arXiv subjects

Sandra Luque

Publications and source records attributed to Sandra Luque.

15 recordsLinked to original sources

arXiv2026

From biodiversity modelling to conservation action: a spatial indicator for prioritising tropical forest protection, restoration, and management

Tropical forests harbour exceptional biodiversity but are increasingly threatened by anthropogenic pressures, making their conservation central to achieving the Convention on Biological Diversity targets. However, national conservation planning is often constrained by heterogeneous monitoring frameworks and limited integration of biodiversity and anthropogenic pressures. We develop a synthetic spatial indicator that combines open-access biodiversity and land-use data to identify refuge and conflict areas between ecological potential and anthropogenic pressure. Using Costa Rica as a national-scale demonstrator, we assess its capacity to support conservation prioritisation and evaluate the current conservation network. Ecological potential was estimated using multi-species distribution models for 254 dominant canopy tree species, serving as an operational proxy for tropical forest biodiversity and ecological structure. We quantified anthropogenic pressure from land-use patterns and combined it with ecological potential to identify refuge and conflict areas. We then tested the spatial significance of these areas and evaluated their representation within the national conservation network. The indicator reveals a clear spatial organisation of biodiversity-pressure interactions: refuge areas coincide with large, continuous forest cores, whereas conflict areas are concentrated in fragmented landscapes and may extend into protected areas. Flexible and transferable, the framework distinguishes areas requiring strict protection from those where restoration, sustainable management, or pressure mitigation should be prioritised. Rather than prescribing conservation actions, the refuge-conflict indicator provides a practical decision-support tool to assess conservation networks, strengthen ecological connectivity, and guide national biodiversity planning.

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arXiv2025

Fire severity and recovery across Europe: insights from forest diversity and landscape metrics

In recent decades, European forests have faced an increased incidence of fire disturbances. This phenomenon is likely to persist, given the rising frequency of extreme events expected in the future. Estimating canopy recovery time after disturbance serves as a critical assessment for understanding forest resilience, which can ultimately help determine the ability of forests to regain their capacity to provide essential ecosystem services. This study estimated fire severity and post-disturbance recovery in European forests using a remote sensing--based time series approach. MODIS Leaf Area Index (LAI) time series data were used to track the evolution of vegetation cover over burned areas from 2001 to 2024. Fire severity was defined relative to pre-disturbance conditions by comparing vegetation status before and after fire events. Recovery intervals were determined from temporal evolution of vegetation greening as the duration required to reach the pre-disturbance LAI baseline. Furthermore, this study analyzed the severity and recovery indicators in relation to forest species diversity and landscape heterogeneity metrics across Europe, offering valuable insights into the spatial variability of forest response dynamics across diverse forest ecosystems across Europe. Results revealed a consistent pattern across vegetation cover types: higher forest species diversity and greater landscape shape complexity were associated with lower fire severity and, notably, shorter recovery times following fire disturbance.

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arXiv2025

Predicted decline in common bird and butterfly species despite conservation policy scenarios in Europe

In response to increasing threats to biodiversity, conservation objectives have been set to halt biodiversity decline by reducing direct anthropogenic drivers. However, the potential effects of these objectives on common species remain rarely studied. We analyse the effect of a range of drivers related to climate, land use and land use intensity, on 265 common bird and 144 common butterfly species from more than 20,000 sites between 2000 and 2021 across 26 European countries. We use land-use and land-use intensity scenarios produced previously using the IPBES Nature Futures Framework, and climate change scenarios in order to project biodiversity drivers in Europe up to 2050. We translate these driver changes into abundance variations for common bird and butterfly species, and for multi-species indicators used to monitor common biodiversity status in Europe. The projected trends relatively improve, while still declining for birds, notably farmland species, under the scenarios meeting conservation objectives, with few effects on butterflies. No scenario shows a stop or a reversal in the average decline in abundance of bird and butterfly species. Our results therefore question the common biodiversity future under current conservation policies and highlight the need for other anticipatory frameworks, not implicitly based on a growing need for natural resources.

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arXiv2025

Modelling species distributions using remote sensing predictors: Comparing Dynamic Habitat Index and LULC

This study compares the predictive capacity of the Dynamic Habitat Index (DHI) - a remote sensing (RS)-based measure of habitat productivity and variability - against traditional land-use/land-cover (LULC) metrics in species distribution modelling (SDM) applications. RS and LULC-based SDMs were built using distribution data for eleven bird, amphibian, and mammal species in \^Ile-de-France. Predictor variables were derived from Sentinel-2 RS data and LULC classifications, with the latter incorporating Euclidean distance to habitat types. Ensemble SDMs were built using nine algorithms and evaluated with the Continuous Boyce Index (CBI) and a calibrated AUC. Habitat suitability scores and their binary transformations were assessed using niche overlap indices (Schoener, Warren, and Spearman rank correlation coefficient). Both RS and LULC approaches exhibited similar predictive accuracy overall. After binarisation however, the resulting niche maps diverged significantly. While LULC-based models exhibited spatial constraints (habitat suitability decreased as distance from recorded occurrences increased), RS-based models, which used continuous data, were not affected by geographic bias or distance effects. These results underscore the need to account for spatial biases in LULC-based SDMs. The DHI may offer a more spatially neutral alternative, making it a promising predictor for modelling species niches at regional scales.

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arXiv2025

Unlocking tropical forest complexity: How tree assemblages in secondary forests boost biodiversity conservation

Secondary forests now dominate tropical landscapes and play a crucial role in achieving COP15 conservation objectives. This study develops a replicable national approach to identifying and characterising forest ecosystems, with a focus on the role of secondary forests. We hypothesised that dominant tree species in the forest canopy serve as reliable indicators for delineating forest ecosystems and untangling biodiversity complexity. Using national inventories, we identified in situ clusters through hierarchical clustering based on dominant species abundance dissimilarity, determined using the Importance Variable Index. These clusters were characterised by analysing species assemblages and their interactions. We then applied object-oriented Random Forest modelling, segmenting the national forest cover using NDVI to identify the forest ecosystems derived from in situ clusters. Freely available spectral (Sentinel-2) and environmental data were used in the model to delineate and characterise key forest ecosystems. We finished with an assessment of distribution of secondary and old-growth forests within ecosystems. In Costa Rica, 495 dominant tree species defined 10 in situ clusters, with 7 main clusters successfully modelled. The modelling (F1-score: 0.73, macro F1-score: 0.58) and species-based characterisation highlighted the main ecological trends of these ecosystems, which are distinguished by specific species dominance, topography, climate, and vegetation dynamics, aligning with local forest classifications. The analysis of secondary forest distribution provided an initial assessment of ecosystem vulnerability by evaluating their role in forest maintenance and dynamics. This approach also underscored the major challenge of in situ data acquisition.

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arXiv2024

Implication of modelling choices on connectivity estimation: A comparative analysis

We focus on connectivity methods used to understand and predict how landscapes and habitats facilitate or impede the movement and dispersal of species. Our objective is to compare the implication of methodological choices at three stages of the modelling framework: landscape characterisation, connectivity estimation, and connectivity assessment. What are the convergences and divergences of different modelling approaches? What are the implications of their combined results for landscape planning? We implemented two landscape characterisation approaches: expert opinion and species distribution model (SDM); four connectivity estimation models: Euclidean distance, least-cost paths (LCP), circuit theory, and stochastic movement simulation (SMS); and two connectivity indices: flux and area-weighted flux (dPCflux). We compared outcomes such as movement maps and habitat prioritisation for a rural landscape in southwestern France. Landscape characterisation is the main factor influencing connectivity assessment. The movement maps reflect the models' assumptions: LCP produced narrow beams reflecting the optimal pathways; whereas circuit theory and SMS produced wider estimation reflecting movement stochasticity, with SMS integrating behavioural drivers. The indices highlighted different aspects: dPCflux the surface of suitable habitats and flux their proximity. We recommend focusing on landscape characterisation before engaging further in the modelling framework. We emphasise the importance of stochasticity and behavioural drivers in connectivity, which can be reflected using circuit theory, SMS or other stochastic individual-based models. We stress the importance of using multiple indices to capture the multi-factorial aspect of connectivity.

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arXiv2024

Coupling in situ and remote sensing data to assess $\alpha$- and $\beta$-diversity over biogeographic gradients

The mapping of plant biodiversity represents a fundamental stage in establishing conservation priorities, particularly in identifying groups of species that share ecological requirements or evolutionary histories. This is often achieved by assessing different spatial diversity patterns in plant population distributions. In this paper, we present two primary data sources crucial for biodiversity monitoring: in situ measurements from botanical observations and remote sensing (RS). In situ methods involve directly collecting data from specific sites, providing detailed insights into ecological patterns but often constrained by resource limitations. Integrating in situ and RS data highlights their complementary strengths, which depend on factors such as study scale, resolution, and logistical feasibility. While in situ approaches are characterized by precision, RS offers efficiency and extensive, repeated coverage. This research integrates in situ and RS data to analyze plant and spectral diversity across France at a spatial resolution of 5 km, encompassing over 23,000 grid cells. We employ four established diversity metrics leveraging the spatial distribution of 6,650 plant species and 250 spectral clusters (derived from MODIS data at a 500-meter resolution). Through bioregionalization network analysis combining these data sources, we identified five distinct bioregions that capture the biogeographical structure of plant biodiversity in France. Additionally, we explore the relationship between plant species diversity and spectral cluster diversity within and between these bioregions, offering novel insights into the spatial dynamics of plant biodiversity.

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arXiv2024

Cartographie de l'habitat de reproduction du t\'etras-lyre (Lyrurus tetrix) dans les Alpes fran\c{c}aises

The Black Grouse (Lyrurus tetrix) is an emblematic alpine species with high conservation importance. The population size of these mountain bird tends to decline on the reference sites and shows differences according to changes in local landscape characteristics. Habitat changes are at the centre of the identified pressures impacting part or all of its life cycle, according to experts. Hence, an approach to monitor population dynamics, is trough modelling the favourable habitats of Black Grouse breeding (nesting sites). Then, coupling modelling with multi-source remote sensing data (medium and very high spatial resolution), allowed the implementation of a spatial distribution model of the species. Indeed, the extraction of variables from remote sensing helped to describe the area studied at appropriate spatial and temporal scales: horizontal and vertical structure (heterogeneity), functioning (vegetation indices), phenology (seasonal or inter-annual dynamics) and biodiversity. An annual time series of radiometric indices (NDVI, NDWI, BI {\ldots}) from Sentinel-2 has made it possible to generate Dynamic Habitat Indices (DHIs) to derive phenological indications on the nature and dynamics of natural habitats. In addition, very high resolution images (SPOT6) provided access to the fine structure of natural habitats, i.e. the vertical and horizontal organisation by states identified as elementary (mineral, herbaceous, low and high woody). Indeed, one of the essential limiting factors for brood rearing is the presence of a well-developed herbaceous or ericaceous stratum in the northern Alps and larch forests in the southern region. A deep learning model was used to classify elementary strata. Finally, Biomod2 R platform, using an ensemble approach, was applied to model, the favourable habitat of Black Grouse reproduction. Of all the models, Random Forest and Extreme Boosted Gradient are the best performing, with TSS and ROC scores close to 1. For the SDM, we selected only Random Forest models (ensemble modelling) because of their low susceptibility to overfitting and coherent predictions (after comparing model predictions).In this ensemble model, the most important explanatory variables are altitude, the proportion of heathland, and the DHI (NDVI Max and NDWI Max). Results from the habitat model can be used as an operational tool for monitoring forest landscape shifts and changes. In addition, to delimiting potential areas to protect the species habitat, which constitute a valuable decision-making tool for conservation management of mountain open forest.

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arXiv2022

Habitat connectivity in agricultural landscapes improving multi-functionality of constructed wetlands as nature-based solutions

The prevention of biodiversity loss in agricultural landscapes to protect ecosystem stability and functions is of major importance in itself and for the maintenance of associated ecosystem services. Intense agriculture leads to a loss in species richness and homogenization of species pools as well as the fragmentation of natural habitats and groundwater pollution. Constructed wetlands stand as nature-based solutions (NBS) to buffer the degradation of water quality by intercepting the transfer of particles, nutrients and pesticides between crops and surface waters. In karstic watersheds where sinkholes short-cut surface water directly to groundwater increasing water resource vulnerability, constructed wetlands are recommended to mitigate agricultural pollutants. Constructed wetlands also have the potential to improve landscape connectivity by providing refuge and breeding sites for wildlife, especially for amphibians. We propose here a methodology to identify optimal locations for water pollution mitigation using constructed wetlands from the perspective of habitat connectivity. We use ecological niche modelling at the regional scale to model the potential of habitat suitability for nine amphibian species, and to infer how the landscape impedes species movements. We combine those results to graph theory to identify connectivity priorities at the operational scale of an agricultural catchment area. Our framework allowed us to identify optimal areas from the point of view of the species, to analyze the effect of multifunctional constructed wetlands aiming to both reduce water pollution and to improve amphibian species habitat overall connectivity. More generally, we show the potential of habitat connectivity assessment to improve multifunctionality of NBS for pollution mitigation.

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arXiv2022

Protection gaps in Amazon floodplains will increase with climate change: Insight from the world's largest scaled freshwater fish

The Amazon floodplains represent important surfaces of highly valuable ecosystems, yet they remain neglected from protected areas. While the efficiency of the protected area network of the Amazon basin may be jeopardised by climate change, floodplains are exposed to important consequences of climate change but are omitted from species distribution models and protection gap analyses. We modelled the current and future (2070) distribution of the giant bony-tongue fish Arapaima sp. accounting for climate and habitat requirements, with consideration of dam presence (already existing and planned constructions) and hydroperiod (high- and low-water stages). We further quantified the amount of suitable environment which falls inside and outside the current network of protected areas to identify spatial conservation gaps. We predict climate change to cause the decline of environmental suitability by 16.6% during the high-water stage, and by 19.4% during the low-water stage. We found that about 70% of the suitable environments of Arapaima sp. remain currently unprotected, which is likely to increase by 5% with future climate change effects. Both current and projected dam constructions may hamper population flows between the central and the Bolivian and Peruvian parts of the basin. We highlight protection gaps mostly in the southwestern part of the basin and recommend the extension of the current network of protected areas in the floodplains of the upper Ucayali, Juru\`a and Purus Rivers and their tributaries. This study showed the importance of taking into account hydroperiods and dispersal barriers in forecasting the distribution of freshwater fish species, and stresses the urgent need to integrate floodplains to the protected area networks.

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arXiv2021

Dispersal-based species pools as sources of connectivity area mismatches

Context - Prioritising is likely to differ depending on the species considered for connectivity assessments, leading to a lack of consensual decisions for territorial planning. Objectives - The objective was to assess the relevance of identifying priority areas for connectivity for groups of species based on common dispersal abilities. We aimed to assess the impact of target groups choices on predicted priority areas. Method - The study was located at the Thau Lagoon territory to demonstrate the methodological approach. Ecological niche modelling was used to quantify species resistance and to identify suitable habitat patches. We coupled the least-cost path methodology with circuit theory to assess species connectivity. We classified connectivity from high to low levels and averaged the results by dispersal groups. Results - We found important differences in identified priority areas between groups with dissimilar dispersal abilities, with little overlap between highly connected areas. We identified a gap between the level of protection of low dispersal species and highly connected areas. We found mismatches between existing corridors and connectivity in low dispersal species, and a greater impact in areas of expected urban sprawl projects on favourably connected areas for species with high dispersal capabilities. Conclusion - We have demonstrated that a diversity of dispersal capacity ranges must be accounted for in order to identify ecological corridors in programmes that aim to restore habitat connectivity at territorial levels. Our findings are oriented to support the decisions of planning initiatives, at both local and regional scale.

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arXiv2021

Assessing the effect of sample bias correction in species distribution models

Open-source biodiversity databases contain a large amount of species occurrence records, but these are often spatially biased, which affects the reliability of species distribution models based on these records. Sample bias correction techniques include data filtering at the cost of record numbers or require considerable additional sampling effort. However, independent data are rarely available and assessment of the correction technique must rely on performance metrics computed with subsets of the only available (biased) data, which may be misleading. Here we assess the extent to which an acknowledged sample bias correction technique is likely to improve models' ability to predict species distributions in the absence of independent data. We assessed the variation in model predictions induced by the correction and model stochasticity. We present an index of the effect of correction relative to model stochasticity, the Relative Overlap Index (ROI). We tested whether the ROI better represented the effect of correction than classic performance metrics and absolute overlap metrics using 64 vertebrate species and 21 virtual species with a generated sample bias. When based on absolute overlaps and cross-validation performance metrics, we found no effect of correction, except for cAUC. When considering its effect relative to model stochasticity, the effect of correction depended on the site and the species. Virtual species enabled us to verify that the correction actually improved distribution predictions and the biological relevance of the selected variables at the sites with a clear gradient of sample bias, and when species distribution predictors are not correlated with sample bias patterns.

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arXiv2019

Comprehensive decision-strategy space exploration for efficient territorial planning strategies

GIS-based Multi-Criteria Decision Analysis is a well-known decision support tool that can be used in a wide variety of contexts. It is particularly useful for territorial planning in situations where several actors with different, and sometimes contradictory, point of views have to take a decision regarding land use development. While the impact of the weights used to represent the relative importance of criteria has been widely studied in the recent literature, the impact of the order weights used to combine the criteria have rarely been investigated. This paper presents a spatial sensitivity analysis to assess the impact of order weights determination in GIS-based Multi-Criteria Analysis by Ordered Weighted Averaging. We propose a methodology based on an efficient exploration of the decision-strategy space defined by the level of risk and trade-off in the decision process. We illustrate our approach with a land use planning process in the South of France. The objective is to find suitable areas for urban development while preserving green areas and their associated ecosystem services. The ecosystem service approach has indeed the potential to widen the scope of traditional landscape-ecological planning by including ecosystem-based benefits, including social and economic benefits, green infrastructures and biophysical parameters in urban and territorial planning. We show that in this particular case the decision-strategy space can be divided into four clusters. Each of them is associated with a map summarizing the average spatial suitability distribution used to identify potential areas for urban development. We also demonstrate the pertinence of a spatial variance within-cluster analysis to disentangle the relationship between risk and trade-off values. At the end, we perform a site suitability ranking analysis to assess the relationship between the four detected clusters.

stat.AP↗

arXiv2018

Multiscale socio-ecological networks in the age of information

Interactions between people and ecological systems, through leisure or tourism activities, form a complex socio-ecological spatial network. The analysis of the benefits people derive from their interactions with nature -- also referred to as cultural ecosystem services (CES) -- enables a better understanding of these socio-ecological systems. In the age of information, the increasing availability of large social media databases enables a better understanding of complex socio-ecological interactions at an unprecedented spatio-temporal resolution. Within this context, we model and analyze these interactions based on information extracted from geotagged photographs embedded into a multiscale socio-ecological network. We apply this approach to 16 case study sites in Europe using a social media database (Flickr) containing more than 150,000 validated and classified photographs. After evaluating the representativeness of the network, we investigate the impact of visitors' origin on the distribution of socio-ecological interactions at different scales. First at a global scale, we develop a spatial measure of attractiveness and use this to identify four groups of sites. Then, at a local scale, we explore how the distance traveled by the users to reach a site affects the way they interact with this site in space and time. The approach developed here, integrating social media data into a network-based framework, offers a new way of visualizing and modeling interactions between humans and landscapes. Results provide valuable insights for understanding relationships between social demands for CES and the places of their realization, thus allowing for the development of more efficient conservation and planning strategies.

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arXiv2018

Biogeographical network analysis of plant species distribution in the Mediterranean region

The delimitation of bioregions helps to understand historical and ecological drivers of species distribution. In this work, we performed a network analysis of the spatial distribution patterns of plants in south of France (Languedoc-Roussillon and Provence-Alpes-C\^ote d'Azur) to analyze the biogeographical structure of the French Mediterranean flora at different scales. We used a network approach to identify and characterize biogeographical regions, based on a large database containing 2.5 million of geolocalized plant records corresponding to more than 3500 plant species. This methodology is performed following five steps, from the biogeographical bipartite network construction, to the identification of biogeographical regions under the form of spatial network communities, the analysis of their interactions and the identification of clusters of plant species based on the species contribution to the biogeographical regions. First, we identified two sub-networks that distinguish Mediterranean and temperate biota. Then, we separated eight statistically significant bioregions that present a complex spatial structure. Some of them are spatially well delimited, and match with particular geological entities. On the other hand fuzzy transitions arise between adjacent bioregions that share a common geological setting, but are spread along a climatic gradient. The proposed network approach illustrates the biogeographical structure of the flora in southern France, and provides precise insights into the relationships between bioregions. This approach sheds light on ecological drivers shaping the distribution of Mediterranean biota: the interplay between a climatic gradient and geological substrate shapes biodiversity patterns. Finally this work exemplifies why fragmented distributions are common in the Mediterranean region, isolating groups of species that share a similar eco-evolutionary history.

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