Overcoming the rare species modelling paradox: A novel hierarchical framework applied to an Iberian endemic plant

• Tipo: Artigo em Revista Científica Internacional
• Data: 2010

Autores

Lomba, A (Autor) Outra; Pellissier, L (Autor) Outra; Randin, C (Autor) Outra; Vicente, J (Autor) Outra; Moreira, F (Autor) Outra; Honrado, J (Autor) FCUP; Guisan, A (Autor) Outra

Revista

Título: Biological Conservation

Vol. 143

Páginas: 2647-2657

ISSN: 0006-3207

Editora: Elsevier

Indexação

ISI Web of Knowledge - 161 Citações

Scopus - 172 Citações

Classificação Científica

FOS: Ciências exactas e naturais > Ciências biológicas

Outras Informações

ID Authenticus: P-003-185

DOI: 10.1016/j.biocon.2010.07.007

Abstract (EN): Rare species have restricted geographic ranges, habitat specialization, and/or small population sizes. Datasets on rare species distribution usually have few observations, limited spatial accuracy and lack of valid absences; conversely they provide comprehensive views of species distributions allowing to realistically capture most of their realized environmental niche. Rare species are the most in need of predictive distribution modelling but also the most difficult to model. We refer to this contrast as the "rare species modelling paradox" and propose as a solution developing modelling approaches that deal with a sufficiently large set of predictors, ensuring that statistical models are not over-fitted. Our novel approach fulfils this condition by fitting a large number of bivariate models and averaging them with a weighted ensemble approach. We further propose that this ensemble forecasting is conducted within a hierarchic multi-scale framework. We present two ensemble models for a test species, one at regional and one at local scale, each based on the combination of 630 models. In both cases, we obtained excellent spatial projections, unusual when modelling rare species. Model results highlight, from a statistically sound approach, the effects of multiple drivers in a same modelling framework and at two distinct scales. From this added information, regional models can support accurate forecasts of range dynamics under climate change scenarios, whereas local models allow the assessment of isolated or synergistic impacts of changes in multiple predictors. This novel framework provides a baseline for adaptive conservation, management and monitoring of rare species at distinct spatial and temporal scales.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 11