Study design and parameter estimability for spatial and temporal ecological models. Issue 2 (30th December 2016)

• Record Type: Journal Article
• Authors: Peacock, Stephanie Jane; Krkošek, Martin; Lewis, Mark Alun; Lele, Subhash
• Is Part Of: Ecology and evolution. Volume 7:Issue 2(2017:Feb.)
• Page Start: 762
• Page End: 770
• Publication Date: 2016-12-30
• Subjects: modeling -- spatial or time series -- statistics; Ecology -- Periodicals; Evolution -- Periodicals; 577.05
• DOI: 10.1002/ece3.2618
• Languages: English
• ISSNs: 2045-7758

Abstract

Abstract: The statistical tools available to ecologists are becoming increasingly sophisticated, allowing more complex, mechanistic models to be fit to ecological data. Such models have the potential to provide new insights into the processes underlying ecological patterns, but the inferences made are limited by the information in the data. Statistical nonestimability of model parameters due to insufficient information in the data is a problem too‐often ignored by ecologists employing complex models. Here, we show how a new statistical computing method called data cloning can be used to inform study design by assessing the estimability of parameters under different spatial and temporal scales of sampling. A case study of parasite transmission from farmed to wild salmon highlights that assessing the estimability of ecologically relevant parameters should be a key step when designing studies in which fitting complex mechanistic models is the end goal. Abstract : Ecological systems are complex and so are some of the models that describe them. Parameters in these models may not be estimable given available data, but few ecologists check parameter estimability. We use a case study to illustrate how data cloning, a new statistical tool, can be used in simulation studies to check parameter estimability and guide study design.