Estimability of density dependence in models of time series data

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Estimability of density dependence in models of time series data. / Knape, Jonas.

In: Ecology, Vol. 89, No. 11, 2008, p. 2994-3000.

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Knape, Jonas. / Estimability of density dependence in models of time series data. In: Ecology. 2008 ; Vol. 89, No. 11. pp. 2994-3000.

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TY - JOUR

T1 - Estimability of density dependence in models of time series data

AU - Knape, Jonas

N1 - 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)

PY - 2008

Y1 - 2008

N2 - 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.

AB - 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.

KW - time series analysis

KW - density dependence

KW - state-space models

U2 - 10.1890/08-0071.1

DO - 10.1890/08-0071.1

M3 - Article

VL - 89

SP - 2994

EP - 3000

JO - Ecology

JF - Ecology

SN - 0012-9658

IS - 11

ER -