Estimability of density dependence in models of time series data

Research output: Contribution to journalArticle

Abstract

Estimation of density dependence from time series data on population abundance is hampered in the presence of observation or measurement errors. Fitting state-space models has been proposed as a solution that reduces the bias in estimates of density dependence caused by ignoring observation errors. While this is often true, I show that, for specific parameter values, there are identifiability issues in the linear state-space model when the strength of density dependence and the observation and process error variances are all unknown. Using simulation to explore properties of the estimators, I illustrate that, unless assumptions are imposed on the process or observation error variances, the variance of the estimator of density dependence varies critically with the strength of the density dependence. Under compensatory dynamics, the stronger the density dependence the more difficult it is to estimate in the presence of observation errors. The identifiability issues disappear when density dependence is estimated from the state-space model with the observation error variance known to the correct value. Direct estimates of observation variance in abundance censuses could therefore prove helpful in estimating density dependence but care needs to be taken to assess the uncertainty in variance estimates.

Details

Authors
  • Jonas Knape
Organisations
Research areas and keywords

Subject classification (UKÄ) – MANDATORY

  • Ecology

Keywords

  • time series analysis, density dependence, state-space models
Original languageEnglish
Pages (from-to)2994-3000
JournalEcology
Volume89
Issue number11
Publication statusPublished - 2008
Publication categoryResearch
Peer-reviewedYes

Bibliographic note

The information about affiliations in this record was updated in December 2015. The record was previously connected to the following departments: Theoretical ecology (Closed 2011) (011006011)