Adjusting age and stage distributions for misclassification errors.

Ecologists often use samples from the age or stage structure of a population to make inferences about population-level processes and to parameterize matrix models. Typically, researchers make a simplifying assumption that age and stage classes are determined without error, when in fact some level of misclassification often can be expected. If unaccounted for, misclassification will lead to overly optimistic levels of precision and can cause biased estimates of age or stage structure. Although several studies have used information from known-age individuals to quantify errors in age or stage distribution, the problem of estimating the age or stage structure in face of such errors has received comparably little attention. In this paper, we describe a general statistical framework for estimating the true stage distribution of a sample when misclassification rates can be estimated. The estimation process requires auxiliary information on misclassification rates, such as data from individuals of known age. We analyze age-structured harvest records from black bears in Pennsylvania to illustrate how incorporating misclassification errors leads to changes in point estimates and provides a measure of precision.

[1]  James H. Torrie,et al.  Principles and procedures of statistics: a biometrical approach (2nd ed) , 1980 .

[2]  Stuart A. Reeves,et al.  A simulation study of the implications of age-reading errors for stock assessment and management advice , 2003 .

[3]  David A. Fournier,et al.  A General Theory for Analyzing Catch at Age Data , 1982 .

[4]  P. Holgate,et al.  Matrix Population Models. , 1990 .

[5]  G. Seber.  The estimation of animal abundance and related parameters , 1974 .

[6]  J. Millspaugh,et al.  Wildlife Demography: Analysis of Sex, Age, and Count Data , 2005 .

[7]  R. Beamish,et al.  The Forgotten Requirement for Age Validation in Fisheries Biology , 1983 .

[8]  D. Garshelis,et al.  Reconstructing reproductive histories of black bears from the incremental layering in dental cementum , 1992 .

[9]  David R. Anderson,et al.  Model selection and multimodel inference : a practical information-theoretic approach , 2003 .

[10]  D. Heisey,et al.  The fitting of general force-of-infection models to wildlife disease prevalence data. , 2006, Ecology.

[11]  Russell B. Millar,et al.  Bayesian stock assessment using a state-space implementation of the delay difference model , 1999 .

[12]  P. Zager,et al.  Statistical models for population reconstruction using age-at-harvest data , 2002 .

[13]  B. Ballachey,et al.  Estimating survival rates with age-structure data , 1998 .

[14]  J. Nichols,et al.  Comparative demography of new world populations of thrushes (Turdus spp.): Comment , 2005 .

[15]  R. Green.  A new method for estimating the adult survival rate of the Corncrake Crex crex and comparison with estimates from ring-recovery and ring-recapture data , 2004 .

[16]  Graeme Caughley,et al.  Analysis of vertebrate populations , 1977 .

[17]  Jon T. Schnute,et al.  Statistical Models for the Analysis of Ageing Error , 1992 .

[18]  J. Gaillard,et al.  Tests of estimation of age from tooth wear on roe deer of known age: variation within and among populations , 1999 .

[19]  D. Diefenbach,et al.  Analysis of error from cementum-annuli age estimates of known-age Pennsylvania black bears , 1998 .

[20]  A method for ageing adult Corncrakes Crex crex , 2001 .

[21]  J. Laake,et al.  SPATIO-TEMPORAL AND DEMOGRAPHIC VARIATION IN THE HARVEST OF BLACK BEARS: IMPLICATIONS FOR POPULATION ESTIMATION , 2004 .

[22]  R. Ricklefs.  COMPARATIVE DEMOGRAPHY OF NEW WORLD POPULATIONS OF THRUSHES (TURDUS SPP.) , 1997 .

[23]  Nicholas J. Gotelli,et al.  A Primer of Ecology , 1995 .