Research Paper
Coupling spatial modeling with expert opinion approaches to restore multispecies connectivity of major transportation infrastructure

https://doi.org/10.1016/j.landurbplan.2022.104371Get rights and content

Highlights

  • Reducing the barrier effect due to major transportation infrastructures.

  • Maximizing connections between species habitats from a multi-scale perspective.

  • Improvement of existing road structures and creation of new wildlife crossings.

  • Coupling simulation approach with field expertise.

Abstract

It is widely recognized that major transportation infrastructures such as highways degrade habitats, increase the risk of collision mortality, and cause alterations in landscape connectivity by reducing connections between habitats due to the barrier effect. Wildlife crossings (underpasses and overpasses) have therefore become standard practice to reduce these effects on wildlife. Most studies aim to identify relevant locations for the creation of new wildlife crossings, but it is very rare for results based on modeling tools or animal movement records, for example, to be confronted with expert opinion and knowledge. Our paper provides a methodological framework combining modeling tools and field expertise from a multi-scale perspective. We used complementary connectivity metrics from graph theory to accurately quantify and spatialize the most relevant areas to improve habitat connectivity with multispecies conservation goals. The connectivity analysis was coupled with field expertise to take into account the local context, and technical and land feasibility. We prioritized existing road structures for improvement, found the best locations for new wildlife crossings, and made specific, costed technical recommendations. We empirically tested this approach on a large highway network in Northern France. This allowed us to define work principles to improve 109 existing road structures and to create underpasses or overpasses on 15 road sections. This innovative study for operational purposes has been a useful tool to assist the decision-making process of land-use planners who wish to improve wildlife crossings on major transportation infrastructures.

Introduction

Interactions between human societies and biodiversity are becoming increasingly intense and extensive to the point that they affect wildlife and the functioning of ecosystems (Ahmed, Zafar and Ali, 2020). The decline in biodiversity is mainly due to the rapid loss and fragmentation of natural habitats, particularly caused by urban artificial structures and associated transportation networks (Barrientos and Borda-de-Água, 2017, Bennett, 2017). It is widely recognized that large transportation infrastructure such as highways degrade habitats, create a barrier effect by preventing animals from moving across the landscape (Coulon et al., 2006), increase the risk of collision mortality (González-Gallina et al., 2013), and thus lead to severe alterations in landscape connectivity by reducing connections between habitats over several tens of kilometers (Bourgeois and Sahraoui, 2020, Girardet et al., 2013, Torres et al., 2016). Although the behavior of each species when encountering a road affects mortality risk (Jacobson et al., 2016), some studies showed that mammals, reptiles and amphibians are more frequently impacted overall. Furthermore, the negative effects of roads are commensurate with the needs of species for mobility and habitat areas (Holderegger and Di Giulio, 2010, Rytwinski and Fahrig, 2012).

In this context, effective mitigation strategies can be developed to improve connections between species habitats, and to promote species and population flows, biotic interactions, and thus biodiversity conservation (Ceia‐Hasse, Borda‐de‐Água, Grilo, & Pereira, 2017). Among these measures, the construction of wildlife crossings (underpasses and overpasses) has become common over the past three decades to reduce the barrier effects of roads on wildlife induced by preexisting or new transportation infrastructure (Rytwinski et al., 2016, Seiler and Bhardwaj, 2020, Van der Ree et al., 2009). Crossings are generally built as close as possible to ecological corridors to ensure their effectiveness. The location of the road section where habitat connectivity should be restored is often determined based on expert opinion and/or landscape composition and configuration (Clevenger et al., 2002, Cushman et al., 2014, Liu et al., 2018, Wierzchowski et al., 2019). Biological data such as animal movements or wildlife-vehicle collision records can also be used (Ford et al., 2011, Hernández-Pérez et al., 2020, Serieys et al., 2021). Nevertheless, collecting data on wildlife movements and roadkills is time-consuming and therefore costly, and these data are very complex to collect for all species (O’Brien et al., 2006). Studies on medium and large vertebrates are indeed favored by the detectability of carcasses and their easier identification (Ciocheti et al., 2017, Grilo et al., 2018).

Some studies in recent years demonstrated the interest of ecological network modeling and evaluation to improve decision-making processes in terms of landscape defragmentation (Gurrutxaga and Saura, 2014). Graph-based methods that focus on landscape connectivity, in relation to the degree to which the landscape facilitates or hinders species movement between resource patches (Taylor et al., 1993), emerge as a very interesting approach (Foltête, 2019). Landscape graphs are representations of ecological networks where habitat patches appear as nodes, and potential movements of individuals or flows between patches as links that connect pairs of nodes (Urban et al., 2009). Over the last few years, they have gained popularity (Darabi, Hashemi, & Lotfi, 2021). Some recent studies specifically improved the understanding and appropriation of modeling tools based on graph theory to assess fragmentation and habitat loss due to major transportation infrastructure and to identify the location of potential wildlife crossings in order to restore connectivity. However, there are few if any studies that seek to locate existing road structures that were not designed for connectivity purposes (e.g., culverts, access for farm equipment, bridges, etc.) that could be ecologically improved for wildlife (but see Mimet, Clauzel and Foltête, 2016). These existing underpasses or overpasses are not always suitable for all species, and some may increase the risk of mortality in the vicinity (Klar, Herrmann and Kramer-Schadt, 2009). Whether these structures are functional or not, improving and securing them is essential to reducing the effects of highways on biodiversity (van der Ree, Gagnon and Smith, 2015). In addition, Sijtsma et al. (2020) showed that new wildlife crossings such as overpasses generated the greatest ecological gain, but were the most costly measures (see also McGuire et al., 2020). Improving existing underpasses and overpasses is generally the most cost-effective solution, with the added benefit that multiple small wildlife crossings are more efficient than a single large one in mitigating the effect of a barrier for the same investment (Karlson, Seiler and Mörtberg, 2017).

Regardless of the type of planned wildlife structures, van der Grift et al. (2013) argue that designers may jeopardize the viability of wildlife populations and inefficiently use financial resources by creating structures that are not effective. There are indeed significant uncertainties about mitigation success (Denneboom, Bar-Massada and Shwartz, 2021), which is partly due to non-strategic choices of location and/or type of crossings. Considering habitat connectivity, not only landscape configuration and composition (e.g.,Schuster, Römer and Germain, 2013), is of crucial importance to increase the chances of successful mitigation measures (Gómez-Fernández, Alcocer and Matesanz, 2016). Karlson, Seiler and Mörtberg (2017) recommend developing multispecies approaches that are relevant to achieving large-scale biodiversity conservation goals (Brodie et al., 2015). Models are therefore developed from a large panel of species with distinct ecological requirements and needs in terms of habitats and mobility (see e.g. Sahraoui et al., 2017, Tarabon et al., 2020)

Most studies aim to identify the relevant location to create new wildlife crossings, but it is very rare that the results (e.g. based on modeling tools or animal movement records) are confronted with expert opinion and knowledge (Perera, Drew and Johnson, 2011). Yet, the integration of field expertise is necessary to meet operational requirements (see Zhang et al., 2019). This allows, for example, large-scale recommendations based on ecological connectivity to be confirmed and/or adjusted, taking into account the local context and providing technical recommendations. Thus, improving the identification of preexisting and new wildlife crossings coupled with expert opinion constitutes a real challenge in the decision-making and operational process. We propose to address this issue by tackling the following question: how to combine modeling and field approaches to reduce the barrier effects of major transportation infrastructure? In France, the highway network represents about 12,000 km (according to the Ministry of Transport, Statistical Surveys and Information Service) which highlights the need to preserve large-scale ecological networks. In this study, we defined a methodological framework using graph theory and field expertise to i) prioritize existing road structures for improvement, ii) find the best locations for new wildlife crossings, and iii) make precise and costed technical recommendations specifying what needs to be done. We empirically tested this approach on a large highway network in Northern France.

Section snippets

Study area

In this study, we examined the 1,800 km of the highway network managed by a French toll road operator (www.sanef.com), mainly located in Northern France (Fig. 1). We restricted the connectivity analysis based on the largest dispersal capacity value of the studied species (see Section 2.3.) and applied a 37.5 km buffer zone on each side of the roads, based on the largest dispersal capacity value of the studied species (i.e., 1.5 × Dmax of the roe deer; Table 1). The total study area covers

Local connectivity analysis and prioritization of existing road structures for improvement

The EFI was performed for each cell in the study area for each species, each habitat group, and then for all species for a multispecies result. The average EFI values around each existing road structure were used to prioritize them (Fig. 4 and Table 2). The calculated EFI values for all species (EFIALL) ranged from 0.015 to 0.202 for all existing road structures studied. The maximum EFI values for habitat groups (EFIHG) were higher for the forest area (000–0.423), followed by the open and

A global methodological framework

Wildlife-human conflicts along transportation infrastructure are inevitable, but solutions are possible Seiler and Bhardwaj (2020). In this study, we planned and localized mitigation measures for biodiversity conservation in order to improve species flows and thus biotic interactions. Following the recommendations of Ascensão et al., 2019, Fabrizio et al., 2019, we focused entirely on habitat connectivity, targeting the ecological functionality of habitat areas that may be in some cases remote

Conclusion

This study was not simply a matter of targeting road sections with high biodiversity conservation issues. We proposed a precise large-scale analysis in which local environmental factors are taken into account to identify relevant mitigation measures. This resulted in the definition of working principles for improving 109 existing road structures and creating underpasses or overpasses on 15 road sections. The design principles for each wildlife crossing are consistent with the species that move

Acknowledgments

This study was conducted with the financial support of the French highway operator SANEF for which the operational aspects were implemented. The field expertise was carried out thanks to the Systra agency. The computations were performed on the supercomputer of the Arp-Astrance agency. The authors particularly thank Hervé Moal for his support, Gilles Vuidel for his support in optimizing the calculations and Diane Deplante for the revision of the English manuscript.

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