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Estimability of density dependence in models of time series data

Knape, Jonas LU (2008) In Ecology 89(11). p.2994-3000
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... (More)
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. (Less)
Please use this url to cite or link to this publication:
author
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
time series analysis, density dependence, state-space models
in
Ecology
volume
89
issue
11
pages
2994 - 3000
publisher
Ecological Society of America
external identifiers
  • wos:000261053500004
  • scopus:63849211706
ISSN
0012-9658
DOI
10.1890/08-0071.1
language
English
LU publication?
yes
id
23bbeab8-16cd-49a3-9050-c77e978c6f06 (old id 1307972)
date added to LUP
2009-03-19 13:01:47
date last changed
2017-10-29 03:56:38
@article{23bbeab8-16cd-49a3-9050-c77e978c6f06,
  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.},
  author       = {Knape, Jonas},
  issn         = {0012-9658},
  keyword      = {time series analysis,density dependence,state-space models},
  language     = {eng},
  number       = {11},
  pages        = {2994--3000},
  publisher    = {Ecological Society of America},
  series       = {Ecology},
  title        = {Estimability of density dependence in models of time series data},
  url          = {http://dx.doi.org/10.1890/08-0071.1},
  volume       = {89},
  year         = {2008},
}