Population responses to perturbations: predictions and responses from laboratory mite populations

Abstract

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.

Multiple life stages with multiple replicated density levels are required to estimate density dependence for plants

“…Secondly, we examined how the number of replicates per density level affects the accuracy of density dependence estimates. If there is little spatial variation, estimates of the strength of density dependence are expected to remain quite constant (Benton et al 2004), which would allow parameterization of density dependence from a small number of replicates. We again used shoot mass as a model rate and sub‐sampled blocks without replacement from the full observed data set (20 blocks) with sample sizes of 3, 5, 10, 15 and 19 blocks.…”

Eco-Evolutionary Interactions as a Consequence of Selection on a Secondary Sexual Trait

“…We used bootstrap resampling to estimate 95% confidence intervals (CIs) for the mean number and the mean size of individuals of relevant life stages per treatment group (cf. Benton et al, 2004). We furthermore divided the experimental period into three periods, day 100-190, day 191-280 and day 281-365, and calculated 95% CIs per period to assess long-term (instead of transient) temporal changes in life stage number and mean body size over time within each treatment group.…”

Integral Projection Models for Species with Complex Demography

“…In the kittiwake Rissa tridactyla, survival was age dependent, and individuals with high survival probability were also more likely to breed, presumably because of between-individual variation in quality . Failure to account for such latent between-individual differences can lead to systematic overestimation of population variability and extinction risk Fox 2002, 2003), underestimation of the uncertainty in population forecasts (Clark 2003), very large biases in estimates of demographic rates (Clark et al , 2004, and incorrect predictions of population responses to demographic perturbations (Benton et al 2004).…”

“…In the kittiwake Rissa tridactyla, survival was age dependent, and individuals with high survival probability were also more likely to breed, presumably because of between-individual variation in quality . Failure to account for such latent between-individual differences can lead to systematic overestimation of population variability and extinction risk Fox 2002, 2003), underestimation of the uncertainty in population forecasts (Clark 2003), very large biases in estimates of demographic rates (Clark et al , 2004, and incorrect predictions of population responses to demographic perturbations (Benton et al 2004).When several variables are needed to predict demographic performance, estimating a matrix model becomes difficult because many between-class transitions have to be estimated (Law 1983;Caswell 2001). For example, "the construction of models using both size and age … may be impractical because of the large numbers of categories required" (Caswell 1988, p. 94).…”