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      SDMtoolbox 2.0: the next generation Python-based GIS toolkit for landscape genetic, biogeographic and species distribution model analyses

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          Abstract

          SDMtoolbox 2.0 is a software package for spatial studies of ecology, evolution, and genetics. The release of SDMtoolbox 2.0 allows researchers to use the most current ArcGIS software and MaxEnt software, and reduces the amount of time that would be spent developing common solutions. The central aim of this software is to automate complicated and repetitive spatial analyses in an intuitive graphical user interface. One core tenant facilitates careful parameterization of species distribution models (SDMs) to maximize each model’s discriminatory ability and minimize overfitting. This includes carefully processing of occurrence data, environmental data, and model parameterization. This program directly interfaces with MaxEnt, one of the most powerful and widely used species distribution modeling software programs, although SDMtoolbox 2.0 is not limited to species distribution modeling or restricted to modeling in MaxEnt. Many of the SDM pre- and post-processing tools have ‘universal’ analogs for use with any modeling software. The current version contains a total of 79 scripts that harness the power of ArcGIS for macroecology, landscape genetics, and evolutionary studies. For example, these tools allow for biodiversity quantification (such as species richness or corrected weighted endemism), generation of least-cost paths and corridors among shared haplotypes, assessment of the significance of spatial randomizations, and enforcement of dispersal limitations of SDMs projected into future climates—to only name a few functions contained in SDMtoolbox 2.0. Lastly, dozens of generalized tools exists for batch processing and conversion of GIS data types or formats, which are broadly useful to any ArcMap user.

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          Most cited references16

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          Selecting pseudo-absences for species distribution models: how, where and how many?

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            Making better Maxentmodels of species distributions: complexity, overfitting and evaluation

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              Spatial filtering to reduce sampling bias can improve the performance of ecological niche models

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                Author and article information

                Contributors
                Journal
                PeerJ
                PeerJ
                peerj
                peerj
                PeerJ
                PeerJ Inc. (San Francisco, USA )
                2167-8359
                5 December 2017
                2017
                : 5
                : e4095
                Affiliations
                [-1] Department of Zoology, Cooperative Wildlife Research Laboratory, Southern Illinois University at Carbondale , Carbondale, IL, USA
                Article
                4095
                10.7717/peerj.4095
                5721907
                29230356
                780e9c58-f60f-453f-86e3-7d2600627ade
                ©2017 Brown et al.

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited.

                History
                : 25 July 2017
                : 6 November 2017
                Funding
                The authors received no funding for this work.
                Categories
                Biodiversity
                Biogeography
                Bioinformatics
                Conservation Biology

                geographic information systems,maxent bias files,arcgis,ecological niche models,spatial jackknifing,rarefy occurrences,canape categorization

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