Bayesian Hierarchical Multi-Population Multistate Jolly–Seber Models With Covariates: Application to the Pallid Sturgeon Population Assessment Program

ABSTRACT

Estimating abundance for multiple populations is of fundamental importance to many ecological monitoring programs. Equally important is quantifying the spatial distribution and characterizing the migratory behavior of target populations within the study domain. To achieve these goals, we propose a Bayesian hierarchical multi-population multistate Jolly–Seber model that incorporates covariates. The model is proposed using a state-space framework and has several distinct advantages. First, multiple populations within the same study area can be modeled simultaneously. As a consequence, it is possible to achieve improved parameter estimation by “borrowing strength” across different populations. In many cases, such as our motivating example involving endangered species, this borrowing of strength is crucial, as there is relatively less information for one of the populations under consideration. Second, in addition to accommodating covariate information, we develop a computationally efficient Markov chain Monte Carlo algorithm that requires no tuning. Importantly, the model we propose allows us to draw inference on each population as well as on multiple populations simultaneously. Finally, we demonstrate the effectiveness of our method through a motivating example of estimating the spatial distribution and migration of hatchery and wild populations of the endangered pallid sturgeon (Scaphirhynchus albus), using data from the Pallid Sturgeon Population Assessment Program on the Lower Missouri River. Supplementary materials for this article are available online.

KEYWORDS:

• Capture-recapture
• Data augmentation
• Endangered species
• Lower Missouri River
• Pallid sturgeon
• State-space

Supplementary Materials

The supplemental Appendix contains full conditional distributions, a proof of Lemma 1, a description of the sampling algorithm for latent variables, and a definition of the log-likelihood function for the complete data.

Acknowledgments

The authors thank the editor, associate editor, and three reviewers for providing comments that helped strengthen this manuscript. The authors also thank Mark Wildhaber and Janice Albers of the United States Geological Survey (USGS) for their scientific input (including study design and model development) and assistance with data issues on earlier versions of this manuscript. Additionally, the authors thank Tim Welker of the United States Army Corps of Engineers (USACE) for his assistance surrounding data issues.

Funding

Funding for the methodological research was partially provided through the U.S. National Science Foundation (NSF) and the U.S. Census Bureau under NSF grant SES-1132031, funded through the NSF-Census Research Network (NCRN) program, and through the USGS Science Support Program, and the U.S. Army Corps of Engineers.