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Population responses to perturbations: predictions and responses from laboratory mite populations
Author(s) -
BENTON T. G.,
CAMERON T. C.,
GRANT A.
Publication year - 2004
Publication title -
journal of animal ecology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.134
H-Index - 157
eISSN - 1365-2656
pISSN - 0021-8790
DOI - 10.1111/j.0021-8790.2004.00859.x
Subject(s) - population , population model , density dependence , vital rates , population density , population size , ecology , statistics , variable (mathematics) , population growth , econometrics , biology , mathematics , demography , mathematical analysis , sociology
Summary1 Mathematical models are frequently used to make predictions of the response of a population to management interventions or environmental perturbations, but it is rarely possible to make controlled or replicated tests of the accuracy of these predictions. 2 We report results from replicated laboratory experiments on populations of a soil mite, Sancassania berlesei , living in ‘constant’ or ‘variable’ environments. We experimentally perturbed vital rates, via selective harvesting, and examined the population‐level responses. The response depends on the stage manipulated and whether there is environmental variability. Increased mortality usually decreased population size and increased population variability. However, egg mortality in a variable environment increased total population size. 3 We used time‐series analysis to construct a stage‐based population model of this system, incorporating the responses to both density and variation in food supply. 4 The time‐series model qualitatively captures the population dynamics, but does not predict well the way the populations will respond to the change in mortality. Elasticity analysis, conducted on the model's output, therefore did not lead to accurate predictions. 5 The presence of indirect positive population effects of a negative perturbation, but only in a variable environment, suggests that predicting the population response will require the incorporation of density dependence and environmental stochasticity. That the considerable biological complexity of our time‐series model did not allow accurate predictions suggests that accurate prediction requires modelling processes within a stage class rather than trying to make do with simple functions of total density.