SDMtoolbox: a python-based GIS toolkit for landscape genetic, biogeographic and species distribution model analyses

Autor(es)

Brown, Jason L. ^[1]

Afiliação do(s) autor(es)

^[1] Duke Univ, Dept Biol, Durham, NC 27705 - USA

• Tipo de documento: Artigo Científico
• Fonte: METHODS IN ECOLOGY AND EVOLUTION; v. 5, n. 7, p. 694-700, JUL 2014.
• Citações Web of Science: 240

Resumo

1. Species distribution models (SDMs) are broadly used in ecological and evolutionary studies. Almost all SDM methods require extensive data preparation in a geographic information system (GIS) prior to model building. Often, this step is cumbersome and, if not properly done, can lead to poorly parameterized models or in some cases, if too difficult, prevents the realization of SDMs. Further, for many studies, the creation of SDMs is not the final result and the post-modelling processing can be equally arduous as other steps. 2. SDMtoolbox is designed to facilitate many complicated pre- and post-processing steps commonly required for species distribution modelling and other geospatial analyses. SDMtoolbox consists of 59 Python script-based GIS tools developed and compiled into a single interface. 3. A large set of the tools were created to complement SDMs generated in Maxent or to improve the predictive performance of SDMs created in Maxent. However, SDMtoolbox is not limited to analyses of Maxent models, and many tools are also available for additional analyses or general geospatial processing: for example, assessing landscape connectivity of haplotype networks (using least-cost corridors or least-cost paths); correcting SDM over-prediction; quantifying distributional changes between current and future SDMs; or for calculating several biodiversity metrics, such as corrected weighted endemism. 4. SDMtoolbox is a free comprehensive python-based toolbox for macroecology, landscape genetic and evolutionary studies to be used in ArcGIS 10.1 (or higher) with the Spatial Analyst extension. The toolkit simplifies many GIS analyses required for species distribution modelling and other analyses, alleviating the need for repetitive and time-consuming climate data pre-processing and post-SDM analyses. (AU)

• Processo FAPESP: 13/50297-0 - Dimensions US-BIOTA São Paulo: integrando disciplinas para a predição da biodiversidade da Floresta Atlântica no Brasil
• Beneficiário: Cristina Yumi Miyaki
• Linha de fomento: Auxílio à Pesquisa - Programa BIOTA - Temático