Species-specific tuning increases robustness to sampling bias in models of species distributions: An implementation with Maxent

• Published in: Ecological modelling 2011-08, Vol.222 (15), p.2796-2811
• Main Authors: Anderson, Robert P., Gonzalez, Israel
• Format: Article
• Language: English
• Subjects: Animal, plant and microbial ecology; Biological and medical sciences; Complexity; Cryptotis; Fundamental and applied biological sciences. Psychology; General aspects. Techniques; Maximum entropy; Methods and techniques (sampling, tagging, trapping, modelling...); Niche; Noise; Overfitting; Sample size
• ISSN: 0304-3800; 1872-7026
• DOI: 10.1016/j.ecolmodel.2011.04.011

► Models of species niches and distributions require optimal levels of model complexity. ► We evaluated sampling bias and small sample sizes for models of a shrew, Cryptotis meridensis. ► We tuned Maxent model settings to achieve optimal model complexity and reduce overfitting. ► Performance varied with changes in regularization and feature classes but not among sample sizes. ► Species-specific tuning of model settings can have great benefits over the use of default settings. Various methods exist to model a species’ niche and geographic distribution using environmental data for the study region and occurrence localities documenting the species’ presence (typically from museums and herbaria). In presence-only modelling, geographic sampling bias and small sample sizes represent challenges for many species. Overfitting to the bias and/or noise characteristic of such datasets can seriously compromise model generality and transferability, which are critical to many current applications – including studies of invasive species, the effects of climatic change, and niche evolution. Even when transferability is not necessary, applications to many areas, including conservation biology, macroecology, and zoonotic diseases, require models that are not overfit. We evaluated these issues using a maximum entropy approach (Maxent) for the shrew Cryptotis meridensis, which is endemic to the Cordillera de Mérida in Venezuela. To simulate strong sampling bias, we divided localities into two datasets: those from a portion of the species’ range that has seen high sampling effort (for model calibration) and those from other areas of the species’ range, where less sampling has occurred (for model evaluation). Before modelling, we assessed the climatic values of localities in the two datasets to determine whether any environmental bias accompanies the geographic bias. Then, to identify optimal levels of model complexity (and minimize overfitting), we made models and tuned model settings, comparing performance with that achieved using default settings. We randomly selected localities for model calibration (sets of 5, 10, 15, and 20 localities) and varied the level of model complexity considered (linear versus both linear and quadratic features) and two aspects of the strength of protection against overfitting (regularization). Environmental bias indeed corresponded to the geographic bias between datasets, with differences in median and observed range (minima and/or maxima) for som