English   español  
Por favor, use este identificador para citar o enlazar a este item: http://hdl.handle.net/10261/122943
logo share SHARE logo core CORE   Add this article to your Mendeley library MendeleyBASE

Visualizar otros formatos: MARC | Dublin Core | RDF | ORE | MODS | METS | DIDL
Exportar a otros formatos:

Uncertainty associated with survey design in species distribution models

AutorTessarolo, Geiziane; Rangel, Thiago F.; Araújo, Miguel B. ; Hortal, Joaquín
Fecha de publicación27-jun-2014
EditorJohn Wiley & Sons
CitaciónDiversity and Distributions 20(11): 1258-1269 (2014)
Resumen© 2014 John Wiley & Sons Ltd. Aim: Species distribution models (SDM) can be used to predict the location of unknown populations from known species occurrences. It follows that how the data used to calibrate the models are collected can have a great impact on prediction success. We evaluated the influence of different survey designs and their interaction with the modelling technique on SDM performance. Location: Iberian Peninsula. Methods: We examine how data recorded using seven alternative survey designs (random, systematic, environmentally stratified by class and environmentally stratified using P-median, biased due to accessibility, biased by human density aggregation and biased towards protected areas) could affect SDM predictions generated with nine modelling techniques (BIOCLIM, Gower distance, Mahalanobis distance, Euclidean distance, GLM, MaxEnt, ENFA and Random Forest). We also study how sample size, species' characteristics and modelling technique affected SDM predictive ability, using six evaluation metrics. Results: Survey design has a small effect on prediction success. Characteristics of species' ranges rank highest among the factors affecting SDM results: the species with lower relative occurrence area (ROA) are predicted better. Model predictions are also improved when sample size is large. Main conclusions: The species modelled - particularly the extent of its distribution - are the largest source of influence over SDM results. The environmental coverage of the surveys is more important than the spatial structure of the calibration data. Therefore, climatic biases in the data should be identified to avoid erroneous conclusions about the geographic patterns of species distributions.
Identificadoresdoi: 10.1111/ddi.12236
e-issn: 1472-4642
issn: 1366-9516
Aparece en las colecciones: (MNCN) Artículos
Ficheros en este ítem:
Fichero Descripción Tamaño Formato  
accesoRestringido.pdf15,38 kBAdobe PDFVista previa
Mostrar el registro completo

Artículos relacionados:

NOTA: Los ítems de Digital.CSIC están protegidos por copyright, con todos los derechos reservados, a menos que se indique lo contrario.