Développement d'une approche de planification systématique ...

Développement d’une approche de planification systématique de la conservation des milieux humides

intégrant les services écologiques

Thèse

Jérôme Cimon-Morin

Doctorat en biologie végétale

Philosophiae Doctor (Ph.D.)

Québec, Canada

© Jérôme Cimon-Morin, 2015

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Résumé

Globalement, le statut de la plupart des services écologiques (SE) est jugé préoccupant. Des actions de

conservation permettraient de maintenir l’apport de certains SE à des emplacements cruciaux pour le maintien

du bien-être humain. Néanmoins, comparée aux approches de conservation traditionnelles, la conservation des

SE requiert des considérations particulières afin de capter le lien spatial qui unit les flux de services à leurs

bénéficiaires. Or, ces connaissances demeurent fragmentaires. Cette thèse s’insère donc dans ce contexte et

tente de répondre à trois questions principales, soit : (1) comment considérer le lien spatial entre les flux de SE

et leurs bénéficiaires lors de la sélection des réserves?; (2) comment peut-on aligner la conservation de la

biodiversité à celle des SE le plus efficacement possible?; et (3) quelles sont les conséquences de retarder la

mise en œuvre des actions de conservation sur l’atteinte des objectifs? Nous avons d’abord montré que lorsque

la demande pour les services à échelle locale était directement intégrée dans les procédés de planification

systématique de la conservation, cela favorisait la sélection de sites qui étaient jusqu’à trois fois plus efficaces

pour combler la demande des bénéficiaires par rapport à des approches qui ne ciblaient que l’apport des

services uniquement. Cette nouvelle approche permet donc de concentrer les efforts de conservation aux

endroits où les ressources investies contribueront le plus au bien-être humain. Ensuite, en utilisant cette

dernière approche conjointement avec des cibles de biodiversité par l’entremise d’approche de sélection basée

sur la complémentarité des sites, nous avons montré qu’il était possible d’atteindre toutes les cibles de

conservation pour seulement 6 % de superficie supplémentaire à protéger. Lorsque l’atteinte de toutes les

cibles de conservation est désirée, miser sur la congruence spatiale entre la biodiversité et les SE était de deux

à cinq fois moins efficace que l’utilisation d’une approche basée sur la complémentarité entre les sites.

Finalement, dans un contexte d’augmentation des pressions provenant des activités industrielles, nous avons

évalué les effets du retard dans la mise en œuvre de la conservation sur le coût de remplacement des réseaux

de conservation. Nous avons montré que ce coût peut s’élever jusqu’à 15 % lorsque la conservation est

effectuée après le début du développement.

V

Abstract

The current global status of most ecosystem services (ES) is precarious. Conservation actions could help

secure their provision in locations deemed critical for the maintenance of human well-being. However,

compared to traditional conservation planning approaches, ES conservation requires particular considerations

to capture the spatial link between human beneficiaries and services flows; this knowledge is still fragmentary.

This thesis examines one such context and tries to answer the following three main research questions: (1)

How do we identify important sites for ES conservation? (2) How do we best align biodiversity and ES

conservation? (3) What are the consequences of the delayed implementation of ES conservation actions? We

first showed that considering demand in systematic conservation planning procedures fostered the selection of

sites that may be up to three times more efficient in fulfilling beneficiary demand. This approach enables

conservation efforts to focus on locations where resource investment will yield the greatest return for human

well-being. Then, using this novel approach simultaneously with wetland biodiversity features within a

complementary based selection procedure, we showed that it was possible to achieve all biodiversity and ES

targets for only six percent of the additional area to be protected. When all conservation targets are sought to

be a attained, counting on the spatial congruence between biodiversity and ES may be two to five times less

efficient than using a complementarity based approach. Finally, in a context of increasing pressures from

industrial development, we assessed the role of timing where the implementation of ES conservation actions is

concerned on the replacement cost of the resultant networks. We showed that a slight increase in the

percentage of the study area subjected to development can raise the replacement cost of conservation

networks of up to fifteen percent.

VII

Table des matières

Résumé ................................................................................................................................................................ III

Abstract ................................................................................................................................................................. V

Table des matières .............................................................................................................................................. VII

Liste des tableaux ................................................................................................................................................ XI

Liste des figures ................................................................................................................................................. XIII

Remerciements .................................................................................................................................................. XV

Avant-propos .................................................................................................................................................... XVII

CHAPITRE 1 ......................................................................................................................................................... 1

Introduction générale ........................................................................................................................................ 1

Mise en contexte .......................................................................................................................................... 2

Les services écologiques ............................................................................................................................. 3

L’historique de la conservation au Québec................................................................................................... 4

L’intégration des services écologiques dans les objectifs de conservation du Québec ................................ 7

Les services écologiques rendus par les milieux humides ........................................................................... 8

Contexte nordique du projet ......................................................................................................................... 9

Organisation de la thèse ............................................................................................................................. 10

Hypothèses et prédictions .......................................................................................................................... 11

CHAPITRE 2 ....................................................................................................................................................... 13

Fostering synergies between ecosystem services and biodiversity in conservation planning: a review ......... 13

Résumé ...................................................................................................................................................... 14

Abstract ...................................................................................................................................................... 15

Introduction ................................................................................................................................................. 16

Method ....................................................................................................................................................... 17

Review structure .................................................................................................................................... 17

Definitions .............................................................................................................................................. 18

How do we identify important sites for ES conservation? ........................................................................... 20

How can we maximize synergy between biodiversity and ES during conservation planning? ................... 22

Spatial congruence between ES and biodiversity .................................................................................. 22

Complementarity between ES and biodiversity in reserve selection ...................................................... 25

Does integrating the concept of ES provide new tools to facilitate biodiversity conservation? ................... 27

Measuring the cost-benefit ratio of conservation .................................................................................... 27

VIII

The use of payments for ecosystem services ........................................................................................ 30

Conclusion .................................................................................................................................................. 31

CHAPITRE 3 ....................................................................................................................................................... 33

Towards systematic conservation planning adapted to the local flow of ecosystem services ......................... 33

Résumé ...................................................................................................................................................... 34

Abstract ...................................................................................................................................................... 35

Introduction ................................................................................................................................................. 36

Method ........................................................................................................................................................ 38

Study area and wetlands mapping ......................................................................................................... 38

Mapping ecosystem services supply and demand ................................................................................. 39

Conservation assessment ...................................................................................................................... 44

Conservation planning software ......................................................................................................... 44

Conservation scenarios ..................................................................................................................... 44

Conservation networks analysis ........................................................................................................ 45

Results ........................................................................................................................................................ 47

Assessing the effects of mapping ES using a beneficiaries-based approach ........................................ 47

Integrating demand into identification of local flow ES priority areas ..................................................... 48

Discussion .................................................................................................................................................. 50

Assessing the effects of mapping ES using a beneficiaries-based approach ........................................ 50

Integrating demand into identification of local flow ES priority areas ..................................................... 51

Conclusion .................................................................................................................................................. 53

CHAPITRE 4 ....................................................................................................................................................... 55

Site complementarity between biodiversity and ecosystem services in conservation planning of

sparsely-populated regions ............................................................................................................................. 55

Résumé ...................................................................................................................................................... 56

Abstract ...................................................................................................................................................... 57

Introduction ................................................................................................................................................. 58

Method ........................................................................................................................................................ 60

The study area ....................................................................................................................................... 60

Wetland biodiversity surrogates ............................................................................................................. 60

Wetland types .................................................................................................................................... 61

Wetland composition classes ............................................................................................................. 62

Wetland richness classes .................................................................................................................. 62

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Ecosystem services features ................................................................................................................. 62

Conservation software and scenarios .................................................................................................... 63

Conservation planning software......................................................................................................... 63

The biodiversity scenario (or BD) ....................................................................................................... 64

The ecosystem services biophysical supply scenario (or BS) ........................................................... 64

The ecosystem services actual-use supply (or AUS)......................................................................... 65

The ecosystem services biophysical supply and biodiversity scenario (or BS-BD) ........................... 65

The ecosystem services actual-use supply and biodiversity scenario (or AUS-BD) .......................... 65

The AUS-BD unconstrained scenario (or AUS-BD unconstrained) ................................................... 65

Conservation networks analysis ........................................................................................................ 66

Results ....................................................................................................................................................... 66

Ecosystem services mapping ................................................................................................................. 66

Planning for biodiversity or ES alone ..................................................................................................... 66

Planning for biodiversity and ES simultaneously .................................................................................... 68

Discussion and conclusion ......................................................................................................................... 74

CHAPITRE 5 ....................................................................................................................................................... 79

Replacement cost of ecosystem services conservation networks in sparsely populated areas subjected

to industrial development ................................................................................................................................ 79

Résumé ...................................................................................................................................................... 80

Abstract ...................................................................................................................................................... 81

Introduction ................................................................................................................................................. 82

Method ....................................................................................................................................................... 83

Study area .............................................................................................................................................. 83

Mapping ecosystem services ................................................................................................................. 84

Mapping future industrial development sites and development scenarios ............................................. 85

Conservation software and scenarios .................................................................................................... 86

Conservation planning software......................................................................................................... 86

Conservation networks and analysis ................................................................................................. 87

Results ....................................................................................................................................................... 88

Effect of development on conservation of ES actual-use supply............................................................ 88

Discussion .................................................................................................................................................. 92

Conclusion .................................................................................................................................................. 95

CHAPITRE 6 ....................................................................................................................................................... 97

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Conclusion générale ....................................................................................................................................... 97

Synthèse des résultats ............................................................................................................................... 98

Applicabilité au Québec de l’approche développée .................................................................................... 99

Limite d’une étude de cas en planification systématique de la conservation ............................................ 100

Limite méthodologique et contraintes rencontrées ................................................................................... 102

Bibliographie...................................................................................................................................................... 107

Annexe 1 ........................................................................................................................................................... 119

Ecosystem services expand the biodiversity conservation toolbox – A response to Deliège and

Neuteleers..................................................................................................................................................... 119

Annexe 2 ........................................................................................................................................................... 121

Ecosystem services mapping description ..................................................................................................... 121

Provisioning services ................................................................................................................................ 121

Cultural services ....................................................................................................................................... 122

Regulating services .................................................................................................................................. 123

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Liste des tableaux

Table 2.1. Keywords used to search for scientific articles. ................................................................................. 18

Table 2.2. Spatial relationship between biodiversity and ecosystem services .................................................... 26

Table 3.1. Indicators and data used to map ES supply and demand across the study area .............................. 42

Table 4.1. Mean deviation (%) from targets for ecosystem services features under the six conservation

scenarios tested. ................................................................................................................................................. 71

Table 4.2. Mean deviation (%) from targets for wetland biodiversity surrogates under the six conservation

scenarios tested. ................................................................................................................................................. 73

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Liste des figures

Figure 1.1. La cascade des services écologiques. ............................................................................................... 5

Figure 2.1. Theoretical relationship between the provision of ES (Y axis) and the degree of biodiversity

loss for different levels of anthropogenic disturbances (X axis). R: sum of regulation services; P: sum of

provisioning services; Cr: sum of cultural-recreation value; Ci: sum of cultural-information value; ESL: sum

of all the ES. ........................................................................................................................................................ 19

Figure 2.2. ES spatial flow scales. ...................................................................................................................... 21

Figure 3.1. The location of the study area (in red) across North America (A); the extent of road networks

and the location of the major towns, First Nations communities and vacation leases are shown in (B). ............. 39

Figure 3.2. The spatial delivery range of the biophysical supply and of the potential-use supply of ES. ............ 43

Figure 3.3. Example of conservation networks assembled under the four conservation scenarios.................... 46

Figure 3.4. Proportion of conservation network that secured the actual-use supply of local flow ES under

the four conservation scenarios. ......................................................................................................................... 48

Figure 3.5. Efficiency of four conservation scenarios to capture demand of local flow ES (see Figure 3.4.

for scenarios). ..................................................................................................................................................... 49

Figure 3.6. Similarity between conservation scenarios. ...................................................................................... 50

Figure 4.1. Location of the study area (A) as well as roads, major towns, First Nations communities and

vacation leases (B). ............................................................................................................................................. 61

Figure 4.2. Basic framework describing the relationship between the capacity of major wetland types to

supply ES and beneficiaries’ access to wetland ecosystems. ............................................................................. 67

Figure 4.3. Networks assembled under the six conservation scenarios. ............................................................ 69

Figure 4.4. Deviation from conservation targets of individual ES features protected under each

conservation scenario ......................................................................................................................................... 70

Figure 4.5. Deviation from conservation targets of individual wetland biodiversity features protected under

each conservation scenario. ................................................................................................................................ 72

Figure 4.6. Increase in total area required to achieve all conservation feature targets for the three

conservation scenarios expressed as a proportion of the budget threshold, which is a fixed percentage of

the study area. .................................................................................................................................................... 74

Figure 5.1. The location of the study area (in red) within North America (A); the extent of road networks

and location of the major towns and First Nations communities (B) .................................................................... 84

Figure 5.2. Planning units where future development is most likely to occur for each type of industrial

activity ................................................................................................................................................................. 87

Figure 5.3. Example of conservation networks at different stages of development. ........................................... 90

Figure 5.4. Difference in total area and total number of selected sites in conservation networks

established at different stages of development ................................................................................................... 91

Figure 5.5. Similarity between the referential network and the network established following different

stages of development. ....................................................................................................................................... 92

XIV

Figure 6.1. Les différentes méthodes pour cartographier les services écologiques en fonction des

données disponibles. ......................................................................................................................................... 103

XV

Remerciements

J’aimerais d’abord remercier tous ceux et celles qui ont travaillé sur le comité de ce projet : Isabelle Falardeau

(présentement au ministère des Transports) et Pierre-Michel Fontaine du ministère du Développement durable,

de l’Environnement et de la Lutte contre les changements climatiques, Louise Émond et Stéphane Lapointe

d’Hydro-Québec ainsi que Stéphanie Boudreau de Canards Illimités Canada (présentement chez l’association

des Producteurs de Tourbe Horticole du Québec). Je désirerais aussi souligner l’aide de Michel Bergeron du

ministère du Développement durable, de l’Environnement et de la Lutte contre les changements climatiques

lors de certaines phases conceptuelles du projet. J’aimerais remercier les gestionnaires de chez Canards

Illimités Canada (bureau de Québec) de m’avoir permis d’effectuer un doctorat en milieu de pratique. Merci au

personnel de Canards Illimités Canada, particulièrement à Sylvie Picard et à Jason Beaulieu pour leur aide en

géomatique, ainsi qu’à Pierre Dulude pour tous les conseils qu’il m’a prodigués au cours de ces quatre

dernières années. Je tiens aussi à remercier Simon Perreault d’avoir passé d’innombrables heures à me former

sur ArcGIS et à cartographier les services écologiques. J’aimerais apporter une reconnaissance spéciale à

Stéphane Bergeron avec qui j’ai eu de bonnes discussions, qui nous ont menés respectivement à mieux

comprendre notre sujet de recherche; nos projets n’auraient pu être les mêmes sans ces interactions. Je désire

souligner le travail exceptionnel de Karen Grislis pour la révision stylistique de mes quatre articles. Finalement,

je désire remercier ma conjointe, Selena Romein, pour son appui constant au cours de ces dernières années et

pour ses révisions linguistiques, généralement demandées à l’improviste.

Pour le financement du projet, je tiens à remercier Hydro-Québec, le Conseil de recherches en sciences

naturelles et en génie du Canada ainsi que le Fonds de recherche du Québec – Nature et technologies. Merci

au Centre de la Science de la Biodiversité du Québec de m’avoir octroyé un prix d’excellence qui m’a permis de

participer à une conférence aux États-Unis. Pour terminer, je voudrais évoquer le travail exceptionnel et

souligner ma grande reconnaissance envers ma directrice Monique Poulin et mon codirecteur Marcel Darveau

de m’avoir guidé tout au long de ce projet.

http://www.google.ca/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&uact=8&ved=0CB4QFjAA&url=http%3A%2F%2Fwww.frqnt.gouv.qc.ca%2Fen%2F&ei=wN3XU5bqBYKWyASMr4F4&usg=AFQjCNFnWtZz60nhPnQG422FS6rsTsRhrg&bvm=bv.71778758,d.aWw

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Avant-propos

Cette thèse est composée de six chapitres, incluant une introduction et une conclusion générales rédigées en

français, ainsi que quatre chapitres écrits en anglais, selon la structure d’un article scientifique. Tous les

chapitres de ce document ont été écrits en entier par le candidat au doctorat, avec les conseils et les

commentaires apportés par la directrice et le codirecteur de recherche, madame Monique Poulin (professeure

titulaire au département de phytologie de l’Université Laval) et monsieur Marcel Darveau (chef du programme

de recherche et conservation boréales pour le Québec chez Canards Illimités Canada).

Le premier chapitre est une introduction générale qui présente notamment l’historique de la conservation au

Québec ainsi que le contexte nordique du projet. Ce chapitre a été rédigé en entier par le candidat et a été

révisé par la directrice et le codirecteur.

Le chapitre 2, soit le premier article de la thèse, est une revue de la littérature intitulée «Fostering synergies

between ecosystem services and biodiversity in conservation planning: A review » et a été publié dans la revue

« Biological Conservation » (2013). Cet article a été nommé le « choix de l’éditeur » de l’édition du mois

d’octobre 2013 de la revue. Le candidat a effectué la revue de la littérature, a synthétisé les connaissances

tirées de la littérature et a écrit le manuscrit en entier. Les coauteurs, Monique Poulin et Marcel Darveau, ont

commenté et révisé plusieurs versions du manuscrit jusqu’à sa publication. En janvier 2014, nous avons été

invités par l’éditeur en chef de la revue à soumettre une communication sous forme de « lettre à l’éditeur » afin

de répondre à deux scientifiques qui critiquaient les conclusions de notre article (Deliège, G. et S. Neuteleers.

2014. Ecosystem services as an argument for biodiversity preservation: Why its strength is its problem – Reply

to Cimon-Morin et al. Biological Conservation 172: 218). La lettre qui a été soumise en réponse à leur critique

est fournie à l’annexe 1 de la thèse. Cette lettre s’intitule « Ecosystem services expand the biodiversity

conservation toolbox - A response to Deliège and Neuteleers » et est aussi disponible dans la revue «

Biological Conservation 172: 219-220 ». Cette lettre a été rédigée en entier par le candidat, avec les

commentaires, suggestions et révisions de Monique Poulin et de Marcel Darveau.

Le chapitre 3 est un article de recherche proposant une modification des approches traditionnelles de

planification systématique de la conservation afin de les adapter à la conservation des services écologiques. Ce

dernier se nomme « Towards systematic conservation planning adapted to the local flow of ecosystem services

» et a été publié dans « Global Ecology and Conservation » (2014). Le candidat au doctorat a élaboré la

recherche, réalisé les analyses et rédigé le manuscrit. Les coauteurs, Monique Poulin et Marcel Darveau, ont

commenté, révisé et proposé des suggestions à toutes les étapes de production de l’article.

XVIII

Le chapitre 4 est un article de recherche où les connaissances acquises dans le cadre du chapitre précédent

ont été conjointement appliquées à une étude de planification de la conservation de la biodiversité des milieux

humides. Ce chapitre porte le titre « Site complementarity between biodiversity and ecosystem services in

conservation planning of sparsely-populated regions » et a été soumis pour publication à la revue «

Environmental Conservation » le 29 août 2014. Le candidat au doctorat a élaboré la recherche, réalisé les

analyses et rédigé le manuscrit. Les coauteurs, Monique Poulin et Marcel Darveau, ont commenté, révisé et

proposé des suggestions à toutes les étapes de production de l’article.

Le dernier article de recherche de la thèse, le chapitre 5, visait à mesurer les conséquences de l’augmentation

des activités industrielles et du délai de mise en œuvre des actions de conservation sur l’atteinte des objectifs

de la conservation des SE. Ce chapitre s’intitule « Replacement cost of ecosystem services conservation

networks in sparsely populated areas subjected to industrial development ». Il est prévu de soumettre ce

chapitre à la revue « Ecosystem Services » au courant de l’automne 2014. Le candidat au doctorat a élaboré la

recherche, réalisé les analyses et rédigé le manuscrit. Les coauteurs, Monique Poulin et Marcel Darveau, ont

commenté, révisé et proposé des suggestions à toutes les étapes de production de l’article.

Finalement, le chapitre 6 est la conclusion générale de la thèse. Une brève synthèse des résultats y est

présentée, ainsi que des pistes pour améliorer les choix de conservation pour les services écologiques. Ce

dernier chapitre a été rédigé en entier par le candidat et a été révisé par la directrice et le codirecteur de

l’étudiant.

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CHAPITRE 1

Introduction générale


2

Mise en contexte

Au cours des dernières décennies, l’être humain a dégradé les écosystèmes au point où une diminution

globale de la biodiversité sans précédent a été observée (Pimm et al. 1995, Vitousek et al. 1997, Chapin et al.

2000, MA 2005, Butchart et al. 2010). L’accroissement constant de la population humaine engendre une

demande grandissante pour les ressources naturelles, ce qui fait en sorte d’augmenter les pressions sur les

territoires naturels. Si les tendances actuelles se maintiennent, les pertes de biodiversité risquent de

s’accentuer au cours du siècle prochain (Jetz et al. 2007). La biologie de la conservation est une science qui a

pris naissance dans les années 1970 afin de répondre à cette crise. Cette science a notamment comme

objectif d’interrompre la vague actuelle d’extinction afin de permettre l’existence de la diversité biologique pour

les générations futures (Margules et Sarkar 2007).

L’atteinte des objectifs de conservation passe d’abord et avant tout par l’établissement d’un réseau d’aires

protégées. L'Union Internationale pour la Conservation de la Nature (UICN; Dudley 2008) définit une aire

protégée comme : « Un espace géographique clairement défini, reconnu, consacré et géré, par tout moyen

efficace, juridique ou autre, afin d’assurer à long terme la conservation de la nature, ainsi que les services

écologiques et les valeurs culturelles qui lui sont associés ». Le but des aires protégées est non seulement de

séparer les entités protégées (espèce, écosystème, paysage, etc.) des pressions qui menacent leur existence,

mais aussi de mettre en place des infrastructures qui facilitent leur appréciation et leur utilisation à des fins

récréatives et scientifiques (Margules et Pressey 2000, Kalamandeen et Gillson 2007). Un grand défi de la

biologie de la conservation est d’identifier les sites où des efforts de conservation seront entrepris (Margules et

Sarkar 2007, Moilanen et al. 2009c). La sélection des sites pour l’établissement d’aires protégées a longtemps

été exempte de toute démarche scientifique (Pressey et Tully 1994), mais diverses approches ont été

développées au cours des dernières années afin de systématiser la conservation de la biodiversité (Margules

et Sarkar 2007, Moilanen et al. 2009c).

La planification systématique de la conservation est une approche qui a été développée afin d’identifier le plus

petit jeu d’unités territoriales à protéger pour atteindre les objectifs de conservation, notamment en réponse au

fait que les fonds disponibles pour la conservation sont limités et que ceux-ci doivent par conséquent être

alloués plus stratégiquement (Margules et Pressey 2000, Margules et Sarkar 2007). En d’autres mots, elle

cherche à optimiser les réseaux de conservation en identifiant, par une approche itérative, le minimum de sites

nécessaires (en termes de nombre ou de superficie) pour atteindre les cibles de conservation (Pressey et

Nicholls 1989, Pressey et al. 1996). L’atteinte de l’optimalité permet notamment de réduire les coûts associés

à la conservation (Pressey et al. 1996). Des algorithmes de sélection sont utilisés afin d’effectuer le choix le

plus optimal possible quant aux nouveaux sites à conserver. Pour être optimaux, les sites choisis pour la

création des réserves doivent être complémentaires entre eux, en regard des objectifs établis (Pressey et al.

3

1993, Kukkala et Moilanen 2013); en d’autres mots, cela évoque, par exemple, que lorsqu’un nouveau site

vient s’ajouter au réseau, il n’est pas nécessairement celui qui possède le plus d’espèces, mais plutôt celui qui

possède le plus de nouvelles espèces non représentées par le réseau déjà existant (Pressey et al. 1996).

L’utilisation des algorithmes de sélection permet aussi de tenir compte de certains critères ou de certaines

contraintes, déterminés a priori par l’utilisateur, tels que la grosseur minimale ou maximale des sites à

conserver, la connectivité entre les sites sélectionnés, et des critères de design et de conception. En résumé,

la planification systématique de la conservation permet d’intégrer à la fois les objectifs de conservation,

l’atteinte de l’optimalité et certains autres critères de conception dans les algorithmes de sélection (Margules

et Pressey 2000). Depuis trois décennies, la planification systématique de la conservation s’est montrée

beaucoup plus efficace pour créer des réseaux de conservation plus représentatifs de la biodiversité que les

méthodes ad hoc utilisées dans le passé, qui reposaient davantage sur des approches multicritères ou

d’attribution de scores. Un des avantages de la planification systématique de la conservation par rapport aux

méthodes ad hoc est qu’elle réduit le biais associé au choix de l’emplacement des aires protégées (Groves et

al. 2002, Reyers et al. 2007).

Dans les années 2000, on a montré que les perturbations anthropiques entrainaient aussi une perte globale

de la plupart des services écologiques (SE; Foley et al. 2005, MA 2005) et que celle-ci est préoccupante étant

donné que leur apport influence directement le bien-être et la santé des êtres humains (Daily 1997, MA 2005,

Díaz et al. 2006, Cardinale et al. 2012). Parallèlement, il est à prévoir que l’augmentation de la population

humaine fera en sorte d’accroître notre dépendance envers les SE (Guo et al. 2010).

Les services écologiques

Bien que l’être humain dépende de la nature pour survivre et combler ses besoins essentiels depuis son

origine, l’émergence du concept de SE dans la littérature remonte seulement à la fin des années 1970. À cette

époque, Westman (1977) avait suggéré que la valeur sociale des bénéfices générés par les écosystèmes soit

prise en compte dans l’aménagement du territoire (Fisher et al. 2009). Westman utilisait alors le terme «

service de la nature » pour faire référence à ces bénéfices. Ce n’est que quelques années plus tard que le

terme « service écologique » a été utilisé pour la première fois par Ehrlich et Ehrlich (1981, Fisher et al. 2009).

Cependant, c’est à partir des années 1990, grâce notamment à des publications comme celle de Costanza et

al. (1997), que les SE ont véritablement commencé à susciter l’intérêt des scientifiques. Costanza et al. (1997)

avaient alors montré que la valeur monétaire globale de 17 SE pourrait s’élever en moyenne à 33 billions

(1012) de dollars par année alors que le produit intérieur brut global n’était que d’environ 18 billions de dollars.

Pour une des premières fois, les scientifiques détenaient des arguments pour faire prendre conscience de la

4

valeur économique rattachée aux SE et justifier l’importance de la protection des écosystèmes. Depuis ce jour,

le nombre de publications scientifiques sur les SE n’a cessé d’augmenter exponentiellement (Fisher et al.

2009, Potschin et Haines-Young 2011). Avec environ une dizaine d’articles par année traitant des SE au début

des années 1990, ce chiffre atteignait près de 1 200 publications par année en 2010 (Potschin et Haines-

Young 2011). En septembre 2014, 11 244 articles scientifiques ont été répertoriés sur le moteur de recherche

« Web of science » en effectuant une recherche avec le mot-clé « ecosystem service ».

Les SE sont définis comme étant les bénéfices que l’être humain obtient des écosystèmes (MA 2005). Ils sont

généralement classifiés sous quatre catégories, soit (1) les services d’approvisionnement (nourriture, matériel,

etc.), (2) les services de régulation (régulation du climat, régulation du débit de l’eau, etc.), (3) les services de

support (le cycle des nutriments, la pollinisation, etc.) et (4) les services socioculturels (esthétisme, récréation;

MA 2005). Néanmoins, les derniers développements dans la classification des SE relèguent les catégories de

services d’approvisionnement, de régulation et socioculturels à un niveau hiérarchique inférieur à celui des

services de support pour des raisons d’évaluation économique (voir Figure 1.1.; Boyd et Banzhaf 2007, Fisher

et al. 2009, Balmford et al. 2011, Fu et al. 2011, Potschin et Haines-Young 2011). En effet, certains auteurs

ont soulevé le risque de surestimation de la valeur des SE engendrée par l’utilisation d’une classification mal

adaptée (Balmford et al. 2011, Fu et al. 2011). Un SE peut être le produit de plusieurs fonctions écologiques

alors qu’une seule fonction écologique peut contribuer à plusieurs SE. Or, il est maintenant bien reconnu que

les services de support sous-tendent la production des trois autres catégories de services (Mace et al. 2012).

Pour cette raison, on distingue maintenant les services finaux (qui regroupent les catégories de services

d’approvisionnement, de régulation et socioculturels) des services intermédiaires (les services de support).

Cette nouvelle classification hiérarchique permet de faire la distinction entre les services, les processus et les

fonctions écologiques (Fisher et al. 2009, Balmford et al. 2011, Fu et al. 2011). Seuls les produits finaux

générés par les processus et les fonctions des écosystèmes, soit les véritables « services », c'est-à-dire les

bénéfices que retirent directement les humains, sont évalués économiquement (Fu et al. 2011). On évite ainsi

la surestimation de la valeur des fonctions et des processus écologiques en ne les intégrant pas directement

dans l’évaluation économique.

L’historique de la conservation au Québec

Avant les années 1970, les initiatives de conservation au Québec étaient rares et à la pièce. Avec

l’intensification des pressions d’utilisation et l’émergence de la biologie de la conservation à l’échelle mondiale

(Pressey et Tully 1994), le besoin d’actions coordonnées et stratégiques s’est accru. Ainsi, il y a une

quarantaine d’années, le Québec a commencé à se doter d’outils qui rendraient la conservation plus

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systématique, notamment en se dotant de deux de ses premiers outils juridiques pour préserver son territoire

avec l’adoption de la Loi sur les réserves écologiques (1974) et de la Loi sur les parcs (1977). L’objectif des

réserves écologiques était alors de conserver intégralement et de manière permanente des échantillons

représentatifs de la diversité et de la richesse écologique et génétique du patrimoine naturel du Québec. En

plus de ces objectifs, les réserves écologiques possédaient aussi des missions d’éducation, de recherche

scientifique, et de sauvegarde des espèces menacées ou vulnérables de la flore et de la faune.

Figure 1.1. La cascade des services écologiques. Les services intermédiaires, soit les processus et les

fonctions des écosystèmes, sous-tendent la production des services finaux. Les bénéfices sont ensuite

dérivés des services finaux en combinant ces derniers avec d’autres formes de capital. C’est la quantité ainsi

que la qualité de ces bénéfices qui influencent directement le bien-être humain. Ces bénéfices ont une valeur

économique qui peut être calculée. Figure adaptée de Potschin et Haines-Young (2011).

Du côté des parcs, la Loi sur les parcs (1977) attribuait à ces territoires, lors de leur établissement, une

vocation soit en tant que parc de conservation ou en tant que parc de récréation. La désignation reflétait

l’objectif prioritaire de leur création. Les parcs de conservation avaient comme objectif premier d’assurer la

protection permanente d’échantillons représentatifs de chacune des régions naturelles du Québec. Ils

servaient aussi à protéger certains territoires jugés exceptionnels. Quant aux parcs de récréation, leur objectif

fondamental était de favoriser la pratique d’activités récréatives en plein air dans un environnement naturel de

qualité près des grands centres de population. Dans un contexte où la rareté des espèces naturelles est en

croissance, cette catégorie de parc avait donc pour but de répondre à la demande grandissante pour des

activités de plein air, tout en assurant un certain niveau de protection du milieu naturel.

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En 1992, lors du Sommet de la Terre à Rio, le gouvernement du Canada signa la Convention sur la diversité

biologique. Le gouvernement du Québec adhéra donc officiellement aux objectifs de la Convention sur la

diversité écologique et décida de les mettre en œuvre sur son territoire. L’atteinte des objectifs de la

Convention demandait alors d’établir un réseau d’aires protégées. L’identification des aires protégées devait

être faite dans le but de préserver la diversité biologique du territoire. Le Québec se dota d’une stratégie sur la

diversité écologique (1996 ; révisée en 2004) assortie d’un plan d’action pour les années 1996-2000.

L’élaboration d’une telle stratégie demandait la préparation d’un ensemble de documents comportant

notamment des portraits de la situation, des réflexions et des orientations stratégiques. Ainsi, en 1999, le

gouvernement du Québec publia le «Portrait synthèse des données sur les aires protégées au Québec»

(Ministère de l’Environnement 1999). Parmi les constats mis de l’avant dans ce bilan, on y nota que le Québec

était en retard quant à ses engagements sur la conservation de la diversité biologique sur son territoire. En

effet, à cette époque, seulement 2,75 % du territoire québécois était constitué d’aires protégées (pour environ

1 100 aires) répondant aux classifications de l’UICN (1994). Ces aires protégées représentaient cinq des six

catégories de l’UICN (i.e. aucune aire de catégorie V) et se répartissaient sous 17 désignations québécoises

(Ministère de l’Environnement 1999). À ce moment, 62 % de la superficie du réseau d’aires protégées était

désignée catégorie VI (aire protégée où l’utilisation durable des ressources naturelles est permise) et 16 %

était des parcs nationaux (catégorie II); 14 % de la superficie protégée était répartie sous forme d’aires de

gestion des habitats ou des espèces (catégories IV) ne laissant que 8 % pour les catégories I et III (réserve

naturelle intégrale et monument naturel) ainsi que pour les aires sans catégorie. En plus de ce constat, le

portrait permettait aussi de remarquer que la plupart des aires protégées étaient de petites superficies

(moyenne d’environ = 43 km2) et localisées majoritairement dans la vallée du Saint-Laurent. C’est pourquoi,

en 2000, le gouvernement du Québec adopta des orientations stratégiques en vue de doter le Québec d’un

réseau d’aires protégées plus représentatif de l’ensemble de sa diversité biologique et qui couvrirait une

superficie totale d’au moins 8 % du territoire. Par ces orientations, le gouvernement reconnaissait l’importance

et les bénéfices des aires protégées sur les plans écologique, économique et social pour l’ensemble du

Québec. En adoptant des objectifs et des mesures en vue de l’expansion du réseau actuel d’aires protégées, il

veilla à axer ses efforts sur la sauvegarde d’échantillons représentatifs de toute la diversité biologique, tant

terrestre, aquatique et estuarienne que marine. Il s’intéressera également à la préservation des milieux fragiles

ou exceptionnels ainsi qu’aux habitats d’espèces menacées ou vulnérables.

En 2001, le gouvernement du Québec apporta d’importantes modifications à la Loi sur les parcs, abolissant

ainsi les classifications de « récréation » et de « conservation ». De cette manière, tous les parcs nationaux du

Québec poursuivraient dorénavant les mêmes objectifs. Les parcs sont maintenant définis comme étant des

aires protégées « dont l’objectif prioritaire est d’assurer la conservation et la protection permanente de

territoires représentatifs des régions naturelles du Québec ou de sites naturels à caractère exceptionnel,

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notamment en raison de leur diversité biologique, tout en les rendant accessibles au public pour des fins

d’éducation et de récréation extensive ».

En 2002, le Québec adapta la Loi sur la conservation du patrimoine naturel. Cette loi instaura trois nouveaux

statuts juridiques de protection conférant davantage de souplesse et une gamme d’outils plus étendue pour

protéger la biodiversité. Cette loi permettait de protéger plus efficacement la diversité biologique de vastes

territoires en fonction de leurs spécificités écologiques et sociales, et ce, tout en permettant l’utilisation durable

de certains de leurs éléments constitutifs. Cette loi permettait aussi la protection temporaire mais légale

(réserve de biodiversité projetée et réserve aquatique projetée) de certains territoires. En évitant les délais et

en autorisant la protection ces territoires dès l’attribution de l’une de ces désignations, cet ajout constituait un

outil législatif performant pour la sauvegarde de territoires d’intérêt écologique.

En 2009, un nouveau portrait des aires protégées fut réalisé et, cette fois, on y remarqua notamment que la

superficie en conservation avait bondi à 8,35 % du territoire québécois (Brassard et al. 2009). On retrouvait

alors 2 488 aires protégées gérées sous 23 différentes désignations juridiques ou administratives

québécoises. Malgré que la taille moyenne des aires protégées n’ait augmenté que d’un peu plus de 10 km2,

pour atteindre 54,5 km2, certaines des nouvelles aires protégées couvrent tout de même des milliers de km2.

Le réseau est aussi maintenant mieux réparti sur le territoire québécois, couvrant notamment le nord. Par

exemple, sur le territoire du Plan Nord (au nord du 49e parallèle), la proportion d’aires protégées est passée de

2,4 % à 9,4 % entre 2002 et 2009. Le réseau comprend dorénavant 81 % de sa superficie en aires protégées

strictes (catégories I à III), alors qu’elle ne couvrait qu’environ 20 % du réseau en 2002. De plus, pratiquement

absentes en 2002 (1 % du réseau), les aires protégées de catégorie III couvrent maintenant 50 % de la

superficie en conservation au Québec. Les parcs nationaux ont aussi connu une augmentation marquée

passant de 16 % du réseau, en 2002, à près de 30 % aujourd’hui. Ces augmentations ont cependant été au

détriment des aires de catégories VI, qui sont passées de 62 % de la superficie du réseau, en 2002, à

seulement 3 % en 2009. Finalement, c’est aussi en 2009 que le gouvernement a annoncé une nouvelle cible

pour la constitution d’aires protégées, soit 12% du territoire québécois d’ici 2015.

L’intégration des services écologiques dans les objectifs de conservation du

Québec

Malgré que les SE n’ont été que récemment explicitement intégrés en biologie de la conservation (Egoh et al.

2007, Luck et al. 2012), ces derniers ont incidemment fait partie des objectifs de la conservation depuis des

décennies. Par exemple, au Québec, déjà dans les années 1970, avec la Loi sur les parcs et celle sur les

réserves écologiques, des territoires étaient protégés pour des raisons récréatives, d’éducation et de

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recherche scientifique. Or, ces vocations sont vraisemblablement des SE de la catégorie socioculturelle.

Aujourd’hui, les SE font partie intégrante des objectifs internationaux de conservation. Par exemple, un des

buts du Plan stratégique sur la diversité biologique 2011-2020, adopté en 2010 lors de la 10e Conférence des

Parties de la Convention sur la diversité biologique, est de : « Renforcer les avantages retirés pour tous de la

diversité biologique et des services fournis par les écosystèmes » (Convention sur la diversité biologique

2014). Les parties signataires de la Convention ont accepté de traduire les objectifs généraux du Plan en

stratégies nationales. Au Québec, avec la publication des nouvelles orientations gouvernementales en matière

de diversité biologique (Gouvernement du Québec 2013a), on s’aperçoit que les SE occupent maintenant une

place bien définie dans les objectifs de conservation.

On constate notamment que les SE sont particulièrement ciblés dans l’enjeu I des orientations

gouvernementales, soit celui de la « conservation de la diversité biologique et maintien des services

écologiques ». Cet enjeu se divise en deux orientations, dont la première stipule de « protéger les

écosystèmes afin de maintenir la production des services écologiques essentiels ». Concrètement, cela se

traduit par l’élaboration d’un réseau d’aires protégées ciblant spécifiquement le maintien des SE. Les

avantages d’introduire les SE dans les plans de conservation sont multiples. Premièrement, l’évaluation

économique des services écologiques pourrait faciliter les décisions d’aménagement en convertissant les

actifs et les ressources de la nature sous un dénominateur commun aux autres facteurs de décisions, soit

l’argent (Kerkhof et al. 2010). Deuxièmement, en mettant l’accent sur le maintien du bien-être humain, une

approche de conservation basée sur les SE permettrait de favoriser l’acceptation sociale et le soutien politique

de la conservation, aidant ainsi à l’accomplissement des actions de conservation (Knight et al. 2006a, Egoh et

al. 2007, Goldman et al. 2008).

Les services écologiques rendus par les milieux humides

Les milieux humides sont définis comme étant « une terre saturée d’eau pendant une période suffisamment

longue pour que naissent des processus de terre humide ou aquatique, qui se caractérisent par un faible

drainage des sols, des espèces hydrophytes et différentes sortes d’activités biologiques adaptées aux milieux

humides » (Warner et Rubec 1997). Les milieux humides, qui comptent plusieurs types d’écosystèmes très

différents, tels que les tourbières ombrotrophes (bogs), les tourbières minérotrophes (fens), les eaux peu

profondes, les marais et les marécages, sont particulièrement reconnus pour produire un large éventail de SE

(ten Brink et al. 2013). Parmi les SE les plus importants rendus par les milieux humides, on note : la protection

contre les inondations, l’épuration de l’eau, le stockage du carbone, la chasse, la pêche, la cueillette, le

tourisme, la recherche scientifique, la protection côtière et l’approvisionnement en eau. Les valeurs

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économiques des SE générées par les milieux humides surpassent généralement celles des autres types

d'écosystèmes (Barbier 2011, ten Brink et al. 2013, Batker et al. 2014). Par exemple, en termes de SE rendus,

un hectare de milieux humides aurait en moyenne une valeur économique de plus de six fois supérieure à

celle d’un hectare de forêt (ten Brink et al. 2013). Les SE produits par des milieux humides naturels ont aussi

généralement une valeur économique plus élevée que les gains qui peuvent être espérés à la suite de la

conversion et de l’exploitation de ce même écosystème (Balmford et al. 2002). Notamment, au Canada, une

étude a montré que les bénéfices tirés de la chasse, de la pêche ainsi que du piégeage réalisés de manière

durable dans un marais intact excédaient du double les bénéfices qui pourraient être obtenus de l’agriculture

si ce milieu humide était drainé et exploité (van Vuuren et Roy 1993, Balmford et al. 2002). Malgré

l’importance disproportionnée des milieux humides naturels par rapport aux SE qu’ils génèrent, il n’en

demeure pas moins qu’ils sont des écosystèmes très sensibles aux perturbations d’origine anthropique. Par

conséquent, ces écosystèmes sont souvent dégradés ou perdus sous l’effet d'une production agricole

intensive, de l'irrigation, de l'extraction de l'eau à des fins domestiques et industrielles, de l'urbanisation, du

développement industriel et des infrastructures, et de la pollution.

Actuellement au Québec, les milieux humides couvrent 12,5 % (189 593 km2) du territoire, dont la grande

majorité, soit 85 %, sont des tourbières (Pellerin et Poulin 2013). Il a été estimé qu’environ 3 733 km2 de

tourbières auraient été perturbés par l’homme au cours des cinquante dernières années au Québec et que ce

chiffre est conservateur, car il n’inclurait pas l’effet des activités minières, dans le Nord, et du développement

résidentiel dans les régions situées plus au sud (Rochefort et al. 2011). Dans les Basses-terres du Saint-

Laurent cependant, 19 % de la superficie totale des milieux humides (toutes les classes de milieux humides

confondues) a été perturbé sur une période de 22 ans, notamment par des activités agricoles et sylvicoles

(Pellerin et Poulin 2013). Finalement, toujours selon ces auteurs, seulement 1 % du territoire québécois serait

voué à la protection de milieux humides, ce qui fait que 8 %, ou 15 313 km2, de la superficie totale de milieux

humides du Québec fait partie d’une aire protégée.

Contexte nordique du projet

Les écosystèmes boréaux sont encore de nos jours considérés comme l’une des dernières grandes frontières

au développement économique. Alors que la majorité des activités industrielles y est concentrée dans leur

portion la plus méridionale, la distance grandissante avec les marchés, le prix du transport et le climat

défavorable (Foote et Krogman 2006) ont limité l’expansion de l’exploitation vers les régions situées plus au

nord. Cela fait en sorte que la plupart des régions nordiques n’ont pas encore été perturbées par l’homme, ou

très peu, et que les écosystèmes qu’on y trouve sont toujours dans leur état naturel ou présentent une faible

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empreinte humaine. Cependant, en regard de l’augmentation de la population, de l’expansion des zones

urbaines et de la raréfaction de la disponibilité des ressources dans les zones habitées, il est à prévoir que ces

territoires nordiques représenteront, dans un avenir rapproché, un potentiel croissant pour l’accès aux

ressources naturelles. C’est d’ailleurs le cas au Québec où le Plan Nord a comme objectif la mise en valeur

des territoires nordiques par le développement et l’exploitation des ressources naturelles au nord du 49e

parallèle. Cette imminente augmentation des pressions environnementales causées par la croissance des

activités industrielles risque de dégrader, ou même de convertir, plusieurs écosystèmes clés, notamment les

milieux humides (Foote et Krogman 2006, Schindler et Lee 2010).

Il importe donc de s’attarder à la mise en place de procédures assurant la pérennité et l’intégrité des milieux

humides nordiques, étant donné que ces écosystèmes sont fragiles et qu’ils fournissent une large gamme de

services écologiques (Foote et Krogman 2006, Schindler et Lee 2010), tout en abritant une grande

biodiversité. Alors que les outils et les approches pour la conservation de la biodiversité reposent sur quelques

décennies de travail et de développement (Margules et Pressey 2000, Margules et Sarkar 2007), l’intégration

des SE en conservation en est toujours à ses balbutiements (Egoh et al. 2007, Luck et al. 2012). Néanmoins,

de plus en plus de chercheurs ont appliqué des procédures de planification systématique de la conservation à

la conservation des SE (Chan et al. 2006, Egoh et al. 2011, Larsen et al. 2011). Cependant, en raison de la

valeur instrumentale qui définit les SE, l’identification des sites importants pour leur conservation doit être

basée sur des considérations spatiales supplémentaires par rapport à celles développées pour la protection

de la biodiversité. Comme nous le verrons dans cette thèse, les connaissances sur la priorisation spatiale des

SE est en plein essor, mais un grand bout de chemin reste encore à faire afin d’effectuer des choix efficaces

en matière de conservation.

Organisation de la thèse

Cette thèse a donc comme principal objectif de développer les connaissances afin d’améliorer l’identification

des sites prioritaires lors de processus de planification systématique de la conservation des SE. Cette thèse

contient quatre chapitres de recherche. Dans le deuxième chapitre, trois questions principales seront

abordées, soit : (1) comment intégrer les services écologiques dans la conservation?; (2) quelle est la relation

spatiale entre la biodiversité et les services écologiques?; et finalement, (3) comment les services écologiques

peuvent-ils contribuer à préserver la biodiversité? Les résultats provenant de cette revue de littérature ont

permis d’identifier des lacunes dans la conservation des services écologiques et ont mené à l’élaboration des

questions de recherche qui formeront le noyau des trois chapitres subséquents. Ainsi, dans le cadre du

chapitre trois, une approche de planification systématique de la conservation mieux adaptée au contexte des

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services écologiques que les approches traditionnelles de conservation est proposée. Cette approche a

ensuite été reprise dans les deux chapitres suivants et est appliquée à différents problèmes de conservation.

Dans le quatrième chapitre, les possibilités de conservation simultanée entre la biodiversité des milieux

humides et de dix de leurs services écologiques ont été explorées. Pour terminer, le chapitre cinq traite du

délai de la mise en œuvre de la conservation de services écologiques dans un contexte de pressions

croissantes et imminentes de développement industriel. Une étude de cas menée en Minganie, Québec,

intégrant seize types de milieux humides et dix de leurs services écologiques aura permis d’appuyer les

chapitres 3, 4 et 5 de résultats concrets.

Hypothèses et prédictions

Hypothèse 1. L’identification des sites prioritaires pour la conservation de SE basée sur les sites d’utilisation

réelle (actual-use supply) devrait favoriser la création de réseaux de conservation qui sont plus efficaces pour

répondre aux objectifs de conservation que lorsque l’on considère uniquement leur apport dans les choix de

conservation.

Prédiction 1. L’objectif de la conservation des services est que leurs bénéfices soient accessibles à un groupe

de bénéficiaires donné. Cependant, tous les services ne procurent pas des bénéfices aux êtres humains

partout où ils sont produits, et ce, pour des raisons d’inaccessibilité, d’absence de demande pour ces services,

ou simplement de restrictions (par exemple, les restrictions sur la chasse, la pêche et la cueillette dans

certains parcs nationaux). Donc, en identifiant les endroits où les services sont accessibles ainsi qu’en

demande, et en sélectionnant les aires à protéger parmi ces sites, je devrais être en mesure d’augmenter la

capacité des réseaux de conservation à remplir leurs objectifs.

Hypothèse 2. Étant donné les contraintes spatiales amenées par la sélection des sites à utilisation réelle des

SE lors de planification de la conservation, l’harmonisation de la conservation des SE et de la biodiversité

devrait être plus efficace que lorsqu’on considère uniquement l’apport des SE et la biodiversité.

Prédiction 2. La sélection des sites prioritaires parmi les sites à utilisation réelle des SE engendrera un

rapprochement des aires protégées avec les zones de concentration de la population pour deux raisons.

Premièrement, l’apport des SE qui est accessible dépend de la distribution des routes et des infrastructures

humaines. Deuxièmement, la demande pour les SE diminue avec l’augmentation de la distance par rapport

aux foyers de population et aux routes. Donc, considérer les sites d’utilisation réelle des SE apportera des

contraintes spatiales lors des choix de conservation qui diminuera la quantité de biodiversité qu’on peut

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protéger dans ces réseaux. En effet, la sélection des réserves s’effectuera dans un bassin de sites beaucoup

plus limité que si on considérait seulement l’apport des SE (sans inclure de lien avec les bénéficiaires).

Hypothèse 3. Le coût de remplacement (en termes de superficie nécessaire) d’un réseau de conservation

pour les SE augmentera proportionnellement avec le délai de la mise en œuvre de la conservation dans un

contexte d’accroissement des activités industrielles reliées à l’extraction des ressources naturelles.

Prédiction 3. La conservation des sites à utilisation réelle rapproche les aires protégées de SE près des zones

habitées (où la demande est la plus forte). Similairement, les sites à haut potentiel de conversion pour

l’extraction des ressources naturelles sont généralement situés aux pourtours des zones habitées. Il est donc

à prévoir que l’accroissement des activités industrielles, jumelé au délai de la mise en œuvre des actions de

conservation, entrainera une perte de sites, initialement jugés prioritaires pour la conservation de SE. Pour

atteindre les objectifs de conservation, ces sites devront être remplacés. Je prévois donc que le coût de

remplacement d’un réseau de conservation devrait augmenter proportionnellement avec l’accroissement des

activités industrielles et du délai de la conservation.

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CHAPITRE 2

Fostering synergies between ecosystem services and biodiversity

in conservation planning: a review

Jérôme Cimon-Morin, Marcel Darveau & Monique Poulin

Copie de l’article « Cimon-Morin, J., M. Darveau, and M. Poulin. 2013. Fostering synergies between

ecosystem services and biodiversity in conservation planning: A review. Biological Conservation. 166:144-

154 ».

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Résumé

Notre dépendance envers la biodiversité et les services écologiques (SE) est de plus en plus importante en

raison de l’accroissement de la population humaine et du développement économique. Par conséquent, la

préservation de la biodiversité et le maintien de la durabilité de l’apport des SE devraient systématiquement

faire partie des objectifs de conservation. Nous avons passé en revue 238 articles scientifiques afin de

répondre à trois questions principales : (1) Comment identifier les sites importants pour la conservation des

SE? (2) Comment pouvons nous maximiser la synergie entre la biodiversité et les SE lors de la planification

de la conservation? (3) Est-ce que les SE procurent de nouveaux outils qui pourraient faciliter la conservation

de la biodiversité? Nous avons trouvé que la méthode la plus efficace pour identifier les sites prioritaires de SE

doit être basée sur des indicateurs biophysiques quantifiables ainsi que sur l’échelle spatiotemporelle de leurs

flux. De plus, nous avons déterminé que le manque général de concordance spatiale entre la biodiversité et

les SE est attribuable : (i) au type de données utilisées pour cartographier les SE; (ii) au fait que la diversité

fonctionnelle prédit plus efficacement l’apport de SE que les autres mesures de biodiversité; (iii) au fait que la

biodiversité est généralement corrélée positivement aux services de régulation alors que les services

d’approvisionnements le sont négativement. L’utilisation de procédures de planification systématique de la

conservation qui sont basées sur la complémentarité des sites permet d’accroitre l’efficacité de la conservation

de la biodiversité et de SE. L’évaluation économique des SE, notamment les analyses coût:bénéfice,

permettent de justifier la conservation de la nature en montrant que les bénéfices financiers qu’on en retire

excèdent grandement ses coûts. De plus, les paiements pour les SE peuvent créer des incitatifs et de

nouvelles sources de financement pour la conservation de la biodiversité. Nous concluons en proposant des

pistes pour des recherches futures afin de favoriser davantage la synergie dans la conservation de la

biodiversité et des SE.

15

Abstract

Our dependence on biodiversity and ecosystem services (ES) is increasing, due to population expansion and

economic growth. Consequently, maintaining biodiversity and sustaining ES supply should consistently be

incorporated into conservation project objectives. We reviewed 238 scientific articles to evaluate current

knowledge, guided by three questions: (1) How do we identify important sites for ES conservation? (2) How

can we maximize synergy between biodiversity and ES during conservation planning? (3) Does integrating the

concept of ES provide new tools to facilitate biodiversity conservation? We found that the most effective

approach to identifying ES priority areas for conservation is based on quantifiable biophysical indicators as

well as their spatiotemporal flow scale. Moreover, we found that the general lack of spatial congruence

between biodiversity and ES is attributable to: (i) the type of data used for ES mapping; (ii) the greater

accuracy of functional diversity, compared to other biodiversity features, in predicting ES provision; (iii) the

higher positive spatial correlation of regulating services with biodiversity, whereas provisioning services are

negatively correlated. Systematic conservation planning procedures based on site complementarity would

increase the efficiency of both biodiversity and ES conservation. Economic valuation of ES, such as through

cost-benefit analysis, could help to justify conservation actions by showing that the financial benefits of nature

conservation greatly exceed the cost. Moreover, payments for ecosystem services could create new incentives

and funding sources for the conservation of biodiversity. We conclude by proposing areas for further research

for the fostering of conservation synergies between biodiversity and ES.

16

Introduction

Anthropogenic disturbances have altered the majority of the biosphere’s ecosystems, causing a global loss in

biodiversity (Vitousek et al. 1997, Foley et al. 2005). The current rate of species extinction is 100 to 1000 times

greater than pre-human rates as documented in fossil records, and could rise tenfold during the next century

(Pimm et al. 1995). Ecosystem services (ES) show a similar trend: it is estimated that the sustainability of

nearly two thirds of our planet’s ES supply is currently in jeopardy (Vitousek et al. 1997, Chapin et al. 2000,

Foley et al. 2005, Millennium Ecosystem Assessment (MA) 2005). For instance, while more than two billion

people now have an inadequate supply of drinking water, their numbers could very well double in the next few

decades (Millennium Ecosystem Assessment (MA) 2005). At the same time, increasing population and

economic growth mean that our dependence on biodiversity and need for ES will only continue to grow (Guo et

al. 2010).

Biodiversity is defined as the sum of all biotic variation, including the genetic diversity between individuals of

the same population as well as the diversity of landscapes and ecosystems (Chapin et al. 2000, Purvis et

Hector 2000). ES have been defined by the Millennium Ecosystem Assessment (Millennium Ecosystem

Assessment (MA) 2005) as the benefits that humans obtain from ecosystems, and classified according to four

categories: provisioning, regulating, supporting and cultural services. The link between biodiversity and ES is

complex, given that biodiversity has a key role at all levels of ES production, as a regulator of ecosystem

functions (e.g. pollinator species), as an ES (e.g. cultivated species) and as a good (e.g. charismatic,

aesthetic, and other species that have value of their own) directly consumed by humans (Mace et al. 2012).

Due to the intricate dependency of ES supply on biodiversity (Hooper et al. 2005, Balvanera et al. 2006,

Costanza et al. 2007, Hector et Bagchi 2007, Gamfeldt et al. 2008, Quijas et al. 2010, Cardinale et al. 2011),

species loss could threaten human well-being (Díaz et al. 2005, Díaz et al. 2006, Cardinale et al. 2012).

Until now, most conservation projects have focused on preserving biodiversity (Balvanera et al. 2001). Yet,

conserving all of nature’s biodiversity can be difficult to justify, since most individuals interpret biodiversity as

goods, whose worth is mainly determined by cultural, aesthetic, recreational, existential and intrinsic values

(Chapin et al. 2000, Mace et al. 2012). Thus, international biodiversity conservation efforts and strategies have

often been limited to biodiversity hotspots and have focused on the preservation of species of interest

(charismatic, iconic or exploited species), rare species, threatened or endangered species, and exceptional

landscapes (Lindenmayer et Possingham 1996, Hof et Raphael 1997, Margules et Pressey 2000, Mace et al.

2003, Brambilla et al. 2013). Thus, conservation reserves are frequently located in remote regions where they

conveniently do not interfere with economic development and land conversion (Sánchez-Azofeifa et al. 2003,

Gaston et al. 2008, Joppa et Pfaff 2009, Moilanen et al. 2009a). For example, current networks of protected

areas across 147 countries are biased towards higher elevation, steeper slopes and greater distances to roads

17

and cities (Joppa et Pfaff 2009); these conditions hamper land conversion, agriculture and the exploitation of

natural resources by poor accessibility, less productive ecosystems and rough terrain (Sánchez-Azofeifa et al.

2003).

Recently, the decrease in ES supply worldwide and recognition of the direct impact of ES on human well-being

have sparked interest in incorporating them into conservation choices to ensure their sustainability (Daily 1997,

Millennium Ecosystem Assessment (MA) 2005, Balvanera et al. 2006, Chan et al. 2006, Egoh et al. 2007,

Turner et al. 2007). It is often assumed that preserving biodiversity will also sustain the provision of a range of

ES (Balvanera et al. 2001, Egoh et al. 2007). However, given the complexity of the relationship between

biodiversity and ES, traditional conservation strategies, which are mostly oriented towards biodiversity as a

good, may not be effective in protecting ES (Balvanera et al. 2001, Mace et al. 2012). On the other hand,

conservation projects targeting ES in order to meet society’s needs could result in sacrificing some biodiversity

(Balvanera et al. 2001, Chan et al. 2006), notably, diluting already feeble conservation efforts and the few

available resources.

It is imperative that we identify the opportunities and trade-offs that may stem from including ES in

conservation targets, to attain the ultimate objective of ensuring conservation of both biodiversity and ES. The

current review will examine these issues by addressing three main questions: (1) How do we identify important

sites for ES conservation? (2) How can we maximize synergy between biodiversity and ES during

conservation planning? (3) Does integrating the concept of ES provide new tools to facilitate biodiversity

conservation?

Method

Review structure

Peer-reviewed articles were systematically selected from the search engines Web of Knowledge and Google-

Scholar based on combinations of three keywords. These combinations included “biodiversity” as the first

keyword, “ecosystem service” or “ecological service” as the second keyword and a third term from among the

list presented in Table 2.1. The initial search identified 374 potential documents. Relevance to the research

questions was first assessed by a preliminary reading of each abstract and conclusion. Studies were excluded

from the review if: (1) their findings were not relevant to at least one of the research questions, (2) or if their

findings were largely irrelevant to the conservation of both biodiversity and ES. Relevant articles were then

read attentively and appropriate references were also consulted. A total of 238 scientific documents were

selected for analysis and 153 of these are included in this literature review. Data on the spatial correlation

between the distribution of biodiversity and ES, as well as, data on their hotspot overlap was gathered from

18

appropriate papers when values were available in order to highlight trends in their spatial relationship. The

data was classified according to the spatial scale of analysis, ES categories, and by specific ES. No statistical

analyses were performed on the data.

Table 2.1. Keywords used to search for scientific articles. The terms “ecosystem service” and “ecological

services” were used interchangeably as the second keyword. The third keyword was chosen from among the

terms under the heading “third keyword.”

First Keyword Second Keyword Third Keyword

Biodiversity AND

Ecosystem service

AND

Area selection Ecosystem function

Conservation Hotspot

OR

Conservation network Management

Conservation planning Payment

Conservation value Protected area

Ecological service Cost-benefit Priority area

Ecosystem value Reserve selection

Definitions

In the literature, ES has been defined and classified numerous times to meet various objectives (Costanza et

al. 1997, Daily 1997, de Groot et al. 2002, Millennium Ecosystem Assessment (MA) 2005, Boyd et Banzhaf

2007, Wallace 2007, Costanza 2008, Fisher et al. 2009, TEEB 2010, Balmford et al. 2011, Haines-Young et

Potschin 2013). Given that most of the studies cited in this review use the MA (2005) classification, the same

terminology was used to ensure consistency. By definition, ecosystem functions and processes only become

ES where there are humans to benefit from them (Fisher et al. 2009). In addition, the extent of human impact

on an ecosystem is a key factor in assessing the provision of different categories of ES (see Figure 2.1.;

DeFries et al. 2004, Tscharntke et al. 2005, Bennett et Balvanera 2007, Bennett et al. 2009, Polasky et al.

2010). For instance, provisioning services (provision of food, materials, and drinking water, etc.) and the

majority of cultural services (recreation, ecotourism, etc.) are considered to be nonexistent or low for an

ecosystem in its natural state (i.e. non-human modified ecosystems; de Groot et al. 2010). Even if the natural

ecosystem’s production potential is high for these services, their value as an ES is nil as long as the region is

inaccessible for human use or exploitation (Hörnsten et Fredman 2000, Eigenbrod et al. 2009, de Groot et al.

2010, Holland et al. 2011). In contrast, regulating services (e.g. climate regulation) and supporting services

(e.g. soil formation) are considered to be at a maximum in non-human disturbed ecosystems (de Groot et al.

2010). Thus, different combinations of land-use and land-cover types generate specific bundles of ES (see

Figure 1; Foley et al. 2005, Bennett et Balvanera 2007, Bennett et al. 2009, Burkhard et al. 2009, Egoh et al.

2009, Raudsepp-Hearne et al. 2010a, Bai et al. 2011, Burkhard et al. 2012).

19

Figure 2.1. Theoretical relationship between the provision of ES (Y axis) and the degree of biodiversity loss for

different levels of anthropogenic disturbances (X axis). R: sum of regulation services; P: sum of provisioning

services; Cr: sum of cultural-recreation value; Ci: sum of cultural-information value; ESL: sum of all the ES.

Figure adapted from de Groot et al. 2010.

Systematic conservation planning (SCP; Margules et Pressey 2000, Cowling et Pressey 2003, Margules et

Sarkar 2007) is defined as a multi-component stage-wise approach to identifying conservation areas and

devising management policy, with feedback, revision, and reiteration, where needed, at any stage (Sarkar et

Illoldi-Rangel 2010). SCP aims to optimize conservation networks through iterative identification of the smallest

set of territorial units (in terms of number or area) required to meet conservation targets (Pressey et al. 1996,

Margules et Pressey 2000). Achieving optimality mainly reduces costs associated with conservation (Pressey

et al. 1996, Naidoo et al. 2006) by selecting sites that are complementary to one another. Thus, the sites

selected at each iteration are not the most diverse, but those which add the most species (or ES) to the initial

network (Margules et Pressey 2000). SCP is increasingly recommended to ensure not only biodiversity

conservation, but also ES provision (Egoh et al. 2008).

20

How do we identify important sites for ES conservation?

For effective conservation and the long-term protection of desired natural features, it is essential that priority

zones for conservation action be identified. Priority areas for ES conservation have been generally described

solely on the spatial assessment of their supply. The term hotspot has often been used in the literature to refer

to an area that provides a maximum supply of a given service; for instance, hotspots account for 5 to 10

percent of the most important sites (Egoh et al. 2009, Bai et al. 2011, Egoh et al. 2011, Larsen et al. 2011,

Onaindia et al. 2013). Quantifiable biophysical indicators can facilitate the identification of ES hotspots for

conservation planning (Heal 2000, Cowling et al. 2008). Examples of such indicators include cubic meters of

drinking water and tons of carbon stored by hectare (Egoh et al. 2010, Raudsepp-Hearne et al. 2010a, Bai et

al. 2011, Egoh et al. 2011, Burkhard et al. 2012, Maes et al. 2012, Kandziora et al. 2013). Other indicators,

such as monetary value (Costanza et al. 1997, Sutton et Costanza 2002, Turner et al. 2007) and those based

on expert knowledge (Burkhard et al. 2009, Burkhard et al. 2012, Busch et al. 2012), are more likely than

biophysical indicators to fluctuate at different spatial and temporal analysis scales (Maes et al. 2012). Yet, less

than 30% of studies that have mapped ES have used biophysical indicators (Seppelt et al. 2011). Determining

the best possible indicator to quantify and represent each ES is thus a challenge (Seppelt et al. 2011,

Burkhard et al. 2012; for examples of indicators see de Groot et al. 2010 and Kandziora et al. 2013).

Selecting a good biophysical indicator may require prior study of the ES ecology in order to identify the ES

providers (Kremen 2005, Kremen et Ostfeld 2005). These are features that collectively ensure the supply of a

particular ES and that can be found at one or several different ecological levels, including populations, species,

functional groups, trophic networks, and habitat types. Once ES providers have been identified, a measurable

unit representative of these featured ecological levels must be determined; one that is sufficiently influential to

serve as an indicator.

However, for effective conservation, the identification of ES priority areas must also take into consideration the

spatiotemporal variability of ES production and demand (ES flow; Figure 2.2.). This refers to the time and

place a service is produced in relation to the time and place that it will actually be used by humans (see also

de Groot et al. 2002, Hein et al. 2006, Balmford et al. 2008, Costanza 2008, Fisher et al. 2009, Balmford et al.

2011). For the majority of ES, the benefits associated with use by humans are only perceivable at the local

scale (spatial scales 1, 2, and 3 in Figure 2), but some are observable on a broader scale; one example being

the global impact of carbon storage. Sites for maintaining global flow ES can thus be selected solely according

to the potential biophysical production of sites, while the selection of sites that promote local flow ES must also

take into account the presence of human beneficiaries (see Chan et al. 2006). Therefore, the integration of

demand for ES in conservation assessments can generate a spatial decoupling between potential hotspots of

ES, as defined by the biophysical capacity of production, and the sites that are actually used by human

21

beneficiaries. For example, demand for recreation services is more driven by the proximity to roads and the

size and the distance to nearby population centres than by the actual biophysical potential of a site (Chan et al.

2006, Holland et al. 2011). In other words, the actual conservation value of sites for ensuring the sustainability

of locally produced ES decreases with increasing distance from humans (Hörnsten et Fredman 2000,

Eigenbrod et al. 2009, Holland et al. 2011, Kozak et al. 2011). In this regard, ES could justify the need for the

establishment of protected areas closer to human populations (see Joppa et Pfaff 2009).

Figure 2.2. ES spatial flow scales. The light colored area shows the zone where ES are produced, while the

dark encircled area illustrates the zone where ES are used. Figure adapted from Balmford et al. 2008.

In regions dominated by humans, where demand for ES is widespread, ES priority areas may be identified

based only on the biophysical potential (e.g. hotspots). In less populated landscapes (e.g. remote regions),

22

where demand for ES is not globally distributed, ES quantification stems instead from the distribution of

beneficiaries in relation to the spatiotemporal configuration of ES flows. This means that the actual priority

areas to be protected are not necessarily zones of high biophysical potential, but rather the zones that are

most important for ensuring the continuous supply and delivery of these services to their beneficiaries. While

most studies have mapped the biophysical potential of ES (Egoh et al. 2009, Maes et al. 2012, Schneiders et

al. 2012) and their demand (van Jaarsveld et al. 2005, Naidoo et Ricketts 2006, Luck et al. 2009, Burkhard et

al. 2012), there does not seem to be a framework for identifying ES priority areas based on their provision,

proximity to beneficiaries, accessibility, demand, and spatial configuration of ES flows (Maes et al. 2012).

However, for the time being, economic methods, such as contingent valuation, could be used to complement

the biophysical assessment of ES by assessing spatial and temporal demand for ES (Cowling et al. 2008,

Burkhard et al. 2012).

How can we maximize synergy between biodiversity and ES during

conservation planning?

One of the objectives of conservation is to ensure the long-term survival of targeted features (e.g. species or

ES); notably, by excluding pressures and threats to their conservation (Margules et Pressey 2000). Although

anthropogenic disturbances have altered biodiversity around the world (Vitousek et al. 1997, Millennium

Ecosystem Assessment (MA) 2005), it may be impossible to spatially separate most ES under conservation

from their connections with their human beneficiaries (Reyers et al. 2012). Joint conservation actions for

biodiversity and ES can be undertaken (1) if their respective priority areas are spatially congruent (i.e. if

biodiversity can represent ES provision; Turner et al. 2007) or (2) when a complementary set of sites can be

identified (Chan et al. 2006). An increasing number of researchers are investigating the congruence and

complementarity between biodiversity and ES. Their studies represent a substantial source of information for

clarifying the circumstances and approaches that could maximize this synergy.

Spatial congruence between ES and biodiversity

Some experts have advocated for global conservation priorities, given the glaring worldwide lack of funding

(Mittermeier et al. 1998, Brooks et al. 2006, Wilson et al. 2006), in order to focus international efforts on zones

where cost-effectiveness is high (i.e. amount of biodiversity protected per unit of cost; Balmford et al. 2003).

Analysis has shown a positive spatial relationship between biodiversity and ES hotspots on a global scale,

particularly in tropical forests (see Table 2.2.; Sutton et Costanza 2002, Turner et al. 2007, Luck et al. 2009,

Strassburg et al. 2010, Larsen et al. 2011, Larsen et al. 2012). In these regions, investment and strategies to

23

preserve ES are more likely to simultaneously conserve biodiversity (Balmford et al. 2003, Millennium

Ecosystem Assessment (MA) 2005, Naidoo et al. 2008).

However, most of the other regions in the world (e.g. arid, boreal, temperate, and polar regions) are subject to

conservation trade-offs since global biodiversity and ES hotspots showed weak spatial overlap (Sutton et

Costanza 2002, Turner et al. 2007, Strassburg et al. 2010, Larsen et al. 2011). Therefore, setting conservation

priorities based on the global congruence between biodiversity and ES risks to leave some important

ecosystems for ES provision, to go unprotected and vice-versa (Kareiva et Marvier 2003, Lavorel et al. 2011).

Furthermore, managing ES conservation on a global scale would fail to take into account that most ES provide

benefits locally. ES conservation would therefore be better ensured at this scale, where there are human

beneficiaries.

The spatial congruence between biodiversity and global flow ES also seem to diminish when evaluated at

scales appropriate for ES conservation planning (e.g. national to local scales). For example, on a global scale,

carbon storage correlates positively with biodiversity (Strassburg et al. 2010, Siikamäki et Newbold 2012),

notably in tropical zones but it has been shown that habitats most important for biodiversity do not coincide

spatially, at a finer scale of analysis, with habitats richest in carbon (Nelson et al. 2008, Anderson et al. 2009,

Paoli et al. 2010). This is particularly evident in Indonesia, where the protection of carbon-rich habitats

redirects pressures and threats onto neighboring habitats even richer in biodiversity (Paoli et al. 2010). Thus,

when synergies seem possible, analyses at a finer scale often reveal trade-off zones between some services

and biodiversity (Naidoo et al. 2008).

At scales compatible with conservation planning of most ES (national to local), the spatial relationship between

biodiversity and ES is positive, but generally ranges from low to moderate (see Table 2.2.; Chan et al. 2006,

Anderson et al. 2009, Egoh et al. 2009, Paoli et al. 2010, Bai et al. 2011, Chan et al. 2011, Holland et al. 2011,

Izquierdo et Clark 2012, Schneiders et al. 2012, Onaindia et al. 2013). Several factors could explain this

finding. First, it has been attributed to the type of data used in mapping (Eigenbrod et al. 2010a, 2010b,

Martínez-Harms et Balvanera 2012). Lacking primary data, most studies that aim to map ES distribution use

proxies related to land-use and land-cover type (Yapp et al. 2010, Seppelt et al. 2011, Haines-Young et al.

2012, Maes et al. 2012, Martínez-Harms et Balvanera 2012). These proxies are often obtained using the

benefit-transfer method (Plummer 2009, Eigenbrod et al. 2010a, Eigenbrod et al. 2010b), which involves

estimating monetary or non-monetary value of an ES that has been measured at a very small scale and then

applying this value at a larger scale or to another region (Costanza et al. 1997, Balmford et al. 2002, Sutton et

Costanza 2002, Chan et al. 2006, Egoh et al. 2007). ES mapped using the benefit-transfer method show a

weak spatial correlation when compared with the same ES but mapped according to primary data (Eigenbrod

24

et al. 2010a, Eigenbrod et al. 2010b). For example, the degree of hotspot overlap can range from 17% to 62%

depending on the ES considered (Eigenbrod et al. 2010b). The benefit-transfer is even more problematic when

several ES are considered simultaneously because biases tend to compound, hindering the identification of

ES conservation priorities and their spatial congruence with biodiversity (Eigenbrod et al. 2010a, Eigenbrod et

al. 2010b). Yet, more than 60% of ES studies still use secondary data to map ES distribution (Seppelt et al.

2011, Martínez-Harms et Balvanera 2012).

Second, the weak correlation observed between biodiversity and ES can also be linked to the categories of

assessed ES. Non-human disturbed ecosystems seem to provide maximal regulating and supporting services,

while the highest supply of one or several provisioning and cultural services arise from ecosystems that are

used or exploited by humans (Figure 2.1.). Provisioning services, especially food services, are weakly or

negatively correlated with other types of services, as well as with biodiversity (Table 2.2.; Egoh et al. 2008,

Anderson et al. 2009, Raudsepp-Hearne et al. 2010a, Raudsepp-Hearne et al. 2010b, Holland et al. 2011,

Maes et al. 2012, Schneiders et al. 2012). Actions taken to maximize production of provisioning services for a

given territory risk substantially altering the production of all other ES, and threatening biodiversity (Foley et al.

2005, Bennett et al. 2009, Raudsepp-Hearne et al. 2010b). Nevertheless, some provisioning ES could be

compatible with biodiversity conservation with restrictions on practices (low land-use intensity) to ensure

sustainability and no biodiversity loss (e.g. controlled hunting, recreational angling, subsistence uptake; Chan

et al. 2011, Schneiders et al. 2012). Regulating services, in turn, tend to show a significant and positive spatial

correlation among each other and with a greater diversity of services and biodiversity (Raudsepp-Hearne et al.

2010a, Maes et al. 2012, Reyers et al. 2012, Schneiders et al. 2012). Therefore, regulating, supporting, and

most cultural ES seem compatible with biodiversity conservation. Yet, nearly half of the studies that have

mapped ES, especially those that studied the spatial congruence between biodiversity and ES, consider only

five or fewer ES simultaneously (Seppelt et al. 2011). ES selection is often limited to those that are easily

mapped, those for which data is available or those that are involved in decision making (e.g. carbon related

services, food production and recreation; Seppelt et al. 2011, Martínez-Harms et Balvanera 2012); the latter

which are often those negatively or weakly correlated to biodiversity (see Table 2.2.). Many ES of prime

importance to human welfare or ecosystem maintenance, such as cultural services and most regulating

services, are still rarely addressed in these studies (Martínez-Harms et Balvanera 2012). It is likely that

increasing the number and diversity of ES evaluated and the use of primary data will increase the possibility of

finding a combination that will allow opportunities for the simultaneous conservation of biodiversity.

Third, the weak congruence observed between biodiversity and ES also rises from the fact that functional

diversity (i.e. the value, range, distribution, and relative abundance of functional traits of the organisms that

make up an ecosystem), rather than species richness, is the most significant biodiversity feature explaining the

25

presence and the production of ES at a given location (Díaz et Cabido 2001, Balvanera et al. 2006, Cadotte et

al. 2011, Díaz et al. 2011). For example, ES production may be linked to the presence of a functional trait

existent only in dominant species (abundant trait), while other ES originate from the range or variety of

functional attributes, or from the presence of a specific species (e.g. particular material or symbolic value; Díaz

et al. 2011). However, a significant spatial discrepancy may exist between the distribution of functional

diversity and species richness, or at the very least, the link between these two types of diversity is not constant

across different trophic levels and spatial scales (Cadotte et al. 2011). While, the level of diversity required to

provide an ES is often low, many ES are provided by organisms not normally considered during biodiversity

conservation planning. For example, soil organisms are important contributors to food and water purification

services but they are rarely assessed. Moreover, ES could be used as surrogates to represent functional

diversity during conservation planning and, thus, contribute to maintenance of ecosystem functions.

The limited congruence between biodiversity and ES suggests that efforts to conserve them will be

inefficient unless they are both explicitly considered in conservation planning (Chan et al. 2006, Naidoo et al.

2008, Larsen et al. 2011, Onaindia et al. 2013). A complementary approach, such as

systematic conservation planning, could therefore be used to better align these two objectives.

Complementarity between ES and biodiversity in reserve selection

It has been shown that a systematic conservation planning (SCP; see definition section) approach focused

exclusively on biodiversity targets may conserve only a moderate ES supply (Chan et al. 2006, Naidoo et al.

2008, Chan et al. 2011, Egoh et al. 2011, Larsen et al. 2012, Thomas et al. 2012). The proportion of each ES’s

targets unmet by a biodiversity network alone may range from 0% to 83% (Chan et al. 2006, Naidoo et al.

2008, Larsen et al. 2011), and sometimes ensures less ES provision than a random site selection (Thomas et

al. 2012). Conversely, a SCP scenario focused only on ES targets would prove equally inappropriate for

meeting biodiversity targets, as shown by several studies in which 36% to 95% of biodiversity targets remained

unmet (Chan et al. 2006, Naidoo et al. 2008, Larsen et al. 2011, Thomas et al. 2012, Onaindia et al. 2013).

These results reflect the lack of spatial congruence between biodiversity and ES. However, better results can

be obtained using SCP scenarios that integrate conservation targets for both biodiversity and ES

simultaneously (Chan et al. 2006, Larsen et al. 2011, Thomas et al. 2012, Onaindia et al. 2013). For example,

one study found that upwards of 90% of both biodiversity and carbon storage targets were obtained using a

biodiversity-carbon combined approach that selected sites based on their complementarity (Thomas et al.

2012). Such an approach makes it possible to reduce the total number of sites in the network required to meet

the conservation targets for both objectives (Egoh et al. 2010). This is especially true for regions where many

alternatives exist; that is, regions where many undisturbed or natural sites are available for conservation.

26

Table 2.2. Spatial relationship between biodiversity and ecosystem services (--: medium negative, -: low negative, 0: no correlation or weak overlap, +: low positive, ++: medium positive, +++:

high positive). Correlation is calculated between ES value and biodiversity value across planning units in the studied area. Hotspot overlap is the number of planning units that co-occur between

biodiversity hotspots and ES hotspots in the studied area.

Spatial scale ES category ES Spatial Relationship

References Correlation1 Hotspot overlap2

Global or

continental

Provisioning Water supply ++ + Larsen et al. 20112, Luck et al. 20091

Regulating Carbon storage ++ to +++ + Larsen et al. 20112, Luck et al. 20091, Siikamäki and Newbold 20121, Strassburg et al. 20101

Water flow regulation ++

Luck et al. 20091

ES economic value ----------------- ++ Turner et al. 20071

National Provisioning Agricultural value + to ++ 0 Anderson et al. 20091,2, Holland et al. 20111

Water supply + + Egoh et al. 20091,2

Regulating Carbon storage -- to + + Anderson et al. 20091,2, Egoh et al. 20091,2, Eigenbrod et al. 2010b1, Holland et al. 20111

Water flow regulation + ++ Egoh et al. 20091,2

Cultural Recreation - to 0 0 Anderson et al. 20091,2, Holland et al. 20111

Supporting Soil accumulation + ++ Egoh et al. 20091,2

Soil retention + ++ Egoh et al. 20091,2

Local to

Regional

Provisioning Provisioning ES -

Schneiders et al. 20121

Angling +

Chan et al. 20111

Crops --

Newton et al. 20121

Forage production -

Chan et al. 20061

Food ES --


Non-food ES +


Timber -

Chan et al. 20111

Water supply - to ++ ++ Bai et al. 20111,2, Chan et al. 20061, Izquierdo and Clark 20121

Regulating Regulating ES ++


Carbon storage 0 to +++ ++ Bai et al. 20111,2, Chan et al. 20111, Chan et al. 20061, Izquierdo and Clark 20121

Pollination - to 0 0 Bai et al. 20111,2, Chan et al. 20061

Water flow regulation + to ++

Chan et al. 20061, Newton et al. 20121

Water quality - 0 Bai et al. 20111,2

Cultural Cultural ES ++


Recreation + to ++

Chan et al. 20061, Newton et al. 20121

Supporting Soil retention ++ to +++ + Bai et al. 20111,2, Izquierdo and Clark 20121

Mean for twenty ES ----------------- ++ Schneiders et al. 20121

1Study from which correlation data were collected. 2Study from which hotspot overlap data were collected.

27

For the time being, the best strategy for maximizing the likelihood of maintaining high levels of biodiversity

within conservation plans that also aim to sustain ES might involve a strategic choice of ES, omitting those

known to correlate negatively with biodiversity (Chan et al. 2006). For example, biodiversity losses were

reduced by more than 20% when two ES that were negatively associated with biodiversity were excluded from

the six initial ones (Chan et al. 2006). Specific conservation or management measures would be required to

ensure the supply of ES not included in biodiversity conservation plans.

Does integrating the concept of ES provide new tools to facilitate

biodiversity conservation?

Biodiversity is still experiencing a rapid decline, primarily because conservation costs are perceived as being

too great (Balmford et al. 2003, Balmford et Whitten 2003). Biodiversity conservation generates spatially

heterogeneous costs that include acquisition, management, damage, transaction and opportunity expenses

(Naidoo et al. 2006), which are in some cases detrimental to local human populations (Díaz et al. 2006, Linnell

et al. 2011). In addition, given that resources are very limited, such investments must be attributed strategically

(Margules et Pressey 2000, Naidoo et Ricketts 2006). When projected conservation costs outweigh the

benefits to be gained from biodiversity, there is a risk that conservation will be limited to distant, unproductive

and uninteresting localities (Moilanen et al. 2009a, Arponen et al. 2010).

Although human valuation of biodiversity is low and may be insufficient to promote conservation, the obligation

to prioritize human well-being by incorporating ES into conservation plans could prove to be a powerful

incentive for conserving nature (Balmford et al. 2002, Goldman et al. 2008). Thus, when spatial overlap

between biodiversity and ES is possible, the economic valuation of the latter would allow for the expression of

natural assets using the same common denominator as the other deciding factors; that is to say, using money

(Kerkhof et al. 2010). Monetizing ES would promote both the social acceptance of ES conservation and its

attainment, by providing reasons other than ethical and cultural ones for biodiversity conservation (Knight et al.

2006a, Egoh et al. 2007, Goldman et al. 2008). Among the possible applications for the economic valuation of

ES (Liu et al. 2010, Billé et al. 2012), cost-benefits analysis and payments for ES could each support

biodiversity conservation directly.

Measuring the cost-benefit ratio of conservation

In this context, measuring the cost-benefit ratio consists of quantifying the economic advantages and the costs

related to the conservation of a given territory. Comparison of the spatial distribution of costs and benefits with

28

that of biodiversity helps to delineate zones in which conservation would have an economic advantage (costs

< benefits) for both humans and biodiversity, as well as trade-off zones in which costs and benefits are high

(Naidoo et Ricketts 2006).

Cost-benefit studies have shown that ecosystems under conservation generally provide substantial benefits

that, in most cases, are equal to or greater than the cost of conservation (Kremen et al. 2000, Balmford et al.

2002, Naidoo et Adamowicz 2005, Naidoo et Ricketts 2006, Naidoo et al. 2009, Hein et van der Meer 2012,

Polasky et al. 2012, Ruiz-Frau et al. 2013). As a result, the benefits are frequently driven by the value of

carbon storage, which often provides greater benefits than when multiple other ES are combined (Kremen et

al. 2000, Balmford et al. 2003, Naidoo et Ricketts 2006, Naidoo et al. 2009, Nelson et al. 2009, Newton et al.

2012, Reichhuber et Requate 2012). The economic benefits associated with conservation are therefore

sensitive to the carbon price which is currently highly volatile. Thus, when conservative or lower estimates of

current international carbon prices are used, such as voluntary market prices, the value of carbon alone may

be insufficient to compete against the most economically attractive land-use options, for example oil palm

agriculture or timber harvest (Butler et al. 2009, Venter et al. 2009, Hein et van der Meer 2012, Reichhuber et

Requate 2012, Irawan et al. 2013). The competitiveness of carbon may increase if reasonable levels of

international carbon prices are used (Butler et al. 2009, Venter et al. 2009, Hein et van der Meer 2012, Irawan

et al. 2013). However, when lower estimates of carbon prices are used, conservation may become an

economically viable land-use option when the economic benefits of others ES are aggregated to the value of

carbon (Naidoo et al. 2009, Hein et van der Meer 2012). In fact, the greater the number of ES included in the

calculation of conservation benefits, the greater the value of a given parcel of land (Kremen et al. 2000, Naidoo

et Ricketts 2006, Hein et van der Meer 2012, Polasky et al. 2012). Yet, most of the studies that have

calculated the benefits of conservation have considered only five or fewer ES simultaneously. When multiple

ES are considered, the cost to benefit ratio of conservation might even be as high as 1:2 to 1:100 from a

regional to a global scale (Costanza et al. 1997, Balmford et al. 2002, Anielski et Wilson 2009, Polasky et al.

2012). In addition, conservation practices that allow for light land-use often present the highest economic value

since some provisioning and more cultural ES can be added to the valuation (see Figure 1), notably the

sustainable exploitation of non-timber forest products and ecotourism (Kremen et al. 2000, Naidoo et

Adamowicz 2005, Naidoo et Ricketts 2006, Naidoo et al. 2011, Hein et van der Meer 2012, Reichhuber et

Requate 2012, Ruiz-Frau et al. 2013). It might be advantageous to determine effective strategies for the

development and use of these ES (Kremen et al. 2000, Naidoo et Adamowicz 2005, Naidoo et al. 2011). A

more representative inventory of all ES produced in a given territory could be a key piece of data for promoting

conservation, since it can increase projected conservation benefits over costs (Naidoo et Ricketts 2006,

Newton et al. 2012). According to these findings, different ES bundles that accrue more local benefits could

foster the identification and preservation of zones where conservation costs are closer to demand, while

29

services with a high economic value, such as carbon, could be used in complementarity to extend

conservation beyond those territories.

During the last decade, integrating cost data into systematic conservation planning (SCP) approaches has

proved to increase efficiency in reserve selection; in other words, it helped achieve conservation targets at

least cost or achieve the most conservation given a limited budget (Naidoo et al. 2006). While provisioning ES

associated with the dominant anthropogenic land-use (e.g. agriculture, timber harvest, commercial fisheries)

have been previously used as estimates of opportunity costs of conservation, ES associated with conservation

benefits have mostly been integrated in SCP as targeted features to be protected. Recently, it has been shown

that incorporating ES into SCP approaches as co-benefits or costs, rather than targeted features, could yield a

much more cost-effective conservation network (Chan et al. 2011). In order to maximize the net economic

benefits of conservation, the economic value of each ES is added (co-benefits) or subtracted (costs) from the

total value of each planning unit according to the change in ES provision associated with conservation. For

example, by adding to the other cost measures the value of carbon storage and recreational angling as co-

benefits, and timber harvest as opportunity cost, it has been possible to further reduce the total cost of a

conservation network by 15% (Chan et al. 2011).

However, conservation planning guided primarily by cost-benefit analysis is likely to favor the services (and

their associated species) whose markets are more lucrative, and have negative impacts on biodiversity and

ecosystem sustainability (Redford et Adams 2009, TEEB 2010, Admiraal et al. 2013). This could be especially

true when only a small number of ES are included in the calculation. In fact, only 20% of ES have a value that

is currently represented in the global economic market (Costanza et al. 1997, Daily et al. 2000, de Groot et al.

2002, Turner et al. 2003, Balmford et al. 2008, de Groot et al. 2010, Salles 2011). Even the market value, for

those ES that have one, can fluctuate, as does any other traded item (e.g. the price of carbon stock; Redford

et Adams 2009). Furthermore, all of the numerous economic approaches that can be used to assess the value

of indirect-use or intangible-value ES (Liu et al. 2010), have significant limitations and potential sources of bias

(Balmford et al. 2011, Fu et al. 2011, Tisdell 2011).

Despite these conflicting findings, cost-benefit analysis constitutes an important justification for biodiversity

conservation. In addition, given that the costs of conservation are not always paid by those who derive the

benefits, the inclusion of ES in the cost-benefit analysis makes it possible to identify the real conservation

beneficiaries (Balmford et Whitten 2003). In a world where the majority of costs are covered by local

communities —especially in developing countries— the identification of new stakeholders could help secure

additional funding sources. For example, the conservation of global flow ES could be assured by the

international community, and the conservation of other ES at the national level. This would seem appropriate

30

given that the global and national levels generally benefit most from conservation, yet tend to pay the least for

it.

The use of payments for ecosystem services

Despite the investments made by the international community to stem the loss of biodiversity in developing

countries, losses continue (Ferraro et Kiss 2002). Traditional conservation strategies, such as the

establishment of protected areas, generate not only high opportunity costs for local communities, but also lack

sustainable funds for management and safeguard of the protection and integrity of biodiversity (Wendland et

al. 2010, Gross-Camp et al. 2012). To rectify these short-comings, one approach would be to offer payments

for ecosystem services (PES; Ferraro et Kiss 2002), whereby landowners would receive financial rewards for

producing, protecting or restoring ES on behalf of the users who would benefit from them directly (Wunder

2007, Engel et al. 2008, Porras 2012). In Costa Rica, for example, PES programs have been integrated into

the economy for nearly 15 years (Pagiola 2008, Porras 2012). PES foster new funding sources that can boost

the value of an ecosystem and promote biodiversity conservation as a viable and competitive land-use option

while generating substantial benefits for local communities in need (Adams et al. 2004, Pagiola et al. 2005,

Wendland et al. 2010). Outside protected areas, especially where unsustainable activities are still practiced as

a source of income or food supply (e.g. slash and burn agriculture), PES could be viewed as a fairer and less

costly alternative than the establishment of reserves to protect biodiversity (Wendland et al. 2010). Also, PES

offer a more favorable cost-efficiency ratio for biodiversity conservation (e.g. more biodiversity conservation

per dollar spent) than traditional conservation tools such as protected areas (Wendland et al. 2010).

PES schemes could foster biodiversity conservation by targeting regulating, supporting and cultural ES; in

other words, those ES most compatible with biodiversity. Among regulating ES, great importance has been

placed on the value of carbon (Kremen et al. 2000, Naidoo et Ricketts 2006, Nelson et al. 2008, Wendland et

al. 2010, Newton et al. 2012). The emergence of voluntary markets and payment methods for carbon, such as

Reducing Emissions from Deforestation and Forest Degradation (REDD+), could contribute to the

conservation of several ecosystems and their related biodiversity (Venter et al. 2009, Strassburg et al. 2010,

Thomas et al. 2012). Other regulating services have also been used in the creation of PES, including water

flow regulation (Guo et al. 2000, Guo et al. 2007, Fisher et al. 2010, Porras 2012), water purification

(Wendland et al. 2010, Van Hecken et al. 2012) and groundwater recharge (Roumasset et Wada 2013).

Based on the principle that biodiversity must pay for itself by generating economic benefits, ecotourism, as a

cultural ES, shows great potential in establishing a PES efficient enough to safeguard biodiversity by creating

additional revenue and local employment opportunities (Gössling 1999, Naidoo et Adamowicz 2005, Naidoo et

31

al. 2011, Ruiz-Frau et al. 2013). For example, in a Ugandan reserve, the sole optimization of admission fees

and distribution of this new revenue among local populations (a form of PES) justifies the conservation of a

large part of the reserve and suffices to adequately protect the majority of bird species (Naidoo et Adamowicz

2005). Likewise, the revenue generated by ecotourism and trophy hunting in Namibia has fostered biodiversity

conservation by generating more benefits for the local rural population (Naidoo et al. 2011). In view of these

benefits, it has recently been suggested that the wildlife and nature media industry should contribute to

conservation, paying for the entertainment value they derive from nature, for example, by redistributing a share

of the income perceived by distributors and broadcasters (Jepson et al. 2011).

The implementation of PES still faces a number of challenges, particularly in developing countries; for

example, how to ensure equitable redistribution of payments, and how to ensure the continuity of payments

when confronted with changing external conditions or project time limits (Engel et al. 2008, Wendland et al.

2010, Gross-Camp et al. 2012). For the moment, PES programs continue to evolve and constitute a promising

approach for the simultaneous conservation of biodiversity and ES.

Conclusion

Increasing anthropogenic pressures due to global population growth are expected to shrink natural

ecosystems and, as a result, cause a worldwide decline in biodiversity and ES (Vitousek et al. 1997,

Millennium Ecosystem Assessment (MA) 2005). This review has confirmed that there is a general lack of

spatial congruence between biodiversity and ES. Thus, making conservation choices according to priority

areas of either biodiversity or ES could result in the dilution of the resources available for conservation, as well

as an inability to meet conservation targets. Complementarity based approaches, such as systematic

conservation planning (SCP), could therefore be used to increase the efficiency in reserves selection. In

addition, the inclusion of ES in conservation is likely to generate more advantages than disadvantages where

biodiversity conservation is concerned. In this regard, ES increase not only the social acceptance and

attainment of conservation project objectives, but their economic valuation also raises new arguments and

tools in favor of biodiversity conservation.

Moreover, this review has highlighted critical knowledge gaps whose assessment could help to further foster

the conservation of both biodiversity and ES. First of all, more research is needed to determine whether the

use of primary data for ES mapping (instead of secondary data) and the use of an increasing number and

diversity of ES in conservation assessments will increase the likelihood of a win-win scenario between

biodiversity and ES. Secondly, further research into the identification of ES priority areas for conservation is

32

required. For instance, determining the best biophysical indicators to represent each ES’s provision could help

the assessment of the biophysical potential of sites. Also, in order to efficiently fulfill and sustain the demand

for ES by their human beneficiaries, it is necessary to develop a framework that would identify ES priority

areas for conservation by combining the biophysical potential of ES with spatially explicit proxies of human

demand and the spatiotemporal flow scale of ES. Furthermore, the complementarity between biodiversity and

ES needs to be evaluated when the latter are mapped using such a framework, as opposed to relying

exclusively on the amount of ES supply. However, it is still unclear how best to explicitly integrate ES into SCP

procedures; in other words, how best to integrate ES as co-benefits and costs, as opposed to targeted

features. Where resources for conservation are limited, the economic value of ES may be integrated into SCP

to favor a better cost-effectiveness ratio in reserve selection. More research still needs to be conducted in

order to determine whether using ES to maximize the net economic benefit of reserve network will effectively

ensure their provision in different contexts and scenarios.

33

CHAPITRE 3

Towards systematic conservation planning adapted to the local

flow of ecosystem services


Copie de l’article « Cimon-Morin, J., M. Darveau, and M. Poulin. 2014. Towards systematic conservation

planning adapted to the local flow of ecosystem services. Global Ecology and Conservation. 2:11-23 ».

34

Résumé

Les services écologiques (SE) sont de plus en plus considérés dans la planification de la conservation à

travers le monde afin de maintenir leur capacité à combler les besoins des êtres humains. En raison de la

valeur instrumentale inhérente des SE, les sites prioritaires pour leur conservation devraient être sélectionnés

en fonction de leur capacité (1) à assurer un apport accessible et (2) à répondre à la demande des

bénéficiaires. Cependant, une telle méthode n’a pas encore été mise au point. Dans l’objectif d’adapter les

approches de planification systématique de la conservation aux SE, nous avons réalisé une étude de cas dans

l’est du Canada considérant dix SE et 16 types de milieux humides. Nous avons commencé par délimiter

l’apport des SE qui est accessible pour l’utilisation humaine à partir de leur apport biophysique totale. Nous

avons aussi cartographié la demande pour chacun des SE. Deuxièmement, nous avons assemblé des

réseaux de conservation qui ciblaient l’apport accessible ainsi que la demande et comparé ces derniers avec

des réseaux qui ne ciblait uniquement que l’apport biophysique ou l’apport accessible des SE. Nous avons

trouvé qu’en ciblant seulement l’apport des SE, les sites sélectionnés pouvaient ne pas être en demande et

les réseaux pouvaient être jusqu’à trois fois moins performants pour répondre à la demande pour les SE à

échelle locale. Ainsi, ne pas considérer la demande dans les choix de conservation échouerait à positionner

les réserves là où l’apport des services est susceptible d’être le plus utile. Établir des cibles de conservation

pour l’apport et la demande des SE pourrait donc favoriser l’atteinte des objectifs de conservation.

35

Abstract

Ecosystem services (ES) are increasingly included in conservation assessment worldwide to sustain their

ability to fulfill human needs. Due to the instrumental value inherent in ES, priority areas for their conservation

should be selected based on their capacity to both ensure an available supply and meet beneficiary demands.

However, such a methodology has yet to be developed. Aiming to adapt systematic conservation planning

procedures to include ES, we conducted a case study in eastern Canada focusing on ten ES for 16 wetland

types. We first delimited the ES supply accessible for human use from the total biophysical supply and

mapped demand for each ES. Secondly, we assembled conservation networks targeting the accessible supply

and demand and compared them with networks targeting either ES biophysical supply or accessible supply.

We found that targeting only ES supply resulted in selecting sites that are not in demand and may be up to

three times less efficient in fulfilling the demands of beneficiaries for local flow ES. Thus, not considering

demand in ES conservation assessment fails to position reserves where ES supply is likely to be most useful.

Setting conservation targets for ES supply and demand could therefore help to achieve ES conservation

objectives.

36

Introduction

Steady expansion of the world’s population and economic growth will continue to increase pressure on natural

ecosystems and accelerate the decline of the supply of most ecosystem services (ES) observed around the

globe (Vitousek et al. 1997, Chapin et al. 2000, Foley et al. 2005, Millennium Ecosystem Assessment (MA)

2005). ES have been defined as the benefits that humans obtain from ecosystems and have been classified

according to four categories: provisioning, regulating, supporting and cultural services (Millennium Ecosystem

Assessment (MA) 2005). In the short term, modern land use practices can increase the supply of most

provisioning services (i.e. food and material), but in the long term they undermine the capacity of ecosystems

to provide other services, such as freshwater supply, climate regulation and recreational opportunities (Foley

et al. 2005, Millennium Ecosystem Assessment (MA) 2005). The growing awareness of the importance of ES

for human well-being has increased interests in securing their sustainability, notably through land protection

and related conservation actions (Millennium Ecosystem Assessment (MA) 2005, Balvanera et al. 2006, Chan

et al. 2006, Egoh et al. 2007, Turner et al. 2007). Human societies’ demand for and dependence on ES is

expected to grow (Guo et al. 2010), and along with it, the need to sustain ES availability.

ES provide benefits on different spatial flow scales (i.e. ranging from local to global), depending on where a

service is produced (source) relative to where its benefits can be perceived (sink) by human beneficiaries

(Fisher et al. 2009, Balmford et al. 2011, Bagstad et al. 2013, Cimon-Morin et al. 2013).Protected areas for ES

have to be identified based on their capacity to provide a continuous flow of ES to their specific beneficiaries.

From a conservation perspective, most ES have a local spatial flow scale; for this reason beneficiaries must

approach or enter the protected area where the ES is supplied to obtain its benefits (thereafter referred as

“local flow ES”). For example, recreational angling in a protected area requires the angler to capture (sink) the

fish species within the protected area (source), established to conserve nature and its associated ES, even if

the benefit (i.e. the meat) can be consumed elsewhere. Moreover, demand for protecting ES, or the sum of the

benefits currently obtained in a particular area (Burkhard et al. 2012), is spatially heterogeneous (van

Jaarsveld et al. 2005, Burkhard et al. 2012, Nedkov et Burkhard 2012). Demand for local flow ES generally

diminishes with increasing distance from beneficiaries because far fewer people are willing to travel great

distances to obtain benefits from nature (Chan et al. 2006, Holland et al. 2011). A spatial mismatch can thus

occur between local flow ES supply (i.e. the amount of benefits) and the sites most used by human

beneficiaries (i.e. highest demand). For example, demand for recreation services is driven more by the

proximity to roads and the size of and distance to nearby population centres than by the capacity of a site to

provide the services per se (Chan et al. 2006, Holland et al. 2011). Accordingly, local flow ES do not

necessarily provide actual benefits to human populations everywhere they are supplied, either due to lack of

37

physical access or demand or restrictions by institutional arrangement (e.g. land-use constraints in national

parks restrict access to provisionng services; Tallis et al. 2012).

Systematic conservation planning (SCP) is increasingly recommended for safeguarding ES provision (Chan et

al. 2006, Egoh et al. 2008, Cimon-Morin et al. 2013). SCP is a multi-component stage-wise approach to

identifying conservation areas and devising management policy, with feedback, revision, and reiteration, where

needed(Margules et Sarkar 2007, Pressey et Bottrill 2008, Sarkar et Illoldi-Rangel 2010, Kukkala et Moilanen

2013). SCP notably involves identifying priority areas to effectively achieve conservation goals; traditionally,

these goals include representativeness, persistence and cost-efficiency (Margules et Sarkar 2007). However,

due to the anthropocentric focus and instrumental value associated with ES (Reyers et al. 2012), these goals

must be expanded to address the spatial relationships between ES supply and their human beneficiaries

(Chan et al. 2006, Egoh et al. 2007). Specifically, ES conservation areas should be targeted as a

complementary set of sites selected according to their capacity to ensure a sustainable and accessible supply

of ES as well as deliver these benefits where they are needed (Cimon-Morin et al. 2013).

Although an increasing number of studies have included ES in conservation assessments (Chan et al. 2006,

Egoh et al. 2007, Egoh et al. 2008, Naidoo et al. 2008, Luck et al. 2009, Larsen et al. 2011), there is still a

knowledge gap on how to effectively prioritize areas based on ES provision, accessibility to beneficiaries and

demand (Egoh et al. 2007, Tallis et Polasky 2009, Maes et al. 2012, Cimon-Morin et al. 2013). The aim of this

study is therefore to suggest a modification of SCP procedures that would increase the effectiveness of local

flow ES conservation. For this purpose, we conducted a case study in eastern Canada focusing on 16 wetland

and aquatic habitats and an associated set of 10 ES (five provisioning, three cultural and two regulating

services). We first mapped for each planning unit the biophysical supply of each ES and then used proxies of

human occupancy of the territory to define the supply’s potential-use spatial range, that is to say, the supply

accessible for human use. Concurrently, we mapped ES demand as the probability that a planning unit would

be used by beneficiaries in order to obtain the benefits of a specific ES. We compared conservation networks

resulting from site-selection algorithms based on the biophysical supply of ES, the potential-use supply or the

combination of potential-use supply and demand (i.e. the actual-use supply). The concept of actual-use supply

originates from the assumption that the real contribution to human well-being is not only when ES are supplied

and the benefits are accessible but also when a minimal amount of demand is fulfilled. Accordingly, the actual-

use supply of an ES is defined as when both accessible supply (i.e. potential use supply) and demand occur at

the same site. We hypothesized that prioritizing areas based on actual-use supply would foster conservation

choices more efficiently towards ES conservation objectives. Finally, we evaluated how to best integrate data

on ES demand in SCP to assemble conservation networks that are the most appropriate for satisfying the

needs of beneficiaries.

38

Method

Study area and wetlands mapping

The study was undertaken in the Lower North-Shore Plateau ecoregion and in a southern portion of the

Central Labrador ecoregion of boreal eastern Canada (Figure 3.1.; Li et Ducruc 1999). The study area covers

over 137 565 km2, most of it part of the black spruce-moss vegetation domain (Saucier et al. 2009). Of the

approximately 12 350 inhabitants (0.09 inhabitants/km2), 9 800 are dispersed among fifteen municipalities and

2 550 in four First Nations communities (Gouvernement du Québec 2013b). The minimal mapping unit of the

Natural-Capital Inventory dataset (Ducruc 1985), a dataset originally built for the ecological classification of the

territory, was used to divide the study area into 16 026 planning units. These units are irregular in shape and

size (mean of 8.5 ± 15 km2) because they are delimited by significant and permanent environmental features,

such as landscape topography, surface deposits and water bodies. All mapping was performed using ArcGIS

10.0 (ESRI 2012). The study area is currently minimally developed but its large freshwater reserves,

commercial forests and rivers which are great potential sources of hydroelectricity, as well as the presence of

important mineral deposits makes it a great candidate for future industrial development (Berteaux 2013).

Assuming that the various wetland and aquatic habitat types differ in their capacity to supply ES, it was

decided to map 16, the largest number possible using the best available complete data. We used the Natural-

Capital Inventory dataset (Ducruc 1985), which contains aggregated information on descriptive variables at the

planning unit scale, such as surface deposit (e.g. organic or mineral), drainage and vegetation cover, to infer

the relative coverage proportion of 10 peatland and one mineral wetland types (including marshes and

swamps). We differentiated four types of ombrotrophic peatlands (bogs) based on the presence of

ombrotrophic organic deposit and using peat depth (thick or thin; threshold of ± 1 m) and vegetation cover

(forested or not) attributes. We also discriminated six minerotrophic peatlands (fens) among the minerotrophic

organic deposits using peat depth (thick or thin; threshold of ± 1 m) and vegetation cover (forested or not and

also presence/absence of strings) attributes. Four types of aquatic habitats (streams, rivers, ponds and lakes)

were extracted from the CanVec v8.0 dataset (Natural Resources Canada (NRC) 2011). Lakes were further

divided into shallow (littoral zones, < 2 meters deep) and deep water zones (pelagic zones) using a 100 meter

distance buffer from the shoreline (Lemelin et Darveau 2008). This division was based on the premise that

these two types of habitats differ in their capacity to generate ES supply, notably for waterfowl related ES

(Lemelin et al. 2010). Aquatic habitats were converted into relative coverage proportion for each planning

units. Freshwater wetlands, mostly peatlands and shallow waters, cover 10% of the study area, while another

10% is composed of deep freshwater (> 2 m deep).

39

Figure 3.1. The location of the study area (in red) across North America (A); the extent of road networks and

the location of the major towns, First Nations communities and vacation leases are shown in (B).

Mapping ecosystem services supply and demand

For the purpose of this study, we selected five provisioning, two regulating and three cultural services provided

by wetlands for which the sustainability of supply is important for tourism and local communities. Regulating

and cultural ES are generally compatible with most protected area categories and especially strict

conservation status (e.g. IUCN I-III status; Dudley 2008). Provisioning services can also be included in

conservation if there are restrictions on practices (low land-use intensity; e.g. recreational angling) to ensure

40

sustainability and preclude biodiversity loss (Cimon-Morin et al. 2013). The protection of provisioning ES

should require the use of protected area categories that allow some resource extraction for local use while also

excluding industrial activity (e.g. IUCN IV-VI status; Dudley 2008). For each ES, supply and demand were

mapped quantitatively (Table 3.1., see Annexe 2 for detailed description). Experts were consulted for the

quantitative assessment and to validate the mapping of each ES supply and demand.

ES supply was first mapped according to biophysical supply (BS), also known as natural capital. Assuming

that any ES can be protected wherever it is supplied, this mapping approach only takes into account the

biophysical capacity of wetland types to provide an ES in each planning unit (Table 3.1. and Figure 3.2.).

Secondly, ES were mapped with regard to potential-use supply (PUS). In other words, the spatial flow scale of

each ES and proxies of human occupancy were used to identify the set of planning units in which humans can

potentially perceive ES benefits (Table 3.1. and Figure 3.2.). Among the ten ES chosen for this study, seven

have a local, one a regional and two have a global flow scale. To identify the set of planning units providing

potential-use benefits to people, the following proxies of accessibility and of human occupancy were used for

local flow ES (except for cultural site ES; see below): (1) a 1 km buffer zone around all types of roads and

human settlements, such as leases of vacation lots on public lands (mostly used for fishing- and hunting-

related activities), and (2) the area occupied by outfitters offering the targeted ES. While these proxies may be

a conservative estimate of planning unit accessibility, we believe that the majority of human uses for the

targeted local flow ES will take place within these limits. Therefore, planning units that fall

outside the spatial range of benefit delivery of an individual ES were considered to provide no accessible

benefits and were not considered for the conservation of this ES’ supply (i.e. the planning unit feature value

was set to nil). For the sole regional flow scale ES, that is, flood control, only the planning units present in

watersheds containing human infrastructures were retained in the PUS. For the two global flow ES, the BS and

the PUS were identical.

Although the ES supply of a planning unit may be accessible to humans, the benefits are not necessarily in

real demand. In order to identify the planning unit providing actual-use benefits (AU), we mapped demand as

the probability of a planning unit to be used by beneficiaries in order to obtain the benefits of a specific ES

across its potential-use supply spatial range. Demand for global flow ES was considered equal across their

PUS range. For regional flow ES (i.e. flood control), the demand may vary according to human population

density and the presence of human infrastructures (e.g. roads, bridges, etc.). We were not able to establish

precise demand values for each watershed. For the purpose of this study, we assumed that demand for

regional flow ES is also equal across the spatial range of their PUS. This raises the need to develop methods

for estimating complex spatial demand values for ES. Demand for most local flow scale ES often involves the

movement of their beneficiaries, who must go to where the ES is supplied in order to benefit from it. For moose

41

hunting and salmon angling, primary data about demand was available. Demand for the other local flow ES

was modeled using proxies of human usage, such as (1) a 30 km buffer zone to the nearest towns, (2) a 1 km

buffer zone to vacation leases, (3) the area occupied by outfitters. The 30 km distance from towns was

preferred over a distance decay function because in this remote region people have good knowledge of the

land and tend to use specific spots for an ES repeatedly. These proxies, as well as those used to map the

PUS, are context-specific and were weighted by previous social assessments and expert knowledge of human

use of the territory (e.g. quantity of possible users and the permanency of use; Hydro-Québec 2007). For

example, outfitters and vacation leases are strong predictors of demand for angling but are less predictive of

wild fruit picking. Thus, a planning unit containing an outfitter and vacation leases will have a greater demand

score for angling than a planning unit that does not contain these features.

42

Table 3.1. Indicators and data used to map ES supply and demand across the study area. See Annexe 2 for a detailed description.

ES Spatial

flow scale

Indicators used to map ES supply PUS supply/BS supply ratio

Indicators used to map ES demand Biophysical supply (BS) Potential-use supply (PUS)

Moose hunting Local Composition of planning units in aquatic and wetland moose habitats (Timmermann and McNicol 1988; Hydro-Québec 2007, Tecsult 2006, Lamontagne and Lefort 2004)

PUS obtained using accessibility proxies2

13% Number of moose hunted per planning unit between 1991-2011 (Ministry of Natual Ressources, Personal commun.)

Salmon angling Local Salmon river layer (Ministry of Natural Resources, Personal commun.), mean number of salmon migrating upstream per river per year (Caron et al. 2006), zones permitting salmon fishing (MRNF 2012b)

PUS obtained using accessibility proxies

25%

Mean number of salmon fished per river between 2008-2012 (MRNF 2012a) and proxies of human demand for salmon angling2

Brook trout angling

Local Composition of planning in aquatic and wetland trout habitats (Hydro-Québec 2007), inaccessible water body by fish layer (Bellavance and Gagné 2012)

PUS obtained using accessibility proxies 19%

Proxies of human demand for brook trout angling

Black duck hunting

Local Habitat selection ratio of waterfowl (Lemelin et al. 2010; Guérette Montminy et al. 2009; Lemelin et al. 2004)

PUS obtained using accessibility proxies 18%

Proxies of human demand for duck hunting

Cloudberry picking

Local Fruit yield per wetlands type (C. Naess, Personal commun.) PUS obtained using accessibility proxies 27%

Proxies of human demand for cloudberry picking

Aesthetics Local – proximal1

Wetland and aquatic habitats composition and heterogeneity (Pâquet 1997)

Distance buffer of 500 m from all human infrastructure (Pâquet 2003, Pâquet and Bélanger 1998) 12%

Demand was estimated according to: (1) the appeal of human infrastructures, (2) mean duration of users’ frequentation, (3) and observation, (4) users’ expectations and (4) the number of users per planning unit (Pâquet 2003)

Cultural site for First Nations subsistence uptake

Local Wetland and aquatic habitats composition of harvested species (Charest 1996; Walsh 2005)

Zones actually used by First Nations (Charest 2005)

86%

Delimitation of high and low uptake zone (Charest 2005) and uptake intensity in each zone (Walsh 2005)

Existence value of woodland caribou

Global Mean probability of occurrence per planning unit (Environment Canada 2008), Buffer zones of avoidance from human disturbances

PUS equals the BS 100%

Demand was set equal across the spatial range of the PUS

Flood control Regional The capacity of each planning unit to reduce and stabilize the water that flows through it (Gouvernement du Québec 1993)

PUS mapped only in watersheds containing human infrastructure

66% Demand was set equal across the spatial range of the PUS

Carbon storage Global Carbon stock value for bogs (Magnan et al. 2011 and personal commun.), for fens (Tarnocai and Lacelle 1996), for mineral wetlands (Horwath 2007) were used, carbon stock for lakes and ponds were modelled using the equation provided by Ferland et al. (2012)

PUS equals the BS

100%

Demand was set equal across the spatial range of PUS

1Local proximal means that the benefits of this ES can be perceived not only at the point of production, but also at a certain distance from where it is supplied. 2See method, section 2.2.

43

Figure 3.2. The spatial delivery range of the biophysical supply and of the potential-use supply of ES. The

biophysical supply area represents the zones where the ES is supplied but not necessarily accessible for

consumption. The potential-use area illustrates the zones where the ES is supplied and potentially accessible

for consumption. The potential-use area is a subset of the biophysical supply area. The not-supplied area

shows the zone where the ES is not produced.

44

Conservation assessment

Conservation planning software Conservation networks were assembled using C-Plan v4.0 conservation planning software (Pressey et al.

2009). The C-Plan site selection algorithm is primarily based on irreplaceability measures, i.e. the likelihood

that a given site will need to be selected in order to efficiently achieve conservation objectives (Kukkala et

Moilanen 2013). Planning units were selected first based on irreplaceability measures. When two or more sites

had equal irreplaceability values, the area of the planning units was used as a proxy of cost (Naidoo et al.

2006) to identify the minimum set of sites that attain conservation targets for all features while minimising the

total selected area. One network was assembled per target level for each conservation scenario (see below).

Conservation scenarios 1- The biophysical supply scenario (BS)

The biophysical supply scenario uses the BS maps of ES. Because the amount of the PUS for moose and

duck hunting, salmon and trout angling, cloudberry picking and aesthetics is less than 27% of their BS supply

(see Table 3.1.), we decided to restrict the maximum targets of the BS scenario to 30%. Other targets tested

were 5, 10, 15, 20, and 25% of their BS supply.

2- The potential-use supply scenario (PUS)

The potential-use supply scenario uses the PUS maps of ES. Targets for moose and duck hunting, salmon

and trout angling, cloudberry picking and aesthetics were set at 5, 10, 15, 20, 25, 30, 40, 50, 60 and 75% of

their PUS supply. Because the PUS of cultural sites, flood control, carbon storage and the existence value of

caribou ES had a greater spatial extent, we adjusted their targets in order to ensure that they did not

disproportionally influence irreplaceability values or site selection (e.g. 99.4% of the study area is covered by

planning units containing the PUS of carbon storage, while only 5.3% of the study area is covered by planning

units containing the PUS of cloudberry picking). To proportionally weight their targets, we first calculated the

proportion of the mean spatial extent of the PUS of the six local ES compared to the spatial extant of their

PUS. These proportions were 0.25 for carbon storage, 0.35 for the existence value of caribou, 0.40 for flood

control, and 0.33 for cultural sites. Finally, we multiplied each of the aforementioned conservation targets by

these proportions to properly adjust the targets of these widespread ES.

45

3-The actual-use supply scenario using demand as a site selection rule (AUS-Algo)

Demand for ES could be integrated into the site selection process by either assigning a conservation target to

each ES demand or by including the total demand value for each planning unit as a rule in the site selection

algorithms. Given that resources (land or money) available for conservation are limited worldwide (Margules et

Pressey 2000, Balmford et al. 2003), we used demand data for ES to assemble reserve networks that

maximize demand fulfillment per unit of cost (i.e. demand-efficiency). Demand data for each local flow ES was

standardized and summed for each planning unit. We did not consider demand for global and regional flow ES

because their demand is equal across their PUS range; in other words, any selection of sites that achieves

their supply targets will contribute equally to demand. The summed demand data was then integrated into the

site selection algorithm in order to encourage the selection of sites with both high irreplaceability for ES supply

and the highest summed demand. The maximisation of demand rule was integrated before the minimisation of

area rule. Targets for all ES supply were the same as above.

4-The actual-use supply scenario using demand as a target (AUS-Target)

In the AUS-Target scenario, conservation targets were set for the potential-use supply of all ES and

specifically for local flow ES demand only. For the same reason as the AUS-Algo scenario, no demand targets

were set for regional and global flow ES. Targets for ES supply were again set as 5, 10, 15, 20, 25, 30, 40, 50,

60 and 75% of their PUS. The same thresholds were used to set demand targets. Including targets for local

flow ES demand should increase the irreplaceability value of sites containing both actual supply and demand

of local flow ES, making them even more essential for meeting conservation objectives.

Conservation networks analysis Conservation networks of all four scenarios were compared based on their performance in selecting the

actual-use supply of ES and on their efficiency in securing ES demand (see Figure 3.3. for network examples).

Fostering the selection of the actual-use supply of ES does not necessarily guarantee that the conservation

network will secure high demand value. Accordingly, efficiency is a complementary analysis in assessing

whether or not our scenarios increase ES conservation effectiveness. We chose to focus on local flow ES

since they are mainly affected by the spatial distribution of both their beneficiaries and demand. Therefore,

even if the four conservation scenarios were assembled to secure the provision of the ten ES, we interpreted

the results only for the six local flow ES, which are moose and duck hunting, salmon and trout angling,

cloudberry picking and aesthetics. Considering regional and global flow ES could allow us to assess if gains in

the effectiveness of local flow ES conservation could be achieved even when conservation planning does not

exclusively focus on them. Moreover, we decided not to consider the cultural sites’ ES with the other local flow

ES in reporting the performance and efficiency results. This choice was made because this ES demand (and

46

PUS) extends far beyond the PUS spatial range of the other local flow ES. Accordingly, almost any selected

site would contribute to fulfilling demand for this ES. Therefore, this ES would have inflated the “true”

performance and efficiency of conservation scenarios for securing local flow ES demand, particularly the BS

and the PUS scenarios.

Figure 3.3. Example of conservation networks assembled under the four conservation scenarios. (A) Network

established under the biophysical supply scenario to secure 10% of the biophysical supply of ten ES. (B)

Network established under the potential-use supply scenario to secure 10% of the potential-use supply of ten

ES. (C) Network assembled under the AUS-Algo scenario to secure 10% of the potential-use supply of the ten

ES while selecting sites that possess higher summed demand value (D) Network assembled under the AUS-

Target scenario to secure 10% of the potential-use supply of the ten ES and demand of local flow ES.

During a preliminary phase of this study, we also examined the performance of two other scenarios that

targeted either ES demand or an index obtained by combining the standardized value of both ES supply and

demand. However, because the demand value for a particular ES was not explicitly linked to the planning unit

area, putting too much weight on ES demand in site selection resulted in networks containing a greater

proportion of small sites with high demand values. Consequently, we chose not to report the results of these

scenarios because they constantly secured an unpredictable low amount of ES supply.

47

Finally, no actual cost dataset was available for the study region. Using the planning unit area as proxy for cost

may incorrectly assume that the costs are homogenous across the study area because areas with high

demand (i.e. near where people live) are likely to be more expensive than other areas. However, while the use

of actual cost data may have changed the spatial configuration of conservation networks, we believe that the

interpretation of the results would not have diverged from the ones presented here. Hence, the minimisation of

area (or cost) rule in the C-Plan algorithm was applied to choose between sites having equal irreplaceability

measures.

Results

Assessing the effects of mapping ES using a beneficiaries-based approach

The potential-use supply (PUS) of the six local flow ES (e.g. the supply currently accessible to humans)

consisted of only 12 to 27% of their total biophysical supply (BS; Table 3.1. and Figure 3.2.). In comparison,

the PUS of regional flow ES (i.e. flood control) was 66% of its BS. Despite its local flow scale, the PUS of

cultural sites for First Nations subsistence uptake was as much as 86% of its total BS. Given that the

accessibility of sites is not an issue for global flow ES, the PUS and BS for both carbon storage and existence

value were unsurprisingly similar. We compared the PUS scenario with the biophysical supply (BS) scenario to

assess the effect of using a beneficiaries-based approach to map ES supply to prioritize areas for ES

conservation. When considering only the PUS for selecting planning units, conservation networks contributed

only partly to the actual-use supply of local flow ES (i.e. a combination of supply and demand of a particular

ES). More precisely, only 45% of the selected planning units (Figure 3.4.a) or of their total area (Figure 3.4.b)

contributed to secure an actual-use supply of local flow ES. Meanwhile, the BS scenario had only about 20%

of its networks composed of planning units securing an actual-use supply of local flow ES. Going from

targeting the biophysical supply to targeting the potential-use supply increased the chances of incidentally

selecting a site with demand for local flow ES by decreasing the number of possible candidate sites

considered at each iteration. Nevertheless, these results suggest that the BS scenario, and to a certain extent

the PUS scenario, selected planning units that are not in demand or, more importantly, planning units that

contain inaccessible local flow ES supply (see Figure 3.3.). Nonetheless, even though demand was not used

in both BS and PUS scenarios, the latter was two to three times more efficient than the former in choosing

sites that fulfill demand for local flow ES (Figure 3.5.).

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Figure 3.4. Proportion of conservation network that secured the actual-use supply of local flow ES under the

four conservation scenarios. One network was assembled at each target level for each scenario. The actual-

use supply of an ES is the simultaneous presence of demand and access to supply in a planning unit. (A) The

proportion per number of selected sites was calculated according to the number of planning units securing the

actual-use supply of local flow ES divided by the total number of planning units in the network. (B) The

proportion per total area selected was calculated according to the area contributing to secure the actual-use

supply of local flow ES divided by the total area of the network. The biophysical supply scenario targeted only

the biophysical supply of ES, the potential-use supply scenario targeted only the potential-use supply of ES

(i.e. the accessible supply), the AUS-target scenario targeted both ES potential-use supply and local flow ES

demand, and the AUS-Algo targeted only ES potential-use supply among the sites with the highest summed

demand. Networks were assembled targeting the ten ES but the values are reported considering only the

actual-use supply of the local flow ES (i.e. moose and duck hunting, salmon and trout angling, cloudberry

picking and aesthetics).

Integrating demand into identification of local flow ES priority areas

Scenarios considering demand, either as a selection rule (AUS-Algo) or as a target (AUS-Target), were

compared with the PUS scenario to evaluate whether considering demand can further increase the

effectiveness of ES conservation. At first glance, adding demand to conservation choices brought conservation

areas closer to human populations (see Figure 3.1. and Figure 3.3.). The advantages of using demand in

identification of priority areas, rather than targeting potential-use supply (PUS) alone, were particularly

apparent at low conservation targets (≤ 30%; Figure 3.4.). As a result, consideration for demand forced the

algorithm to restrict most of its choices to sites with demand for multiple local flow ES. However, at higher

target levels the differences between the scenarios decreased as the chances of incidentally having a higher

proportion of overlapping sites between them increased (see below). As a result, the AUS-Target and AUS-

Algo scenarios established conservation networks that secured a higher proportion of actual-use supply of

local flow ES (Figure 3.4.). Below targets of 30%, the efficiency of AUS-Target and the AUS-Algo scenarios to

secure the actual-use supply of local flow ES was on average nearly 15% to 20% higher than the PUS

49

scenario conservation networks (Figure 3.4.). Therefore, these two scenarios better ensured that the

accessible supply of local flow ES would be useful to human beneficiaries (i.e. in demand) almost everywhere

that supply is secured. Surprisingly, the PUS and AUS-Algo scenarios showed a similar proportion of actual-

use supply secured per conservation network unit of area at conservation targets greater than 40% (Figure

3.4.B). This could be explained by the fact that at later stages of site selection under the AUS-Algo (and at

high targets level), it was mostly the existence value of caribou and flood control targets that remained

unachieved. At this stage, sites with the highest irreplaceability were mostly located outside the spatial range

of local flow ES. Among these sites, the algorithm selected those that also had demand for cultural sites, while

minimizing the total area selected. Nevertheless, cultural site demand was not considered in the calculation of

the network’ performance (Figure 3.4.). As a result, the networks’ performance values per km2 of AUS-Algo

were slightly lowered.

Figure 3.5. Efficiency of four conservation scenarios to capture demand of local flow ES (see Figure 3.4. for

scenarios). (A) The efficiency of conservation scenarios per number of sites selected in the networks. (B). The

efficiency of conservation scenarios per km2 of selected sites in the networks. Efficiency values were

calculated as the ratio of the total local flow ES demand secured per number of planning units selected in the

networks or per km2 of selected planning units in the networks. The efficiency values were standardized to

enable easier comparisons of efficiency per number of selected sites with the efficiency per km2 of selected

sites. Networks were assembled targeting the ten ES but the efficiency values are reported considering only

local flow ES demand (i.e. moose and duck hunting, salmon and trout angling, cloudberry picking and

aesthetics).

Moreover, the planning units chosen with the two scenarios using demand typically secured a higher

proportion of the total demand for local flow ES, as reflected by the higher efficiency of their networks in

fulfilling demand (Figure 3.5.). As a direct result of our scenario’s design, the AUS-Algo was more efficient in

fulfilling demand when considering the number of planning units required to achieve targets (Figure 3.5.a),

50

while the AUS-Target scenario was slightly more efficient per km2 of selected planning units (Figure 3.5.b).

Finally, integrating demand in the identification of priority areas led to a different composition and configuration

of conservation networks, as suggested by the low proportion of planning units shared between scenarios

(Figure 3.6.). More importantly, only 30% of the planning units selected by the two scenarios considering

demand (AUS-Target and the AUS-Algo) overlapped spatially at a target of 10%. While similarity between

conservation networks increased as conservation targets increased, only half of the sites overlapped for the

two scenarios considering demand at targets ranging from 10 to 40%. This indicates that even the method

chosen to integrate demand in identification of priority areas could introduce great spatial discrepancies in the

resulting networks.

Figure 3.6. Similarity between conservation scenarios. The proportion of overlapping planning units was

calculated as a fraction of the number of selected sites in the smallest network considered in the comparison.

Discussion

Assessing the effects of mapping ES using a beneficiaries-based approach

By definition, ecosystem processes, structures and functions only give rise to ES where there are humans to

benefit from them (Fisher et al. 2009, Potschin et Haines-Young 2011). Therefore, to make effective and

relevant conservation choices, it is important to spatially link ES supply to human beneficiaries. Our analyses

showed that using a beneficiaries-based approach (the potential-use supply scenario) nearly doubled the

proportion of sites providing an actual-use supply of local flow scale ES when compared to relying solely on

the biophysical supply (the biophysical supply scenario). Mapping the biophysical supply (BS) could be useful

for planning for future development and natural capital accounting. However, making immediate conservation

51

choices using the BS would result in diluting and spending limited conservation funds on sites that do not

provide benefits to humans. Using a beneficiaries-based approach to map the potential-use supply of each ES

(PUS) should thus ensure that each site selected for ES protection provides accessible benefits.

In this study, proxies of human occupancy were used to assess which planning units provide an ES supply

accessible to human populations. Mapping ES using this method showed that less than 27% of the total

biophysical supply (i.e. PUS/BS ratio) of the six local flow ES was available for human use, reflecting a supply

mostly inaccessible to humans in the study area. This is primarily due to the fact that the region is minimally

developed and most human settlements are concentrated in the southern part (see Figure 3.1.). Future

regional development for natural resources extraction is expected to occur in the study area (Berteaux 2013).

As development occurs, the resulting expansion of road networks will improve accessibility to the territory.

Accordingly, as new ES supply becomes available to human populations, the PUS/BS ratio of local flow ES will

increase and new conservation opportunities will be created. However, despite the increase in the PUS supply

of local flow ES, land cover changes and ecosystem conversions resulting from development will also cause a

net decrease in the study area’s BS of all ES. The extent of anthropogenic disturbances can indeed have a

huge impact on the capacity of ecosystems to provide different categories of ES (Foley et al. 2005, de Groot et

al. 2010, Cimon-Morin et al. 2013). For instance, regulating (e.g. carbon storage and flood control) and some

cultural ES (e.g. the existence value of caribou) are considered to be at a maximum in natural or slightly used

ecosystems, while the flow of provisioning ES are considered to be non-existent to low in such ecosystems.

Integrating demand into identification of local flow ES priority areas

For any given conservation target, there are often multiple combinations of sites (solutions) that would make it

possible to achieve these objectives. This is especially true for regions where many undisturbed or natural

sites are still available for conservation, as in our study area. Choosing the best solution among all possible

alternatives may require the inclusion of new criteria directly into the systematic conservation planning

procedure. In this study, we posited that using demand for ES could increase the effectiveness of the priority

areas identified by fostering the selection of sites where there is a high level of need for these supplies. Using

demand caused great spatial discrepancies, compared to the conservation networks assembled using only the

potential-use supply scenario (PUS), particularly at low representation targets (Figure 3.6.). This supports the

suggestion that there is a spatial trade-off between ES supply and demand (Chan et al. 2006, Holland et al.

2011, Burkhard et al. 2012) and clearly illustrates its consequences for conservation assessments. A very

different set of selected sites could result from approaches that include demand as opposed to those that do

not. Furthermore, this indicates that mapping ES using a beneficiaries-based approach may not be sufficient

52

for efficient conservation of local flow ES demand particularly, since actual conservation targets are often low

due to the lack of available resources.

In order to assess how to best use demand data, we tested two different approaches: (1) the AUS-Algo, for

which conservation networks were assembled to maximize demand fulfillment (2) the AUS-Target, where ES

demand was used as targeted features. The comparison of overlapping sites selected by both scenarios also

showed great spatial discrepancies (Figure 5). This illustrates that the method used to include demand can

result in very different conservation networks. The AUS-Algo does not guarantee that each particular ES

demand will be sufficiently or equally represented, but rather ensures that each new planning unit added to the

network will be selected among the ones with the highest summed demand of local flow ES, regardless of

which ES demand is currently fulfilled. Thus, this approach may not be suitable when the spatial congruence of

different ES demands is low. On the other hand, using demand as targets conserved at least a minimal

amount of demand for each ES, but its resulting networks were larger in both total area and number of

planning units selected than when integrating demand into the selection process. Targets for demand were

perhaps set too high in comparison to ES supply’s targets, and additional area was needed to achieve the

local flow ES demand targets. However, in the context of our study, setting targets for ES demand seemed to

be the best way of ensuring that each ES demand had a specific degree of representation (i.e. an exact

amount) secured into conservation networks.

Conservation actions often create significant economic loss in the form of opportunity costs to local human

populations by causing the foreclosure of future land-use options (Adams et al. 2004, Linnell et al. 2011).

Thus, when projected conservation costs outweigh benefits, there is a risk that conservation will be limited to

distant, unproductive and uninteresting localities (Moilanen et al. 2009a). Nevertheless, ES provide a means

for valuing human’s well-being in conservation projects and can contribute to improve the societal acceptance

and implementation of conservation actions (Goldman et al. 2008, Cimon-Morin et al. 2014a) by making local

communities benefit from them. ES offer the possibility to better align conservation and human usage of

ecosystems by enabling the pursuit of some local population livelihood activities linked to nature in protected

areas. Provisioning services conservation tends more toward the maintenance and sustainable uptake of

harvestable species rather than the preservation of biodiversity specifically. For example, setting adequate

conservation targets for moose supply should ensure the conservation of its habitats (or populations), while the

use of demand will protect moose populations where they can also be hunted by beneficiaries. Thereby, our

results showed that using demand in sites selection procedures could further increase the number of local

people who benefit from conservation when compared to the PUS scenario. It resulted indeed in conservation

networks containing a higher proportion of planning units providing potential-use supply and having a higher

efficiency in fulfilling demand of local flow ES (Figures 3.4. and 3.5.). Thus, the effective conservation of local

53

flow ES could justify the need to bring conservation actions closer to human population, that is to say, in the

most threatened and costly ecosystem to protect.

Similarly to ES supply, demand for ES is not static and is likely to change over time as the region’s population

grows larger and expands spatially with the spread of human settlements and infrastructures (e.g. more

vacation leases, residential development, expansion of road networks, etc.). Nevertheless, considering the

dynamic nature of both ES supply and demand, we believe that regional development should not proceed at

the expense of ES conservation. Since demand for local flow ES decreases with increasing distance from

beneficiaries (Chan et al. 2006, Holland et al. 2011), the protection of more distant sites (or newly accessible

distant sites) may not adequately fulfill demand and is not likely to be sufficient to sustain current and future

levels of well-being for most local ES users.

In this study, spatially-defined field data about demand (i.e. primary data) was available for only two ES

(moose hunting and salmon angling), while secondary data was used to estimate demand for the other local

flow ES. Such use of secondary data resulted in maps with a higher proportion of planning units sharing the

same demand value, e.g. similar in terms of composition in the proxies used for estimating demand. The use

of secondary data to map ES demand thus creates uncertainty in reserve selection because it does not ensure

that the planning units that meet the criteria for high demand are actually used more by humans than others.

For example, the comparison of primary demand data for moose hunting with demand scores that could have

been obtained using our secondary data modeling method showed a moderate positive correlation (r = 0.32, p

< 0.0001; results not shown). Likewise, it has also been reported that using secondary data to map ES supply

could hinder the identification of priority areas (Eigenbrod et al. 2010a, Eigenbrod et al. 2010b). Although

primary data should enable more relevant conservation choices, our approach illustrates the imperative of

considering demand in systematic conservation planning of ES.

Conclusion

In order to halt the global loss of ES supply, efforts have been made to include ES in conservation

assessments (Chan et al. 2006, Egoh et al. 2007). However, shifting the focus of conservation to safeguarding

human well-being also requires broadening traditional conservation goals to better spatially link conservation

actions to human beneficiaries. In this study, we posited that ES conservation networks should secure an

accessible ES supply, in a location where ES are greatly needed by their human beneficiaries. We showed

that targeting the actual-use supply of ES, which is a combination of ES potential-use supply and demand,

using systematic conservation planning procedures enabled conservation networks to achieve ES

conservation objectives more effectively. For the time being, setting conservation targets for ES supply and

54

demand seems the best approach for selecting the actual-use supply of ES, particularly in regard to other

conservation goals such as cost-efficiency. Our results constitute a first step towards adapting SCP

procedures for ES conservation.

Despite the fact that this study was conducted in a remote region, our results are relevant for ES conservation

assessments worldwide for a number of reasons. In more human dominated regions, for example, local flow

ES may provide accessible benefits to human almost everywhere they are supplied, yet their demand remains

spatially heterogeneous. Therefore, as we demonstrated, failure to consider demand during site selection

could result in choosing planning units that are not the most efficient with respect to demand fulfillment, and

ultimately achieving ES conservation objectives. Moreover, priority areas for biodiversity and ES conservation

tend to lack spatial congruence globally (Cimon-Morin et al. 2013). Since funds available for conservation are

often limited, even a slight increase in the effectiveness of ES conservation is critical to lowering the impact on

resource allocation and better aligning biodiversity and ES conservation.

55

CHAPITRE 4

Site complementarity between biodiversity and ecosystem

services in conservation planning of sparsely-populated regions


Copie de l’article « Cimon-Morin, J., M. Darveau, and M. Poulin. Site complementarity between biodiversity

and ecosystem services in conservation planning of sparsely-populated regions ». Cet article, tel qu’il apparait

dans cette thèse, a été soumis à la revue « Environmental Conservation » en août 2014. Après révision par

les pairs, une version révisée a été renvoyée à la revue le 2 février 2015.

56

Résumé

Les conséquences de l’inclusion des services écologiques (SE) dans les objectifs de conservation sont encore

largement débattues. Le degré de succès dépendra sur la mesure dans laquelle la biodiversité et les SE

peuvent être protégés par des actions conjointes de conservation. Contrairement à la biodiversité, la

conservation des SE est inséparablement liée à leurs bénéficiaires humains. Concilier la conservation de la

biodiversité et des SE dans les zones peu peuplées risque d’être particulièrement difficile. Pour cette raison,

nous avons réalisé une étude de cas dans une région peu peuplée de l’est du Canada, en se concentrant sur

la biodiversité des milieux humides ainsi que sur dix des SE qu’ils génèrent. Sous une superficie de

conservation maximale donnée, nos résultats ont montré que planifier uniquement pour la biodiversité sous-

représentait l’apport des SE à échelle locale par 57 % and leur demande par 61 % dans les réseaux de

conservation. Planifier pour les SE uniquement, sous-représentait nos substituts de la biodiversité des milieux

humides par 34 %. En considérant la biodiversité et les SE simultanément, toutes les cibles de biodiversité et

de SE ont été atteintes pour une augmentation moyenne de seulement 6 % de la superficie à protéger.

Atteindre toutes les cibles de conservation en partant d’un réseau formé uniquement pour protéger la

biodiversité ou les SE était de deux à cinq fois moins efficace qu’en considérant la biodiversité et les SE

simultanément. Nous proposons une méthode pour traduire ces synergies spatiales en actions de

conservation conjointes.

57

Abstract

The consequences of considering ecosystem services (ES) in conservation assessments is still widely

debated. The degree of success depends on the extent to which biodiversity and ES can be secured under

joint conservations actions. Unlike biodiversity, ES conservation is inseparably linked to human beneficiaries.

Reconciling biodiversity with ES and conservation can be particularly challenging in sparsely populated areas.

For this purpose, we conducted a case study a sparsely populated region of eastern Canada, focusing on

freshwater wetland biodiversity and ten ES provided by wetlands. Within a given maximal total area, our

results showed that planning for biodiversity underrepresented local flow ES supply by 57% and demand by

61% in conservation networks. Planning for ES alone underrepresented our wetland biodiversity surrogates by

an average of 34%. Considering both biodiversity and ES simultaneously, all of our biodiversity and ES targets

were achieved with only a 6% mean increase in area. Achieving all conservation targets starting from a

network that was primarily built for either ES or biodiversity features alone was two to five times less efficient

than considering both ES and biodiversity simultaneously in conservation assessment. We propose a

framework to translate these spatial synergies into effective joint conservations actions.

58

Introduction

Traditionally, conservation strategies have relied on the intrinsic value that people place on nature to generate

support for protecting biodiversity and thereby restrain current global species loss (Butchart et al. 2010).

Biodiversity conservation projects face many challenges, including limited funding, social and political support,

as well as pressure to sustain economic development, which together can hinder implementation. More

recently, the supply of ecosystem services (ES) has also been recognized as increasingly precarious. Since

ES are so important for human well-being, there has been increasing interest in ensuring their sustainability,

notably through land protection and related conservation actions (MA 2005, Margules et Sarkar 2007). Yet,

some conservationists have expressed concerns that protecting ES may distract conservation efforts from a

broader focus on protecting all biodiversity, and tend to neglect or exclude species that provide no ES

(McCauley 2006, Redford et Adams 2009, Deliège et Neuteleers 2014). On the other hand, the potential of ES

to promote the social acceptance as well as the political and economical support for implementing

conservation projects has also been identified (Goldman et al. 2008, Reyers et al. 2012, Cimon-Morin et al.

2014a). ES could thus enable the use of supplementary strategies for protecting biodiversity (Reyers et al.

2012, Cimon-Morin et al. 2014a) if the conservation of both can be aligned through overlapping actions.

While all ES imply some level of biodiversity, the exact nature of their relationship is not easily determined

(Mace et al. 2012, Reyers et al. 2012, Harrison et al. 2014). Joint conservation actions are only possible if (1)

areas prioritized for biodiversity and ES are spatially congruent (e.g. if biodiversity represents ES provision) or

(2) when a complementary set of sites can be identified. However, a growing number of studies have found

low to moderate spatial concordance between priority areas for ES and biodiversity in conservation

assessments (Cimon-Morin et al. 2013). To ensure more efficient conservation solutions, it has been

suggested that systematic conservation planning procedures focus on both objectives through site

complementarity rather than on congruence (Thomas et al. 2012, Cimon-Morin et al. 2013). Complementarity

implies that conservation areas can be linked synergistically by their inherent features to achieve conservation

objectives efficiently (Kukkala et Moilanen 2013). This may be a way to identify priority areas and promote the

conservation of localised biodiversity features even if they occur in environments that provide few ES, and

vice-versa.

Remote regions, such as the Canadian borealis, contain some of the world’s last remnants of wilderness and

still harbour near-pristine ecosystems. Not only do they represent unique opportunities for biodiversity

conservation (Schindler et Lee 2010, Berteaux 2013, McCauley et al. 2013), these regions also provide

important local to global flow scale ES to human populations (Schindler et Lee 2010, McCauley et al. 2013).

Preserving provisioning and cultural services can be particularly important in remote regions, since inhabitants

generally draw a higher portion of their necessities of life from their surrounding ecosystems than do urban

59

dwellers. Some populations, often indigenous communities, even directly depend, at least seasonally, on the

resources they can obtain from ecosystems (Foote et Krogman 2006). Often considered self-protected simply

due to their remoteness, these regions have generally not benefited from conservation efforts, especially in

regard to protecting ES. At the same time, the scarcity of natural resources in the vicinity of densely populated

areas, human population expansion, new technologies and extension of transportation networks have all

increased interest in developing out-lying regions (Foote et Krogman 2006, Kramer et al. 2009). Intervention to

secure and manage both ES supply and biodiversity in these areas is thus long overdue (McCauley et al.

2013).

In contrast to strategies for biodiversity conservation, setting aside areas for protecting ES that are located at a

distance from human beneficiaries may be irrelevant for most ES (Reyers et al. 2012, Cimon-Morin et al.

2013). Indeed, most ES do not provide benefits where they are biophysically supplied, for example, due to lack

of physical access or demand (Tallis et al. 2012). This is particularly true for local flow scale ES (i.e.

provisioning and most cultural services), whose benefits must be obtained at or near the location where their

supply is protected. To be effective, ES conservation must focus on actual-use supply, which is defined as

accessible supply and demand occurring simultaneously at the same site (see Cimon-Morin et al. 2014b). In a

previous study, we found that identifying priority areas based on ES supply alone resulted in the selection of

sites that were up to five times less in demand or not even accessible to beneficiaries when compared with

approaches targeting the actual-use supply (Cimon-Morin et al. 2014b). However, theses novel approaches

may introduce a spatial bias toward sites in proximity to human settlements, which could further undermine the

already weak congruence between priority areas for ES and those for biodiversity.

Considering the sparse distribution of human populations in remote regions, it is still unclear whether

biodiversity and actual-use supply of ES could overlap sufficiently to enable the development of conservation

strategies that would safeguard both simultaneously. To evaluate the complementarity between biodiversity

and ES, we conducted a case study of systematic conservation planning in a remote, sparsely populated

region of eastern Canada, focusing on biodiversity and ten ES associated to freshwater wetlands. Wetland

conservation is of particular interest because these ecosystems are rich in biodiversity, while also being

generous providers of important ES (Foote et Krogman 2006, Schindler et Lee 2010). We first assessed the

impact of the sparsely distributed human populations on the bundle of ES provided by wetlands. Second, we

investigated the spatial congruence between ES and biodiversity during conservation assessment by looking

at the amount of each ES captured incidentally by a conservation plan that targeted wetland biodiversity and

vice-versa. Then, we compared three conservation scenarios that considered both biodiversity and ES

features simultaneously. We chose to compare networks targeting either ES biophysical supply or actual-use

supply to determine the extent to which actual-use supply brings additional spatial constraints in reserve

60

selection that could affect the level of spatial congruence with biodiversity. Finally, we considered strategies for

aligning ES and biodiversity conservation under such circumstances to preserve both most effectively.

Method

The study area

The study was undertaken in an area extending across the Lower North Shore Plateau ecoregion and into the

southern portion of the Central Labrador ecoregion in boreal Quebec (Figure 4.1.; Li et Ducruc 1999), which

corresponds to the eastern half of the continental part of the Central Laurentians and Mecatina Plateau

ecoregion level III of North America (Wiken et al. 2011). It covers 137,565 km2 and encompasses

approximately 12,350 inhabitants (0.09 inhabitants/km2), of whom 9,800 are dispersed across fifteen

municipalities and 2,550 are distributed in four First Nations communities (Gouvernement du Québec 2013b).

The minimal mapping unit of the Natural Capital Inventory dataset (Ducruc 1985), originally compiled for

ecological classification of the territory, was used to divide the study area into 16,026 planning units. The

planning units are of irregular shape and size (mean of 8.5 ± 15 km2) because they are delimited by significant

and permanent environmental features, such as landscape topography, surface deposits and water bodies. All

mapping was performed using ArcGIS 10.0 (ESRI 2012).

Wetland biodiversity surrogates

Data on species distribution are scare for the study area, as for many regions worldwide, and cannot be

collected easily, given time and budget constraints. To rely on most complete data available, we used a

combination of three wetland biodiversity surrogates to represent the study area’s wetland biodiversity

(Margules et Sarkar 2007). First, we mapped 16 wetlands types. We then assessed the composition and

richness of wetland types at the scale of the planning unit to generate two wetland assemblages surrogates.

Traditionally, assemblages represent different combinations of species, community, habitat type, etc., as well

as the interactions between them and therefore reflect greater ecological complexity than individual taxa

(Margules et Sarkar 2007). Wetland composition classes were obtained using a clustering procedure that

aggregated planning units according to the similarity of their wetland composition; while wetland richness

classes were based on the number of wetland types within each planning unit.

61

Figure 4.1. Location of the study area (A) as well as roads, major towns, First Nations communities and

vacation leases (B).

Wetland types We mapped 16 wetland and aquatic habitat types, the largest number possible using the most complete date

available. We used the Natural Capital Inventory dataset (Ducruc 1985), which contains aggregated

information on descriptive variables at the planning unit scale, such as surface deposit (e.g. organic or

mineral), drainage and vegetation cover, to infer the relative coverage proportion of one mineral wetland type

(including both marshes and swamps) and 10 peatland types. More specifically, we differentiated four types of

ombrotrophic peatland (bogs) based on the presence of ombrotrophic organic deposits, peat depth (thick or

thin; threshold of ± 1 m) and vegetation cover (forested or not). We also distinguished between six

minerotrophic peatland types (fens) among the minerotrophic organic deposits using peat depth (thick or thin;

threshold of ± 1 m) and vegetation cover (forested or not; presence/absence of patterns or strings). Five

aquatic habitats (streams, rivers, ponds, shallow zones of lakes, deep zones of lakes; Ménard et al. 2013)

62

were extracted from the CanVec v8.0 dataset (NRC 2011). We used a 100-meter distance buffer from the

shoreline to discriminate between shallow zones of lakes (littoral zones, < 2 meters deep) and deepwater

zones (Lemelin et Darveau 2008). This distinction was based on the premise that these two types of habitats

differ in their capacity to generate ES supply, notably for waterfowl related ES (Lemelin et al. 2010). While

streams, rivers, ponds, and shallow zones of lakes are part of the shallow water class (> 2 meters deep) of the

Canadian wetlands classification system (NWWG 1997), we also decided to consider deep water zones of

lakes in our conservation assessments (hereafter included in “wetland type features”). This decision was made

to facilitate management and conservation decisions, since shallow and deep water zones are directly

associated and the boundary between them may fluctuate over time (i.e. at least seasonally; Cowardin et

Golet 1995). The total relative coverage of the five aquatic habitats was calculated for each planning unit. The

area covered by linear vector features, such as streams and rivers, was estimated by generating a raster grid

with a 25-meter pixel resolution. Ten percent of the study area is covered by wetlands and another 17% by

aquatic habitats.

Wetland composition classes A wetland composition class represents a set of planning units that are similar in terms of frequency and

abundance of wetland types. The cascade KM function from the VEGAN package (Borcard et al. 2011,

Legendre et Legendre 2012) of R version 3.0.1 software (R Development Core Team 2013) was used to

identify the optimal number of different classes based on our data. The function proposed 11 composition

classes. Then, the K-Means portioning analysis in R (Borcard et al. 2011, Legendre et Legendre 2012), a non-

hierarchical clustering method, was used to associate each planning unit to one of the 11 wetland composition

classes.

Wetland richness classes Wetland richness class was measured as the number of each wetland type per planning unit. Wetland

richness classes ranged from 0 to 13, as no unit had the full array of 16 wetland types.

Ecosystem services features

Communities in remote regions and sparsely populated areas generally have a more utilitarian view of nature

and its conservation (Berteaux 2013, Failing et al. 2013) than people living in urban centres or biodiversity

conservationists. For example, a recent survey revealed that among the non-aboriginal inhabitants of our study

63

area, 27% practice moose hunting, 15% waterfowl hunting, 51% trout angling and 53% wild berry picking

(Bergeron 2014). Among those surveyed, 74% had consumed moose meat, 80% trout and 71% berries

originating from the study area at least once in the last year. All these proportions could be expected to be

even higher for the First Nations communities.

We thus selected ten wetland ES compatible with conservation actions, for which the sustainability of supply is

important, notably for local communities and tourism (five provisioning, three cultural and two regulating

services). Seven have a local flow scale: moose and waterfowl hunting, salmon and trout angling, cloudberry

picking, aesthetics and cultural site used by First Nations for subsistence uptake. First Nations subsistence

uptake, which includes all hunting, fishing and picking activities practiced by these communities, was

considered distinctly from the others services since it does not occur at the same sites. Applied to a

conservation context, a local spatial flow scale means that beneficiaries must approach or enter the protected

area where the ES is supplied to obtain its benefits (hereafter referred as “local flow ES”). One ES, flood

control, has a regional flow, and the final two have a more global importance, e.g. the existence value of

woodland caribou (i.e. an iconic species in Canada) and carbon storage. We mapped the biophysical supply

(BS) of all ES quantitatively (for detailed mapping methodology see chapter 3 and Annexe 2). Then, for the

seven local and the single regional flow scale ES, we used proxies of human occupancy of the study area to

identify where these ES can provide accessible benefits to beneficiaries (hereafter referred to as the potential-

use supply of ES). We also mapped demand for the seven local flow ES quantitatively, since the effective

conservation of local flow ES may require the selection of sites that contain actual-use supply, which are sites

containing a combination of both potential-use supply and demand (Cimon-Morin et al. 2014b). For example,

setting adequate conservation targets for moose supply should ensure the conservation of moose populations

and habitats, while demand will ensure that the moose are protected where they are also hunted by

beneficiaries. Experts were consulted for both quantitative assessment and validation of supply and demand

mapping for each ES.

Conservation software and scenarios

Conservation planning software Systematic conservation planning (SCP) is a multi-step operational approach to planning and implementation

of conservation (Margules et Sarkar 2007). SCP procedures for identifying priority areas, which are based on

site complementarity, were developed notably to increase conservation efficiency. We assembled conservation

networks using C-Plan v4.0 conservation planning software (Pressey et al. 2009). C-Plan algorithm selects

sites primarily based on their irreplaceability measures, that is to say, the likelihood that a given site will need

64

to be selected for the efficient achievement of conservation objectives (Kukkala et Moilanen 2013). Calculating

irreplaceability generally implicitly integrates complementarity (Kukkala et Moilanen 2013). We used the area

of planning units as a proxy for budget constraint in order to identify the minimum set of sites that attain

conservation targets for all features while minimizing the total selected area (i.e. budget).

The biodiversity scenario (or BD) The extent of wetlands that need to be protected to ensure their persistence is not well documented.

Therefore, we chose to assemble conservation networks to protect six samples of 1%, 5%, 10%, 15%, 20%

and 25% of all our wetland biodiversity surrogates (see above). Within these samples, we used the coarse-

filter approach to set quantitative targets for each particular wetland biodiversity features. The coarse-filter

implies that the conservation of a representative sample of all ecosystem types and natural communities

inventoried should “ensure representation” (of the constituents of a particular ecological level) and provide

habitats for the majority of species in the region, even up to 90% under some circumstances (Noss 1987,

Lemelin et Darveau 2006, Hunter et Schmiegelow 2011). A coarse filter is generally complemented by a fine

filter, which is an approach that would capture, on an individual basis, threatened or endangered species not

protected by the coarse filter. In data-scarce regions such as our study area, the coarse-filter is generally

applied at the ecoregion scale (~ 100,000 km2) without the fine filter, due to the lack of data available at a finer

scale (Lemelin et Darveau 2006). Representative targets for wetland types were based on their total area,

while targets for wetland composition classes and richness classes were based on their total number of

occurrences. It took approximately 1%, 6%, 10%, 14%, 19% and 24% of the study area to achieve the six

samples of wetland biodiversity features respectively. These area thresholds were then used as budget

limitations in the other conservation scenarios (see below) to establish networks of similar total area.

The ecosystem services biophysical supply scenario (or BS) Setting quantitative conservation targets for ES features is a difficult task (Egoh et al. 2011). Consequently,

instead of setting arbitrary targets, we chose to use the optimal amount of the ten ES that can be secured

given a fixed budget (i.e. area) thresholds. The budget thresholds we used were the same as the total area

required to achieve all targets in the biodiversity scenario above. Accordingly, we used 1%, 6%, 10%, 14%,

19% and 24% of the study area as budget limitations to assemble the six networks. These networks targeted

the ES biophysical supply. Assuming that any ES can be protected wherever it is supplied, this scenario only

takes into account the biophysical capacity of wetland types to provide the ten ES. After each budget threshold

was reached, the site selection process ended and we assessed the amount of each ES biophysical supply

65

secured within the network. We used these values to set ES biophysical supply targets in the scenario seeking

both ES biophysical supply and a representative sample of biodiversity as described below.

The ecosystem services actual-use supply (or AUS) This scenario is similar to the BS scenario with the distinction that it considered ES potential-use supply and

demand in site selection, rather than ES biophysical supply. The results obtain from this were used to set

conservation targets for ES potential-use supply and demand in the scenario seeking both ES actual-use

supply and a representative sample of biodiversity as described below.

The ecosystem services biophysical supply and biodiversity scenario (or BS-BD) Given a maximum total budget, this scenario considers both ES biophysical supply and biodiversity features

simultaneously. Targets for wetland biodiversity features were the same as for the biodiversity scenario, while

targets for ES biophysical supply were based on the optimal amount of each that could be secured under the

BS scenario. We used 1%, 6%, 10%, 14%, 19% and 24% of the study area as budget limitations to assemble

six networks. Once the network reached the total budget allowed, the selection process ended even though all

conservation feature targets had not been reached.

The ecosystem services actual-use supply and biodiversity scenario (or AUS-BD) The same procedure used in the BS-BD scenario was applied for the AUS-BD scenario, but with the difference

that ES potential-use supply, ES demand and biodiversity features were targeted simultaneously. Targets for

wetland biodiversity features were the same as for the biodiversity scenario, while targets for ES potential-use

supply and demand were based on the optimal amount of each ES feature secured under the AUS scenario.

The AUS-BD unconstrained scenario (or AUS-BD unconstrained) This scenario is similar to the AUS-BD scenario, with the sole distinction that no budget limitations were used.

Each ES potential-use supply, ES demand and biodiversity feature was therefore allowed to reach its assigned

target.

66

Conservation networks analysis

Conservation networks were compared based on the fraction of features that reached (or exceeded) their

targets and based on the utility value of targeted features. The utility value represent the mean level of

representation of targeted features.

Results

Ecosystem services mapping

Mapping ES potential-use supply, which is the accessible subset of biophysical supply, showed that wetland

ecosystems provide different bundles of ES according to the access human beneficiaries have to the sites that

contain them (see Figure 4.2). On the one hand, accessible wetlands provided a maximum supply of

provisioning (i.e. hunting, angling and berry-picking activities) and cultural services (i.e. aesthetics and cultural

sites), especially in the case of peatlands. On the other hand, inaccessible wetlands provided no tangible

benefits in regard to the provisioning services we analyzed, even if they had the biophysical capacity to supply

them, simply because no beneficiaries could gain access. These wetlands also provided less of the cultural ES

we examined than accessible sites, since some cultural ES provide benefits on a local spatial flow scale, such

as aesthetics. For most wetland types, accessibility was accompanied by a much wider spectrum of ES than

inaccessible wetlands could provide. Inaccessible rivers and streams supplied none of the ten services

assessed. Moreover, some ES were supplied by a single wetland type, such as salmon angling in rivers and

streams, and cloudberry picking in peatlands, while others were provided by several wetland types, such as

carbon storage and cultural sites. Wetland types also differed in their capacity to supply particular services. For

example, peatlands stored more carbon than lakes, ponds, marshes and swamps, and on average they also

provided a greater bundle of the assessed ES than other wetland types.

Planning for biodiversity or ES alone

By quantifying the biodiversity and ES features captured incidentally by the scenarios based on either

biodiversity (BD), biophysical supply (BS) or actual-use supply (AUS), we were able to determine to what

extent ES and biodiversity could represent each other during conservation planning in the context of sparsely

populated areas. These three scenarios resulted in spatially distinct networks (see Figure 4.3.). While the sites

selected by the biodiversity and biophysical supply (BS) scenarios were widely distributed across the study

area, the actual-use supply scenario (AUS) resulted in a selection of sites on average closer to the human

population (see Figure 4.1. and Figure 4.3.). Moreover, compared to the BD networks, the BS and AUS

networks contained fewer but, on average, larger sites.

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Figure 4.2. Basic framework describing the relationship between the capacity of major wetland types to supply

ES and beneficiaries’ access to wetland ecosystems. The summed “inaccessible” and “accessible” capacities

of each wetland type represent their biophysical capacity to supply ES. When the accessibility of the wetland

ecosystem is taken into account, different bundles of ES potential-use supply are provided by the same

wetland type. A value of 0 signifies that the service is not produced, a value of 1 signifies that this wetland type

is a producer of the service, while a value of 2 signifies that the wetland type is a major producer of the

service. Accessible wetlands are not necessarily altered by anthropogenic disturbances. The terms

“accessible” and “inaccessible” refer to the capacity of the beneficiaries of each particular ES to have physical

access to the wetland and ultimately, to obtain the benefits it provides. For example, cultural site beneficiaries

are solely indigenous communities; accordingly, their access to the territory, and to the wetlands, is much

greater than that of the beneficiaries of other local flow provisioning ES (i.e. the non-indigenous population).

Provisioning services group together salmon and trout angling, moose and duck hunting and cloudberry

picking. Regulating services are carbon storage and flood control, while cultural services are aesthetic value

and iconic species.

Planning for biodiversity conservation without explicitly including ES (i.e. the BD scenario) assembled reserve

networks that incidentally protected only a low level of ES potential-use supply and demand (Figure 4.4.B, C).

The BD scenario was more effective in representing the biophysical supply of ES than their potential-use

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supply features (see Table 4.1. for exact values). Excluding flood control and aesthetics, which were

particularly overrepresented in terms of biophysical supply, the other examples of ES biophysical supply were

in general 34% more represented than their homologous potential-use supply. The BD scenario was in fact

particularly inefficient for evaluating local flow ES potential-use supply, which was on average

underrepresented by 57%. Similarly, by excluding demand for cultural sites that exceeded the target by 225%,

demand for the other local flow ES was underrepresented by 61%.

Planning for ES alone, either by considering biophysical (BS) or actual-use supply (AUS), represented

biodiversity inadequately (Figure 4.5.). Both scenarios represented wetland types more effectively than

wetland composition or richness classes. The BS scenario was more effective in representing most

biodiversity features than the AUS scenario with respectively 30% (versus 20%) reaching their targets (see

Table 4.2. for exact values). The mean level of representation of the BS scenario was 22% and 16% higher

for wetlands types and wetland richness classes, but 9% lower for the composition classes than the AUS

scenario.

Planning for biodiversity and ES simultaneously

When biodiversity and ES features were considered simultaneously (i.e. BS-BD and AUS-BD), conservation

networks reached a higher proportion of targets for overall conservation features compared to scenarios that

targeted either biodiversity or ES under the same budget limitations (Figure 4.4. and 4.5.). For ES potential-

use supply and demand features, 82% attained their targets under the AUS-BD scenario, while only 18% did

under the BS-BD scenario (see Table 4.1. for exact values). On the other hand, despite the fact that 52% of

biodiversity features reached their targets in both scenarios, their mean level of representation was 13% higher

for the BS-BD than in the AUS-BD scenario (see Figure 4.5. and Table 4.2. for exact values). The difference in

the level of representation reached, notably, 17% for wetland composition classes and 19% for wetland types.

With no budget limitation, targeting the actual-use supply of ES and biodiversity (AUS-BD unconstrained

scenario) achieved all conservation feature targets with a mean increase of only 6 % (± 2%) of the total area

selected when compared with the budget constraints used in the other scenarios (Figure 4.6.). This slight

increase in selected area was disproportionally efficient for protecting biodiversity features. Indeed, comparing

the AUS-BD scenario with the AUS-BD unconstrained scenario, the mean level of representation for wetland

types, wetland composition classes and for wetland richness classes increased by 19%, 56% and 36%

respectively (Table 4.2.). By contrast, the mean increase in area needed to achieve the conservation targets of

all features a posteriori would average 34% (± 13%) for the BD scenario and 13 % (± 3%) for the AUS

scenario.

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Figure 4.3. Networks assembled under the six conservation scenarios. Except for the AUS-BD unconstrained

scenario (13.6%; F), all networks cover the same total area, which is 10% of the study area.

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Figure 4.4. Deviation from conservation targets of individual ES features protected under each conservation

scenario. Each point represents the mean deviation from the target of the six networks assembled by each

scenario. Several features can have the same deviation value and thus their points are positioned on top of

each other (see Table 4.1. for exact value). A point located above the zero line signifies that on average this

feature exceeded its target, while the points situated below the zero line refer to features that did not meet their

targets. We show the mean deviation for the ten ES biophysical supply (A), the mean deviation for the seven

local flow ES potential-use supply (B), the mean deviation for the seven local flow ES demand (C) and the

mean deviation of one regional and two global flow ES potential-use supply (D). Refer to the method section

for a description of conservation scenarios.

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Table 4.1. Mean deviation (%) from targets for ecosystem services features under the six conservation scenarios tested. A negative value indicates that the target was not met, a zero value indicates that the target was met, while a positive value indicates that the target was exceeded. The mean values were calculated based on the results of six networks for each conservation scenario.

Ecosystem services features Conservation scenarios

BD BS AUS BS-BD AUS-BD AUS-BD

unconstrained

Biophysical supply of

ecosystem services

Moose hunting 6 0 0 1 0 15

Salmon angling 17 0 125 25 137 154

Duck hunting -17 0 -2 -7 -4 0

Trout angling 2 0 -10 -1 -5 6

Berry picking -39 0 42 0 33 44

Aesthetic 475 0 92 307 204 515

Cultural sites -13 0 4 1 8 20

Flood control 627 0 83 384 225 647

Carbon storage -29 0 23 -3 19 29

Iconic species 3 0 -38 -4 -47 -29

Potential-use supply

of ecosystem

services

Moose hunting -62 -62 0 -63 3 4

Salmon fishing -77 -75 0 -68 5 2

Duck hunting -64 -50 0 -49 2 3

Trout fishing -57 -54 0 -49 1 9

Berry picking -71 -43 0 -43 1 2

Aesthetic -44 -73 0 -51 3 20

Cultural sites -22 -6 0 -6 -19 6

Flood control 250 -48 0 102 58 259

Carbon storage -39 -12 0 -15 -6 3

Iconic species 51 45 0 34 -20 11

Demand for local flow

ecosystem services

Moose hunting -62 -67 0 -64 -3 3

Salmon fishing -78 -75 0 -69 3 2

Duck hunting -69 -79 0 -66 1 8

Trout fishing -54 -77 0 -58 3 15

Berry picking -68 -79 0 -65 2 10

Aesthetic -34 -77 0 -49 11 28

Cultural sites 225 -50 0 97 55 236

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Figure 4.5. Deviation from conservation targets of individual wetland biodiversity features protected under

each conservation scenario. Each point represents the mean deviation from conservation targets for a

particular wetland feature obtained for the six targets tested under each conservation scenario. The position of

several features can be superimposed (see Table 4.2. for exact value). A point located above the zero line

signifies that on average this feature exceeded its target, while the points situated below the zero line

represent features that did not meet their targets. We show the mean deviation for the 16 wetland types (A),

11 wetland composition classes (B), and 13 wetland richness classes (C). Refer to the method section for a

description of conservation scenarios.

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Table 4.2. Mean deviation (%) from targets for wetland biodiversity surrogates under the six conservation scenarios tested. A negative value indicates that the target was not met, a zero value indicates that the target was met, while a positive value indicates that the target was exceeded. The mean values were calculated based on the results of six networks for each conservation scenario.

Wetland biodiversity surrogates Conservation scenarios

BD BS AUS BS-BD AUS-BD AUS-BD

unconstrained

Wetland types

Lake -littoral zone 17 6 -10 4 4 9

Lake - pelagic zone 34 -26 -36 15 21 17

Pond 37 92 93 70 85 95

River 26 1 4 22 0 39

Stream 10 -3 0 -2 1 18

Forested thick bogs 30 -71 -59 35 54 62

Open thick bogs 45 188 378 171 341 372

Forested thin bogs 30 -5 -40 45 7 35

Open thin bogs 39 35 -11 76 9 37

Forested thick fens 175 -65 -80 206 86 151

Open thick fens with strings 201 376 114 418 213 162

Open thick fens 57 120 111 111 108 152

Forested thin fens 27 -11 -44 24 29 39

Open thin fens with strings 42 67 -18 56 35 40

Open thin fens with strings 10 8 -40 13 -14 13

Mineral wetlands 24 12 10 17 -7 32

Wetland composition

classes

Composition1 0 -84 -71 -34 -55 2

Composition2 0 -86 -90 -33 -50 10

Composition3 0 -69 -10 -33 -14 22

Composition4 0 -81 -74 -33 -56 3

Composition5 0 -96 -94 -33 -52 11

Composition6 0 -83 -79 -36 -59 0

Composition7 0 -88 -57 -31 -44 6

Composition8 0 -98 -96 -34 -54 4

Composition9 0 -74 -88 -32 -51 10

Composition10 0 -99 -97 -34 -55 1

Composition11 0 -83 -87 -36 -63 2

Wetland richness classes

Richness1 0 -100 -100 -28 -16 63

Richness2 0 -96 -93 -34 -57 3

Richness3 0 -92 -85 -36 -63 1

Richness4 0 -86 -75 -36 -61 1

Richness5 0 -82 -70 -36 -57 2

Richness6 0 -74 -63 -32 -47 5

Richness7 0 -61 -61 -24 -21 8

Richness8 0 -53 -66 -11 0 17

Richness9 0 -22 -77 -9 2 11

Richness10 0 -28 -77 -6 -1 17

Richness11 0 -8 -84 0 0 13

Richness12 0 28 -30 0 17 33

Richness13 0 0 0 0 0 0

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Figure 4.6. Increase in total area required to achieve all conservation feature targets for the three conservation

scenarios expressed as a proportion of the budget threshold, which is a fixed percentage of the study area.

The x-axis shows the area threshold used as budget limitations. The AUS-BD unconstrained scenario was

allowed to reach all conservation targets without budget limitations. As a basis for comparison, we show the

increase in area required by the biodiversity scenario to achieve all ES features (i.e. potential-use supply and

demand) a posteriori, while the AUS shows the increase in area needed to achieve all biodiversity targets a

posteriori.

Discussion and conclusion

As reported in other studies, our results suggest that even in remote regions, where much of the landscape

remains essentially undisturbed and available for conservation, the capacity of biodiversity and ES to

represent each other in conservation assessment is limited (Chan et al. 2006, Naidoo et al. 2008, Larsen et al.

2011). We found that on the one hand, planning for biodiversity alone did not adequately represent local flow

ES potential-use supply and demand, e.g. actual-use supply, which is the key ES to maintain in order to

sustain the well-being and livelihood of the local population. On the other hand, planning for actual-use supply

of ES alone (AUS scenario) represented most biodiversity features equally ineffectively (Chan et al. 2006,

Larsen et al. 2011, Thomas et al. 2012). Unsurprisingly, targeting the biophysical supply of ES (i.e. BS

scenario) represented biodiversity features better than the AUS scenario, as measured by the fraction of

features that met their set targets. Since biophysical supply is independent of beneficiaries’ accessibility and

demand, we were however expecting that the BS scenario would represent biodiversity surrogates much more

effectively. The fact that it did not could have implied that the sparsely distributed human populations at the

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ecoregion scale did not introduce spatial constraints in reserve selection for the actual-use supply of ES (i.e.

AUS scenario). However, the AUS scenario networks showed a clear selection bias toward the southern part

of the study area, where most human settlements are located (see Figure 4.3.B vs 4.3.C). We assume that

both the aggregation in the southern part of the study area and the widespread distribution of some

biodiversity features may have incidentally buffered these constraints.

The inability of ES based scenarios (whether biophysical or potential-use supply) to represent specific

biodiversity features can be attributed to two main factors. First, some of the ES we assessed were supplied

by a single wetland type, such as rivers for salmon angling or peatland bogs for cloudberry picking, while

others such as carbon storage and cultural sites were provided by several wetland types (see Figure 4.2.).

Consequently, the ten ES considered in our conservation assessment resulted in the selection of

unrepresentative samples of wetland type features. Considering a different spectrum of ES in our conservation

assessment, particularly services provided by multiple wetland types and those less dependent on

beneficiaries’ accessibility, could have increased the capacity of ES to represent biodiversity features for which

their targets were set as a proportion of their total area (e.g. wetland types). However, we selected ES with a

higher relevance for local populations, tourism or global significance (i.e. carbon storage and iconic species)

and to represent each ES category (i.e. provisioning, cultural and regulating). Second, since ES targets were

set to secure a specific amount of ES supply, they were less capable of representing biodiversity features

whose targets were based on number of occurrences (i.e. wetland composition and richness classes). Indeed,

while a high number of planning units needed to be protected to achieve targets based on a representative

number of occurrences, the AUS and BS scenarios rather resulted in networks containing only a few large

planning units, thereby incidentally failing to represent the expected number of occurrences. Nevertheless, our

results clearly illustrate that efforts to conserve biodiversity and provide ES that are in demand will be

inefficient unless both are explicitly considered in the planning process (Larsen et al. 2011, Cimon-Morin et al.

2013).

Considering both biodiversity and ES simultaneously using systematic conservation planning procedures

based on site complementarity has been shown to achieve overall conservation targets more efficiently (Chan

et al. 2006, Larsen et al. 2011, Thomas et al. 2012; this study, Cimon-Morin et al. 2013). Using a fixed budget,

the biodiversity and ES (i.e. BS-BD and the AUS-BD) scenarios showed a good level of complementarity even

though a great number of features did not meet their targets (Figure 4.4. and 4.5.). In a target-based

conservation planning approach, the representativeness and persistence goals are generally translated into

quantitative targets that enable calculation of the conservation value of new reserves during the site selection

process (Margules et Pressey 2000, Kukkala et Moilanen 2013). Thus, failure to reach targets may

compromise the long term success of conservation plans. While biodiversity targets can be based on

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ecological knowledge and thresholds, such as minimum reserve size or minimum viable population, targets for

ES supply and demand should reflect societal needs or goals as well as biophysical and ecological thresholds

(Egoh et al. 2011). Lacking such data for ES, we set targets as the optimal amount of each ES feature that can

be secured under a fixed budget. This method may have resulted in disproportionally high targets that could

not be achieved for most ES features in the two budget-constrained scenarios. We believe that lowering, or

even using a different weighting for ES targets, could increase the level of complementarity between ES and

biodiversity.

As expected, the scenario combining both the biophysical supply and biodiversity (BS-BD) optimized utility

value of biodiversity in conservation networks (Moilanen et al. 2009b) when compared with the scenario

targeting the actual-use supply and biodiversity (AUS-BD) scenario. The utility value of conservation network is

measured in terms of the mean level of representation of biodiversity features, rather than as the fraction of

biodiversity features reaching their targets (Figure 4.5.). These differences support the assumption that

planning for actual-use supply of ES introduced a spatial constraint that lowered its complementarity with

biodiversity. Nevertheless, substituting ES actual-use supply for biophysical supply in conservation

assessment would not improve the likelihood of meeting ES conservation objectives because there is a risk

that the selected sites will not be accessible or will not be able to fulfil demand (Cimon-Morin et al. 2014b).

Finally, without a budget constraint (i.e. the AUS-BD unconstrained scenario), it was possible to achieve all

conservation targets for both ES and biodiversity with a mean increase of only 6% of the total selected area. In

comparison, selecting an entirely new area to achieve all conservation targets a posteriori would have required

nearly two to five times more area respectively for the AUS or BD scenarios (Figure 4.6.). These results clearly

show that targeting both biodiversity and ES features in conservation assessments facilitates finding a solution

that achieves all conservation targets more efficiently.

Overall, our results illustrated that there is great potential for spatial synergies between ES and biodiversity

conservation. Indeed, nearly all selected sites contained both biodiversity and ES features. These synergies

could be translated into joint conservation actions by determining the best management options to ensure that

each site adequately fulfils its primary objective(s), which could be to protect either (1) biodiversity features, (2)

one or several ES features, or (3) jointly, biodiversity and one or several ES features. The challenge is to

determine the conservation designation (e.g. IUCN categories; Dudley 2008) that most closely matches each

combination of objectives, ranging from an ecological reserve for preserving a rare ecosystem type to a multi-

use area. The choice should be guided by the needs and urgency of biodiversity conservation, the

opportunities for delivering ES benefits, the needs of local human communities, and the influence of the

surrounding area. There is a current conservation paradigm that assumes that strict conservation status is

incompatible with most ES flows and are the most effective towards wilderness and biodiversity protection

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(Kalamandeen et Gillson 2007). However, even the most strictly protected area (e.g. I-III IUCN status; Dudley

2008) can provide a wide range of regulating (carbon storage, flood control, etc.) and cultural services

(recreation, scientific knowledge, aesthetics, etc.) to beneficiaries. According to another paradigm, the least

strictly protected area designations (e.g. IV-VI IUCN status) are ecosystem services-oriented and ineffective

for biodiversity conservation, and consequently should be second-order designations, or even eliminated

entirely. In fact, these least strict designations can be very helpful in preserving particular biodiversity features

for which various degrees of habitat management are considered necessary (Kalamandeen et Gillson 2007).

The conciliation of biodiversity and ES conservation objectives would obviously lead to a revision of such

conflicting paradigms.

An emerging vision is that effective within-reserve zoning (or micro-zoning; Lin 2000, del Carmen Sabatini et

al. 2007, Geneletti et van Duren 2008) should be considered for multi-use protected areas. Micro-zoning

schemes enable optimal delineation of zones that allow various activities and degrees of extraction and

protection, while also increasing the likelihood of biodiversity protection. Several protection levels are

commonly considered in parks, ranging for example from zones with strict protection to zones of game species

uptake and recreation development, while also minimizing anthropogenic disturbances (Geneletti et van Duren

2008). Achieving both conservation and sustainable development objectives implies the judicious use of all

protected area categories. Considering ES and biodiversity in conservation assessments will certainly bring

greater complexity to the science of conservation planning and management of targeted areas.

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CHAPITRE 5

Replacement cost of ecosystem services conservation networks

in sparsely populated areas subjected to industrial development


Copie de l’article « Cimon-Morin, J., M. Darveau, and M. Poulin. Replacement cost of ecosystem services

networks in sparsely populated areas subjected to industrial development ». Cet article sera soumis à la revue

« Ecosystem Services » à l’hiver 2015.

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Résumé

Le développement pour l’accès aux ressources naturelles est en expansion vers des régions de plus en plus

éloignées. Les services écologiques (SE) sont particulièrement importants en régions éloignées, car ils

constituent une part importante des moyens de subsistance, de la santé et de l’économie des populations

locales. Les changements d’affectation du territoire se traduisent souvent par la perte d’opportunités de

conservation. Encore aujourd’hui, nous ne sommes pas en mesure d’évaluer les conséquences de la mise en

œuvre tardive des actions de conservation par rapport à différents stades de développement. Pour combler ce

manque, nous avons réalisé une étude de cas de planification systématique de la conservation dans une

région éloignée de l’est du Canada, en considérant dix SE produits par les milieux humides. Nous avons

simulé huit différents stades de développement et évalué le coût de remplacement des solutions alternatives

de conservation. Nous avons trouvé que les alternatives étaient en général plus dispendieuses, pouvant

atteindre 15 % de plus. Cependant, lors des derniers stades de développement, le coût de remplacement

diminuait car les solutions alternatives devenaient composées d’un plus grand nombre de sites, mais de taille

moyenne plus petite. Par conséquent, la mise en œuvre tardive de la conservation résultait en des réseaux de

conservation qui étaient beaucoup plus fragmentés. La considération de la valeur économique des SE lors de

la planification de la conservation pourrait aider à optimiser les compromis entre les différentes options

d’aménagement ainsi que faciliter le choix entre des affectations des terres concurrentes.

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Abstract

Development for natural resources extraction is expanding toward increasingly remote areas. Ecosystem

services (ES) are particularly important to preserve in such regions, where they form a great part of

inhabitants’ livelihoods, health and economy. Land-use change may translate into the direct loss of ES

conservation opportunities. Yet, we are unable to evaluate the consequences of implementing conservation

actions to secure ecosystem services at one stage of development versus another. To fill this gap, we

conducted a systematic conservation planning case study in a remote region of eastern Canada, focusing on

ten ES provided by wetlands. We simulated eight different stages of development and assessed the

replacement cost of alternative conservation solutions. We found that alternatives were generally more costly,

even up to 15%. However, at later stages of development, replacement cost decreased because alternative

solutions involved many more sites with a lower mean area. Consequently, implementing ES conservation at a

later stage of development resulted in much more fragmented networks. Taking the economic benefits of ES

conservation into account may help optimize trade-offs between different ecosystem management options and

facilitate the choice between competing land-uses.

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Introduction

Over recent decades, humans have modified ecosystems more rapidly and extensively than in any

comparable period of time in human history (Foley et al. 2005, MA 2005). This process is driven by growing

demand for food, fresh water, raw materials and energy, which all contribute to substantial short-term gains in

human welfare. In the long run however, the degradation of natural ecosystems undermines their capacity to

provide vital ecosystem services (ES), with possible negative consequences for livelihoods, health and

economy. The groups of people affected by local losses in ES supply often differ spatially (distant urban

dwellers vs local populations) and temporally (current vs future generations) from those benefitting from land

development (MA 2005, Kosmus et al. 2012). It is thus important to plan development projects to allow for both

economic and social progress without infringing on ES supply or provoking negative impacts on the well-being

of current and future local populations.

The scarcity of natural resources in proximity to densely populated areas is pushing the frontiers of

development toward increasingly remote areas. For generations, logistical barriers of distance, transportation

or even weather made remote regions less appealing for development, affording their ecosystems de facto

natural protection (Foote et Krogman 2006). However, new technologies, the expansion of transportation

networks and the world’s human population, coupled with economic growth, have altered expected

cost:income ratios to favor development in these remote regions (Foote et Krogman 2006, Kramer et al. 2009).

It is therefore of critical importance to intensify ES conservation efforts in remote regions, where, compared to

urban dwellers, inhabitants tend to (1) experience a stronger link with ecosystems and (2) to draw a greater

proportion of their necessities of life from surrounding ecosystems (McCauley et al. 2013). Some populations,

often indigenous communities, are even directly dependent on the benefits they can derive from ecosystems,

at least seasonally (Foote et Krogman 2006).

Systematic conservation planning (SCP), a multi-step operational approach to planning and implementation of

conservation (Margules et Sarkar 2007), is increasingly recommended for safeguarding ES provision (Cimon-

Morin et al. 2013). SCP has notably been developed to identify priority areas and design conservation

networks that make it possible to achieve conservation goals with the least cost or invested effort (Margules et

Sarkar 2007). Once priority areas for conservation have been identified, it is often assumed that they will be

protected rapidly. In practice, decisions are made sequentially due to insufficient budgets, limited site

availability, lack of political support or conflicts with stakeholders (Costello et Polasky 2004, Snyder et al.

2004, Strange et al. 2006, Sabbadin et al. 2007, Haight et Snyder 2009, Possingham et al. 2009, Schapaugh

et Tyre 2014). Accordingly, continued development brings uncertainties about future site availability, while the

degradation (or even loss) of future expected reserves may drive down their conservation value (Costello et

Polasky 2004, Harrison et al. 2008). The effects of development generally translate into the loss of

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conservation opportunities and the available alternatives generally involve a choice between sites with less

ecological value (i.e. ES value) or increased cost (Cabeza et Moilanen 2006). The loss incurred by an

optimized conservation network if certain areas cannot be protected is referred to as “replacement cost”

(Cabeza et Moilanen 2006, Moilanen et al. 2009a). Applied to the context of developing remote regions, it is

possible that the availability of a great number of undisturbed or natural sites for conservation may provide a

suitable alternative that may be particularly useful as a response to lost opportunities, and may buffer the

replacement cost of a conservation network to a certain degree (Wilson et al. 2009).

We conducted a case study in a remote region of eastern Canada to assess the effect of development on

conservation networks, notably by calculating replacement cost. This region of boreal eastern Canada is

currently marginally disturbed, but its large freshwater reserves, commercial forest, rivers with great potential

as a source of hydroelectricity and important mineral deposits make it an ideal candidate for industrial

development (Berteaux 2013). Specifically, our research question was: what is the cost of postponing

conservation actions until after different stages of development? For this purpose, we compared a referential

conservation network established prior to development with networks established at different stages of

simulated development scenarios. Networks were assembled to secure various target levels of ten ES

provided by wetland ecosystems. Targets were set for both the potential-use supply of the ten ES (i.e.

accessible supply) and the demand related to a subset of seven local flow scale ES (Cimon-Morin et al.

2014b). We hypothesized that the replacement cost of ES conservation networks would increase

proportionally with increasing development due to the loss of valuable sites for ES conservation.

Method

Study area

The study was undertaken in the Lower North-Shore Plateau ecoregion and in a southern portion of the

Central Labrador ecoregion of boreal eastern Canada (Figure 5.1.; Li et Ducruc 1999). It extends over 137,565

km2 and has a population of approximately 12,350 (0.09 inhabitants/km2) of which 9,800 inhabitants are

dispersed across fifteen municipalities and 2,550 are distributed in four First Nations communities

(Gouvernement du Québec 2013b). The minimal mapping unit of the Natural-Capital Inventory dataset (Ducruc

1985), originally compiled for ecological classification of the territory, was used to divide the study area into

16,026 planning units. The planning units are of irregular shape and size (mean of 8.5 ± 15 km2) because they

are delimited by significant and permanent environmental features, such as landscape topography, surface

deposits and water bodies. We mapped the proportion of relative coverage of 16 wetland types across

planning units using the most complete data available (see chapter 3 for detailed methodology). Ten peatland

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types and mineral wetlands (including marshes and swamps) were differentiated using the Natural-Capital

Inventory dataset (Ducruc 1985), while five aquatic wetland habitats were mapped using the CanVec v8.0

dataset (NRC 2011). All mapping was performed using ArcGIS 10.0 (ESRI 2012).

Figure 5.1. The location of the study area (in red) within North America (A); the extent of road networks and

location of the major towns and First Nations communities (B)

Mapping ecosystem services

Remote regions are great providers of important local to global flow scale ES for human populations (Schindler

et Lee 2010, Berteaux 2013, McCauley et al. 2013, Cimon-Morin et al. 2014b). We selected ten wetland ES

compatible with conservation actions, for which the sustainability of supply is important, notably for local

communities and tourism (five provisioning, three cultural and two regulating services). Seven have a local

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flow scale: moose and waterfowl hunting, salmon and trout angling, cloudberry picking, aesthetics and cultural

site for First Nations subsistence uptake. From a conservation perspective, a local flow scale means that

beneficiaries must approach or enter the protected area (where the ES is supplied) in order to obtain the

service’s benefits. One selected ES, flood control, has a regional flow. Two ES have a more global importance:

the existence value of woodland caribou (i.e. an iconic species in Canada) and carbon storage. We mapped

the biophysical supply (BS) of all ES quantitatively, taking into account the biophysical capacity of wetland

types to provide an ES in each planning unit (for detailed methodology see chapter 3). Then, for the seven

local and the single regional flow scale ES, we used proxies of human occupancy of the study area to refine

maps of their biophysical supply in order to identify where these ES can provide benefits accessible to human

populations, hereafter referred to as the potential-use supply of ES. We also mapped demand for the seven

local flow ES quantitatively. Demand for global flow ES was considered equal across their PUS range, since

demand for these ES is theoretically equal across their potential-use supply spatial range (i.e. any selection of

sites will contribute equally to demand). Because we were unable to establish demand values for flood control

service (a regional flow ES) precisely, we also assumed that demand for this ES was equal across the spatial

range of its potential-use supply, even if it may actually vary according to human population density and the

presence of human infrastructures (e.g. roads, bridges, etc.). Mapping ES potential-use and demand allows

the selection of their actual-use supply in conservation networks, which we defined as the presence of

accessible ES supply and demand at the same site (Cimon-Morin et al. 2014b). This definition follows from the

assumption that a real contribution to human well-being is made not only when ES are supplied and the

benefits are accessible, but also when a minimal demand is fulfilled. Experts were consulted for the

quantitative assessment and to validate the mapping of each ES supply and demand.

Mapping future industrial development sites and development scenarios

In the study area, there are four major types of industrial activities: (1) peat mining, (2) mineral mining, (3)

forestry and (4) hydroelectric energy production (i.e. dams and reservoirs). While these industrial activities are

already underway, they can be expected to expand in the coming years (Berteaux 2013). Therefore, we

mapped the potential sites where future industrial development is likely to take place (see Figure 5.2.). We first

gathered data on active peat (MRN 2012, personal communication) and mineral mining titles (MRN 2014) to

identify the planning units in our study most susceptible to future mining industry development. We used

forestry maps (MRNF 2012b) to identify productive stands (mostly coniferous and mixedwood stands) lying

inside the forest management units delimited in the study area (MRN 2003). Currently, less than 1 % of these

productive stands have been subjected to forestry interventions (MRNF 2012b). Finally, we mapped the five

hydroelectric projects that could be developed in the study area in the future (CRÉ 2010). We simulated eight

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intensities of development, corresponding roughly to various percentages of the study area subjected to

development (i.e. 3, 5, 7, 9, 11, 13, 15 and 17 %). These eight thresholds may also be interpreted as temporal

development sequences (i.e. time scale), where 3 % may represent the first stage of development and 17 %

the last stage. We stopped our simulations at 17 % of the study area subjected to development because it was

the maximal spatial extent covered by all four industrial activities combined. In our simulations, we chose to

randomly vary which hydroelectric projects were considered, except for the project called “la Romaine” which

was considered automatically in each simulation since the construction of dams has already begun. We used a

combined map of peat mining, mineral mining and forestry development sites to randomly select planning units

until the total area thresholds were attained. Because each site associated to a particular hydroelectric project

may not be chosen by random selection, we began our simulations by manually including the sites associated

to each proposed hydroelectric project. Although our simulations consider the development probability for each

site to be independent of the development status of neighboring sites, the fact that most future development

sites are located in proximity to each other (see Figure 5.2.) incidentally resulted in more or less contagious

distribution of selected development sites in all of our simulations (i.e. which is more representative of forestry

development). To account for the uncertainty as to which particular set of sites will be developed, we carried

out five different development simulations per percentage of development intensity tested, except for the 17 %

threshold, for which only one simulation was possible.

Conservation software and scenarios

Conservation planning software Systematic conservation planning (Margules et Sarkar 2007), is increasingly used and recommended for ES

conservation (Chan et al. 2006, Egoh et al. 2011, Cimon-Morin et al. 2014b). Accordingly, networks were

assembled using C-Plan v4.0 conservation planning software (Pressey et al. 2009). The C-Plan site selection

algorithm is primarily based on irreplaceability measures, defined as the likelihood that a given site will need to

be selected for efficient achievement of conservation objectives (Kukkala et Moilanen 2013). Irreplaceability

measures of sites generally vary according to target levels. Because no cost dataset was available for the

study region, the area of the planning units (km2) was used as a proxy for cost (Naidoo et al. 2006) to identify

the minimum set of sites that attain conservation targets for all features while minimizing the total cost

(hereafter “cost” will refer to the area). This rule was used to choose between sites of equal irreplaceability.

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Figure 5.2. Planning units where future development is most likely to occur for each type of industrial activity.

(A) Planning units containing at least one active peat mining titles. (B) Planning units containing at least one

active metal mining titles. (C) Planning units susceptible to experiencing forestry development. (D) Planning

units where five potential hydroelectricity projects may be developed.

Conservation networks and analysis We first assembled a reference conservation network by identifying priority areas predating any industrial

development. We tested a wide range of targets, since it is currently unknown precisely how much ES supply

and demand needs to be protected to enable their sustainability and actual demand fulfillment. Thus, we

assembled reserve networks to secure 10, 15, 20, 25, 30, 35, and 40% of each ES actual-use supply. These

targets were set for both the ten ES potential-use supply and the demand of the seven local flow ES in order to

foster the selection of their actual-use supply (see section 2.2.; Cimon-Morin et al. 2014b). Assuming that once

a site has been selected for industrial development it will not be available for conservation, we excluded the

sites disturbed under our different development simulations. Then, we re-optimized conservation networks to

secure the same ES targets as above in order to find a new near-optimal solution. For each ES target level, we

calculated the replacement cost of the conservation network by comparing the referential network with the

alternative solutions (i.e. networks established at the different stages of development). In target-based

conservation planning, replacement cost is measured as the changes in cost (or area) needed to achieve the

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targets after the exclusion of a site (or set of sites; Cabeza et Moilanen 2006, Kukkala et Moilanen 2013).

Therefore, we focused analysis on the difference in terms of the total area and the total number of sites

needed to achieve all conservation targets. Normally, reserve selection is a dynamic process that would last

throughout the development temporal sequence. However, to answer our main research question, we

assumed that each network identified following development simulations could be implemented as such. While

this approach simplifies the site selection process, we believe that our results provide conservative lower

bound estimates of replacement costs. A replacement-cost value of zero means that there is an alternative

solution with the same properties as the current optimal solution, i.e. that achieves ES targets at the same

cost. A replacement cost greater than zero means that any alternative solution excluding the sites subjected to

development will have a greater cost than the referential one.

To better describe replacement cost value, we decided to support our results by assessing the effect of

development on the frequency of selected sites based on their total area. We therefore plotted the probability

density function of conservation networks using R version 3.0.1 software (R Development Core Team 2013).

We used a two-sample Kolmogorov-Smirnov test (Zar 2010) to compare the referential network’s (i.e. prior to

development) frequency distribution with the replacement network’s (i.e. following development) frequency

distribution. For simplicity, we report only the probability density function of networks established at 5%, 11%

and 15% of development and for target levels of 10%, 25% and 35%, which represents low, moderate and

high ranges of the target levels and development stages tested. The five network samples that were

established per combination of target level and development stage were aggregated to form a unique

distribution that was compared to the referential network’s distribution. Finally, we assessed the proportion of

overlapping planning units between the referential networks and those established following development.

Once again for simplicity, we only reported the values for 10%, 25% and 35% target levels.

Results

Effect of development on conservation of ES actual-use supply

It took between 4,405 km2 (3.2% of the study area) and 22,441 km2 (16.3% of the study area) to secure 10%

to 40% respectively of the actual-use supply of ES prior to development (see Figure 5.3. for networks

example). Even at the maximal extent of development (i.e. 17% of the study area), it was possible to achieve

all ES actual-use supply conservation targets. In general, the replacement cost, measured as the difference in

the total area required to achieve all conservation targets compared with the referential network (i.e. prior to

development), was positive (Figure 5.4., except F and G), meaning that the alternative solutions were more

expensive. At low target levels, the “alternative solutions” required fewer sites than the referential network,

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suggesting that the sites lost to development were replaced by fewer sites but covered a mean larger area

than sites of the referential network. Nevertheless, the number of sites in the alternative solutions increased

disproportionally as both development and target level increased. The increase in the number of sites

coincided with a decrease of the replacement cost. The number of sites in the alternative solutions became

greater than the referential network at a target level of 25% and 15% of development. As target levels

increased further, the number of sites exceeded those of the referential network at earlier stages of

development, up to 11% of development at a 40% target level. Surprisingly, at higher target levels,

replacement cost was sometimes negative (Figure 5.4.F and G). This result is counter-intuitive since it

suggests that if conservation cannot be implemented before development, it could be less expensive, in terms

of total area protected, to wait until later stages of development before identifying priority areas.

The value of the replacement cost was influenced by both the target level and the stage of development. In

fact, increasing these two factors raised the irreplaceability value of the remaining available sites. First, at a

fixed target level, since developed sites were not considered as being available for conservation, increasing

the percentage of development resulted in an increase of the irreplaceability of the residual sites. Indeed, the

probability that these sites will be needed to achieve the target increased when compared with the referential

network (i.e. no development). Second, at a given development stage, increasing ES target levels also

increased the probability of each available site to be needed to achieve the target. Thus, since the

minimization-of-area rule in the site-selection algorithm was used to separate sites with equal irreplaceability

values, this rule was much more influential at both higher conservation target levels and stages of

development. This resulted in replacing losses with fewer moderately sized sites at low target levels with many

small sized sites at high target levels. This shift in the influence of the minimization-of-area rule seems to have

coincided with the stage of development where the replacement cost curve stopped increasing and began to

decrease.

The probability density function analysis of the conservation networks revealed great differences between the

referential network and those established following development. At a 10% target level, the referential network

was significantly different from the networks assembled at 5% (p < 0.001), 11% (p < 0.001) and 15% (p <

0.001) of development. At this target level, the referential network contained a higher proportion of small sized

sites and a smaller proportion of moderately sized sites. The same results apply to the 25% target level; the

referential network was significantly different from all others (p < 0.001) in that it was composed of a smaller

proportion of moderate to larger sized sites. At a conservation target level of 35% however, the referential

network was only significantly different from the network assembled at 15% of development (p < 0.001). In this

instance, the referential network contained a lower proportion of smaller sites than the network established

after 15% of development. These results suggest that at high target levels, a shift in site selection procedures

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occurs. That is to say, the composition of alternative solutions shifts from a higher proportion of larger sized

sites to a higher proportion of smaller sized sites when compared with the referential network.

Finally, there were great spatial discrepancies between the referential conservation networks and those

established following development (Figure 5.5.). First of all, the proportion of overlapping sites diminished with

decreasing target levels. This is simply due to the fact that higher target level networks required a greater

number of sites, which resulted in an increase of the chances that a particular site would be selected

incidentally. Second, the proportion of overlapping sites also decreased with increasing development. As a

result, implementing ES conservation after development greatly changed the spatial configuration of

conservation networks, since the alternatives (for all targets levels) had less than 40% of sites in common with

their referential network at 17% of development.

Figure 5.3. Example of conservation networks at different stages of development. We show networks

established to secure our 10% target for the ten ES actual-use supply prior to development (A) and at three

stages of development, 5% (B), 11% (C) and 15% (D).

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Figure 5.4. Difference in total area and total number of selected sites in conservation networks established at

different stages of development. Difference in total area refers to replacement cost. Mean deviation is from the

referential network that was established prior to development. Bars represent the standard deviation calculated

from the five networks established for each target level and each percentage of industrial development. In (G)

all values for the total area selected are negative except at 3% of development.

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Figure 5.5. Similarity between the referential network and the network established following different stages of

development. The proportion of overlapping planning units was calculated as a fraction of the number of

selected sites in the alternative solutions. The similarity is shown for three ES conservation target levels, 10%,

25% and 35% of their actual-use supply. Bars represent the standard deviation calculated from the five

networks established for each target level and each proportion of the study area subjected to development.

Discussion

Future development is most likely to occur in the southern part of the study area, where the population is

concentrated and in locations closer to existing road networks (Figure 5.1. and Figure 5.2.). At the same time,

these sites are also currently among the most important in providing an accessible supply of local flow ES and

in fulfilling demand for these ES (Cimon-Morin et al. 2014b), as beneficiaries’ accessibility and demand

generally decrease with increasing distance from roads and urban centers (Chan et al. 2006, Holland et al.

2011). As expected, development caused the direct loss of ES conservation opportunities (i.e. initial expected

priority areas). At 3% of development, there was already a minimum of 30% of non-overlapping sites between

the referential network and alternative solutions (Figure 5.5.). Our results illustrate that, from the beginning, ES

conservation and development compete for the same planning units (see Figure 5.2. and 5.3.). Nevertheless,

our results show that there was a high degree of flexibility for finding alternative conservation solutions in our

study area. In fact, it was possible to protect our highest ES targets, which is 40% of the ten ES actual-use

supply, even after 17% of the study area was allocated to industrial activities.

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As we were expecting, alternative solutions were in general more expensive, but surprisingly, cost began to

decrease at later stages of development for all target levels. Decreasing replacement costs have been

reported previously (Moilanen et al. 2009a), but in the context of a dynamic site selection process. These

researchers found that the replacement cost of a site with high loss rate probability decreased with increasing

planning period length because it became increasingly less likely that such a site would be acquired for

conservation. In the context of our study, however, cost was measured at the scale of the entire conservation

network. Despite these unexpected results, the actual adverse effects of development on conservation

networks may not be detected by focusing analysis on replacement cost alone (Moilanen et al. 2009a). We

believe that there are multiple reasons to begin implementing ES conservation actions prior to development.

First, all of the conservation solutions pointed to by the analysis in this study could have been identified prior to

development, even those with a negative replacement cost. Why were these solutions not identified? The

answer lies in the fact that increasing development (and ES target levels) inflated the irreplaceability of the

remaining available sites by causing the loss of alternative sites (Moilanen et al. 2009a). Since our

minimization of site area rule (i.e. cost) in the site selection algorithm was used to choose between sites of

equal irreplaceability value, increasing development resulted in a greater influence of the area minimization

rule. This produced alternative solutions that were more likely to have a lower initial irreplaceability value, i.e.

sites that possessed a lower initial ecological value for ES conservation. The same mechanism explains why

replacement cost decreased (or even became negative) at later stages of development. When costs dominate

the reserve selection process, there is a risk that unproductive, uninteresting and more distant locations will be

protected (Sabbadin et al. 2007, Moilanen et al. 2009a, Arponen et al. 2010). The most cost-efficient solution

is generally sub-optimal from ecological and conservation perspectives, therefore cost-efficiency should not

be the only measure of conservation success (Arponen et al. 2010).

Second, our findings suggest that waiting until development commences before reserving land may result in

much smaller reserves than a strategy of reserving land prior to development (Sabbadin et al. 2007). Indeed,

as target levels and stages of development increased, alternative solutions comprised many more sites of a

lower mean area (Figure 5.4.). The effect of fragmentation is well documented for biodiversity (Fahrig 2003,

Fischer et Lindenmayer 2007, Krauss et al. 2010), causing decreases in population size and ultimately

increasing the risk of extinction of local populations. Habitat and conservation networks fragmentation may

thus result in a failure of conservation objectives. We do not yet fully understand how increased fragmentation

may impact the effectiveness of ES conservation networks and the fulfillment of demand (Mitchell et al. 2013).

Habitat loss is most likely to have negative effects on service provision. Indeed, fragmentation may affect ES

provision directly, by restricting the rate of biotic, abiotic and matter (e.g. water) flow so important for service

provision, and indirectly by affecting the level of ecosystem functioning, which underlies ES provision

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(Cardinale et al. 2012, Mitchell et al. 2013). Nevertheless, the effect of habitat loss on ES conservation

effectiveness might be service specific. For example, is it better to protect the sites most important for

landscape aesthetics or to protect several less important sites that will be dispersed in the landscape but

globally protect the same quantitative amount of the service? In addition, few studies have examined the

relationship between ES supply and demand, and little is known about the level of supply that must be

maintained in a location to fulfill a particular level of demand. Nor do we know whether it is better to protect ES

actual-use with a network containing a low number of larger sites, also highly in demand, or a network

containing several smaller sites that are less in demand. Moreover, in our study, local flow ES demand scores,

unlike those for supply, have not been directly linked to planning unit area, but rather to geographic location in

relation to beneficiaries. In other words, demand scores, notably for local flow ES, diminish with increasing

distance from beneficiaries (Chan et al. 2006, Holland et al. 2011, Cimon-Morin et al. 2014b). Therefore, as

development increases, the loss of a site important to fulfill demand (e.g. a site in proximity to human

settlements) may be replaced by two or more distant sites.

Furthermore, no actual cost data was available that would have allowed us to properly calculate replacement

cost. Nevertheless, we can assume that excluding sites susceptible to future industrial development redirected

our reserve selection toward sites with current lower opportunity costs. In other words, sites with current no

potential for any future industrial activities may, for the time being, have a lower probability of conversion. At

the maximum extent of development, is it possible that the conservation networks for each target level would

be among the least expensive in actual economic cost values. The choice of developing or protecting a site is

generally influenced by a cost:benefit ratio where the benefit is related to the expected production value of

marketed products, which in this case would include wood, peat, mineral and energy production. However, the

economic value of other ES, such as regulating and cultural services (and the value of provisioning ES

compatible with conservation) has not usually been taken into account in development planning. A great body

of evidence suggests that the economic value of such ES provided by intact landscape outweighs the

marketed economic benefits of land conversion and development (Balmford et al. 2002, Hein et van der Meer

2012, Polasky et al. 2012, Cimon-Morin et al. 2013, Ruiz-Frau et al. 2013). In fact, even when only a few ES

are considered, their conservation may be a more economical land-use than conversion. Accordingly, the true

“cost” of a site may be the land price minus the value of ES it provides (Costello et Polasky 2004, Chan et al.

2011). Thus, the actual long term profitability of the study area may be decreasing with increasing delay of ES

conservation planning and implementation. Considering a wide spectrum of ES in development planning

should enable decisions to be made on the basis of the best possible information and may lead to more

economically profitable land-use decisions.

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Finally, we assumed that all priority areas identified at a specific stage of development could be immediately

protected. In reality, however, reserve selection is a sequential decision-making process and it is impossible to

protect all priority areas instantly given limited resources or site availability (Costello et Polasky 2004, Snyder

et al. 2004, Haight et Snyder 2009, Schapaugh et Tyre 2014). Given these constraints, it is important for

conservation practitioners to choose wisely among available potential sites to maximize conservation

outcomes. Applied to ES conservation, such a dynamic process should focus primarily on those ES more

vulnerable to immediate threats (Luck et al. 2012). Consequently, we recommend that regulating and cultural

services as well as local flow range ES be given first priority. Regulating and cultural services are among the

services most vulnerable to anthropogenic disturbances (Foley et al. 2005, Bennett et al. 2009), while local

flow ES cannot be replaced spatially and their conservation should compete more actively with development

for land acquisition. Priority areas with a high demand score should also be given preference, since they are

particularly important for sustaining the delivery of ES benefits to a greater number of people. Moreover, the

sequence of reserve acquisition should prioritize sites subject to high development pressure (Moilanen et al.

2009a) since, as observed elsewhere, most remaining sites are likely to remain natural or in their current state

for some time, even if not formally protected (Strange et al. 2006, Sabbadin et al. 2007).

Conclusion

The sustainability of activities related to the well-being and livelihood of local populations is more closely linked

to conservation actions than development projects. Elaborating a regional development and conservation plan

that is ecologically, socially and politically optimized is less likely to be possible after development is underway.

Even if all our ES targets were achievable at the maximum extent of development tested, our results also

suggest caution regarding the effectiveness of late conservation planning. Moreover, development may

certainly exceed the levels simulated in this study. In such cases, conservation would be pushed back toward

areas even more remote, to the point that late ES conservation may eventually become pointless.

Conservation compatible ES, such as those considered in this study, may provide economic and social

arguments for protecting sites that are the most vulnerable and with high opportunity costs. There is an urgent

need to measure the economic benefits of ES in order to make relevant land-use planning decisions.

Considering ES may help optimize trade-offs between different ecosystem management options and choosing

between competing land-uses (Kosmus et al. 2012).

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CHAPITRE 6

Conclusion générale


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Cette étude a été entreprise dans l’objectif de développer une approche pour planifier de manière efficace la

conservation des services écologiques dans les milieux nordiques du Québec. En guise de conclusion

générale, je ferai un bref retour sur les résultats, je soulèverai les principales limites et contraintes rencontrés,

puis je discuterai de la possibilité d’appliquer mes résultats à la conservation au Québec.

Synthèse des résultats

Le chapitre 2 a permis d’évaluer les bases de connaissances sur la conservation des services écologiques.

J’ai notamment déterminé que l’identification des sites prioritaires à protéger pour les SE doit être basée sur

des indicateurs biophysiques quantifiables ainsi que sur l’échelle spatiotemporelle des flux de services. J’ai

aussi montré que la faible congruence spatiale entre la biodiversité et les SE est attribuable (1) au manque de

données précises pour cartographier les SE ; (2) au fait que la diversité fonctionnelle représenterait avec plus

d’exactitude la distribution spatiale des SE par rapport aux autres mesures de biodiversité ; (3) au fait que les

services d’approvisionnement sont souvent négativement spatialement corrélés à la biodiversité. J’ai suggéré

que l’utilisation d’approches de sélection des sites prioritaires basées sur la complémentarité des sites

augmente l’efficacité de la conservation simultanée de la biodiversité et des SE. Finalement, j’ai montré

comment les SE peuvent promouvoir la conservation de la biodiversité, notamment grâce à des arguments

économiques.

Dans le cadre du chapitre 3, j’ai suggéré une adaptation des approches traditionnelles de planification

systématique de la conservation qui permet d’identifier les sites prioritaires à protéger pour les services

écologiques, tout en considérant le lien qui les unit à leurs bénéficiaires. J’ai d’abord montré comment utiliser

les connaissances sur l’échelle spatiale de flux de services pour cartographier leur apport qui est accessible

aux bénéficiaires. Puis, l’intégration de la demande pour les services écologiques dans le processus de

sélection des sites a permis de générer des réseaux de conservation qui comblaient de deux à cinq fois plus la

demande des bénéficiaires que lorsqu’on ciblait seulement l’apport des services. Cette approche favorise donc

la sélection de sites qui procurent un apport accessible d’un service donné là où il est en demande par les

bénéficiaires (les sites à utilisation réelle). J’ai aussi comparé deux méthodes pour intégrer la demande dans

les processus de sélection des sites : (1) en établissant des cibles de conservation pour la demande de

chacun des SE au même titre que pour leur apport ; (2) en établissant une règle de sélection dans

l’algorithme, qui forçait ce dernier à considérer en premier les sites ayant la plus grande demande totale. Mes

résultats ont montré que d’établir des cibles de conservation précises pour la demande était la seule des deux

approches qui permettait de protéger une quantité minimale désirée.

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Dans le chapitre 4, j’ai montré que la conservation de sites à utilisation réelle de SE induit une diminution dans

la capacité des SE à représenter la biodiversité dans les réseaux de conservation par rapport à lorsque je

ciblais uniquement leur apport biophysique. Cette diminution est notamment attribuable aux contraintes

spatiales résultant d’une conservation des SE plus axée vers leurs bénéficiaires. En fait, la considération des

relations spatiales entre les flux de services et leurs bénéficiaires dans les choix de conservation rapproche

les sites sélectionnés des zones habitées. Néanmoins, il a été possible d’optimiser le niveau de représentation

de la biodiversité et des SE en les considérant simultanément lors des processus d’identification des sites

prioritaires. J’ai montré qu’il était beaucoup plus efficace d’atteindre toutes les cibles de conservation de

biodiversité et de SE dans un seul et même exercice de planification systématique en misant sur la

complémentarité des sites plutôt qu’en misant sur leur concordance spatiale ou en complétant un réseau déjà

établi (par exemple en complétant un réseau protégeant la biodiversité pour atteindre des cibles de SE).

Dans le chapitre 5, j’ai montré que les activités industrielles reliées à l’extraction des ressources naturelles

vont être en compétition spatiale avec les sites les plus importants à protéger pour la conservation des SE en

Minganie. J’ai d’ailleurs constaté qu’à seulement 3% de développement, les réseaux alternatifs présentaient

déjà seulement de 50 à 70% de sites sélectionnés en commun avec le réseau de référence généré avant le

développement. De plus, j’ai aussi illustré que planifier la conservation des SE, après le début développement,

induit des coûts de remplacement, en termes de superficie totale à protéger pour atteindre toutes les cibles de

conservation, pouvant s’élever jusqu’à 15%. Parmi les autres conséquences de la mise en œuvre tardive de la

conservation, j’ai aussi constaté que plus le développement s’intensifiait et plus les réseaux alternatifs

devenaient formés d’un grand nombre de sites dont la taille moyenne diminuait. Ainsi, à partir du moment où

13% du territoire était soumis au développement, mes résultats suggèrent un effet de fragmentation dans les

réseaux de conservation qui devient de plus en plus perceptible.

Applicabilité au Québec de l’approche développée

Cette thèse a permis de développer un cadre de référence de base pour la planification systématique de la

conservation des SE. Ce cadre de référence est sans aucun doute exportable à d’autres régions du Québec

(nordiques ou autres) et ailleurs dans le monde. En effet, seulement le choix des services à intégrer dans la

conservation ainsi que la cartographie de leur apport et de leur demande variera d’une région d’étude à l’autre.

Les concepts et les connaissances mis de l’avant dans cette thèse pourront certainement contribuer et

alimenter l’approche de planification écologique envisagée par le gouvernement du Québec afin de répondre

aux nouvelles orientations gouvernementales en manière de diversité écologique, dont le maintien de la

production des SE essentiels.

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Limite d’une étude de cas en planification systématique de la conservation

Il importe de mentionner que les résultats quantitatifs obtenus dans le cadre de cette thèse ne peuvent se

rapporter qu’au contexte de l’aire d’étude. Par exemple, dans le cadre du chapitre 4, j’ai montré que toutes les

cibles de conservation pour la biodiversité et les SE pouvaient être atteintes pour seulement 6% de superficie

supplémentaire à protéger. Or, ce 6% ne se rapporte concrètement qu’à l’aire d’étude en question et cette

valeur pourrait bien être supérieure ou même inférieure si l’étude avait été réalisée ailleurs. Néanmoins, les

constats de nature plus qualitatifs provenant des différents chapitres, par exemple dans ce cas-ci, le fait que

des approches de conservation basées sur la complémentarité soient plus effìcaces que les approches misant

sur la concordance spatiale, sont généralisables à d’autres contextes. Une étude de cas comme celle-ci

simplifie souvent la réalité afin de tester différentes hypothèses de recherche Certains aspects essentiels de la

planification systématique de la conservation ont été simplifiés dans cette thèse mais doivent être pris en

compte dans un contexte réel.

Malgré que le cadre de référence pour la conservation des SE présenté dans cette thèse peut être appliqué

dans d’autre régions, il n’est pas pour autant tout à fait opérationnel pour plusieurs raisons. Les réseaux

identifiés par une étude de cas portant sur l’identification des sites prioritaires pour la conservation comme

celle présentée dans cette thèse, sont rarement mis en œuvre tels quels sur le terrain (Knight et al. 2006a,

Knight et al. 2006b). En effet, les informations recueillies dans le cadre d’une telle analyse ne peuvent pas à

elles seules engendrer des mesures concrètes de conservation; elles doivent plutôt être intégrées à une

approche plus opérationnelle. On distingue généralement « l’évaluation systématique de la conservation », qui

implique d’identifier spatialement les priorités de conservation (c’est-à-dire, la sélection de sites), du concept

plus général de la « planification de la conservation ». La planification de la conservation cherche à compléter

la phase d’évaluation systématique par le développement d’une stratégie de mise en œuvre de la

conservation (Knight et al. 2006b). Ainsi, la « planification systématique de la conservation » est un modèle

opérationnel qui compte généralement entre 11 et 13 étapes (Pressey et Bottrill 2008, Kukkala et Moilanen

2013). Suivant le modèle suggéré par Pressey et Bottrill (2008), ces onzes étapes sont : (1) délimiter la région

de planification; (2) identifier toutes les parties prenantes; (3) décrire le contexte de la conservation, ce qui

implique d’évaluer le contexte social, économique et politique ainsi que les contraintes et les opportunités de

conservation; (4) identifier les objectifs de la conservation; (5) obtenir des données socio-économiques de la

région sous étude; (6) recueillir des données sur la biodiversité, sur les services écologiques ou d’autres

caractéristiques naturelles; (7) établir des cibles de conservation; (8) évaluer le réseau existant d’aires

protégées, par exemple une analyse de carences permet de constater la proportion des cibles de conservation

déjà atteintes dans le réseau existant; (9) sélectionner de nouvelles aires de conservation; (10) mettre en

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œuvre des actions de conservation dans les sites sélectionnés; (11) maintenir et surveiller les zones de

conservation.

Dans cette étude, je n’ai vraisemblablement réalisé que trois étapes du processus de la planification

systématique de la conservation, soit la délimitation de l’aire d’étude (1), la collecte de données sur les

services écologiques et la biodiversité (6) et la sélection des aires protégées (9). Ainsi, cette étude n’a pas été

réalisée dans l’objectif de fournir un réseau d’aires protégées qui serait implanté sur le terrain, mais plus

simplement de développer les connaissances pour réaliser une évaluation systématique de la conservation

adaptée aux services écologiques. Les réseaux qui sont présentés dans cette thèse ne devraient pas être

interprétés comme étant les réseaux qui devraient concrètement être mis en œuvre pour protéger les services

écologiques.

Ensuite, la planification systématique de la conservation implique trois objectifs de base, qui sont (1) la

représentation, (2) la persistance et (3) l’économie. Afin d’adapter la conservation aux services écologiques,

nous avons suggéré d’élargir ces objectifs pour en inclure un quatrième, soit la notion d’utilité. Ainsi, les sites

sélectionnés pour protéger un apport de services écologiques doivent (4) fournir des bénéfices accessibles là

où ils sont en demande par les bénéficiaires. L’approche d’évaluation systématique de la conservation que j’ai

développée pour les services écologiques répond bien aux objectifs de représentation, d’économie et d’utilité.

En effet, j’ai établi des cibles minimales de conservation pour l’apport et la demande des services écologiques.

Grâce à la complémentarité entre les sites, ces cibles ont été atteintes en sélectionnant un minimum de

superficie et j’ai montré que les sites sélectionnés optimisent l’apport utile (apport accessible et en demande)

des services. J’ai aussi montré que la complémentarité entre la biodiversité et les services écologiques rendait

leur conservation beaucoup plus compatible et optimisait leur degré de représentation respectif.

L’approche développée dans ce projet est cependant une version un peu tronquée de la planification de la

conservation car elle ne garantit pas la persistance des services écologiques et de la biodiversité dans le

réseau, qui est l’objectif ultime de la conservation. Généralement, la persistance des entités protégées est

assurée par le maintien des structures, des processus et des fonctions écologiques. Or, les services finaux,

comme ceux considérés dans cette thèse, sont justement le fruit de l’action des processus et des fonctions

écologiques (autrement dit les services intermédiaires; voir Figure 2.1.). Ainsi, la persistance à long terme des

services écologiques finaux, c’est-à-dire les services qui rendent des bénéfices directs aux êtres humains

(comme les services considérés dans cette thèse), doit impliquer une prise en compte des processus et des

fonctions au-delà des frontières procurant un apport accessible en services finaux. L’apport des services

finaux pourra être maintenu en s’assurant que les fonctions et les processus écologiques (soit les services

intermédiaires) sont conservés de manière durable soit directement dans l’aire protégée (service local

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ponctuel; par exemple l’esthétisme), soit dans le grand écosystème environnant l’aire protégée dans le cas de

certains services locaux non-directionnels (par exemple la chasse à l’orignal), soit en amont de l’aire protégée

dans le cas des services locaux directionnels (par exemple, le saumon et la sauvagine).

Opérationnellement, la planification de la persistance se traduit par l’accroissement de la connectivité entre les

aires protégées par l’inclusion des critères de conception et de configuration spatiale du réseau lors de la

sélection des sites ou a posteriori. Elle doit généralement inclure des considérations biologiques,

sociopolitiques, spatiales et temporelles qui vont bien au-delà d’assurer un simple niveau de représentation

(c’est-à-dire d’atteindre les cibles de conservation) dans le réseau. D’un côté, les processus écologiques qui

sont facilement cartographiables (c’est-à-dire associés à des attributs spatialement fixes des écosystèmes)

comme les bandes riveraines (qui peuvent servir de couloir de migration), peuvent être inclus directement lors

de la sélection de sites en leur attribuant des cibles de conservation. Par exemple, on peut chercher à

sélectionner dans le réseau 100% des bandes riveraines identifiées. D’un autre côté, les processus qui varient

dans l’espace (comme les gradients environnementaux; gradient climatique est-ouest ou nord-sud, gradient

hautes terres-basses terres) et qui sont plus difficilement cartographiables sont plutôt considérés par la

création de corridors. Des critères de connectivité (sélection de sites adjacents à ceux déjà sélectionnés) ainsi

que de taille des sites sélectionnées (minimale ou maximale) peuvent généralement être employés pour

favoriser cet objectif lors de la sélection des sites. Cependant, ces considérations n’étaient pas disponibles

dans le logiciel de planification systématique de la conservation que nous avons utilisé (C-Plan).

Limite méthodologique et contraintes rencontrées

En examinant les résultats de ce projet, j’ai été en mesure de soulever certaines lacunes ou contraintes qui

devraient être le sujet de recherches futures afin de bien intégrer les services dans les objectifs de

conservation.

Tout d’abord, le manque de données disponibles concernant la cartographie de l’apport et de la demande des

SE (notamment pour les SE ayant une échelle locale et régionale dans le cas de la demande) a été une des

principales limites de ce projet. En effet, un des plus grands obstacles à la cartographie des SE est le manque

de données primaires (c’est-à-dire provenant d’un échantillon mesuré à même l’aire d’étude), pour la plupart

des services dans la grande majorité des régions du monde (Eigenbrod et al. 2010b, Eigenbrod et al. 2010a).

C'est pourquoi, encore aujourd’hui, les approches basées sur les données secondaires sont principalement

utilisées pour produire les cartes de SE (voir Figure 6.1.). Toutefois, des auteurs ont montré que l’utilisation de

données secondaires engendre une erreur de cartographie, appelée l’erreur de généralisation, qui provoque

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une faible concordance spatiale lorsqu’on compare le même service cartographié à l’aide de données

primaires et à l’aide de données secondaires (Plummer 2009, Eigenbrod et al. 2010a, Eigenbrod et al. 2010b).

Les cartes résultant de données secondaires sont donc moins représentatives de la réalité que celles issues

de données primaires. Cela tend à créer des erreurs de commission (fausses présences), où on déclare un

SE présent dans une unité d’aménagement alors qu’il y est en fait absent ou qu’il présente un apport existant

beaucoup plus faible que celui estimé. Cet aspect est problématique, car un réseau optimisé pour atteindre

toutes les cibles de conservation risquerait, dans la réalité, de voir certains SE sous-représentés. La

persistance de l’apport de ces SE, soit un des objectifs de la conservation, pourrait donc être compromise.

Dans le cadre ce projet, la cartographie de plusieurs services, notamment la demande pour la plupart des

services à échelle locale, a été basée sur des données secondaires.

Figure 6.1. Les différentes méthodes pour cartographier les services écologiques en fonction des données

disponibles. Adaptée de Martínez-Harms et Balvanera (2012).

Je n’ai pas été en mesure de quantifier l’impact de l’utilisation de données secondaires lors de l’identification

des sites prioritaires dans les articles de cette thèse. Néanmoins, je recommande l’utilisation des meilleures

données disponibles et, possiblement, de réaliser des inventaires ou des projets qui résulteront en la création

de bases de données pour mieux cartographier les SE les plus importants ; c’est-à-dire, ceux qu’ils seraient

désirables d’inclure dans les objectifs de conservation.

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La deuxième contrainte méthodologique à laquelle j’ai été confronté est que je n’ai jamais été en mesure

d’établir des cibles précises de conservation que ce soit pour l’apport ou pour la demande des SE. Dans la

réalité, des cibles trop élevées pourraient rendre la tâche d’harmoniser la conservation de services à celle de

la biodiversité plus difficile, (1) en engendrant des contraintes spatiales dans la sélection des sites, mais aussi

(2) en entrainant une allocation disproportionnée des ressources disponibles pour la conservation. Dans un

contexte où les ressources pour la conservation sont limitées, une telle situation risquerait de résulter en un

réseau qui serait moins efficace pour représenter la biodiversité. Des cibles trop faibles cependant risqueraient

d’affecter la durabilité du bien-être des populations locales. Aussi, il faudra veiller à balancer les cibles de

conservation entre l’apport et la demande d’un service donné. En effet, établir des cibles trop faibles pour

l’apport du service par rapport à sa demande risquerait d’épuiser localement les flux de ce service. À l’inverse,

une cible plus élevée pour l’apport par rapport à la demande pourrait assurer une plus grande stabilité dans

les flux de ce service. Pour certains services, je recommande que des études socio-écologiques soient

réalisées dans chacune des zones de planification de la conservation afin d’établir des cibles précises qui

représenteront adéquatement les besoins des bénéficiaires locaux.

Troisièmement, le logiciel de planification systématique de la conservation que j’ai utilisé, C-Plan, est un

logiciel qui ne propose qu’une seule solution de conservation (un seul réseau) lorsque les paramètres

demeurent inchangés (par exemple, les règles de l’algorithme, les cibles de conservation, la disponibilité des

sites, etc.). Dans un contexte de paysage fortement transformé par l’homme, comme le Sud du Québec, où la

disponibilité et la quantité de sites potentiels pour la conservation sont limitées, le nombre de solutions

alternatives risque d’être faible. Ainsi, l’unique réseau proposé par C-Plan peut représenter la meilleure

configuration spatiale des sites à protéger. Cependant, dans un contexte d’une aire d’étude peu développée et

où la grande majorité des unités d’aménagement sont disponibles pour la conservation, il devrait y avoir en

théorie plusieurs alternatives possibles, ou plusieurs configurations différentes de réseaux optimisés pour

atteindre toutes les cibles de conservation. L’évaluation de l’ensemble des solutions alternatives permet de

faire des compromis et de prendre de meilleures décisions quant au choix des sites qui formeront le réseau

final. D’autres logiciels, notamment Marxan (Ball et al. 2009), proposent plusieurs solutions de conservation

sans avoir à changer les paramètres de base et permettent d’inclure des critères de connectivité ou de taille

minimale ou maximale lors de la sélection des sites. Si C-Plan est utilisé pour planifier la conservation de

d’autres territoires peu développés du Québec, je recommande que les utilisateurs du logiciel se basent aussi

sur les cartes de valeurs d’irremplaçabilité générées par le logiciel afin de connaître l’importance individuelle

de l’ensemble des unités d’aménagement, et non seulement de celles de l’unique solution proposée.

Ensuite, alors que j’ai intégré l’aspect spatial des flux de services écologiques dans mes analyses, je n’ai pas

été en mesure d’intégrer des aspects temporels. En effet, mes analyses ont considéré la demande ainsi que

105

l’apport accessible des services comme étant statique. Or, il est certain que de nouvelles routes issues de

projets de développements miniers, hydroélectriques ou forestiers ouvriront le territoire et changeront par

conséquent l’accès aux sites. Dans cette optique, une analyse de l’apport biophysique des services pourrait

permettre d’identifier soit (1) les sites sensibles à la création d’une route et qui pourraient perdre leur capacité

à fournir certains services à la suite d’une perturbation de ce type ou (2) les sites où la construction d’une

route permettrait d’augmenter l’accessibilité et l’apport local de certains services. Néanmoins, il demeure que

la demande pour les services à échelle locale diminue avec l’augmentation de la distance par rapport aux

zones habitées. Ainsi, miser sur la protection de sites nouvellement accessibles par la construction d’une route

risque d’être moins efficace que de protéger les sites possédant une grande demande actuellement. En

d’autres mots, il faudra peut-être protéger davantage de sites pour répondre à la même quantité de demande.

Il aurait été intéressant dans le cadre du chapitre 5 d’intégrer l’aspect temporel. Il est donc souhaitable que ce

sujet soit abordé dans des recherches futures.

Finalement, il faudra absolument intensifier la recherche sur l’effet de la fragmentation d’un réseau de

conservation de SE sur sa capacité à procurer un apport constant et à combler la demande. Alors que les

connaissances sur la configuration spatiale des réseaux de conservation pour la biodiversité sont en plein

essor (pour exemples, voir Moilanen et al. 2009c), l’importance de la connectivité et de la taille des réserves

pour le maintien des SE est toujours peu étudiée. Par exemple, comme nous en avons discuté dans le

chapitre 5, la différence réelle d’efficacité entre un réseau de conservation formé de petits sites par rapport à

un réseau formé de grands sites est peu documentée. La persistance des flux de SE dans le temps risque de

nécessiter des connaissances sur la conception et la configuration spatiale des sites formant le réseau de

conservation. Il serait notamment important d’établir le lien qui existe entre la quantité de l’apport d’un SE

protégé dans un site donné et sa capacité à combler une quantité de demande donnée.

107

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Annexe 1

Ecosystem services expand the biodiversity conservation toolbox

– A response to Deliège and Neuteleers

Jérôme Cimon-Morin, Marcel Darveau & Monique Poulin In reviewing the literature on the synergies between ecosystem services (ES) and biodiversity in conservation

planning, we posited that “the inclusion of ES in conservation is likely to generate more advantages than

disadvantages where biodiversity conservation is concerned” (Cimon-Morin et al. 2013). Commenting on our

review, Deliège and Neuteleers (2014) called for caution in using the ES-argument to promote biodiversity

conservation. We would like to address their concerns and also highlight the complex interrelationship among

conservation approaches that may in fact be complementary.

First, we completely agree with Deliège and Neuteleers (2014) that the intrinsic value of biodiversity and moral

arguments should be the primary motivation for biodiversity conservation. At the same time, biodiversity

conservation projects worldwide face many challenges to their implementation and success, including

perpetual lack of funding and social and political support, as well as pressures related to economic

development. To meet these challenges, the conservation community needs to improve the “toolbox” at its

disposal and diversify its strategies. As we and others have suggested, ES have the potential to be a means

towards the end of biodiversity conservation and surmount some of the aforementioned constraints (Reyers et

al. 2012, Cimon-Morin et al. 2013). Although our review counsels caution since areas rich in biodiversity and

ES are not necessarily congruent, ES conservation could foster biodiversity protection in certain contexts.

One of the main concerns expressed by Deliège and Neuteleers (2014) is that the ES-argument may modify

conservation objectives by targeting “useful species or ecosystems, so that useless or non-functional

biodiversity can be extinguished”. In fact, conservation objectives emerge from a society-wide debate and

represent a great diversity of human values. Thus, the aim is not to substitute ES for biodiversity as the

conservation objective (unless society were to decide so) but rather to consider it as a complement or an

extension. Many biological features that are “ES providers”, such as functional biodiversity, are not normally

considered during biodiversity conservation assessment, while some ES originate from ecosystem functions

that may be optimized under rich biodiversity. Even conservation plans that target a particular species because

of its ES contribution must often include the entire associated community or ecosystem. Hence, ES could

expand biodiversity conservation by contributing to the security of a wide array of species as well as their

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related functional diversity. In most circumstances, for example, the protection of the Catskills-Delaware

watershed in the state of New York (USA), such an approach can still be less costly than relying on a

technological replacement for the ES. In addition, since ES conservation projects attract new funding sources

and engage a more diverse set of funding agencies and conservation partners (Goldman et al. 2008), they are

less likely to draw upon the financial resources available for important biodiversity features that require specific

protection.

Furthermore, Deliège and Neuteleers (2014) stated that the ES-argument could damage the social acceptance

of conservation because “most conservationists will not be motivated to preserve biodiversity because of its

functional roles”. However, we believe that social and, ultimately, political support are key for the effective

implementation and funding of biodiversity conservation. It has increasingly been documented that actions to

preserve biodiversity can be accompanied by constraints on future land use options that affect local human

populations, resulting in a significant loss of economic opportunities. The strict conservation of biodiversity may

also conflict with the well-being of most populations that still draw a portion of their necessities of life directly

from ecosystems (e.g. subsistence uptake and other benefits), especially in emerging countries, remote

regions or sparsely-populated areas of industrialized countries. Moreover, urban dwellers tend to lose their

connection with nature and fail to experience the full extent of its benefits, which is likely to impede their

propensity to appreciate the intrinsic value of biodiversity. ES offer conservationists a promising way to align

people’s needs with conservation actions by establishing reserves that could fulfil their well-being.

To conclude, the ES-argument is too often interpreted with a narrow focus on use value, markets and

payments, which overlooks the aesthetic, spiritual, educational, scientific, recreational, existence and non-use

values of ES (Reyers et al. 2012). As Reyers et al. (2012) have suggested, there is “an urgent need for the

[conservation] community to move beyond the either biodiversity or ES debate to one that acknowledges that

both biodiversity and ES are important arguments in stemming the tide of biodiversity”. We believe that the ES-

argument is not an exclusive but rather a supplementary means for protecting biodiversity, especially where

conservation costs are high and lack social support. Careful use of ES in conservation assessment is more

likely to expand the opportunities for preserving biodiversity by diversifying the conservation toolbox.

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Annexe 2

Ecosystem services mapping description

Provisioning services

Moose hunting

The biophysical supply (BS) of moose (Alces alces) hunting was quantified using the extent of its aquatic and

wetland habitat requirements (Timmermann et McNicol 1988, Tecsult 2006). All planning units containing

moose aquatic and wetland habitats were considered as contributing to the BS. It was assumed that the

density of this species was homogenous across the study area (Lamontagne et Lefort 2004). Potential-use

supply (PUS) was mapped using the accessibility proxies for local flow ES. Point locations of moose hunting in

the study area for the past twenty years were obtained from the Ministry of Natural Resources of Quebec

(MRN, unpubl. data 2012). The sum of moose hunted per planning unit was used as demand data.

Atlantic salmon angling

The BS of Atlantic salmon (Salmo salar) angling was mapped for each planning unit using a map of salmon-

inhabited rivers from the Natural Resources Ministry of Quebec (MRN, unpubl. data 2012), which takes into

account the impassible physical barriers to upstream salmon migration, and the mean number of salmon

migrating upstream each year per river (Caron et al. 2006). The sections of these rivers where salmon fishing

is legally permitted (MRNF 2012c) were also used to refine the BS spatial range. The PUS was modeled using

proxies of accessibility. Demand was mapped using the mean number of catch per specific river for the last

five years (MRNF 2012a). However, because the number of catches had been noted per river, the demand for

salmon angling was further spatially estimated using demand proxies. These two variables were then

standardized and summed.

Brook trout angling

Brook trout (Salvelinus fontinalis) is a species of fish present in almost every water body and waterway within

the study area (Hydro-Québec 2007), with the exception of those located upstream a slope of 40% and at an

altitude higher than 500 meters above sea level (i.e. those that could not be naturally colonized following the

last glaciation). The BS was modelled using the extent of the species’ habitat availability in each planning unit.

A map of the inaccessible water bodies (Bellavance et Gagné 2012) was used to identify the planning units

that were out of reach of this species. PUS was modelled using proxies of accessibility. Demand was also

mapped using proxies because no catch data was available.

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American black duck hunting

The BS of American black duck (Anas rubripes) hunting was modeled using its aquatic and wetland habitat

selection ratio (Lemelin et al. 2010) and the area covered by these types of habitats in each planning unit. It

was assumed that the duck’s density was uniform across the study area (Lemelin et al. 2004, Guérette

Montminy et al. 2009). PUS and demand were mapped using proxies.

Cloudberry picking

Cloudberry (Rubus chamaemorus) is a plant species that grows in ombrotrophic peatlands (bogs). However,

according to an expert, only non-forested bogs (i.e. open bogs) show a berry production high enough to enable

picking (C. Naess, personal communication). The BS of this provisioning ES was mapped using mean yield

value from field samples measured in the study area (C. Naess, unpubl. data 2012) and the area occupied by

open bogs. PUS and demand were mapped using proxies.

Cultural services

Aesthetic features of wetlands

Open peatlands, rivers, lakes and ponds are the wetlands and aquatic habitats that contribute most to the

aesthetics of the landscape in the study area, according to a previous social assessment and expert

knowledge (Pâquet 1997, Hydro-Québec 2007). We mapped the wetland aesthetic BS by scoring planning

units according to four categories: (1) proportion of ponds and lakes, (2) proportion of rivers, (3) proportion of

open peatlands (i.e. non-forested bogs and fens) and (4) wetland and aquatic habitat heterogeneity (i.e. the

total proportion of ponds, lakes, rivers and open peatlands; Pâquet 1997). Each of these categories was

divided into four proportion ranges, for which thresholds were specifically determined using the natural breaks

in ArcGIS (ESRI 2012). A score was associated with each proportion range and the total BS value of the

aesthetics for a planning unit was obtained by summing the scores obtained under all four categories (Pâquet

1997). For the PUS, we combined the accessibility proxies with a distance buffer of 500 m around human

infrastructures (Pâquet et Bélanger 1998, Pâquet 2003) in order to identify where the wetlands are part of the

aesthetic features of the landscape. Demand for aesthetics was mapped by scoring each planning unit

according to (1) appeal in regard to infrastructures (i.e. local, regional or national appeal), (2) users’

expectations and interests in landscape quality (i.e. low, moderate, high), (3) mean duration of users’

frequentation (i.e. occasional, seasonal, annual), (4) mean duration of users’ observations (i.e. from seconds

to extended time periods), and (5) the number of potential viewers (i.e. low, moderate or high; Pâquet 2003).

Cultural sites for First Nations communities subsistence uptake

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The Montagnais First Nations communities (Innu) in the study area harvest several wetland and aquatic

species for subsistence, including waterfowl, beaver, muskrat, moose, freshwater fishes and berries (Charest

1996, Walsh 2005). Thus, the BS for this ES was mapped according to the extent of habitat availability for

subsistence uptake in each planning unit. The PUS was refined from the BS using maps of the “community

territories” which are the zones across the study area that each of the four First Nations communities actually

uses for their subsistence uptake activities (Charest 2005). Inside these zones, we were further able to

delineate high and low uptake areas (Charest 2005). The average harvest intensities according to the total

weight and total number of catches in these low and high uptake areas were used to map demand (Walsh

2005).

Existence value of caribou

Woodland caribou (Rangifer tarandus caribou) is an iconic and endangered subspecies of the Canadian boreal

forest whose conservation is of global concern (Environment Canada 2008). The study area contains nearly a

fifth of the total distribution range of woodland caribou in the province of Quebec. In addition to their terrestrial

forested habitats, the caribou in the study are known to select water bodies and open and forested wetlands

for their seasonal habitat requirements (Environment Canada 2008). Because this subspecies is sensitive to

anthropogenic disturbances and seems to avoid disturbed habitats, the mean avoidance distance from each

type of human disturbance in the study area was gathered from the literature (Dyer et al. 2001, Seip et al.

2007, Vistnes et Nellemann 2007, Vors et al. 2007, Vistnes et Nellemann 2008, Fortin et al. 2013). Caribou

avoidance buffer zones were then mapped and planning units that fell inside them were excluded from the BS.

In the remaining units, the mean occurrence probability of caribou (Environment Canada 2008), based on

environmental niche models, was used to map the feature value for the protection of woodland caribou. All

planning units containing a probability of occurrence higher than zero were considered as contributing to the

BS. Due to the global spatial flow scale associated with this ES, the demand was set equal across each unit

containing this feature.

Regulating services

Flood control

Flood control was mapped by modeling the capacity of each planning unit to reduce and stabilize the water

that flows through it using the proportion of wetlands in each unit and its position in the watershed

(Gouvernement du Québec 1993). Ombrotrophic peatlands (bogs) were excluded from the model since by

definition there is great uncertainty regarding their role in controlling floods. Rivers and streams were also not

considered. The position of the planning units in the watershed unit was estimated by calculating the mean

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Strahler order for each unit using ArcGIS 10.0 (ESRI 2012). After analysis, we considered planning units with a

mean Strahler order of less than 2.5 as being headwaters. Because the spatial flow scale of this ES is

regional, the PUS was restricted to only those watersheds containing human populations and infrastructures.

The demand was set equal across the spatial range of the PUS.

Carbon storage

We chose to focus on storage rather than sequestration because of the considerable uncertainty regarding

carbon sequestration in wetlands (M. Garneau, personal communication). Stored carbon for each type of

wetland soil was modeled using a sample of primary data from the study area for bog peatlands (Magnan et al.

2011 and personal communication) and from the Soil Organic Carbon Digital Database of Canada for fen

peatlands (Tarnocai et Lacelle 1996). Marsh and swamp carbon stock were estimated using a mean value for

mineral wetlands (Horwath 2007). Lake and pond carbon stocks were calculated using an equation that

established a relationship between their area and their carbon stock (Ferland et al. 2012). Because this ES

has a global flow, each planning unit containing stored carbon could provide benefits to humans (i.e. the BS

equals the PUS) and therefore has equal demand.

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