Biodiversity assessments use a variety of data and models. We propose best-practice standards for studies in these assessments.

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. 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 in ENMeval use (2015–2019). ENMeval 2.0 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 how ENMeval 2.0 can help address these issues. This redesigned and expanded version can promote progress in the field and improve the information available for decision‐making. ​

Scientific research increasingly calls for open‐source software that is flexible, interactive, and expandable, while providing methodological guidance and reproducibility. Currently, many analyses in ecology are implemented with “black box” graphical user interfaces (GUIs) that lack flexibility or command‐line interfaces that are infrequently used by non‐specialists. To help remedy this situation in the context of species distribution modeling, we created Wallace, an open and modular application with a richly documented GUI with underlying R scripts that is flexible and highly interactive. Wallace guides users from acquiring and processing data to building models and examining predictions. Additionally, it is designed to grow via community contributions of new modules to expand functionality. All results are downloadable, along with code to reproduce the analysis. Wallace provides an example of an innovative platform to increase access to cutting‐edge methods and encourage plurality in science and collaboration in software development.

Species distribution models, also known as ecological niche models or habitat suitability models, have become commonplace for addressing fundamental and applied biodiversity questions. Although the field has progressed rapidly regarding theory and implementation, key assumptions are still frequently violated and recommendations inadvertently overlooked. This leads to poor models being published and used in real‐world applications. In a structured, didactic treatment, we summarize what in our view constitute the ten most problematic issues, or hazards, negatively affecting implementation of correlative approaches to species distribution modeling (specifically those that model suitability by comparing the environments of a species' occurrence records with those of a background or pseudoabsence sample). For each hazard, we state relevant assumptions, detail problems that arise when violating them, and convey straightforward existing recommendations. We also discuss five major outstanding questions of active current research. We hope this contribution will promote more rigorous implementation of these valuable models and stimulate further advancements.

Released 4 years ago, the Wallace EcoMod application (R package wallace ) provided an open‐source and interactive platform for modeling species niches and distributions that served as a reproducible toolbox and educational resource. wallace harnesses R package tools documented in the literature and makes them available via a graphical user interface that runs analyses and returns code to document and reproduce them. Since its release, feedback from users and partners helped identify key areas for advancement, leading to the development of wallace 2. Following the vision of growth by community expansion, the core development team engaged with collaborators and undertook a major restructuring of the application to enable: simplified addition of custom modules to expand methodological options, analyses for multiple species in the same session, improved metadata features, new database connections, and saving/loading sessions. wallace 2 features nine new modules and added functionalities that facilitate data acquisition from climate‐simulation, botanical and paleontological databases; custom data inputs; model metadata tracking; and citations for R packages used (to promote documentation and give credit to developers). Three of these modules compose a new component for environmental space analyses (e.g., niche overlap). This expansion was paired with outreach to the biogeography and biodiversity communities, including international presentations and workshops that take advantage of the software's extensive guidance text. Additionally, the advances extend accessibility with a cloud‐computing implementation and include a suite of comprehensive unit tests. The features in wallace 2 greatly improve its expandability, breadth of analyses, and reproducibility options, including the use of emerging metadata standards. The new architecture serves as an example for other modular software, especially those developed using the rapidly proliferating R package shiny , by showcasing straightforward module ingestion and unit testing. Importantly, wallace 2 sets the stage for future expansions, including those enabling biodiversity estimation and threat assessments for conservation.