Abstract
BackgroundPredicting species’ potential geographical range by species distribution models (SDMs) is central to understand their ecological requirements. However, the effects of using different modeling techniques need further investigation. In order to improve the prediction effect, we need to assess the predictive performance and stability of different SDMs.MethodologyWe collected the distribution data of five common tree species (Pinus massoniana, Betula platyphylla, Quercus wutaishanica, Quercus mongolica and Quercus variabilis) and simulated their potential distribution area using 13 environmental variables and six widely used SDMs: BIOCLIM, DOMAIN, MAHAL, RF, MAXENT, and SVM. Each model run was repeated 100 times (trials). We compared the predictive performance by testing the consistency between observations and simulated distributions and assessed the stability by the standard deviation, coefficient of variation, and the 99% confidence interval of Kappa and AUC values.ResultsThe mean values of AUC and Kappa from MAHAL, RF, MAXENT, and SVM trials were similar and significantly higher than those from BIOCLIM and DOMAIN trials (p<0.05), while the associated standard deviations and coefficients of variation were larger for BIOCLIM and DOMAIN trials (p<0.05), and the 99% confidence intervals for AUC and Kappa values were narrower for MAHAL, RF, MAXENT, and SVM. Compared to BIOCLIM and DOMAIN, other SDMs (MAHAL, RF, MAXENT, and SVM) had higher prediction accuracy, smaller confidence intervals, and were more stable and less affected by the random variable (randomly selected pseudo-absence points).ConclusionsAccording to the prediction performance and stability of SDMs, we can divide these six SDMs into two categories: a high performance and stability group including MAHAL, RF, MAXENT, and SVM, and a low performance and stability group consisting of BIOCLIM, and DOMAIN. We highlight that choosing appropriate SDMs to address a specific problem is an important part of the modeling process.
Highlights
Species distribution models (SDMs), known as climate envelope models, habitat suitability models, and ecological niche models, use environment data for sites of occurrence of a species to predict a response variable, such as suitability, for a site where the environmental conditions are suitable for that species to persist and so may be expected to occur [1,2,3,4,5,6,7]
The mean values of AUC and Kappa from MAHAL, RF, MAXENT, and SVM trials were similar and significantly higher than those from BIOCLIM and DOMAIN trials (p,0.05), while the associated standard deviations and coefficients of variation were larger for BIOCLIM and DOMAIN trials (p,0.05), and the 99% confidence intervals for AUC and Kappa values were narrower for MAHAL, RF, MAXENT, and SVM
According to the prediction performance and stability of species distribution models (SDMs), we can divide these six SDMs into two categories: a high performance and stability group including MAHAL, RF, MAXENT, and SVM, and a low performance and stability group consisting of BIOCLIM, and DOMAIN
Summary
Species distribution models (SDMs), known as climate envelope models, habitat suitability models, and ecological niche models, use environment data for sites of occurrence (presence) of a species to predict a response variable, such as suitability, for a site where the environmental conditions are suitable for that species to persist and so may be expected to occur [1,2,3,4,5,6,7]. There are substantial discrepancies in predicting species’ distributions by SDMs with different predictive modeling method, which have highlighted the uncertainties of prediction results [14,19,20,21]. These uncertainties of prediction may puzzle stakeholders and policy makers, and cast doubt on the reliability of species distribution predictions by SDMs. critical assessment of the predictive performance and stability of SDMs need to be performed. In order to improve the prediction effect, we need to assess the predictive performance and stability of different SDMs
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