M
Mevin B. Hooten
Researcher at Colorado State University
Publications - 198
Citations - 7371
Mevin B. Hooten is an academic researcher from Colorado State University. The author has contributed to research in topics: Population & Inference. The author has an hindex of 40, co-authored 177 publications receiving 5958 citations. Previous affiliations of Mevin B. Hooten include University of Missouri & United States Geological Survey.
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Journal ArticleDOI
A guide to Bayesian model selection for ecologists
Mevin B. Hooten,N.T. Hobbs +1 more
TL;DR: This guide serves as a reference for ecologists using Bayesian methods, so that they can better understand their options and can make an informed choice that is best aligned with their goals for inference.
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Iterative near-term ecological forecasting: Needs, opportunities, and challenges
Michael Dietze,Andrew M. Fox,Lindsay M. Beck-Johnson,Julio L. Betancourt,Mevin B. Hooten,Catherine S. Jarnevich,Timothy H. Keitt,Melissa A. Kenney,Christine Laney,Laurel G. Larsen,Henry W. Loescher,Henry W. Loescher,Claire Lunch,Bryan C. Pijanowski,James T. Randerson,Emily K. Read,Andrew T. Tredennick,Rodrigo Vargas,Kathleen C. Weathers,Ethan P. White +19 more
TL;DR: The need to start forecasting is now; the time for making ecology more predictive is here, and learning by doing is the fastest route to drive the science forward.
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Practical guidance on characterizing availability in resource selection functions under a use–availability design
TL;DR: This work examined the influence of availability on statistical inference through simulations and analysis of serially correlated mule deer GPS data, finding bias in estimates arose from incorrectly assessing and sampling the spatial extent of availability.
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On the use of log‐transformation vs. nonlinear regression for analyzing biological power laws
TL;DR: Using Monte Carlo simulations, it is demonstrated that the error distribution determines which method performs better, with NLR better characterizing data with additive, homoscedastic, normal error and LR better characterizes data with multiplicative, heteroscedastics, lognormal error.