Study design and parameter estimability for spatial and temporal ecological models

Submitted May 11, 2016 to Methods in Ecology and Evolution Revised August 9, 2016 with increased number of clones in the Workspaces folder

Stephanie J Peacock (stephanie.j.peacock at gmail.com), Martin Krkosek, Mark Lewis, and Subhash Lele

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 non-estimability 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.

Keywords: modelling, spatial or time-series, statistics

Contents of repository

This repository contains three folders with the following files:

1. Code

• figures.R: loads .RData workspace containing results from the Workspaces subfolder and creates the figures for the main text of the paper
• model.R: the model function for JAGS, called by the SeaLice_dclone_xxx.R files
• sim_model.R: version of model.R in R syntax that takes parameters and gives expected number of lice per fish
• SeaLice_dclone_lessSpread.R: simulates data for the less-spread data scenario and fits the model using data cloning
• SeaLice_dclone_moreSpread.R: simulates data for the more-spread data scenario and fits the model using data cloning
• SeaLice_dclone_original.R: fits the model to the original dataset using data cloning

2. Data

• Leps.txt contains the sea louse data
• Summary.txt contains the site data, including distance along migration route

3. Supplement: contains code, workspaces and figures for the supplemental material looking at 4 different prior assumptions.