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Where to Now?

  • George A. F. SeberEmail author
  • Matthew R. Schofield
Chapter
Part of the Statistics for Biology and Health book series (SBH)

Abstract

This short chapter does some crystal ball gazing about the future of capture–recapture. Clearly, computer software will continue to develop along with increasingly sophisticated computers. As models get more complicated, the number of unknown parameters goes up steeply. In contrast, even if capture–recapture, resighting, and dead recovery data are available, the amount of data is still limited, even if packaged in different ways. Two methods for parameter reduction are using covariates usually via logistic transformations of the parameters, and using random effects with Bayesian models. What is particularly needed is more concentration on experimental design and on applying various types of residuals using, for example, box plots. Another approach is to combine capture–recapture with other sampling methods such as adaptive sampling, count methods, aerial censusing, distance sampling, and using catch–effort data, usually referred to as integrated data analysis. Also, different types of tags can be used together such as DNA and photo identification.

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of StatisticsUniversity of AucklandAucklandNew Zealand
  2. 2.Department of Mathematics and StatisticsUniversity of OtagoDunedinNew Zealand

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