Volunteer-based surveys offer enhanced opportunities for biodiversity monitoring across broad spatial extent
| Autor: | David Pinaud, Vincent Bretagnolle, Kévin Le Rest |
|---|---|
| Přispěvatelé: | Centre d'Études Biologiques de Chizé - UMR 7372 (CEBC), Université de La Rochelle (ULR)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Centre National de la Recherche Scientifique (CNRS)-Institut National de la Recherche Agronomique (INRA)-Université de La Rochelle (ULR), Institut National de la Recherche Agronomique (INRA)-Université de La Rochelle (ULR)-Centre National de la Recherche Scientifique (CNRS) |
| Jazyk: | angličtina |
| Rok vydání: | 2015 |
| Předmět: |
0106 biological sciences
Computer science Population Biodiversity Inference Feature selection 010603 evolutionary biology 01 natural sciences Overdispersion 010104 statistics & probability Econometrics 0101 mathematics education Spatial analysis Ecology Evolution Behavior and Systematics Species distribution Count data education.field_of_study Ecology Raptors Applied Mathematics Ecological Modeling Uncertainty Sampling (statistics) 15. Life on land Data science Computer Science Applications [STAT]Statistics [stat] Computational Theory and Mathematics Modeling and Simulation [SDE]Environmental Sciences Spatial autocorrelation |
| Zdroj: | Ecological Informatics Ecological Informatics, Elsevier, 2015, 30, pp.313-317. ⟨10.1016/j.ecoinf.2015.08.007⟩ |
| ISSN: | 1574-9541 |
| DOI: | 10.1016/j.ecoinf.2015.08.007⟩ |
| Popis: | The growing public interest in biodiversity projects provides great opportunities to monitor biodiversity across broad geographic areas at low cost. Such volunteer-based surveys should however need careful consideration during statistical analysis since the presence of residual spatial autocorrelation and over-heterogeneity can lead to misguided inference. The recent development of new statistical tools allows accounting for these problems in all steps of the statistical analysis. Especially, the spatial leave-one-out method allows accounting for spatial autocorrelation in the variable selection step while the R-INLA tool box provides a useful way to estimate complex spatial hierarchical models in a minimum computation time. We applied such tools on a dataset collected by volunteers between 2000 and 2013 giving the relative abundance of 12 raptors breeding in France. We then estimated their spatial distribution, population sizes and trends with a particular emphasis in quantifying the uncertainty of our estimations. Our results suggest that broad-scale volunteer-based surveys offer enhanced opportunities for monitoring widespread species but may fail in giving accurate information for less common species, especially when sampling is too scattered. Providing uncertainty of estimations helps in identifying species and areas from which estimations are the more reliable and thus gives more powerful information for conservation practitioners. |
| Databáze: | OpenAIRE |
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