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Sampling-Variance Effects on Detecting Density Dependence from Temporal Trends in Natural Populations

1998; Wiley; Volume: 68; Issue: 3 Linguagem: Inglês

10.2307/2657247

1557-7015

Tanya M. Shenk, Gary C. White, Kenneth P. Burnham,

Statistical Methods and Bayesian Inference

Ecological MonographsVolume 68, Issue 3 p. 445-463 Article SAMPLING-VARIANCE EFFECTS ON DETECTING DENSITY DEPENDENCE FROM TEMPORAL TRENDS IN NATURAL POPULATIONS Tanya M. Shenk, Tanya M. Shenk Department of Fishery and Wildlife Biology, Colorado State University, Fort Collins, Colorado 80523 USA Colorado Division of Wildlife, 317 West Prospect Street, Fort Collins, Colorado 80526 USA.Search for more papers by this authorGary C. White, Gary C. White Department of Fishery and Wildlife Biology, Colorado State University, Fort Collins, Colorado 80523 USASearch for more papers by this authorKenneth P. Burnham, Kenneth P. Burnham Colorado Cooperative Fish and Wildlife Research Unit, U.S. Geological Survey, 201 Wagar Building, Fort Collins, Colorado 80523 USASearch for more papers by this author Tanya M. Shenk, Tanya M. Shenk Department of Fishery and Wildlife Biology, Colorado State University, Fort Collins, Colorado 80523 USA Colorado Division of Wildlife, 317 West Prospect Street, Fort Collins, Colorado 80526 USA.Search for more papers by this authorGary C. White, Gary C. White Department of Fishery and Wildlife Biology, Colorado State University, Fort Collins, Colorado 80523 USASearch for more papers by this authorKenneth P. Burnham, Kenneth P. Burnham Colorado Cooperative Fish and Wildlife Research Unit, U.S. Geological Survey, 201 Wagar Building, Fort Collins, Colorado 80523 USASearch for more papers by this author First published: 01 August 1998 https://doi.org/10.1890/0012-9615(1998)068[0445:SVEODD]2.0.CO;2Citations: 133Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat Abstract Monte Carlo simulations were conducted to evaluate robustness of four tests to detect density dependence, from series of population abundances, to the addition of sampling variance. Population abundances were generated from random walk, stochastic exponential growth, and density-dependent population models. Population abundance estimates were generated with sampling variances distributed as lognormal and constant coefficients of variation (cv) from 0.00 to 1.00. In general, when data were generated under a random walk, Type I error rates increased rapidly for Bulmer's R, Pollard et al.'s, and Dennis and Taper's tests with increasing magnitude of sampling variance for n > 5 yr and all values of process variation. Bulmer's R* test maintained a constant 5% Type I error rate for n > 5 yr and all magnitudes of sampling variance in the population abundance estimates. When abundances were generated from two stochastic exponential growth models (R = 0.05 and R = 0.10), Type I errors again increased with increasing sampling variance; magnitude of Type I error rates were higher for the slower growing population. Therefore, sampling error inflated Type I error rates, invalidating the tests, for all except Bulmer's R* test. Comparable simulations for abundance estimates generated from a density-dependent growth rate model were conducted to estimate power of the tests. Type II error rates were influenced by the relationship of initial population size to carrying capacity (K), length of time series, as well as sampling error. Given the inflated Type I error rates for all but Bulmer's R*, power was overestimated for the remaining tests, resulting in density dependence being detected more often than it existed. Population abundances of natural populations are almost exclusively estimated rather than censused, assuring sampling error. Therefore, because these tests have been shown to be either invalid when only sampling variance occurs in the population abundances (Bulmer's R, Pollard et al.'s, and Dennis and Taper's tests) or lack power (Bulmer's R* test), little justification exists for use of such tests to support or refute the hypothesis of density dependence. Citing Literature Volume68, Issue3August 1998Pages 445-463 RelatedInformation