Summary Recent studies have demonstrated a need for increased rigour in building and evaluating ecological niche models (ENMs) based on presence‐only occurrence data. Two major goals are to balance goodness‐of‐fit with model complexity (e.g. by ‘tuning’ model settings) and to evaluate models with spatially independent data. These issues are especially critical for data sets suffering from sampling bias, and for studies that require transferring models across space or time (e.g. responses to climate change or spread of invasive species). Efficient implementation of procedures to accomplish these goals, however, requires automation. We developed ENMeval, an R package that: (i) creates data sets for k‐fold cross‐validation using one of several methods for partitioning occurrence data (including options for spatially independent partitions), (ii) builds a series of candidate models using Maxent with a variety of user‐defined settings and (iii) provides multiple evaluation metrics to aid in selecting optimal model settings. The six methods for partitioning data are n−1 jackknife, random k‐folds ( = bins), user‐specified folds and three methods of masked geographically structured folds. ENMeval quantifies six evaluation metrics: the area under the curve of the receiver‐operating characteristic plot for test localities (AUCTEST), the difference between training and testing AUC (AUCDIFF), two different threshold‐based omission rates for test localities and the Akaike information criterion corrected for small sample sizes (AICc). We demonstrate ENMeval by tuning model settings for eight tree species of the genus Coccoloba in Puerto Rico based on AICc. Evaluation metrics varied substantially across model settings, and models selected with AICc differed from default ones. In summary, ENMeval facilitates the production of better ENMs and should promote future methodological research on many outstanding issues.

Spatial thinning of species occurrence records can help address problems associated with spatial sampling biases. Ideally, thinning removes the fewest records necessary to substantially reduce the effects of sampling bias, while simultaneously retaining the greatest amount of useful information. Spatial thinning can be done manually; however, this is prohibitively time consuming for large datasets. Using a randomization approach, the ‘thin’ function in the spThin R package returns a dataset with the maximum number of records for a given thinning distance, when run for sufficient iterations. We here provide a worked example for the Caribbean spiny pocket mouse, where the results obtained match those of manual thinning.

No abstract

1. Quantitative evaluations to optimize complexity have become standard for avoiding overfitting of ecological niche models (ENMs) that estimate species' potential geographic distributions. ENMeval was the first R package to make such evaluations (often termed model tuning) widely accessible for the Maxent algorithm.

Based on our own empirical data and a literature review, we explore the possibility that biotic interactions, specifically competition, might be responsible for creating, and/or maintaining, geographic isolation. Ecological niche modeling was first used to test whether the distributions of 2 species of Neotropical marsupials (Marmosa robinsoni and M. xerophila) fit the predicted geographic pattern of competitive exclusion: one species predominates in areas environmentally suitable for both species along real contact zones. Secondly, we examined the connectivity among populations of each species, interpreted in the light of the niche models. The results show predominance of M. xerophila along its contact zone with M. robinsoni in the Península de Paraguaná in northwestern Venezuela. There, M. robinsoni has an extremely restricted distribution despite climatic conditions suitable for both species across the peninsula and its isthmus. The latter two results suggest that M. xerophila may be responsible for the geographic isolation of the peninsular populations of M. robinsoni with respect to other populations of the latter species in northwestern Venezuela. These results may represent an example of allopatry caused, or at least maintained, by competition. Our results and a review of numerous studies in which biotic interactions restrict species distributions (including at the continental scale) support a previously overlooked phenomenon: biotic interactions can isolate populations of a species. We propose 2 general mechanisms, intrusion and contraction, to classify allopatric conditions caused by various classes of biotic interactions. We present a necessary modification of the concept of ecological vicariance to include biotic interactions as possible vicariant agents regardless of whether genetic differentiation occurs or not.