title: Study design and parameter estimability for spatial and temporal ecological models.
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.
authors.affiliation: Ecology and Evolutionary Biology University of Toronto Toronto ON Canada; Biological Sciences University of Alberta Edmonton AB Canada; Present address: Biological Sciences University of Calgary Calgary AB Canada.
authors.forename: Stephanie Jane
authors.initials: SJ
authors.lastname: Peacock
authors.affiliation: Ecology and Evolutionary Biology University of Toronto Toronto ON Canada; Salmon Coast Field Station Simoom Sound BC Canada.
authors.forename: Martin
authors.initials: M
authors.lastname: Krkošek
authors.affiliation: Biological Sciences University of Alberta Edmonton AB Canada; Mathematical and Statistical Sciences University of Alberta Edmonton AB Canada.
authors.forename: Mark Alun
authors.initials: MA
authors.lastname: Lewis
authors.affiliation: Mathematical and Statistical Sciences University of Alberta Edmonton AB Canada.
authors.forename: Subhash
authors.initials: S
authors.lastname: Lele
keywords: modeling; spatial or time series; statistics