The DOI matches its Crossref record; 23 of the 23 references it cites were found in a registry or an index.
SDM toolbox: a python‐based GIS toolkit for landscape genetic, biogeographic and species distribution model analyses
Abstract
Summary: Species distribution models (SDM s) 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 SDM s. Further, for many studies, the creation of SDM s is not the final result and the post‐modelling processing can be equally arduous as other steps. SDM toolbox is designed to facilitate many complicated pre‐ and post‐processing steps commonly required for species distribution modelling and other geospatial analyses. SDM toolbox consists of 59 P ython script‐based GIS tools developed and compiled into a single interface. A large set of the tools were created to complement SDM s generated in M axent or to improve the predictive performance of SDM s created in M axent. However, SDM toolbox is not limited to analyses of M axent 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 SDM s; or for calculating several biodiversity metrics, such as corrected weighted endemism. SDM toolbox is a free comprehensive python‐based toolbox for macroecology, landscape genetic and evolutionary studies to be used in Arc GIS 10.1 (or higher) with the S patial A nalyst 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.
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Cites
A statistical explanation of MaxEnt for ecologists
Elith, J., Phillips, S. J., Hastie, T., Dudík, M., Chee, Y. E., & Yates, C. J. (2011) · Diversity and Distributions · doi:10.1111/j.1472-4642.2010.00725.x · Cited by 6,699
Merow, C., Smith, M. J., & Silander, J. A. (2013) · Ecography · doi:10.1111/j.1600-0587.2013.07872.x · Cited by 3,392
Phillips, S. J., Dudík, M., Elith, J., Graham, C. H., Lehmann, A., Leathwick, J., & Ferrier, S. (2009) · Ecological Applications · doi:10.1890/07-2153.1 · Cited by 2,996
BIOMOD – a platform for ensemble forecasting of species distributions
Thuiller, W., Lafourcade, B., Engler, R., & Araújo, M. B. (2009) · Ecography · doi:10.1111/j.1600-0587.2008.05742.x · Cited by 2,749
Selecting pseudo‐absences for species distribution models: how, where and how many?
Barbet‐Massin, M., Jiguet, F., Albert, C. H., & Thuiller, W. (2012) · Methods in Ecology and Evolution · doi:10.1111/j.2041-210x.2011.00172.x · Cited by 2,716
Barve, N., Barve, V., Jiménez-Valverde, A., Lira-Noriega, A., Maher, S. P., Peterson, A. T., Soberón, J., & Villalobos, F. (2011) · Ecological Modelling · doi:10.1016/j.ecolmodel.2011.02.011 · Cited by 1,881
Ecological Niches and Geographic Distributions (MPB-49)
Peterson, A. T., Soberón, J., Pearson, R. G., Anderson, R. P., Martínez-Meyer, E., Nakamura, M., & Araújo, M. B. (2011) · Princeton University Press eBooks · doi:10.23943/princeton/9780691136868.001.0001 · Cited by 1,828
Making better M axent models of species distributions: complexity, overfitting and evaluation
Radosavljevic, A., & Anderson, R. P. (2014) · Journal of Biogeography · doi:10.1111/jbi.12227 · Cited by 1,723
Spatial filtering to reduce sampling bias can improve the performance of ecological niche models
Boria, R. A., Olson, L. E., Goodman, S. M., & Anderson, R. P. (2014) · Ecological Modelling · doi:10.1016/j.ecolmodel.2013.12.012 · Cited by 1,524
Ecological niches and geographic distributions
(2012) · Choice Reviews Online · doi:10.5860/choice.49-6266 · Cited by 1,343
Research works printed but not linked: 1
- ESRI, 2012, ArcGIS Desktop and Spatial Analyst Extension: Release 10.1
Cited by
Want to model a species niche? A step-by-step guideline on correlative ecological niche modelling
Sillero, N., Arenas-Castro, S., Enriquez‐Urzelai, U., Vale, C. G., Sousa-Guedes, D., Martínez-Freiría, F., Real, R., & Barbosa, A. M. (2021) · Ecological Modelling · doi:10.1016/j.ecolmodel.2021.109671 · Cited by 456
Bouchet, P. J., & Meeuwig, J. J. (2015) · Ecosphere · doi:10.1890/es14-00380.1 · Cited by 59
Spiers, J. A., Oatham, M. P., Rostant, L. V., & Farrell, A. D. (2018) · Biodiversity and Conservation · doi:10.1007/s10531-018-1578-y · Cited by 51
Freeman, B., Roehrdanz, P. R., & Peterson, A. T. (2019) · Biodiversity and Conservation · doi:10.1007/s10531-018-01684-6 · Cited by 38
Mendonça, P., Carneiro, L. S., Leandro‐Silva, V., Aleixo, A., Araripe, J., & Rêgo, P. S. (2025) · Ecology and Evolution · doi:10.1002/ece3.70331
Metadata
- Authors
- Brown, J. L.
- Venue
- Methods in Ecology and Evolution
- Publisher
- Wiley
- Published
- 2014-05-21
- Volume
- 5
- Issue
- 7
- Pages
- 694-700
- ISSN
- 2041-210X (ISSN-L)
- OpenAlex
- W2132143030
- Cited by
- 1,254
- References
- 23
- FWCI
- 32.80
- Type
- journal article
- Language
- en
- Open access
- closed
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