Methodological advancements for improving performance and generating ensemble ecological niche models

This study employs spatial filtering of occurrence data with the aim of reducing overfitting to sampling bias in ecological niche models (ENMs). Sampling bias in geographic space leads to localities that may also be biased in environmental space. If so, the model can overfit to those biases. As a preliminary test addressing this issue, we used Maxent, bioclimatic variables, and occurrence localities of a broadly distributed Malagasy tenrec, Microgale cowani (Family Tenrecidae: Subfamily Oryzorictinae). We modeled the abiotically suitable area of this species using three distinct datasets: unfiltered, spatially filtered, and rarefied unfiltered localities. To quantify overfitting and model performance, we calculated evaluation AUC, the difference between calibration and evaluation AUC (= AUCdiff), and omission rates. Models made with the filtered dataset showed lower overfitting and better performance than the other two suites of models, having lower omission rates and AUCdiff, and a higher AUCevaluation. Additionally, the rarefied unfiltered dataset performed better than the unfiltered one for three evaluation metrics, likely because the larger datasets reinforced the biases. These results indicate that spatial filtering of occurrence localities may allow biogeographers to produce better models

Predicting global invasion risks: a management tool to prevent future introductions

Predicting regions at risk from introductions of non-native species and the subsequent invasions is a fundamental aspect of horizon scanning activities that enable the development of more effective preventative actions and planning of management measures. The Asian cyprinid fish topmouth gudgeon Pseudorasbora parva has proved highly invasive across Europe since its introduction in the 1960s. In addition to direct negative impacts on native fish populations, P. parva has potential for further damage through transmission of an emergent infectious disease, known to cause mortality in other species. To quantify its invasion risk, in regions where it has yet to be introduced, we trained 900 ecological niche models and constructed an Ensemble Model predicting suitability, then integrated a proxy for introduction likelihood. This revealed high potential for P. parva to invade regions well beyond its current invasive range. These included areas in all modelled continents, with several hotspots of climatic suitability and risk of introduction. We believe that these methods are easily adapted for a variety of other invasive species and that such risk maps could be used by policy-makers and managers in hotspots to formulate increased surveillance and early-warning systems that aim to prevent introductions and subsequent invasions

A tale of two rodents: Understanding past distribution of genetic diversity, population structure, and areas of refugia

Forecasting how biodiversity will change in the future due to natural and anthropogenic impacts is a primary focus of both ecology and evolutionary biology. By understanding the historical processes influencing the current geographic distribution of biodiversity, we can determine the relative importance of different factors shaping biodiversity, now and in the future. The main question that drove this dissertation: What historical processes led to the current distribution of diversity across the landscape? One omnipresent influence on the geographic distribution of diversity at multiple levels– e.g., genes and species– is climate. Understanding the spatial distributions of intraspecific genetic diversity and the role of climate and climate refugia in evolutionary and ecological processes is important because it shapes species potential for persistence in the face of future climate change. My dissertation focuses on how populations have responded to past climate change, and how the historical distributions and past areas of climate refugia will influence future climate change responses, using mammals as the study system.Studying population histories through time, we can uncover how different populations with similar genetic reservoirs respond differently to the same environmental stressor (climate). Determining how the distribution of intraspecific diversity of North American taxa was directly influenced by climate and landscape changes may illuminate broad-scale patterns of species’ responses to other climatic events, or more generally, to barriers impeding or constraining gene flow. My dissertation research utilized an interdisciplinary approach– next generation sequencing; GIS data; ecological modeling; bioinformatics– to understand how historical events have shaped the current distribution of genetic diversity within mammals. My aim was to study how mammal populations respond to climate change through time by determining the range dynamics and potential areas of refugia (Chapter 2 and 3). Understanding the spatial distributions of intraspecific genetic diversity and the role of climate refugia in the evolutionary and ecological processes of populations is important because it may determine their potential for persistence in the face of future climate change. My dissertation examined how two small mammals responded to the glacial cycles of the Pleistocene in North America. First, I determined Neotoma fuscipes has three historical populations in California—two northern and one southern population (Chapter 2). The major split between the northern and southern populations is older than 1.7 million years and occurred in the San Francisco Bay-Delta region, a historically significant region with high lineage diversification in mammals, amphibians, and reptiles. I detected multiple refugia within the species, including several origins of expansions and contractions (particularly with the northern populations). Second, I examined two western lineages within Peromyscus maniculatus and identified three main populations: 1) southern California; 2) a small population nested within the broader Pacific Northwest; 3) the Pacific Northwest through central California and across the Rocky Mountains (Chapter 3). These populations diverged within the last 160,000 years with very little migration, and evidence of recent population expansion. I found evidence for multiple areas of refugia including southeastern Alaska, a known refugia for several mammal species. In order to connect patterns and processes observed in the past with projections of change for the future, I pair my focus on generating empirical genetic & genomic data with different modeling types. Specifically, ecological niche models (ENM) are powerful tools for approximating the abiotically suitable area of a species by comparing environmental conditions at localities where the species occurs with the overall conditions available in the study region. Many ENM studies struggle with small sample sizes, but modeling widely distributed and well-sampled cosmopolitan species raises computational issues as well (Chapter 1). I thus determined the number of localities needed to model the distribution of a cosmopolitan species (Peromyscus maniculatus) with many occurrence records. I discovered when modeling species with a large number of occurrence records, it may not be necessary to use all localities for ENMs and could potentially affect model performance negatively

A tale of two rodents: Understanding past distribution of genetic diversity, population structure, and areas of refugia

Forecasting how biodiversity will change in the future due to natural and anthropogenic impacts is a primary focus of both ecology and evolutionary biology. By understanding the historical processes influencing the current geographic distribution of biodiversity, we can determine the relative importance of different factors shaping biodiversity, now and in the future. The main question that drove this dissertation: What historical processes led to the current distribution of diversity across the landscape? One omnipresent influence on the geographic distribution of diversity at multiple levels– e.g., genes and species– is climate. Understanding the spatial distributions of intraspecific genetic diversity and the role of climate and climate refugia in evolutionary and ecological processes is important because it shapes species potential for persistence in the face of future climate change. My dissertation focuses on how populations have responded to past climate change, and how the historical distributions and past areas of climate refugia will influence future climate change responses, using mammals as the study system.Studying population histories through time, we can uncover how different populations with similar genetic reservoirs respond differently to the same environmental stressor (climate). Determining how the distribution of intraspecific diversity of North American taxa was directly influenced by climate and landscape changes may illuminate broad-scale patterns of species’ responses to other climatic events, or more generally, to barriers impeding or constraining gene flow. My dissertation research utilized an interdisciplinary approach– next generation sequencing; GIS data; ecological modeling; bioinformatics– to understand how historical events have shaped the current distribution of genetic diversity within mammals. My aim was to study how mammal populations respond to climate change through time by determining the range dynamics and potential areas of refugia (Chapter 2 and 3). Understanding the spatial distributions of intraspecific genetic diversity and the role of climate refugia in the evolutionary and ecological processes of populations is important because it may determine their potential for persistence in the face of future climate change. My dissertation examined how two small mammals responded to the glacial cycles of the Pleistocene in North America. First, I determined Neotoma fuscipes has three historical populations in California—two northern and one southern population (Chapter 2). The major split between the northern and southern populations is older than 1.7 million years and occurred in the San Francisco Bay-Delta region, a historically significant region with high lineage diversification in mammals, amphibians, and reptiles. I detected multiple refugia within the species, including several origins of expansions and contractions (particularly with the northern populations). Second, I examined two western lineages within Peromyscus maniculatus and identified three main populations: 1) southern California; 2) a small population nested within the broader Pacific Northwest; 3) the Pacific Northwest through central California and across the Rocky Mountains (Chapter 3). These populations diverged within the last 160,000 years with very little migration, and evidence of recent population expansion. I found evidence for multiple areas of refugia including southeastern Alaska, a known refugia for several mammal species. In order to connect patterns and processes observed in the past with projections of change for the future, I pair my focus on generating empirical genetic & genomic data with different modeling types. Specifically, ecological niche models (ENM) are powerful tools for approximating the abiotically suitable area of a species by comparing environmental conditions at localities where the species occurs with the overall conditions available in the study region. Many ENM studies struggle with small sample sizes, but modeling widely distributed and well-sampled cosmopolitan species raises computational issues as well (Chapter 1). I thus determined the number of localities needed to model the distribution of a cosmopolitan species (Peromyscus maniculatus) with many occurrence records. I discovered when modeling species with a large number of occurrence records, it may not be necessary to use all localities for ENMs and could potentially affect model performance negatively

Genome-wide genetic variation coupled with demographic and ecological niche modeling of the dusky-footed woodrat (Neotoma fuscipes) reveal patterns of deep divergence and widespread Holocene expansion across northern California

Understanding how species have responded to past climate change may help refine projections of how species and biotic communities will respond to future change. Here, we integrate estimates of genome-wide genetic variation with demographic and niche modeling to investigate the historical biogeography of an important ecological engineer: the dusky-footed woodrat, Neotoma fuscipes. We use RADseq to generate a genome-wide dataset for 71 individuals from across the geographic distribution of the species in California. We estimate population structure using several model-based methods and infer the demographic history of regional populations using a site frequency spectrum-based approach. Additionally, we use ecological niche modeling to infer current and past (Last Glacial Maximum) environmental suitability across the species' distribution. Finally, we estimate the directionality and possible spatial origins of regional population expansions. Our analyses indicate this species is subdivided into three regionally distinct populations, with the deepest divergence occurring ~1.7 million years ago across the modern-day San Francisco-Bay Delta region; a common biogeographic barrier for the flora and fauna of California. Our models of environmental suitability through time coincide with our estimates of population expansion, with relative long-term stability in the southern portion of the range, and more recent expansion into the northern end of the range. Our study illustrates how the integration of genome-wide data with spatial and demographic modeling can reveal the timing and spatial extent of historic events that determine patterns of biotic diversity and may help predict biotic response to future change

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