changeRangeR: An R package for reproducible biodiversity change metrics from species distribution estimates

Conservation planning and decision-making rely on evaluations of biodiversity status and threats that are based upon species' distribution estimates. However, gaps exist regarding automated tools to delineate species' current ranges from distribution estimates and use those estimates to calculate both species- and community-level biodiversity metrics. Here, we introduce changeRangeR, an R package that facilitates workflows to reproducibly transform estimates of species' distributions into metrics relevant for conservation. For example, by combining predictions from species distribution models (SDMs) with other maps of environmental data (e.g., suitable forest cover), researchers can characterize the proportion of a species' range that is under protection, metrics used under the IUCN Criteria A and B guidelines (Area of Occupancy and Extent of Occurrence), and other more general metrics such as taxonomic and phylogenetic diversity and endemism. Further, changeRangeR facilitates temporal comparisons among biodiversity metrics to inform efforts toward complementarity and consideration of future scenarios in conservation decisions. changeRangeR also provides tools to determine the effects of modeling decisions through sensitivity tests. Transparent and repeatable workflows for calculating biodiversity change metrics from SDMs such as those provided by changeRangeR are essential to inform conservation decision-making efforts and represent key extensions for SDM methodology and associated metadata documentation.journal articl

wallace 2: a shiny app for modeling species niches and distributions redesigned to facilitate expansion via module contributions

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.journal articl

ENM2020 : A FREE ONLINE COURSE AND SET OF RESOURCES ON MODELING SPECIES NICHES AND DISTRIBUTIONS

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.Peer reviewe

Expanding the Role of the Temporal Dimension in Ecological Niche Models: A Study on Mexican Montane Small Mammals

Ecological niche models (ENMs) are commonly used to estimate the potential geographic distributions of species and have various uses, such as predicting invasive species\u27 potential distribution, assessing vulnerability to climate change, or estimating paleodistributions. However, ENM developments have primarily focused on solving spatially related issues, underemphasizing the temporal dimensionality of occurrence and environmental data collected over the last century. One limitation of ENMs is the common practice of using recent environmental averages that fail to capture the gradual environmental change over the time span of the occurrence records and can hide trends in environmental variables, a critical piece of information that could help understand the impact of ongoing climate change. Moreover, the traditional ENM framework has been criticized for its limited use of the timing of species occurrences, which is typically associated with a single average value of the environmental condition over a long period of time (e.g., 30-year climate baselines) even if the records corresponded to decades earlier. Another challenge occurs when transferring models to different time periods with dissimilar environmental conditions from those in which the model was trained. Despite available recommendations for transferring to novel conditions, tools that facilitate decision-making focused on characteristics of the model chosen for transfer are lacking. To help fill these gaps and using two montane small non-volant mammals in Mexico, i) I introduce a new method for detecting changes in environmental suitability by using a time series of recent data, ii) examine the effectiveness of temporal matching between occurrences and environmental conditions, and iii) present examples and visualization tools to help in the decision-making process and implementation when transferring to novel environmental conditions. In this dissertation, I present three chapters that collectively expand the role of the temporal dimension in ecological niche modeling. In the first chapter, I focus on predicting potential changes in the distribution limits of the Mexican small-eared shrew (Cryptotis mexicanus). Instead of comparing model predictions between two time periods (present vs. a future scenario), I used a time series of environmental data over the past four decades to identify temporal trends of environmental suitability. The findings indicate that changes in environmental suitability do not align with the simple poleward or upslope shifts expected for a montane species. Instead, variation in regional precipitation, rather than temperature, is the primary factor that corresponds with changes in suitability affecting the distribution limits. The approach used here could be a valuable supplement for forecasting potential geographic range changes across different time periods. In the second chapter, I temporally match occurrences of C. mexicanus with the environmental conditions experienced prior to the date of observation (i.e., one, five, and ten years) and compared resulting models against those from a standard 30-year average to evaluate their performance and ecological plausibility. The results showed that the ten-year temporal resolution performed equally to or better than the standard approach (based on withheld omission rate). Temporal matching could therefore improve model performance and geographic predictions, even for species with low mobility. Future studies should focus on selecting an optimal temporal resolution that considers population responses to climate change. In the third chapter, I use the Black-eared mouse (Peromyscus melanotis) as a case study for transferring models to novel (“non-analog”) conditions, also known as environmental extrapolation. I develop visualization tools to help in the decision-making process for extrapolating models when transferring to novel environments. This study reaffirms the importance of inspecting response curves and simultaneously assessing the quantity of pixels under novel conditions to make appropriate choices regarding extrapolation methods. The recommendations in this study offer additional guidance for obtaining more reliable geographic predictions in novel conditions, which is becoming increasingly crucial in a rapidly changing world. Overall, this dissertation contributes to the methodological development of incorporating the temporal dimension in ecological niche modeling through empirical examples and code that can be used broadly by biogeographers and spatial ecologists

Ecological niche comparison among closely related tree species of Lauraceae using climatic and edaphic data

Edaphic specialization is considered to promote ecological differentiation among closely related species of Damburneya (Lauraceae) occurring in sympatry. However, little is known about the effects of soil and other key environmental factors like climate on the ecological niche and distributionof these tree species. Here, we assessed the role of climate and soil on niche divergence and potential distribution of four Damburneya species whose distributions span Central America and Mexico. We performed ecological niche modeling with MaxEnt using three sets of environmental data: climatic-only, edaphic-only, and a combination of both, to characterize species niches and suitable distribution areas. Niche overlap was quantified, and niche similarity was tested to assess niche differentiation among species. Climate and soil determined species’ potential distribution. While climatic niches were mostly similar, edaphic niches tended to differ. Warm and moist tropical forests with no water deficit and low seasonality in precipitation are the most suitable environments for the four species. This study supports previous reports of wide ecological plasticity of Damburneya salicifolia based on its distribution and leaf trait variation, as it occurred in drier environments with wider temperature and soil pH ranges than the other species. The observed patterns of niche similarity did not reflect the phylogenetic relationships between species, suggesting that the modeled niches do not necessarily reflect past evolutionary processes but rather the current environmental variation across the geographical ranges of the species. The results suggest that the studied species are similarly constrained by climate and toleratewide edaphic variation, supporting a potential role for soils on ecological divergence within the genus. On the other hand, performance and predictions varied between models built with different datasets. This research supports the utility of including climate and soil data in ecological niche models to comprehensively analyze the niche and distribution of plant species

ENMeval 2.0: Redesigned for customizable and reproducible modeling of species’ niches and distributions

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. It also provided multiple methods for partitioning occurrence data and reported various performance metrics.２．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).３．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.４．This redesigned and expanded version can promote progress in the field and improve the information available for decision-making

ENMeval 2.0: Redesigned for customizable and reproducible modeling of species’ niches and distributions

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. It also provided multiple methods for partitioning occurrence data and reported various performance metrics.２．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).３．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.４．This redesigned and expanded version can promote progress in the field and improve the information available for decision-making

changeRangeR: An R package for reproducible biodiversity change metrics from species distribution estimates

Abstract Conservation planning and decision‐making rely on evaluations of biodiversity status and threats that are based upon species' distribution estimates. However, gaps exist regarding automated tools to delineate species' current ranges from distribution estimates and use those estimates to calculate both species‐ and community‐level biodiversity metrics. Here, we introduce changeRangeR, an R package that facilitates workflows to reproducibly transform estimates of species' distributions into metrics relevant for conservation. For example, by combining predictions from species distribution models (SDMs) with other maps of environmental data (e.g., suitable forest cover), researchers can characterize the proportion of a species' range that is under protection, metrics used under the IUCN Criteria A and B guidelines (Area of Occupancy and Extent of Occurrence), and other more general metrics such as taxonomic and phylogenetic diversity and endemism. Further, changeRangeR facilitates temporal comparisons among biodiversity metrics to inform efforts toward complementarity and consideration of future scenarios in conservation decisions. changeRangeR also provides tools to determine the effects of modeling decisions through sensitivity tests. Transparent and repeatable workflows for calculating biodiversity change metrics from SDMs such as those provided by changeRangeR are essential to inform conservation decision‐making efforts and represent key extensions for SDM methodology and associated metadata documentation

Operationalizing expert knowledge in species' range estimates using diverse data types

Estimates of species’ ranges can inform many aspects of biodiversity research and conservation-management decisions. Many practical applications need high-precision range estimates that are sufficiently reliable to use as input data in downstream applications. One solution has involved expert-generated maps that reflect on-the-ground field information and implicitly capture various processes that may limit a species’ geographic distribution. However, expert maps are often subjective and rarely reproducible. In contrast, species distribution models (SDMs) typically have finer resolution and are reproducible because of explicit links to data. Yet, SDMs can have higher uncertainty when data are sparse, which is an issue for most species. Also, SDMs often capture only a subset of the factors that determine species distributions (e.g., climate) and hence can require significant post-processing to better estimate species’ current realized distributions. Here, we demonstrate how expert knowledge, diverse data types, and SDMs can be used together in a transparent and reproducible modeling workflow. Specifically, we show how expert knowledge regarding species’ habitat use, elevation, biotic interactions, and environmental tolerances can be used to make and refine range estimates using SDMs and various data sources, including high-resolution remotely sensed products. This range-refinement approach is primed to use various data sources, including many with continuously improving spatial or temporal resolution. To facilitate such analyses, we compile a comprehensive suite of tools in a new R package, maskRangeR, and provide worked examples. These tools can facilitate a wide variety of basic and applied research that requires high-resolution maps of species’ current ranges, including quantifications of biodiversity and its change over time