Predicting wildlife distribution patterns in New England USA with expert elicitation techniques
Understanding the impacts of landscape change on species distributions can help inform decision-making and conservation planning. Unfortunately, empirical data that span large spatial extents across multiple taxa are limited. In this study, we used expert elicitation techniques to develop species di...
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doaj-b844de7123e940fa878b799a555174522020-11-25T01:30:39ZengElsevierGlobal Ecology and Conservation2351-98942020-03-0121Predicting wildlife distribution patterns in New England USA with expert elicitation techniquesSchuyler B. Pearman-Gillman0Jonathan E. Katz1Ruth M. Mickey2James D. Murdoch3Therese M. Donovan4Vermont Cooperative Fish and Wildlife Research Unit, Rubenstein School of Environment and Natural Resources, University of Vermont, Burlington, VT, 05405, USAVermont Cooperative Fish and Wildlife Research Unit, University of Vermont, Burlington, VT, 05405, USADepartment of Mathematics and Statistics, College of Engineering and Mathematical Sciences, University of Vermont, Burlington, VT, 05405, USAWildlife and Fisheries Biology Program, Rubenstein School of Environment and Natural Resources, University of Vermont, Burlington, VT, 05405, USAU.S. Geological Survey, Vermont Cooperative Fish and Wildlife Research Unit, Rubenstein School of Environment and Natural Resources, University of Vermont, Burlington, VT, 05405, USA; Corresponding author.Understanding the impacts of landscape change on species distributions can help inform decision-making and conservation planning. Unfortunately, empirical data that span large spatial extents across multiple taxa are limited. In this study, we used expert elicitation techniques to develop species distribution models (SDMs) for harvested wildlife species (n = 10) in the New England region of the northeastern United States. We administered an online survey that elicited opinions from wildlife experts on the probability of species occurrence throughout the study region. We collected 3396 probability of occurrence estimates from 46 experts, and used linear mixed-effects methods and landcover variables at multiple spatial extents to develop SDMs. The models were in general agreement with the literature and provided effect sizes for variables that shape species occurrence. With the exception of gray fox, models performed well when validated against crowdsourced empirical data. We applied models to rasters (30 × 30 m cells) of the New England region to map each species’ distribution. Average regional occurrence probability was highest for coyote (0.92) and white-tailed deer (0.89) and lowest for gray fox (0.42) and moose (0.52). We then stacked distribution maps of each species to estimate and map focal species richness. Species richness (s) varied across New England, with highest average richness in the least developed states of Vermont (s = 7.47) and Maine (s = 7.32), and lowest average richness in the most developed states of Rhode Island (s = 6.13) and Massachusetts (s = 6.61). Our expert-based approach provided relatively inexpensive, comprehensive information that would have otherwise been difficult to obtain given the spatial extent and range of species being assessed. The results provide valuable information about the current distribution of wildlife species and offer a means of exploring how climate and land-use change may impact wildlife in the future. Keywords: AMSurvey, Expert elicitation, Harvested species, New England, Occupancy, Species distribution modeling (SDM)http://www.sciencedirect.com/science/article/pii/S2351989419304433 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Schuyler B. Pearman-Gillman Jonathan E. Katz Ruth M. Mickey James D. Murdoch Therese M. Donovan |
spellingShingle |
Schuyler B. Pearman-Gillman Jonathan E. Katz Ruth M. Mickey James D. Murdoch Therese M. Donovan Predicting wildlife distribution patterns in New England USA with expert elicitation techniques Global Ecology and Conservation |
author_facet |
Schuyler B. Pearman-Gillman Jonathan E. Katz Ruth M. Mickey James D. Murdoch Therese M. Donovan |
author_sort |
Schuyler B. Pearman-Gillman |
title |
Predicting wildlife distribution patterns in New England USA with expert elicitation techniques |
title_short |
Predicting wildlife distribution patterns in New England USA with expert elicitation techniques |
title_full |
Predicting wildlife distribution patterns in New England USA with expert elicitation techniques |
title_fullStr |
Predicting wildlife distribution patterns in New England USA with expert elicitation techniques |
title_full_unstemmed |
Predicting wildlife distribution patterns in New England USA with expert elicitation techniques |
title_sort |
predicting wildlife distribution patterns in new england usa with expert elicitation techniques |
publisher |
Elsevier |
series |
Global Ecology and Conservation |
issn |
2351-9894 |
publishDate |
2020-03-01 |
description |
Understanding the impacts of landscape change on species distributions can help inform decision-making and conservation planning. Unfortunately, empirical data that span large spatial extents across multiple taxa are limited. In this study, we used expert elicitation techniques to develop species distribution models (SDMs) for harvested wildlife species (n = 10) in the New England region of the northeastern United States. We administered an online survey that elicited opinions from wildlife experts on the probability of species occurrence throughout the study region. We collected 3396 probability of occurrence estimates from 46 experts, and used linear mixed-effects methods and landcover variables at multiple spatial extents to develop SDMs. The models were in general agreement with the literature and provided effect sizes for variables that shape species occurrence. With the exception of gray fox, models performed well when validated against crowdsourced empirical data. We applied models to rasters (30 × 30 m cells) of the New England region to map each species’ distribution. Average regional occurrence probability was highest for coyote (0.92) and white-tailed deer (0.89) and lowest for gray fox (0.42) and moose (0.52). We then stacked distribution maps of each species to estimate and map focal species richness. Species richness (s) varied across New England, with highest average richness in the least developed states of Vermont (s = 7.47) and Maine (s = 7.32), and lowest average richness in the most developed states of Rhode Island (s = 6.13) and Massachusetts (s = 6.61). Our expert-based approach provided relatively inexpensive, comprehensive information that would have otherwise been difficult to obtain given the spatial extent and range of species being assessed. The results provide valuable information about the current distribution of wildlife species and offer a means of exploring how climate and land-use change may impact wildlife in the future. Keywords: AMSurvey, Expert elicitation, Harvested species, New England, Occupancy, Species distribution modeling (SDM) |
url |
http://www.sciencedirect.com/science/article/pii/S2351989419304433 |
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