期刊
ECOLOGY
卷 89, 期 2, 页码 532-541出版社
ECOLOGICAL SOC AMER
DOI: 10.1890/06-1996.1
关键词
agriculture; field studies; Aphis gossypii; Bayesian and frequentist evidence; computational statistics methodology; density dependence; Markov chain Monte Carlo; Monte Carlo kernel likelihood; population dynamics; San Joaquin Valley; California; USA; stage-structure; state-space model
类别
Robust analyses of noisy, stage-structured, irregularly spaced, field-scale data incorporating multiple sources of variability and nonlinear dynamics remain very limited, hindering understanding of how small-scale studies relate to large-scale population dynamics. We used a novel, complementary Bayesian and frequentist state-space model analysis to ask how density, temperature, plant nitrogen, and predators affect cotton aphid (Aphis gossypii) population dynamics in weekly data from 18 field-years and whether estimated effects are consistent with small-scale studies. We found clear roles of density and temperature but not of plant nitrogen or predators, for which Bayesian and frequentist evidence differed. However, overall predictability of field-scale dynamics remained low. This study demonstrates stage-structured state-space model analysis incorporating bottom-up, top-down, and density-dependent effects for within-season (nearly continuous time), nonlinear population dynamics. The analysis combines Bayesian posterior evidence with maximum-likelihood estimation and frequentist hypothesis testing using average one-step-ahead residuals.
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