Estimating age-specific hazards from wildlife telemetry data

We illustrate 2 techniques for estimating age-specific hazards with wildlife telemetry data: Siler’s (Ecology 60:750–757, 1979) competing risk model fit using maximum likelihood and a penalized likelihood estimate that only assumes the hazard varies smoothly with age. In most telemetry studies, anim...

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Bibliographic Details
Published in:Environmental and ecological statistics 2011-06, Vol.18 (2), p.209-222
Main Authors: Fieberg, John, DelGiudice, Glenn D.
Format: Article
Language:English
Subjects:
Age
Animals
Biomedical and Life Sciences
Chemistry and Earth Sciences
Computer Science
Datasets
Deer
Ecology
Emigration
Environmental monitoring
Estimates
Females
Health Sciences
Life Sciences
Math. Appl. in Environmental Science
Medicine
Natural resources
Neonates
Odocoileus virginianus
Physics
Predation
Random variables
Statistics for Engineering
Statistics for Life Sciences
Studies
Telemetry
Theoretical Ecology/Statistics
Wildlife
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Summary:We illustrate 2 techniques for estimating age-specific hazards with wildlife telemetry data: Siler’s (Ecology 60:750–757, 1979) competing risk model fit using maximum likelihood and a penalized likelihood estimate that only assumes the hazard varies smoothly with age. In most telemetry studies, animals enter at different points in time (and at different ages), leading to data that are left-truncated. In addition, death times may only be known to occur within an interval of time (interval-censoring). Observations may also be right-censored (e.g., due to the end of the study, radio-collar failure, or emigration from the study area). It is important to consider the observation process, since the contribution of each individual’s data to the likelihood will depend on whether data are left-truncated or censored. We estimate age-specific hazards using telemetry data collected in two Phases during a 13-year study of white-tailed deer ( Odocoileus virginianus ) in northern Minnesota. The hazards estimated from the two methods were similar for the full data set that included 302 adults and 76 neonates (followed since or shortly after birth). However, estimated hazards for early-aged individuals differed considerably for subsets of the data that did not include neonates. We discuss the advantages and disadvantages of these two modeling approaches and also compare the estimators using a short simulation study.
ISSN:1352-8505
1573-3009
DOI:10.1007/s10651-009-0128-x