Sampling-variance effects on detecting density dependence from temporal trends in natural populations

摘要: Monte Carlo simulations were conducted to evaluate robustness of four tests detect density dependence, from series population abundances, the addition sampling variance. Population abundances generated random walk, stochastic exponential growth, and density-dependent models. abundance es- timates with variances distributed as lognormal constant coefficients variation (CV) 0.00 1.00. In general, when data under a Type I error rates increased rapidly for Bulmer's R, Pollard et al.'s, Dennis Taper's increasing magnitude variance n . 5y r all values process variation. R* test maintained 5% rate 5 yr magnitudes in estimates. When two growth models (R 0.05 R 0.10), errors again variance; higher slower growing population. Therefore, inflated rates, invalidating tests, except test. Comparable estimates model estimate power tests. II influenced by relationship initial size carrying capacity ( K), length time series, well error. Given but R*, was overestimated remaining resulting depen- dence being detected more often than it existed. natural popu- lations are almost exclusively estimated rather censused, assuring because these have been shown be either invalid only occurs (Bulmer's tests) or lack test), little justification exists use such support refute hypothesis dependence.