# Riparian vegetation variability in natural boreal forests: Lessons for delineating functional forested strips at the margin of aquatic habitats

Philippe Janssen, Audrey Comtois, Marcel Darveau, Bérenger Bourgeois, David Pothier, Monique Poulin

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## To cite this version:

Philippe Janssen, Audrey Comtois, Marcel Darveau, Bérenger Bourgeois, David Pothier, et al. "Riparian vegetation variability in natural boreal forests: Lessons for delineating functional forested strips at the margin of aquatic habitats." Forest Ecology and Management, 2025, 595, pp.123044. 10.1016/j.foreco.2025.123044. hal-05216100

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Submitted on 20 Aug 2025

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# Riparian vegetation variability in natural boreal forests: Lessons for delineating functional forested strips at the margin of aquatic habitats

Philippe Janssen<sup>a, *</sup> <sup> ID </sup>, Audrey Comtois<sup>b, c</sup>, Marcel Darveau<sup>d ID</sup>, Bérenger Bourgeois<sup>b, c ID</sup>, David Pothier<sup>d</sup>, Monique Poulin<sup>b, c</sup>

## ARTICLE INFO

### Keywords:

Biodiversity conservation
Boreal forest
Buffer strips
Eastern Canada
Management zones
Riparian plants

## ABSTRACT

Riparian zones are biodiversity hotspots that play a fundamental role in the functioning of aquatic habitats. In logged boreal forests, the common practice is to maintain a fixed width of vegetation along rivers and lakes, without any other protective measures, and often neglecting small streams and ponds. To preserve the ecosystem services provided by riparian zones, management practices need to better integrate the natural variability in vegetation attributes along the lateral connectivity gradient in different hydrogeomorphic contexts. This study compared patterns of plant diversity and composition at the margin of five aquatic habitats, in four ecoregions of the Canadian boreal forest. Our results showed that species richness, the mean values for wetness requirements and the magnitude of changes in taxa composition decreased consistently along the lateral gradient, but that this effect was mediated by hydrogeomorphic settings. Specifically, we highlighted significant interactions between distance or elevation difference from the waterline and ecoregions or aquatic habitats, showing that the structure and extent of riparian vegetation along the local topographic gradient are shaped differently by the regional (climate) and hydrogeomorphic (flow regime) contexts. In addition, model ranking revealed that elevation difference best predicted changes in riparian vegetation structure and composition than distance from the waterline. For delineating functional forested strips, our results revealed thus the importance of taking into account both elevation and distance criteria but also of modulating the spatial extent of these strips according to the regional climatic context and the types of aquatic habitat.

## 1. Introduction

The boreal forest forms a belt stretching from North America through Scandinavia to Russia, making it one of the world's largest biomes (Gauthier et al., 2015). Long considered an inexhaustible resource (Simberloff, 2001), the boreal forest has been intensively logged, particularly in Fennoscandia, where almost all the original forest has been replaced by second-growth forests (Kuuluvainen, 2002). In Canada, the boreal forest remains one of the forest ecosystems least disturbed by humans, and contains a significant proportion of the world's surface freshwater, including numerous types of wetlands (Gauthier et al., 2015; Wells et al., 2020). Linking terrestrial habitats to freshwater habitats, riparian zones generally represent a narrow portion

of the landscape but are of disproportionate ecological importance (González et al., 2017). However, due to multiple anthropogenic pressures, of which the Canadian boreal forest is no exception (Li et al., 2024), a decrease in the extent of boreal riparian zones associated with a loss of biodiversity and functionality of these ecotones has been reported (Hoppenreiks et al., 2022). This directly threatens the quality of aquatic habitats and the diversity of associated organisms (e.g., Guimond et al., 2024; Luke et al., 2007).

To mitigate the negative effects of natural resources extraction in boreal forests, of which forestry practices remains one of the most damaging factors for riparian vegetation (Hoppenreiks et al., 2022), the maintenance of forest strips at the margin of aquatic habitats has become a standard (Blinn and Kilgore, 2001; Broadmeadow and Nisbet,

<footnote><sup>a</sup> Université Grenoble Alpes, INRAE, LESSEM, Grenoble, France</footnote>

<footnote><sup>b</sup> Département de Phytologie, Faculté des Sciences de l'Agriculture et de l'Alimentation, Université Laval, 2425 rue de l'Agriculture, Québec QC G1V 0A6, Canada</footnote>

<footnote><sup>c</sup> Québec Centre for Biodiversity Science, McGill University, 1205 Dr. Penfield Avenue, Montreal, QC H3A 1B1, Canada</footnote>

<footnote><sup>d</sup> Département des sciences du bois et de la forêt, Faculté de Foresterie, de Géographie et de Géomatique, Université Laval, 2405 rue de la Terrasse, Québec QC G1V 0A6, Canada</footnote>

<footnote>* Corresponding author.</footnote>

<footnote>E-mail address: philippe.janssen@inrae.fr (P. Janssen).</footnote>
2004: Richardson et al., 2012). Initially implemented to protect water quality and control soil erosion in logged areas, the retention of forested “buffer” strips has evolved from a unidirectional vision, aimed at controlling the effects of terrestrial environments on aquatic environments (Barling and Moore, 1994), to a more integrative vision recognizing the role of ecotone of riparian zones (Naiman and Decamps, 1997). Notwithstanding that shift to a new management paradigm encompassing the maintenance of riparian ecosystems functions and associated ecosystem services, the practical implementation of these forest strips is still largely based on fixed widths, through “one-size-fits-all” guidelines that have been drawn up with more regard to regulatory boundaries than natural processes (Lee et al., 2004). For example, in Quebec, the legislation requires essentially the maintenance of a 20-meter-wide forested strip along the shores of lakes and rivers (Ministère des Forêts, de la Faune et des Parcs, 2025), while in Sweden and Finland, the obligation to set up protection zones is not clearly defined in law, and no width is specified (Ring et al., 2017). Worse still, riparian forest strips are sometimes not even mandatory or implemented in the vicinity of certain types of aquatic habitats, such as small and intermittent streams (Kuglerová et al., 2020).

Although fixed-width forest strips are simple to position and monitor in the field, they are in most cases implemented without distinguishing between aquatic habitat types, e.g., river and lake banks, and local environmental contexts, e.g., climate and topography (Richardson et al., 2012). It is well known, however, that the extent of riparian zones varies considerably, from a very narrow strip in mountain streams to several kilometers in the floodplains of large rivers, and that many broadscale and fine-scale environmental parameters shape the structure and composition of related communities (Gregory et al., 1991; Naiman and Decamps, 1997). While the landscape context associated with climate, geology or landform determines the type, form and distribution of wetland and aquatic habitats on a large scale (Federal Geographic Data Committee, 2013; Ménard et al., 2013), site characteristics associated with topography, hydrology or soil properties determine the taxonomic and functional structure of riparian vegetation on a fine scale (Janssen et al., 2020; Turner et al., 2004). Specifically, it was shown that the response of riparian plant communities to the lateral connectivity gradient, expressed as distance or elevation from the waterline, varies according to hydrogeomorphic settings (Van Appledorn and Baker, 2023). This demonstrates the difficulty of transferring guidelines from one environmental context to another, and therefore calls into question the current practice of using fixed widths to delimit forest strips (Sweeney and Newbold, 2014), even more on the scale of very large territories presenting a wide range of hydro-geomorphological contexts.

Here, we aimed to study changes in the structure and composition of plant communities in riparian areas adjacent to natural boreal forests in Canada in order to set targets for sustainable management (i.e., sensu Landres et al., 1999). Specifically, our work sought to assess the extent of natural variability in riparian vegetation attributes between four ecoregions, representing different climatic, soil and landform conditions, and five aquatic habitat types (both lentic and lotic) of the Canadian boreal forest. This was done to evaluate and compare the relative influence of local and large-scale environmental gradients, as well as to study the variability of ecological contexts encountered in the boreal forest but not always taken into account when determining forest strip widths. In each riparian zone studied, we also measured changes in plant community attributes along the local topographic gradient, comparing the effect of distance from the waterline with that of elevation relative to the waterline. To this end, vegetation was sampled in the first 50 m of riparian and terrestrial environments bordering aquatic habitats, which includes the range of values currently recommended in forestry for riparian forest strips in boreal forests (Kuglerová et al., 2020). This was done to quantify possible interactions between the properties of the local lateral connectivity gradient and different hydro-geomorphological contexts, but also to question the usefulness of using distance as the sole criterion for designing riparian forest strips. Within this framework,

we addressed the following three research questions: How do tree basal area, vascular taxa richness and composition, and vascular plant wetness requirements vary in riparian zone between ecoregions and aquatic habitat types in the Canadian boreal forest? (ii) Is the response of riparian plant communities to the topographical gradient different between ecoregions and aquatic habitats? (iii) When controlling for differences between ecoregions and aquatic habitats, which parameter best explains changes in basal area, taxa richness and composition, and plant wetness requirements between distance and elevation relative to the waterline? We believe that answers to these three questions will provide new information on terrestrial biodiversity patterns for a geographical context that has received little attention in studies of riparian vegetation (Hoppenreiks et al., 2022) and thus help define optimal management measures that contribute to the conservation of riparian ecotones.

## 2. Materials and methods

### 2.1. Study area and sampling design

The study area covers approximately 1000 km from west to east in the boreal shield ecozone of Quebec, Canada, and extends over 4 ecoregions (Fig. 1). In each ecoregion and for logistical reasons, a sampling area within two hours' drive of a town or forestry camp was selected. Ecoforest maps (1:20,000, Létourneau et al., 2009) were used to restrict sampling to natural areas not modified by forestry, recreational or industrial activities, or by recent major natural disturbances (fire or insect outbreak). Two ecoregions (Abitibi Lowlands; Central Laurentians) were sampled from June to August 2007, a third (Lower North-Shore Plateau) in July 2011, and a fourth (Mistassini Highlands) in July 2012. All four ecoregions have a sub-polar, sub-humid continental climate. However, the two central ecoregions (Mistassini Highlands, annual mean temp. = 0.0°C; Central Laurentians, annual mean temp. = -1.5°C) have slightly lower mean temperatures and fewer growing days compared to peripheral ecoregions (Abitibi Lowlands, annual mean temp. = 1.1°C; Lower North-Shore, annual mean temp. = 1.2°C) (variations in climatic conditions between years for the four ecoregions are presented in Appendix S1). The ecoregions also differ in terms of snow fraction and total precipitation, which increase from west to east (Couillard et al., 2019). Furthermore, the western ecoregions have a flat topography, while the relief becomes increasingly rugged towards the east and along this west to east gradient, fine surface deposits are progressively replaced by coarser sand and gravel deposits, with numerous rocky outcrops. These environmental gradients are associated with changes in forest stand composition, from the south-western balsam fir-white birch bioclimatic vegetation domain to the northeastern black spruce-moss domain (Couillard et al., 2019).

In each ecoregion, five types of aquatic habitats were randomly selected from all accessible waterbodies and watercourses surrounded by near-pristine boreal forests. Aquatic habitats were defined and extracted from ecoforest maps (1:20,000, Létourneau et al., 2009) and from the 1:50,000 hydrographic features of the CanVec series (Topographic Data of Canada, https://open.canada.ca/data/en/dataset). For waterbodies a distinction was made between ponds (area < 8 ha) and lakes (area > 8 ha); for watercourses a distinction was made between intermittent streams, permanent streams (width < 6 m) and rivers (width > 6 m). This distinction is based on the Classification of wetlands and deepwater habitats of the United States (Federal Geographic Data Committee, 2013) and is in line with the Ramsar Convention (https://www.ramsar.org). A total of 63 sites distributed between the four ecoregions (Abitibi Lowlands, n = 11; Mistassini Highlands, n = 20; Central Laurentians, n = 14; Lower North-Shore, n = 18) and the five aquatic habitats (ponds, n = 10; lakes, n = 13; intermittent streams, n = 14; streams, n = 13; rivers, n = 13) were sampled, with the constraint that each aquatic habitat be sampled at least once in each ecoregion (see Appendix S2 for details).
<figure><img src="image_4.png" /><figcaption>**Fig. 1.** Location of the four ecoregions and the four sampling areas (black dots) within each ecoregion in Canada. The grey polygons show the boundaries of the four boreal ecoregions in the province of Quebec (AL = Abitibi Lowlands, MH = Mistassini Highlands, CL = Central Laurentians, NS = Lower North-Shore). The grey area corresponds to the Canadian boreal forest boundary, and the bold black line to the commercial boreal forest boundary.</figcaption></figure>

## 2.2. Vegetation data

For each site, two transects were randomly established perpendicularly to the waterline (n = 126). Transects were at least 100 m apart from each other, except for a few intermittent streams and ponds where the shoreline was *per se* too short. The two transects established along a watercourse were positioned preferably on the left and right banks. Along each transect, i.e., the lateral gradient, four circular plots of 11.28 m diameter (100 m²) were positioned side by side until a distance of 45 m was reached (n = 504) (Comtois, 2013). In each plot, forest stand characteristics were assessed by inventorying all tree species and measuring the diameter at breast height (i.e., ≥ 9.1 cm) of standing living trees and snags using a factor 2 prism. Within plot, three subplots of 1.13 m radius (4 m²) were equidistantly positioned (longitudinally to the transect direction) in order to carry out botanical surveys (then combined in the analyses). The percentage cover of ferns, herbs and shrubs was assessed visually at the species or genus level, with the exception of *Poaceae* for which the percentage cover was estimated at the family level (for details see Appendix S3). In addition, for the moss layer, i.e., non-vascular plants, a simple distinction was made between mosses, sphagna and lichens, and their respective percentage cover was also visually assessed.

To analyze differences in plant wetness requirements, we extracted the wetland indicator status of each species from the national wetland plant list of the US Army Corps of Engineers, using data from the North Central and Northeast US subregional area (https://wetland-plants.sec.usace.army.mil). This five-level factor, which make distinction between “obligate wetland” (OBL), “facultative wetland” (FACW), “Facultative” (FAC), “Facultative upland” (FACU) and “upland” (UPL) taxa, were

transformed to ordinal values (5 – OBL, 4 = FACW, 3 = FAC, 2 = FACU, 1 = UPL). Of the 173 taxa identified, this information was missing for all taxa identified at genus (n = 22) or family (n = 1) levels and for 5 taxa identified at species level. For these taxa, as well as for non-vascular plants, a value was defined based on expert knowledge of the ecology of the genus and/or the dominant values of other species belonging to the same genus (e.g., *Carex* genus was considered as OBL, *Poaceae* family as FAC, *Fragaria* genus as FACU) (Appendix S3). We then computed the community-weighted mean (CWM) and functional dispersion (FDis) values of wetness indicator for each plot (FD package, Laliberté et al., 2014). CWM is defined as the mean of the trait values weighted by the relative cover of each species bearing each value (Lavorel et al., 2008). FDis is defined as the mean distance of individual species to the weighted centroid of all species in the community (Laliberté and Lavorel, 2010).

## 2.3. Environmental data

Within each 100-m² circular plot, topographical and pedological variables were recorded. To characterize the position of each plot center relative to the waterline, the horizontal distance was measured using a decimeter and the slope inclination (%) was measured using a clinometer. From these two measurements, the elevation of the plot center relative to the waterline was estimated by trigonometry. We preferred this method to GPS, which was not very accurate for measuring Z coordinates (altitude) at the time field surveys were carried out, and to centimetric LiDAR-derived Digital Elevation, which in 2025 is still unavailable for one of the 4 ecoregions. To characterize local soil properties, drainage classes ranging from rapid (= 1) to very poor (= 6),
granulometric texture classes of surface deposits for mineral soils ranging from clayey (= 1) to rocky (= 9) and the presence of organic soils were assessed with a Dutch pedological probe using standardized protocols used for characterizing ecological data in forest environments (Saucier et al., 1994).

## 2.4. Statistical analyses

To analyse variations in riparian boreal plant communities between ecoregions (four-level factor) and aquatic habitats (five-level factor), as well as along the local topographic gradient, assessed by distance (continuous) and elevation (continuous) from the waterline, six dependent variables were considered: the basal area of softwood and hardwood tree species (live and dead), the richness of herbaceous taxa (including non-vascular taxa), the richness of shrub taxa, the mean and the dispersion values of the wetness indicator. For variations in taxa composition, species inventoried in herbaceous strata, including non-vascular taxa, and shrub strata were grouped together in the following analysis.

### 2.4.1. Changes in environmental parameters between ecoregions and aquatic habitats

To determine whether topographical and pedological variables varied between ecoregions and aquatic habitats, plot-scale data were analysed. For environmental parameters, i.e., distance, elevation, slope inclination, drainage and texture classes, we used one-way ANOVA and Tukey's HSD post hoc tests for pairwise comparisons (car package, Fox and Weisberg, 2019). In addition, for the presence or absence of organic soil, we used Pearson's Chi-square with post hoc tests.

### 2.4.2. Influence of ecoregions and aquatic habitats on riparian plant communities

To determine whether the biotic responses of plant communities varied between ecoregions and aquatic habitats, plot-scale data were combined at the transect scale. We used one-way ANOVA and Tukey's HSD post hoc tests for pairwise comparisons (car package, Fox and Weisberg, 2019) to test for significant differences in basal area, richness and wetness indicator values between factor levels. In addition, we used PERMANOVA (i.e., between-groups) and PERMDISP (i.e., within-group) analyses (Anderson and Walsh, 2013) with 999 permutations (vegan package, Oksanen et al., 2024) to determine whether taxa composition varied between ecoregions and aquatic habitats. These analyses were based on a Bray-Curtis distance matrix using the log-transformed percentage cover of taxa present in at least 5 transects. To test the statistical significance of the associations of individual taxa with each ecoregion and aquatic habitat, we used the phi coefficient of association with 999 permutations (indicspecies, De Cáceres and Legendre, 2009). Finally, because the level of replication was too low to statistically test the interaction between ecoregions and aquatic habitats, we used a hierarchical cluster analysis (Ward's method) on the same Bray-Curtis distance matrix. The optimum number of clusters was determined using the gap statistics (Tibshirani et al., 2001) (factoextra package, Kassambara and Mundt, 2020) and the distribution of transects between ecoregions and aquatic habitats was then graphically described using a circlized dendrograms (dendextend package, Galili, 2015).

### 2.4.3. Influence of the topographic gradient, between ecoregions and aquatic habitats, on riparian plant communities

To determine whether the biotic response of plant communities varied along the topographic gradient between ecoregions and aquatic habitats, plot-scale data were analysed. We used mixed effect linear models (lme4 package, Bates et al., 2015) with Normal, Log-normal or Poisson distribution to respectively test how basal area, richness and wetness indicator values varied with elevation or distance, and whether these trends differed significantly between ecoregions or aquatic habitats. We considered two sets of 4 *a priori* models including the factors

"Ecoregion" and "Habitat" as covariates and tested the additive and interactive effects of elevation or distance with ecoregions on the one hand, and with aquatic habitats on the other. Each set of candidate models was compared with a null model including only the covariates (i. e., Y = Ecoregion + Habitat). Prior to analyses, topographic variables were scaled (Schielzeth, 2010), and it was verified that the variance inflation factor was less than 3 for each of the models. Since observations within each site were not independent, we used site identity as a random effect (n = 63). Variance explained by models was estimated using the marginal coefficient of determination for fixed effect parameters alone (Nakagawa et al., 2017). We used the Akaike information criterion corrected for small sample sizes (AICc) to rank the models, and for each response variable, excluding the covariates, we extracted parameter estimates and associated unconditional standard errors and performed model averaging based on all a priori models (MuMin package, Barton, 2015).

We used multivariate Generalized Linear Mixed Models (spAbundance package, Doser et al., 2024) with a Negative Binomial distribution to test how taxa composition (i.e., absolute values of log-transformed percentage cover of taxa present in at least 5 plots) varied with elevation or distance, and whether these trends differed significantly between ecoregions or aquatic habitats. We considered the same sets of a priori models as previously, i.e., we tested the additive and interactive effects of elevation or distance with ecoregions on the one hand, and with aquatic habitats on the other. We fitted models in a Bayesian framework using Markov chain Monte Carlo methods. We used the default values for all hyperparameter in the prior distributions (Doser et al., 2024) and ran each model by considering three chains, each with 5000 iterations with a burn-in period of 500 iterations and a thinning rate of 10. Model convergence were assessed using the potential scale reduction factor (i. e., PSRF, Gelman and Rubin, 1992) and model goodness of fit was assessed with Bayesian *p*-values using the Freeman-Tukey statistic as a measure of posterior predictive checks (Hoobs and Hooten, 2015). We used the Widely Applicable Information Criterion (WAIC, Watanabe, 2010) to rank the models, and for each set of models, we extracted parameter estimates and associated unconditional standard errors (excluding covariates) from the two top-ranked models.

Analyses were performed with R version 4.4.1 (R Core Team, 2024).

## 3. Results

### 3.1. Variations in environmental conditions between ecoregions and aquatic habitats

One-way ANOVAs and Chi-square tests showed that all environmental variables varied significantly between ecoregions (Appendix S4). Pairwise comparisons revealed that the elevation and the slope inclination had significantly higher values in Lower North-Shore than in the other three ecoregions (Appendix S5). For soil texture classes, mean values were significantly lower in Abitibi Lowlands (i.e., dominated by clay-loam and clay-sand soils) and significantly higher in Mistassini Highlands (i.e., dominated by sandy and rocky soils) than in the other two ecoregions (i.e., dominated by sandy-loam soils). The presence of organic soils in the study sites was more frequent in Mistassini Highlands but less frequent in Abitibi Lowlands than in the other two ecoregions. Finally, soils were better drained in Lower North-Shore, whereas they were less well drained in Mistassini Highlands, compared to the other two ecoregions. For aquatic habitats, one-way ANOVAs and Chi-square tests showed that only the mean values of soil texture classes varied significantly between factors levels (Appendix S6). Pairwise comparisons revealed that mean values were significantly lower along permanent streams than along lakes and intermittent streams (Appendix S7).
## 3.2. Variations in basal area, richness and wetness indicator values between ecoregions and aquatic habitats

One-way ANOVAs showed that all dependent variables tested varied significantly between the four ecoregions (Fig. 2-A, Appendix S8). Pairwise comparisons revealed that softwood basal area was significantly higher in Lower North-Shore, as compared to others, and in Mistassini Highlands as compared to Abitibi Lowlands (Appendix S9). For hardwoods, basal area was significantly higher in Abitibi Lowlands. Herbaceous richness was significantly lower in Central Laurentians, while shrub richness was significantly higher in Abitibi Lowlands and significantly lower in Lower North-Shore. Reversely, for the wetness indicator, the mean value was significantly lower in Abitibi Lowlands, while the dispersion value was significantly lower in Lower North-Shore and Mistassini Highlands and significantly higher in Abitibi Lowlands and Central Laurentians. For aquatic habitats, one-way ANOVAs showed that only hardwood basal area and richness of herbaceous taxa varied significantly (Fig. 2-B, Appendix S10). Pairwise comparisons revealed that hardwood basal area was significantly higher along intermittent streams as compared to ponds (Appendix S11). Herbaceous richness was also significantly lower along ponds and lakes, as compared with intermittent streams, to a lesser extent, permanent streams.

## 3.3. Variations in plant composition between ecoregions and aquatic habitat types

PERMANOVA analyses revealed a significant difference in plant composition (pseudo-F<sub>3.122</sub> = 30.22, R<sup>2</sup> = 42.6 %, p = 0.001) between ecoregions (Fig. 3-A). Pairwise comparisons revealed that all ecoregions

varied significantly from one another. Furthermore, PERMDISP (pseudo- F<sub>3.122</sub> = 7.28, p = 0.001) revealed significant differences between ecoregions in the degree of site dispersion around the centroids (i. e., within each ecoregion). In particular, the within-group value for Abitibi Lowlands (AL = 0.383) and Lower North-Shore (NS = 0.360) was significantly higher than for the other two ecoregions (MH = 0.306, CL = 0.261). This indicates that plant composition within the Abitibi Lowlands and Lower North-Shore ecoregions was more diverse among the sites studied, compared to the other ecoregions (i.e., location and dispersion effects are confounded). With regard to the association of individual taxa with ecoregions, the phi coefficient revealed that 28 taxa were significantly associated with Abitibi Lowlands, 26 taxa with Mistassini Highlands, 6 taxa with Central Laurentians and 10 taxa with Lower North-Shore (Appendix S12).

For aquatic habitats, PERMANOVA analyses also revealed a significant difference in taxa composition between the five types (pseudo- F<sub>3.122</sub> = 2.34, R<sup>2</sup> = 7.2 %, p = 0.001) (Fig. 3-A). Pairwise comparisons revealed that ponds and lakes differed significantly in taxa composition from intermittent and permanent streams. Furthermore, PERMDISP (pseudo- F<sub>3.122</sub> = 2.40, p = 0.053) revealed no significant differences between aquatic habitats in the degree of site dispersion around the centroids, indicating that the difference in taxon composition is indeed due to location. With regard to the association of individual taxa with aquatic habitats, the phi coefficient revealed that 4 taxa were significantly associated with ponds, 2 taxa with lakes, 6 taxa with intermittent streams, 6 taxa with permanent streams and 2 taxa with rivers (Appendix S13).

Finally, the hierarchical classification of transects confirmed that ecoregions had the strongest influence on taxa composition, over the

<figure><img src="image_5.png" /><figcaption>**Fig. 2.** Boxplots showing variations in basal area, richness and wetness indicator values between A) the four ecoregions (AL = Abitibi Lowlands, MH = Mistassini Highlands, CL = Central Laurentians, NS = Lower North-Shore) and B) the five aquatic habitats (PD = ponds, LK = lakes, IS = intermittent streams, PS = permanent streams, RV = rivers) in boreal forest, Eastern Canada. Boxplots with different letters indicate statistical significant differences (p < 0.05) among factor levels, based on Tukey's post hoc tests.</figcaption></figure>
<figure><img src="image_6.png" /><figcaption>**Fig. 3.** Nonmetric multidimensional scaling (NMDS, stress value = 0.1651) ordination plot (A) and cirlced dendrogram plot (B) of the Bray-Curtis distance matrix, illustrating differences in plant composition between the four ecoregions and five aquatic habitats (i.e., PD = ponds, LK = lakes, IS = intermittent streams, PS = permanent streams, RV = rivers) in boreal forest, Eastern Canada. The classification of transects into clusters was done using a hierarchical cluster analysis (Ward's method). Colored branches represent transect clusters, as defined by the gap statistic (n = 9, i.e., from C_1 to C_9), and colored labels refer to the ecoregion in the same way as in the NMDS plot.</figcaption></figure>

type of aquatic habitat (Fig. 3-B). Thus, of the 9 clusters formed, 5 included transects belonging only to the same ecoregion (i.e., C_1, C_3, C_4, C_5, C_7), while none included transects only belonging to the same type of aquatic habitat. Thus, in four of these five clusters, almost all aquatic habitats were represented. Only one cluster, comprising transects from the Lower North-Shore ecoregion, grouped together transects from intermittent and permanent streams (i.e., C_5). The other four clusters included transects from different ecoregions, but sometimes with a strong dominance of one ecoregion, as in the case of Mistassini Highlands (C_8) and Lower North-Shore (C_6), and always with transects positioned on the banks of waterbodies and watercourses.

## 3.4. Variations in basal area, richness and wetness indicator values along the topographic gradient, between ecoregions and aquatic habitats

According to AICc values, variations in the dependent variables were, in most cases, better explained by models including an interaction between elevation or distance and ecoregions or aquatic habitats (Table 1). The goodness of fit of the top-ranked models varied from 0.09 for the dispersion values of the wetness indicator, to 0.55 for the mean values of the wetness indicator. In addition, for all dependent variables except for the dispersion values of the wetness indicator, the Null model ranked last, indicating that the inclusion of topographic variables improved model performance (Appendix S14 & S15). Finally, models including elevation rank in most cases before models including distance.

### 3.4.1. Interactions between the topographic gradient and ecoregions

Model averaging revealed significant interactions between elevation or distance and ecoregions (Table 2 and Fig. 4). The basal area of soft-wood increased significantly with elevation in all ecoregions except in Lower North-Shore, where it decreased significantly. The basal area of hardwood increased significantly with distance only in Abitibi Lowlands. The richness of herbaceous taxa decreased significantly with elevation and distance, but this trend was more pronounced along the elevation gradient in Mistassini Highlands and Central Laurentians. The richness of shrubs also decreased significantly with elevation and

distance in all ecoregions, except in Abitibi Lowlands, where it increased significantly. The mean values of the wetness indicator decreased significantly with elevation and distance, but this trend was less pronounced along the topographic gradient in Lower North-Shore. Finally, the dispersion values of the wetness indicator decreased slightly or remained stable with elevation in all ecoregions, except in Abitibi Lowlands, where it increased significantly.

### 3.4.2. Interactions between the topographic gradient and aquatic habitats

Model averaging revealed few significant interactions between elevation or distance and aquatic habitats (Table 3 and Fig. 5). The basal area of softwood increased significantly with elevation along all aquatic habitats and the basal area of hardwood increased significantly with distance along all aquatic habitats. However, the basal area of hardwood decreased slightly with elevation along ponds, but increased slightly along intermittent streams. The richness of herbaceous taxa decreased significantly with elevation and distance along all aquatic habitats. The richness of shrubs also decreased significantly with elevation and distance along all aquatic habitats, but less pronounced along watercourses than waterbodies. The mean values of the wetness indicator decreased significantly with elevation and distance along all aquatic habitats, except along intermittent streams, where it increased significantly with elevation or remained stable with distance. Finally, the dispersion values of the wetness indicator decreased slightly with elevation and distance along ponds but increased slightly along lakes and permanent streams.

## 3.5. Variations in taxa composition along the topographic gradient, between ecoregions and aquatic habitats

According to WAIC, variations in taxa composition was best explained by the model including the interaction between elevation and ecoregions for the set of models testing the additive and interactive effects of elevation or distance with ecoregions, and by the model including the interaction between elevation and aquatic habitats for the set of models testing the additive and interactive effects of elevation or
<caption>**Table 1**</caption>

Top-ranking models predicting variations in basal area, richness and wetness indicator values along the topographic gradient, taking into account interactions with ecoregions on the one hand, and interactions with aquatic habitats on the other, in boreal forest, Eastern Canada, as assessed with Akaike's information criterion corrected for small sample size (AICc). The number of parameters estimated (k), AICc, AICc weight (W) and adjusted coefficient of determination for fixed only (R²) are provided (see Appendix S9 for detailed results of all candidate models).

<table><thead><tr><th>Variables</th><th></th><th>Candidate models (Y = Ecoregion + Habitat +)</th><th>k</th><th>AICc</th><th>W</th><th>R<sup>2</sup></th></tr></thead><tbody><tr><td rowspan="5">Ecoregions</td><td>Basal area softwood</td><td>Elevation + Ecoregion: Elevation + (1|Site)</td><td>14</td><td>1313.8</td><td>0.752</td><td>0.307</td></tr><tr><td>Basal area hardwood</td><td>Distance + Ecoregion: Distance + (1|Site)</td><td>14</td><td>1045.7</td><td>0.983</td><td>0.287</td></tr><tr><td>Richness herbaceous</td><td>Elevation + Ecoregion: Elevation + (1|Site)</td><td>13</td><td>2553.0</td><td>1.000</td><td>0.408</td></tr><tr><td>Richness shrubs</td><td>Elevation + Ecoregion: Elevation + (1|Site)</td><td>13</td><td>2177.2</td><td>0.972</td><td>0.385</td></tr><tr><td>CWM wetness ind.</td><td>Elevation + Ecoregion: Elevation + (1|Site)</td><td>14</td><td>477.9</td><td>1.000</td><td>0.549</td></tr><tr><td rowspan="5">Aquatic habitats</td><td>FDis wetness ind.</td><td>Elevation + Ecoregion: Elevation + (1|Site)</td><td>14</td><td>-328.8</td><td>0.800</td><td>0.112</td></tr><tr><td>Basal area softwood</td><td>Elevation + Habitat: Elevation + (1|Site)</td><td>15</td><td>1309.6</td><td>0.961</td><td>0.315</td></tr><tr><td>Basal area hardwood</td><td>Elevation + Habitat: Elevation + (1|Site)</td><td>15</td><td>1046.6</td><td>0.983</td><td>0.312</td></tr><tr><td>Richness herbaceous</td><td>Distance + (1|Site)</td><td>10</td><td>2582.0</td><td>0.654</td><td>0.335</td></tr><tr><td>Richness shrubs</td><td>Elevation + Habitat: Elevation + (1|Site)</td><td>14</td><td>2194.1</td><td>0.932</td><td>0.360</td></tr><tr><td></td><td>CWM wetness ind.</td><td>Elevation + Habitat: Elevation + (1|Site)</td><td>15</td><td>511.3</td><td>0.973</td><td>0.517</td></tr><tr><td></td><td>FDis wetness ind.</td><td>Distance + (1|Site)</td><td>11</td><td>-324.8</td><td>0.405</td><td>0.092</td></tr></tbody></table>

distance with aquatic habitats (Table 4). In both sets of a priori models the Null model ranked last, indicating that the inclusion of topographic variables improved model performance. In addition, the ranking of models including elevation was always higher than models including distance. Overall, the convergence of the models, assessed for each individual parameter, was satisfactory, since the values of the potential scale reduction factor were all very close to 1.00 (PSRF, Table 4). The goodness of fit of the models was also quite good since the Bayesian p-value varied between 0.7 and 0.8, indicating that the models adequately represented the variability of the observed data.

### 3.5.1. Interactions between the topographic gradient and ecoregions

Parameter estimates revealed significant negative interactions between elevation or distance and ecoregions, but with a greater magnitude of the effect for elevation (Table 5). This indicates that changes in

taxa composition decreased consistently along the lateral topographic gradient, but more strongly with changes in elevation and differently between the four ecoregions. Specifically, changes in taxa composition decreased more strongly along the topographic gradient, both for elevation and distance, in Mistassini Highlands, Central Laurentians and Lower North-Shore than in Abitibi Lowlands.

### 3.5.2. Interactions between the topographic gradient and aquatic habitats

Parameter estimates revealed significant positive interactions between elevation and two aquatic habitats, i.e., intermittent streams and rivers (Table 5). This indicates that taxa composition changed significantly along the elevation gradient, but differently between aquatic habitats. Specifically, changes in taxa composition decreased with elevation along the banks of all aquatic habitats but less rapidly along the banks of intermittent streams and rivers. However, no significant interactions were found between distance and aquatic habitats. This revealed that taxa composition changed consistently with distance along the banks of the five aquatic habitats, i.e., changes in taxa composition decreased in the same way with distance to waterline.

## 4. Discussion

To preserve biodiversity and ecosystem services provided by riparian zones in boreal forests, management practices need to be based on detailed knowledge of the natural range of variation in plant communities along the lateral connectivity gradient in different ecological contexts. This study compared patterns of plant diversity and composition in four ecoregions with different climatic, geological and topographical characteristics, along the shores of five aquatic habitats, and found clear differences in community structure. Specifically, we demonstrated that herbaceous species richness, as well as mean values for wetness requirements, decreased consistently along the lateral topographic gradient, but that this effect was mediated by ecoregions and aquatic habitats. However, we have also highlighted significant interactions between the local topographic gradient and ecoregions or aquatic habitats, showing that the regional context and the hydrological regime can strongly influence the structuring and extent of riparian vegetation laterally. Furthermore, compared to distance to the water line, widely used to delimit riparian forest strips, our results revealed that elevation difference was a better predictor of changes in riparian vegetation along the lateral gradient. To better protect the aquatic and riparian biodiversity of the boreal forest, the use of a fixed distance to delimit forest strips should be coupled with elevation gradient and a minimal distance from the waterline should be maintained in accordance with general riparian management principles, i.e., bearing in mind that the extent of riparian and aquatic habitats varies from headwater streams to large bottomland rivers and lakes.

### 4.1. Riparian plant diversity and composition differ between ecoregions and aquatic habitat types

By selecting four ecoregions along a strongly structuring longitudinal gradient, i.e., linked to the increase in rugged topography and precipitations from west to east (Couillard et al., 2019), and five aquatic habitats representing the main freshwater wetlands in the landscapes, our study sought to represent the range of hydrogeomorphic settings in Quebec's boreal forest (Luke et al., 2007). Thus, our results revealed clear differences in environmental conditions within riparian zones between ecoregions and, to a lesser extent, between aquatic habitats. As expected, the easternmost ecoregion, i.e., Lower North-Shore, had a steeper lateral topography and better-drained soils, while the westernmost ecoregions, Abitibi Lowlands were dominated by clay soils and Mistassini Highlands by a high proportion of poorly drained organic soils. As for the variation in environmental conditions between aquatic habitats, only soil texture classes varied, with overall finer textures in the riparian zones of permanent streams and rivers than in those of
<caption>**Table 2**</caption>

Estimate coefficients (Estimate (± SE)) and confidence intervals (95 % CI) for each environmental parameter, excluding covariates, used to predict variations in basal area, richness and wetness indicator values along the topographic gradient between ecoregions (MH = Mistassini Highlands, CL = Central Laurentians, NS = Lower North-Shore), in boreal forest, Eastern Canada (values in bold exclude zero from the confidence interval; for the four-level ecoregion factor, the Abitibi Lowlands was used as the reference level).

<table><thead><tr><th rowspan="2">Parameters</th><th colspan="2">Basal area softwood</th><th colspan="2">Basal area hardwood</th><th colspan="2">Richness herbaceous</th></tr><tr><th>Estimate (±SE)</th><th>(95 % CI)</th><th>Estimate (±SE)</th><th>(95 % CI)</th><th>Estimate (±SE)</th><th>(95 % CI)</th></tr></thead><tbody><tr><td>Elevation</td><td><strong>0.416 (±0.133)</strong></td><td>(0.155; 0.677)</td><td>0.087 (±0.074)</td><td>(-0.058; 0.231)</td><td>-0.011 (±0.044)</td><td>(-0.098; 0.076)</td></tr><tr><td>Elevation:MH</td><td>-0.145 (±0.144)</td><td>(-0.427; 0.137)</td><td>0.208 (±0.111)</td><td>(-0.009; 0.425)</td><td><strong>-0.303 (±0.062)</strong></td><td>(-0.425; -0.181)</td></tr><tr><td>Elevation:CL</td><td>-0.197 (±0.157)</td><td>(-0.505; 0.110)</td><td>-0.024 (±0.121)</td><td>(-0.261; 0.212)</td><td><strong>-0.267 (±0.077)</strong></td><td>(-0.417; -0.117)</td></tr><tr><td>Elevation:NS</td><td><strong>-0.375 (±0.134)</strong></td><td>(-0.637; -0.113)</td><td>0.107 (±0.103)</td><td>(-0.095; 0.309)</td><td>-0.040 (±0.055)</td><td>(-0.148; 0.067)</td></tr><tr><td>Distance</td><td>0.115 (±0.078)</td><td>(-0.038; 0.268)</td><td><strong>0.299 (±0.063)</strong></td><td>(0.175; 0.423)</td><td><strong>-0.085 (±0.031)</strong></td><td>(-0.146; -0.025)</td></tr><tr><td>Distance:MH</td><td>0.106 (±0.107)</td><td>(-0.103; 0.315)</td><td>-0.210 (±0.078)</td><td>(-0.362; -0.057)</td><td>-0.078 (±0.043)</td><td>(-0.162; 0.007)</td></tr><tr><td>Distance:CL</td><td>-0.181 (±0.115)</td><td>(-0.406; 0.043)</td><td><strong>-0.300 (±0.083)</strong></td><td>(-0.463; -0.136)</td><td>-0.039 (±0.053)</td><td>(-0.143; 0.064)</td></tr><tr><td>Distance:NS</td><td>-0.059 (±0.109)</td><td>(-0.272; 0.155)</td><td><strong>-0.322 (±0.079)</strong></td><td>(-0.477; -0.167)</td><td>0.030 (±0.043)</td><td>(-0.055; 0.115)</td></tr><tr><td>Parameters</td><td colspan="2">Richness shrubs</td><td colspan="2">CWM wetness ind.</td><td colspan="2">FDIs wetness ind.</td></tr><tr><td></td><td>Estimate (±SE)</td><td>(95 % CI)</td><td>Estimate (±SE)</td><td>(95 % CI)</td><td>Estimate (±SE)</td><td>(95 % CI)</td></tr><tr><td>Elevation</td><td>0.102 (±0.055)</td><td>(-0.006; 0.211)</td><td><strong>-0.321 (±0.047)</strong></td><td>(-0.414; -0.229)</td><td><strong>0.059 (±0.024)</strong></td><td>(0.012; 0.106)</td></tr><tr><td>Elevation:MH</td><td><strong>-0.404 (±0.079)</strong></td><td>(-0.558; -0.249)</td><td>0.130 (±0.063)</td><td>(0.007; 0.253)</td><td>-0.095 (±0.028)</td><td>(-0.150; -0.040)</td></tr><tr><td>Elevation:CL</td><td><strong>-0.356 (±0.088)</strong></td><td>(-0.527; -0.184)</td><td>-0.074 (±0.068)</td><td>(-0.208; 0.060)</td><td>-0.090 (±0.031)</td><td>(-0.150; -0.030)</td></tr><tr><td>Elevation:NS</td><td><strong>-0.321 (±0.080)</strong></td><td>(-0.478; -0.164)</td><td><strong>0.299 (±0.058)</strong></td><td>(0.185; 0.414)</td><td>-0.075 (±0.026)</td><td>(-0.126; -0.024)</td></tr><tr><td>Distance</td><td>0.067 (±0.041)</td><td>(-0.013; 0.148)</td><td><strong>-0.116 (±0.039)</strong></td><td>(-0.193; -0.039)</td><td>-0.011 (±0.011)</td><td>(-0.033; 0.011)</td></tr><tr><td>Distance:MH</td><td><strong>-0.220 (±0.054)</strong></td><td>(-0.325; -0.115)</td><td>0.023 (±0.049)</td><td>(-0.072; 0.119)</td><td>-0.015 (±0.021)</td><td>(-0.056; 0.027)</td></tr><tr><td>Distance:CL</td><td><strong>-0.189 (±0.059)</strong></td><td>(-0.305; -0.073)</td><td>-0.018 (±0.052)</td><td>(-0.120; 0.085)</td><td>-0.021 (±0.023)</td><td>(-0.066; 0.023)</td></tr><tr><td>Distance:NS</td><td><strong>-0.315 (±0.065)</strong></td><td>(-0.443; -0.187)</td><td><strong>0.133 (±0.050)</strong></td><td>(0.036; 0.231)</td><td>-0.034 (±0.022)</td><td>(-0.076; 0.008)</td></tr></tbody></table>

<figure><img src="image_7.png" /><figcaption>**Fig. 4.** Variations in basal area, richness and wetness indicator values along the topographic gradient between the four ecoregions (AL = Abitibi Lowlands, MH = Mistassini Highlands, CL = Central Laurentians, NS = Lower North-Shore) in boreal forest, Eastern Canada.</figcaption></figure>

ponds and lakes. As a result, plant community responses to aquatic habitat type can be attributed, more directly, to differences in hydrological regime between waterbodies and watercourses (Rolls et al., 2018). This includes disturbance regimes, i.e., variations in flood depth, frequency and duration (Poff et al., 1997), but also, in the context of boreal forests, the mechanical effect of ice on riparian vegetation, particularly during ice break-up periods on watercourses (Lind et al., 2014).

In response to these varied hydrogeomorphic contexts, plant communities showed significant variations between the four ecoregions, but less so between the five aquatic habitats. Specifically, the riparian zone of the Lower North Shore ecoregion included the greatest basal area of softwoods, but the lowest richness of shrubs, while that of the Abitibi Lowlands included the greatest basal area of hardwoods and the highest

richness of shrubs. These changes may be linked to modifications in ecological gradients (climate, soil, topography) but also in the dominant disturbance regime along the longitudinal gradient, with a higher occurrence of fire in the Abitibi Lowlands and Mistassini Highlands ecoregions, and a higher occurrence of insect outbreaks and windthrow in the Central Laurentians and Lower North Shore ecoregions (Bergeron et al., 2002; De Grandpré et al., 2000; Harper et al., 2002). Indeed, fire is a non-selective disturbance that leads to more frequent and extensive rejuvenation of surfaces and, in boreal forests, favors pioneer species of deciduous trees and shrubs (Anyomi et al., 2022; De Grandpré et al., 2003; Harper et al., 2002). Specifically, and in line with the results of the *phi* coefficient of association, it was shown that taxa such as *Rubus* spp. and *Ribes* spp. were more associated with clay deposits (Légaré et al., 2001), as is the case in the Abitibi Lowlands, and that ericaceous species
<caption>**Table 3**</caption>

Estimate coefficients (Estimate (± SE)) and confidence intervals (95 % CI) for each environmental parameter, excluding covariates, used to predict variations in basal area, richness and wetness indicator values along the topographic gradient between aquatic habitats (LK = lakes, IS = intermittent streams, PS = permanent streams, RV = rivers), in boreal forest, Eastern Canada (values in bold exclude zero from the confidence interval: for the five-level factor aquatic habitats, pond was used as the reference level).

<table><thead><tr><th rowspan="2">Parameters</th><th colspan="2">Basal area softwood</th><th colspan="2">Basal area hardwood</th><th colspan="2">Richness herbaceous</th></tr><tr><th>Estimate (±SE)</th><th>(95 % CI)</th><th>Estimate (±SE)</th><th>(95 % CI)</th><th>Estimate (±SE)</th><th>(95 % CI)</th></tr></thead><tbody><tr><td>Elevation</td><td><strong>0.218 (±0.110)</strong></td><td>(0.003; 0.433)</td><td>0.057 (±0.085)</td><td>(-0.109; 0.224)</td><td><strong>-0.133 (±0.022)</strong></td><td>(-0.177; -0.089)</td></tr><tr><td>Elevation:LK</td><td>0.186 (±0.149)</td><td>(-0.107; 0.479)</td><td>-0.176 (±0.115)</td><td>(-0.401; 0.048)</td><td>0.019 (±0.070)</td><td>(-0.119; 0.156)</td></tr><tr><td>Elevation:IS</td><td>-0.222 (±0.151)</td><td>(-0.518; 0.075)</td><td><strong>0.236 (±0.117)</strong></td><td>(0.007; 0.466)</td><td>0.022 (±0.067)</td><td>(-0.110; 0.153)</td></tr><tr><td>Elevation:PS</td><td>-0.051 (±0.150)</td><td>(-0.346; 0.243)</td><td>0.119 (±0.116)</td><td>(-0.108; 0.347)</td><td>-0.009 (±0.068)</td><td>(-0.142; 0.123)</td></tr><tr><td>Elevation:RV</td><td>0.282 (±0.152)</td><td>(-0.015; 0.580)</td><td>0.178 (±0.117)</td><td>(-0.052; 0.408)</td><td>0.050 (±0.071)</td><td>(-0.089; 0.189)</td></tr><tr><td>Distance</td><td>0.125 (±0.075)</td><td>(-0.021; 0.272)</td><td><strong>0.078 (±0.033)</strong></td><td>(0.012; 0.143)</td><td><strong>-0.097 (±0.022)</strong></td><td>(-0.141; -0.054)</td></tr><tr><td>Distance:LK</td><td>0.035 (±0.120)</td><td>(-0.200; 0.269)</td><td>-0.096 (±0.089)</td><td>(-0.270; 0.077)</td><td>0.017 (±0.055)</td><td>(-0.091; 0.124)</td></tr><tr><td>Distance:IS</td><td>-0.216 (±0.118)</td><td>(-0.447; 0.015)</td><td>-0.098 (±0.087)</td><td>(-0.269; 0.073)</td><td>-0.030 (±0.050)</td><td>(-0.128; 0.069)</td></tr><tr><td>Distance:PS</td><td>-0.121 (±0.120)</td><td>(-0.356; 0.113)</td><td>0.012 (±0.089)</td><td>(-0.161; 0.186)</td><td>-0.081 (±0.053)</td><td>(-0.185; 0.022)</td></tr><tr><td>Distance:RV</td><td>0.050 (±0.120)</td><td>(-0.185; 0.285)</td><td>-0.009 (±0.089)</td><td>(-0.182; 0.165)</td><td>-0.029 (±0.055)</td><td>(-0.137; 0.079)</td></tr><tr><td>Parameters</td><td colspan="2">Richness shrubs</td><td colspan="2">CWM wetness ind.</td><td colspan="2">FDIs wetness ind.</td></tr><tr><td></td><td>Estimate (±SE)</td><td>(95 % CI)</td><td>Estimate (±SE)</td><td>(95 % CI)</td><td>Estimate (±SE)</td><td>(95 % CI)</td></tr><tr><td>Elevation</td><td><strong>-0.351 (±0.077)</strong></td><td>(-0.502; -0.201)</td><td><strong>-0.221 (±0.050)</strong></td><td>(-0.319; -0.123)</td><td>-0.047 (±0.030)</td><td>(-0.107; 0.012)</td></tr><tr><td>Elevation:LK</td><td>0.087 (±0.096)</td><td>(-0.101; 0.276)</td><td>-0.039 (±0.068)</td><td>(-0.171; 0.093)</td><td><strong>0.063 (±0.029)</strong></td><td>(0.005; 0.121)</td></tr><tr><td>Elevation:IS</td><td><strong>0.323 (±0.097)</strong></td><td>(0.134; 0.512)</td><td><strong>0.190 (±0.069)</strong></td><td>(0.055; 0.325)</td><td>0.049 (±0.030)</td><td>(-0.009; 0.107)</td></tr><tr><td>Elevation:PS</td><td><strong>0.251 (±0.096)</strong></td><td>(0.063; 0.439)</td><td>-0.029 (±0.068)</td><td>(-0.163; 0.105)</td><td><strong>0.095 (±0.029)</strong></td><td>(0.037; 0.152)</td></tr><tr><td>Elevation:RV</td><td><strong>0.284 (±0.099)</strong></td><td>(0.089; 0.478)</td><td>0.050 (±0.069)</td><td>(-0.085; 0.185)</td><td>0.049 (±0.030)</td><td>(-0.009; 0.108)</td></tr><tr><td>Distance</td><td><strong>-0.182 (±0.053)</strong></td><td>(-0.287; -0.077)</td><td><strong>-0.136 (±0.046)</strong></td><td>(-0.226; -0.045)</td><td>-0.017 (±0.013)</td><td>(-0.043; 0.008)</td></tr><tr><td>Distance:LK</td><td>-0.029 (±0.069)</td><td>(-0.164; 0.106)</td><td>0.050 (±0.055)</td><td>(-0.057; 0.157)</td><td>0.021 (±0.024)</td><td>(-0.025; 0.067)</td></tr><tr><td>Distance:IS</td><td><strong>0.165 (±0.067)</strong></td><td>(0.034; 0.296)</td><td><strong>0.146 (±0.054)</strong></td><td>(0.041; 0.252)</td><td>0.034 (±0.023)</td><td>(-0.011; 0.080)</td></tr><tr><td>Distance:PS</td><td>0.092 (±0.068)</td><td>(-0.042; 0.225)</td><td>0.018 (±0.055)</td><td>(-0.089; 0.126)</td><td><strong>0.048 (±0.024)</strong></td><td>(0.001; 0.094)</td></tr><tr><td>Distance:RV</td><td><strong>0.144 (±0.069)</strong></td><td>(0.008; 0.279)</td><td><strong>0.119 (±0.055)</strong></td><td>(0.012; 0.226)</td><td>0.015 (±0.024)</td><td>(-0.031; 0.061)</td></tr></tbody></table>

<figure><img src="image_8.png" /><figcaption>**Fig. 5.** Variations in basal area, richness and wetness indicator values along the topographic gradient between the five types of aquatic habitats (PD = ponds, LK = lakes, IS = intermittent streams, PS = permanent streams, RV = rivers) in boreal forest, Eastern Canada.</figcaption></figure>

were more dominant in the center part of the Québec boreal forest (Thiffault et al., 2015), as is the case in the Mistassini Highlands. Conversely, in eastern Quebec (Bouchard et al., 2008), forest stands are more stable and mature, with an understory dominated by non-vascular plant communities, that require more time to develop but then limit the establishment of shrubs and herbaceous plants (Hart and Chen, 2006). Overall, our results illustrate that the shift in forest stand composition along the longitudinal gradient, from the balsam fir-white birch domain to the black spruce-moss domain (Couillard et al., 2019), are also associated with changes in riparian zones and therefore, that each ecoregion is characterized by a pool of plant species and a set of

structural attributes that differentiate them from one another.

With regard to plant wetness requirements, the Abitibi Lowlands ecoregion was distinguished by a lower mean value and a higher dispersion value, i.e., a greater co-occurrence of taxa with different affinities for soil moisture. This can be explained on the one hand by the high coverage of facultative upland shrub taxa, e.g., *Corylus cornuta*, *Lonicera canadensis* and *Acer spicatum*, and on the other hand by micro-topographic heterogeneity along the relatively flat lateral gradient, which favors obligate wetland taxa in depressions, and facultative wetland and upland taxa on soil mounds (e.g., Raulings et al., 2010). However, no significant differences were found in the wetness
<caption>**Table 4**</caption>

All candidate models predicting variations in taxa composition along the topographic gradient, taking into account interactions with ecoregions on the one hand, and interactions with aquatic habitats on the other, in boreal forest, Eastern Canada. For each model the Widely Applicable Information Criterion (WAIC) and the goodness-of-fit statistic, as assessed by Bayesian *p*-value (i.e., Freeman-Tukey statistic), are provided.

<table><thead><tr><th>Variables</th><th>Candidate model</th><th>WAIC</th><th>p-value</th></tr></thead><tbody><tr><td rowspan="4">Ecoregions</td><td>Taxa composition</td><td>Ecoregion + Habitat + Elevation + Ecoregion: Elevation + (1|Site)</td><td>67463.8</td><td>0.7869</td></tr><tr><td>Taxa composition</td><td>Ecoregion + Habitat + Distance + Ecoregion: Distance + (1|Site)</td><td>67631.6</td><td>0.7953</td></tr><tr><td>Taxa composition</td><td>Ecoregion + Habitat + Elevation + (1|Site)</td><td>67766.4</td><td>0.7922</td></tr><tr><td>Taxa composition</td><td>Ecoregion + Habitat + Distance + (1|Site)</td><td>67886.8</td><td>0.7989</td></tr><tr><td rowspan="4">Aq. habitats</td><td>Taxa composition</td><td>Ecoregion + Habitat + (1|Site)</td><td>68471.8</td><td>0.8036</td></tr><tr><td>Taxa composition</td><td>Ecoregion + Habitat + Elevation + (1|Site)</td><td>67579.4</td><td>0.7876</td></tr><tr><td>Taxa composition</td><td>Ecoregion + Habitat + Distance + Habitat: Distance + (1|Site)</td><td>67742.3</td><td>0.7942</td></tr><tr><td>Taxa composition</td><td>Ecoregion + Habitat + Elevation + (1|Site)</td><td>67766.4</td><td>0.7922</td></tr><tr><td>Taxa composition</td><td>Ecoregion + Habitat + Distance + (1|Site)</td><td>67886.8</td><td>0.7989</td></tr><tr><td>Taxa composition</td><td>Ecoregion + Habitat + (1|Site)</td><td>68471.8</td><td>0.8036</td></tr></tbody></table>

requirements of the taxa between the five aquatic habitats studied. More generally, only the basal area of hardwood trees and the richness of herbaceous taxa varied significantly between aquatic habitats, with higher values observed in the riparian zone of intermittent rivers. As observed in other environmental contexts (e.g., Katz et al., 2012; Milner et al., 2023), this increase in plant richness can be explained by greater temporal variability in environmental conditions, allowing co-occurrence of taxa with different ecological requirements. In fact, although the results were not significantly different between aquatic habitats, the highest dispersion values for wetness requirements were observed in intermittent streams. Overall, and beyond the few differences mentioned above, our results highlight a more structuring effect of ecoregions compared to aquatic habitats, with both a greater effect on taxa composition and a greater number of individual taxa associated with each ecoregion than with each aquatic habitat. This shows that, on a broad spatial scale, aquatic habitat types have a less structuring effect on riparian plant communities than regional environmental contexts, which themselves condition the local hydro-geomorphological

properties of lentic and lotic environments, from headwaters to bottomlands. From a management point of view, these results underline the fact that the application of fixed-width forest strips across vast territories, as is the case for the province of Quebec, is based more on practical than ecological considerations. It is therefore urgently needed to better integrate regional specificities in the delimitation of forest strips, e.g., by taking into account the effect of climate, soil, landform and disturbance dynamics on the extent and properties of riparian vegetation.

## 4.2. The local topographical gradient strongly structures riparian communities, but its effect differs between ecoregions and aquatic habitats

The local topographic gradient was found to have a significant and important effect in explaining variations in basal area, richness and wetness indicator values of riparian plant communities, but also changes in taxa composition. In fact, adding elevation or distance from the waterline as explanatory variables in the models improved their performance in all cases, and explained up to 50 % of the variation in mean values for wetness requirements. Specifically, we showed that the plots most connected to aquatic habitats, in all four ecoregions, hosted the greatest diversity of taxa, both herbaceous and shrubs, but the lowest basal area value of hardwood trees, and that, in general, these values tended to decrease with increasing distance and elevation from the waterline. This reinforces the idea that riparian zones play an important role in biodiversity conservation (González et al., 2017; Naiman and Decamps, 1997; Sabo et al., 2005), even along small streams in boreal forests (Kuglerová et al., 2014b), and highlights the fact that lightly forested areas in the direct vicinity of aquatic habitats contribute significantly to the regional species pool. Indeed, our results also showed that the magnitude of changes in the taxonomic composition of plant communities was higher near the waterline, with more hygrophilous plants, and decreased along the topographic gradient, with more mesophilous plants. This shift in plant composition reveals the original and unique character of the species pool associated with the most connected riparian zones and, to a certain extent, reveals a trend toward more uniform communities in the most disconnected zones. Overall, these results underline the important structuring effect of the lateral connectivity gradient on riparian vegetation in boreal forests and stresses the need for forest strips along all types of aquatic habitat, including small and intermittent streams (Hylander and Weibull, 2012; Selonen and Kotiaho, 2013).

Beyond the constant structuring effect of the local topographic gradient on riparian plant communities, different responses were also observed between ecoregions and to a lesser extent between aquatic habitats. Indeed, models including interactions with ecoregions or aquatic habitats came out on top, meaning that the response of riparian plants to elevation or distance to the waterline differed according to

<caption>**Table 5**</caption>

Estimate coefficients (Estimate (± SE)), confidence intervals (95 % CI) and potential scale reduction factor (PSRF) for each environmental parameter, excluding covariates, of top-ranked models predicting variations in taxa composition along the topographic gradient in interaction with ecoregions (MH = Mistassini Highlands, CL = Central Laurentians, NS = Lower North-Shore) on the one hand, and with aquatic habitats (LK = lakes, IS = intermittent streams, PS = permanent streams, RV = rivers) on the other, in boreal forest, Eastern Canada (values in bold exclude zero from the confidence interval).

<table><thead><tr><th rowspan="2">Parameters</th><th colspan="3">Taxa composition</th><th rowspan="2">Parameters</th><th colspan="3">Taxa composition</th></tr><tr><th>Estimate (±SE)</th><th>(95 % CI)</th><th>PSRF</th><th>Estimate (±SE)</th><th>(95 % CI)</th><th>PSRF</th></tr></thead><tbody><tr><td>Elevation</td><td>0.098 (±0.105)</td><td>(-0.108; 0.300)</td><td>1.006</td><td>Elevation</td><td>-0.713 (±0.130)</td><td>(-0.968; -0.449)</td><td>1.044</td></tr><tr><td>Elevation:MH</td><td>-1.239 (±0.176)</td><td>(-1.617; -0.910)</td><td>1.008</td><td>Elevation:LK</td><td>0.106 (±0.103)</td><td>(-0.312; 0.085)</td><td>1.067</td></tr><tr><td>Elevation:CL</td><td>-1.134 (±0.211)</td><td>(-1.568; -0.709)</td><td>1.022</td><td>Elevation:IS</td><td>0.339 (±0.135)</td><td>(0.078; 0.600)</td><td>1.025</td></tr><tr><td>Elevation:NS</td><td>-0.577 (±0.097)</td><td>(-0.774; -0.396)</td><td>1.011</td><td>Elevation:PS</td><td>0.169 (±0.101)</td><td>(-0.043; 0.366)</td><td>1.014</td></tr><tr><td></td><td></td><td></td><td></td><td>Elevation:RV</td><td>0.311 (±0.097)</td><td>(0.120; 0.504)</td><td>1.041</td></tr><tr><td>Distance</td><td>-0.036 (±0.072)</td><td>(-0.173; 0.105)</td><td>1.006</td><td>Distance</td><td>-0.378 (±0.082)</td><td>(-0.542; -0.222)</td><td>1.005</td></tr><tr><td>Distance:MH</td><td>-0.565 (±0.116)</td><td>(-0.808; -0.340)</td><td>1.018</td><td>Distance:LK</td><td>-0.077 (±0.080)</td><td>(-0.223; 0.092)</td><td>1.010</td></tr><tr><td>Distance:CL</td><td>-0.425 (±0.116)</td><td>(-0.669; -0.221)</td><td>1.006</td><td>Distance:IS</td><td>0.085 (±0.071)</td><td>(-0.049; 0.228)</td><td>1.000</td></tr><tr><td>Distance:NS</td><td>-0.445 (±0.093)</td><td>(-0.627; -0.272)</td><td>1.011</td><td>Distance:PS</td><td>-0.073 (±0.078)</td><td>(-0.225; 0.078)</td><td>1.002</td></tr><tr><td></td><td></td><td></td><td></td><td>Distance:RV</td><td>0.056 (±0.088)</td><td>(-0.131; 0.226)</td><td>1.032</td></tr></tbody></table>
hydrogeomorphic setting (see also, Van Appledorn and Baker, 2023). Specifically, our results revealed that the response of plant communities to the lateral gradient was fairly homogeneous for the Mistassini Highlands and Central Laurentian ecoregions, but sometimes quite different for the Abitibi Lowlands and Lower North-Shore ecoregions. For example, along the topographic gradient, the basal area of softwood trees decreased only on the Lower North-Shore, while the richness of shrubs increased only in the Abitibi Lowlands. This can be attributed to the strong difference in environmental drivers between the westernmost ecoregion with flat landform, dry climate and high fire occurrence, and the easternmost ecoregion with rugged landform, humid climate and low fire occurrence (Couillard et al., 2019). For aquatic habitats, fewer significant interactions were found, and the differences were mainly related to the magnitude of the effect, not its direction. Only the mean value of the wetness indicator increased along the topographical gradient around lakes, whereas it systematically decreased along ponds, streams and rivers. This may be explained by the higher establishment capacities of aquatic species along undisturbed lake shores, compared to riverbanks, as already demonstrated in boreal environments (Nilsson et al., 2002). Overall, these differences in the response of plant communities to the lateral connectivity gradient highlight the fact that insights acquired in one environmental context are in most case difficult to transfer to another ecological context (Van Appledorn and Baker, 2023), and therefore, pointed out the inadequacy of systematically implementing fix-width forested strips in different hydrogeomorphic setting.

Finally, and compared to distance from the waterline, we found some evidence that elevation difference was a better predictor of changes in the structural and compositional attributes of riparian vegetation in the Canadian boreal forests. Indeed, and with the exception of changes in basal area of hardwood trees, all other parameters, including changes in taxa composition, were best explained by models accounting for difference in elevation between plot position and waterline. Furthermore, our results showed that the magnitude of the effect was more pronounced when elevation difference was taken into account, indicating a greater structuring effect on plant communities (see also, Turner et al., 2004; Van Appledorn and Baker, 2023). Although distance and elevation are indirect indicators of the hydrological connectivity gradient (Breton et al., 2023; Johnson et al., 1976; Robertson and Augspurger, 1999), which is known to strongly shape plant community structure (Rolls et al., 2018), these two parameters are not always strongly correlated, as was the case in our study area (i.e., r = 0.39). Thus, within a relatively flat area, elevation changes of the order of a few tens of centimeters have been shown to deeply influence plant communities, either by modifying access to groundwater or exposure to overland flow, or by promoting topographical heterogeneity at fine spatial scale (Douda et al., 2012; Raulings et al., 2010). In the riparian zone of Canada's boreal forest, the delimitation of ecologically relevant forest strips would therefore benefit from being based on topographical parameters incorporating differences in elevation.

## 5. Conclusion and management implications

Although riparian forest strips have been shown to be quite effective in mitigating the transfer of pollutants and sediments from upland to water bodies in agricultural and forest ecosystems (Cole et al., 2020; Lind et al., 2019; Yuan et al., 2009), there is yet no consensus on the widths required to maintain biodiversity and the ecological functions of riparian and aquatic habitats. Indeed, reviews and meta-analyses on the subject (Lind et al., 2019; Marczak et al., 2010; Richardson and Béraud, 2014; Sweeney and Newbold, 2014) have generally shown that the response is profoundly influenced by the type of organism studied, and that riparian forest strips that are too narrow produce the most negative results. In addition, forestry activities such as selective cutting in riparian forest strips and clear-cutting in the surrounding area have been shown to generate interactive or confounding effects on riparian stand properties (Guimond et al., 2024; Mäenpää et al., 2020) and associated

biodiversity (Johansson et al., 2018; Kardynal et al., 2011). As our study was carried out in almost natural boreal forests, we can assume that the effects of anthropogenic disturbances or hydrological alterations are minimized and, therefore, that the patterns observed here reflect the natural range of variability in the boreal forest of eastern Canada.

From this context, our results have three main implications. Firstly, and in accordance with previous studies (Chellaiah and Kuglerová, 2021; Kuglerová et al. 2014a,b), we show the importance of taking regional and site-specific conditions into account to create functional and effective forest strips. This means that national and other large-scale jurisdictional regulations on the width of riparian strips would benefit from being more contextualized, e.g., by favoring wider strips in regions with flat topography, or by using the limits of flood overflow areas at the margins of lotic environments. Secondly, we have highlighted that the riparian zones of all aquatic habitat types, including those adjacent to intermittent streams and small streams in the boreal forest (Guzy et al., 2019; Hagan et al., 2006; Ring et al., 2018), would benefit from the establishment of forest strips with adapted conservation strategies (tree retention, no machinery circulation, etc.). This has particularly important implications, given the lack of consideration given to these types of aquatic habitats in national legislations (Kuglerová et al., 2020), but also the significant disturbance these numerous linear structures suffer as a result of forestry activities (Kuglerová et al., 2021). Thirdly, we have shown that using the sole criterion of distance from an aquatic habitat to delineate riparian zones is not always relevant in the boreal forest of eastern Canada, the only exceptions being flat topographies. Although numerous studies have evaluated the effectiveness of forest strips of varying widths for biodiversity (Chellaiah and Kuglerová, 2021; Darveau et al., 2001; Jyväsjärvi et al., 2020; Tange et al., 2024), with the conclusion that the wider the better (Lind et al., 2019; Oldén et al., 2019; Sweeney and Newbold, 2014), our results point out the importance of elevation in structuring riparian plant communities. Integrating the elevation criterion in addition to the distance criterion can now be done more easily and effectively on a large scale, thanks to the development of LIDAR technology and digital elevation models, which can be used to produce high-resolution topographic maps of forest landscapes (Huylenbroeck et al., 2020). By coupling these maps with field observations and environmental variables related to geology, climate or hydrology (see e.g., Ågren et al., 2021), it should be possible to delineate natural riparian zones boundaries for different ecoregions and aquatic habitats. This information will eventually make it possible to define buffer zones adapted to regional and site-specific conditions, much more in line with the ecosystem management paradigm than fixed-width strips.

## CRediT authorship contribution statement

**Audrey Comtois:** Writing – review & editing, Methodology, Investigation, Data curation, Conceptualization. **Philippe Janssen:** Writing – original draft, Formal analysis, Data curation. **Pothier David:** Writing – review & editing, Validation, Supervision, Project administration, Methodology, Funding acquisition, Conceptualization. **Bérenger Bourgeois:** Writing – review & editing, Visualization, Validation. **Marcel Darveau:** Writing – review & editing, Validation, Supervision, Funding acquisition, Conceptualization. **Monique Poulin:** Writing – review & editing, Validation, Supervision, Project administration, Funding acquisition, Conceptualization.

## Declaration of Competing Interest

The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Marcel Darveau reports financial support was provided by Ducks Unlimited Canada. Monique Poulin reports financial support was provided by Ministry of Natural Resources. Monique Poulin reports financial support was provided by Ministry of Sustainable Development,
Environment, wildlife and Parks. Monique Poulin reports financial support was provided by Hydro-Québec. Monique Poulin reports financial support was provided by Quebec Research Fund Nature and Technology. Audrey Comtois reports financial support was provided by Natural Sciences and Engineering Research Council of Canada. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

## Acknowledgment

The authors would like to thank Catherine Landry for initiating the project and for her involvement between 2006 and 2007. We also thanks Maxime Beaudoin, Stéphanie Murray, Marie-Élise Samson and Stéphanie Lefebvre-Ruel for their help during the field work and Mathieu Vaillancourt for his support in producing Fig. 1. We would like to thank our major project partners for their financial and technical support: Ducks Unlimited Canada; The Ministry of Natural Resources; The Ministry of Sustainable Development, Environment, wildlife and Parks; Hydro-Québec; Quebec Research Funds for Nature and Technology (FRONT); and The Natural Sciences and Engineering Research Council of Canada (NSERC; BMP grants to AC).

## Appendix A. Supporting information

Supplementary data associated with this article can be found in the online version at doi:10.1016/j.foreco.2025.123044.

## Data availability

The data are accessible via the Canadian dataverse repository boralis, https://borealisdata.ca/dataset.xhtml?persistentId=doi:10.5683/SP3/PMPQU8

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