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Quantitative evaluations to optimize complexity have become standard for avoiding overfitting of ecological niche models (ENMs) that estimate species’ potential geographic distributions.ENMevalwas the first R package to make such evaluations (often termed model tuning) widely accessible for the Maxent algorithm. It also provided multiple methods for partitioning occurrence data and reported various performance metrics.

Requests by users, recent developments in the field, and needs for software compatibility led to a major redesign and expansion. We additionally conducted a literature review to investigate trends inENMevaluse (2015–2019).

ENMeval2.0 has a new object‐oriented structure for adding other algorithms, enables customizing algorithmic settings and performance metrics, generates extensive metadata, implements a null‐model approach to quantify significance and effect sizes, and includes features to increase the breadth of analyses and visualizations. In our literature review, we found insufficient reporting of model performance and parameterization, heavy reliance on model selection with AICc and low utilization of spatial cross‐validation; we explain howENMeval2.0 can help address these issues.

This redesigned and expanded version can promote progress in the field and improve the information available for decision‐making.

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As geographic range estimates for the IUCN Red List guide conservation actions, accuracy and ecological realism are crucial. IUCN’s extent of occurrence (EOO) is the general region including the species’ range, while area of occupancy (AOO) is the subset of EOO occupied by the species. Data‐poor species with incomplete sampling present particular difficulties, but species distribution models (SDMs) can be used to predict suitable areas. Nevertheless, SDMs typically employ abiotic variables (i.e., climate) and do not explicitly account for biotic interactions that can impose range constraints. We sought to improve range estimates for data‐poor, parapatric species by masking out areas under inferred competitive exclusion. We did so for two South American spiny pocket mice:Heteromys australis(Least Concern) andHeteromys teleus(Vulnerable due to especially poor sampling), whose ranges appear restricted by competition. For both species, we estimated EOO using SDMs and AOO with four approaches: occupied grid cells, abiotic SDM prediction, and this prediction masked by approximations of the areas occupied by each species’ congener. We made the masks using support vector machines (SVMs) fit with two data types: occurrence coordinates alone; and coordinates along with SDM predictions of suitability. Given the uncertainty in calculating AOO for low‐data species, we made estimates for the lower and upper bounds for AOO, but only make recommendations forH. teleusas its full known range was considered. The SVM approaches (especially the second one) had lower classification error and made more ecologically realistic delineations of the contact zone. ForH. teleus, the lower AOO bound (a strongly biased underestimate) corresponded to Endangered (occupied grid cells), while the upper bounds (other approaches) led to Near Threatened. As we currently lack data to determine the species’ true occupancy within the post‐processed SDM prediction, we recommend that an updated listing forH. teleusinclude these bounds for AOO. This study advances methods for estimating the upper bound of AOO and highlights the need for better ways to produce unbiased estimates of lower bounds. More generally, the SVM approaches for post‐processing SDM predictions hold promise for improving range estimates for other uses in biogeography and conservation.

 

The field of distributional ecology has seen considerable recent attention, particularly surrounding the theory, protocols, and tools for Ecological Niche Modeling (ENM) or Species Distribution Modeling (SDM). Such analyses have grown steadily over the past two decades—including a maturation of relevant theory and key concepts—but methodological consensus has yet to be reached. In response, and following an online course taught in Spanish in 2018, we designed a comprehensive English-language course covering much of the underlying theory and methods currently applied in this broad field. Here, we summarize that course, ENM2020, and provide links by which resources produced for it can be accessed into the future. ENM2020 lasted 43 weeks, with presentations from 52 instructors, who engaged with >2500 participants globally through >14,000 hours of viewing and >90,000 views of instructional video and question-and-answer sessions. Each major topic was introduced by an “Overview” talk, followed by more detailed lectures on subtopics. The hierarchical and modular format of the course permits updates, corrections, or alternative viewpoints, and generally facilitates revision and reuse, including the use of only the Overview lectures for introductory courses. All course materials are free and openly accessible (CC-BY license) to ensure these resources remain available to all interested in distributional ecology. 

Abstract Reporting specific modelling methods and metadata is essential to the reproducibility of ecological studies, yet guidelines rarely exist regarding what information should be noted. Here, we address this issue for ecological niche modelling or species distribution modelling, a rapidly developing toolset in ecology used across many aspects of biodiversity science. Our quantitative review of the recent literature reveals a general lack of sufficient information to fully reproduce the work. Over two-thirds of the examined studies neglected to report the version or access date of the underlying data, and only half reported model parameters. To address this problem, we propose adopting a checklist to guide studies in reporting at least the minimum information necessary for ecological niche modelling reproducibility, offering a straightforward way to balance efficiency and accuracy. We encourage the ecological niche modelling community, as well as journal reviewers and editors, to utilize and further develop this framework to facilitate and improve the reproducibility of future work. The proposed checklist framework is generalizable to other areas of ecology, especially those utilizing biodiversity data, environmental data and statistical modelling, and could also be adopted by a broader array of disciplines.