bioRxivEcology
The persistence in time of distributional patterns in marine megafauna impacts zonal conservation strategies
Abstract
The main type of zonal conservation approach corresponds to Marine Protected Areas (MPAs), which are spatially defined and generally static entities aiming at the protection of some target populations by the implementation of a management plan. For highly mobile species the relevance of an MPA over time might be hampered by temporal variations in distributions or home ranges. In the present work, we used habitat model-based predicted distributions of cetaceans and seabirds within the Bay of Biscay from 2004 to 2017 to characterise the aggregation and persistence of mobile species distributional patterns and the relevance of the existing MPA network. We explored the relationship between population abundance and spatial extent of distribution to assess the aggregation level of species distribution. We used the smallest spatial extent including 75% of the population present in the Bay of Biscay to define specific core areas of distributions, and calculated their persistence over the 14 studied years. We inspected the relevance of the MPA network with respect to aggregation and persistence. We found that aggregation and persistence are two independent features of marine megafauna distributions. Indeed, strong persistence was shown in both aggregated (bottlenose dolphins, auks) and loosely distributed species (northern gannets), while some species with aggregated distributions also showed limited year-to-year persistence in their patterns (black-legged kittiwakes). We thus have demonstrated that both aggregation and persistence have potential impact on the amount of spatio-temporal distributional variability encompassed within static MPAs. Our results exemplified the need to have access to a minimal temporal depth in the species distribution data when aiming to designate new site boundaries for the conservation of mobile species.
- organism: multiple species
- assay: spatial transcriptomics
- research focus: ecology & biodiversity
- approach: statistical model
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