# Using landscape graphs to assess the ecological relevance of biodiversity conservation plans for forest habitat networks

Anissa Bellil, Jean-Christophe Foltête, Gilles Vuidel, Emeline Comby, Paul Savary

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## To cite this version:

Anissa Bellil, Jean-Christophe Foltête, Gilles Vuidel, Emeline Comby, Paul Savary. Using landscape graphs to assess the ecological relevance of biodiversity conservation plans for forest habitat networks. Land Use Policy, 2025, 158, pp.107740. 10.1016/j.landusepol.2025.107740. hal-05227320

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# Using landscape graphs to assess the ecological relevance of biodiversity conservation plans for forest habitat networks

Anissa Bellil<sup>a, *</sup>, Jean-Christophe Foltête<sup>a</sup>, Gilles Vuidel<sup>a</sup>, Emeline Comby<sup>b</sup>, Paul Savary<sup>a</sup>

## ARTICLE INFO

**Keywords:**

Ecological connectivity

Planning document

Landscape graph

Forest habitat networks

SCoT

## ABSTRACT

Conservation measures integrated into planning documents are designed for the protection and management of natural habitats. They are part of planning strategies for maintaining biodiversity and the functional connectivity of habitat networks. However, spatial planning strategies need to consider a wide range of socio-economic requirements in addition to biodiversity issues. This leads to a mismatch between the planning documents emerging from stakeholders' discussions and the most ecologically relevant conservation strategies for habitat networks. Here, we use a generic spatial modelling approach based on landscape graphs to model forest habitat networks in four areas matching the boundaries of French inter-municipal planning instruments (SCoTs). This enables us to consistently compare our modelling outputs with the habitat networks defined in the corresponding planning documents, for a representative set of ecological profiles. Independently, we assess ecological connectivity using metrics focusing on several key components of habitat connectivity, i.e., habitat patch area, inter-patch dispersal, and network traversability. Our results reveal both similarities and differences among SCoTs in the way they map and conserve habitat networks for different species profiles. The SCoTs differed particularly in the importance accorded to each component of habitat connectivity, although the metric related to habitat area was overall well accounted for. Our connectivity modelling framework provides a testing ground to support decision-making when mapping spatial habitat networks for biodiversity conservation and land-use planning.

## 1. Introduction

The recognition that human activities strongly impact biodiversity has largely affected land-use policies. Over recent decades, ecological theories have progressively influenced policy-making, and conservation strategies are now an integral part of planning tools (Chester and Hilty, 2010; Hilty et al., 2020; Worboys et al., 2010). Natural habitat destruction has been identified as a main driver of biodiversity erosion (Díaz et al., 2019), placing measures for conserving landscape-scale habitat connectivity among the most common strategies for biodiversity conservation (Brennan et al., 2022; Gonzalez et al., 2017; Keeley et al., 2019; Maxwell et al., 2020). Habitat connectivity ensures long-term species survival by enabling movements among habitat patches and access to resources needed by species to accomplish their life-cycles. As connectivity depends on the spatial distribution of habitat patches and the degree to which the landscape allows or impedes the movement of organisms (Taylor et al., 2006), it is important in aligning

land-use policies with ecological requirements.

Although connectivity has become central to conservation strategies, we do not yet have the level of understanding needed to maintain it effectively (Beunen and Opdam, 2011; Opdam, 2018; Opdam et al., 2006; Beunen and Hagens, 2009). Transposing ecological theories into practical planning tools and documents is far from trivial. First, ongoing scientific work has yet to reach clear-cut conclusions about the effects of connectivity on biodiversity (Crooks and Sanjayan, 2006; Tischendorf and Fahrig, 2000; Turner and Gardner, 2015). Moreover, practical connectivity conservation is still limited and poorly integrated into decision-making (Bergsten and Zetterberg, 2013; Bormpoudakis and Tzanopoulos, 2019). Finally, conservation plans targeting habitat connectivity must strike a balance between socio-economic and ecological objectives. This raises a critical challenge when it comes to identifying habitat networks for land-use planning. Mismatches can arise between planning documents and the ecologically most effective conservation strategies. It is crucial to identify such mismatches and understand their

<footnote><sup>a</sup> Marie & Louis Pasteur University, CNRS, ThéMA, Besançon F-25000, France</footnote>

<footnote><sup>b</sup> Department of Geography, UMR 5600 EVS CNRS, Université Lumière Lyon 2, Lyon 69007, France</footnote>

<footnote>* Corresponding author.</footnote>

<footnote>E-mail addresses: anissa.bellil@univ-fcomte.fr (A. Bellil), jean-christophe.foltete@univ-fcomte.fr (J.-C. Foltête), gilles.vuidel@univ-fcomte.fr (G. Vuidel), e.comby@univ-lyon2.fr (E. Comby), paul.savary@univ-fcomte.fr (P. Savary).</footnote>
main drivers if we are to both implement land-use policies and effec-
tively prevent the erosion of biodiversity.

Comparison of planning documents can provide key insights into these mismatches (Ledda et al., 2023). In most countries, connectivity conservation directives are mainly enforced by creating large inter-connected habitat areas nationally and regionally, as these connections support key ecological processes (Bennett, 1999; Bennett et al., 2006; Cole et al., 2023). However, planning documents often protect habitat networks at local scales in independent ways. Consequently, differing interpretations of supra-level incentives and context-dependent factors may result in discrepancies among local-scale planning documents. These differences may be contingent upon stakeholders' commitments (Czechowski et al., 2015; Tiemann and Siebert, 2009), local governance organisations (Beunen and Hagens, 2009; Nassauer and Opdam, 2008), or the differing understandings of governance arrangements linked to the transcription of local habitat networks (Wyborn, 2015b, 2015a). Besides, several tools are available for mapping habitat networks and choosing among them is still a difficult task (Correa Ayram et al., 2016; Honeck et al., 2020). The choice, complexity, and precision of the methods used ultimately depend on the needs of the users, as well as on the time and resources allocated (Kukkala and Moilanen, 2013; Moilanen et al., 2009). The use of different methods could lead to varying and potentially sub-optimal conservation strategies for the same habitat types. This stems from the fact that the local and independent implementation of these methods commonly entails different conceptions of habitat networks, further increasing the discrepancies among conservation strategies (Honeck et al., 2020; Perrin et al., 2022).

In this context, some means of systematically describing and un-
derstanding how ecological connectivity is considered in planning
documents would be timely. Since each conservation plan is designed to
take into account the specific issues of each area considered, the com-
parison of several conservation plans located in a similar geographical
context requires a common baseline and prioritisations for promoting
connectivity (Hanson et al., 2022). The use of field observations (i.e.,
species presence or abundance, community diversity), despite reflecting
ecological reality, falls short in providing such a baseline, for several
reasons including the heterogeneity of data acquisition protocols and
still complex and/or uncertain empirical inferences about species
movements (Jacobson and Peres-Neto, 2010). Consequently,
spatially-explicit models based on generic ecological parameters seem to
provide the most relevant baseline assessment of ecological connectiv-
ity, making it possible to compare conservation plans in a transparent
and systematic way across multiple study areas. Among these models,
landscape graphs are particularly easy to implement for representing
ecological flows while considering both the amount of habitat and
connectivity (Foltête, 2019; Urban et al., 2009). Landscape graph
modelling therefore constitutes a methodological advance, as it applies a
spatially explicit, graph-based framework to the outputs of spatial
planning documents. While such models have demonstrated strong po-
tential to inform biodiversity planning and improve the integration of
ecological connectivity into land-use strategies (Foltete, 2019), their
application to planning documents has so far been limited (Ortega et al.,
2023; Vidal-Llamas et al., 2025), and rarely oriented towards comparing
planning documents themselves. Our study addresses this gap by
quantitatively assessing the alignment of connectivity elements
embedded in spatial plans, thereby linking models with planning
practices.

In response to growing incentives to consider connectivity, France
has been implementing the “Trame Verte et Bleue” (TVB) strategy,
which aims to establish a coherent habitat network, from the national to
the local level (Decree No. 2014-45 of 20 January 2014). In this context,
the inter-municipal level has emerged as a relevant geographical scale
between regional and municipal levels to implement this strategy and
integrate the resulting conservation schemes within the other French
conservation strategies (Sanseverino-Godfrin et al., 2013). The SCOT
(Schéma de cohérence territoriale), a French planning instrument designed

at the inter-municipal level, serves as the strategic tool for integrating
and implementing the TVB in land-use planning. These planning docu-
ments spatially define the TVB through a set of maps presenting many
features (Vimal et al., 2012, 2017). Although these maps are valuable
tools for land-use planning, they limit planners' ability to fully represent
the complexity of the ecological connectivity of habitat networks.

Our study aims to compare different TVBs integrated into SCoTs to characterise how they handle ecological connectivity, with a specific focus on forest habitat networks. This approach offers a novel practical contribution in the French context, where such evaluations of planning documents remain limited. Angelstam et al. (2021) point out that effective forest governance requires regionally adapted area and place-based landscape approaches. Besides, our focus on forest areas is justified by the fact that these areas make up a significant proportion of protected areas, allowing for some leeway in the long-term management and conservation of public forests in eastern France.

We use landscape graphs (LG) modelling to address two main ob-
jectives: (i) assessing the consistency between forest habitat networks
defined in the SCoTs and those derived from ecological modelling, and
(ii) comparing the potential contribution of each SCOT to ecological
connectivity, based on a set of ecological profiles with varying dispersal
capacities. We estimate ecological connectivity using connectivity
metrics derived from forest habitat networks at the scale of each SCOT.
Through this approach, we aim to provide a testing ground to support
decision-making and align territorial planning tools with biodiversity
conservation requirements. Our analysis also highlights the challenges
that remain for operationalising ecological connectivity in land use
planning. Our analysis stresses crucial points for the ecological rele-
vance of planning outputs and their institutional and methodological
limitations. In the following sections, we first describe the study area
and the modelling approach. We then assess the alignment between
forest habitat networks delineated in the SCoTs and those derived from
ecological modelling, in order to evaluate their consistency and
ecological relevance. Accordingly, we then quantify and compare the
potential contribution of each SCOT to ecological connectivity, using
landscape graph metrics applied to species with distinct dispersal pro-
files. In the discussion section, we interpret the findings in light of the
challenges of operationalising ecological connectivity in planning
practices, reflecting on methodological limitations and implications for
enhancing the ecological effectiveness of planning frameworks.

## 2. Material and methods

2.1. *Study area*

We studied four SCoTs located in the Bourgogne Franche-Comté (BFC) region, eastern France, namely: Grand Avallonnais, Territoire de Belfort, Grand Nevers and Autunois Morvan (Fig. 1). These SCoTs have similar forest cover (between 37 % and 44 %), but they differ in other land-cover categories. The SCOT of Grand Avallonnais covers 1 290 km² and is occupied by cereal crops in the northern part of the Bourgogne plateau. In the South, it covers the northern part of the Morvan massif, a granitic basement mainly occupied by forests. The SCOT of Territoire de Belfort (609 km²) is centred on the midsize urban area of Belfort (~80 000 inhabitants). Its northern part is dominated by the large coniferous forests of the Vosges massif. The SCOT of Grand Nevers (2 486 km²) is crossed by the Loire Valley near the midsize city of Nevers (~60 000 inhabitants) and is characterised by a mix of crops, grasslands and for- ests. Finally, the SCOT of Autunois Morvan (1 208 km²) is centred on the small municipality of Autun (< 15 000 inhabitants). It is covered by the Morvan massif in the North and is characterised by a mix of agricultural plains, valleys and forested plateaus.
<figure><img src="image_4.png" /><figcaption>Fig. 1. Study area and analysed SCoTs of the Bourgogne Franche-Comté region.</figcaption></figure>

## 2.2. SCoTs forest habitat networks mappings and their role for connectivity conservation

The SCoTs are strategic inter-municipal planning documents that guide territorial development and land-use decisions. Their primary purpose is to ensure coherence among urban, economic, and environmental policies by providing a harmonised and spatially-explicit planning document to implement actions across municipalities. In this respect, SCoTs translate regional environmental strategies into operational spatial plans. They function at the interface of spatial planning and biodiversity conservation, integrating ecological concerns into land-use policies and thus represent a crucial entry point for embedding ecological connectivity objectives into planning documents. Unlike local urban plans, which operate at the municipal level with detailed regulatory functions, SCoTs occupy an intermediate position in the planning hierarchy, between broad-scale regional schemes and local plans. SCoTs hold both strategic and prescriptive authority over subordinate plans, making them key instruments for integrating ecological networks into legally binding planning frameworks.

The mapping of forest habitats networks within SCoTs is based on ecological expertise, institutional guidelines, and planning priorities. However, methodologies vary among territories depending on local constraints, planning cultures, and technical capacities. These mappings are developed through a collaborative, multi-stakeholder process involving scientific experts, technical professionals, environmental organisations and political representatives (Charvolin et al., 2011). This participatory framework allows for the inclusion of ecological knowledge in planning, but also reflects negotiated compromises between conservation goals and competing land-use interests. The resulting interpretations are translated into spatial representations (see Appendix A), which serve both as tools for decision-making and as instruments for policy implementation.

## 2.3. GIS data on the forest habitat networks provided by official institutions

We collected the spatial digital data representing the cartographic elements of the forest habitat networks for each SCOT from official

institutions. The forest habitat networks are made up of two components: (1) core areas, so-called “biodiversity reservoirs”, including protected areas and other areas added by experts because of their ecological potential for forest species, and (2) linkages or “ecological corridors” that represent the pathways through which movement between biodiversity reservoirs can occur. The structure of forest habitat networks varies among the SCoTs because of differences in design and mapping methods. The biodiversity reservoirs are represented as polygons in the GIS, whereas ecological corridors include both polygons and lines. In order to standardize the representation of corridors, we converted lines into polygons by assigning them a width equivalent to the average width of the SCOT ecological corridors.

## 2.4. Ecological connectivity analysis

### 2.4.1. Land-cover mapping

We produced a land-cover map by combining multiple data types from several sources: forest areas from BD FORET® (IGN 2018); artificial entities from BD TOPO® (IGN 2018); agricultural areas from Registre Parcellaire Graphique 2017; and the OSO land cover raster map (CES 2018) as a base map for covering the areas without data from the aforementioned databases. The resulting land-cover raster map has a spatial resolution of 5 m and is composed of 14 classes: high-speed railways and motorways, buildings, artificial areas, main roads, minor roads, railways, rivers and wetlands, crops, other open areas, orchards, moors and poplar stands, meadows, herbaceous vegetation and forests. For each SCOT, the land-cover map extends approximately 10 km beyond the administrative boundaries, to prevent boundary effects from impacting the spatial analyses.

### 2.4.2. Species selection

We defined three ecological profiles following the approach of Vos et al. (2001) and considering generic forest species that varied in terms of sensitivity to fragmentation, home range and dispersal distances. As in Minor and Lookingbill (2010) and Tannier et al. (2016), these three ecological profiles represented species with either (1) a short dispersal distance and small home range, (2) a medium dispersal distance and medium home range, or (3) a long dispersal distance and large home
range (Table 1). These profiles ensure that our modelling of forest habitat networks in the SCoTs encompasses a broad range of species and represents a range of scales of conservation planning.

While these profiles offer a broad gradient of ecological traits relevant for forest connectivity analysis, they mainly represent mammal species and do not account for taxa with very different dispersal capacities, such as birds or amphibians. However, other taxa might disperse similarly given the broad range considered. Besides, this taxonomic focus was chosen due to the availability of consistent dispersal and home range parameters across forest-dwelling mammals, and their ecological relevance in forest landscapes.

### 2.4.3. Landscape graph modelling

A landscape graph is defined as a set of suitable habitat patches (nodes) connected by potential movement paths (Galpern et al., 2011), which are mostly dispersal paths (links). From the land cover maps, we modelled landscape graphs using Graphab 2.8 software (Foltête et al., 2021). The habitat patches for the three ecological profiles were forested habitat patches with a minimal area of 1 ha, 10 ha, and 100 ha, respectively. The inter-patch links were weighted by least-cost distances computed by assigning a specific resistance value to each land-cover category (Table 2). These resistance values are used to distinguish different zones within the landscape matrix: habitat, suitable, unfavourable, or barrier to movement. For each ecological profile, we created a minimal planar graph (Fall et al., 2007) linking only neighbouring habitat patches. This graph structure (i.e., topology) provides a good approximation of the complete graph and allows for time-efficient computations of weighted connectivity metrics.

### 2.4.4. Resistance maps used to model landscape graph corridors

To model landscape graph (LG) corridors, we focused on potential movements among habitat patches, using a resistance map as the model's main input. This map had to reflect the probability of movements of species between habitat patches (Matisziw et al., 2015; Pinto and Keitt, 2009). For this, our approach consisted in defining a distributed probability gradient of species dispersal. The transformation we used to create the probability gradient of dispersal from cost-distances is based on the dispersal kernel relying on a negative exponential function and commonly used in metapopulation models (Hanski, 1994) and, by extension, in connectivity models (Urban and Keitt, 2001). This function is widely adopted because it captures the empirically observed decline in dispersal probability with increasing distance while maintaining a

<caption>**Table 2**</caption>

Cost values characterising each land-cover category.

<table><thead><tr><th>Land-cover class</th><th>Ecological function</th><th>Cost value</th></tr></thead><tbody><tr><td>Forests</td><td>Habitat</td><td>1</td></tr><tr><td>Herbaceous vegetation</td><td>Suitable</td><td>10</td></tr><tr><td>Orchards</td><td>Suitable</td><td>10</td></tr><tr><td>Meadows</td><td>Suitable</td><td>10</td></tr><tr><td>Moors and poplar stands</td><td>Suitable</td><td>10</td></tr><tr><td>Rivers and wetlands</td><td>Unfavourable</td><td>100</td></tr><tr><td>Crops</td><td>Unfavourable</td><td>100</td></tr><tr><td>Other open areas</td><td>Unfavourable</td><td>100</td></tr><tr><td>Railways</td><td>Unfavourable</td><td>100</td></tr><tr><td>Minor Roads</td><td>Unfavourable</td><td>100</td></tr><tr><td>Main Roads</td><td>Unfavourable</td><td>1 000</td></tr><tr><td>Artificial areas</td><td>Unfavourable</td><td>1 000</td></tr><tr><td>Buildings</td><td>Barrier</td><td>10 000</td></tr><tr><td>High-speed railways and motorways</td><td>Barrier</td><td>10 000</td></tr></tbody></table>

mathematically tractable form. By applying this function to the distance between each pixel and the least-cost path, we considered all potential paths included inside the corridor (Fig. 2.A), such that:

$$P_{k_{ij}} = e^{-\alpha(d_{k_{ij}} - d_{ij})}$$

where $P_{k_{ij}}$ is the probability of dispersal from patch $i$ to patch $j$ passing through pixel $k$, $d_{ij}$ is the distance between patches $i$ and $j$, $d_{k_{ij}}$ is the distance between patches $i$ and $j$ passing through pixel $k$, and $\alpha$ is a parameter that represents the intensity of the probability decay as distance increases.

The resistance map plotted defines a probability gradient for each pair of habitat patches, considering the cost-distances computed for each ecological profile. It represents a decreasing field from pixels situated along the least-cost path having a value of 1, indicating the highest concentration of movement probability, to unreachable pixels having a value of 0 (Fig. 2.B).

### 2.4.5. Connectivity metrics

There are different ways to identify how habitat patches contribute to population dynamics. We focused on three metrics defined from the three main functions identified by Urban and Keitt (2001): (1) recruitment, (2) dispersal flux and (3) traversability. The first metric noted (A), related to the area, represents the potential recruitment or demographic capacity of each focal patch and is independent of the features of other habitat patches. The second metric noted (F) estimates the dispersal flux.

<caption>**Table 1**</caption>

Ecological profiles selected according to their dispersal distance and home range.

<table><thead><tr><th>Ecological profile</th><th>Examples of representative species</th><th>Home range (ha)</th><th>Dispersal distance (m)</th><th>References</th></tr></thead><tbody><tr><td>Profile 1</td><td>Eurasian red squirrel (Sciurus vulgaris)</td><td>1</td><td>1 000</td><td>(L. Wauters et al., 1994; L. A. Wauters et al., 2010)</td></tr><tr><td>Profile 2</td><td>Roe deer (Capreolus capreolus), Bechstein's bat (Myotis bechsteinii)</td><td>10</td><td>5 000</td><td>(Coulon et al., 2008; Jepsen and Topping, 2004; Smith et al., 2003; Sordello et al., 2013; Sutherland et al., 2000)</td></tr><tr><td>Profile 3</td><td>European wildcat (Felis silvestris), Alpine Chamois (Rupicapra rupicapra), Red deer (Cervus elaphus)</td><td>100</td><td>10 000</td><td>(Loe et al., 2009; Ryckman et al., 2010; Smith et al., 2003; Sordello et al., 2013; Stahl et al., 1988; Sutherland et al., 2000)</td></tr></tbody></table>

<figure><img src="image_5.png" /><figcaption>**Fig. 2.** Diagram of LG corridor modelling. A) LG corridors created from the probability gradient defined for a pair of LG habitat patches. B) Values representing the probability gradient of species movement, ranging from 1 along the least-cost path to 0 in the unreachable pixels.</figcaption></figure>
It is related to the patch's capacity to function as a source or receptor of individuals dispersing from nearby patches, based on their recruitment potential but independently of the recruitment potential of the focal patch. It is calculated as follows:

$$F_i = \sum_{\substack{j=1 \\ j \neq i}}^{n} A_j e^{-\alpha d_{ij}}$$

where n is the total number of habitat patches, A_j represents the capacity of the habitat patch j, -αd_{ij} is the probability of dispersal between patches i and j, and α is a parameter representing the intensity of the distance effect.

The third metric (CF) measures the role of habitat patches for the traversability of the whole network. Based on electric circuit theory (McRae et al., 2008), it quantifies the flows through each patch by taking into account all possible alternative paths among all pairs of habitat patches, while not focusing exclusively on the least-cost paths between these pairs of patches (Carroll et al., 2012; Girardet et al., 2015). It thus provides a realistic estimate of the potential role of a patch for dispersal. In contrast to the commonly used betweenness centrality index, it does not artificially favour patches located on specific least-cost paths between sets of patches if almost equivalent least-cost paths are also to be found. It is calculated as follows:

$$CF_i = \sum_{j}^{n} C_i^j$$

where C_i^j is current through the patch i emitted from all the patches towards the patch j, and n is the total number of habitat patches.

In order to assess the connectivity of every pixel on LG corridors, we also used the CF metric. This allowed for the relative representation of paths on the basis of the potential flow along them. In this case, we used the resistance maps plotted and assigned a CF value to each pixel on these maps. Pixels located on the least-cost paths take the metric value associated with those paths, which corresponds to the maximal value possible. The metric values exponentially decrease as the pixels' distance from the least-cost path increases, until they reach the value 0 for pixels considered unreachable for the species. The resulting matrices are then aggregated so that the value of each pixel corresponds to the maximum metric value. This approach means LG corridors between each pair of LG habitat patches can be modelled, and this process was repeated for each of the ecological profiles defined.

## 2.5. Comparison of SCoT forest habitat networks and LG forest habitat networks

The maps of SCoT forest habitat networks were compared with the LG forest habitat networks for each ecological profile and their biodiversity reservoirs/habitat patches and corridors examined separately (Fig. 3).

LG habitat patches were included (in) in the SCoT biodiversity reservoirs when the area of overlap equalled or exceeded the home range of the species of the ecological profile. Otherwise they were excluded (out). These two types of patches and their respective metric values (i.e., in and out of SCoT biodiversity reservoirs were then compared via one-way analysis of variance (ANOVA).

Similarly, LG corridors could overlap SCoT corridors (in) or not (out). Connectivity metric values were extracted using random sampling points. A total of 2000 points were considered for each SCoT, 1000 points located in the intersecting corridors and 1000 points located outside the intersecting corridors (Fig. 3.B). The metric values calculated inside and outside the SCoT corridors were then compared via a one-way analysis of variance as for the SCoT biodiversity reservoirs.

Comparing the maps based on the calculation of connectivity metrics

<figure><img src="image_6.png" /><figcaption>Fig. 3. Diagram of the principle of comparing the LG forest habitat networks and the SCoT forest habitat networks, and the overlap considered for the definition of points (in and out) when calculating connectivity metrics. A) LG habitat patches/SCoT biodiversity reservoirs, B) LG corridors/SCoT corridors.</figcaption></figure>

inside and outside SCoT biodiversity reservoirs and SCoT corridors led us to identify matches and mismatches between LG forest habitat networks and SCoT forest habitat networks.

# 3. Results

## 3.1. Comparison of SCoTs by connectivity metrics

The analysis of variance conducted on the sets of LG habitat patches located inside (in) and outside (out) the SCoT biodiversity reservoirs reveals substantial variations in the values of connectivity metrics among SCoTs and ecological profiles (Fig. 4). The results are qualitatively consistent across the SCoTs, despite large differences in metric absolute values. Most of the tests are statistically significant and reveal that, overall, all three metrics take higher average values inside than outside SCoT biodiversity reservoirs. The areas of LG habitat patches exhibit the largest differences, which are consistently greater inside than outside the SCoT biodiversity reservoirs for short and medium-distance dispersers (profiles 1 and 2). This trend is general across all SCoTs. For long-distance dispersers (profile 3), differences are smaller and not statistically significant in some cases, notably in the Territoire de Belfort and Autunois Morvan SCoTs. The latter results stem from the method used to exclude LG habitat patches from SCoT biodiversity reservoirs, which depend on the home range size associated with each profile. The other two metrics considered (i.e., F and CF) depend more heavily on dispersal capacities and differ less between LG habitat patches whether or not they are included in SCoT biodiversity reservoirs. This is especially the case for long-distance dispersers, for which differences are not significant in the SCoTs of Territoire de Belfort and Autunois Morvan. Note that CF values tend to decrease from short to long-distance dispersers because they are calculated independently of the dispersal parameter.

The areas (A) of LG habitat patches located inside SCoT biodiversity reservoirs are, on average, greater than those located outside them, with the exception of the Territoire de Belfort SCoT for profile 3, where the difference is not significant.

The F metric values related to the dispersal flux, calculated inside biodiversity reservoirs, tend to be higher on average than those calculated outside biodiversity reservoirs in the SCoTs of Grand Avallonnais and Autunois Morvan when considering profile 2. Conversely, for the same profile, the F values calculated inside and outside biodiversity reservoirs are similar and not statistically different in the SCoTs of Territoire de Belfort and Grand Nevers. For profile 3, the F metric values calculated inside and outside biodiversity reservoirs show similar values
<figure><img src="image_7.png" /><figcaption>**Fig. 4.** Box plots of connectivity metric values calculated inside (green) and outside (yellow) the SCoT biodiversity reservoirs: A) Grand Avallonnais, B) Territoire de Belfort, C) Grand Nevers, D) Autunois Morvan. We display the log-transformed values of connectivity metrics. The significance levels are: p-value> 0.05 (ns), < 0.05 (*), < 0.01 (**), < 0.001 (***).</figcaption></figure>

with no significant difference in the SCoTs of Grand Avallonnais, Territoire de Belfort and Autunois Morvan. However, within the SCoT of Grand Nevers, for the same profile, the *F* values calculated outside biodiversity reservoirs are significantly higher than those calculated inside biodiversity reservoirs.

The *CF* values, measuring the traversability of LG habitat patches, consistently exhibit higher averages when calculated inside biodiversity reservoirs compared to those calculated outside biodiversity reservoirs in the SCoTs of Grand Avallonnais, Territoire de Belfort and Autunois Morvan for profile 2. Conversely, within the SCoT of Grand Nevers and for the same profile, the *CF* values calculated inside and outside biodiversity reservoirs are similar and not statistically different. For profile 3, the *CF* values calculated inside biodiversity reservoirs tend to be higher on average than those calculated outside biodiversity reservoirs in the

SCoTs of Grand Nevers and Autunois Morvan. However, within the SCoTs of Grand Avallonnais and Territoire de Belfort, differences in *CF* values are not statistically significant.

The analysis of variance conducted on the point samples located inside (in) and outside (out) SCoT ecological corridors (Fig. 5), shows statistically significant results on metric *CF*, except for the SCoT of Grand Nevers when considering the short disperser (profile 1). All the other tests were statistically significant and revealed that, overall, the *CF* metric took higher average values inside than outside corridors for all SCoTs. However, in the SCoT of Grand Nevers, the maximum values of *CF* are observed outside corridors.
<figure><img src="image_8.png" /><figcaption>**Fig. 5.** Box plots of CF metric values calculated inside (green) and outside (yellow) the SCoT corridors: A) Grand Avallonnais, B) Territoire de Belfort, C) Grand Nevers, D) Autunois Morvan. The significance levels are: p-value > 0.05 (ns), < 0.05 (*), < 0.01 (**), < 0.001 (***)</figcaption></figure>

## 3.2. Matches and mismatches between LG habitat network maps and SCoT maps

Comparison of the maps, based on the calculation of the *F* metric for profile 1, provides insights into the matches and mismatches between LG forest habitat networks and SCoT forest habitat networks (Fig. 6). Overall, a significant proportion of LG habitat patches with high *F* values are located within SCoT biodiversity reservoirs in the SCoTs of Grand Avallonnais, Grand Nevers, and Autunois Morvan (Fig. 6 A, 6 C and 6D, respectively). However, comparison of the maps also reveals the occurrence of LG habitat patches with high *F* metric values located outside SCoT biodiversity reservoirs.

In particular, well-connected LG habitat patches, that are excluded from the SCoT of Grand Avallonnais, are found in the Cure and Cousin valleys (Fig. 6 A.a), encompassing forests and herbaceous vegetation. In the SCoT of Territoire de Belfort, such habitat patches are found predominantly in the Vosges foothills (Fig. 6 B.a), and these habitat patches tend to be larger than in other SCoTs. Similar patterns are observed in the SCoT of Grand Nevers, where ecologically important LG habitat

patches, though excluded from the maps, are located in the wooded Nivernais region in the north (Fig. 6 C.a). For the SCoT of Autunois Morvan, these LG habitat patches are located, among others, in the southern part of the Morvan massif (Fig. 6 D.a).

Comparison of the maps serves in identifying another problematic type of mismatch, i.e., the occurrence of LG habitat patches with low connectivity values inside SCoT biodiversity reservoirs. In the SCoT of Grand Avallonnais, LG habitat patches with low *F* values are mainly located in the northern part of the territory, particularly on the plateaus of Bourgogne (Fig. 6 A.b). These LG habitat patches are smaller and are found around the large agricultural fields of the Serein Valley. In the SCoT of Territoire de Belfort, these LG habitat patches are mainly located in the Sundgau region in the south (Fig. 6 B.b). These LG habitat patches are often surrounded by extensive woodlands and are bordered by agricultural areas. In the SCoT of Grand Nevers, smaller LG habitat patches with low *F* values are predominantly found in the Loire Valley and between the rivers Loire and Allier (Fig. 6 C.b). Lastly, in the SCoT of Autunois Morvan, such LG habitat patches are mainly observed in the alluvial valleys of the rivers Arroux and Dheune (Fig. 6 D.b). These
<figure><img src="image_9.png" /><figcaption>**Fig. 6.** Flux metric *F* calculated within LG habitat patches, superimposed on the SCoT biodiversity reservoirs: A) Grand Avallonnais, B) Territoire de Belfort, C) Grand Nevers, D) Autunois Morvan. The *F* values are calculated for profile 1 and displayed with dots, with sizes proportional to the values. This allows for the identification of: a) LG habitat patches with high *F* values located outside SCoT biodiversity reservoirs, and b) LG habitat patches with low *F* values located inside SCoT biodiversity reservoirs.</figcaption></figure>

mismatches highlight areas of ecological importance that are over-
looked when considering the *F* metric. SCoT biodiversity reservoirs are
delineated based on known land-use types or habitat cores. The
observed mismatches reflect differences in the way spatial priorities for
connectivity are identified in planning documents and ecological
modelling. Their spatial clustering, such as well-connected patches
systematically located outside SCOTs maps, points to areas underrepre-
sented in current planning strategies.

The ecological corridor maps also highlight cases where the LG
corridors match the SCoT corridors and cases where mismatches occur,
based on the calculation of metric CF for profile 1 (Fig. 7). The SCoT of

Grand Avallonnais (Fig. 7 A) shows a fairly close match between the
areas with high CF values and those covered by the SCoT corridors. This
is particularly obvious with the LG corridors connecting the habitat
patches of the northern plateaus of Bourgogne to the southern habitat
patches characterised by high CF values. Similarly, in the SCoT of Ter-
ritoire de Belfort (Fig. 6 B), a substantial match between SCoT corridors
and LG corridors with high CF values can be observed, especially in the
southern Sundgau region. These LG corridors connect habitat patches
that do not lie within SCoT biodiversity reservoirs.

Nonetheless, comparing the maps also reveals significant LG corri-
dors with high CF values that do not coincide with the SCoT corridors, as
<figure><img src="image_10.png" /><figcaption>**Fig. 7.** Modelled LG corridors and corresponding CF values calculated for profile 1, superimposed on the SCoT corridors: A) Grand Avallonnais, B) Territoire de Belfort, C) Grand Nevers, D) Autunois Morvan. The LG corridors with high CF values are shown in dark colours, while those with low CF values are shown in light colours. Unreachable areas appear in white.</figcaption></figure>

seen in the southern region of the SCoT of Grand Nevers (Fig. 7 C). In this case, the LG corridors display high CF values around the biodiversity reservoirs, while the SCoT corridors extend to areas with lower values. In the SCoT of Autunois Morvan (Fig. 7 D), despite a close match between areas displaying high CF values and those covered by the SCoT corridors, there are areas with high CF values in the Autunois hills and the wooded massif of Morvan that are not encompassed by the SCoT corridors. Instead, the SCoT features an adjacent corridor linking the northern SCoT biodiversity reservoirs to those situated in the south.

## 4. Discussion

This study aimed to assess the consistency between forest habitat networks defined in planning instruments (SCoTs) and those derived from ecological modelling, and to compare the potential of each SCoT to support ecological connectivity across different ecological profiles. In line with these objectives, we expected LG habitat patches located inside

SCoT biodiversity reservoirs and LG corridors within SCoT corridors to be better interconnected than those located outside. This prediction was confirmed in most cases, and for different ecological profiles. However, comparison of maps enabled us to identify both matches and mismatches between conservation plans and what is ecologically relevant. Indeed, these comparisons revealed crucial areas for ecological connectivity that were not included in the SCoT forest habitat networks. This addresses our first objective by assessing the spatial correspondence between planning instruments and ecological connectivity models. Our generic approach provides a reproducible basis for other SCoT habitat networks, and it offers a basis for informed decision-making. Furthermore, by using a multi-profile ecological modelling approach in combination with planning documents, this study provides a framework to evaluate how well spatial planning captures the ecological processes (i. e., mostly dispersal movements here) sustaining the life-cycles of multiple taxa.
## 4.1. Do planning documents maintain ecological connectivity in an ecologically relevant way?

Overall, and in accordance with our expectations, metric values were, on average, higher inside SCoT biodiversity reservoirs and SCoT corridors than outside. This can be related to supra-level frameworks that encompass ecological connectivity considerations and parameters (Perrin et al., 2022), applied across different methodological and normative contexts within the SCoT (Kohler, 2023). In the most urbanised SCoT (Territoire de Belfort), planning choices were the least consistent with the LG modelling results, while the SCoT of Autunois Morvan (the least urbanised) was the most consistent. This reflects a common fact that highly urbanised areas are less aligned with ecological connectivity, since urban ecosystems are recognised as highly heterogeneous (LaPoint et al., 2015).

However, our results revealed differences among the SCoTs based on the connectivity metrics considered (A, F, and CF). When assessing biodiversity reservoirs, overall, habitat area is well considered across the SCoTs, indicating that SCoT biodiversity reservoirs generally encompass forest habitat patches providing sufficiently vast home ranges for most of the ecological profiles. Conversely, the metrics used to estimate dispersal flux F and habitat patch traversability CF, i.e., emphasising connectivity per se, showed more variation among the SCoTs. It is worth noting that considering habitat patch area is crucial for maintaining populations (Bender et al., 1998), since it takes into account the intrinsic properties of habitat patches independently of the network. However, ecological connectivity metrics related to dispersal flux and habitat patch traversability can be used to quantify the flow between habitat patches, thus characterising their centrality within the network (Urban and Keitt, 2001). Therefore, it is essential to include these metrics in conservation plans, as they reflect crucial functions of habitat patches in contributing to ecological connectivity.

Furthermore, our results showed that profile 1, characterised by a short dispersal distance, consistently matches the modelling compared to profiles 2 and 3, which feature medium and large dispersal distances, respectively. It is widely recognised that species with limited dispersal abilities are particularly affected by landscape fragmentation (Meyer et al., 2008; Perović et al., 2015; Wootton and Pfister, 2013). However, recent studies have demonstrated that species with medium and large dispersal distances are also vulnerable, especially given the increased risk of mortality associated with long movements, challenging the assumption that species with short dispersal abilities are inherently vulnerable (Martin et al., 2023). This underscores the importance of prioritising the conservation of species with different dispersal abilities. Our results showed that profiles characterised by medium and large dispersal distances were less consistent when considering connectivity metrics related to dispersal flux and habitat patch traversability. This is in line with the preference for structural connectivity, where the area-related metric (A) is more significant. Profiles with limited dispersal capacity involve fewer movements, which aligns them more closely with the “structural” aspect of connectivity.

Moreover, the response of SCoTs to ecological connectivity also varies depending on whether it is examined within biodiversity reservoirs or along ecological corridors. We noticed that the matching of modelling results for biodiversity reservoirs does not necessarily entail a similar matching for ecological corridors within the same SCoT. The SCoTs of Territoire de Belfort and Grand Nevers present contrasting situations with the modelling results in both cases. This finding highlights the importance of examining these two components separately when assessing the response to ecological connectivity in conservation plans (Bennett et al., 2006), as both components are essential to the ecological functioning of habitat networks.

For SCoT corridors, our findings indicate that the LG corridors were consistent when considering the Current Flow (CF) metric, except for the SCoT of Grand Nevers when considering profile 1. However, our results also highlight mismatches in terms of maps. Our modelling approach

identified crucial zones for dispersal flow that are not integrated into SCoTs. These mismatches emphasise the limitations associated with the cartographic representations of SCoT corridors, resulting from varying methodological and conceptual frameworks (Perrin et al., 2022; Vimal and Mathevet, 2011). These findings respond to our second objective, by identifying gaps in the integration of ecological connectivity into planning tools and showing how spatial modelling can reveal overlooked functional areas.

## 4.2. Challenges and limitations of planning for connectivity conservation

Despite the overall match between conservation plans and modelling, mismatches emerged concerning dispersal flux (F) and habitat patch traversability (CF). This highlights the limitations of conservation plans for maintaining ecological connectivity in every respect. Ecological connectivity, like other environmental challenges, undoubtedly reveals the spatial planning system's limitations concerning TVB planning (Perrin et al., 2022). Indeed, biodiversity conservation within planning documents is often oriented by sectoral thinking driven by laws and planning regulations (Moscarelli, 2016). Whereas the TVB represents a dynamic phenomenon of species movement (Mougenot and Melin, 2000), conservation plans are structured by a “static” logic, defining biodiversity reservoirs through zoning that aligns with a structural connectivity rationale. Consequently, the responses provided remain inherently limited in part, raising a fundamental issue within spatial planning – its limited capacity to accommodate dynamic ecological processes.

Among the factors explaining the mismatches between SCoTs, the approaches and development contexts of each SCoT play a role in the consideration of ecological connectivity. Habitat networks are mapped using various methodologies and design approaches within planning documents. Moreover, the territorial and spatial disparities between SCoTs imply an adaptation of conservation measures to specific issues regarding biodiversity conservation (Moscarelli, 2016). Recent research has highlighted the multifaceted character of methodologies used for habitat network mapping in conservation plans (Honeck et al., 2020). Despite conceptual and methodological advances, practical conservation efforts for ecological habitat networks remain challenging, and the identification of optimal approaches remains a subject of ongoing debate (Khosravi and Hemami, 2019; Krosby et al., 2015; Marrec et al., 2020; Théau et al., 2015). These variations may also influence how conservation is defined, framed, and spatially delineated within conservation plans (Beunen and Opdam, 2011; Opdam, 2018). This diversity of responses to ecological connectivity can lead to inconsistencies and hinder efforts to promote ecological connectivity on a larger scale (Gonzalez et al., 2017).

In this context, the identification of mismatches between conservation plans allowed us to highlight the spatial disparities at play. Our study contributes to the existing literature in two complementary ways: i) on a theoretical level, it offers a replicable spatial modelling framework to evaluate ecological coherence across different planning contexts (Beger et al., 2022; Jung et al., 2024; Kukkala and Moilanen, 2013). The use of spatially explicit models based on landscape graphs for analysing habitat networks contributes to mitigating uncertainties surrounding their diversity and offers avenues for improvement and guidance (Foltête, 2019; Urban and Keitt, 2001). Second, ii) on a practical level, the approach supports the development of operational tools that facilitate dialogue among stakeholders and help planners identify areas that require greater attention in conservation strategies (Bergsten and Zetterberg, 2013; Bormpoudakis and Tzanopoulos, 2019; Lechner et al., 2015). By providing actionable spatial information, it strengthens planners' capacity to design more ecologically coherent habitat networks. Ultimately, our findings underscore the need to establish robust criteria for integrating ecological connectivity into spatial planning, thereby contributing to broader biodiversity conservation goals (Bhola et al., 2021; Locke et al., 2019).
Finally, it is important to acknowledge that our results are inevitably influenced by modelling choices, including the resistance values and dispersal distances used to simulate connectivity. These parameters strongly affect model outcomes (Zeller et al., 2018), particularly when resistance is derived from land-cover maps (Préau et al., 2022). Future work could apply sensitivity analyses, such as randomisation of cost values, or testing alternative dispersal thresholds, to evaluate the robustness of connectivity patterns under different modelling assumptions. Such analyses would help quantify uncertainty and reinforce the reproducibility of the modelling framework. Furthermore, our approach focused on the spatial responses of three ecological profiles associated with forests and akin to mammal species in their needs, but it did not address the question for all ecological profiles dependent on forest environments, such as birds, amphibians, or invertebrates, which may exhibit different sensitivities to fragmentation, as well as distinct home range and dispersal capacities. This might lead to different results because areas avoided by mammals, such as small physical or aquatic barriers, may not necessarily be barriers for birds (Waits et al., 2015). Applying this modelling framework to a broader range of ecological profiles would enhance its ecological representativeness and allow for a more comprehensive assessment of connectivity across multiple taxonomic groups. Our approach should thus be applied to other ecological profiles and enhanced accordingly. The cross-referencing of the results obtained would provide a solid decision-support basis. The integration of this modelling framework into spatial planning practices represents an innovative contribution, as it bridges ecological modelling and operational planning tools. Our approach opens up new perspectives for better integrating biodiversity into planning processes, fostering further reflection and exploration.

## 5. Conclusion

Using a generic landscape graph modelling approach, we compared conservation plans integrated into SCoTs (French inter-municipal planning instruments) by assessing ecological connectivity for a set of ecological profiles with different dispersal abilities and home ranges. The results showed that the SCoTs matched the modelling results overall, but differed in terms of habitat connectivity, i.e., the connectivity metrics used and the ecological profiles considered. These findings and the comparison enabled us to distinguish between SCoTs in terms of their convergence and divergence with the modelling results and to point out consequential differences in terms of conservation efforts. The cartographic analysis precisely identified cases where conservation should be considered more carefully.

Our approach was applied to forest habitat networks but could be reproduced for other conservation plans in environments where different habitats dominate the landscape or require special attention (e. g., wetlands, dry grasslands). Similarly, including a broader set of ecological profiles would enhance the representativity of the results. It is important to note that our approach relies on specific assumptions about the movements, habitat preferences, and landscape resistance of the considered species. This simplifies the complexity of actual ecological processes in a way that makes the results tractable and interpretable. These parameters would need to be adapted to other contexts, and thus, their level of complexity adjusted for striking the ideal balance between simplicity and realism.

Additionally, our study revealed that ecological connectivity is better preserved for species with short dispersal abilities, while mismatches become more frequent for medium- and large-distance dispersers. This questions the ability of conservation plans to capture functional connectivity at different scales and for more mobile species. The contrast between biodiversity reservoirs and ecological corridors also suggests that structural components might be better considered in planning than dynamic processes such as species movement. Finally, for ensuring progress on similar spatial conservation planning approaches, we recommend a constant dialogue between scientists and policymakers, as

it could further bridge the gap between scientific knowledge and operational implementation.

## CRediT authorship contribution statement

**FOLTETE Jean-Christophe:** Writing – review & editing, Validation, Supervision, Project administration, Methodology, Funding acquisition, Conceptualization. **BELLIL Anissa:** Writing – review & editing, Writing – original draft, Visualization, Resources, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. **COMBY Emeline:** Writing – review & editing. **VUIDEL Gilles:** Validation, Software, Resources, Methodology. **SAVARY Paul:** Writing – review & editing, Validation, Methodology.

## Declaration of Competing Interest

None.

## Acknowledgements

We thank Christopher Sutcliffe for his assistance in enhancing the quality of the English text.

Computations were performed on the supercomputer facilities of the Mésocentre de calcul de Franche-Comté.

## Appendix A. Supporting information

Supplementary data associated with this article can be found in the online version at doi: 10.1016/j.landusepol.2025.107740.

## Data Availability

Data will be made available on request.

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