Simon Tarabon
email: simon.tarabon@imbe.fr

Calvet Coralie

Vincent Delbar

Françis Thierry Dutoit

Coralie Calvet

Francis Isselin-Nondedeu

Integrating a

published or not. The documents may come

Introduction

In developed countries, fragmentation and habitat loss, mainly due to urban growth, are major causes of biodiversity decline [START_REF] Maxwell | Biodiversity: The ravages of guns, nets and bulldozers[END_REF]. Urban sprawl and the resulting artificialization of semi-natural areas affect landscape connectivity and biodiversity conservation [START_REF] Haverland | Examining the occurrence of mammal species in natural areas within a rapidly urbanizing region of Texas[END_REF]. Landscape connectivity means "the degree to which the landscape facilitates or impedes species movement among resource patches" [START_REF] Taylor | Connectivity is a vital element of landscape structure[END_REF]. It depends on the quantity and spatial arrangement of the landscape, including the landscape matrix, core habitat areas (i.e. where individuals perform their life cycle functions like feeding, breeding or daily dispersing), the corridors between habitat patches, as well as the species' dispersal ability [START_REF] Forman | Some general principles of landscape and regional ecology[END_REF][START_REF] Saura | Stepping stones are crucial for species' longdistance dispersal and range expansion through habitat networks[END_REF]. Recent studies have highlighted the need to conserve ecological networks at a large scale, because their integrity allows individuals to move and exchange genes, thereby ensuring populations' survival [START_REF] Boitani | Ecological networks as conceptual frameworks or operational tools in conservation[END_REF][START_REF] Kiesecker | Development by design: Blending landscape-level planning with the mitigation hierarchy[END_REF].

To date, few effective tools have been developed to properly assess landscape connectivity [START_REF] Kool | Population connectivity: Recent advances and new perspectives[END_REF]). Among the most common models are cost-distance analysis [START_REF] Adriaensen | The application of 'least-cost'modelling as a functional landscape model[END_REF], circuit theory [START_REF] Mcrae | Isolation by resistance[END_REF][START_REF] Mcrae | Using circuit theory to model connectivity in ecology, evolution, and conservation[END_REF], stochastic simulation models [START_REF] Allen | An individual-based modelling approach to estimate landscape connectivity for bighorn sheep (Ovis canadensis)[END_REF][START_REF] Tracey | Individual-based modeling as a tool for conserving connectivity[END_REF] and graph-theory-based models [START_REF] Minor | A graph-theory framework for evaluating landscape connectivity and conservation planning[END_REF][START_REF] Urban | Graph models of habitat mosaics[END_REF]. Graph theory is a relevant mathematical framework for the analysis of functional connectivity and for ecological conservation issues [START_REF] Correa Ayram | Habitat connectivity in biodiversity conservation: A review of recent studies and applications[END_REF][START_REF] Foltête | How ecological networks could benefit from landscape graphs: A response to the paper by Spartaco Gippoliti and Corrado Battisti[END_REF] requiring connectivity metrics [START_REF] Rayfield | Connectivity for conservation: A framework to classify network measures[END_REF][START_REF] Saura | A common currency for the different ways in which patches and links can contribute to habitat availability and connectivity in the landscape[END_REF]. Landscape graphs are simplified representations of ecological networks where habitat patches appear as nodes. The potential movements of individuals or fluxes between patches appear as links that connect pairs of nodes [START_REF] Urban | Graph models of habitat mosaics[END_REF]. They have recently grown in popularity as a way to easily identify ecological networks (see for instance [START_REF] Clauzel | Apports de la modélisation spatiale pour la gestion de la trame verte et bleue, Cybergeo: European[END_REF][START_REF] Mechai | Evidence for an effect of landscape connectivity on Borrelia burgdorferi sensu stricto dispersion in a zone of range expansion[END_REF][START_REF] Niculae | Evaluating the functional connectivity of Natura 2000 forest patch for mammals in Romania[END_REF], to assess the effects of fragmentation and loss of landscape connectivity [START_REF] Clauzel | Evaluating and mitigating the impact of a high-speed railway on connectivity: A case study with an amphibian species in[END_REF][START_REF] Liu | Assessing landscape eco-risk associated with hilly construction land exploitation in the southwest of China: Trade-off and adaptation[END_REF][START_REF] Tournant | Evaluating the effect of habitat connectivity on the distribution of lesser horseshoe bat maternity roosts using landscape graphs[END_REF], and to wholly or partially implement the mitigation hierarchy (for instance [START_REF] Bergès | Environmental mitigation hierarchy and biodiversity offsets revisited through habitat connectivity modelling[END_REF][START_REF] Clauzel | Integrating regional-scale connectivity in habitat restoration: An application for amphibian conservation in eastern France[END_REF]Tarabon, Bergès, Dutoit, & Isselin-Nondedeu, 2019a, b). Moreover, graph-theory-based models can be combined with species distribution models (SDMs) to identify habitat patches (see Tarabon et al., 2019a). SDMs relate species distribution records to environmental data and can be used to produce maps of habitat suitability [START_REF] Elith | Novel methods improve prediction of species' distributions from occurrence data[END_REF]. Nevertheless, SDMs require largescale databases of species occurrences, making this type of modeling challenging [START_REF] Bigard | Strategic landscape-scale planning to improve mitigation hierarchy implementation: An empirical case study in Mediterranean France[END_REF].

In response to biodiversity losses mainly caused by urban development [START_REF] Haddad | Habitat fragmentation and its lasting impact on Earth's ecosystems[END_REF], national regulations in many countries require developers to implement a three-step mitigation hierarchy aimed at first avoiding, and failing this, reducing impacts on biodiversity and then compensating for them [START_REF] Maron | The many meanings of no net loss in environmental policy[END_REF]. The mitigation hierarchy has been part of environmental policies since the 1970s in many countries, but only applied since the 2000s following recent regulatory developments. It aims to reach the goal of no net loss (NNL) or net gain of biodiversity [START_REF] Gibbons | Offsets for land clearing: No net loss or the tail wagging the dog?[END_REF][START_REF] Maron | Taming a wicked problem: Resolving controversies in biodiversity offsetting[END_REF]. In the last compensation step, developers have to implement biodiversity offsets (BO) providing ecological gains elsewhere through restoration or habitat creation actions on public or private land, in a quantity at least equal to what was impacted. Depending on the environmental policy, they generally have to comply with offset ratios (between the amount of area negatively impacted and the compensation area) to reduce the effects of delays and uncertainties [START_REF] Bull | Categories of flexibility in biodiversity offsetting, and their implications for conservation[END_REF][START_REF] Moilanen | Fifteen operationally important decisions in the planning of biodiversity offsets[END_REF]. However, neither France nor Europe imposes any standard equivalence assessment method (EAM) of calculating the offset ratios, which can lead to extensive requirements for land [START_REF] Bezombes | Ecological equivalence assessment methods: What trade-offs between operationality, scientific basis and comprehensiveness?[END_REF]. In other countries, such as in the United States, there is a standard calculation method such as Florida's Uniform Mitigation Assessment Method (UMAM) [START_REF] Reiss | Florida Uniform Mitigation Assessment Method[END_REF]). Yet there is a lack of clear guidelines and methods that could help developers and regulators properly anticipate and assess the actual impacts of development on biodiversity, and the relevance of the proposed BO [START_REF] Gardner | Biodiversity offsets and the challenge of achieving no net loss[END_REF]Maron, Dunn, McAlpine, & Apan, 2010a;[START_REF] Moreno-Mateos | The true loss caused by biodiversity offsets[END_REF][START_REF] Thorn | Effectiveness of biodiversity offsets: An assessment of a controversial offset in Perth[END_REF].

In addition, despite recent European regulatory requirements (e.g. the Green Infrastructure Strategy in Europe, the green and blue corridors recommended by the "Grenelle de l'Environnement" in France), little attention is paid to the territorial organization of BO. Anticipating, organizing and pooling offsets are key to reaping BO benefits [START_REF] Bigard | Strategic landscape-scale planning to improve mitigation hierarchy implementation: An empirical case study in Mediterranean France[END_REF], particularly in territories where the pressure of urbanization is relatively strong and where the landscape suffers from fragmentation [START_REF] Haverland | Examining the occurrence of mammal species in natural areas within a rapidly urbanizing region of Texas[END_REF]. The mitigation hierarchy is also only applied to certain components of biodiversity, when it should apply to the whole range [START_REF] Bezombes | A set of organized indicators to conciliate scientific knowledge, offset policies requirements and operational constraints in the context of biodiversity offsets[END_REF][START_REF] Gamarra | Accounting for no net loss: A critical assessment of biodiversity offsetting metrics and methods[END_REF]. For example, landscape connectivity is not a primary criterion within the mitigation hierarchy, nor for land-use and urban planning, as pointed out by different authors [START_REF] Bergsten | To model the landscape as a network: A practitioner's perspective[END_REF][START_REF] Bigard | The inclusion of biodiversity in environmental impact assessment: Policy-related progress limited by gaps and semantic confusion[END_REF][START_REF] Kujala | Towards strategic offsetting of biodiversity loss using spatial prioritization concepts and tools: A case study on mining impacts in Australia[END_REF]. In practice, BO implementation has so far taken little account of issues related to the spatial configuration of ecosystems in the landscape. Yet this should be a major component of BO, since connectivity appears to be a key factor in BO successes [START_REF] May | Are offsets effective? An evaluation of recent environmental offsets in Western Australia[END_REF][START_REF] Scolozzi | A multi-scale qualitative approach to assess the impact of urbanization on natural habitats and their connectivity[END_REF][START_REF] Van Teeffelen | Ecological and economic conditions and associated institutional challenges for conservation banking in dynamic landscapes[END_REF]. Adopting a landscape approach also appears essential to implement the three steps of the mitigation hierarchy; this should be based on scientific methodology and tools that can properly anticipate future negative impacts of urban development and to implement their avoidance [START_REF] Bigard | Strategic landscape-scale planning to improve mitigation hierarchy implementation: An empirical case study in Mediterranean France[END_REF]. Although a growing literature addresses BO from an ecological perspective [START_REF] Calvet | Tracking the origins and development of biodiversity offsetting in academic research and its implications for conservation: A review[END_REF], as well as the use of spatial tools in conservation planning [START_REF] Almenar | Assessing habitat loss, fragmentation and ecological connectivity in Luxembourg to support spatial planning[END_REF], no work so far has linked these two issues.

We propose to fill this gap by addressing the following question: how can we better anticipate and plan mitigation hierarchy implementation at territorial scales in relation to future urban development? We developed a methodological framework to implement mitigation hierarchy planning within a landscape connectivity approach. We considered both ecological connectivity and urban dynamics, seeking to (1) minimize the ecological impacts of land development, and (2) enhance the application of the mitigation hierarchy.

We first simulated future urbanization needs under two different development scenarios using a bespoke urban model called SimUrba developed by [START_REF] Calvet | Biodiversity facing development choices: A modeling approach applied to the Occitanie region[END_REF] and described here. We then assessed their impact on ecological networks using the spatial ecology tool Graphab [START_REF] Foltête | A software tool dedicated to the modelling of landscape networks[END_REF]. Then, we implemented the mitigation hierarchy, first by avoiding ecological impacts from urbanization and, when necessary, by identifying the most appropriate locations for BO (corresponding to new habitat patches) from two different sets of potential sites (public and private land) and using two different offset ratios (2:1 and 1:1).

We empirically tested this approach with a case study located in the French Occitanie region: the Toulouse conurbation, closely concerned by these planning issues. Indeed, its territorial cohesion program (SCoT) is currently undergoing revision aimed at reconciling spatially balanced economic growth with environmental objectives, especially under recent French regulatory changes (e.g. Council Directive 85/337/ EEC, Directive 2014/52/EU, French laws n°2015-991 and n°2 016-1087; [START_REF] Wende | Introduction of a European Strategy on No Net Loss of Biodiversity[END_REF]. In this context, local authorities have been given greater responsibility for urban and environmental planning.

This paper is organized in three sections. First, we present the overall methodological approach. Then we apply it to the Toulouse conurbation case study. Lastly, we conclude on the lessons learned in the context of BO, offering further research and policy recommendations.

Methods

The overall methodological approach

We propose a four-step method aimed at: 1) mapping the ecological value of a study area and its ecological networks based on several representative species and a global connectivity index (Equivalent Connectivity; [START_REF] Saura | Network analysis to assess landscape connectivity trends: Application to European forests (1990-2000)[END_REF], 2) simulating future urban sprawl according to two development scenarios (a past-trend scenario and an urban sprawl reduction scenario called "factor 4"), 3) assessing the ecological impacts of these two scenarios, and 4) implementing the mitigation hierarchy including appropriate and ecologically relevant biodiversity offsets based on landscape networks. Fig. 1 summarizes this overall approach.

Study area

We selected a study area where our approach could be tested as part of the review process for a land-use planning scheme (SCoT), and for which data was accessible: the Toulouse conurbation, in the Occitanie region of Southern France, (Fig. 2a). The Toulouse conurbation's SCoT covers an area of 1,181 km 2 and encompasses 964,000 inhabitants spread over 114 municipalities and 5 municipal communities. The Metropolis of Toulouse is very attractive, and the SCoT predicts 250,000 to 300,000 additional inhabitants and 140,000 new jobs by 2030. Nearly 10,000 additional inhabitants yearly should settle in the suburban area and along the main lines of transportation. Therefore, meeting current needs and those of future generations requires real control of space consumption. Given the diversity and spatial proximity of land uses, and the high stakes regarding biodiversity conservation and the maintenance of a still-thriving agriculture, this is a particularly great challenge. To avoid the edge effect in urban and connectivity Fig. 1. Summary of the overall method proposed. The first steps of the mitigation hierarchy are applied from 1c by avoiding urbanization of areas of highest ecological value, before implementing BO if necessary (step 3). Note that steps 1a, 1b and 1c are concurrent, numbering of the steps is not chronological. analysis, we applied a buffer zone of 5 km around the administrative boundary of the Toulouse conurbation (Fig. 2b). We defined this distance according to the availability of land-use and environmental geodata, the computation times of models, and the dispersal distance of the focal species.

Species data

Landscape graphs are intended for modeling the ecological networks of single species by focusing on well-identified habitat. However, the development of efficient mitigation hierarchy strategies requires measures of benefit to overall biodiversity, including both rare and more common species with various habitat preferences. Thus, a large set of representative species needed to be used to model ecological networks and cover all the issues related to offsetting in this study area with multispecific conservation goals [START_REF] Sahraoui | A multi-species approach for assessing the impact of land-cover changes on landscape connectivity[END_REF].

We selected species of conservation interest in the context of the mitigation hierarchy for the Toulouse region, identifying 20 species, 16 vertebrate and 4 invertebrate, particularly affected by development projects there [START_REF] Biotope | Decision support study for implementing an mitigation hierarchy strategy at the regional scale[END_REF]. Of these, 15 are protected under the EU Habitats and Birds Directives, and 14 are included in the French list for national consistency of the green and blue corridor (cohérence nationale de la Trame Verte et Bleue) defined by the French National Museum of Natural History [START_REF] Sordello | Review on needs relating to movements and ecological functionality of 39 species proposed for the French list for national consistency of the green and blue corridor Muséum national d'histoire naturelle (MNHN)-Service du Patrimoine naturel et Office pour les insectes et leur environnement[END_REF]. Two of these species, the roe deer and fire salamander, are not protected outside France but are highly affected by human activities and road development [START_REF] Jiang | Effects of human activities on the spatial distribution of eastern roe deerCapreolus pygargus bedfordi in the Lesser Khingan Mountains, northeastern China[END_REF][START_REF] Vincenz | Road Effects on a Fire Salamander Population (Salamandra salamandra terrestris)[END_REF].

Based on their habitat preferences, we then grouped them under 5 types of area: open and semi-open, cultivated, forest, watercourses and ponds (Table 1). See Appendix 1 for more details on the species' habitat preferences and dispersal capacities.

Mapping of ecological value of landscape and ecological networks

The first step in implementing a planned and strategic mitigation hierarchy is to identify areas with high ecological value. Habitat quality will be used to assess the capacity of habitat patches other than surface area (see below in this Section), and then to begin the mitigation hierarchy process by avoiding urbanization of those areas of highest ecological value (see Section 2.7).

Here, we mapped the ecological value of landscape based on landscape ecology indicators, as proposed by [START_REF] Bigard | Strategic landscape-scale planning to improve mitigation hierarchy implementation: An empirical case study in Mediterranean France[END_REF]. We selected eight relevant indicators in the landscape ecology framework related to landscape structure, nature of land cover and use, and species occurrence. Indeed, the ecological value of landscape relies not only on LULC characteristics (including different biodiversity indicators such as type of LULC, form, inventory and protection areas, and ecological functionality level) but also on the composition and configuration of the landscape (such as density, rarity or shape of habitats) (see Table 2 and related literature). We produced a map of landscape ecological values that is not species-specific, although we are aware that the set of indicators is not always appropriate for all species. However, various considerations make this approach relevant. First, the values of the different indicators are weighted together (see below) and the average value within a habitat patch then enables the area of this patch to be weighted. Thus, if nuances according to species appear for some indicators, they will have very little impact on the analysis of connectivity and the calculation of the global connectivity index. Moreover, this approach is more easily reproducible for stakeholders than species-byspecies mapping, which is tedious. In addition, weighting the surface area of habitat patches with the average value of overall ecological importance within them renders connectivity analyses more likely to be performed.

Data used to map the ecological value of landscape are presented in Table 3. As the initial typology included many different land covers, we classified them into 23 land cover categories to model ecological networks (see Appendix 2 for details).

We used Land Facet Corridor Designer [START_REF] Beier | Use of land facets to plan for climate change: Conserving the arenas, not the actors[END_REF] to calculate the Local Shannon Index [START_REF] Shannon | A Mathematical Theory of Communication[END_REF], and the SDMtoolbox [START_REF] Brown | SDMtoolbox 2.0: The next generation Python-based GIS toolkit for landscape genetic, biogeographic and species distribution model analyses[END_REF] for species richness. The Fractal Dimension Index (FRAC; Eq. ( 1)) for a landscape element i (according to LULC) is given by the following formula [START_REF] Crossman | Systematic landscape restoration in the rural-urban fringe: Meeting conservation planning and policy goals[END_REF]:

= × FRAC p a 2ln(0.25 ) ln i i (1)
where a i is the area and p i the perimeter of the landscape element.

We first tested collinearity and estimated the effect of multicollinearity among variables with the variance inflation factor (VIF), taking a stepwise approach where a VIF is calculated with the usdm Table 1 List of targeted species used in the study. Some are included in the French list for national consistency of the green and blue corridor (cohérence nationale de la Trame Verte et Bleue: TVB) and/or protected under the EU Habitats and Birds Directives. Then, we produced a 20-m resolution raster map by combining variables. The value of each cell is the average of each indicator value using a uniform weighting.

Next, we used Graphab (version 2.4; [START_REF] Foltête | A software tool dedicated to the modelling of landscape networks[END_REF]; see http:// thema.univ-fcomte.en/productions/graphab/) to model the ecological networks and conduct the connectivity analysis from landscape graphs and an associated connectivity metric, the Equivalent Connectivity (EC) index proposed by [START_REF] Saura | Network analysis to assess landscape connectivity trends: Application to European forests (1990-2000)[END_REF].

We prepared a 20 m-resolution raster map by combining different vector data. Areas showing transport infrastructure (roads, railways and associated land) were dilated by 10 m to avoid any breaks due to the rasterizing of the initial vector data. We based the analysis on several vertebrate and invertebrate species from the study area (see Section 2.3). The nodes of landscape graphs were defined according to the landcover categories favored as habitat by each species. These were derived from the biological literature, as were the minimum areas of nodes (Appendix 2). Species distribution models (SDMs) could not be used here due to a lack of species occurrence data at the scale of the study area. Then, the different land-cover categories defined during data compilation were assigned to six resistance classes based on the species' ability to move within them: highly suitable, suitable, neutral, unfavorable, highly unfavorable or barrier to animal movement [START_REF] Mimet | Locating wildlife crossings for multispecies connectivity across linear infrastructures[END_REF].

Habitat connectivity was assessed from the Equivalent Connectivity index (EC; [START_REF] Saura | Network analysis to assess landscape connectivity trends: Application to European forests (1990-2000)[END_REF], an ecologically relevant metric appropriate to this mitigation hierarchy context. Indeed, the EC index uses the concept of 'amount of reachable habitat' for a species at the landscape scale [START_REF] Saura | Network analysis to assess landscape connectivity trends: Application to European forests (1990-2000)[END_REF][START_REF] Saura | A common currency for the different ways in which patches and links can contribute to habitat availability and connectivity in the landscape[END_REF]. The index is derived from the Probability of Connectivity index (PC; [START_REF] Saura | A new habitat availability index to integrate connectivity in landscape conservation planning: Comparison with existing indices and application to a case study[END_REF], defined as "the probability that two animals randomly placed within the landscape move to habitat areas that are reachable from each other." Thus, the EC index fulfils all the requirements of a connectivity metric, meeting conservation objectives linked to habitat loss and landscape fragmentation [START_REF] Bergès | Environmental mitigation hierarchy and biodiversity offsets revisited through habitat connectivity modelling[END_REF]. EC is a combined measure of habitat amount and habitat connectivity, and the index is based on node attributes (here, on quality-weighted habitat area) and link attributes transformed into probability of dispersal p ij between nodes i and j. p ij values are calculated with a decreasing exponential function of the distance d ij between patch i and j, taking into account the dispersal capacity of the focal species (Eq. ( 2)):

= p e ij dij ( 2
)
where α is a cost distance-decay coefficient: α is usually set so that p ij = 0.5 when d is the median dispersal distance , or p ij = 0.05 when d is the maximum dispersal distance [START_REF] Saura | A new habitat availability index to integrate connectivity in landscape conservation planning: Comparison with existing indices and application to a case study[END_REF].

Here, we took into account a cumulative dispersal distance (cost distance) at the metapopulation level. These metapopulation dynamics are key factors in population viability [START_REF] Saunders | Biological consequences of ecosystem fragmentation: A review[END_REF]. Dispersal distances were extracted from existing literature reviews; however, no literature review gave maximum dispersal distances for birds. Therefore, we used an allometric relationship from [START_REF] Sutherland | Scaling of natal dispersal distances in terrestrial birds and mammals[END_REF], as proposed in [START_REF] Sahraoui | A multi-species approach for assessing the impact of land-cover changes on landscape connectivity[END_REF], which links diet types and species body mass, as well as dispersal capacity, given by the following formula: 13.1 × M 0.63 . Bird body size (M, in kg) was extracted from the data set of [START_REF] Lislevand | Avian body sizes in relation to fecundity, mating system, display behavior, and resource sharing[END_REF] containing 3,769 bird species. Then, dispersal distance values in metric units (Dist M ) were converted into cost units (Dist) using a linear regression between link topological distance and link cost distance for all the links of the graph, as given in

Table 2

Indicators related to landscape structure, land-use and land-cover (LULC), land form and species occurrence selected and used to assess the ecological value of each cell at 20-m resolution.

Indicators Justification Categories Value

LULC rarity A rare environment considered important and requiring special attention [START_REF] Crenam | Identifying potential biodiversity reservoirs. Ecological networks of the Massif Central project: IPAMAC project[END_REF]. We considered the proportion of each land cover (except urban areas) relative to the total area of natural and semi-natural areas.

High: < 1% 1 Medium: 1-5% 0.6 Low: > 5% 0.3 Not affected: water bodies and courses 0 LULC diversity A high diversity or local richness of habitats considered beneficial for biodiversity. Particularly favorable habitats are homogeneous in size [START_REF] Crenam | Identifying potential biodiversity reservoirs. Ecological networks of the Massif Central project: IPAMAC project[END_REF][START_REF] Mcgarigal | The gradient concept of landscape structure[END_REF]. The richness and homogeneity of areas are summarized by the Local Shannon Index [START_REF] Shannon | A Mathematical Theory of Communication[END_REF].

High to low 0 to 1 Biodiversity potential Different LULC offer differing potential for the development or maintenance of biodiversity [START_REF] Kujala | Towards strategic offsetting of biodiversity loss using spatial prioritization concepts and tools: A case study on mining impacts in Australia[END_REF][START_REF] Letourneau | Methodological note for identifying areas important for the biodiversity and ecological continuities conservation in Languedoc region, Annexe au SRCE Languedoc-Roussillon[END_REF]. We ranked all types of land cover based on scoring by experts according to their biodiversity potential.

High Ecotones are also very important in biological processes. We used the Fractal Dimension Index (FRAC) as a measure of complexity derived from the perimeter/area ratio [START_REF] Crossman | Systematic landscape restoration in the rural-urban fringe: Meeting conservation planning and policy goals[END_REF].

High to low 1 to 0 Species richness Species richness is an indicator of the number of species recorded in a cell [START_REF] Gotelli | Estimating species richness[END_REF].

High to low 1 to 0 the following formula (Eq. ( 3)):

= + Dist e intercept slope Dist ( )

M

(3)

See Appendix 3 for further details on data used and calculations of EC index.

Simulation of dynamics with SimUrba

We used an ad hoc model, SimUrba, developed by [START_REF] Calvet | Biodiversity facing development choices: A modeling approach applied to the Occitanie region[END_REF] and presented here, which simulates urban dynamics based on the consumption of space related to future needs (including housing, commercial and industrial areas, roads and parking) per inhabitant under different development scenarios. SimUrba is an open source model available at: https://gitlab.com/vidlb/simurbapy. Data used in this section are presented in Table 3.

The simulation approach of SimUrba combines a cellular automaton and a geographic information system. The literature contains other models that model urban systems (e.g. Mup-City, UrbanSim, SLEUTH as well as GeOpenSim, etc.). These were used as a starting point, but we decided to create our own model to better respond to our research problem and available data (more detail in Appendix 4). Our model differs in the way we estimate housing needs from census data, so we can also focus our analysis of the results on comparing scenarios with different degrees of urban sprawl. We also added fine building data, to estimate population density at the individual building level, to our diachronic analysis of the trend in per capita space consumption. Thus, we were able to choose the type of results we wanted to obtain from the simulation output. For example, we were able to separate the numerical results on the consumption of built versus floor space, the average height of buildings in a cell, and many other indicators that we wanted to include in the sensitivity analysis.

The model estimates the probability of a cell (spatial unit) becoming urbanized in the future. The urbanization process is modeled according to estimated future needs in terms of built space. To do so, the model first assesses past trends in space consumption per inhabitant, and subsequently evaluates the future urban needs of the selected area based on its estimated population growth. Future projections of population growth are based on INSEE data (Institut national de la statistique et des études économiques). The time period chosen depends on the data available for the selected area. Here, we used data from 2009 to 2015 to assess past trends (see Section 2.3).

The probability of a cell becoming urbanized, in our case by 2040, depends on several "restriction" and "urbanization potential" parameters. For instance, our "urbanization potential" parameters included location indicators such as "being in areas that will be urbanized" according to the local development plans. Moreover, a cell close to amenities and services (such as roads, shops, schools and healthcare services) is more likely to be urbanized in the future. SimUrba also identifies areas where urbanization is restricted or not allowed (such as regulatory zones, existing biodiversity offsetting areas, floodplains and areas with slopes greater than 30%), called the "restriction" parameters.

To implement the first steps of the mitigation hierarchy (i.e. avoidance and reduction of ecological impacts), we added two further indicators to the simulations of urban dynamics: the ecological value of the study area (provided by the previous step of the method), and agricultural dynamics and land pressure estimated locally from existing studies or databases [START_REF] Biotope | Decision support study for implementing an mitigation hierarchy strategy at the regional scale[END_REF]. Our objective was to identify areas of potential conflict between agriculture, urbanization, and biodiversity conservation. Thus, we favored particularly intense urbanization for cells of less ecological or agricultural importance.

Lastly, two different development scenarios were simulated: (1) a trend-based scenario (hereafter Sc Trend ) based on a diachronic analysis (2009)(2010)(2011)(2012)(2013)(2014)(2015), where the past trend in per capita space consumption is maintained up to 2040, and which can be described as "business as usual", and (2) a scenario where space consumption reduces in accordance with regulatory targets (e.g. Plan Biodiversité 2018 in France), the "factor 4 scenario" (hereafter Sc F4 ), the per capita space consumption calculated in the trend-based scenario being divided by 4 by 2040.

Assessment of ecological impacts

We calculated the ecological impacts of the urban development scenarios on habitat connectivity with the following formula (Eq. ( 4)):

= × X X X X 100 2040 2015 2015 (4)
where X 2015 and X 2040 are the metric values of habitat connectivity (amount of reachable habitat on EC index with habitat area weighted by ecological value) assessed for each species before (in 2015) and after (in 2040) the simulated future urbanization. X provides information on the habitat patches and ecological connectivity that will be lost or reduced due to urbanization.

Implementation of planned and relevant biodiversity offsets

To address the mitigation hierarchy, we first considered whether the plots were public or private, assuming that public plots would be more readily available for BO. This yielded a first set of all-public potential plots, called "P BO1 ". Then, we added plots of private land located near major ecological corridors identified in the territorial cohesion program (SCoT), with the objective of increasing the number of plots available, including the most ecologically relevant sites, and supporting ecological networks like the "green and blue corridors" [START_REF] Chaurand | Articulating the green and blue corridor policy and the mitigation hierarchy: Complementarities and limits for an effective biodiversity conservation in France[END_REF]. We called this mixed public and private set of potential plots "P BO2 ". In addition, farming was taken into account when selecting BO sites to avoid possible conflicts with agricultural activities (we excluded irrigated areas and major agricultural interests like famous wine-producing areas) thanks to a collaboration with SAFER (French land development and rural establishment agencies), the agency responsible for rural land development.

Certain landscape and area criteria were also considered in BO site selection. We restricted to areas of 0.5 ha or more located outside future urban areas, wetlands and existing BO sites. Since our approach was species-centered, we were also able to exclude habitats unsuitable for the species considered (e.g. we excluded forest patches for species from open/semi-open and cultivated areas). After identifying areas for BO, we addressed ecological connectivity using Graphab's "patch addition process" as described by [START_REF] Foltête | A methodological framework for the use of landscape graphs in land-use planning[END_REF]. This enabled us to identify where BO would maximize species' connectivity, by iteratively adding a virtual node (corresponding to a new habitat patch) in the centroid of each pre-targeted plot and adding new links from this node to the other nodes. This stepwise procedure is currently applied in studies aimed at identifying the most strategic habitats to prioritize for biodiversity preservation or restoration, as well to propose landscape management actions to protect terrestrial mammals, amphibians and birds [START_REF] Clauzel | Integrating regional-scale connectivity in habitat restoration: An application for amphibian conservation in eastern France[END_REF][START_REF] Clauzel | Coupling a landscape-based approach and graph theory to maximize multispecific connectivity in bird communities[END_REF][START_REF] Mimet | Locating wildlife crossings for multispecies connectivity across linear infrastructures[END_REF]. It results in a map of favorable habitat patches for the species classified under a habitat type, created by overlaying the prioritization maps of individual species.

Then, we identified plots from both P BO1 and P BO2 (see above) as candidates for BO to compensate for negative impacts on habitat area, using two standard offset ratios from current BO practice: 1:1 and 2:1 (called "R 1:1 " and "R 2:1 ", respectively) [START_REF] Curran | Is there any empirical support for biodiversity offset policy?[END_REF]. Landscape graphs being species-specific, ecological impacts are different for each species, even within the same group. Therefore, we used the highest habitat area loss value among species belonging to the same group to define BO objectives in terms of area, according to the offset ratio applied.

Lastly, we assessed residual impacts on habitat connectivity from BO sites identified using the R 2:1 offset ratio, assuming this to be more likely to reach the NNL objective. When the ratio was not met, we attempted to identify the appropriate minimum ratio to ensure NNL in habitat connectivity [START_REF] Bergès | Environmental mitigation hierarchy and biodiversity offsets revisited through habitat connectivity modelling[END_REF].

Results

Mapping of ecological value

We calculated a VIF for each indicator selected to map the ecological value of landscape. All values were below the threshold (VIF < 10). Therefore, there were no further exclusions among the variables presented in Table 2.

The ecological value map of the Toulouse region used to assess habitat connectivity is presented in Fig. 3. The gradient represents the cells' ecological importance, ranging from very high to low or zero.

In this study case, urban areas are generally the least valuable in terms of biodiversity. Ecological value is mainly derived from natural or semi-natural areas preserved from urbanization, inventory and protection areas, and green and blue infrastructures.

Simulation of urban dynamics

Future projections of urban sprawl are based on an increase of + 1.464% in inhabitants each year, generating supplementary urbanization in 2040 of 6,108 ha for Sc Trend and 4,286 ha for Sc F4 (Fig. 4). New urbanization is mainly distributed around urban centers offering the widest urbanization potential. The greatly reduced urban sprawl obtained in Sc F4 (-30%) is due to the densification of existing buildings (via a fitting method which takes into account height rules for surrounding buildings) to meet estimated needs.

Assessment of ecological impacts

This result reveals the potential areas of conflict between future development and biodiversity conservation issues. Both urban development scenarios led to a decrease in total patch surface area (S) and in habitat connectivity (EC index). The average decreases in S and EC values for Sc Trend were -5.6% and -6.3%, respectively, and for Sc F4 , -4.1% and -4.8%, respectively. The most impacted species were those from open/ semi-open and cultivated areas, particularly Cisticola juncidis and Burhinus oedicnemus: on average, these groups lost more than 8% and 10% of surface area and habitat connectivity, respectively, under the worst-case urbanization scenario (Sc Trend ). Others were very little affected, such as species linked to watercourses (for details, see Table 4).

Implementation of appropriate biodiversity offsets

Considering P BO1 (exclusively public plots), appropriate sites for BO were identified on 1,164 ha of public land distributed over 452 cells of 50 ha for species associated with forest areas, watercourses and ponds.

For species from open/semi-open and cultivated areas, 577.3 ha were identified, distributed over 391 cells. Considering P B02 (including private plots), we added another 3,788.8 ha for species associated with forest areas, watercourses and ponds, and 2,918.0 ha for the other species, together distributed over 1,159 and 1,238 cells, respectively. The position of the plots is illustrated in Appendix 6.

For easier readability, we focus our results on the more optimistic "factor 4" scenario (Sc F4 ). It can be assumed that the Sc Trend scenario will show more alarming results, since dense urbanization generates more impacts on habitat networks [START_REF] Calvet | The biodiversity offsetting dilemma: Between economic rationales and ecological dynamics[END_REF][START_REF] Tannier | Impact of urban developments on the functional connectivity of forested habitats: A joint contribution of advanced urban models and landscape graphs[END_REF]. Thus, for each species pool and sets of plots P BO1 and P BO2, we identified the most promising cells for BO implementation, i.e. where habitat creation (to compensate for negative effects on habitat area) generated the highest gain in habitat connectivity (based on EC index). Fig. 5 shows the most promising areas in red, while those generating lower gains in connectivity tend towards white. From the areas maximizing habitat connectivity, we selected the BO sites required to meet the NNL objective for each habitat group under both P BO1 and P BO2 (for each habitat group, the highest impact value among species belonging to the group was used). BO site requirements are illustrated in Fig. 5 by green and purple squares corresponding to offset ratios of 1:1 and 2:1, respectively.

Assessment of residual impacts on habitat connectivity

Habitat connectivity (regarding EC index) was maintained for most species, with the exception of 4 (in bold font in Table 5). The residual impacts vary between -0.3% and -1.9%. For these species, we thus estimated the minimum offset ratio that would ensure compliance with the NNL objective in terms of connectivity at from 217% to 338% (Table 5).

Discussion

Our paper provides a combined methodological framework to anticipate and plan the mitigation hierarchy. Future urban dynamics are modeled and a landscape connectivity standpoint is taken to assess ecological impacts. We use complementary indicators to precisely quantify and spatialize the ecological impacts of urban sprawl, and to locate their most relevant areas for biodiversity offsetting. Our analyses emphasize the role of development scenarios in space consumption and thus in biodiversity conservation.

Planning biodiversity offsets at territorial scale

Based on the case study of the Toulouse conurbation, we identified areas of conflict between future development and biodiversity conservation issues. Our analysis highlighted the BO needs for each habitat type impacted, revealing the effects of urban sprawl. In this thriving agricultural area, it was mainly species associated with open/semi-open and cultivated areas that were affected, principally due to their number and the low ecological value of these areas (see Section 2.4).

The most appropriate locations for creating habitats (i.e. maximizing habitat connectivity) were identified after following the different steps of the mitigation hierarchy (avoidance/reduction steps are integrated into the simulation in the urban sprawl model). Gains in connectivity are generated either by reinforcing existing habitats (for instance, here, forest areas; Fig. 5e,f) or by creating within the landscape matrix new habitats that serve as stepping stones [START_REF] Saura | Stepping stones are crucial for species' longdistance dispersal and range expansion through habitat networks[END_REF]. BO should, in this case, be more effective in conserving biodiversity [START_REF] Resasco | Meta-analysis on a Decade of Testing Corridor Efficacy: What New Have we Learned?[END_REF], provided the measures are not overambitious [START_REF] Guillet | Policy flaws of biodiversity offsetting as a conservation strategy[END_REF] or the BO needs are not disproportionate to the

Table 4

Assessment of ecological impacts for the trend-based (Sc Trend ) and "factor 4" (Sc F4 ) scenarios based on variation in habitat area (ΔS) and EC index (ΔEC). ΔS moy and ΔEC moy indicate the average value for each species group (see Appendix 5 for area values in ha.). The orange boxes represent maximum values of losses within habitat groups or between them. supply of potential sites (for instance here, open, semi-open, and cultivated areas; Fig. 5a,b).

However, since this was done here in parallel for each species group, it generated several conflict areas involving different BO needs watercourses and ponds are not illustrated here for readability issues) under the Sc F4 scenario for two sets of available areas P BO1 (a, c and e) and P BO2 (b, d and f), the first consisting exclusively of public plots and the second including both public and private plots located near major ecological corridors (see Section 2.6). Purple squares represent priority cells for BO, i.e. plots where gains in connectivity are maximized, with an offset ratio of 1:1 (of impacts on habitat area); green squares represent cells added to reach 2:1. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.) 2019), affects the feasibility of BO, making it impossible to implement all the offsets. Here, including under potential BO sites the private plots of land located near the main corridors of the SCoT proved to be one solution to this problem. In our study case, it significantly reduced the areas in conflict (Table 6).

Moreover, where private plots of land were used for BO (P BO2 ), fewer were needed (for instance, Fig. 5b,d) because they were on average much larger than the public plots (+21.6%). Using fewer plots means fewer long-term constraints, like monitoring and management of compensatory plots (i.e. fewer sites to manage, fewer actors to coordinate, etc.). But it should be noted that multiple offset sites can limit the risk of larger-scale ecological failure [START_REF] Moilanen | How much compensation is enough? A framework for incorporating uncertainty and time discounting when calculating offset ratios for impacted habitat[END_REF], and may thus be more successful in promoting specific types of biodiversity [START_REF] Herrera | A distribution-oriented approach to support landscape connectivity for ecologically distinct bird species[END_REF][START_REF] Wintle | Global synthesis of conservation studies reveals the importance of small habitat patches for biodiversity[END_REF].

While we are aware that land-use conflicts cannot be reduced solely by using private plots, it should be remembered that the public plots commonly used by developers to implement BO are often subject to criticism concerning their ecological relevance. However, environmental and urban planners have little control over private land, which in practice limits access to relevant potential BO sites. Stakeholders would thus find it advantageous to adopt a mixed approach, for example considering private-public partnerships.

Adopting a landscape perspective to pursue the NNL objective has implications for ecological equivalence assessment [START_REF] Quétier | Assessing ecological equivalence in biodiversity offset schemes: Key issues and solutions[END_REF]. Here, we proposed to estimate the minimum offset ratio that would ensure NNL of biodiversity for several species in terms of habitat connectivity, consistent with the warnings of [START_REF] Bergès | Environmental mitigation hierarchy and biodiversity offsets revisited through habitat connectivity modelling[END_REF] on the riskiness of using a standard offset ratio.

Implications and opportunities for biodiversity conservation

Our methodological framework provides a relevant and comprehensive tool at the territorial scale, meeting two needs. First, identifying urban dynamics is a challenge because there is no standard simulation model for urban sprawl, despite the many developed to meet different research objectives. SimUrba was developed to address biodiversity conservation in the context of land-use planning scenarios and the application of the mitigation hierarchy. Second, the precise identification of local conservation issues requires specific ecological indicators and methods. Here, a habitat network connectivity model based on landscape graphs was used to estimate the impacts of projected urban dynamics on species' habitats in an ecologically relevant way [START_REF] Foltête | How ecological networks could benefit from landscape graphs: A response to the paper by Spartaco Gippoliti and Corrado Battisti[END_REF]Gamarra, Lassoie, & Milder, 2018).

Applying the mitigation hierarchy highlights areas of tension between future urbanization and conservation issues that can be taken into account in planning the best sites for biodiversity offsets. Bunton, Ernst, Hanson, Beyer, Hammill, Runge, Venter, Possingham, and Rhodes (2015) showed the usefulness of coordination between linear infrastructure planning and conservation offset planning. In our case, this approach helps to identify the most appropriate locations for biodiversity offsets in terms of functional ecology. The hierarchization of BO sites enables relevant conservation sites to be selected, so as to reconnect existing ecological corridors and to integrate BO into broader conservation actions that make sense locally. This is consistent with [START_REF] Dupont | Biodiversity offset and green and blue corridor: A combination to explore for biodiversity[END_REF], who pointed out the many advantages of pooling BO and green and blue infrastructure, provided that the principle of additionality is respected: i.e., measures are taken (or planned) in addition to existing public or private commitments for the protection of the environment [START_REF] Dupont | The law for the reconquest of biodiversity: Towards a strengthening of the legal regime of biodiversity offsetting?[END_REF]. This form of BO organization strengthens local biodiversity conservation and helps ensure BO success through natural recolonization during site restoration, a major factor in meeting the NNL of biodiversity objective [START_REF] Resasco | Meta-analysis on a Decade of Testing Corridor Efficacy: What New Have we Learned?[END_REF]. Our method is similar in its anticipation of offsets to the mitigation bank model [START_REF] Bekessy | The biodiversity bank cannot be a lending bank[END_REF], which was shown to outperform project-by-project BO [START_REF] Scemama | The emergence of the US wetland compensation market: Impacts on organizational patterns and transaction characteristics[END_REF]. Our projections should therefore make offsetting sites more ecologically relevant.

Improving the methodological framework

The main limitations of the proposed methodology are related to the data used, particularly to prepare the LULC map. Although landscape graphs are a good compromise between the information yielded and the data requirements of landscape connectivity analysis [START_REF] Foltête | How ecological networks could benefit from landscape graphs: A response to the paper by Spartaco Gippoliti and Corrado Battisti[END_REF][START_REF] Saura | Connectivity as the amount of reachable habitat: conservation priorities and the roles of habitat patches in landscape networks[END_REF], combined models require a lot of data, and there is a risk of over-simplification or inconsistent results depending on the sources [START_REF] Simpkins | Assessing the performance of common landscape connectivity metrics using a virtual ecologist approach[END_REF]. Here, our modeling approaches involve closely-related results; variations can stem both from the mapping of landscape's ecological value and from

Table 5

Assessment of residual impacts based on variation in EC index (ΔEC) and on the minimum offset ratio (R min ; expressed as a percentage) required to meet the NNL objective for habitat connectivity. We applied the analysis from urbanization scenario Sc F4 and offset ratio R 2:1 . Assessment of the proportion of areas where conflict arises over the differing offsets required. A value of 1 means there is no conflict while values from 2 to 5 represent the number of species pools in conflict situations. This analysis covers both offset ratios applied (R 1:1 and R 2:1 ) and highlights differences in conflict area between the two sets of potential BO offsetting sites (ΔS BO ). In this paper, we defined resistance surface values by transforming a qualitative classification of resistance according to the well-documented preferences of each species. However, [START_REF] Simpkins | Assessing the performance of common landscape connectivity metrics using a virtual ecologist approach[END_REF] acknowledge that the performance of resistance surfaces is contextspecific. Different populations of the same species in different landscape matrices could differ in their tolerance to unsuitable land use and land covers, which could affect the resistance surfaces. Here, we did not generate values through expert knowledge [START_REF] Le Roux | Conservation planning with spatially explicit models: A case for horseshoe bats in complex mountain landscapes[END_REF] nor based on context-specific empirical values [START_REF] Reed | Modeling landscape connectivity for bobcats using expert-opinion and empirically derived models: How well do they work?[END_REF]. Therefore, the values of the resistance surfaces may not be reliable enough to be used in a detailed study in the same part of France. But they are good enough for the purposes of this paper, which does not aim for the most accurate resistance surfaces available nor apply state-of-the-art methods to obtain them. Alternatives could be used in similar studies. For instance, some authors proposed using SDM-derived maps to calculate a species-specific landscape resistance surface [START_REF] Duflot | Combining habitat suitability models and spatial graphs for more effective landscape conservation planning: An applied methodological framework and a species case study[END_REF][START_REF] Rödder | Coupling satellite data with species distribution and connectivity models as a tool for environmental management and planning in matrix-sensitive species[END_REF]. However, SDMs require the acquisition of species occurrence data. At this spatial scale, databases like the French SINP nature and landscape system (Système d'Information sur la Nature et les Paysages; http:// inventaire.naturefrance.fr/) can be used, but information is still too incomplete and inaccurate (only 28% of SINP data is considered to be spatially and temporally accurate; ONB, 2018) to make SDM results robust, as discussed by [START_REF] Bigard | Strategic landscape-scale planning to improve mitigation hierarchy implementation: An empirical case study in Mediterranean France[END_REF].

In the same way, [START_REF] Moilanen | On the limitations of graph-theoretic connectivity in spatial ecology and conservation[END_REF] noted that habitat patches are not always mapped explicitly in landscape graphs. Here, for technical feasibility reasons (quantity of data), we defined habitat patches according to the land-cover map, which does not take into account possible ecological constraints that may limit species' presence (neighboring roads, topography, etc.). Using a SDM could provide a practical and relevant solution (see Tarabon et al., 2019a), especially in a changing global context, since we are studying the impacts of urbanization by 2040. Will species still occupy the same habitats in areas vulnerable to climate change, like the Mediterranean basin (Allen, Antwi-Agyei, Aragon-Durand, Babiker, Bertoldi, Bind, Brown, Buckeridge, Camilloni, & Cartwright, 2019)? Although most of the species distribution remains adapted to all the climate scenarios considered, some species may well disappear from some regions [START_REF] Ofori | Influence of adaptive capacity on the outcome of climate change vulnerability assessment[END_REF]. Where the use of SDM is feasible, adding climate variables to models would be relevant [START_REF] Dilts | Multiscale connectivity and graph theory highlight critical areas for conservation under climate change[END_REF], as the question of species' vulnerability to climate change represents a real challenge in terms of the dimensioning of BO [START_REF] Quétier | Assessing ecological equivalence in biodiversity offset schemes: Key issues and solutions[END_REF].

Another possible limitation is that the offsetting step in our approach using landscape graphs is based on only two offset ratios. Depending on the ecological issues involved, this may not be enough to represent local or regional environmental policies, thus possibly leading to underestimations. Moreover, this methodological framework is based solely on the creation of habitats (adding habitat patches). We did not include (except in the avoiding steps) assessment and management of existing natural areas to enhance population viability [START_REF] Hodgson | Habitat area, quality and connectivity: Striking the balance for efficient conservation[END_REF]. Evaluating the potential for ecological gain at this spatial scale of analysis is difficult, and would require a detailed study of the ecological quality of the offset sites [START_REF] Weissgerber | Biodiversity offsetting: Certainty of the net loss but uncertainty of the net gain[END_REF]. Demonstrating the positive impact of BO on habitat connectivity would involve numerous non-measurable values and components of biodiversity (Calvet et al., 2015;[START_REF] Moreno-Mateos | The true loss caused by biodiversity offsets[END_REF]. Research perspectives raised here include improving the accuracy and reliability of our simulation models. Some authors have shown that improving overall and local connectivity can be effective as offsetting measures, and an alternative to creating new habitats [START_REF] Dalang | Trading connectivity improvement for area loss in patch-based biodiversity reserve networks[END_REF]. A wider approach could balance mitigation efforts between restoring habitat areas and reducing patch isolation [START_REF] Fahrig | Ecological responses to habitat fragmentation per se[END_REF]. Thus, our models could be improved by incorporating more qualitative developments, such as wildlife crossings. Wildlife crossings lead to landscape defragmentation and reconnect environments with each other, thereby enhancing the overall connectivity of ecological networks [START_REF] Bergès | Environmental mitigation hierarchy and biodiversity offsets revisited through habitat connectivity modelling[END_REF][START_REF] Mimet | Locating wildlife crossings for multispecies connectivity across linear infrastructures[END_REF]Tarabon, Bergès, Dutoit, & Isselin-Nondedeu, 2019b).

Finally, a wider territorial scale (from towns or municipalities to provinces or regions) could be relevant in the context of spatial BO planning [START_REF] Bigard | The inclusion of biodiversity in environmental impact assessment: Policy-related progress limited by gaps and semantic confusion[END_REF]. For instance, in France, the regional sustainable development plan, called "SRADDET" (Schéma Régional d'Aménagement, de Développement Durable et d'Égalité des Territoires), uses the regional scale, promoting the principles of sustainable development within broader territorial and ecosystem dynamics. This provides an overall view of the main organizational processes at work in the area, but not at such a fine and accurate scale. Analysis such as that performed in this study would be difficult on a wider scale: data acquisition would be too complex and calculations too lengthy or unfeasible. In this study, we applied a buffer zone of several kilometers around the administrative boundary of the Toulouse conurbation, a compromise that took into account the availability of land-use and environmental geo-data, the computation times of models, and the dispersal distance of the focal species. The buffer zone we considered here was not always, i.e. for four species, at least equal to their maximal dispersal distance, as suggested by [START_REF] Bergès | Environmental mitigation hierarchy and biodiversity offsets revisited through habitat connectivity modelling[END_REF] and [START_REF] Fletcher | The negative effects of habitat fragmentation operate at the scale of dispersal[END_REF]. Data acquisition and computation time permitting, this rule should be respected as far as possible in similar studies.

Conclusion

To meet the need for spatial tools in applying BO policies, we propose a methodological framework that can be used to implement a planning strategy respecting the mitigation hierarchy. Its originality lies in combining a simulation of projected urbanization up to 2040 with a simulation of the impacts on ecological networks within landscape ecology. We show through the case study of the Metropolis of Toulouse that simulations of projected urban development and mapping can be used to enhance land-use and offset planning.

This approach can improve the application of the mitigation hierarchy, especially by anticipating future land and conservation conflict areas, and by proposing appropriate biodiversity offsets. Moreover, this framework should support public decision-makers by taking into account relevant local factors, with several advantages as highlighted by [START_REF] Regnery | Biodiversity offsett: Concepts and limits for conserving biodiversity[END_REF]. First, the assessment of the residual impacts of future urbanization could give local stakeholders a better understanding of the limits of BO. Second, its territorial organization offers a comprehensive overview of the plans, policies and instruments related to the offsetting, so as to develop a framework of clear biodiversity objectives. Third, planning biodiversity offsets upstream could foster forward-looking territorial management, developing social dialogue and promoting the integration of ecological, social and economic issues.

Fig. 2 .

 2 Fig. 2. Location (a) and land-use map (b) of the study area of Toulouse conurbation.

Fig. 3 .

 3 Fig. 3. Ecological value map according to the multi-criteria analysis detailed in Section 2.4. The denser the green, the higher the ecological value. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

Fig. 4 .

 4 Fig. 4. Simulation of urban sprawl (in red) modeled with SimUrba for a) the trend-based scenario (Sc Trend ) and b) the "factor 4" scenario (Sc F4 ). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

  within the same plot (for instance BO linked to forest areas and open areas), especially where the offset ratio used was high. This issue, previously raised by many authors (see for instance Maron, Dunn, McAlpine, & Apan, 2010b; Weissgerber, Roturier, Julliard, & Guillet,

Fig. 5 .

 5 Fig. 5. Potential gains in habitat connectivity (EC index) for three habitat groups (a and b: open and semi-open areas; c and d: cultivated areas; e and f: forest areas;watercourses and ponds are not illustrated here for readability issues) under the Sc F4 scenario for two sets of available areas P BO1 (a, c and e) and P BO2 (b, d and f), the first consisting exclusively of public plots and the second including both public and private plots located near major ecological corridors (see Section 2.6). Purple squares represent priority cells for BO, i.e. plots where gains in connectivity are maximized, with an offset ratio of 1:1 (of impacts on habitat area); green squares represent cells added to reach 2:1. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

Table 3

 3 Sources of data used to map the ecological value of landscape and to simulate ecological networks and urban dynamics.

	Period Source Description	2015 The French National Geographical Institute (IGN) Hedges and bushes	The European Environment Agency Urbanized areas	2015 The French National Geographical Institute (IGN) Forest types	2016 The French Record of Agricultural Plots (RPG) Grasslands and other agricultural areas	2017 The Conseil Général de la Haute-Garonne Wetlands	2005-2015 The French SINP (Système d'Information sur la Nature et les Recorded species occurrences	Paysages) of the National Institute of Natural Heritage	(INPN)	2009-2015 The French Institut national de la statistique et des études Housing and demographical census	économiques (INSEE)		2009-2016 The French National Geographical Institute (IGN) National topographical database	2015 The French National Geographical Institute (IGN) Digital Elevation Model	2017 The French Direction générale des Finances publiques (DGFiP) Land register and associated housing data		2017 Metropolis of Toulouse Local urban planning scheme	2017 The French Centre d'Etudes Spatiales de la BIOsphère (CESBIO) Land cover from Sentinel 2 satellite data		2017 See Section 2.4 of the paper Computed ecological values		2018 The National Institute of Natural Heritage (INPN) Heavily protected natural areas	2018 The French Ministère de la Transition Écologique et Solidaire Biodiversity offset areas	2018 The French Institut national de la statistique et des études All public or private establishments	économiques (INSEE)	2018 OpenStreetMap Contributors Bus stops, missing from BDTOPO® database	2018 Direction Départementale des Territoires (31) Flood-risk areas, a main component of the French	plan de prévention du risque inondation (PPRI)	2018 SAFER (Société d'aménagement foncier et d'établissement rural) Regulatory pressures linked to agricultural	activities	2017 The French Direction Générale des Finances Publiques Land register (identifying public or private plots)
	Data	BD TOPO®	European Urban Atlas	BD FORET®	Agricultural areas	Wetland inventory	Occurrences of species			Sub-communal base IRIS (Ilots	Regroupés pour l'Information	Statistique)	BDTOPO®	BDALTI® 5m	-MAJIC (Mise À Jour des Informations	Cadastrales)	Local Urban Plan	CES OSO		Ecological value of landscape		Natural areas	Existing biodiversity offsets	SIRENE database		OSM Public Transport	Atlas of Floodplains	Appellation d'origine contrôlée (AOC)	MAJIC (Mise À Jour des Informations	Cadastrales)
	Detail	Preparation of the land-	cover map				Calculation of species	richness		Measuring demography			Measuring artificialization	Computing slope	Identifying non-residential	buildings	Fitting to local planning	Measuring land-cover	change	Preserving important	ecological areas	Building restrictions		Locating shops and services		Locating public transport	Building restrictions	Identifying plots for	compensation
	Step	Ecological value and habitat	connectivity mapping							Simulation of urbanization process																		Hierarchization of biodiversity	offsetting targets

Table 6

 6

  estimation of urban sprawl. For instance, we combined several relatively precise and reliable sources of data (European Urban Atlas provided by the European Environment Agency, BD TOPO by the French National Geographical Institute, the French Record of Agricultural Plots, etc.; see Table3). However, data availability is a frequent challenge in this kind of study, and stakeholders may, depending on study areas (for example, the Urban Atlas only covers large urban zones with more than 100,000 inhabitants), have to use less detailed databases (e.g. Corine Land Cover in Europe). Photointerpretation could be an alternative way to produce LULC maps, but this requires significant technical and human resources[START_REF] Hay | Geographic Object-Based Image Analysis (GEOBIA): A new name for a new discipline[END_REF]. Regarding the indicators in ecological value mapping, stakeholders must avoid using oversimplified indicators derived only from land cover maps (rarity, diversity, potential, wetland). Some used here are examples of relevant indicators (e.g. inventory and protection areas, species richness, ecological functionality). Moreover, if sufficient species occurrence data are available (for instance from regional NGOs), species distribution models (SDMs) can be used to determine suitable habitats according to[START_REF] Bigard | Strategic landscape-scale planning to improve mitigation hierarchy implementation: An empirical case study in Mediterranean France[END_REF]. Note that this study used an indicator related to species richness. However, if collaborative databases are not available or if they contain observations from different sources using inconsistent sampling strategies, we suggest that stakeholders use this indicator with caution, and only if it is consistent with their other data.

	Value	R 1:1			R 2:1
		P BO1	P BO2	ΔS BO	P BO1	P BO2	ΔS BO
	5	0 (0.0%)	0 (0.0%)	0 (0.0%)	0 (0.0%)	0 (0.0%)	0 (0.0%)
	4	6.15 (0.9%)	0.11 (0.0%)	-6.04 (-0.9%)	44.01 (6.1%)	25.45 (2.9%)	-18.56 (-3.2%)
	3	107.43 (16.3%)	37.23 (6.4%)	-70.20 (-9.9%)	96.40 (13.4%)	74.84 (8.6%)	-21.56 (-4.8%)
	2	281.56 (42.8%)	205.81 (35.5%)	-75.75 (-7.3%)	426.93 (59.2%)	310.12 (35.4%)	-116.81 (-23.8%)
	1	263.19 (40.0%)	336.54 (58.1%)	+73.35 (+18.1%)	153.37 (21.3%)	464.40 (53.1%)	+311.06 (+31.8%)

the

Acknowledgements

Simon Tarabon thank the Association for Research and Technology (ANRT) funded by the Ministry for Higher Education and Research, and Fabrice Vullion and Fabienne Theuriau (Soberco Environnement) for funding and involvement under the CIFRE agreements (n°2016/1378). Coralie Calvet thank the Labex CeMeb for funding, and Paul Chapron, Mickaël Brasebin and Julien Perret (from the IGN research lab) for their participation in the development of SimUrba. Lastly, authors thank Marjorie Sweetko for revising the English manuscript.

CRediT authorship contribution statement

Simon Tarabon: Conceptualization, Methodology, Validation, Formal analysis, Writing -original draft, Visualization. Coralie Calvet: Methodology, Validation, Investigation, Software, Writing -original draft. Vincent Delbar: Formal analysis, Software. Thierry Dutoit: Supervision. Francis Isselin-Nondedeu: Supervision, Writing -original draft.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Appendix A. Supplementary data

Supplementary data to this article can be found online at https:// doi.org/10.1016/j.landurbplan.2020.103871.


Mirko Mikolasek

Karl Ridier

Dimitrios Bessas

Valerio Cerantola

Gautier Félix

Grégory Chaboussant

Mario Piedrahita-Bello

José Elias Angulo-Cervera

Léa Godard

William Nicolazzi

Elias Angulo-Cervera

Lionel Salmon

Gábor Molnár

Azzedine Bousseksou
email: azzedine.bousseksou@lcc-toulouse.fr

Phase Stability of Spin-Crossover Nanoparticles Investigated by Synchrotron Mössbauer Spectroscopy and Small-Angle Neutron Scattering

Keywords: Spin crossover nanoparticles, Phase stability, Mössbauer spectroscopy, Small-angle neutron scattering

come   L'archive ouverte pluridisciplinaire

interesting questions since both the phase stability and the transformation kinetics are highly dependent on the object size.

1 At the nanoscale, one of the major contributions to nite- size eects is related to the large surface-to-volume ratio. Due to the surface free energies, the equilibrium conditions of surface atoms or molecules may be considerably dierent from those of the bulk material. This is observed in numerous well known phase transitions such as the melting process in which the melting point of nanoparticles shifts as function of the size and environment.

2 Strong surface eects can induce structural phase transition 3

or surface relaxations which may lead to topological transitions (e.g., electric, magnetic, morphological, electronic, etc.) with the size reduction.

4,5 Consequently, the characterization of the surface species is of paramount importance to understand the size-dependent properties of the material.

In the past decade, the synthesis of Spin-CrossOver (SCO)

68 nanomaterials and the study of size reduction and surface eects have attracted much attention.

818 Unfortunately, in many cases, the switching properties between the so-called low-spin (LS) and high-spin (HS) states appear strongly aected by the size reduction. In particular, an increasing fraction of the SCO centers becomes often inactive at reduced sizes, which limits the completeness of the transition and thus the scope of applications.

This eect is particularly visible in nanoparticles of Fe(II)(pyrazine)[Ni(CN) 4 ] that is a 3D-coordination network of the Hofmann clathrate family. [START_REF] Volatron | Spin-Crossover Coordination Nanoparticles[END_REF][START_REF] Larionova | Towards the Ultimate Size Limit of the Memory Eect in Spin-Crossover Solids[END_REF][START_REF] Peng | Re-Appearance of Cooperativity in Ultra-Small Spin-Crossover [Fe(pz){Ni(CN)4}] Nanoparticles[END_REF] In this compound, a decrease of the cooperativity, a stabilization of the HS state and the existence of a HS residual fraction are observed at low temperature with size reduction. In addition, a LS residual fraction is also observed at high temperature for particle sizes below ca. 10-20 nm.

Because the HS residual fraction ts with the estimated fraction of surface metallic centers, these latter were assumed to be 'frozen' in the HS state.

From a thermodynamical point of view, most observations made on SCO nanoparticules larger than ca. 5-10 nm can be explained by a lower surface energy of the HS state, which is surface metallic centers in the HS state was modelled using three dierent approaches. The rst approach assumes ad hoc that surface SCO sites are frozen in the HS state, 2023 assuming implicitly a drastic change of the ligand eld of these surface metallic centers. To take more explicitly into account the underlying mechanism, other models introduced an additional surface free energy term in the Hamiltonian, 2430 leading to potential multi-step transitions (shift of the transition temperature/pressure of the surface metallic centers) tuned by the surface energies. More recently, it was also suggested that elastic surface relaxations can be also accounted for the emergence of HS surface states. In this last approach, surface and core metallic centers are not aected by the same stress and strain, leading also to a multi-step transition.

31 Despite the predicted key role of this residual HS fraction in SCO nano-objects, to our best knowledge, there is no experimental report on the switching properties and the spatial distribution of the `frozen' HS molecules.

In this letter, we investigate the stability and spatial distribution of the HS residual frac- 15 for synthesis and characterization). The as-synthesized particles were carefully washed with ethanol and dried under vacuum. In all experiments, the particles were used in their solvated form i.e. without prior heat treatment.

Fe(pyrazine)[Ni(CN) 4 ] in its bulk form exhibits a rst-order spin transition around 285 K.

32 As displayed in Figure 1, the LS-HS phase stability is strongly modied with size reduction, leading to a sizeable downshift of the transition temperature (ca. 263 K) and the appearance of inactive residual fractions since only 23% of the metallic centers exhibit SCO. It is also interesting to note that the HS residual fraction is stable down to liquid helium temperatures.

Zero-field splitting

LS residual fraction

(53 %) nanoparticle sample under pressure (see Figure S6 in Supporting Information).

HS residual fraction (24 %)

SCO-active fraction (23 %)

Taking into account the Clapeyron-slope, which links the transition temperature to the applied pressure (see the thermodynamical considerations in the SI), we can estimate that the spin transition temperature of the HS residual fraction at ambient pressure is shifted to -142 ± 118 K. (The large standard deviation is due to the experimental uncertainties of volume and entropy changes.) In other words, the HS residual centers are stabilized by ca.

400 K (3.3 kJ • mol -1 ) with respect to the bulk material due to the size reduction eect. In the present case, this strong stabilization of the HS state leads thus to an incomplete tran- Measurements performed under various values of the magnetic eld (see Figure 3(b-c))

unambiguously demonstrate the magnetic nature of this signal, which disappears at zero eld and whose magnitude follows the magnetization curve obtained from SQUID measurements at 5 K. This magnetic scattering can only originate from the SCO sites whose spin state is `blocked' in the paramagnetic HS state at low temperature, and thus constitutes the signature, in terms of POLSANS patterns, of the residual HS fraction within the nanoparticles.

Fig. 3(d) shows the square of the magnetic form factor F 2 M (Q) of the 56 Fe nanoparticles derived from I sc (Q) at 5 K and 5 T (see details in Supporting Information). To analyse the magnetic POLSANS data, we have considered a core-shell parallelepiped particle model in which a shell of variable thickness t only contributes to the total magnetization and where all the structural parameters (size and size distribution) are xed to their values obtained from the analysis of the nuclear form factor of the particles F 2 N (Q) (see Figure S4 and Table 1 in Supporting Information). When small shell thicknesses are considered, a large mismatch occurs above Q ≈ 0.04 Å -1 since an enhanced scattered intensity is expected in the Q = 2π/t range associated with the characteristic thickness t of the shell.

Fig. 3(d) shows that the simulation is substantially improved (in particular in the large-Q range) when the thickness of the magnetic shell is increased and, nally, it appears that the best t to the data is obtained when a uniform magnetization density is considered throughout the particles. Similar results were obtained for two other non-enriched particle batches with dierent sizes a = 14.4 ± 1.7 nm and 79.1 ± 16.9 nm (see Figure S5 in Supporting Information).

These POLSANS results tend to show that the magnetization associated with the residual HS sites is uniformly distributed within the particles rather than concentrated at the surface.

These experimental observations are in disagreement with the theoretical predictions, but obviously call for further conrmations through dierent experimental techniques.

In the case of the present 3D-coordination network particles, an important aspect is the presence of both HS and LS residual fractions. While the general thermodynamical trend would be the stabilization of the HS state on the surface due to its lower surface energy, we may suggest that the coordination of certain species as well as the existence of surface distortions/deformations might be possible contributions that favor the strong ligand eld conguration (LS state) on the particle surface. As for the residual HS fraction and by analogy with Prussian blue analogue materials in which the inuence of lattice defects and vacancies is well established, 36,37 the porous 3D network may contain defects and/or guest molecules, which may locally stabilize the HS state of a certain amount of iron ions within the whole particle volume.

In conclusion, we have studied the stability and the spatial distribution of the high-spin residual fraction in spin-crossover nanoparticles of Fe(pyrazine)[Ni(CN) 4 ] by synchrotron high-pressure Mössbauer spectroscopy and polarized small-angle neutron scattering. These experiments revealed that the `frozen' HS centers are actually SCO active and can be switched to the LS state at room temperature by applying a pressure of 1.5 GPa. This result indicates that initially fully LS materials can become SCO active at reduced sizes. Surprisingly, the HS residual centers appear homogeneously distributed within the particles -in contrast to what is assumed in general from theoretical considerations. We have tentatively ascribed this nding to the particular porous 3D network structure of the investigated compound. While these considerations remain necessarily speculative at this stage, our results clearly show that beyond the general considerations of phase stability, the specic chemistry and crystal structure of each SCO compound must be carefully taken into account when interpreting size eects. Powerful experimental techniques, such as POLSANS, will certainly play an increasing role in this respect in the future.

  tion in a set of nanoparticles of dierent sizes of the SCO compound Fe(pyrazine)[Ni(CN) 4 ] by high-pressure Synchrotron Mössbauer Spectroscopy (SMS) and POLarized Small-Angle Neutron Scattering (POLSANS).The studied nanoparticles of Fe(pyrazine)[Ni(CN) 4 ] have a typical parallelepipedic shape with one dimension signicantly shorter than the others (a ≈ b > c). From Transmission Electronic Microscopy (TEM) and POLSANS measurements, the average in-plane and outof-plane particle sizes were estimated to a = 41.1±6.5 nm and c = 8±3 nm, respectively.A second batch of particles with in-plane size a = 17.1 ± 5.3 nm were additionally enriched to ∼ 90% in 57 Fe in order to carry out synchrotron Mössbauer spectroscopy with good statistics in reasonable time. Particles are embedded in a NaAOT (dioctyl sodium sulfosuccinate) polymer matrix (see ref.

Figure 1 :Figure 2 :

 12 Figure 1: Thermal dependence of the HS fraction n HS of the 56 Fe(pyrazine)[Ni(CN) 4 ] nanoparticles determined by magnetic measurements. Two Mössbauer spectra were used at 300 K (n HS = 0.47) and 80 K (n HS = 0.24) to calibrate the HS fraction. Below 15 K, the slight decrease of the magnetic susceptibility can be attributed to a zero-eld splitting 33 (associated with magnetic anisotropy) and the HS fraction is not anymore relevant. Inset: Size-distribution histogram and selected TEM image of the particles.

Figure 3 :

 3 Figure 3: POLSANS measurements performed on the 56 Fe nanoparticles at 5 K. (a) I sc (Q) = I + (Q) -I -(Q) signal obtained on the two-dimensional detector at 5 K and under a magnetic eld of 5 T. The arrow indicates the direction of the applied magnetic eld. (b) Angular evolution of the scattered intensity I sc (Q) for various values of the magnetic eld. Solid lines depict the ts using a sin 2 function as described in Equation S1. (c) Modulation amplitude of I sc (Q) with respect to the magnetic eld. These amplitudes (circles) are compared with magnetic data obtained from SQUID measurements (triangles). (d) Magnetic form factor F 2 M (Q) of the nanoparticles extracted from the measured intensities I + (Q) and I -(Q) at 5T (see details in SI). Solid lines are the result of simulations using a core-shell parallelepiped particle model whose thickness of the magnetic shell was varied from t = 10 Å to that of a homogeneous magnetic parallelepiped.

Acknowledgement

The European Synchrotron Radiation Facility (ESRF) is acknowledge for beamtime provision at the Nuclear Resonance beamline ID18. The authors thank Jeroen Jacobs and Jean-Philippe Celse for their technical assistance during the high-pressure experiment as well as Aleksandr I. Chumakov and Rudolf Rüer for helpful discussions. The authors also thank Nicolas Martin for his technical help with SANS experiments. This work was partially founded by the European Commission through the SPINSWITCH project (H2020-MSCA-RISE-2016, Grant Agreement No. 734322). MPB and EAC thank the Federal University of Toulouse and the CONACYT, respectively, for their PhD grants.

Supporting Information Available

The following les are available free of charge. SI.tex: this le contains the results for the other studied samples, additional information about POLSANS and the full description of the thermodynamical model. (37) Bleuzen, A.; Escax, V.; Ferrier, A.; Villain, F.; Verdaguer, M.; Münsch, P.; Itié, J.-P.

Thermally induced electron transfer in a CsCoFe Prussian blue derivative: the specic role of the alkali-metal ion. Angew. Chem. Int. Ed. 2004, 43, 37283731.