A methodology to assess habitat fragmentation effects through regional indexes: illustration with forest biodiversity hotspots

[Departement_IRSTEA]Ecotechnologies [TR1_IRSTEA]INSPIRE [ADD1_IRSTEA]Dynamique et fonctionnement des écosystèmes

International audience

إنجليزي. The fragmentation of natural environments is a critical issue involving major challenges for biodiversity conservation and ecosystem management. Large-scale information on areas sensitive to fragmentation is needed to improve the effectiveness of planning efforts. One promising metric combining the landscape spatial configuration with species characteristics is the metapopulation capacity λ, which can be used to rank differentfragmented landscapes in terms of their capacity to support viable metapopulations. A methodology to globally derive a fragmentation metric based on metapopulation capacity, at appropriate and meaningful spatial scales for fragmentation mechanisms, was developed. To illustrate the applicability and interest of the methodology, worldwide regionalised fragmentation indexes, calculated with a dispersal distance of 1 km valid for a broad range of species, were provided for all forest ecoregions included in the biodiversity hotspots. Ecoregions were divided by a virtual grid and a statistical analysis of metapopulation capacity values calculated at the grid square scale was performed to obtain a Forest Fragmentation Potential FFP at three levels of spatial aggregation within the ecoregion (highly converted forest, entire forest, and the whole ecoregion). The results highlighted significant intra- and inter-ecoregions differences, showing great potential to extend the use of these indexes to land use planning and areas prioritisation for both ecological protection and restoration. The influence of the different parameters used in the proposed approach is discussed as well as the limitations of the main assumptions. One important result is that the derived methodology can be easily adapted to a large number of species, scales, or regions to improve the coverage of fragmentation indexes.