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A quantitative synthesis of the importance of variables used in MaxEnt species distribution models
Bradie, J.; Leung, B. (2017). A quantitative synthesis of the importance of variables used in MaxEnt species distribution models. J. Biogeogr. 44(6): 1344-1361. https://dx.doi.org/10.1111/jbi.12894
In: Journal of Biogeography. Wiley-Blackwell: Oxford. ISSN 0305-0270; e-ISSN 1365-2699
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| Author keywords |
environmental variables;habitat suitability models;MaxEnt;model transferability;niche determinants;niche models;species distribution models;variable importance |
| Abstract |
AimTo synthesize the species distribution modelling (SDM) literature to inform which variables have been used in MaxEnt models for different taxa and to quantify how frequently they have been important for species’ distributions.LocationGlobal.MethodsWe conducted a quantitative synthesis analysing the contribution of over 400 distinct environmental variables to 2040 MaxEnt SDMs for nearly 1900 species representing over 300 families. Environmental variables were grouped into 24 related factors and results were analysed by examining the frequency with which variables were found to be most important, the mean contribution of each variable (at various taxonomic levels), and using TrueSkill™, a Bayesian skill rating system.ResultsPrecipitation, temperature, bathymetry, distance to water and habitat patch characteristics were the most important variables overall. Precipitation and temperature were analysed most frequently and one of these variables was often the most important predictor in the model (nearly 80% of models, when tested). Notably, distance to water was the most important variable in the highest proportion of models in which it was tested (42% of 225 models). For terrestrial species, precipitation, temperature and distance to water had the highest overall contributions, whereas for aquatic species, bathymetry, precipitation and temperature were most important.Main conclusionsOver all MaxEnt models published, the ability to discriminate occurrence from reference sites was high (average AUC = 0.92). Much of this discriminatory ability was due to temperature and precipitation variables. Further, variability (temperature) and extremes (minimum precipitation) were the most predictive. More generally, the most commonly tested variables were not always the most predictive, with, for instance, ‘distance to water’ infrequently tested, but found to be very important when it was. Thus, the results from this study summarize the MaxEnt SDM literature, and can aid in variable selection by identifying underutilized, but potentially important variables, which could be incorporated in future modelling efforts. |
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