Determining the Influence of Abiotic and Biotic Predictors on Ecological Niche Models
type: CreativeWork
description: Biotic variables, including those reflecting interactions such as competition and parasitism, are generally assumed to have negligible effects on broadscale species distributions and are typically excluded from ecological niche models (ENMs). However, the role such interactions play in shaping species distributions is increasingly recognized, sparking the development of methods for their integration into ENMs. Among the most common approaches are those that limit the ENM output of the focal species by the distribution of biotic interactors (post-processing) and those that incorporate biotic interactors as predictor variables. These endeavors hold widespread and critical usages, such as predicting the impacts of climate change on species distributions, invasive species control, and disease risk mapping. Despite the importance and growing number of methods and studies incorporating biotic interactors in ENMs, similar developments to evaluate whether model performance is in fact improved by their inclusion are lagging. This is also true of methods to guide appropriate biotic variable selection and inferences regarding their biological relevance. Here, I present three chapters that make methodological advances to evaluate model performance and biotic variable contributions when accounting for them in ENMs. The first chapter evaluates the impacts of accounting for biotic interactions via post- processing on model performance and explores its potential implications for ENM applications. In it, I demonstrate that accounting for biotic interactions via post-processing balances model omission and commission error rates for biological entities with presumed parapatric distributions (i.e., vegetation types). In the second chapter, I develop a novel null model test to enable comparisons of model performance between two or more ENMs built with different predictor variable sets by extending a null model framework proposed by Bohl et al. (2019). Here, I demonstrate how ENM perform…
abstract: Biotic variables, including those reflecting interactions such as competition and parasitism, are generally assumed to have negligible effects on broadscale species distributions and are typically excluded from ecological niche models (ENMs). However, the role such interactions play in shaping species distributions is increasingly recognized, sparking the development of methods for their integration into ENMs. Among the most common approaches are those that limit the ENM output of the focal species by the distribution of biotic interactors (post-processing) and those that incorporate biotic interactors as predictor variables. These endeavors hold widespread and critical usages, such as predicting the impacts of climate change on species distributions, invasive species control, and disease risk mapping. Despite the importance and growing number of methods and studies incorporating biotic interactors in ENMs, similar developments to evaluate whether model performance is in fact improved by their inclusion are lagging. This is also true of methods to guide appropriate biotic variable selection and inferences regarding their biological relevance. Here, I present three chapters that make methodological advances to evaluate model performance and biotic variable contributions when accounting for them in ENMs. The first chapter evaluates the impacts of accounting for biotic interactions via post- processing on model performance and explores its potential implications for ENM applications. In it, I demonstrate that accounting for biotic interactions via post-processing balances model omission and commission error rates for biological entities with presumed parapatric distributions (i.e., vegetation types). In the second chapter, I develop a novel null model test to enable comparisons of model performance between two or more ENMs built with different predictor variable sets by extending a null model framework proposed by Bohl et al. (2019). Here, I demonstrate how ENM perform…
author: Johnson, Erica
keywords: Bioinformatics, Biology, Ecology and Evolutionary Biology, Ecological niche models, Species distribution modeling, Biotic interactions, Vegetation models, Parasite distributions, Disease risk mapping
date published: 2023-09-01T07:00:00Z
date modified: 2026-07-24T01:59:05Z
provider: City University of New York - Graduate Center
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: The Graduate School and University Center of The City University of New York