Improving estimates of abundance by aggregating sparse capture-recapture data

Andrea R. Litt, Robert J. Steidl

Research output: Contribution to journalArticlepeer-review

11 Scopus citations


Inferences about abundance often are based on unadjusted counts of individuals observed, in part, because of the large amount of data required to generate reliable estimates of abundance. Where capture-recapture data are sparse, aggregating data across multiple sample elements by pooling species, locations, and sampling periods increases the information available for modeling detection probability, a necessary step for estimating abundance reliably. The process of aggregating sample elements involves balancing trade-offs related to the number of aggregated elements; although larger aggregates increase the amount of information available for estimation, they often require more complex models. We describe a heuristic approach for aggregating data for studies with multiple sample elements, use simulated data to evaluate the efficacy of aggregation, and illustrate the approach using data from a field study. Aggregating data systematically improved reliability of model selection and increased accuracy of abundance estimates while still providing estimates of abundance for each original sample unit, an important benefit necessary to maintain the design and sampling structure of a study. Within the framework of capture-recapture sampling, aggregating data improves estimates of abundance and increases the reliability of subsequent inferences made from sparse data. Additional tables and datasets may be found in the online supplements.

Original languageEnglish (US)
Pages (from-to)228-247
Number of pages20
JournalJournal of Agricultural, Biological, and Environmental Statistics
Issue number2
StatePublished - Jun 2010


  • Abundance estimation
  • Data aggregation
  • Mark-recapture
  • Population parameters
  • Program CAPTURE
  • Program MARK

ASJC Scopus subject areas

  • Statistics and Probability
  • Agricultural and Biological Sciences (miscellaneous)
  • Environmental Science(all)
  • Agricultural and Biological Sciences(all)
  • Statistics, Probability and Uncertainty
  • Applied Mathematics


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