|Document Type:||Journal Article|
|Title:||The importance of spatial models for estimating the type and strength of density dependence|
|Author:||James T. Thorson, Hans Skaug, Kasper Kristensen, Andrew O. Shelton, E. J. Ward, J. Harms, J. A. Benante|
|Keywords:||Gompertz model, spatial variation, density dependence, random field, autoregressive model, Pacific rockfish (Sebastes),|
Identifying the existence, form, and magnitude of density dependence is one of the oldest concerns in ecology. Periodically over forty years, ecologists have aimed to estimate density dependence in population and community data by fitting a simple autoregressive (Gompertz) model for density dependence to time series of abundance for an entire population. However, it is increasingly recognized that spatial heterogeneity in population densities has implications for population and community dynamics. We therefore adapt the Gompertz model to approximate local densities over continuous space instead of population-wide abundance, and to allow productivity to vary spatially. Using simulated data generated from a spatial model, we show that the conventional (non-spatial) Gompertz model will result in biased estimates of density dependence, e.g., identifying oscillatory dynamics when not present. By contrast, the spatial Gompertz model provides accurate estimates of density dependence for a variety of simulation scenarios and data availabilities. These results are corroborated when comparing spatial and non-spatial models for data from 10 years and ~100 sampling stations for three[JH1] long-lived rockfishes (Sebastes spp.) off the California Coast. In this case, the nonspatial model estimates implausible oscillatory dynamics on an annual time scale, while the spatial model estimates strong autocorrelation and is supported by model selection tools.
|Full Text URL:||http://www.esajournals.org/doi/abs/10.1890/14-0739.1|
|Theme:||Recovery and rebuilding of marine and coastal species|
Characterize the population biology of species, and develop and improve methods for predicting the status of populations.
Develop methods to use physiological, biological and behavioral information to predict population-level processes.