# Riparian habitat connectivity restoration in an anthropized landscape: A multi-species approach based on landscape graph and soil bioengineering structures

Etienne Boncourt, Laurent Bergès, Maria Alp, Blandine Dupont, Timothée Herviault, André Evette

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## ► To cite this version:

Etienne Boncourt, Laurent Bergès, Maria Alp, Blandine Dupont, Timothée Herviault, et al. | Riparian habitat connectivity restoration in an anthropized landscape: A multi-species approach based on landscape graph and soil bioengineering structures. Environmental Management, 2024, pp.1-42. 10.1007/s00267-024-01959-5 | hal-04599263

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Submitted on 3 Jun 2024

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# Riparian habitat connectivity restoration in an anthropized landscape: A multi-species approach based on landscape graph and soil bioengineering structures

Etienne Boncourt, Laurent Bergès, Maria Alp, Blandine Dupont, Timothée Herviault, André Evette

► To cite this version:

Etienne Boncourt, Laurent Bergès, Maria Alp, Blandine Dupont, Timothée Herviault, et al. Riparian habitat connectivity restoration in an anthropized landscape: A multi-species approach based on landscape graph and soil bioengineering structures. Environmental Management, 2024, pp.1-42. 10.1007/s00267-024-01959-5. hal-04599263

HAL Id: hal-04599263

https://hal.science/hal-04599263v1

Submitted on 3 Jun 2024

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
1

Riparian habitat connectivity restoration in an anthropized landscape: a multi-species approach

2

based on landscape graph and soil bioengineering structures

3

4

Statements and Declarations:

5

This study was funded by the Agence de l'eau Rhône Méditerranée Corse (French water agency). The

6

authors declare no competing interests that could affect the integrity of this study.

7

Authors (names, first names):

8

Boncourt, Etienne¹

9

Bergès, Laurent¹

10

Alp, Maria²

11

Dupont, Blandine¹

12

Herviault, Timothée¹

13

Evette, André¹

14

Affiliation (of all authors):

15

1 Univ. Grenoble Alpes, INRAE, LESSEM, F-38402 St-Martin-d'Hères, FR

16

2 RiverLy, INRAE, F-69625 Villeurbanne, France

17

Corresponding author:

18

Etienne Boncourt etienne.boncourt@gmail.com

19

Orcid:

20

EB: 0000-0001-8233-0190

21

LB: 0000-0003-0408-7900
1

Riparian habitat connectivity restoration in an anthropized landscape: a multi-species approach

2

based on landscape graph and soil bioengineering structures

3

4

Statements and Declarations:

5

This study was funded by the Agence de l'eau Rhône Méditerranée Corse (French water agency). The

6

authors declare no competing interests that could affect the integrity of this study.

7

Authors (names, first names):

8

Boncourt, Etienne¹

9

Bergès, Laurent¹

10

Alp, Maria²

11

Dupont, Blandine¹

12

Herviault, Timothée¹

13

Evette, André¹

14

Affiliation (of all authors):

15

1 Univ. Grenoble Alpes, INRAE, LESSEM, F-38402 St-Martin-d'Hères, FR

16

2 RiverLy, INRAE, F-69625 Villeurbanne, France

17

Corresponding author:

18

Etienne Boncourt etienne.boncourt@gmail.com

19

Orcid:

20

EB: 0000-0001-8233-0190

21

LB: 0000-0003-0408-7900
22 MA: 0000-0003-4295-6361

23 AE: 0000-0002-0927-0037

24 Total number of words (captions and tables included, but not abstract and reference list):

25 8316

26 Total number of words (abstract):

27 259
22 MA: 0000-0003-4295-6361

23 AE: 0000-0002-0927-0037

24 Total number of words (captions and tables included, but not abstract and reference list):

25 8316

26 Total number of words (abstract):

27 259
28 **Abstract**

29 In urbanized areas, rivers and riparian ecosystems are often the only ecological corridors available for
30 wildlife movement. There, riverbanks are often protected by civil engineering structures (dykes,
31 riprap). This can lead to habitat degradation and loss of landscape connectivity. Fascines (willow
32 bundles tied together) could be an alternative to riprap, since they maintain the quality of the natural
33 ecosystems by using native vegetal species instead of rocks, but their potential positive impact needs
34 to be assessed.

35 We proposed a landscape-scale decision-making method for river managers who want to restore banks
36 by transforming riprap into fascines to improve landscape connectivity. We applied our methodology
37 to a case study involving a 25km-stretch of the Arve river, France. We selected four target vertebrate
38 species based on biological traits to cover a wide range of dispersal capacities. For each species, we
39 used landscape graphs to assess habitat connectivity under different contrasted riverbank scenarios.
40 Scenarios included replacing all natural banks with ripraps or replacing all ripraps with fascines. In
41 addition, we systematically tested the effect of replacing individual 100 or 500m sections of ripraps by
42 fascines, to locate where riverbank restoration would maximize connectivity gain.

43 The four species selected responded very differently to the scenarios (up to +14% and +46% change in
44 Probability of Connectivity for common toads and Eurasian beavers, respectively, 0% for common
45 sandpipers and barred grass snakes). The restoration of specific riverbank sections could result in
46 important gains in PC (up to +33% for one single section for one species) but no section maximized
47 connectivity gain for all the target species.

48

49 **Keywords**

50 Ecological connectivity restoration, graph theory, riverbank, riverine landscape management,
51 vertebrates
28 **Abstract**

29 In urbanized areas, rivers and riparian ecosystems are often the only ecological corridors available for
30 wildlife movement. There, riverbanks are often protected by civil engineering structures (dykes,
31 riprap). This can lead to habitat degradation and loss of landscape connectivity. Fascines (willow
32 bundles tied together) could be an alternative to riprap, since they maintain the quality of the natural
33 ecosystems by using native vegetal species instead of rocks, but their potential positive impact needs
34 to be assessed.

35 We proposed a landscape-scale decision-making method for river managers who want to restore banks
36 by transforming riprap into fascines to improve landscape connectivity. We applied our methodology
37 to a case study involving a 25km-stretch of the Arve river, France. We selected four target vertebrate
38 species based on biological traits to cover a wide range of dispersal capacities. For each species, we
39 used landscape graphs to assess habitat connectivity under different contrasted riverbank scenarios.
40 Scenarios included replacing all natural banks with ripraps or replacing all ripraps with fascines. In
41 addition, we systematically tested the effect of replacing individual 100 or 500m sections of ripraps by
42 fascines, to locate where riverbank restoration would maximize connectivity gain.

43 The four species selected responded very differently to the scenarios (up to +14% and +46% change in
44 Probability of Connectivity for common toads and Eurasian beavers, respectively, 0% for common
45 sandpipers and barred grass snakes). The restoration of specific riverbank sections could result in
46 important gains in PC (up to +33% for one single section for one species) but no section maximized
47 connectivity gain for all the target species.

48

49 **Keywords**

50 Ecological connectivity restoration, graph theory, riverbank, riverine landscape management,
51 vertebrates
## 52 Introduction

Riparian habitats are transition areas, or ecotones, at the interface between terrestrial and aquatic habitats. They offer habitat to a wide variety of animal species, whether aquatic (e.g. for spawning and nursery of juvenile fish (Mouton et al., 2012)), terrestrial (such as forest species), or dependent on these specific environments (e.g. beaver). Riparian habitats are also crucial for connectivity across these different types of environments for amphibious species, whose life cycles require both terrestrial and aquatic habitats (Wells, 2007). These habitats are therefore of disproportionate ecological importance in relation to their size as they are often only a few dozen metres wide (Sabo et al., 2005; González et al., 2017; Hunter, 2017). The specific linear nature of rivers makes them potential biological corridors, particularly in human-modified landscapes (Naiman et al., 1993; Erós et al., 2011; de la Fuente et al., 2018). Biological corridors are areas that allow organisms to move through a landscape. In particular, they enable gene flow, which is of great importance for biodiversity conservation. Their conservation is often integrated into public environmental policies (Rosenberg et al., 1997; Van Der Windt & Swart, 2007).

Under natural conditions, rivers and riparian zones are highly dynamic systems changing their position and extensions in the landscape following hydrogeomorphological processes at various temporal scales (Tabacchi et al., 1990; Benda et al., 2004). Riverbank erosion along rivers is one of these natural processes and is necessary to create new pioneer habitat and feed the river's sediment transport flow (Florsheim et al 2008). To limit this erosion in urban areas, riverbanks are often stabilized using bank stabilization structures like dikes, riprap (civil engineering structures made of rocks or concrete) or other civil engineering works.

Therefore, riverbeds and riparian habitats are subject to numerous pressures linked to anthropization, in particular through changes in hydrological regime, installation of dams, and channelization of watercourses. Damming and channelization can lead to a loss of habitat and functional connectivity – i.e. the way the landscape structure affects ecological processes like gene flow across landscapes
## 52 Introduction

Riparian habitats are transition areas, or ecotones, at the interface between terrestrial and aquatic habitats. They offer habitat to a wide variety of animal species, whether aquatic (e.g. for spawning and nursery of juvenile fish (Mouton et al., 2012)), terrestrial (such as forest species), or dependent on these specific environments (e.g. beaver). Riparian habitats are also crucial for connectivity across these different types of environments for amphibious species, whose life cycles require both terrestrial and aquatic habitats (Wells, 2007). These habitats are therefore of disproportionate ecological importance in relation to their size as they are often only a few dozen metres wide (Sabo et al., 2005; González et al., 2017; Hunter, 2017). The specific linear nature of rivers makes them potential biological corridors, particularly in human-modified landscapes (Naiman et al., 1993; Erós et al., 2011; de la Fuente et al., 2018). Biological corridors are areas that allow organisms to move through a landscape. In particular, they enable gene flow, which is of great importance for biodiversity conservation. Their conservation is often integrated into public environmental policies (Rosenberg et al., 1997; Van Der Windt & Swart, 2007).

Under natural conditions, rivers and riparian zones are highly dynamic systems changing their position and extensions in the landscape following hydrogeomorphological processes at various temporal scales (Tabacchi et al., 1990; Benda et al., 2004). Riverbank erosion along rivers is one of these natural processes and is necessary to create new pioneer habitat and feed the river's sediment transport flow (Florsheim et al 2008). To limit this erosion in urban areas, riverbanks are often stabilized using bank stabilization structures like dikes, riprap (civil engineering structures made of rocks or concrete) or other civil engineering works.

Therefore, riverbeds and riparian habitats are subject to numerous pressures linked to anthropization, in particular through changes in hydrological regime, installation of dams, and channelization of watercourses. Damming and channelization can lead to a loss of habitat and functional connectivity – i.e. the way the landscape structure affects ecological processes like gene flow across landscapes
77 (Wainwright et al., 2011; Baguette et al., 2013). This has been observed in many species such as aquatic macroinvertebrates and fish (Smith et al. 2009; Van Looy et al., 2014), but also in riparian plants (Merritt & Wohl, 2006) and mammals, such as otters (Leoncini et al., 2023), whose dispersal is impeded by dams. River embankments with civil engineering structures are usually linked to a loss and/or degradation of natural riparian habitats as they consist in replacing native woody and herbaceous riparian plants to bare ground or rock (Ward & Stanford, 1995; Van Looy et al., 2003; Dudgeon et al., 2006). They also lead to a loss of lateral connectivity by inducing differences between upstream and downstream plant communities (Van Looy et al., 2003; Martin et al., 2021). Loss of connectivity is one of the main causes of biodiversity loss (Haddad et al., 2015; Thomson et al., 2017; Pardini, 2018).

86 Soil water bioengineering (SWBE) bank protection structures are an alternative to 'hard' civil engineering structures such as riprap. SWBE structures make it possible to maintain habitats with higher quality and functional diversity (Cavaillé et al., 2015; Janssen et al., 2019), and suitable for more terrestrial and aquatic species (Cavaillé et al., 2013, 2018). For instance, fascine toes (small-diameter willow bundles tied together to form a solid structure) show a higher vegetation cover, a higher abundance of plants, a higher species richness and a lower abundance of invasive alien species than riprap (Cavaillé et al., 2013; Martin et al., 2021). Soil water bioengineering structures can help protect riverbank, while restoring habitats (Rey et al., 2019) and improving habitat connectivity (Martin et al. 2021). In general, restoring riparian habitats can greatly improve landscape connectivity (Rojas et al., 2020). River managers may therefore wish to restore riparian habitats by replacing civil engineering structures with SWBE structures. However, in highly urbanized areas, hosting a large number of civil engineering bank protection structures, it is unrealistic to replace all of them simply because of financial and technical constraints.

99 Therefore, efforts to identify where restoration would improve habitat connectivity the most can facilitate implementation of SWBE structure. To maximize the positive impact of new restoration projects on landscape connectivity, we need to identify key areas for the dispersal of organisms.
77 (Wainwright et al., 2011; Baguette et al., 2013). This has been observed in many species such as aquatic macroinvertebrates and fish (Smith et al 2009; Van Looy et al., 2014), but also in riparian plants (Merritt & Wohl, 2006) and mammals, such as otters (Leoncini et al., 2023), whose dispersal is impeded by dams. River embankments with civil engineering structures are usually linked to a loss and/or degradation of natural riparian habitats as they consist in replacing native woody and herbaceous riparian plants to bare ground or rock (Ward & Stanford, 1995; Van Looy et al., 2003; Dudgeon et al., 2006). They also lead to a loss of lateral connectivity by inducing differences between upstream and downstream plant communities (Van Looy et al., 2003; Martin et al., 2021). Loss of connectivity is one of the main causes of biodiversity loss (Haddad et al., 2015; Thomson et al., 2017; Pardini, 2018).

86 Soil water bioengineering (SWBE) bank protection structures are an alternative to 'hard' civil engineering structures such as riprap. SWBE structures make it possible to maintain habitats with higher quality and functional diversity (Cavaillé et al., 2015; Janssen et al., 2019), and suitable for more terrestrial and aquatic species (Cavaillé et al., 2013, 2018). For instance, fascine toes (small-diameter willow bundles tied together to form a solid structure) show a higher vegetation cover, a higher abundance of plants, a higher species richness and a lower abundance of invasive alien species than riprap (Cavaillé et al., 2013; Martin et al., 2021). Soil water bioengineering structures can help protect riverbank, while restoring habitats (Rey et al., 2019) and improving habitat connectivity (Martin et al 2021). In general, restoring riparian habitats can greatly improve landscape connectivity (Rojas et al., 2020). River managers may therefore wish to restore riparian habitats by replacing civil engineering structures with SWBE structures. However, in highly urbanized areas, hosting a large number of civil engineering bank protection structures, it is unrealistic to replace all of them simply because of financial and technical constraints.

99 Therefore, efforts to identify where restoration would improve habitat connectivity the most can facilitate implementation of SWBE structure. To maximize the positive impact of new restoration projects on landscape connectivity, we need to identify key areas for the dispersal of organisms.
102 Restoration ecology and landscape ecology have provided a wealth of literature on how to prioritize
103 areas for restoration to maximize landscape connectivity (e.g. Hodgson et al., 2011; Tarabon et al.,
104 2019; Bergès et al. 2020). These studies focus in particular on habitat patch creation (Hodgson et al.,
105 2011; Clauzel et al., 2015) or the detection of barriers to movement to remove in priority (McRae et
106 al., 2012; Roy & Le Pichon 2017; Kraft et al., 2019). How to optimize the location of the terrestrial
107 habitats to be restored has been extensively addressed in the literature, but not specifically for riparian
108 habitats. The applied techniques range from least-cost distance calculations based on estimated
109 resistance surfaces (McRae et al., 2012 ; Roy & Le Pichon 2017) to more complex spatially explicit
110 models, e.g. ecological network modeling based on landscape graphs (Clauzel et al., 2015; Avon &
111 Bergès, 2016; Kraft et al., 2019; Clauzel & Godet, 2020). Landscape graphs are models derived from
112 graph theory, a mathematical discipline dedicated to the study of networks. Graph theory applied to
113 landscape ecology borrows some of its concepts from the theory of island metapopulation models. In
114 this conceptual framework, target organisms frequent patches of adapted habitats located in a matrix
115 of non-adapted habitats (Urban et al. 2001). The ease with which organisms can move through the
116 matrix from one habitat patch to another depends on their movement capabilities and the
117 characteristics of the elements composing the matrix. Resistance surfaces are used to model such
118 movements of animals between habitat patches across heterogeneous landscapes. Resistance
119 surfaces are raster layers in which all the elements of the landscape are assigned values indicating their
120 propensity to movement of target organisms. Even if resistance surfaces are largely used for
121 connectivity analyses (Zeller et al., 2012), there is no consensus on how to parameterize them.
122 Resistance values can ideally be derived from empirical data collected in the study area, such as genetic
123 data (landscape genetics), movement data (telemetry), species habitat model (Duflot et al., 2018;
124 Lalechère & Bergès, 2021) or from a combination of data from the literature and expert opinion (Dutta
125 et al., 2022). The use of landscape graphs and resistance surfaces in landscape ecology makes it
126 possible to calculate specific metrics for quantifying the connectivity of habitats in a spatial network.
127 In particular, the concept of amount of reachable habitat (ARH) focuses on habitat accessibility,
102 Restoration ecology and landscape ecology have provided a wealth of literature on how to prioritize
103 areas for restoration to maximize landscape connectivity (e.g. Hodgson et al., 2011; Tarabon et al.,
104 2019; Bergès et al. 2020). These studies focus in particular on habitat patch creation (Hodgson et al.,
105 2011; Clauzel et al., 2015) or the detection of barriers to movement to remove in priority (McRae et
106 al., 2012; Roy & Le Pichon 2017; Kraft et al., 2019). How to optimize the location of the terrestrial
107 habitats to be restored has been extensively addressed in the literature, but not specifically for riparian
108 habitats. The applied techniques range from least-cost distance calculations based on estimated
109 resistance surfaces (McRae et al., 2012 ; Roy & Le Pichon 2017) to more complex spatially explicit
110 models, e.g. ecological network modeling based on landscape graphs (Clauzel et al., 2015; Avon &
111 Bergès, 2016; Kraft et al., 2019; Clauzel & Godet, 2020). Landscape graphs are models derived from
112 graph theory, a mathematical discipline dedicated to the study of networks. Graph theory applied to
113 landscape ecology borrows some of its concepts from the theory of island metapopulation models. In
114 this conceptual framework, target organisms frequent patches of adapted habitats located in a matrix
115 of non-adapted habitats (Urban et al. 2001). The ease with which organisms can move through the
116 matrix from one habitat patch to another depends on their movement capabilities and the
117 characteristics of the elements composing the matrix. Resistance surfaces are used to model such
118 movements of animals between habitat patches across heterogeneous landscapes. Resistance
119 surfaces are raster layers in which all the elements of the landscape are assigned values indicating their
120 propensity to movement of target organisms. Even if resistance surfaces are largely used for
121 connectivity analyses (Zeller et al., 2012), there is no consensus on how to parameterize them.
122 Resistance values can ideally be derived from empirical data collected in the study area, such as genetic
123 data (landscape genetics), movement data (telemetry), species habitat model (Duflot et al., 2018;
124 Lalechère & Bergès, 2021) or from a combination of data from the literature and expert opinion (Dutta
125 et al., 2022). The use of landscape graphs and resistance surfaces in landscape ecology makes it
126 possible to calculate specific metrics for quantifying the connectivity of habitats in a spatial network.
127 In particular, the concept of amount of reachable habitat (ARH) focuses on habitat accessibility,
128

including the surface and quality of the habitat patches themselves and their degree of habitat connectivity (Saura et al., 2011). This ARH can be assessed in the form of a single metric (Saura and Rubio, 2010).

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Connectivity has to be assessed taxon by taxon as species differ in their response to habitat availability and configuration (Vos et al., 2001) and it is rarely possible to carry out detailed analyses for every species. How to select target species to best quantify multi-species landscape connectivity is still under debate in the literature (e.g. Albert et al., 2017; Meurant et al., 2018; Dutta et al., 2023). Meurant et al. (2018) showed that a small number of surrogate species covering different biological traits related to dispersal might adequately represent the needs of the full list of species found in a given habitat type, and provide more informative results than simply maximizing taxonomic diversity of surrogate species. Working with surrogate species is a good way to reconcile conservation objectives with stakeholder constraints and field data availability (Opdam et al., 2008; Wiens et al., 2008).

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In this study, we proposed a method for prioritizing sections of the riparian habitat for restoration to maximize gains in habitat connectivity restoration to a peri-urban river located in the South East of France. First, based on a multivariate analysis of species traits, we selected several surrogate species, which allowed us to characterize the ability of species to reach available habitats within the riparian ecosystem. For each surrogate species, we used landscape graphs to quantify the importance of riparian habitats to total connectivity. Then, we assessed the contribution of existing and potential SWBE structures, using scenarios. Scenarios were chosen to assess the maximum potential gain in connectivity by replacing all riprap with SWBE structures, or to assess the contribution of existing bank protection structures to connectivity. Finally, in order to prioritize restoration areas, we systematically tested the ability of each potentially restorable bank section along the river to improve habitat connectivity for our surrogate species.

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We hypothesize that, as each species has different needs and dispersal capacities, the sections that maximize habitat connectivity will differ from one taxon to another. However, the aim of the study is

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including the surface and quality of the habitat patches themselves and their degree of habitat connectivity (Saura et al., 2011). This ARH can be assessed in the form of a single metric (Saura and Rubio, 2010).

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Connectivity has to be assessed taxon by taxon as species differ in their response to habitat availability and configuration (Vos et al., 2001) and it is rarely possible to carry out detailed analyses for every species. How to select target species to best quantify multi-species landscape connectivity is still under debate in the literature (e.g. Albert et al., 2017; Meurant et al., 2018; Dutta et al., 2023). Meurant et al. (2018) showed that a small number of surrogate species covering different biological traits related to dispersal might adequately represent the needs of the full list of species found in a given habitat type, and provide more informative results than simply maximizing taxonomic diversity of surrogate species. Working with surrogate species is a good way to reconcile conservation objectives with stakeholder constraints and field data availability (Opdam et al., 2008; Wiens et al., 2008).

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In this study, we proposed a method for prioritizing sections of the riparian habitat for restoration to maximize gains in habitat connectivity restoration to a peri-urban river located in the South East of France. First, based on a multivariate analysis of species traits, we selected several surrogate species, which allowed us to characterize the ability of species to reach available habitats within the riparian ecosystem. For each surrogate species, we used landscape graphs to quantify the importance of riparian habitats to total connectivity. Then, we assessed the contribution of existing and potential SWBE structures, using scenarios. Scenarios were chosen to assess the maximum potential gain in connectivity by replacing all riprap with SWBE structures, or to assess the contribution of existing bank protection structures to connectivity. Finally, in order to prioritize restoration areas, we systematically tested the ability of each potentially restorable bank section along the river to improve habitat connectivity for our surrogate species.

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We hypothesize that, as each species has different needs and dispersal capacities, the sections that maximize habitat connectivity will differ from one taxon to another. However, the aim of the study is

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153 to identify optimal sections that will enable a trade-off to be found to increase habitat connectivity for
154 as many species as possible.

155 **Materials and methods**

156 *Study area*

157 The study area is located in the Haute-Savoie department, France. The section of interest is a 25 km
158 long stretch of the Arve river between the towns of Magland (upstream) and Bonneville (downstream).
159 As the spatial analysis methods used require the use of raster layers, the study area considered is a
160 157 km² rectangle encompassing the 25 km stretch of river studied (Fig. 1). The Arve has a mean annual
discharge of 27.4 m³/s (measured at the hydrological station of Sallanches). The elevation in the study
161 site ranges between 440m (Bonneville) and 500m (Magland). It is mainly (54% of the linear) located in
162 an urbanized area (Fig. 1). It is characterized by number of bank protections along the river corridor
to control erosion. These protections consist of riprap (46.1% of the total length, see sample photo in
163 the supplementary material), mixed technique (riprap toe with vegetation on the upper part of the
164 bank, 2.2%) or soil water bioengineering structures (live toe willow fascines, 0.5%, see sample photo
165 in the supplementary material). Natural banks represent 51.2% of the total length. The detailed
166 cartography and typology of the bank protections were provided by the river manager (Syndicat mixte
d'aménagement de l'Arve et de ses affluents hereafter named SM3A) and supplemented by a field
167 observation campaign along the entire length to verify the information and assess the vegetation
168 height.
169

171
153 to identify optimal sections that will enable a trade-off to be found to increase habitat connectivity for
154 as many species as possible.

155 **Materials and methods**

156 *Study area*

157 The study area is located in the Haute-Savoie department, France. The section of interest is a 25 km
158 long stretch of the Arve river between the towns of Magland (upstream) and Bonneville (downstream).
159 As the spatial analysis methods used require the use of raster layers, the study area considered is a
160 157 km² rectangle encompassing the 25 km stretch of river studied (Fig. 1). The Arve has a mean annual
discharge of 27.4 m³/s (measured at the hydrological station of Sallanches). The elevation in the study
161 site ranges between 440m (Bonneville) and 500m (Magland). It is mainly (54% of the linear) located in
162 an urbanized area (Fig. 1). It is characterized by number of bank protections along the river corridor
to control erosion. These protections consist of riprap (46.1% of the total length, see sample photo in
163 the supplementary material), mixed technique (riprap toe with vegetation on the upper part of the
164 bank, 2.2%) or soil water bioengineering structures (live toe willow fascines, 0.5%, see sample photo
165 in the supplementary material). Natural banks represent 51.2% of the total length. The detailed
166 cartography and typology of the bank protections were provided by the river manager (Syndicat mixte
d'aménagement de l'Arve et de ses affluents hereafter named SM3A) and supplemented by a field
167 observation campaign along the entire length to verify the information and assess the vegetation
168 height.
169
<figure><img src="image_3.png" /></figure>

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Fig. 1 Land cover map of the study area. Data from France OSO soil mapping (Inglada et al., 2017). The names of the main towns along the river are shown in white boxes.

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### Selection of surrogate species

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Species differ in their response to landscape configuration, i.e. the size and position of habitat patches and the composition of the landscape matrix (Vos et al., 2001). In order to avoid performing a connectivity analysis for all the species surveyed in the area, we developed a method to select a few surrogate species that cover a diversity of biological traits.

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First, we listed the 54 vertebrate species present in our study area by compiling georeferenced data from inventories carried out by the SM3A, the natural heritage information system SINP (INPN, 2023) as well as the data present in the inventories of the zone of floristic and faunistic ecological interest (INPN, 2023b). As these three sources were not exhaustive in terms of invertebrate inventories, we limited our analysis to the vertebrates found in the area. The use of vertebrates as umbrella species in conservation studies is relevant and very common (Yang et al., 2023). The detailed species list is provided in the Appendix 1 in the supplementary material. Then, for mammals, reptiles and
<figure><img src="image_2.png" /></figure>

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Fig. 1 Land cover map of the study area. Data from France OSO soil mapping (Inglada et al., 2017). The names of the main towns along the river are shown in white boxes.

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### Selection of surrogate species

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Species differ in their response to landscape configuration, i.e. the size and position of habitat patches and the composition of the landscape matrix (Vos et al., 2001). In order to avoid performing a connectivity analysis for all the species surveyed in the area, we developed a method to select a few surrogate species that cover a diversity of biological traits.

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First, we listed the 54 vertebrate species present in our study area by compiling georeferenced data from inventories carried out by the SM3A, the natural heritage information system SINP (INPN, 2023) as well as the data present in the inventories of the zone of floristic and faunistic ecological interest (INPN, 2023b). As these three sources were not exhaustive in terms of invertebrate inventories, we limited our analysis to the vertebrates found in the area. The use of vertebrates as umbrella species in conservation studies is relevant and very common (Yang et al., 2023). The detailed species list is provided in the Appendix 1 in the supplementary material. Then, for mammals, reptiles and
amphibians, we selected only species using watercourses as habitats according to IUCN (environment 188 5.1 in IUCN nomenclature, see IUCN (2022) for detailed information). For birds, we only selected 189 species whose main breeding habitats are moving riverbeds, riparian habitats and torrents, according 190 to the habitat preference classification of Roché et al. (2016), and removed generalist species. Our final 191 list encompassed 18 species that depend on riparian habitats: five amphibians, 10 birds, two mammals 192 and one reptile (Table 1). 193

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Table 1. The 18 candidate species surveyed in the area after retaining only those strictly associated 195 with watercourses. The four variables (biological traits) used to perform the factor analysis are 196 indicated, as is the cluster resulting from the hierarchical clustering. Id = unique identifier used for each 197 species. The paragon species selected for each cluster are in bold. 198

<table><thead><tr><th>Id</th><th>Group</th><th>Scientific name</th><th>Common name</th><th>Maximum dispersion distance</th><th>Maximum longevity (year)</th><th>Fecundity per year</th><th>Main dispersal mode</th><th>Cluster</th></tr></thead><tbody><tr><td>1</td><td>Reptiles</td><td>Natrix helvetica (Lacepède, 1789)<br>Actitis hypoleucos (Linnaeus, 1758)</td><td>Barred grass snake</td><td>100m-1km</td><td>20</td><td>12</td><td>Terrestrial and aquatic</td><td>2</td></tr><tr><td>2</td><td>Birds</td><td>Alcedo atthis (Linnaeus, 1758)<br>Ardea cinerea Linnaeus, 1758<br>Ardeola ralloides (Scopoli, 1769)</td><td>Common sandpiper</td><td>+10km</td><td>14.5</td><td>4</td><td>Aerial</td><td>1</td></tr><tr><td>3</td><td>Birds</td><td>Cettia cetti (Temminck, 1820)<br>Charadrius dubius Scopoli, 1786<br>Mergus merganser Linnaeus, 1758<br>Motacilla cinerea Tunstall, 1771</td><td>Common kingfisher</td><td>+10km</td><td>21</td><td>9</td><td>Aerial</td><td>1</td></tr><tr><td>4</td><td>Birds</td><td></td><td>Grey heron</td><td>+10km</td><td>37.5</td><td>4</td><td>Aerial</td><td>1</td></tr><tr><td>5</td><td>Birds</td><td></td><td>Squacco heron</td><td>+10km</td><td>5.8</td><td>3</td><td>Aerial</td><td>1</td></tr><tr><td>6</td><td>Birds</td><td></td><td>Cetti's warbler</td><td>+10km</td><td>7,6</td><td>6</td><td>Aerial</td><td>1</td></tr><tr><td>7</td><td>Birds</td><td></td><td>Little ringed plover</td><td>+10km</td><td>13</td><td>7</td><td>Aerial</td><td>1</td></tr><tr><td>8</td><td>Birds</td><td></td><td>Common merganser</td><td>+10km</td><td>14.8</td><td>8</td><td>Aerial</td><td>1</td></tr><tr><td>9</td><td>Birds</td><td></td><td>Grey wagtail</td><td>+10km</td><td>8</td><td>9</td><td>Aerial</td><td>1</td></tr></tbody></table>
amphibians, we selected only species using watercourses as habitats according to IUCN (environment 188 5.1 in IUCN nomenclature, see IUCN (2022) for detailed information). For birds, we only selected 189 species whose main breeding habitats are moving riverbeds, riparian habitats and torrents, according 190 to the habitat preference classification of Roché et al. (2016), and removed generalist species. Our final 191 list encompassed 18 species that depend on riparian habitats: five amphibians, 10 birds, two mammals 192 and one reptile (Table 1). 193

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Table 1. The 18 candidate species surveyed in the area after retaining only those strictly associated 195 with watercourses. The four variables (biological traits) used to perform the factor analysis are 196 indicated, as is the cluster resulting from the hierarchical clustering. Id = unique identifier used for each 197 species. The paragon species selected for each cluster are in bold. 198

<table><thead><tr><th>Id</th><th>Group</th><th>Scientific name</th><th>Common name</th><th>Maximum dispersion distance</th><th>Maximum longevity (year)</th><th>Fecundity per year</th><th>Main dispersal mode</th><th>Cluster</th></tr></thead><tbody><tr><td>1</td><td>Reptiles</td><td>Natrix helvetica (Lacepède, 1789)<br>Actitis hypoleucos (Linnaeus, 1758)</td><td>Barred grass snake</td><td>100m-1km</td><td>20</td><td>12</td><td>Terrestrial and aquatic</td><td>2</td></tr><tr><td>2</td><td>Birds</td><td>Alcedo atthis (Linnaeus, 1758)<br>Ardea cinerea Linnaeus, 1758<br>Ardeola ralloides (Scopoli, 1769)</td><td>Common sandpiper</td><td>+10km</td><td>14.5</td><td>4</td><td>Aerial</td><td>1</td></tr><tr><td>3</td><td>Birds</td><td>Cettia cetti (Temminck, 1820)<br>Charadrius dubius Scopoli, 1786<br>Mergus merganser Linnaeus, 1758<br>Motacilla cinerea Tunstall, 1771</td><td>Common kingfisher</td><td>+10km</td><td>21</td><td>9</td><td>Aerial</td><td>1</td></tr><tr><td>4</td><td>Birds</td><td></td><td>Grey heron</td><td>+10km</td><td>37.5</td><td>4</td><td>Aerial</td><td>1</td></tr><tr><td>5</td><td>Birds</td><td></td><td>Squacco heron</td><td>+10km</td><td>5.8</td><td>3</td><td>Aerial</td><td>1</td></tr><tr><td>6</td><td>Birds</td><td></td><td>Cetti's warbler</td><td>+10km</td><td>7,6</td><td>6</td><td>Aerial</td><td>1</td></tr><tr><td>7</td><td>Birds</td><td></td><td>Little ringed plover</td><td>+10km</td><td>13</td><td>7</td><td>Aerial</td><td>1</td></tr><tr><td>8</td><td>Birds</td><td></td><td>Common merganser</td><td>+10km</td><td>14.8</td><td>8</td><td>Aerial</td><td>1</td></tr><tr><td>9</td><td>Birds</td><td></td><td>Grey wagtail</td><td>+10km</td><td>8</td><td>9</td><td>Aerial</td><td>1</td></tr></tbody></table>
<table><tr><td>10</td><td>Birds</td><td>Nycticorax<br>nycticorax<br>(Linnaeus,<br>1758)</td><td>Night heron</td><td>+10km</td><td>21.1</td><td>4</td><td>Aerial</td><td>1</td></tr><tr><td>11</td><td>Birds</td><td>Riparia<br>riaria<br>(Linnaeus,<br>1758)</td><td>Sand martin</td><td>+10km</td><td>10</td><td>6</td><td>Aerial</td><td>1</td></tr><tr><td>12</td><td>Mammals</td><td>Castor fiber<br>Linnaeus,<br>1758</td><td>Eurasian beaver</td><td>+10km</td><td>35</td><td>3</td><td>Terrestrial and<br>aquatic</td><td>3</td></tr><tr><td>13</td><td>Mammals</td><td>Lutra lutra<br>(Linnaeus,<br>1758)</td><td>Eurasian otter</td><td>+10km</td><td>22</td><td>2</td><td>Terrestrial and<br>aquatic</td><td>3</td></tr><tr><td>14</td><td>Amphibians</td><td>Alytes<br>obstetricans<br>(Laurenti,<br>1768)</td><td>Common<br>midwife toad</td><td>100m-1km</td><td>7</td><td>45</td><td>Terrestrial and<br>aquatic</td><td>2</td></tr><tr><td>15</td><td>Amphibians</td><td>Bombina<br>variegata<br>(Linnaeus,<br>1758)</td><td>Yellow-bellied<br>toad</td><td>1-10km</td><td>29</td><td>161</td><td>Terrestrial and<br>aquatic</td><td>3</td></tr><tr><td>16</td><td>Amphibians</td><td>Bufo bufo<br>(Linnaeus,<br>1758)</td><td>Common toad</td><td>1-10km</td><td>40</td><td>9000</td><td>Terrestrial</td><td>4</td></tr><tr><td>17</td><td>Amphibians</td><td>Pelophylax<br>ridibundus<br>(Pallas, 1771)</td><td>Marsh frog</td><td>1-10km</td><td>12</td><td>12000</td><td>Terrestrial</td><td>4</td></tr><tr><td>18</td><td>Amphibians</td><td>Rana<br>temporaria<br>Linnaeus,<br>1758</td><td>Common frog</td><td>1-10km</td><td>27</td><td>3556</td><td>Terrestrial and<br>aquatic</td><td>3</td></tr></table>

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We based our species selection on several biological traits that are representative of species ability to reach distant habitats. As we restricted the analysis to those species dependent on a single habitat type (riparian habitats), we did not include habitat preferences in the factorial analysis. Following Henle et al. (2004), we selected traits linked to vulnerability to habitat change: two functional traits related to dispersal capacity (maximum dispersion distance and dispersal mode) and two life history traits related to population dynamics (maximum longevity and fecundity per year). For each species, we collected information on these traits from the literature, using either existing databases and generalist papers (Sutherland et al., 2000; Grimm et al., 2014; Trochet et al., 2014; Myhrvold et al., 2015; Oliveira et al., 2017) or articles more specific to each taxon (Rouland & Migot, 1990; Smith & Green, 2005; Decout et al., 2010; Meister et al., 2010; Quaglietta et al., 2013). Depending on data precision and accessibility in the literature, we coded the traits either as categorical or as continuous variables. The categorical variables were the maximum dispersion distance (coded into the following
<table><tr><td>10</td><td>Birds</td><td>Nycticorax nycticorax (Linnaeus, 1758)</td><td>Night heron</td><td>+10km</td><td>21.1</td><td>4</td><td>Aerial</td><td>1</td></tr><tr><td>11</td><td>Birds</td><td>Riparia riparia (Linnaeus, 1758)</td><td>Sand martin</td><td>+10km</td><td>10</td><td>6</td><td>Aerial</td><td>1</td></tr><tr><td>12</td><td>Mammals</td><td>Castor fiber Linnaeus, 1758</td><td>Eurasian beaver</td><td>+10km</td><td>35</td><td>3</td><td>Terrestrial and aquatic</td><td>3</td></tr><tr><td>13</td><td>Mammals</td><td>Lutra lutra (Linnaeus, 1758)</td><td>Eurasian otter</td><td>+10km</td><td>22</td><td>2</td><td>Terrestrial and aquatic</td><td>3</td></tr><tr><td>14</td><td>Amphibians</td><td>Alytes obstetricans (Laurenti, 1768)</td><td>Common midwife toad</td><td>100m-1km</td><td>7</td><td>45</td><td>Terrestrial and aquatic</td><td>2</td></tr><tr><td>15</td><td>Amphibians</td><td>Bombina variegata (Linnaeus, 1758)</td><td>Yellow-bellied toad</td><td>1-10km</td><td>29</td><td>161</td><td>Terrestrial and aquatic</td><td>3</td></tr><tr><td>16</td><td>Amphibians</td><td>Bufo bufo (Linnaeus, 1758)</td><td>Common toad</td><td>1-10km</td><td>40</td><td>9000</td><td>Terrestrial</td><td>4</td></tr><tr><td>17</td><td>Amphibians</td><td>Pelophylax ridibundus (Pallas, 1771)</td><td>Marsh frog</td><td>1-10km</td><td>12</td><td>12000</td><td>Terrestrial</td><td>4</td></tr><tr><td>18</td><td>Amphibians</td><td>Rana temporaria Linnaeus, 1758</td><td>Common frog</td><td>1-10km</td><td>27</td><td>3556</td><td>Terrestrial and aquatic</td><td>3</td></tr></table>

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We based our species selection on several biological traits that are representative of species ability to reach distant habitats. As we restricted the analysis to those species dependent on a single habitat type (riparian habitats), we did not include habitat preferences in the factorial analysis. Following Henle et al. (2004), we selected traits linked to vulnerability to habitat change: two functional traits related to dispersal capacity (maximum dispersion distance and dispersal mode) and two life history traits related to population dynamics (maximum longevity and fecundity per year). For each species, we collected information on these traits from the literature, using either existing databases and generalist papers (Sutherland et al., 2000; Grimm et al., 2014; Trochet et al., 2014; Myhrvold et al., 2015; Oliveira et al., 2017) or articles more specific to each taxon (Rouland & Migot, 1990; Smith & Green, 2005; Decout et al., 2010; Meister et al., 2010; Quaglietta et al., 2013). Depending on data precision and accessibility in the literature, we coded the traits either as categorical or as continuous variables. The categorical variables were the maximum dispersion distance (coded into the following

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categories: 0.1-1 km; 1-10 km; >10 km) and the dispersal mode (categories: aerial; terrestrial; terrestrial and aquatic). The continuous variables were the maximum longevity (in years) and the fecundity per year (in number of offspring) (Table1).

To select surrogate species, we performed a k-means clustering on these four variables using the Hierarchical clustering on principal components (HCPC), based on a factor analysis for mixed data (FAMD). To do so, we used the functions FAMD and HCPC of the package FactoMineR (Lê et al., 2008) in R environment (R Core Team, 2022). We kept the number of clusters with the highest relative loss of inertia as recommended by Lê et al. (2008). For each cluster, we selected the species located closest to the barycenter as surrogate (thereafter called paragon species).

## Habitat characterization

SM3A provided us with a detailed mapping of the habitats along the rivers in the study area. For more remote areas, we used Occupation des Sols (OSO) soil mapping (Inglada et al., 2017), which provides a 23-class land cover map at 10m resolution produced from satellite data. For the aquatic environment, the location of obstacles to flow (weirs) was provided by the SM3A.

We treated the riverbanks separately from the rest of the study area. We mapped them using a 10m buffer zone along the Arve River. We determined three characteristics of the banks: type of protection structure, vegetation height and slope. The type of protection structure (5 categories: riprap, lower-bank ripraps with upper-bank plantings, bare rock, natural bank, live toe fascine) and the vegetation height (3 categories: low vegetation cover <25%, mainly herbaceous or shrubby vegetation, mainly tree cover) were mapped in a field survey (2017). The slope was determined using a 1m-resolution digital elevation model obtained by LIDAR and supplied by SM3A. We used two categories: low slope (<60%) and steep slope (>60%). Crossing these three characteristics allowed us to obtain a classification of the banks in 5*3*2=30 categories. 68.1% of the riverbanks were categorized as steep (vs. 32.9% as shallow). Vegetation cover on the banks was sparse (<25%), herbaceous/shrubby and woody on 4.2%, 14.1% and 81.7% of the linear length, respectively.
categories: 0.1-1 km; 1-10 km; >10 km) and the dispersal mode (categories: aerial; terrestrial; terrestrial and aquatic). The continuous variables were the maximum longevity (in years) and the fecundity per year (in number of offspring) (Table1).

To select surrogate species, we performed a k-means clustering on these four variables using the Hierarchical clustering on principal components (HCPC), based on a factor analysis for mixed data (FAMD). To do so, we used the functions FAMD and HCPC of the package FactoMineR (Lê et al., 2008) in R environment (R Core Team, 2022). We kept the number of clusters with the highest relative loss of inertia as recommended by Lê et al. (2008). For each cluster, we selected the species located closest to the barycenter as surrogate (thereafter called paragon species).

## Habitat characterization

SM3A provided us with a detailed mapping of the habitats along the rivers in the study area. For more remote areas, we used Occupation des Sols (OSO) soil mapping (Inglada et al., 2017), which provides a 23-class land cover map at 10m resolution produced from satellite data. For the aquatic environment, the location of obstacles to flow (weirs) was provided by the SM3A.

We treated the riverbanks separately from the rest of the study area. We mapped them using a 10m buffer zone along the Arve River. We determined three characteristics of the banks: type of protection structure, vegetation height and slope. The type of protection structure (5 categories: riprap, lower-bank ripraps with upper-bank plantings, bare rock, natural bank, live toe fascine) and the vegetation height (3 categories: low vegetation cover <25%, mainly herbaceous or shrubby vegetation, mainly tree cover) were mapped in a field survey (2017). The slope was determined using a 1m-resolution digital elevation model obtained by LIDAR and supplied by SM3A. We used two categories: low slope (<60%) and steep slope (>60%). Crossing these three characteristics allowed us to obtain a classification of the banks in 5*3*2=30 categories. 68.1% of the riverbanks were categorized as steep (vs. 32.9% as shallow). Vegetation cover on the banks was sparse (<25%), herbaceous/shrubby and woody on 4.2%, 14.1% and 81.7% of the linear length, respectively.
We carried out the cartographic work using QGIS (QGIS.org, 2023). We combined the collected information in a land use raster map adopting a resolution of 2m, the finest resolution usable by Graphab software (Foltête et al., 2012, 2021) in our computer configuration (Intel Core i7, 32GB RAM).

## Resistance maps

We first determined the main habitat types for each target species, based on literature and discussions with experts. All patches of potential habitats were mapped within the study area. Then, we classified the different elements of the matrix according to their resistance to species movement based on literature and expert opinion. We used the literature to select prior resistance values, which were then corrected and validated by local naturalist experts. The resistance values were attributed on a logarithmic scale according to previous papers that demonstrated the importance of using a contrasted resistance scale in landscape connectivity models (Verbeylen et al., 2003; Clauzel et al., 2013; Clauzel & Godet, 2020). Our resistance values thus ranged from 1 (habitat patch) to 10,000 (barrier), with a value of 10 assigned to elements favorable to movement, a value of 100 to elements neutral to movement and a value of 1000 to elements unfavorable to movement (Table 2 and Table 3). The resistance map obtained covered the entire study area (157km²).

Table 2. Resistance values assigned to the various landscape elements based on literature and expert opinion, for the 4 surrogate species.

<table><thead><tr><th>Land cover class</th><th>Very favorable</th><th>Favorable</th><th>Neutral</th><th>Unfavorable</th><th>Barrier</th></tr></thead><tbody><tr><th>Resistance attributed</th><td>1</td><td>10</td><td>100</td><td>1 000</td><td>10 000</td></tr><tr><th>Eurasian beaver</th><td>Riparian alder and willow groves, rivers</td><td>Other deciduous riparian forests</td><td>Coniferous</td><td>Shrub and bush vegetation, reedbeds, ruderal areas, meadows, crops</td><td>Areas located more than 40m from watercourses, buildings, roads, weirs</td></tr></tbody></table>
We carried out the cartographic work using QGIS (QGIS.org, 2023). We combined the collected information in a land use raster map adopting a resolution of 2m, the finest resolution usable by Graphab software (Foltête et al., 2012, 2021) in our computer configuration (Intel Core i7, 32GB RAM).

## Resistance maps

We first determined the main habitat types for each target species, based on literature and discussions with experts. All patches of potential habitats were mapped within the study area. Then, we classified the different elements of the matrix according to their resistance to species movement based on literature and expert opinion. We used the literature to select prior resistance values, which were then corrected and validated by local naturalist experts. The resistance values were attributed on a logarithmic scale according to previous papers that demonstrated the importance of using a contrasted resistance scale in landscape connectivity models (Verbeylen et al., 2003; Clauzel et al., 2013; Clauzel & Godet, 2020). Our resistance values thus ranged from 1 (habitat patch) to 10,000 (barrier), with a value of 10 assigned to elements favorable to movement, a value of 100 to elements neutral to movement and a value of 1000 to elements unfavorable to movement (Table 2 and Table 3). The resistance map obtained covered the entire study area (157km²).

Table 2. Resistance values assigned to the various landscape elements based on literature and expert opinion, for the 4 surrogate species.

<table><thead><tr><th>Land cover class</th><th>Very favorable</th><th>Favorable</th><th>Neutral</th><th>Unfavorable</th><th>Barrier</th></tr></thead><tbody><tr><th>Resistance attributed</th><td>1</td><td>10</td><td>100</td><td>1 000</td><td>10 000</td></tr><tr><th>Eurasian beaver</th><td>Riparian alder and willow groves, rivers</td><td>Other deciduous riparian forests</td><td>Coniferous</td><td>Shrub and bush vegetation, reedbeds, ruderal areas, meadows, crops</td><td>Areas located more than 40m from watercourses, buildings, roads, weirs</td></tr></tbody></table>
<table><tr><td>Common sandpiper</td><td>Lakes, reservoirs, rivers, alluvial banks with pioneer vegetation</td><td>Reedbeds, wetlands, weirs</td><td>Bushes, artificial meadows</td><td>Forests, gardens, roads, artificial areas</td><td>Buildings, industrial sites, crops</td></tr><tr><td>Barred grass snake</td><td>Lakes and reservoirs, minor watercourses</td><td>Large rivers (Arve), alluvial beds</td><td>Forests, bushes, artificial meadows, agricultural areas</td><td>Gardens, roads, conifer plantations</td><td>Industrial sites, artificial areas, ripraps with joint</td></tr><tr><td>Common toad</td><td>Deciduous forests, lakes and reservoirs</td><td>Minor watercourses, alluvial deposits, reedbeds</td><td>Woodlands, coniferous forests, artificial meadows</td><td>Gardens, roads</td><td>Industrial sites, artificial areas, crops, weirs, ripraps with joint</td></tr></table>

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Table 3. Resistance values assigned to different riverbanks based on literature and expert opinion, for the 4 surrogate species, depending on their protection structures, vegetation cover and slope.

<table><thead><tr><th rowspan="2">Species</th><th rowspan="2">Slope</th><th rowspan="2">Vegetation</th><th colspan="5">Type of bank protection structure</th></tr><tr><th>Bare zone (cliff, concrete)</th><th>Riprap</th><th>Lower-bank ripraps with upper-bank plantings</th><th>Live toe fascine</th><th>Natural bank</th></tr></thead><tbody><tr><td rowspan="3">Eurasian beaver</td><td rowspan="3">Low slope (&lt;60°)</td><td>Absent or very sparse (&lt;25% coverage)</td><td>1000</td><td>1000</td><td>-</td><td>-</td><td>100</td></tr><tr><td>Herbaceous or shrubby vegetation</td><td>-</td><td>100</td><td>10</td><td>1</td><td>1</td></tr><tr><td>Tree cover</td><td>-</td><td>100</td><td>-</td><td>1</td><td>1</td></tr><tr><td rowspan="3">Steep slope (&gt;60°)</td><td rowspan="3">Steep slope (&gt;60°)</td><td>Absent or very sparse (&lt;25% coverage)</td><td>1000</td><td>1000</td><td>-</td><td>100</td><td>100</td></tr><tr><td>Herbaceous or shrubby vegetation</td><td>-</td><td>100</td><td>100</td><td>1</td><td>1</td></tr><tr><td>Tree cover</td><td>1000</td><td>100</td><td>100</td><td>1</td><td>1</td></tr><tr><td rowspan="3">Common sandpiper</td><td rowspan="3">Low slope (&lt;60°)</td><td>Absent or very sparse (&lt;25% coverage)</td><td>10000</td><td>1000</td><td>-</td><td>1</td><td>1</td></tr><tr><td>Herbaceous or shrubby vegetation</td><td>-</td><td>1000</td><td>1000</td><td>1</td><td>1</td></tr><tr><td>Tree cover</td><td>-</td><td>1000</td><td>1000</td><td>100</td><td>100</td></tr><tr><td rowspan="3">Steep slope (&gt;60°)</td><td rowspan="3">Steep slope (&gt;60°)</td><td>Absent or very sparse (&lt;25% coverage)</td><td>1000</td><td>1000</td><td>-</td><td>10</td><td>10</td></tr><tr><td>Herbaceous or shrubby vegetation</td><td>-</td><td>1000</td><td>1000</td><td>10</td><td>10</td></tr><tr><td>Tree cover</td><td>1000</td><td>1000</td><td>1000</td><td>100</td><td>100</td></tr><tr><td rowspan="3">Barred grass snake</td><td rowspan="3">Low slope (&lt;60°)</td><td>Absent or very sparse (&lt;25% coverage)</td><td>1000</td><td>10</td><td>-</td><td>100</td><td>100</td></tr><tr><td>Herbaceous or shrubby vegetation</td><td>-</td><td>1</td><td>1</td><td>1</td><td>1</td></tr><tr><td>Tree cover</td><td>-</td><td>100</td><td>100</td><td>100</td><td>100</td></tr><tr><td rowspan="3">Steep slope (&gt;60°)</td><td rowspan="3">Steep slope (&gt;60°)</td><td>Absent or very sparse (&lt;25% coverage)</td><td>10000</td><td>100</td><td>-</td><td>1000</td><td>1000</td></tr><tr><td>Herbaceous or shrubby vegetation</td><td>-</td><td>10</td><td>10</td><td>100</td><td>100</td></tr><tr><td>Tree cover</td><td>1000</td><td>1000</td><td>1000</td><td>1000</td><td>1000</td></tr><tr><td rowspan="2">Common toad</td><td rowspan="2">Low slope (&lt;60°)</td><td>Absent or very sparse (&lt;25% coverage)</td><td>10000</td><td>10000</td><td>-</td><td>1000</td><td>1000</td></tr><tr><td>Herbaceous or shrubby vegetation</td><td>-</td><td>1000</td><td>1000</td><td>10</td><td>10</td></tr></tbody></table>
<table><tr><td>Common sandpiper</td><td>Lakes, reservoirs, rivers, alluvial banks with pioneer vegetation</td><td>Reedbeds, wetlands, weirs</td><td>Bushes, artificial meadows</td><td>Forests, gardens, roads, artificial areas</td><td>Buildings, industrial sites, crops</td></tr><tr><td>Barred grass snake</td><td>Lakes and reservoirs, minor watercourses</td><td>Large rivers (Arve), alluvial beds</td><td>Forests, bushes, artificial meadows, agricultural areas</td><td>Gardens, roads, conifer plantations</td><td>Industrial sites, artificial areas, ripraps with joint</td></tr><tr><td>Common toad</td><td>Deciduous forests, lakes and reservoirs</td><td>Minor watercourses, alluvial deposits, reedbeds</td><td>Woodlands, coniferous forests, artificial meadows</td><td>Gardens, roads</td><td>Industrial sites, artificial areas, crops, weirs, ripraps with joint</td></tr></table>

256

Table 3. Resistance values assigned to different riverbanks based on literature and expert opinion, for the 4 surrogate species, depending on their protection structures, vegetation cover and slope.

<table><thead><tr><th rowspan="2">Species</th><th rowspan="2">Slope</th><th rowspan="2">Vegetation</th><th colspan="5">Type of bank protection structure</th></tr><tr><th>Bare zone (cliff, concrete)</th><th>Riprap</th><th>Lower-bank ripraps with upper-bank plantings</th><th>Live toe fascine</th><th>Natural bank</th></tr></thead><tbody><tr><td rowspan="3">Eurasian beaver</td><td rowspan="3">Low slope (&lt;60°)</td><td>Absent or very sparse (&lt;25% coverage)</td><td>1000</td><td>1000</td><td>-</td><td>-</td><td>100</td></tr><tr><td>Herbaceous or shrubby vegetation</td><td>-</td><td>100</td><td>10</td><td>1</td><td>1</td></tr><tr><td>Tree cover</td><td>-</td><td>100</td><td>-</td><td>1</td><td>1</td></tr><tr><td rowspan="3">Steep slope (&gt;60°)</td><td rowspan="3">Steep slope (&gt;60°)</td><td>Absent or very sparse (&lt;25% coverage)</td><td>1000</td><td>1000</td><td>-</td><td>100</td><td>100</td></tr><tr><td>Herbaceous or shrubby vegetation</td><td>-</td><td>100</td><td>100</td><td>1</td><td>1</td></tr><tr><td>Tree cover</td><td>1000</td><td>100</td><td>100</td><td>1</td><td>1</td></tr><tr><td rowspan="3">Common sandpiper</td><td rowspan="3">Low slope (&lt;60°)</td><td>Absent or very sparse (&lt;25% coverage)</td><td>10000</td><td>1000</td><td>-</td><td>1</td><td>1</td></tr><tr><td>Herbaceous or shrubby vegetation</td><td>-</td><td>1000</td><td>1000</td><td>1</td><td>1</td></tr><tr><td>Tree cover</td><td>-</td><td>1000</td><td>1000</td><td>100</td><td>100</td></tr><tr><td rowspan="3">Steep slope (&gt;60°)</td><td rowspan="3">Steep slope (&gt;60°)</td><td>Absent or very sparse (&lt;25% coverage)</td><td>1000</td><td>1000</td><td>-</td><td>10</td><td>10</td></tr><tr><td>Herbaceous or shrubby vegetation</td><td>-</td><td>1000</td><td>1000</td><td>10</td><td>10</td></tr><tr><td>Tree cover</td><td>1000</td><td>1000</td><td>1000</td><td>100</td><td>100</td></tr><tr><td rowspan="3">Barred grass snake</td><td rowspan="3">Low slope (&lt;60°)</td><td>Absent or very sparse (&lt;25% coverage)</td><td>1000</td><td>10</td><td>-</td><td>100</td><td>100</td></tr><tr><td>Herbaceous or shrubby vegetation</td><td>-</td><td>1</td><td>1</td><td>1</td><td>1</td></tr><tr><td>Tree cover</td><td>-</td><td>100</td><td>100</td><td>100</td><td>100</td></tr><tr><td rowspan="3">Steep slope (&gt;60°)</td><td rowspan="3">Steep slope (&gt;60°)</td><td>Absent or very sparse (&lt;25% coverage)</td><td>10000</td><td>100</td><td>-</td><td>1000</td><td>1000</td></tr><tr><td>Herbaceous or shrubby vegetation</td><td>-</td><td>10</td><td>10</td><td>100</td><td>100</td></tr><tr><td>Tree cover</td><td>1000</td><td>1000</td><td>1000</td><td>1000</td><td>1000</td></tr><tr><td rowspan="2">Common toad</td><td rowspan="2">Low slope (&lt;60°)</td><td>Absent or very sparse (&lt;25% coverage)</td><td>10000</td><td>10000</td><td>-</td><td>1000</td><td>1000</td></tr><tr><td>Herbaceous or shrubby vegetation</td><td>-</td><td>1000</td><td>1000</td><td>10</td><td>10</td></tr></tbody></table>
<table><tr><td></td><td>Tree cover</td><td>-</td><td>10</td><td>10</td><td>1</td><td>1</td></tr><tr><td rowspan="2">Steep slope (&gt;60°)</td><td>Absent or very sparse (&lt;25% coverage)</td><td>10000</td><td>10000</td><td>-</td><td>100</td><td>100</td></tr><tr><td>Herbaceous or shrubby vegetation</td><td>-</td><td>100</td><td>100</td><td>10</td><td>10</td></tr><tr><td></td><td>Tree cover</td><td>1000</td><td>1000</td><td>1000</td><td>10</td><td>10</td></tr></table>

259

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Assessment of connectivity using landscape graphs

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We constructed landscape graphs for each species/scenario combination using Graphab 2.4 software (Foltête et al., 2012, 2021). To avoid modeling patches too small to constitute real habitats for vertebrate species, we kept habitat patches larger than 500m² as nodes for the graphs. Graph links were constructed between all habitat patches, and we only retained the ones that were shorter than the maximum dispersion distance of each species based on literature. The link impedance between patches was calculated in least-cost distances based on the previously constructed resistance map.

We assessed the landscape connectivity by computing the Probability of Connectivity (PC) index for each graph. The PC accounts for the amount of reachable habitat for the target species (Saura & Pascual-Hortal, 2007). It is defined as the probability that two individuals randomly selected within the landscape are located in interconnected habitat patches. The PC takes into account 1) the total amount of habitat available, 2) the spatial configuration of habitat patches, 3) the resistance of the landscape matrix to species movement and 4) the dispersal capacity of the target species modeled:

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$$PC = \frac{\sum_{i=1}^{n} \sum_{j=1}^{n} a_i a_j p_{ij}^{*}}{A_L^2}$$

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with $a_i$ and $a_j$ the respective capacities of habitat patches i and j and $A_L$ the total landscape capacity. We considered habitat patch capacity to be equal to habitat patch area. $p_{ij}^*$ is the maximum value of the probability of dispersal $p_{ij}$ calculated on all the possible paths connecting patches i and j. $p_{ij}$ is the direct probability of dispersal between the two habitat patches i and j and is a negative exponential function of the distance between them $d_{ij}$ in km. :

279

$$p_{ij} = e^{-kd_{ij}}$$
<table><tr><td></td><td>Tree cover</td><td>-</td><td>10</td><td>10</td><td>1</td><td>1</td></tr><tr><td rowspan="2">Steep slope (&gt;60°)</td><td>Absent or very sparse (&lt;25% coverage)</td><td>10000</td><td>10000</td><td>-</td><td>100</td><td>100</td></tr><tr><td>Herbaceous or shrubby vegetation</td><td>-</td><td>100</td><td>100</td><td>10</td><td>10</td></tr><tr><td></td><td>Tree cover</td><td>1000</td><td>1000</td><td>1000</td><td>10</td><td>10</td></tr></table>

259

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Assessment of connectivity using landscape graphs

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We constructed landscape graphs for each species/scenario combination using Graphab 2.4 software (Foltête et al., 2012, 2021). To avoid modeling patches too small to constitute real habitats for vertebrate species, we kept habitat patches larger than 500m² as nodes for the graphs. Graph links were constructed between all habitat patches, and we only retained the ones that were shorter than the maximum dispersion distance of each species based on literature. The link impedance between patches was calculated in least-cost distances based on the previously constructed resistance map.

We assessed the landscape connectivity by computing the Probability of Connectivity (PC) index for each graph. The PC accounts for the amount of reachable habitat for the target species (Saura & Pascual-Hortal, 2007). It is defined as the probability that two individuals randomly selected within the landscape are located in interconnected habitat patches. The PC takes into account 1) the total amount of habitat available, 2) the spatial configuration of habitat patches, 3) the resistance of the landscape matrix to species movement and 4) the dispersal capacity of the target species modeled:

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$$PC = \frac{\sum_{i=1}^{n} \sum_{j=1}^{n} a_i a_j p_{ij}^{*}}{A_L^2}$$

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with $a_i$ and $a_j$ the respective capacities of habitat patches i and j and $A_L$ the total landscape capacity. We considered habitat patch capacity to be equal to habitat patch area. $p_{ij}^*$ is the maximum value of the probability of dispersal $p_{ij}$ calculated on all the possible paths connecting patches i and j. $p_{ij}$ is the direct probability of dispersal between the two habitat patches i and j and is a negative exponential function of the distance between them $d_{ij}$ in km. :

279

$$p_{ij} = e^{-kd_{ij}}$$
k is a constant expressing the intensity of the decrease in the probability of movement (p) with distance (d). The value of k was fixed such that $p_{ij} = 0.05$ when $d_{ij}$ equals the maximum dispersion distance of the target species

## Contribution of each habitat type to landscape connectivity

To focus on the importance of each habitat type in the ecological network, we classified habitats into three categories: riverbanks, other river-related habitats (e.g. gravel banks in the middle of the watercourse) and offstream habitats (e.g. pond banks, wetlands). To test each species' level of dependence on riverbank versus offstream or other river-related habitats, we created new graphs, each time removing all habitat patches in a given category. We replaced the removed habitats with matrix elements to which we assigned a resistance value equal to the average resistance of the study area. For each graph, we calculated the PC and the loss of PC compared with the current situation (in %).

We scaled this analysis down to the habitat patch level. We also evaluated the importance of each patch of habitats, using the dPC (for delta PC) index calculated with Graphab. dPC is calculated by removing each patch one by one from the network and calculating the associated percentage of PC change. This quantifies the importance of each habitat patch in the spatial ecological network (Saura & Rubio, 2010). To control for habitat patch area, we also calculated the dPC/area index, which quantifies the connectivity importance of each patch per surface unit, which seems appropriate in a context where riverbanks often represent very small surfaces and neighbor larger habitats (e.g. lakes and reservoirs).

## Effect of soil water bioengineering structures on habitat connectivity

To investigate the potential for improvement and further degradation in the studied landscape, we drew up three contrasting scenarios of land use change and compared them with the baseline scenario, reflecting the current landscape configuration: 1) the 'riprap to fascines' scenario, or a best-case scenario, in which all riprap structures would be replaced in the study area with low-slope, live toe
k is a constant expressing the intensity of the decrease in the probability of movement (p) with distance (d). The value of k was fixed such that $p_{ij} = 0.05$ when $d_{ij}$ equals the maximum dispersion distance of the target species

## Contribution of each habitat type to landscape connectivity

To focus on the importance of each habitat type in the ecological network, we classified habitats into three categories: riverbanks, other river-related habitats (e.g. gravel banks in the middle of the watercourse) and offstream habitats (e.g. pond banks, wetlands). To test each species' level of dependence on riverbank versus offstream or other river-related habitats, we created new graphs, each time removing all habitat patches in a given category. We replaced the removed habitats with matrix elements to which we assigned a resistance value equal to the average resistance of the study area. For each graph, we calculated the PC and the loss of PC compared with the current situation (in %).

We scaled this analysis down to the habitat patch level. We also evaluated the importance of each patch of habitats, using the dPC (for delta PC) index calculated with Graphab. dPC is calculated by removing each patch one by one from the network and calculating the associated percentage of PC change. This quantifies the importance of each habitat patch in the spatial ecological network (Saura & Rubio, 2010). To control for habitat patch area, we also calculated the dPC/area index, which quantifies the connectivity importance of each patch per surface unit, which seems appropriate in a context where riverbanks often represent very small surfaces and neighbor larger habitats (e.g. lakes and reservoirs).

## Effect of soil water bioengineering structures on habitat connectivity

To investigate the potential for improvement and further degradation in the studied landscape, we drew up three contrasting scenarios of land use change and compared them with the baseline scenario, reflecting the current landscape configuration: 1) the 'riprap to fascines' scenario, or a best-case scenario, in which all riprap structures would be replaced in the study area with low-slope, live toe
305 fascines and plantings above, considered a priori to be the most favorable situation for connectivity;
306 2) the 'fascines to riprap' scenario, in which the existing live toe fascines would be transformed into
307 riprap; and 3) the 'fascines and natural banks to riprap' scenario, or worst-case scenario, in which the
308 live toe fascines and all natural banks would be replaced by riprap. The first scenario was designed to
309 assess the potential for improving habitat connectivity through SWBE structures. The second would
310 quantify the contribution of existing live toe fascines to the observed connectivity. The third was
311 designed to assess the contribution of existing natural banks to connectivity. We computed the PC for
312 each scenario and each target species. The PC of the baseline scenario was used to compare the overall
313 connectivity of the ecological network between target species.

314 Prioritization of bank sections to restore

315 As the 'riprap to fascines' scenario is not a realistic short-term development scenario, we tested
316 intermediate scenarios where the manager would only have the means to restore one section of the
317 bank. The aim of this approach was to identify the sections that would offer the highest gain in
318 connectivity for the target species. We first isolated the riprap embankments. Using the QGIS plugin
319 Polygon divider, we divided them into sections of around 100 m in length. For each target species, we
320 then systematically changed the resistance of each section one by one, switching from a riprap
321 resistance value to a live toe fascine resistance value. For each stage (one species and one section), we
322 recalculated the least-cost paths and recreated a graph. For each graph, we calculated PC change,
323 expressed as a percentage of change in connectivity (positive or negative) relative to the baseline
324 scenario. We obtained a change in connectivity for each surrogate species. This made it possible to
325 calculate the impacts of the restoration of any 100 m section, and thus to select those that would
326 maximize connectivity gains once restored. Finally, to test for the sensitivity of this analysis to section
327 size considered, we repeated the operation using 500 m sections (an equally plausible landscape
328 change scenario). A total of 214 sections of 100 m and 77 sections of 500 m were tested one by one.

329 **Results**
305 fascines and plantings above, considered a priori to be the most favorable situation for connectivity;
306 2) the 'fascines to riprap' scenario, in which the existing live toe fascines would be transformed into
307 riprap; and 3) the 'fascines and natural banks to riprap' scenario, or worst-case scenario, in which the
308 live toe fascines and all natural banks would be replaced by riprap. The first scenario was designed to
309 assess the potential for improving habitat connectivity through SWBE structures. The second would
310 quantify the contribution of existing live toe fascines to the observed connectivity. The third was
311 designed to assess the contribution of existing natural banks to connectivity. We computed the PC for
312 each scenario and each target species. The PC of the baseline scenario was used to compare the overall
313 connectivity of the ecological network between target species.

314 Prioritization of bank sections to restore

315 As the 'riprap to fascines' scenario is not a realistic short-term development scenario, we tested
316 intermediate scenarios where the manager would only have the means to restore one section of the
317 bank. The aim of this approach was to identify the sections that would offer the highest gain in
318 connectivity for the target species. We first isolated the riprap embankments. Using the QGIS plugin
319 Polygon divider, we divided them into sections of around 100 m in length. For each target species, we
320 then systematically changed the resistance of each section one by one, switching from a riprap
321 resistance value to a live toe fascine resistance value. For each stage (one species and one section), we
322 recalculated the least-cost paths and recreated a graph. For each graph, we calculated PC change,
323 expressed as a percentage of change in connectivity (positive or negative) relative to the baseline
324 scenario. We obtained a change in connectivity for each surrogate species. This made it possible to
325 calculate the impacts of the restoration of any 100 m section, and thus to select those that would
326 maximize connectivity gains once restored. Finally, to test for the sensitivity of this analysis to section
327 size considered, we repeated the operation using 500 m sections (an equally plausible landscape
328 change scenario). A total of 214 sections of 100 m and 77 sections of 500 m were tested one by one.

329 Results
331 The first two components of the HCPC captured 73% (46.6% and 26.4% respectively) of the variation
332 of the four variables reflecting traits related to dispersal capacity in 18 species (Fig. 2). The variables
333 'maximum longevity' and 'fertility per year' were negatively correlated. At the same time, dispersal
334 mode and maximum dispersion distance appeared to be related (Fig. 2A). In particular, species that fly
335 can disperse over longer distances.

<figure><img src="image_4.png" /></figure>

336

337

338 Fig. 2 Factor analysis for mixed data (FAMD) performed on the 18 species and projected into the
339 factorial map formed by the first two components. A: Qualitative and quantitative variables (traits).
340 The continuous variables appear in black while the categorical variables appear in red. B: Species
341 clustered according to their traits. Each point represents a species, and numbers correspond to species
342 identifiers mentioned in Table 2. The hierarchical principal component clustering (HCPC) resulted in
343 four clusters. The silhouettes of some emblematic animals are shown for illustrative purposes

344
331 The first two components of the HCPC captured 73% (46.6% and 26.4% respectively) of the variation
332 of the four variables reflecting traits related to dispersal capacity in 18 species (Fig. 2). The variables
333 'maximum longevity' and 'fertility per year' were negatively correlated. At the same time, dispersal
334 mode and maximum dispersion distance appeared to be related (Fig. 2A). In particular, species that fly
335 can disperse over longer distances.

<figure><img src="image_3.png" /></figure>

336
337

Fig. 2 Factor analysis for mixed data (FAMD) performed on the 18 species and projected into the
factorial map formed by the first two components. A: Qualitative and quantitative variables (traits).
338
339
The continuous variables appear in black while the categorical variables appear in red. B: Species
340
clustered according to their traits. Each point represents a species, and numbers correspond to species
341
342
identifiers mentioned in Table 2. The hierarchical principal component clustering (HCPC) resulted in
343
four clusters. The silhouettes of some emblematic animals are shown for illustrative purposes

344
Hierarchical clustering identified four clusters, each comprising between two and 10 species (Fig. 2B, Table 1). Clusters 1 and 4 were taxonomically homogeneous, being composed of birds and amphibians, respectively. Cluster 2 included one reptile species and one amphibian, while cluster 3 contained two mammals and two amphibians (Table 1). We labeled the clusters according to their main ecological characteristics: “flying animals” (Main dispersal mode = ‘Aerial’), “amphibious weak dispersers” (Main dispersal mode = ‘Terrestrial and aquatic’ and Maximum dispersion distance = “100m–1km”), “amphibious strong dispersers” (Main dispersal mode = ‘Terrestrial and aquatic’ and Maximum dispersion distance = “+10km”) and “walkers” (Main dispersal mode = ‘Terrestrial’). The paragon species of these four clusters were the common sandpiper (*Actitis hypoleucos*), the barred grass snake (*Natrix helvetica*), the Eurasian beaver (*Castor fiber*) and the common toad (*Bufo bufo*), respectively. These four species were used as target species thereafter.

Contribution of each habitat type to landscape connectivity

For the Eurasian beaver, the main habitats identified (0.29km², 0.2% of the total study area) were wooded banks with no ripraps, with a quiet zone of at least 10m around them, free of any infrastructure (roads, tracks, buildings), as well as water bodies less than 30m from the Arve riverbed.

For the common sandpiper, the main habitats (0.29km², 0.2% of the total study area) were gravel banks, riparian vegetation with small cattail and riverbed with herbaceous riparian vegetation. For the barred grass snake, the main habitats (0.52km², 0.3% of the total study area) were lakes and reservoirs banks and low-slope herbaceous stream banks. Finally, for the common toads, main habitats (0.57km², 0.4% of the total study area) were wooded riverbanks with low slopes, lake banks and reservoirs.

The highest probability of connectivity (PC) in the study area was found for the common sandpiper (PC = 3.0*10⁻⁶), followed by the Eurasian beaver (PC = 2.1*10⁻⁶), the common toad (PC = 8.1*10⁻⁷) and the barred grass snake (PC = 6.3*10⁻⁷).

Calculating PC with each habitat type removed one by one, we observed that the Eurasian beaver was the species most dependent on riverbank habitats, which contribute to almost all habitat connectivity
Hierarchical clustering identified four clusters, each comprising between two and 10 species (Fig. 2B, Table 1). Clusters 1 and 4 were taxonomically homogeneous, being composed of birds and amphibians, respectively. Cluster 2 included one reptile species and one amphibian, while cluster 3 contained two mammals and two amphibians (Table 1). We labeled the clusters according to their main ecological characteristics: “flying animals” (Main dispersal mode = ‘Aerial’), “amphibious weak dispersers” (Main dispersal mode = ‘Terrestrial and aquatic’ and Maximum dispersion distance = “100m–1km”), “amphibious strong dispersers” (Main dispersal mode = ‘Terrestrial and aquatic’ and Maximum dispersion distance = “+10km’) and “walkers” (Main dispersal mode = ‘Terrestrial). The paragon species of these four clusters were the common sandpiper (*Actitis hypoleucos*), the barred grass snake (*Natrix helvetica*), the Eurasian beaver (*Castor fiber*) and the common toad (*Bufo bufo*), respectively. These four species were used as target species thereafter.

Contribution of each habitat type to landscape connectivity

For the Eurasian beaver, the main habitats identified (0.29km², 0.2% of the total study area) were wooded banks with no ripraps, with a quiet zone of at least 10m around them, free of any infrastructure (roads, tracks, buildings), as well as water bodies less than 30m from the Arve riverbed.

For the common sandpiper, the main habitats (0.29km², 0.2% of the total study area) were gravel banks, riparian vegetation with small cattail and riverbed with herbaceous riparian vegetation. For the barred grass snake, the main habitats (0.52km², 0.3% of the total study area) were lakes and reservoirs banks and low-slope herbaceous stream banks. Finally, for the common toads, main habitats (0.57km², 0.4% of the total study area) were wooded riverbanks with low slopes, lake banks and reservoirs.

The highest probability of connectivity (PC) in the study area was found for the common sandpiper (PC = 3.0*10⁻⁶), followed by the Eurasian beaver (PC = 2.1*10⁻⁶), the common toad (PC = 8.1*10⁻⁷) and the barred grass snake (PC = 6.3*10⁻⁷).

Calculating PC with each habitat type removed one by one, we observed that the Eurasian beaver was the species most dependent on riverbank habitats, which contribute to almost all habitat connectivity
for this species (Table 4). The common sandpiper main habitats were gravel banks, which were classified as 'other river related habitats'. Habitat connectivity for the common toad depended on both riverbank and offstream habitats. Connectivity for the barred grass snake was mainly linked to offstream habitats (Table 4). Note that it is normal for the sum of PC losses for each species to be >100%, as several habitat patches of different types may be redundant in terms of connectivity.

Table 4. Variation in PC obtained by removing each habitat type one by one. It is expressed as a percentage loss compared with the current situation.

<table><thead><tr><th rowspan="2">Species</th><th colspan="3">Loss of PC if removed</th></tr><tr><th>Riverbank habitats</th><th>Other river related habitats</th><th>Offstream habitats</th></tr></thead><tbody><tr><td>Common sandpiper</td><td>-</td><td>100%</td><td>-</td></tr><tr><td>Common toad</td><td>60.1%</td><td>-</td><td>52.4%</td></tr><tr><td>Eurasian beaver</td><td>99.9%</td><td>-</td><td>4.8%</td></tr><tr><td>Barred grass snake</td><td>28.0%</td><td>-</td><td>75.5%</td></tr></tbody></table>

Calculating the dPC for each habitat patch showed that for the Eurasian beaver and the barred grass snake, each offstream habitat patch contributed more in absolute values to total network connectivity than riverbank habitat patches. To a lesser extent, this was also the case for the common toad. In our landscape, the only habitats of the common sandpiper were gravel banks (classified as 'other river related habitats'), so no comparison across habitat types was possible. When controlling the contribution of habitat patch to overall connectivity by habitat patch area (dPC/area), riverbank habitats appeared to contribute more to overall network connectivity than offstream habitats for the
for this species (Table 4). The common sandpiper main habitats were gravel banks, which were classified as 'other river related habitats'. Habitat connectivity for the common toad depended on both riverbank and offstream habitats. Connectivity for the barred grass snake was mainly linked to offstream habitats (Table 4). Note that it is normal for the sum of PC losses for each species to be >100%, as several habitat patches of different types may be redundant in terms of connectivity.

<table><caption>Table 4. Variation in PC obtained by removing each habitat type one by one. It is expressed as a percentage loss compared with the current situation.</caption><thead><tr><th rowspan="2">Species</th><th colspan="3">Loss of PC if removed</th></tr><tr><th>Riverbank habitats</th><th>Other river related habitats</th><th>Offstream habitats</th></tr></thead><tbody><tr><td>Common sandpiper</td><td>-</td><td>100%</td><td>-</td></tr><tr><td>Common toad</td><td>60.1%</td><td>-</td><td>52.4%</td></tr><tr><td>Eurasian beaver</td><td>99.9%</td><td>-</td><td>4.8%</td></tr><tr><td>Barred grass snake</td><td>28.0%</td><td>-</td><td>75.5%</td></tr></tbody></table>

Calculating the dPC for each habitat patch showed that for the Eurasian beaver and the barred grass snake, each offstream habitat patch contributed more in absolute values to total network connectivity than riverbank habitat patches. To a lesser extent, this was also the case for the common toad. In our landscape, the only habitats of the common sandpiper were gravel banks (classified as 'other river related habitats'), so no comparison across habitat types was possible. When controlling the contribution of habitat patch to overall connectivity by habitat patch area (dPC/area), riverbank habitats appeared to contribute more to overall network connectivity than offstream habitats for the
385 common toad and the Eurasian beaver. The trend remained the same for the barred grass snake (Fig. 3).
386 3).

<figure><img src="image_5.png" /></figure>

387

388 Fig. 3 Boxplot of the distribution of the delta Probability of connectivity (dPC) computed for each habitat patch, differentiating between riverbank habitats, other river-related habitats (mainly gravel banks) and offstream habitats. Top: dPC is shown as a percentage of PC change. Bottom: dPC is divided by patch area to eliminate the ‘area effect’ associated with the PC calculation method. We separated species into two groups for easier reading

393

394 *Impact of landscape change scenarios*

395 The live toe fascines were identified as very favorable environments (resistance = 1) for the Eurasian beaver and common toad, and neutral (resistance = 100) for the common sandpiper and barred grass snake (Table 3). The ‘riprap to fascines’ scenario responded in an average increase in PC of 15% for the four target species. The ‘fascines to riprap’ scenario had no effect (+0%), while the ‘fascines and natural

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The live toe fascines were identified as very favorable environments (resistance = 1) for the Eurasian
beaver and common toad, and neutral (resistance = 100) for the common sandpiper and barred grass
snake (Table 3). The 'riprap to fascines' scenario responded in an average increase in PC of 15% for the
four target species. The 'fascines to riprap' scenario had no effect (+0%), while the 'fascines and natural
385 common toad and the Eurasian beaver. The trend remained the same for the barred grass snake (Fig. 3).
386 3).

<figure><img src="image_4.png" /></figure>

387

388 Fig. 3 Boxplot of the distribution of the delta Probability of connectivity (dPC) computed for each habitat patch, differentiating between riverbank habitats, other river-related habitats (mainly gravel banks) and offstream habitats. Top: dPC is shown as a percentage of PC change. Bottom: dPC is divided by patch area to eliminate the ‘area effect’ associated with the PC calculation method. We separated species into two groups for easier reading

393

394 *Impact of landscape change scenarios*

395 The live toe fascines were identified as very favorable environments (resistance = 1) for the Eurasian beaver and common toad, and neutral (resistance = 100) for the common sandpiper and barred grass snake (Table 3). The ‘riprap to fascines’ scenario responded in an average increase in PC of 15% for the four target species. The ‘fascines to riprap’ scenario had no effect (+0%), while the ‘fascines and natural

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The live toe fascines were identified as very favorable environments (resistance = 1) for the Eurasian
beaver and common toad, and neutral (resistance = 100) for the common sandpiper and barred grass
snake (Table 3). The 'riprap to fascines' scenario responded in an average increase in PC of 15% for the
four target species. The 'fascines to riprap' scenario had no effect (+0%), while the 'fascines and natural
banks to riprap' scenario corresponded to a PC decrease of 2%. However, these values masked strong differences among species. The barred grass snake (amphibious low disperser) and the common sandpiper (aerial disperser by flight) were insensitive to any bank modifications. In contrast, the 'riprap to fascines' scenario increased connectivity by 46% and 14% for the Eurasian beaver (amphibious high disperser) and the common toad (walker), respectively. For these two species, the 'fascines and natural banks to riprap' scenario led to a decrease in PC of 4% and 5%, respectively (Table 5).

Table 5. Percentage change in probability of connectivity (PC) relative to the baseline scenario, for each species and each scenario.

<table><thead><tr><th rowspan="2">Species</th><th colspan="3">Scenarios</th></tr><tr><th>Riprap to fascines</th><th>Fascines to riprap</th><th>Fascines and natural banks to riprap</th></tr></thead><tbody><tr><td>Common sandpiper</td><td>0%</td><td>0%</td><td>0%</td></tr><tr><td>Common toad</td><td>+14%</td><td>0%</td><td>-5%</td></tr><tr><td>Eurasian beaver</td><td>+46%</td><td>0%</td><td>-4%</td></tr><tr><td>Barred grass snake</td><td>0%</td><td>0%</td><td>0%</td></tr></tbody></table>

### Prioritization of bank sections to restore

As the analysis of the different scenarios showed that two of the four species (the common sandpiper and the barred grass snake) were insensitive to bank modifications, the prioritization of the sections to restore was only made for the other two species (Fig. 4). Whatever the section length (100m or 500m), the vast majority of the sections tested had little or no effect on connectivity if they were the only ones to be restored. Thus 98.1% (210/214) of the 100m sections and 94.8% of the 500m sections
banks to riprap' scenario corresponded to a PC decrease of 2%. However, these values masked strong differences among species. The barred grass snake (amphibious low disperser) and the common sandpiper (aerial disperser by flight) were insensitive to any bank modifications. In contrast, the 'riprap to fascines' scenario increased connectivity by 46% and 14% for the Eurasian beaver (amphibious high disperser) and the common toad (walker), respectively. For these two species, the 'fascines and natural banks to riprap' scenario led to a decrease in PC of 4% and 5%, respectively (Table 5).

Table 5. Percentage change in probability of connectivity (PC) relative to the baseline scenario, for each species and each scenario.

<table><thead><tr><th rowspan="2">Species</th><th colspan="3">Scenarios</th></tr><tr><th>Riprap to fascines</th><th>Fascines to riprap</th><th>Fascines and natural banks to riprap</th></tr></thead><tbody><tr><td>Common sandpiper</td><td>0%</td><td>0%</td><td>0%</td></tr><tr><td>Common toad</td><td>+14%</td><td>0%</td><td>-5%</td></tr><tr><td>Eurasian beaver</td><td>+46%</td><td>0%</td><td>-4%</td></tr><tr><td>Barred grass snake</td><td>0%</td><td>0%</td><td>0%</td></tr></tbody></table>

### Prioritization of bank sections to restore

As the analysis of the different scenarios showed that two of the four species (the common sandpiper and the barred grass snake) were insensitive to bank modifications, the prioritization of the sections to restore was only made for the other two species (Fig. 4). Whatever the section length (100m or 500m), the vast majority of the sections tested had little or no effect on connectivity if they were the only ones to be restored. Thus 98.1% (210/214) of the 100m sections and 94.8% of the 500m sections
(73/77) increase the PC of any species by less than 3%. Only four 100m sections and four 500m sections offered greater potential for connectivity improvement. Over the 100m sections, two of them would improve PC for common toad by more than 7.5% and two would improve PC for beaver by more than 12%. For the 500m sections, two significantly improved connectivity (gain above 5%) for common toad (+13.1% and +14.2% respectively), and two for Eurasian beaver (+12.4% and +32.7% respectively). However, the sections that best improved connectivity could only do so for a single target species at a time. In other words, the most favorable sections for the Eurasian beaver did not at all intersect with the most favorable sections for the common toad.

<figure><img src="image_6.png" /><figcaption>Fig. 4 Gains in habitat connectivity for two target species achieved by replacing riprap sections with live toe fascines. The orange dots represent 100 m sections, while the blue triangles represent 500 m sections. The gain in connectivity for each species (Eurasian beaver *Castor fiber* and common toad *Bufo bufo*) is expressed as the PC gain relative to the PC of the baseline scenario. Rectangles highlight sections with a significant gain in connectivity (above 5%) for a given species</figcaption></figure>
(73/77) increase the PC of any species by less than 3%. Only four 100m sections and four 500m sections offered greater potential for connectivity improvement. Over the 100m sections, two of them would improve PC for common toad by more than 7.5% and two would improve PC for beaver by more than 12%. For the 500m sections, two significantly improved connectivity (gain above 5%) for common toad (+13.1% and +14.2% respectively), and two for Eurasian beaver (+12.4% and +32.7% respectively). However, the sections that best improved connectivity could only do so for a single target species at a time. In other words, the most favorable sections for the Eurasian beaver did not at all intersect with the most favorable sections for the common toad.

<figure><img src="image_5.png" /><figcaption>Fig. 4 Gains in habitat connectivity for two target species achieved by replacing riprap sections with live toe fascines. The orange dots represent 100 m sections, while the blue triangles represent 500 m sections. The gain in connectivity for each species (Eurasian beaver *Castor fiber* and common toad *Bufo bufo*) is expressed as the PC gain relative to the PC of the baseline scenario. Rectangles highlight sections with a significant gain in connectivity (above 5%) for a given species</figcaption></figure>
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Fig. 5 confirmed that the sections favorable to the two target species were different and distant. The high-priority sections for the restoration of Eurasian beaver riparian corridors were located in the town of Cluses, on either side of a weir (B). The high-priority sections for the restoration of common toad riparian corridors were located upstream (east) of the study area, under the bridge over which the highway crosses the Arve river (C). The location of the sections to restore did not change when we used 500m sections, which suggests that at this spatial scale the prioritization does not depend on the length of the section considered (see Appendix 3 in the supplementary material).

<figure><img src="image_7.png" /></figure>

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Fig. 5 PC gain obtained by replacing one by one 100m riprap section with a live toe fascine. Sections are represented by circles for visibility. As each section is tested for two species, the circles are divided in two. The gain in connectivity is represented by a gradient of blue (Eurasian beaver) or red (common toad). We provided detailed inserts B, C for greater readability
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Fig. 5 confirmed that the sections favorable to the two target species were different and distant. The high-priority sections for the restoration of Eurasian beaver riparian corridors were located in the town of Cluses, on either side of a weir (B). The high-priority sections for the restoration of common toad riparian corridors were located upstream (east) of the study area, under the bridge over which the highway crosses the Arve river (C). The location of the sections to restore did not change when we used 500m sections, which suggests that at this spatial scale the prioritization does not depend on the length of the section considered (see Appendix 3 in the supplementary material).

<figure><img src="image_6.png" /></figure>

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Fig. 5 PC gain obtained by replacing one by one 100m riprap section with a live toe fascine. Sections are represented by circles for visibility. As each section is tested for two species, the circles are divided in two. The gain in connectivity is represented by a gradient of blue (Eurasian beaver) or red (common toad). We provided detailed inserts B, C for greater readability
We showed that using soil water bioengineering (SWBE) structures instead of civil engineering for riverbank protection could greatly change landscape connectivity for some species, while showing no effect for others. Our results showed that not all tested vertebrate species responded in a similar way to changes in riverbank structures, some of them not responding at all (common sandpiper and barred grass snake). Restoring small, well-positioned bank sections could lead to significant gains in connectivity for certain species like the Eurasian beaver or the common toad. However, sections that simultaneously favor all taxa were not detected.

We used a method for selecting surrogate species based on the use of species traits linked to sensitivity to landscape fragmentation. This method was mainly based on data from generalist databases covering a wide number of species (Grimm et al., 2014; Trochet et al., 2014; Myrvolt et al., 2015) and is therefore easily transposable to other case studies. However, for some species, more specific literature would be required for conducting this analysis. In our study, this was particularly the case for certain reptile and amphibian species, for which the literature is less extensive than for birds and mammals.

Our method enabled us to highlight four clusters of species, and therefore four surrogate species, to represent the diversity of traits related to dispersal capacities and life cycle present in our pool of candidate species. This number of species was slightly lower than the results of Meurant et al. (2018), who used an initial list of 296 vertebrate species – compared with 18 in our study, which probably corresponds to a much greater diversity of biological traits. In this configuration, they demonstrated that selecting 5 to 7 species based on traits was generally sufficient to retain the majority of 84 conservation criteria. The use of surrogate species representative of their cluster, rather than virtual species in connectivity analysis, seems also better suited to communicate with river managers and
442

443

Discussion

444 We showed that using soil water bioengineering (SWBE) structures instead of civil engineering for
445 riverbank protection could greatly change landscape connectivity for some species, while showing no
effect for others. Our results showed that not all tested vertebrate species responded in a similar way
446 to changes in riverbank structures, some of them not responding at all (common sandpiper and barred
447 grass snake). Restoring small, well-positioned bank sections could lead to significant gains in
448 connectivity for certain species like the Eurasian beaver or the common toad. However, sections that
449 simultaneously favor all taxa were not detected.
450

451

Selection of surrogate species

452 We used a method for selecting surrogate species based on the use of species traits linked to sensitivity
to landscape fragmentation. This method was mainly based on data from generalist databases
453 covering a wide number of species (Grimm et al., 2014; Trochet et al., 2014; Myrvolt et al., 2015) and
454 is therefore easily transposable to other case studies. However, for some species, more specific
455 literature would be required for conducting this analysis. In our study, this was particularly the case
456 for certain reptile and amphibian species, for which the literature is less extensive than for birds and
457 mammals.
458

459 Our method enabled us to highlight four clusters of species, and therefore four surrogate species, to
represent the diversity of traits related to dispersal capacities and life cycle present in our pool of
460 candidate species. This number of species was slightly lower than the results of Meurant et al. (2018),
461 who used an initial list of 296 vertebrate species – compared with 18 in our study, which probably
462 corresponds to a much greater diversity of biological traits. In this configuration, they demonstrated
463 that selecting 5 to 7 species based on traits was generally sufficient to retain the majority of 84
464 conservation criteria. The use of surrogate species representative of their cluster, rather than virtual
465 species in connectivity analysis, seems also better suited to communicate with river managers and
466
decision-makers. Generalist species that use riparian habitats may also be sensitive to bank modifications. Nevertheless, we have chosen to include only specialist species in the analysis, which are particularly sensitive to landscape modifications (Devictor et al., 2008; Keinath et al., 2017). We believe this conservative approach is relevant to environmental management research.

Unsurprisingly, the factorial analysis showed that the variables 'maximum longevity' and 'fertility per year' were negatively correlated. This is a good example of the r/K dichotomy (Pianka, 1970), which explains why there's always a trade-off between quality and quantity of offspring. The so-called r species (mainly represented by the amphibians in our study) have a reproductive strategy that enables them to deal with major variations in the availability of vital resources. They are characterized by short life spans and high annual fecundity. K species invest more in the survival of their young in a context of predictable access to resources. They are characterized by a long lifespan and low annual fecundity.

On the other hand, there were a statistical relationship between dispersal mode and maximum dispersal distance. In particular, species capable of flight are able to disperse further, while the walking group disperses over shorter distances. The four clusters identified, "flying animals", "amphibious weak dispersers", "amphibious strong dispersers" and "walkers", appeared to be ecologically coherent.

The fact that they were not all taxonomically homogeneous is consistent with the results of Meurant et al. (2018) who showed that grouping the species by taxon was not necessarily the most appropriate approach.

### Connectivity analysis and riverbank change scenarios

PC ranking mainly reflected the target species's dispersal capacities in the studied landscape. It was logical to find that connectivity of the landscape was highest for the common sandpiper first, as birds are naturally less sensitive to landscape fragmentation and barriers compared to other taxa such as reptiles (Keinath et al., 2017). Next came the Eurasian beaver, which disperses mainly along watercourses (Table 2) followed by the common toad and barred grass snake, which disperse mainly
decision-makers. Generalist species that use riparian habitats may also be sensitive to bank modifications. Nevertheless, we have chosen to include only specialist species in the analysis, which are particularly sensitive to landscape modifications (Devictor et al., 2008; Keinath et al., 2017). We believe this conservative approach is relevant to environmental management research.

Unsurprisingly, the factorial analysis showed that the variables 'maximum longevity' and 'fertility per year' were negatively correlated. This is a good example of the r/K dichotomy (Pianka, 1970), which explains why there's always a trade-off between quality and quantity of offspring. The so-called r species (mainly represented by the amphibians in our study) have a reproductive strategy that enables them to deal with major variations in the availability of vital resources. They are characterized by short life spans and high annual fecundity. K species invest more in the survival of their young in a context of predictable access to resources. They are characterized by a long lifespan and low annual fecundity.

On the other hand, there were a statistical relationship between dispersal mode and maximum dispersal distance. In particular, species capable of flight are able to disperse further, while the walking group disperses over shorter distances. The four clusters identified, "flying animals", "amphibious weak dispersers", "amphibious strong dispersers" and "walkers", appeared to be ecologically coherent.

The fact that they were not all taxonomically homogeneous is consistent with the results of Meurant et al. (2018) who showed that grouping the species by taxon was not necessarily the most appropriate approach.

### Connectivity analysis and riverbank change scenarios

PC ranking mainly reflected the target species's dispersal capacities in the studied landscape. It was logical to find that connectivity of the landscape was highest for the common sandpiper first, as birds are naturally less sensitive to landscape fragmentation and barriers compared to other taxa such as reptiles (Keinath et al., 2017). Next came the Eurasian beaver, which disperses mainly along watercourses (Table 2) followed by the common toad and barred grass snake, which disperse mainly
491 along terrestrial habitats and are therefore more sensitive to the quality of the landscape matrix
492 (Keinath et al., 2017).

493 Interestingly, scenario analysis revealed significant disparities between species (Table 5). The common
494 sandpiper was not sensitive to changes in riverbank structures, which can be explained by its ability to
495 fly and high dispersal distances. Changing from riprap to vegetated banks and vice versa had little
496 impact on it, particularly as its main habitat is gravel banks in the middle of watercourses. The
497 importance of gravel banks in landscape connectivity for this species was therefore significant (Fig. 3).

498 The barred grass snake (*Natrix helvetica*) had some dependence on riverbanks (Table 4). However, it
499 did not appear to be sensitive to riverbank change scenarios at all (Table 5). This can be explained by
500 the fact that riprap banks and fascines were categorized neither as favorable habitats, nor as elements
501 that particularly impede this species movements. Experts therefore assigned them an equal resistance
502 value (100) (Table 3). Consequently, switching from one type of bank to another as part of the scenario
503 did not affect landscape connectivity for this species. This result is interesting from a management
504 perspective, as it shows that ecological restoration is not necessarily favorable for all species,
505 particularly species that are well adapted to human infrastructures or degraded habitat (Walts &
506 Wallace Covington, 2004; Noreika et al., 2015).

507 The analysis of connectivity for Eurasian beaver (*Castor fiber*) and common toad (*Bufo bufo*) showed
508 that both species seemed to be highly dependent on riverbank habitats for their connectivity (Table
509 4). The 'riprap to fascines' scenario greatly improved connectivity, while the 'fascines and natural banks
510 to riprap' scenario reduced it (Table 5). The 'fascines to riprap' scenario only concerned the
511 replacement of the single live toe fascine in the landscape (0.5% of the total bank linear), so its impact
512 on connectivity was expected to be low. The dPC analysis showed that offstream habitats could be
513 very important for habitat connectivity in absolute value (Fig. 4). This was particularly the case for the
514 Eurasian beaver, which occurs in offstream water bodies that can cover large areas of the study zone
515 (Table 2). When divided by surface area (dPC/area), the importance of riverbank habitats for ecological
491 along terrestrial habitats and are therefore more sensitive to the quality of the landscape matrix
492 (Keinath et al., 2017).

493 Interestingly, scenario analysis revealed significant disparities between species (Table 5). The common
494 sandpiper was not sensitive to changes in riverbank structures, which can be explained by its ability to
495 fly and high dispersal distances. Changing from riprap to vegetated banks and vice versa had little
496 impact on it, particularly as its main habitat is gravel banks in the middle of watercourses. The
497 importance of gravel banks in landscape connectivity for this species was therefore significant (Fig. 3).

498 The barred grass snake (*Natrix helvetica*) had some dependence on riverbanks (Table 4). However, it
499 did not appear to be sensitive to riverbank change scenarios at all (Table 5). This can be explained by
500 the fact that riprap banks and fascines were categorized neither as favorable habitats, nor as elements
501 that particularly impede this species movements. Experts therefore assigned them an equal resistance
502 value (100) (Table 3). Consequently, switching from one type of bank to another as part of the scenario
503 did not affect landscape connectivity for this species. This result is interesting from a management
504 perspective, as it shows that ecological restoration is not necessarily favorable for all species,
505 particularly species that are well adapted to human infrastructures or degraded habitat (Walts &
506 Wallace Covington, 2004; Noreika et al., 2015).

507 The analysis of connectivity for Eurasian beaver (*Castor fiber*) and common toad (*Bufo bufo*) showed
508 that both species seemed to be highly dependent on riverbank habitats for their connectivity (Table
509 4). The 'riprap to fascines' scenario greatly improved connectivity, while the 'fascines and natural banks
510 to riprap' scenario reduced it (Table 5). The 'fascines to riprap' scenario only concerned the
511 replacement of the single live toe fascine in the landscape (0.5% of the total bank linear), so its impact
512 on connectivity was expected to be low. The dPC analysis showed that offstream habitats could be
513 very important for habitat connectivity in absolute value (Fig. 4). This was particularly the case for the
514 Eurasian beaver, which occurs in offstream water bodies that can cover large areas of the study zone
515 (Table 2). When divided by surface area (dPC/area), the importance of riverbank habitats for ecological
connectivity appeared to be greater, highlighting their disproportionate ecological importance in relation to their surface. Their importance had already been demonstrated by Sabo et al. (2005), who showed that riparian habitats increased regional species richness by >50% on average worldwide, despite their narrow surface area. Riparian habitats are therefore what Hunter (2017) calls small natural features with large ecological roles. Nevertheless, maintaining favorable offstream habitats in addition to riverbank habitats for semi-aquatic species seems to be a good solution for maintaining or improving landscape connectivity. This is particularly the case for species that are not strictly river-dwelling, such as the common toad and the barred grass snake (Table 3). This result is consistent with previous studies showing the importance of the offstream wetland network in ecological connectivity, particularly when riverbed habitats are subject to change as a result of hydromorphological variations like flooding (Karim et al., 2012). Our study shows that this is also the case in urbanized areas, which are much less subject to hydromorphological variations due to the channelization of rivers. This study is based on resistance surfaces parameterized based on expert opinions and the literature. It could be useful to supplement it with field studies on the actual movements of species, for example using genetics or telemetry.

### Prioritization of bank sections to restore

Our prioritization analysis showed that at the tested scale (100 and 500m-long sections) there was no single section where restoration would maximize connectivity for all the target species. On the contrary, the majority of sections tested had only a limited effect on connectivity when considered separately (PC gain < 3%). The few sections with a strong effect were only effective for one species at a time, either the Eurasian beaver or the common toad (Fig. 4). Multi-species connectivity studies often reveal inter-taxon disparities in response to habitat change (e.g. Albert et al., 2017; Meurant et al., 2018). It is noteworthy that a few sections have a very high potential for increasing connectivity, and their restoration alone would allow to achieve or approach the level of connectivity of the best case ('riprap to fascines') scenario. For example, one 500m section would increase connectivity for common
connectivity appeared to be greater, highlighting their disproportionate ecological importance in relation to their surface. Their importance had already been demonstrated by Sabo et al. (2005), who showed that riparian habitats increased regional species richness by >50% on average worldwide, despite their narrow surface area. Riparian habitats are therefore what Hunter (2017) calls small natural features with large ecological roles. Nevertheless, maintaining favorable offstream habitats in addition to riverbank habitats for semi-aquatic species seems to be a good solution for maintaining or improving landscape connectivity. This is particularly the case for species that are not strictly river-dwelling, such as the common toad and the barred grass snake (Table 3). This result is consistent with previous studies showing the importance of the offstream wetland network in ecological connectivity, particularly when riverbed habitats are subject to change as a result of hydromorphological variations like flooding (Karim et al., 2012). Our study shows that this is also the case in urbanized areas, which are much less subject to hydromorphological variations due to the channelization of rivers. This study is based on resistance surfaces parameterized based on expert opinions and the literature. It could be useful to supplement it with field studies on the actual movements of species, for example using genetics or telemetry.

### Prioritization of bank sections to restore

Our prioritization analysis showed that at the tested scale (100 and 500m-long sections) there was no single section where restoration would maximize connectivity for all the target species. On the contrary, the majority of sections tested had only a limited effect on connectivity when considered separately (PC gain < 3%). The few sections with a strong effect were only effective for one species at a time, either the Eurasian beaver or the common toad (Fig. 4). Multi-species connectivity studies often reveal inter-taxon disparities in response to habitat change (e.g. Albert et al., 2017; Meurant et al., 2018). It is noteworthy that a few sections have a very high potential for increasing connectivity, and their restoration alone would allow to achieve or approach the level of connectivity of the best case ('riprap to fascines') scenario. For example, one 500m section would increase connectivity for common
541 toad by 14%, which is the potential total gain of the 'riprap to fascines' scenario in the studied riverine
542 landscape. For Eurasian beavers, a single section can increase PC by more than 32%, close to the total
543 gain of 46% for the 'riprap to fascines' scenario.

544 A cartographic analysis of the location of the sections tested showed that there were key sectors of
545 the river where the priority sections to restore were concentrated. These were areas whose position
546 in the habitat network made the reduction of their resistance to movement particularly interesting for
547 connectivity. For example, panel B (Fig. 5) showed that the most interesting sectors to restore in terms
548 of habitat connectivity for Eurasian beavers were located on either side of a weir in an urban
549 environment. The weirs were identified as potential barriers to beaver longitudinal movement (Table
550 2). As the concerned weirs were located in urban areas, the environments above the banks were not
551 crossable by beavers (built-up areas). The fact that the beavers could cross the banks laterally (10m
552 wide) were not sufficient to enable them to reach areas favorable to movement. They therefore
553 needed to travel a longer distance along the banks to bypass the weirs and move from one section of
554 the river to another. This cost of movement would be reduced by replacing riprap with SWBE
555 structures, improving the crossability of the banks to improve habitat connectivity. Panel C (Fig. 5)
556 contained the main sectors to restore for the common toad. These were located under the highway
557 bridge. As the highway itself is a strong barrier to toad movement, being able to pass underneath it
558 would increase habitat connectivity between downstream and upstream. This could be made possible
559 by replacing the bank riprap under the freeway bridge with a fascine, which is more permeable to toad
560 movement than riprap.

561 Our analysis therefore showed that the restoration of small, well-chosen areas of riverbanks could
562 enable semi-aquatic animals to avoid ecological barriers present in urbanized landscapes (buildings,
563 roads) or in riverbeds (weirs). Detecting and removing barriers to movement to improve connectivity
564 is a well-known issue in landscape ecology, in both terrestrial (McRae et al., 2012) and aquatic contexts
541 toad by 14%, which is the potential total gain of the 'riprap to fascines' scenario in the studied riverine
542 landscape. For Eurasian beavers, a single section can increase PC by more than 32%, close to the total
543 gain of 46% for the 'riprap to fascines' scenario.

544 A cartographic analysis of the location of the sections tested showed that there were key sectors of
545 the river where the priority sections to restore were concentrated. These were areas whose position
546 in the habitat network made the reduction of their resistance to movement particularly interesting for
547 connectivity. For example, panel B (Fig. 5) showed that the most interesting sectors to restore in terms
548 of habitat connectivity for Eurasian beavers were located on either side of a weir in an urban
549 environment. The weirs were identified as potential barriers to beaver longitudinal movement (Table
550 2). As the concerned weirs were located in urban areas, the environments above the banks were not
551 crossable by beavers (built-up areas). The fact that the beavers could cross the banks laterally (10m
552 wide) were not sufficient to enable them to reach areas favorable to movement. They therefore
553 needed to travel a longer distance along the banks to bypass the weirs and move from one section of
554 the river to another. This cost of movement would be reduced by replacing riprap with SWBE
555 structures, improving the crossability of the banks to improve habitat connectivity. Panel C (Fig. 5)
556 contained the main sectors to restore for the common toad. These were located under the highway
557 bridge. As the highway itself is a strong barrier to toad movement, being able to pass underneath it
558 would increase habitat connectivity between downstream and upstream. This could be made possible
559 by replacing the bank riprap under the freeway bridge with a fascine, which is more permeable to toad
560 movement than riprap.

561 Our analysis therefore showed that the restoration of small, well-chosen areas of riverbanks could
562 enable semi-aquatic animals to avoid ecological barriers present in urbanized landscapes (buildings,
563 roads) or in riverbeds (weirs). Detecting and removing barriers to movement to improve connectivity
564 is a well-known issue in landscape ecology, in both terrestrial (McRae et al., 2012) and aquatic contexts
565

(Kraft et al., 2019). In the case of the Eurasian beaver, it is likely that the same result could be achieved by removing the barrier in the riverbed (weir) as by restoring the surrounding banks.

566

567

The prioritization analysis was not very sensitive to the size of the sections tested. We could expect that switching from 100m to 500m sections should reveal new favorable zones, but the most favorable zones to restore did not change (Appendix 3). The switch just revealed low-priority areas (potential PC gain <3%) for the common toad and the Eurasian beaver. However, high-priority areas were immediately apparent when testing 100m sections. The switch to 500m only confirmed the potential PC gain (Fig. 4, Appendix 3).

568

569

570

571

572

573

## Conclusion: insight for management

574

575

576

577

578

579

580

581

582

583

584

585

586

587

588

589

Our study proposes a valuable decision-making method to help river managers determine where to restore bank protection structures using soil water bioengineering. As many species with variable dispersal capacities may be present in the study area, we recommend that managers rely on accurate, up-to-date species inventories to guide management choices. We showed that it can be difficult to satisfy the needs of all riparian habitat species when selecting a section to restore. This result supports previous studies showing that different riparian species can respond differently to deleterious changes in their environment (e.g. Van Looy et al., 2013). It seems logical to obtain similar results for a priori more favorable changes in the landscape, such as ecological restoration. To improve habitat connectivity in a multi-species context, it may be worthwhile to restore several small, appropriately located sections according to the ecological requirements of several target species. It may also be worthwhile to focus on one or two species of particular concern (e.g., a species with high conservation value) to limit the number of target species with different ecological needs. As the restoration of connectivity is not necessarily at the heart of all restoration projects, this choice of target species can be made as part of a more general framework for defining the objectives of the restoration project (Hallett et al., 2013; Coutinho et al., 2023). We believe that our method will find its place as a decision-making tool for planning restoration projects, alongside others from other fields that may include other
565

(Kraft et al., 2019). In the case of the Eurasian beaver, it is likely that the same result could be achieved by removing the barrier in the riverbed (weir) as by restoring the surrounding banks.

566

567

The prioritization analysis was not very sensitive to the size of the sections tested. We could expect that switching from 100m to 500m sections should reveal new favorable zones, but the most favorable zones to restore did not change (Appendix 3). The switch just revealed low-priority areas (potential PC gain <3%) for the common toad and the Eurasian beaver. However, high-priority areas were immediately apparent when testing 100m sections. The switch to 500m only confirmed the potential PC gain (Fig. 4, Appendix 3).

568

569

570

571

572

573

## Conclusion: insight for management

574

575

576

577

578

579

580

581

582

583

584

585

586

587

588

589

Our study proposes a valuable decision-making method to help river managers determine where to restore bank protection structures using soil water bioengineering. As many species with variable dispersal capacities may be present in the study area, we recommend that managers rely on accurate, up-to-date species inventories to guide management choices. We showed that it can be difficult to satisfy the needs of all riparian habitat species when selecting a section to restore. This result supports previous studies showing that different riparian species can respond differently to deleterious changes in their environment (e.g. Van Looy et al., 2013). It seems logical to obtain similar results for a priori more favorable changes in the landscape, such as ecological restoration. To improve habitat connectivity in a multi-species context, it may be worthwhile to restore several small, appropriately located sections according to the ecological requirements of several target species. It may also be worthwhile to focus on one or two species of particular concern (e.g., a species with high conservation value) to limit the number of target species with different ecological needs. As the restoration of connectivity is not necessarily at the heart of all restoration projects, this choice of target species can be made as part of a more general framework for defining the objectives of the restoration project (Hallett et al., 2013; Coutinho et al., 2023). We believe that our method will find its place as a decision-making tool for planning restoration projects, alongside others from other fields that may include other
590 areas of ecology as well as governance or economic benefit criteria that may be crucial to the chances
591 of success of ecological restoration projects (Hallett et al., 2013).

592 **Authors' contribution**

593 AE, LB and MA designed the study. BD and TH performed the field work. EB performed the analysis
594 and led the writing of the manuscript. AE, LB, MA and EB contributed critically to the drafts and gave
595 final approval for publication.

596 **Acknowledgements**

597 This study was funded by the Agence de l'Eau Rhône-Méditerranée-Corse (AERMC) within the program
598 GéniTrame. Thanks to the Syndicat mixte d'aménagement de l'Arve et de ses affluents (SM3A) which
599 kindly shared their data. Thanks to all the experts who agreed to answer our questions and help us
600 parameterize the resistance surface maps.
590 areas of ecology as well as governance or economic benefit criteria that may be crucial to the chances

591 of success of ecological restoration projects (Hallett et al., 2013).

592 **Authors' contribution**

593 AE, LB and MA designed the study. BD and TH performed the field work. EB performed the analysis

594 and led the writing of the manuscript. AE, LB, MA and EB contributed critically to the drafts and gave

595 final approval for publication.

596 **Acknowledgements**

597 This study was funded by the Agence de l'Eau Rhône-Méditerranée-Corse (AERMC) within the program

598 GéniTrame. Thanks to the Syndicat mixte d'aménagement de l'Arve et de ses affluents (SM3A) which

599 kindly shared their data. Thanks to all the experts who agreed to answer our questions and help us

600 parameterize the resistance surface maps.
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607 https://doi.org/10.1007/s10980-015-0336-8

608 Baguette, M., Blanchet, S., Legrand, D., Stevens, V. M., & Turlure, C. (2013). Individual dispersal,
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611 Benda, L., Poff, N. L., Miller, D., Dunne, T., Reeves, G., Pess, G., & Pollock, M. (2004). The Network
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613 413. https://doi.org/10.1641/0006-3568(2004)054[0413:TNDHHC]2.0.CO;2

614 Bergès, L., Avon, C., Bezombes, L., Clauzel, C., Duflot, R., Foltête, J.-C., Gaucherand, S., Girardet, X., &
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618 Cavaillé, P., Dommanget, F., Daumergue, N., Loucougaray, G., Spiegelberger, T., Tabacchi, E., &
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622 Cavaillé, P., Ducasse, L., Breton, V., Dommanget, F., Tabacchi, E., & Evette, A. (2015). Functional and
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626 Cavaillé, P., Dumont, B., Van Looy, K., Floury, M., Tabacchi, E., & Evette, A. (2018). Influence of riverbank stabilization techniques on taxonomic and functional macrobenthic communities. *Hydrobiologia*, 807(1), 19-35. https://doi.org/10.1007/s10750-017-3380-3
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638
639 Coutinho, A. G., Carlucci, M. B., & Cianciaruso, M. V. (2023). A framework to apply trait-based ecological restoration at large scales. *Journal of Applied Ecology*, 60(8), 1562-1571.
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641
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644
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647
648 Devictor, V., Julliard, R., & Jiguet, F. (2008). Distribution of specialist and generalist species along spatial gradients of habitat disturbance and fragmentation. *Oikos*, 117(4), 507-514.
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650
651 Dudgeon, D., Arthington, A. H., Gessner, M. O., Kawabata, Z.-I., Knowler, D. J., Lévêque, C., Naiman, https://doi.org/10.1111/j.0030-1299.2008.16215.x
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627
628
629 Clauzel, C., Bannwarth, C., & Foltete, J.-C. (2015). Integrating regional-scale connectivity in habitat restoration: An application for amphibian conservation in eastern France. *Journal for Nature Conservation*, 23, 98-107. https://doi.org/10.1016/j.jnc.2014.07.001
630
631
632 Clauzel, C., Girardet, X., & Foltête, J.-C. (2013). Impact assessment of a high-speed railway line on species distribution: Application to the European tree frog (Hyla arborea) in Franche-Comté. *Journal of Environmental Management*, 127, 125-134.
633
634
635 https://doi.org/10.1016/j.jenvman.2013.04.018
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637
638
639 Coutinho, A. G., Carlucci, M. B., & Cianciaruso, M. V. (2023). A framework to apply trait-based ecological restoration at large scales. *Journal of Applied Ecology*, 60(8), 1562-1571.
640
641
642 de la Fuente, B., Mateo-Sánchez, M. C., Rodríguez, G., Gastón, A., Pérez de Ayala, R., Colomina-Pérez, D., Melero, M., & Saura, S. (2018). Natura 2000 sites, public forests and riparian corridors: The connectivity backbone of forest green infrastructure. *Land Use Policy*, 75, 429-441.
643
644
645
646 Decout, S., Manel, S., Miaud, C., & Luque, S. (2010). *Connectivity and landscape patterns in human dominated landscape: A case study with the common frog Rana temporaria*. 11.
647
648 Devictor, V., Julliard, R., & Jiguet, F. (2008). Distribution of specialist and generalist species along spatial gradients of habitat disturbance and fragmentation. *Oikos*, 117(4), 507-514.
649
650
651 Dudgeon, D., Arthington, A. H., Gessner, M. O., Kawabata, Z.-I., Knowler, D. J., Lévêque, C., Naiman, https://doi.org/10.1111/j.0030-1299.2008.16215.x
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654 81(02), 163. https://doi.org/10.1017/S1464793105006950

655 Duflot, R., Avon, C., Roche, P., & Bergès, L. (2018). Combining habitat suitability models and spatial

656 graphs for more effective landscape conservation planning : An applied methodological

657 framework and a species case study. *Journal for Nature Conservation*, 46, 38-47.

658 https://doi.org/10.1016/j.jnc.2018.08.005

659 Dutta, T., De Barba, M., Selva, N., Fedorca, A. C., Maiorano, L., Thuiller, W., Zedrosser, A., Signer, J.,

660 Pflüger, F., Frank, S., Lucas, P. M., & Balkenhol, N. (2023). An objective approach to select

661 surrogate species for connectivity conservation. *Frontiers in Ecology and Evolution*, 11,

662 1078649. https://doi.org/10.3389/fevo.2023.1078649

663 Erős, T., Schmera, D., & Schick, R. S. (2011). Network thinking in riverscape conservation – A graph-

664 based approach. *Biological Conservation*, 144(1), 184-192.

665 https://doi.org/10.1016/j.biocon.2010.08.013

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667 networks. *Environmental Modelling & Software*, 38, 316-327.

668 https://doi.org/10.1016/j.envsoft.2012.07.002

669 Foltête, J.-C., Girardet, X., & Clauzel, C. (2014). A methodological framework for the use of landscape

670 graphs in land-use planning. *Landscape and Urban Planning*, 124, 140-150.

671 https://doi.org/10.1016/j.landurbplan.2013.12.012

672 Foltête, J.-C., Vuidel, G., Savary, P., Clauzel, C., Sahraoui, Y., Girardet, X., & Bourgeois, M. (2021).

673 Graphab : An application for modeling and managing ecological habitat networks. *Software*

674 *Impacts*, 8, 100065. https://doi.org/10.1016/j.simpa.2021.100065

675 González, E., Felipe-Lucia, M. R., Bourgeois, B., Boz, B., Nilsson, C., Palmer, G., & Sher, A. A. (2017).

676 Integrative conservation of riparian zones. *Biological Conservation*, 211, 20-29.

677 https://doi.org/10.1016/j.biocon.2016.10.035
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653 biodiversity : Importance, threats, status and conservation challenges. *Biological Reviews*,

654 81(02), 163. https://doi.org/10.1017/S1464793105006950

655 Duflot, R., Avon, C., Roche, P., & Bergès, L. (2018). Combining habitat suitability models and spatial

656 graphs for more effective landscape conservation planning : An applied methodological

657 framework and a species case study. *Journal for Nature Conservation*, 46, 38-47.

658 https://doi.org/10.1016/j.jnc.2018.08.005

659 Dutta, T., De Barba, M., Selva, N., Fedorca, A. C., Maiorano, L., Thuiller, W., Zedrosser, A., Signer, J.,

660 Pflüger, F., Frank, S., Lucas, P. M., & Balkenhol, N. (2023). An objective approach to select

661 surrogate species for connectivity conservation. *Frontiers in Ecology and Evolution*, 11,

662 1078649. https://doi.org/10.3389/fevo.2023.1078649

663 Erős, T., Schmera, D., & Schick, R. S. (2011). Network thinking in riverscape conservation – A graph-

664 based approach. *Biological Conservation*, 144(1), 184-192.

665 https://doi.org/10.1016/j.biocon.2010.08.013

666 Foltête, J.-C., Clauzel, C., & Vuidel, G. (2012). A software tool dedicated to the modelling of landscape

667 networks. *Environmental Modelling & Software*, 38, 316-327.

668 https://doi.org/10.1016/j.envsoft.2012.07.002

669 Foltête, J.-C., Girardet, X., & Clauzel, C. (2014). A methodological framework for the use of landscape

670 graphs in land-use planning. *Landscape and Urban Planning*, 124, 140-150.

671 https://doi.org/10.1016/j.landurbplan.2013.12.012

672 Foltête, J.-C., Vuidel, G., Savary, P., Clauzel, C., Sahraoui, Y., Girardet, X., & Bourgeois, M. (2021).

673 Graphab : An application for modeling and managing ecological habitat networks. *Software*

674 *Impacts*, 8, 100065. https://doi.org/10.1016/j.simpa.2021.100065

675 González, E., Felipe-Lucia, M. R., Bourgeois, B., Boz, B., Nilsson, C., Palmer, G., & Sher, A. A. (2017).

676 Integrative conservation of riparian zones. *Biological Conservation*, 211, 20-29.

677 https://doi.org/10.1016/j.biocon.2016.10.035
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679 database of European reptile species. *Nature Conservation, 9*, 45-67.

680 https://doi.org/10.3897/natureconservation.9.8908

681 Haddad, N. M., Brudvig, L. A., Clobert, J., Davies, K. F., Gonzalez, A., Holt, R. D., Lovejoy, T. E., Sexton,

682 J. O., Austin, M. P., Collins, C. D., Cook, W. M., Damschen, E. I., Ewers, R. M., Foster, B. L.,

683 Jenkins, C. N., King, A. J., Laurance, W. F., Levey, D. J., Margules, C. R., ... Townshend, J. R.

684 (2015). Habitat fragmentation and its lasting impact on Earth's ecosystems. *Science*

685 *Advances, 1*(2), e1500052. https://doi.org/10.1126/sciadv.1500052

686 Hallett, L. M., Diver, S., Eitzel, M. V., Olson, J. J., Ramage, B. S., Sardinas, H., Statman-Weil, Z., &

687 Suding, K. N. (2013). Do We Practice What We Preach? Goal Setting for Ecological

688 Restoration: Goal setting for ecological restoration. *Restoration Ecology, 21*(3), 312-319.

689 https://doi.org/10.1111/rec.12007

690 Henle, K., Davies, K. F., Kleyer, M., Margules, C., & Settele, J. (2004). Predictors of Species Sensitivity

691 to Fragmentation. *Biodiversity and Conservation, 13*(1), 207-251.

692 https://doi.org/10.1023/B:BIOC.0000004319.91643.9e

693 Hodgson, J. A., Thomas, C. D., Cinderby, S., Cambridge, H., Evans, P., & Hill, J. K. (2011). Habitat re-

694 creation strategies for promoting adaptation of species to climate change : Habitat re-

695 creation and range expansions. *Conservation Letters, 4*(4), 289-297.

696 https://doi.org/10.1111/j.1755-263X.2011.00177.x

697 Hunter, M. L. (2017). Conserving small natural features with large ecological roles : An introduction

698 and definition. *Biological Conservation, 211*, 1-2.

699 https://doi.org/10.1016/j.biocon.2016.12.019Inglada, J., Vincent, A., Arias, M., Tardy, B.,

700 Morin, D., & Rodes, I. (2017). Operational High Resolution Land Cover Map Production at the

701 Country Scale Using Satellite Image Time Series. *Remote Sensing, 9*(1), 95.

702 https://doi.org/10.3390/rs9010095

703 INPN (2023). Accès aux données d'observation sur les espèces. Muséum national d'histoire naturelle.
678 Grimm, A., Prieto Ramírez, A. M., Moulherat, S., Reynaud, J., & Henle, K. (2014). Life-history trait

679 database of European reptile species. *Nature Conservation, 9*, 45-67.

680 https://doi.org/10.3897/natureconservation.9.8908

681 Haddad, N. M., Brudvig, L. A., Clobert, J., Davies, K. F., Gonzalez, A., Holt, R. D., Lovejoy, T. E., Sexton,

682 J. O., Austin, M. P., Collins, C. D., Cook, W. M., Damschen, E. I., Ewers, R. M., Foster, B. L.,

683 Jenkins, C. N., King, A. J., Laurance, W. F., Levey, D. J., Margules, C. R., ... Townshend, J. R.

684 (2015). Habitat fragmentation and its lasting impact on Earth's ecosystems. *Science*

685 *Advances*, 1(2), e1500052. https://doi.org/10.1126/sciadv.1500052

686 Hallett, L. M., Diver, S., Eitzel, M. V., Olson, J. J., Ramage, B. S., Sardinas, H., Statman-Weil, Z., &

687 Suding, K. N. (2013). Do We Practice What We Preach? Goal Setting for Ecological

688 Restoration: Goal setting for ecological restoration. *Restoration Ecology*, 21(3), 312-319.

689 https://doi.org/10.1111/rec.12007

690 Henle, K., Davies, K. F., Kleyer, M., Margules, C., & Settele, J. (2004). Predictors of Species Sensitivity

691 to Fragmentation. *Biodiversity and Conservation*, 13(1), 207-251.

692 https://doi.org/10.1023/B:BIOC.0000004319.91643.9e

693 Hodgson, J. A., Thomas, C. D., Cinderby, S., Cambridge, H., Evans, P., & Hill, J. K. (2011). Habitat re-

694 creation strategies for promoting adaptation of species to climate change : Habitat re-

695 creation and range expansions. *Conservation Letters*, 4(4), 289-297.

696 https://doi.org/10.1111/j.1755-263X.2011.00177.x

697 Hunter, M. L. (2017). Conserving small natural features with large ecological roles : An introduction

698 and definition. *Biological Conservation*, 211, 1-2.

699 https://doi.org/10.1016/j.biocon.2016.12.019Inglada, J., Vincent, A., Arias, M., Tardy, B.,

700 Morin, D., & Rodes, I. (2017). Operational High Resolution Land Cover Map Production at the

701 Country Scale Using Satellite Image Time Series. *Remote Sensing*, 9(1), 95.

702 https://doi.org/10.3390/rs9010095

703 INPN (2023). Accès aux données d'observation sur les espèces. Muséum national d'histoire naturelle.
704 https://inpn.mnhn.fr/espece/extraction-sinp/preambule

705 INPN (2023b). ZNIEFF 820031533 - Ensemble fonctionnel de la rivière Arve et de ses annexes. Muséum

706 national d'histoire naturelle. https://inpn.mnhn.fr/zone/znieff/820031533

707 IUCN (2022). Habitats Classification Scheme (Version 3.1). International union for conservation of

708 nature. https://www.iucnredlist.org/resources/habitat-classification-scheme

709 Janssen, P., Cavaillé, P., Vivier, A., & Evette, A. (2019). Le génie végétal favorise une plus grande

710 diversité de micro-habitats aquatiques et de macro-invertébrés benthiques. *Techniques*

711 *Sciences Méthodes*, 9, 55-64. https://doi.org/10.1051/tsm/201909055

712 Karim, F., Kinsey-Henderson, A., Wallace, J., Arthington, A. H., & Pearson, R. G. (2012). Modelling

713 wetland connectivity during overbank flooding in a tropical floodplain in north Queensland,

714 Australia : HYDROLOGICAL CONNECTIVITY OF FLOODPLAIN WETLANDS. *Hydrological*

715 *Processes*, 26(18), 2710-2723. https://doi.org/10.1002/hyp.8364

716 Keinath, D. A., Doak, D. F., Hodges, K. E., Prugh, L. R., Fagan, W., Sekercioglu, C. H., Buchart, S. H. M.,

717 & Kauffman, M. (2017). A global analysis of traits predicting species sensitivity to habitat

718 fragmentation. *Global Ecology and Biogeography*, 26(1), 115-127.

719 https://doi.org/10.1111/geb.12509

720 Kraft, M., Rosenberg, D. E., & Null, S. E. (2019). Prioritizing Stream Barrier Removal to Maximize

721 Connected Aquatic Habitat and Minimize Water Scarcity. *JAWRA Journal of the American*

722 *Water Resources Association*, 55(2), 382-400. https://doi.org/10.1111/1752-1688.12718

723 Lalechère, E., & Bergès, L. (2021). A Validation Procedure for Ecological Corridor Locations. *Land*,

724 10(12), 1320. https://doi.org/10.3390/land10121320

725 Lê, S., Josse, J., & Husson, F. (2008). FactoMineR : An R Package for Multivariate Analysis. *Journal of*

726 *Statistical Software*, 25(1). https://doi.org/10.18637/jss.v025.i01

727 Leoncini, F., Semenzato, P., Di Febbraro, M., Loy, A., & Ferrari, C. (2023). Come back to stay :

728 Landscape connectivity analysis for the Eurasian otter (Lutra lutra) in the western Alps.
704 https://inpn.mnhn.fr/espece/extraction-sinp/preambule

705 INPN (2023b). ZNIEFF 820031533 - Ensemble fonctionnel de la rivière Arve et de ses annexes. Muséum

706 national d'histoire naturelle. https://inpn.mnhn.fr/zone/znieff/820031533

707 IUCN (2022). Habitats Classification Scheme (Version 3.1). International union for conservation of

708 nature. https://www.iucnredlist.org/resources/habitat-classification-scheme

709 Janssen, P., Cavaillé, P., Vivier, A., & Evette, A. (2019). Le génie végétal favorise une plus grande

710 diversité de micro-habitats aquatiques et de macro-invertébrés benthiques. *Techniques*

711 *Sciences Méthodes*, 9, 55-64. https://doi.org/10.1051/tsm/201909055

712 Karim, F., Kinsey-Henderson, A., Wallace, J., Arthington, A. H., & Pearson, R. G. (2012). Modelling

713 wetland connectivity during overbank flooding in a tropical floodplain in north Queensland,

714 Australia : HYDROLOGICAL CONNECTIVITY OF FLOODPLAIN WETLANDS. *Hydrological*

715 *Processes*, 26(18), 2710-2723. https://doi.org/10.1002/hyp.8364

716 Keinath, D. A., Doak, D. F., Hodges, K. E., Prugh, L. R., Fagan, W., Sekercioglu, C. H., Buchart, S. H. M.,

717 & Kauffman, M. (2017). A global analysis of traits predicting species sensitivity to habitat

718 fragmentation. *Global Ecology and Biogeography*, 26(1), 115-127.

719 https://doi.org/10.1111/geb.12509

720 Kraft, M., Rosenberg, D. E., & Null, S. E. (2019). Prioritizing Stream Barrier Removal to Maximize

721 Connected Aquatic Habitat and Minimize Water Scarcity. *JAWRA Journal of the American*

722 *Water Resources Association*, 55(2), 382-400. https://doi.org/10.1111/1752-1688.12718

723 Lalechère, E., & Bergès, L. (2021). A Validation Procedure for Ecological Corridor Locations. *Land*,

724 10(12), 1320. https://doi.org/10.3390/land10121320

725 Lê, S., Josse, J., & Husson, F. (2008). FactoMineR : An R Package for Multivariate Analysis. *Journal of*

726 *Statistical Software*, 25(1). https://doi.org/10.18637/jss.v025.i01

727 Leoncini, F., Semenzato, P., Di Febbraro, M., Loy, A., & Ferrari, C. (2023). Come back to stay :

728 Landscape connectivity analysis for the Eurasian otter (Lutra lutra) in the western Alps.
729 *Biodiversity and Conservation*, 32(2), 653-669. https://doi.org/10.1007/s10531-022-02517-3
730 Martin, F.-M., Janssen, P., Bergès, L., Dupont, B., & Evette, A. (2021). Higher structural connectivity and resistance against invasions of soil bioengineering over hard-engineering for riverbank stabilisation. *Wetlands Ecology and Management*, 29(1), 27-39.
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733 https://doi.org/10.1007/s11273-020-09765-6
734 McRae, B. H., Hall, S. A., Beier, P., & Theobald, D. M. (2012). Where to Restore Ecological Connectivity? Detecting Barriers and Quantifying Restoration Benefits. *PLoS ONE*, 7(12),
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743 Meurant, M., Gonzalez, A., Doxa, A., & Albert, C. H. (2018). Selecting surrogate species for connectivity conservation. *Biological Conservation*, 227, 326-334.
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748 Mouton, A. M., Buysse, D., Stevens, M., Neucker, T., & Coeck, J. (2012). Evaluation of riparian habitat restoration in a lowland river: evaluation of riparian habitat restoration. *River Research and Applications*, 28(7), 845-857. https://doi.org/10.1002/rra.1500
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751 Myhrvold, N. P., Baldridge, E., Chan, B., Sivam, D., Freeman, D. L., & Ernest, S. K. M. (2015). An amniote life-history database to perform comparative analyses with birds, mammals, and reptiles. *Ecology*, 96(11), 3109-000. https://doi.org/10.1890/15-0846R.1
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755 https://doi.org/10.2307/1941822
729 *Biodiversity and Conservation*, 32(2), 653-669. https://doi.org/10.1007/s10531-022-02517-3
730 Martin, F.-M., Janssen, P., Bergès, L., Dupont, B., & Evette, A. (2021). Higher structural connectivity and resistance against invasions of soil bioengineering over hard-engineering for riverbank stabilisation. *Wetlands Ecology and Management*, 29(1), 27-39.
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733 https://doi.org/10.1007/s11273-020-09765-6
734 McRae, B. H., Hall, S. A., Beier, P., & Theobald, D. M. (2012). Where to Restore Ecological Connectivity? Detecting Barriers and Quantifying Restoration Benefits. *PLoS ONE*, 7(12),
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736 e52604. https://doi.org/10.1371/journal.pone.0052604
737 Meister, B., Hofer, U., Ursenbacher, S., & Baur, B. (2010). Spatial genetic analysis of the grass snake, Natrix natrix (Squamata : Colubridae), in an intensively used agricultural landscape. *Biological Journal of the Linnean Society*, 101(1), 51-58. https://doi.org/10.1111/j.1095-8312.2010.01474.x
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741 Merritt, D. M., & Wohl, E. E. (2006). Plant dispersal along rivers fragmented by dams. *River Research and Applications*, 22(1), 1-26. https://doi.org/10.1002/rra.890
742
743 Meurant, M., Gonzalez, A., Doxa, A., & Albert, C. H. (2018). Selecting surrogate species for connectivity conservation. *Biological Conservation*, 227, 326-334.
744
745
746 https://doi.org/10.1016/j.biocon.2018.09.028
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748 Mouton, A. M., Buysse, D., Stevens, M., Neucker, T., & Coeck, J. (2012). Evaluation of riparian habitat restoration in a lowland river: evaluation of riparian habitat restoration. *River Research and Applications*, 28(7), 845-857. https://doi.org/10.1002/rra.1500
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751 Myhrvold, N. P., Baldridge, E., Chan, B., Sivam, D., Freeman, D. L., & Ernest, S. K. M. (2015). An amniote life-history database to perform comparative analyses with birds, mammals, and reptiles. *Ecology*, 96(11), 3109-000. https://doi.org/10.1890/15-0846R.1
752 Naiman, R. J., Decamps, H., & Pollock, M. (1993). The Role of Riparian Corridors in Maintaining Regional Biodiversity. *Ecological Applications*, 3(2), 209-212.
753
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755 https://doi.org/10.2307/1941822
755 Noreika, N., Kotiaho, J. S., Penttinen, J., Punttila, P., Vuori, A., Pajunen, T., Autio, O., Loukola, O. J., & Kotze, D. J. (2015). Rapid recovery of invertebrate communities after ecological restoration of boreal mires. *Restoration Ecology*, 23(5), 566-579. https://doi.org/10.1111/rec.12237
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758 Oliveira, B. F., São-Pedro, V. A., Santos-Barrera, G., Penone, C., & Costa, G. C. (2017). AmphiBIO, a global database for amphibian ecological traits. *Scientific Data*, 4(1), 170123.
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760 https://doi.org/10.1038/sdata.2017.123
761 Opdam, P., Pouwels, R., Rooij, S. van, Steingröver, E., & Vos, C. C. (2008). Setting Biodiversity Targets in Participatory Regional Planning: Introducing Ecoprofiles. *Ecology and Society*, 13(1), art20.
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764 Pardini, R. (2018). Biodiversity Response to Habitat Loss and Fragmentation. In *Reference Module in Earth Systems and Environmental Sciences* (p. B9780124095489098000). Elsevier.
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766 https://doi.org/10.1016/B978-0-12-409548-9.09824-9Pianka, E. R. (1970). On r- and K-Selection. *The American Naturalist*, 104(940), 592-597. https://doi.org/10.1086/282697
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768 QGIS.org (2023). QGIS Geographic Information System. QGIS Association. http://www.qgis.org
769 Quaglietta, L., Fonseca, V. C., Hájková, P., Mira, A., & Boitani, L. (2013). Fine-scale population genetic structure and short-range sex-biased dispersal in a solitary carnivore, *Lutra lutra*. *Journal of Mammalogy*, 94(3), 561-571. https://doi.org/10.1644/12-MAMM-A-171.1
770
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772 Rey, F., Bifulco, C., Bischetti, G. B., Bourrier, F., De Cesare, G., Florineth, F., Graf, F., Marden, M., Mickovski, S. B., Phillips, C., Peklo, K., Poesen, J., Polster, D., Preti, F., Rauch, H. P., Raymond, P., Sangalli, P., Tardio, G., & Stokes, A. (2019). Soil and water bioengineering: Practice and research needs for reconciling natural hazard control and ecological restoration. *Science of The Total Environment*, 648, 1210-1218. https://doi.org/10.1016/j.scitotenv.2018.08.217
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777 Roché, J.-E., Witté, I., Comolet-Tirman, J., Siblet, J.-P., Cochet, G., Deceuninck, B., Frochot, B., Guillot, G., Muller, Y., Nicolau-Guillaumet, P., & Olioso, G. (2016). Proposition de classification par l'habitat des oiseaux nicheurs de France. Test de l'influence du niveau typologique sur des diagnostics de tendances. *Alauda*, 84(2), 111-144.
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758 Oliveira, B. F., São-Pedro, V. A., Santos-Barrera, G., Penone, C., & Costa, G. C. (2017). AmphiBIO, a global database for amphibian ecological traits. *Scientific Data*, 4(1), 170123.
759
760 https://doi.org/10.1038/sdata.2017.123
761 Opdam, P., Pouwels, R., Rooij, S. van, Steingröver, E., & Vos, C. C. (2008). Setting Biodiversity Targets in Participatory Regional Planning: Introducing Ecoprofiles. *Ecology and Society*, 13(1), art20.
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763 https://doi.org/10.5751/ES-02438-130120
764 Pardini, R. (2018). Biodiversity Response to Habitat Loss and Fragmentation. In *Reference Module in Earth Systems and Environmental Sciences* (p. B9780124095489098000). Elsevier.
765
766 https://doi.org/10.1016/B978-0-12-409548-9.09824-9Pianka, E. R. (1970). On r- and K-Selection. *The American Naturalist*, 104(940), 592-597. https://doi.org/10.1086/282697
767
768 QGIS.org (2023). QGIS Geographic Information System. QGIS Association. http://www.qgis.org
769 Quaglietta, L., Fonseca, V. C., Hájková, P., Mira, A., & Boitani, L. (2013). Fine-scale population genetic structure and short-range sex-biased dispersal in a solitary carnivore, *Lutra lutra*. *Journal of Mammalogy*, 94(3), 561-571. https://doi.org/10.1644/12-MAMM-A-171.1
770
771
772 Rey, F., Bifulco, C., Bischetti, G. B., Bourrier, F., De Cesare, G., Florineth, F., Graf, F., Marden, M., Mickovski, S. B., Phillips, C., Peklo, K., Poesen, J., Polster, D., Preti, F., Rauch, H. P., Raymond, P., Sangalli, P., Tardio, G., & Stokes, A. (2019). Soil and water bioengineering: Practice and research needs for reconciling natural hazard control and ecological restoration. *Science of The Total Environment*, 648, 1210-1218. https://doi.org/10.1016/j.scitotenv.2018.08.217
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774
775
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777 Roché, J.-E., Witté, I., Comolet-Tirman, J., Siblet, J.-P., Cochet, G., Deceuninck, B., Frochot, B., Guillot, G., Muller, Y., Nicolau-Guillaumet, P., & Olioso, G. (2016). Proposition de classification par l'habitat des oiseaux nicheurs de France. Test de l'influence du niveau typologique sur des diagnostics de tendances. *Alauda*, 84(2), 111-144.
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781 Rojas, I. M., Pidgeon, A. M., & Radeloff, V. C. (2020). Restoring riparian forests according to existing regulations could greatly improve connectivity for forest fauna in Chile. *Landscape and Urban Planning, 203*, 103895. https://doi.org/10.1016/j.landurbplan.2020.103895

784 Rosenberg, D. K., Noon, B. R., & Meslow, E. C. (1997). Biological Corridors: Form, Function, and Efficacy. *BioScience*, 47(10), 677-687. https://doi.org/10.2307/1313208

786 Rouland, P., & Migot, P. (1990). La réintroduction du castor (Castor fiber) en France. *Essai de synthèse et réflexions*. *Revue d'Ecologie*, 145-158.

788 Roy, M. L., & Le Pichon, C. (2017). Modelling functional fish habitat connectivity in rivers: A case study for prioritizing restoration actions targeting brown trout. *Aquatic Conservation: Marine and Freshwater Ecosystems*, 27(5), 927-937. https://doi.org/10.1002/aqc.2786

791 Sabo, J. L., Sponseller, R., Dixon, M., Gade, K., Harms, T., Heffernan, J., Jani, A., Katz, G., Soykan, C., Watts, J., & Welter, J. (2005). Riparian zones increase regional species richness by harboring different, not more, species. *Ecology*, 86(1), 56-62. https://doi.org/10.1890/04-0668

794 Saura, S., & Pascual-Hortal, L. (2007). A new habitat availability index to integrate connectivity in landscape conservation planning: Comparison with existing indices and application to a case study. *Landscape and Urban Planning*, 83(2-3), 91-103.
https://doi.org/10.1016/j.landurbplan.2007.03.005

798 Saura, S., & Rubio, L. (2010). A common currency for the different ways in which patches and links can contribute to habitat availability and connectivity in the landscape. *Ecography*.
https://doi.org/10.1111/j.1600-0587.2009.05760.x

801 Saura, S., Estreguil, C., Mouton, C., & Rodríguez-Freire, M. (2011). Network analysis to assess landscape connectivity trends: Application to European forests (1990–2000). *Ecological Indicators*, 11(2), 407-416. https://doi.org/10.1016/j.ecolind.2010.06.011

804 Smith, M. A., & Green, D. M. (2005). Dispersal and the metapopulation paradigm in amphibian ecology and conservation: Are all amphibian populations metapopulations?
781 Rojas, I. M., Pidgeon, A. M., & Radeloff, V. C. (2020). Restoring riparian forests according to existing regulations could greatly improve connectivity for forest fauna in Chile. *Landscape and Urban Planning, 203*, 103895. https://doi.org/10.1016/j.landurbplan.2020.103895

784 Rosenberg, D. K., Noon, B. R., & Meslow, E. C. (1997). Biological Corridors: Form, Function, and Efficacy. *BioScience*, 47(10), 677-687. https://doi.org/10.2307/1313208

786 Rouland, P., & Migot, P. (1990). La réintroduction du castor (Castor fiber) en France. *Essai de synthèse et réflexions*. *Revue d'Ecologie*, 145-158.

788 Roy, M. L., & Le Pichon, C. (2017). Modelling functional fish habitat connectivity in rivers: A case study for prioritizing restoration actions targeting brown trout. *Aquatic Conservation: Marine and Freshwater Ecosystems*, 27(5), 927-937. https://doi.org/10.1002/aqc.2786

791 Sabo, J. L., Sponseller, R., Dixon, M., Gade, K., Harms, T., Heffernan, J., Jani, A., Katz, G., Soykan, C., Watts, J., & Welter, J. (2005). Riparian zones increase regional species richness by harboring different, not more, species. *Ecology*, 86(1), 56-62. https://doi.org/10.1890/04-0668

794 Saura, S., & Pascual-Hortal, L. (2007). A new habitat availability index to integrate connectivity in landscape conservation planning: Comparison with existing indices and application to a case study. *Landscape and Urban Planning*, 83(2-3), 91-103.
https://doi.org/10.1016/j.landurbplan.2007.03.005

798 Saura, S., & Rubio, L. (2010). A common currency for the different ways in which patches and links can contribute to habitat availability and connectivity in the landscape. *Ecography*.
https://doi.org/10.1111/j.1600-0587.2009.05760.x

801 Saura, S., Estreguil, C., Mouton, C., & Rodríguez-Freire, M. (2011). Network analysis to assess landscape connectivity trends: Application to European forests (1990–2000). *Ecological Indicators*, 11(2), 407-416. https://doi.org/10.1016/j.ecolind.2010.06.011

804 Smith, M. A., & Green, D. M. (2005). Dispersal and the metapopulation paradigm in amphibian ecology and conservation: Are all amphibian populations metapopulations?
806 Smith, R. F., Alexander, L. C., & Lamp, W. O. (2009). Dispersal by terrestrial stages of stream insects in urban watersheds: A synthesis of current knowledge. *Journal of the North American Benthological Society*, 28(4), 1022-1037. https://doi.org/10.1899/08-176.1
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809 *Ecography*, 28(1), 110-128. https://doi.org/10.1111/j.0906-7590.2005.04042.x
810 Sutherland, G. D., Harestad, A. S., Price, K., & Lertzman, K. (2000). Scaling of Natal Dispersal Distances in Terrestrial Birds and Mammals. *Conservation Ecology*, 4(1), art16.
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812 https://doi.org/10.5751/ES-00184-040116
813 Tabacchi, E., Planty-Tabacchi, A.-M., & Décamps, O. (1990). Continuity and discontinuity of the riparian vegetation along a fluvial corridor. *Landscape Ecology*, 5(1), 9-20.
814
815 https://doi.org/10.1007/BF00153800
816 Tarabon, S., Bergès, L., Dutoit, T., & Isselin-Nondedeu, F. (2019). Maximizing habitat connectivity in the mitigation hierarchy. A case study on three terrestrial mammals in an urban environment. *Journal of Environmental Management*, 243, 340-349.
817
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819 https://doi.org/10.1016/j.jenvman.2019.04.121
820 Thompson, P. L., Rayfield, B., & Gonzalez, A. (2017). Loss of habitat and connectivity erodes species diversity, ecosystem functioning, and stability in metacommunity networks. *Ecography*, 40(1), 98-108. https://doi.org/10.1111/ecog.02558
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823 Trochet, A., Moulherat, S., Calvez, O., Stevens, V., Clobert, J., & Schmeller, D. (2014). A database of life-history traits of European amphibians. *Biodiversity Data Journal*, 2, e4123.
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825 https://doi.org/10.3897/BDJ.2.e4123
826 Urban, D., & Keitt, T. (2001). Landscape connectivity: A graph-theoretic perspective. *Ecology*, 82(5), 1205-1218. https://doi.org/10.1890/0012-9658(2001)082[1205:LCAGTP]2.0.CO;2
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828 Van Der Windt, H. J., & Swart, J. A. A. (2007). Ecological corridors, connecting science and politics: The case of the Green River in the Netherlands: Ecological corridors, connecting science and politics. *Journal of Applied Ecology*, 45(1), 124-132. https://doi.org/10.1111/j.1365-2664.2007.01404.x
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806 Smith, R. F., Alexander, L. C., & Lamp, W. O. (2009). Dispersal by terrestrial stages of stream insects in urban watersheds: A synthesis of current knowledge. *Journal of the North American Benthological Society*, 28(4), 1022-1037. https://doi.org/10.1899/08-176.1
807
808
809 *Ecography*, 28(1), 110-128. https://doi.org/10.1111/j.0906-7590.2005.04042.x
810 Sutherland, G. D., Harestad, A. S., Price, K., & Lertzman, K. (2000). Scaling of Natal Dispersal Distances in Terrestrial Birds and Mammals. *Conservation Ecology*, 4(1), art16.
811
812 https://doi.org/10.5751/ES-00184-040116
813 Tabacchi, E., Planty-Tabacchi, A.-M., & Décamps, O. (1990). Continuity and discontinuity of the riparian vegetation along a fluvial corridor. *Landscape Ecology*, 5(1), 9-20.
814
815 https://doi.org/10.1007/BF00153800
816 Tarabon, S., Bergès, L., Dutoit, T., & Isselin-Nondedeu, F. (2019). Maximizing habitat connectivity in the mitigation hierarchy. A case study on three terrestrial mammals in an urban environment. *Journal of Environmental Management*, 243, 340-349.
817
818
819 https://doi.org/10.1016/j.jenvman.2019.04.121
820 Thompson, P. L., Rayfield, B., & Gonzalez, A. (2017). Loss of habitat and connectivity erodes species diversity, ecosystem functioning, and stability in metacommunity networks. *Ecography*, 40(1), 98-108. https://doi.org/10.1111/ecog.02558
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823 Trochet, A., Moulherat, S., Calvez, O., Stevens, V., Clobert, J., & Schmeller, D. (2014). A database of life-history traits of European amphibians. *Biodiversity Data Journal*, 2, e4123.
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825 https://doi.org/10.3897/BDJ.2.e4123
826 Urban, D., & Keitt, T. (2001). Landscape connectivity: A graph-theoretic perspective. *Ecology*, 82(5), 1205-1218. https://doi.org/10.1890/0012-9658(2001)082[1205:LCAGTP]2.0.CO;2
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828 Van Der Windt, H. J., & Swart, J. A. A. (2007). Ecological corridors, connecting science and politics: The case of the Green River in the Netherlands: Ecological corridors, connecting science and politics. *Journal of Applied Ecology*, 45(1), 124-132. https://doi.org/10.1111/j.1365-2664.2007.01404.x
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832 Van Looy, K., Honnay, O., Bossuyt, B., & Hermy, M. (2003). The effects of river embankment and forest fragmentation on the plant species richness and composition of floodplain forests in the Meuse valley, Belgium. *Belgium Journal of Botany*.

835 Van Looy, K., Cavillon, C., Tormos, T., Piffady, J., Landry, P., & Souchon, Y. (2013). Are generalist and specialist species influenced differently by anthropogenic stressors and physical environment of riparian corridors? *Riparian Ecology and Conservation, 1*. https://doi.org/10.2478/remc-2013-0004

839 Van Looy, K., Tormos, T., & Souchon, Y. (2014). Disentangling dam impacts in river networks. *Ecological Indicators, 37*, 10-20. https://doi.org/10.1016/j.ecolind.2013.10.006

841 Verbeylen, G., De Bruyn, L., Adriaensen, F., & Matthysen, E. (2003). Does matrix resistance influence Red squirrel (Sciurus vulgaris L. 1758) distribution in an urban landscape? *Landscape Ecology*, 18(8), 791-805. https://doi.org/10.1023/B:LAND.0000014492.50765.05

844 Vos, C. C., Verboom, J., Opdam, P. F. M., & Ter Braak, C. J. F. (2001). Toward Ecologically Scaled Landscape Indices. *The American Naturalist*, 157(1), 24-41. https://doi.org/10.1086/317004

846 Wainwright, J., Turnbull, L., Ibrahim, T. G., Lexartza-Artza, I., Thornton, S. F., & Brazier, R. E. (2011). Linking environmental régimes, space and time : Interpretations of structural and functional connectivity. *Geomorphology*, 126(3-4), 387-404. https://doi.org/10.1016/j geomorph.2010.07.027

850 Waltz, A. E. M., & Wallace Covington, W. (2004). Ecological Restoration Treatments Increase Butterfly Richness and Abundance : Mechanisms of Response. *Restoration Ecology*, 12(1), 85-96. https://doi.org/10.1111/j.1061-2971.2004.00262.x

853 Ward, J. V., & Stanford, J. A. (1995). Ecological connectivity in alluvial river ecosystems and its disruption by flow regulation. *Regulated Rivers: Research & Management*, 11(1), 105-119. https://doi.org/10.1002/rrr.3450110109

856 Wells, K. D. (2007). *The Ecology and Behavior of Amphibians*. University of Chicago Press.
832 Van Looy, K., Honnay, O., Bossuyt, B., & Hermy, M. (2003). The effects of river embankment and forest fragmentation on the plant species richness and composition of floodplain forests in the Meuse valley, Belgium. *Belgium Journal of Botany*.

835 Van Looy, K., Cavillon, C., Tormos, T., Piffady, J., Landry, P., & Souchon, Y. (2013). Are generalist and specialist species influenced differently by anthropogenic stressors and physical environment of riparian corridors? *Riparian Ecology and Conservation, 1*. https://doi.org/10.2478/remc-2013-0004

839 Van Looy, K., Tormos, T., & Souchon, Y. (2014). Disentangling dam impacts in river networks. *Ecological Indicators, 37*, 10-20. https://doi.org/10.1016/j.ecolind.2013.10.006

841 Verbeylen, G., De Bruyn, L., Adriaensen, F., & Matthysen, E. (2003). Does matrix resistance influence Red squirrel (Sciurus vulgaris L. 1758) distribution in an urban landscape? *Landscape Ecology*, 18(8), 791-805. https://doi.org/10.1023/B:LAND.0000014492.50765.05

844 Vos, C. C., Verboom, J., Opdam, P. F. M., & Ter Braak, C. J. F. (2001). Toward Ecologically Scaled Landscape Indices. *The American Naturalist*, 157(1), 24-41. https://doi.org/10.1086/317004

846 Wainwright, J., Turnbull, L., Ibrahim, T. G., Lexartza-Artza, I., Thornton, S. F., & Brazier, R. E. (2011). Linking environmental régimes, space and time : Interpretations of structural and functional connectivity. *Geomorphology*, 126(3-4), 387-404. https://doi.org/10.1016/j geomorph.2010.07.027

850 Waltz, A. E. M., & Wallace Covington, W. (2004). Ecological Restoration Treatments Increase Butterfly Richness and Abundance : Mechanisms of Response. *Restoration Ecology*, 12(1), 85-96. https://doi.org/10.1111/j.1061-2971.2004.00262.x

853 Ward, J. V., & Stanford, J. A. (1995). Ecological connectivity in alluvial river ecosystems and its disruption by flow regulation. *Regulated Rivers: Research & Management*, 11(1), 105-119. https://doi.org/10.1002/rrr.3450110109

856 Wells, K. D. (2007). *The Ecology and Behavior of Amphibians*. University of Chicago Press.
857 Wiens, J. A., Hayward, G. D., Holthausen, R. S., & Wisdom, M. J. (2008). Using Surrogate Species and

858 Groups for Conservation Planning and Management. *BioScience*, **58**(3), 241-252.

859 https://doi.org/10.1641/B580310

860 Yang, N., Price, M., Xu, Y., Zhu, Y., Zhong, X., Cheng, Y., & Wang, B. (2023). Assessing Global Efforts in

861 the Selection of Vertebrates as Umbrella Species for Conservation. *Biology*, **12**(4), 509.

862 https://doi.org/10.3390/biology12040509

863 Zeller, K. A., McGarigal, K., & Whiteley, A. R. (2012). Estimating landscape resistance to movement : A

864 review. *Landscape Ecology*, **27**(6), 777-797. https://doi.org/10.1007/s10980-012-9737-0
857 Wiens, J. A., Hayward, G. D., Holthausen, R. S., & Wisdom, M. J. (2008). Using Surrogate Species and

858 Groups for Conservation Planning and Management. *BioScience*, 58(3), 241-252.

859 https://doi.org/10.1641/B580310

860 Yang, N., Price, M., Xu, Y., Zhu, Y., Zhong, X., Cheng, Y., & Wang, B. (2023). Assessing Global Efforts in

861 the Selection of Vertebrates as Umbrella Species for Conservation. *Biology*, 12(4), 509.

862 https://doi.org/10.3390/biology12040509

863 Zeller, K. A., McGarigal, K., & Whiteley, A. R. (2012). Estimating landscape resistance to movement : A

864 review. *Landscape Ecology*, 27(6), 777-797. https://doi.org/10.1007/s10980-012-9737-0