Running head

Spatial processes in riparian vegetation

Title

Spatial processes structuring riparian plant communities in agroecosystems: implications for

restoration

BÉRENGER BOURGEOIS,1,2 EDUARDO GONZÁLEZ,3,4 ANNE VANASSE,1 ISABELLE AUBIN,5 AND

MONIQUE POULIN1,2

1 Département de Phytologie, Faculté des Sciences de l’Agriculture et de l’Alimentation,

Université Laval, 2425 rue de l’agriculture, Québec, Québec, G1V 0A6, Canada

2 Québec Centre for Biodiversity Science, Department of Biology, McGill University, Stewart

Biology Building, 1205 Dr. Penfield avenue, Montréal, Québec, H3A 1B1, Canada

3 Department of Biological Sciences, University of Denver, F W Olin Hall, Room 102, 2190 E

Iliff avenue, Denver, Colorado, 80208-9010, USA

4 EcoLab, Université Paul Sabatier, Institut National Polytechnique de Toulouse, Centre

National de la Recherche Scientifique, 118 route de Narbonne Bâtiment 4R1, 31062 Toulouse

Cedex 9, France

5 Natural Resources Canada, Canadian Forest Service, Great Lakes Forestry Centre, 1219

Queen St. East, Sault Ste. Marie, Ontario, P6A 2E5, Canada

Corresponding author: Département de Phytologie, Faculté des Sciences de l'Agriculture et de

l'Alimentation, Université Laval, Pavillon Paul-Comtois, 2425, rue de l'Agriculture, Québec

(Québec) G1V 0A6. berenger.bourgeois.1@ulaval.ca; monique.poulin@fsaa.ulaval.ca

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Abstract. The disruption of hydrological connectivity by human activities such as flood

regulation or land-use changes strongly impacts riparian plant communities. However, landscape

scale processes have generally been neglected in riparian restoration projects as opposed to local

conditions, even though such processes might largely influence community recovery. We

surveyed plant composition of field edges and riverbanks in 51 riparian zones restored by tree

planting (565 1-m2 plots) within two agricultural watersheds in southeastern Québec (Canada).

Once the effects of environmental variables (hydrology, soil, agriculture, landscape, restoration)

were partialled out, three models of spatial autocorrelation based on Moran’s Eigenvector Maps

and Asymmetric Eigenvector Maps were compared to quantify the pathways and direction of the

spatial processes structuring riparian communities. The ecological mechanisms underlying

predominant spatial processes were then assessed by regression trees linking species response to

spatial gradients to seed and morphological traits. The structure of riparian communities was

predominantly related to unidirectional spatial gradients from upstream to downstream along

watercourses, which contributed more to species composition than bidirectional gradients along

watercourses or overland. Plant traits selected by regression trees explained 22% of species

response to unidirectional upstream-downstream gradients in field edges, and 24% in riverbanks,

and predominantly corresponded to seed traits rather than morphological traits of the adult

plants. Our study showed that even in agriculturally open landscapes, water flow remains a major

force structuring spatially riparian plant communities by filtering species according to their seed

traits, thereby suggesting long-distance dispersal as a predominant process. Preserving

hydrological connectivity at the watershed-scale and restoring riparian plant communities from

upstream to downstream should be encouraged to improve the ecological integrity of rivers

running through agricultural landscapes.

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Key words: agricultural landscapes; ecological restoration; eigenvector maps; hydrochory;

plant traits; spatial autocorrelation; unidirectional vs. bidirectional spatial processes;

watercourse vs. overland spatial processes.

INTRODUCTION

The rapid expansion of agricultural lands worldwide has led to important land-use changes,

habitat fragmentation and flood regulation that are severely degrading riparian habitats. Flow

regulation, which has been estimated to impact over 50% of the world’s large river systems

(Nilsson et al. 2005), cumulatively reduces hydrological connectivity (Ward and Stanford 1995),

affects water runoff (Postel et al. 1996, Allan 2004) and profoundly modifies riparian

communities (Poff and Zimmerman 2010). Dispersal limitation and loss of recruitment sites due

to flow regulation result in impoverished riparian plant communities with reduced cover of

native trees, shrubs and herbs, and higher cover of exotics (Nilsson et al. 1997, Jansson et al.

2000, Catford et al. 2011). Preserving the dynamism, resilience and diversity of riparian plant

communities thus largely depends on the spatio-temporal connectivity of rivers (Poff et al 1997,

Tabacchi et al. 1998, Bendix et Hupp 2000).

The multidimensional gradients structuring rivers and their associated riparian zones have

been outlined in successive theoretical frameworks (Petts and Amoros 1996, Ward 1989, 1998,

Tockner et al. 2010). The River Continuum Concept first identified the longitudinal spatial

processes driving gradual changes in the physical conditions and biotic communities of rivers

from upstream to downstream (Vannote et al. 1980). This spatial structure was extended to

lateral gradients governing exchanges between river channels and floodplains in the Flood Pulse

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and Flow Pulse Concepts (Junk et al. 1989, Bayley 1991, Tockner et al. 2000). A vertical

dimension was then added to the conceptualization of river functioning through the Hyporheic

Corridor Concept linking groundwater and rivers (Gibert et al. 1990, Stanford and Ward 1993).

In addition to these three spatial gradients, temporal processes were identified as playing a role in

shaping river communities according to a Patch Dynamics Concept, in which disturbances and

colonization events are related to water flow (Townsend 1989). Hydrological Connectivity, i.e.,

the water-mediated transfer of matter, energy and organisms along these four gradients,

ultimately emerged from these concepts as the key driver of the ecological integrity of rivers

(Ward and Stanford 1995, Ward et al. 1999, Pringle 2003). Even if these theoretical frameworks

have led to a better understanding of river systems in the last three decades, they still remain

conceptual; recently developed statistical tools now makes it possible to quantify the

multidimensional spatial drivers of river ecological processes.

Plant dispersal by water (i.e., hydrochory) strongly structures riparian plant communities

(Nilsson et al. 2010). The composition of riparian plant communities is indeed largely influenced

by the traits of seeds and propagules trapped in river sediments and water column (Boedeltje et

al. 2003, Gurnell et al. 2008, Corenblit et al. 2009), particularly those determining species

hydrochorous ability (Catford and Jansson 2014). For example, high seed buoyancy and seed

release phenology synchronous with flood regimes favor the dispersal of riparian plant species

by river flow (Merritt and Wohl 2002, Boedeltje et al. 2004, González et al. 2015a). However,

the role of hydrochory in structuring the distribution of riparian plants has generally been studied

at the local scale, such as the reach of a river, or for particular species such as invasive species or

Salicaceae riparian trees, rather than for entire communities at large scale (Boedeltje et al. 2003,

Stella et al. 2006, Richardson et al. 2007). Due to these geographic and taxonomic restrictions,

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previous studies can only be generalized to a limited extent. Additional seed traits such as mass,

size or longevity have been proposed as relevant plant features for explaining the long-distance

dispersal of riparian plant species (Nilsson et al. 2010), but their contribution to the large-scale

spatial structure of entire plant communities has never been investigated.

The adaptive strategies developed by riparian plant species in response to hydrological

spatial gradients rely on their ability to not only colonize new patches, but also to tolerate flood

disturbances (Lytle and Poff 2004, Bornette et al. 2008, Corenblit et al. 2009). Consequently, the

large-scale spatial structure of riparian plant communities might also depend on morphological

traits that confer tolerance to flood disturbances, such as height, since taller plants can avoid

submergence, or root depth, as greater rooting depth can provide anchorage during flooding and

include storage organs that facilitate resprouting after sediment burial (Lytle and Poff 2004,

Bornette et al. 2008, Catford and Jansson 2014). However, the relative contribution of seed traits

vs. morphological traits to the structure of riparian plant communities at large spatial scales has

never been quantified.

Recognizing the importance of flood regimes for riparian communities has contributed to

the development of hydrological approaches to restoring riparian plant communities that have

been degraded by human activities. These approaches focus on re-creating the physical attributes

of riparian habitats, mainly through renaturalization of flow regimes and manual reconfiguration

of floodplain landforms (Poff et al. 1997, Richter et al. 2003, González et al. 2015b). Sometimes,

nurse species are also introduced during restoration to re-create non-hydrological attributes of

riparian zones (Battaglia et al. 2008, McClain et al. 2011, Harris et al. 2012). Once riparian zones

have been physically reconfigured, plant species are assumed to passively recolonize (Field of

Dreams Hypothesis, “if you build it, they will come”; Palmer et al. 1997). Rivers are indeed

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expected to act as ecological corridors that promote the dispersal of target plant species to

restored riparian zones (Naiman et al. 1993). In agricultural landscapes however, multiple large-

scale environmental factors can impact restoration trajectories and outcomes (Allan 2004, Mayer

and Rietkerk 2004, Tscharntke et al. 2005). Such land-use intensification could cause the low

recolonization of herbaceous species through a number of processes including the

impoverishment of the regional species pool (Brudvig 2011), the homogenization of local abiotic

conditions (Vellend et al. 2007), the decrease of landscape connectivity (Tscharntke et al. 2005)

and the limitation of plant dispersal (Hermy and Verheyen 2007). Furthermore, habitat

fragmentation, land clearing and landscape opening associated with agricultural expansion can

further favor the invasion of restored riparian zones by wind-dispersed generalist or exotic

species and contribute to the homogenization of plant communities in these landscapes (Bossuyt

et al. 1999, Vellend et al. 2007, Grashof-Bokdam 2009). In this context, riparian plant

communities are likely to be structured not only by spatial processes occurring along rivers, but

also by those occurring overland. Surprisingly though, riparian restoration projects have

generally neglected to integrate, at least explicitly, the spatial processes fostering the

recolonization process (González et al. 2015b).

The recent development of multivariate multiscale spatial analyses (Dray et al. 2012) now

makes it possible to disentangle the unidirectional (Asymmetric eigenvector maps, Blanchet et

al. 2008) or bidirectional (Moran’s eigenvector maps, Dray et al. 2006) spatial processes that

structure riparian plant communities at the watershed scale. These spatial analyses represent

innovative tools to deepen our understanding of the mechanisms that structure riparian plant

communities and foster their restoration. They also represent an opportunity to formally test river

ecology theoretical frameworks. Such statistical tools can discriminate between allogenic

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processes related to the spatial structure of environmental variables (i.e., spatial dependence) and

autogenic community processes such as dispersal (i.e., pure spatial autocorrelation, Legendre

1993, Dray et al. 2012). These analyses can also identify the spatial scales at which communities

are structured (Dray et al. 2012). In a restoration context, spatial models can help determine the

dispersal vectors that promote the recovery of plant communities, locate disruptions in ecological

connectivity, or assess the impact of land-use on community structure.

This study aims (1) to quantify the contribution of spatial processes to structuring plant

communities in tree-planted riparian zones of agricultural landscapes and (2) to explore the

underlying ecological mechanisms via plant traits. We hypothesized hydrochory to be the main

operative mechanism, as demonstrated by (1) the spatial structure of riparian communities,

principally corresponding to a unidirectional upstream‒downstream spatial model based on

watercourse distances, as opposed to bidirectional processes along watercourses or overland, and

by (2) the spatial distribution of riparian plants, which is predominantly driven by seed traits that

represent plant dispersal.

METHODS

Study area

This study was conducted in two agricultural watersheds in southeastern Québec, Canada.

The Boyer watershed (46°41' N, 70°55' W) extends over an area of 216 km² that includes

scattered forest fragments (24% of the land) but is primarily agricultural land (66% of the land),

of which 26% is farmed with annual crops (principally wheat, corn and soybean). From 1984 to

1992, watercourses were widely channelized over 73% of the watershed’s 215 km of rivers to

improve soil drainage for crop cultivation (OBV Côte-du-Sud/GIRB 2011). The Bélair

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watershed (46°26' N, 70°56' W) covers an area of 43 km² that is predominantly forested (66%),

with 33% agricultural land, of which 18% are annual crops (MAPAQ unpublished data). In

1987, the province banned agricultural practice from riparian zones extending at least 3 m in

width on each side of any stream, in order to reduce soil erosion and improve water quality

(Gouvernement du Québec 1987). This protection was then complemented with extensive tree

planting programs that were conducted in the two studied watersheds from 1995 to 2009. Trees

were planted every 3‒5 m on the flat edge of agricultural fields, and the following species were

mainly introduced: Fraxinus pennsylvanica, Acer saccharum, Picea glauca, Larix laricina,

Quercus rubra and Quercus macrocarpa.

Sampling design

Fifty-one agricultural riparian zones representing a chronosequence from 3 to 17 years

after tree planting were sampled in the two studied watersheds during the summer of 2012. To be

sampled, riparian zones had to meet four conditions: (1) measure at least 40 m long, (2) have

been planted with trees within a single year, (3) be adjacent to an agricultural field with a single

type of crop, (4) have a uniform vegetation structure. Furthermore, sampled riparian zones were

selected in order to be well distributed within the watersheds and associated with rivers of

uniform width (mean ± standard deviation = 3.38 ± 1.60 m) in order to control for hydro-

geomorphologic variability.

Botanical surveys

To account for intra-site variability in plant communities, the number of plant surveys

performed on each site was proportional to site area. At each site, from two to five equidistant

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transects were established perpendicular to the river (i.e., from the field edge to the river bank)

depending on the length of the site. More precisely, for sites shorter than 100 m (min. length 40

m), two transects were used. Additional transects were placed as site length increased by 50 m,

up to a maximum of five transects for sites longer than 200 m (max. length 1150 m). Along these

transects, a minimum of two 1-m2 plots (one in the field edge, one in the riverbank) up to a

maximum of six plots were staked out, depending on transect length, i.e., one plot every 5 m in

the riverbank and one plot every 10 m in the field edge. Species composition of communities

was inventoried within these 1-m² plots by visual estimation of species cover (%) using classes

with a 5% range. Since a preliminary Principal Component Analysis (PCA) revealed distinct

plant compositions in field edge (262 plots) vs. riverbank plots (302 plots, Appendix S1), all

subsequent analyses were performed separately for these two types of communities. Community-

weighted means (see below) were thus calculated separately for field edge and riverbank

communities, and two inventories of plant abundances were obtained for each site.

Plant traits

Eleven seed and morphological plant traits were selected from the Traits Of Plants In

Canada database (TOPIC, Aubin et al. 2012), and supplemented by a literature review of

scientific articles and local floras (Flora of North America Editorial Committee 1993, Marie-

Victorin et al. 1995). Seed dispersal and seedling establishment strategies were represented by

four seed traits, while competition, resource use and tolerance to disturbance strategies of adult

plants were characterized by six morphological traits (Weiher et al. 1999, Cornelissen et al.

2003, Table 1).

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Explanatory variables

Two types of between-sites geographic distances were measured based on the coordinates

of each site to perform spatial analyses: overland distances (calculated with a minimum spanning

tree) and distance along watercourses (measured on ArcGIS). Seventeen environmental variables

related to tree planting, agriculture, hydrology, soils and landscape structure were also measured

at each site (Table 2). These variables were used to determine the environmental determinants

(either spatially structured or not) of riparian plant communities: their effects were thus removed

prior to performing spatial analyses. This procedure allowed us to investigate the effect of pure

spatial autocorrelation on the composition of riparian plant communities.

Statistical analysis

To partial out the effect of environmental variables on Hellinger-transformed species

abundances, a forward selection was first conducted to retain significant variables.

Environmental variables significantly related to species abundances were next used in a

Redundancy Analysis (RDA). Residuals were extracted and used as response variables in the

spatial analyses described below, as they corresponded to the environment-independent part of

the plant communities.

Three spatial models were then used on the extracted RDA residuals to assess the

importance of spatial processes on species composition: (1) a bidirectional overland model, (2) a

bidirectional watercourse model and (3) a unidirectional upstream-downstream watercourse

model (Fig. 1). The model representing overland bidirectional spatial processes was constructed

using Moran’s Eigenvector Maps (MEM, Dray et al. 2006). A matrix of overland between-site

distances (calculated by minimum spanning tree from XY site coordinates) was analyzed using a

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Principal Coordinates Analysis (PCoA) with resulting eigenvectors corresponding to spatial

components. The second model, representing bidirectional spatial processes along watercourses

was built using the same procedure, based instead on watercourse distances. The third model,

representing unidirectional spatial processes between sites along watercourse in the direction of

water flow (from upstream to downstream), was constructed using Asymmetric Eigenvector

Maps (AEM, Blanchet et al. 2008). A node-by-edge matrix coding unidirectional between-site

connections along watercourse was analyzed by means of a PCA with resulting eigenvectors

corresponding to spatial components.

For each of the three spatial models, we calculated 30 spatial submodels representing

linear, concave-down or concave-up spatial relationships with three different types of geographic

weighting functions, i.e.,

f 1α=1−(

d ij

max (d ij ))

α

, f 2α= 1

(d ij)α and f 3

α=(d ij

max ( dij ))

α

where d ij corresponds to the distances between the sites i and j and α, ranked from 1 to 10 (Dray

et al. 2006, Blanchet et al. 2011). For each submodel, the spatial components representing a

positive and significant spatial autocorrelation were determined using the Moran’s I statistics and

forward selection. These components were then used as explanatory variables in separate RDA

on detrended species abundances for MEM models and on non-detrended species abundances for

AEM models in order to identify the best weighting function (i.e., the best submodel) for each of

the three spatial models. Only the weighting function that resulted in the highest adjusted R2

among the 30 submodels tested was conserved for each spatial model: functions f 13 in field edges

and f 21 in riverbanks for the bidirectional overland model, function f 2

1 in both field edges and

riverbanks for the bidirectional watercourse model, and functions f 11 in field edges and f 1

2 in

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riverbanks for the unidirectional upstream‒downstream watercourse model. In both types of

plant communities (field edge and riverbank), we thus selected the weighting function with the

best explanatory power for each of the three spatial models (Fig. 1).

The three final spatial models were compared based on the adjusted R2 produced by the

RDA, and submitted to variation partitioning to identify the best geographic pathway (overland

vs. watercourse bidirectional processes) and direction (bidirectional vs. unidirectional

watercourse processes) for explaining the spatial structure of non detrended species abundances

in field edge and riverbank riparian plant communities separately.

Ecological mechanisms underlying spatial patterns that drive riparian plant communities

were then identified by the use of plant traits. For this, we selected the best spatial model based

on the above-mentioned adjusted R2 comparison and variation partitioning, and extracted species

scores along the significant RDA axes representing spatial gradients (i.e., linear combinations of

spatial components). These scores were used in a regression tree with the 11 plant traits as

explanatory variables in order to determine the traits responsible for species response to spatial

gradients (Kleyer et al. 2012, Fig. 1). To maximize representativeness, only species with more

than three informed trait values were used in this analysis; these included 95 species for field

edge communities and 116 for riverbank communities.

All analyses were performed on R version 3.0.3 (R Core Team 2014) using packages

vegan (Oksanen et al. 2013), spacemaker (Dray 2013), AEM (Blanchet et al. 2014) and packfor

(Dray et al. 2013).

RESULTS

Environmental control of riparian plant communities

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Field edge and riverbank communities were composed of 124 and 157 species,

respectively, for a total of 167 species inventoried in the 565 1-m² plots sampled within the 51

studied riparian zones (Appendix S2). Four selected environmental variables significantly

explained 18% of the species composition (adjusted R2; F = 2.86, P = 0.005) in the field edges:

elevation above river, soil gravel content, crop type in the adjacent field and tree canopy cover

(Table 3). In riverbank communities, five selected environmental variables significantly

explained 22% of species composition (adjusted R2; F = 2.96, P = 0.005): tree canopy cover, soil

gravel content, crop type in the adjacent field, river width and riverbank width (Table 3). The

effect of the environmental variables (either non-spatially or spatially structured) on plant

community composition was partialled out to conserve only the variability in species

composition that was independent from environmental variables and thereby investigate pure

spatial autocorrelation among these plant communities.

Spatial processes structuring riparian plant communities

The composition of riparian communities was spatially structured predominantly by

watercourse processes as opposed to overland ones and unidirectionally rather than

bidirectionally. Upstream‒downstream processes along watercourses indeed contributed to the

highest explained variation of the residuals of plant composition in both field edges (adjusted R2

= 11.1% which makes a total of 27% of variance explained, if combined with the environmental

variables) and riverbanks (adjusted R2 = 16.5% which makes a total of 34% of variance

explained, if combined with the environmental variables, Table 4), once the effect of

environmental variables was partialled out as explained above. This was especially true for

riverbank communities (closer to the river channel), for which the adjusted R2 of the

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unidirectional watercourse model was 1.2 to 1.5 times higher than the adjusted R2 of the two

bidirectional models. The variation explained by the bidirectional overland and watercourse

models was, however, still quite high in both field edge and riverbank communities (on the order

of 10%). However, variation partitioning indicated that a large part of this variation (7.0% and

10.9% for field edge and riverbank communities, respectively) was shared with the

unidirectional watercourse model (see Appendix S3 for the complete figures). Furthermore, the

variation in non-detrended plant communities solely explained by the unidirectional watercourse

model amounted to 6.7% in field edges and 4.1% in riverbanks and was higher than the variation

in plant communities solely explained by the two bidirectional models.

In the unidirectional model along watercourses, three spatial gradients (i.e., linear

combinations of spatial components) significantly influenced plant composition in field edge

communities (RDA axis 1 to 3; proportion explained = 8.6%, 4.5% and 3.9% respectively) and

five in riverbank communities (RDA axis 1 to 5; proportion explained = 8.9%, 5.7%, 4.8%, 4.4%

and 3.7% respectively). Considering the higher adjusted R2 provided by the unidirectional model

in both field edge and riverbank communities, the significant spatial gradients of this model were

selected for the subsequent analysis, which sought to identify the ecological mechanisms

underlying autogenic spatial patterns found in riparian communities based on plant traits.

Plant traits related to spatial processes

Among the 11 traits tested, community assembly processes under upstream‒downstream

watercourse spatial gradients were predominantly related to seed traits (Fig. 2). In field edge and

riverbank communities, the plant traits selected by regression trees explained 22% and 24% of

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species scores along significant upstream‒downstream spatial gradients and sorted species into

three and six distinct groups respectively.

In field edge communities, seed length predominantly explained the autogenic spatial

structure of plant composition from upstream to downstream along watercourse by separating

short-seeded species from long-seeded, which were negatively correlated with the two first

spatial gradients (seed length threshold = 5.3 mm, RDA axis 1, RDA Axis 2, Fig. 2a). For short-

seeded species, the start of the seed release period secondly influenced the response to

unidirectional upstream‒downstream gradients as species with seeds dispersed in autumn formed

a distinct group from species with seeds dispersed in spring or summer. These latter species with

earlier seed dispersal were then separated according to their seed weight (seed weight threshold =

0.12 mg). Finally, maximum height classified species in two distinct subgroups for species with

light seeds (weight inferior to 0.12 mg) dispersed in spring or summer (maximum height

threshold = 1.3 m) as well as for species dispersing seeds in autumn (maximum height threshold

= 1.4 m).

In riverbanks, species composition was also mostly explained by seed length, which first

separated species with short seeds from those with long seeds (seed length threshold = 6.8 mm),

the latter being negatively correlated with the first unidirectional upstream‒downstream spatial

gradient (RDA Axis1, Fig. 2b). Short-seeded species were secondly sorted into two subgroups

according to their maximum height, with species taller than 2.1 m forming a distinct subgroup.

The seed dispersal vector further subdivided species smaller than 2.1 m, as anemochorous

species responded differently to unidirectional upstream‒downstream spatial gradients than

species with other dispersal vectors. Seed weight (threshold = 0.07 mg) and seed length

(threshold = 3.3 mm) then contributed to species response for anemochorous species while

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species with hydrochorous, myrmechorous, zoochorous and unassisted dispersal were only

classified according to their seed length (threshold = 2.7 mm).

DISCUSSION

Spatial processes structuring riparian plant communities in agricultural landscapes

Our study highlights the prevalence of upstream‒downstream gradients along watercourses

over bidirectional gradients along watercourses or overland. Even for tree-planted sites scattered

in agricultural landscapes, water flow remains the major force contributing to the autogenic

spatial structure riparian communities at the watershed scale. Our study thus extends the well-

known structuring role of rivers (Hupp and Osterkamp 1996, Poff et al. 1997, Tabacchi et al.

1998) to recently restored sites that occur within heavily transformed landscapes. This underlines

the need for upscaling the evaluation of riparian restoration projects, which have mainly been

conducted locally to date (González et al. 2015b), and supports theoretical frameworks that

provide a landscape-scale perspective on riparian communities, as being structured around

hierarchical spatial scales (Gregory et al. 1991, Wu and Loucks 1995, Thorp et al. 2006). Using

similar spatial models, macrophyte, diatom, macroinvertebrate and fish communities have also

been found to be strongly structured by longitudinal gradients along rivers in the direction of

water flow (Landeiro et al. 2011, Liu et al. 2013, Padial et al. 2014). This study therefore extends

the importance of longitudinal gradients previously identified in such aquatic communities to the

riparian ecotone. Moreover, these results together provide quantitative data to support the

importance of hydrological connectivity, a key spatial process for biotic communities of river

ecosystems that was originally intended to be applied at watershed level, but was only

investigated at the scale of river reach (Ward and Stanford 1995, Ward et al. 1999, Pringle 2003).

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Surprisingly, overland gradients contributed to a large part of the spatial autogenic

structure of riparian plant communities, even if most of their explained variation was also shared

by watercourse gradients. This contribution of overland gradients might be attributed to land-use

changes due to agricultural intensification, which are known to impact the spatial connectivity of

riparian zones (Allan 2004, Tetzlaff et al. 2007). While the three environmental variables

representing landscape composition did not affect significantly the composition of riparian plant

communities here, the loss of structural heterogeneity in the studied agricultural landscapes

could still have promoted overland spatial processes. The habitat fragmentation and landscape

simplification that accompany agricultural intensification indeed affect the dispersal of plant

species differently, depending on their means of dispersion (Vellend et al. 2007, Ozinga et al.

2009). Anemochory highly favors species dispersal despite patch isolation and strongly drives

the diversity of plant communities in fragmented areas (Herlin and Fry 2000, Grashof-Bokdam

2009, Ozinga et al. 2009). In addition to this large-scale dispersal through seed rain (Gabriel et

al. 2005, Roschewitz et al. 2005, Buisson et al. 2006), small-scale dispersal from nearby patches

due to neighborhood effects also influences plant communities within agricultural open

landscapes (Kleyer 1999, Roschewitz et al. 2005). In the agricultural watersheds studied here,

the contribution of overland spatial processes to the composition of riparian communities is thus

likely related to a combination of seed rain and neighborhood effect fostering the immigration of

plant species from agricultural fields to riparian zones. Further investigation of the contribution

of overland spatial processes in the context of agricultural landscapes having different

intensification levels is needed, however, in order to better identify the underlying ecological

mechanisms.

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Seed traits drive species responses to upstream-downstream gradients

Riparian plant species responded to upstream‒downstream spatial gradients along

watercourses according to their seed traits. Seed length was the main trait explaining the affinity

of plant species to upstream‒downstream spatial gradients, especially in riverbank communities

closer to the river channel. In our study, long-seeded species were negatively correlated to

upstream‒downstream spatial gradients along watercourses, meaning that water flow had less

influence on the distribution of long-seeded species than on short-seeded ones. Besides seed

length, seed weight, seed dispersal vector and the start of the seed release period secondly

contributed to species sorting. While seed weight is considered a better functional marker (sensu

Garnier et al. 2004) than seed length (Westoby 1998), both are associated with several ecological

mechanisms operating at different plant developmental stages, from seed dispersal to

establishment of seedlings (Weiher et al. 1999, Moles and Westoby 2004, 2006). In general,

long, large and heavy seeds are produced in lower number and tend to travel shorter distances

than smaller seeds (Thompson et al. 1993, Westoby 1998, Leishman et al. 2000), while, in the

case of hydrochory, seed buoyancy, also conditions dispersal distance but remains poorly

informed in the literature (Merritt and Wohl, 2002, Nilsson et al. 2010). The importance of seed

traits over morphological traits in explaining the response of species to spatial gradients along

watercourse from upstream to downstream points to hydrochory as the main mechanism

responsible for the watershed-scale spatial structure of riparian communities. The spatial

structure of riparian communities at the reach scale has been related to hydrochory by several

authors (Nilsson et al. 1991, Boedeltje et al. 2003, Gurnell et al. 2008), but according to our

results, the key influence of this process can also be detected at the watershed-scale.

Furthermore, the distribution of a plant species in a riparian habitat was previously attributed to

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several traits: long seed buoyancy fostering the hydrochorous ability of plant species (Merritt and

Wohl 2002, Boedeltje et al. 2003, 2004), small and compact size that can facilitate burial in

sediment and persistence over a long period of time (Thompson et al. 1993), or large seeds which

produce seedlings that establish more successfully (Xiong et al. 2011). Our study showed that the

spatial structure of riparian plant communities from upstream to downstream was predominantly

related to seed traits, indirectly pointing out the importance of long distance hydrochorous

dispersal for species assembly.

Role of morphological traits in upstream-downstream species distribution

Although hydrochory played a predominant role, additional processes related to

morphological traits also helped to explain community composition along upstream‒downstream

gradients. Maximum plant height indeed contributed to species sorting in both field edge and

riverbank communities. While morphological traits are known to influence competition ability

and resource-use (Pérez-Harguindeguy et al. 2013), their contribution to the spatial structure of

riparian communities in the direction of water flow could be mainly related to tolerance to flow

disturbances. Such adaptation is an important feature of riparian plant strategies (Lytle and Poff

2004, Naiman et al. 2005, Catford and Jansson 2014). For example, tall stems can decrease the

impact of water flow by reducing biomass loss, while the higher resistance of small and compact

species to mechanical damage can explain their positive correlation with upstream‒downstream

spatial gradients observed here in field edges (Karrenberg et al. 2002, Boeger and Poulson 2003,

Puijalon and Bornette 2004). The predominant role of seed traits in structuring riparian

communities spatially and the secondary role of morphological traits suggest the existence of a

hierarchy within the adaptive strategies of plant species to riparian habitats. Alternatively, the

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higher influence of seed vs. morphological traits in structuring riparian plant communities could

be due to the spatial scale considered in this study. Seed traits might be of primary importance

for watershed-scale processes such as dispersal (as shown here) while morphological traits

should rather influence species assembly at the local scale (not assessed in this study) by

conditioning competition ability, resource-use strategies or resistance to disturbance (e.g., plant

height could be related to flood tolerance as higher plants can escape from inundation easier than

shorter plants).

Integrating spatial processes in riparian community restoration

This study demonstrates that longitudinal processes merit greater consideration in

management and restoration strategies. Restoring the longitudinal hydrological connectivity of

rivers through renaturalization of flow regimes (Poff et al. 1997, Poff and Zimmerman 2010),

should be among the first alternatives to be considered by restoration projects, especially in small

rivers running through agricultural landscapes where implementing this approach is

socioeconomically and technically more realistic than in other industrialized areas or larger

rivers. This approach fosters the recovery of the longitudinal gradients that structure river

ecosystems by removing dams, implementing environmental flows and reallocating water

resources, as initial necessary steps for the re-establishment of riparian communities. To

accelerate and optimize restoration efforts, such an approach would preferentially locate active

introductions of plant species in upstream sites, as downstream sites might be naturally colonized

via hydrochorous dispersal, especially in the case of small-seeded species. Restoration of riparian

zone should be implemented from upstream to downstream along river networks (Dietrich et al.

2014). As previously pointed out by Holl et al. (2003), there is indeed an urgent need to expand

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riparian zone restoration to the scale of the watershed. While legacies of past agricultural land-

use, long-lasting degradations and uninvestigated environmental factors could have contributed

to the unexplained variation in plant community composition, our statistical framework provides

an innovative perspective to take into account spatial processes in future restoration projects.

Considering large-scale processes in decision-making and land-use planning via spatial models is

a promising approach for developing efficient management strategies for riparian plant

communities, or, more holistically, entire river ecosystems. Spatial modeling of ecosystem

recovery is a new tool to scale up ecological restoration in a landscape perspective.

ACKNOWLEDGMENTS

We would thank local restoration practitioners, stakeholders and decision-makers from

Organisme de Bassin Versant de la Côte-du-Sud, Club conseil Bélair-Morency, Ministère de

l’Agriculture, des Pêcheries et de l’Alimention du Québec (respectively F. Lajoie, L. Beaulieu

and A. Goudreau) for their help in sampling design; field assistants (A. Rablat, P. Israël-Morin

and M. Vaillancourt) for data collection; P. Legendre from Université de Montréal for statistical

advice; K. Grislis for English revision; all the farmers who welcomed us onto their lands to

conduct this study; and Christer Nilsson and two anonymous reviewers for their help in

improving our manuscript. This project was funded by a research grant from the Ministère de

l’Agriculture, des Pêcheries et de l’Alimention du Québec received by A.V. and M.P., and by a

NSERC discovery grant to M.P. (RGPIN-2014-05663).

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6566

TABLE 1. Description of the traits selected to characterize the 167 plant species inventoried in the

51 sampled riparian zones.

Trait name Trait valueMissing

value (%)

Regeneration and dispersion

Seed length numerical (mm) 7

Seed weight numerical (mg.seed-1) 11

Seed production abundant (> 1000 seeds.ind-1.year-1);

semi-abundant (20-1000 seeds.ind-1.year-

1); scarce (1-20 seeds.ind-1.year-1)

0

Seed dispersal vector anemochore; autochore; barochore;

hydrochore; myrmechore; zoochore

0

Seed release period: start spring; summer; autumn 0

Seed release period:

duration

numerical (seasons) 0

Morphology and adult plant

strategy

Life cycle annual; bi-annual; perennial 0

Maximum height numerical (m) 0

Morphological type fern; grass and sedge; herb; horsetail; 0

34

747

748

6768

vine

Raunkiaer life form chamaephyte; geophyte;

hemicryptophyte; helophyte; micro- and

nano-phanerophyte; therophyte

0

Vegetative propagation absence; presence 0

35

749

750

751

752

753

754

755

756

757

758

759

760

761

6970

TABLE 2. Description of the 17 environmental variables measured in the 51 sampled riparian

zones.

Variable Unit Methodology

Tree planting

Time elapsed since tree

planting

Years Discussions with stakeholders

and landowners

Tree canopy cover % Stereoscopic measurements

taken at 1 m height within each

plot, above understory species

(WINSCANOPY system, Régent

Instruments Inc., Québec,

Canada)

Agriculture

Crop type in the adjacent

field during sampling

Hay, corn,

soybean, wheat

Visual assessment

Hydrology

Elevation above the river

water level

m Height between the plot and

river water level (calculated for

each plot from the slope and the

distance to the river)

36

762

763

7172

River discharge Low, average,

strong

Visual assessment and

discussions with stakeholders

River sinuosity Ordinal categories

from 0 to 3 (0:

rectilinear river

channel)

Visual assessment

Riverbank erosion Ordinal categories

from 0 to 3 (0:

rectilinear river

channel)

"

River width m Measured with a tape

Riverbank width m "

Fiel edge width m "

Soil

Proportion of stones (250-

600 mm)

% Visual assessement of soil

samples (of ca15 000 cm3)

collected with an auger in the 0-

15 cm soil horizon at each plot

Proportion of cobbles (75-

250 mm)

% "

Proportion of gravels (2- % "

377374

75 mm)

Proportion of fine particles

(<2 mm)

% "

Landscape structure

Proportion of forests % Measured in a 500 m-wide

buffer zone around each site

from governemental

georeferenced databases* using

ArcGIS

Proportion of grasslands % "

Proportion of croplands % "

* Base de Données Topographiques du Québec from the Ministère des Ressources Naturelles et de la

Faune and Base de Données des Cultures Assurées from la Financière Agricole du Québec

38

764

765

766

767

768

769

770

771

7576

TABLE 3. Environmental variables significantly influencing understory species composition in

riparian plant communities of field edges and riverbanks, obtained by forward selection (adjusted

R2environmental variables = 18% in field edges and adjusted R2

environmental variables = 22% in riverbanks).

df F Pr > P

Field edge

Elevation above river 1 3.23 0.01

Soil gravel content 1 3.13 0.02

Crop type in the adjacent

field

3 3.00 0.01

Tree canopy cover 1 1.87 0.03

Riverbank

Tree canopy cover 1 8.07 0.01

Soil gravel content 1 2.30 0.02

Crop type in the adjacent

field

3 2.25 0.01

River width 1 1.88 0.01

Riverbank width 1 1.70 0.01

39

772

773

774

775

776

7778

TABLE 4. Proportion of variance (adjusted R2) in plant composition explained by the three spatial

models for field edge and riverbank riparian plant communities (MEM: Moran’s Eigenvector

Maps; AEM: Asymmetric Eigenvector Maps) once the effects of significant environmental

variables were partialled out (see Table 3; adjusted R2environmental variables = 18% in field edges and

adjusted R2environmental variables = 22% in riverbanks).

Adjusted R² (%)

Bidirectional overland

(MEM)

Bidirectional

watercourse (MEM)

Unidirectional

watercourse (AEM)

Field edge 9.94 10.14 11.09

Riverbank 11.16 13.23 16.46

40

777

778

779

780

781

782

783

784

785

786

787

788

789

790

791

792

793

7980

FIG. 1. Diagram of the analytical steps performed with the five datasets to address the two

research objectives of this study.

41

794

795

796

797

798

799

800

801

802

8182

FIG. 2. Traits-based classification of plant species response to upstream‒downstream spatial

process obtained by a regression tree for (a) field edge and (b) riverbank riparian communities.

Species scores along significant axes of the RDA modelling unidirectional upstream-downstream

watercourse spatial gradient (AEM model) were used as dependent variables and a selection of

functional traits (Table 1) as explanatory variables.

42

803

804

805

806

807

808

8384

43

809

8586