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Right-Censored Mixed Poisson Count Models with Detection Times

Abstract

Conducting complete surveys on flora and fauna species within a sampling unit (or quadrat) of interest can be costly, particularly if there are several species in high abundance. A commonly used approach, which aims to reduce time and costs, consists of occurrence data reflecting the status of occupancy of a species– e.g., rather than counting every individual, the survey is stopped as soon as one individual has been observed. Although this approach is cheaper to conduct than a complete survey, some statistical efficiency in model estimators is lost. In this study, we consider occurrence data as a special case of right-censored count data where the collecting process stops until some set threshold on the number of observed individuals is reached. We then propose a new class of regression estimation models for right-censored count data that incorporate information from detection times (or catch effort) collected during sampling. First, we show that incorporating ancillary information in the form of detection times can greatly improve statistical efficiency over, say, right-censored Poisson or negative binomial models. Furthermore, the proposed models retain the same cost-effectiveness as censored-type models. We also consider zero-truncated and zero-inflated models for a variety of count data types. These models can be extended to a more general class of mixed Poisson models. We investigate model performance on simulated data and give two examples consisting of plant abundance data and bat acoustics data.

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Acknowledgements

The authors would like to thank the Handling Editor and two anonymous referees for their valuable comments. The authors would also like to thank R. Pretty for proofreading the manuscript. This work was supported by the Ministry of Science & Technology of Taiwan. The BCI forest dynamics research project was founded by S.P. Hubbell and R.B. Foster and is now managed by R. Condit, S. Lao, and R. Perez under the Center for Tropical Forest Science and the Smithsonian Tropical Research in Panama. Numerous organizations have provided funding, principally the U.S. National Science Foundation, and hundreds of field workers have contributed to this project. The bat data collection was conducted in collaboration with B.C. McLaughlin and W.F. Frick and was funded by the National Institute for Food and Agriculture, U.S. Department of Agriculture, McIntire Stennis project under 1006829.

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Correspondence to Jakub Stoklosa.

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Hwang, WH., Blakey, R.V. & Stoklosa, J. Right-Censored Mixed Poisson Count Models with Detection Times. JABES 25, 112–132 (2020). https://doi.org/10.1007/s13253-019-00381-3

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Keywords

  • Aggregation index
  • Negative binomial distribution
  • Presence–absence data
  • Zero-truncated and zero-inflated models