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Maxence Soubeyrand

Publications and source records attributed to Maxence Soubeyrand.

2 recordsLinked to original sources

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-Côte 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.

q-bio.PE

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