Integrating species pools and abundance distribution in habitat conservation status assessment: A new index

2021 
Abstract Habitat degradation and fragmentation are recognized as major causes of biodiversity loss, and effective management to conserve habitats is highly dependent on our ability to assess their conservation status. In this study we introduce a new index (VCS, for vegetation conservation status) to assess the conservation status of plant communities, which reflect the identity of habitat types. The VCS index is based on the same probabilistic approach than the classical Simpson’s diversity index, but uses the concept of species pools to integrate the influence of ‘typical’ and ‘non-typical’ species on habitat conservation status. In addition to the effect of species identity, this index also allows the detection of change in conservation status because of variation in species-abundance distribution. As an example we applied the VCS index to two heathland habitats in French Brittany and we compared the values provided by the index to qualitative assessments by heathland experts. We also compared the performance of the VCS index against three other indices: species richness, species diversity and a more recent index of ‘favourable conservation status’. Among the four indices tested, the VCS index was the most effective in assessing the vegetation conservation status when compared against qualitative assessment by heathland experts. Moreover the VCS index, coupled with variance partitioning methods, allowed to quantify the contribution of expected causes of habitat degradation. This study demonstrates that the use of habitat-specific species pools to distinguish between typical and non-typical species, as well as the consideration of species abundances, are critical for an accurate assessment of the vegetation conservation status. The VCS index should therefore be a valuable tool for both managers and researchers involved in habitat conservation.
    • Correction
    • Source
    • Cite
    • Save
    • Machine Reading By IdeaReader
    46
    References
    1
    Citations
    NaN
    KQI
    []