Simple statistical models can be sufficient for testing hypotheses with population time-series data.

Autor: Wenger SJ; Odum School of Ecology University of Georgia Athens Georgia USA., Stowe ES; Odum School of Ecology University of Georgia Athens Georgia USA., Gido KB; Division of Biology Kansas State University Manhattan Kansas USA., Freeman MC; U.S. Geological Survey Eastern Ecological Science Center Athens Georgia USA., Kanno Y; Department of Fish, Wildlife, and Conservation Biology Colorado State University Fort Collins Colorado USA., Franssen NR; U.S. Fish and Wildlife Service Albuquerque New Mexico USA., Olden JD; School of Aquatic and Fishery Sciences University of Washington Seattle Washington USA., Poff NL; Department of Biology Colorado State University Fort Collins Colorado USA., Walters AW; U.S. Geological Survey Wyoming Cooperative Fish and Wildlife Research Unit, Department of Zoology and Physiology and Program in Ecology University of Wyoming Laramie Wyoming USA., Bumpers PM; Odum School of Ecology University of Georgia Athens Georgia USA., Mims MC; Department of Biological Sciences Virginia Tech Blacksburg Virginia USA., Hooten MB; Department of Statistics and Data Sciences The University of Texas at Austin Austin Texas USA., Lu X; Department of Fish, Wildlife, and Conservation Biology Colorado State University Fort Collins Colorado USA.
Jazyk: angličtina
Zdroj: Ecology and evolution [Ecol Evol] 2022 Sep 27; Vol. 12 (9), pp. e9339. Date of Electronic Publication: 2022 Sep 27 (Print Publication: 2022).
DOI: 10.1002/ece3.9339
Abstrakt: Time-series data offer wide-ranging opportunities to test hypotheses about the physical and biological factors that influence species abundances. Although sophisticated models have been developed and applied to analyze abundance time series, they require information about species detectability that is often unavailable. We propose that in many cases, simpler models are adequate for testing hypotheses. We consider three relatively simple regression models for time series, using simulated and empirical (fish and mammal) datasets. Model A is a conventional generalized linear model of abundance, model B adds a temporal autoregressive term, and model C uses an estimate of population growth rate as a response variable, with the option of including a term for density dependence. All models can be fit using Bayesian and non-Bayesian methods. Simulation results demonstrated that model C tended to have greater support for long-lived, lower-fecundity organisms (K life-history strategists), while model A, the simplest, tended to be supported for shorter-lived, high-fecundity organisms (r life-history strategists). Analysis of real-world fish and mammal datasets found that models A, B, and C each enjoyed support for at least some species, but sometimes yielded different insights. In particular, model C indicated effects of predictor variables that were not evident in analyses with models A and B. Bayesian and frequentist models yielded similar parameter estimates and performance. We conclude that relatively simple models are useful for testing hypotheses about the factors that influence abundance in time-series data, and can be appropriate choices for datasets that lack the information needed to fit more complicated models. When feasible, we advise fitting datasets with multiple models because they can provide complementary information.
Competing Interests: The authors declare no conflicts of interest.
(© 2022 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd.)
Databáze: MEDLINE
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