Modelling species abundance in a river by Negative Binomial hidden Markov models

L. Spezia, S.L. Cooksley, M.J. Brewer, D. Donnelly and A. Tree

Computational Statistics & Data Analysis, 2014, vol. 71, issue C, 599-614

Abstract: The investigation of species abundance in rivers involves data which are inherently sequential and unlikely to be fully independent. To take these characteristics into account, a Bayesian hierarchical model within the class of hidden Markov models is proposed to map the distribution of freshwater pearl mussels in the River Dee (Scotland). In order to model the overdispersed series of mussel counts, the conditional probability function of each observation, given the hidden state, is assumed to be Negative Binomial. Both the transition probabilities of the hidden Markov chain and the state-dependent means of the observed process depend on covariates obtained from a hydromorphological survey. Bayesian inference, model choice, and covariate selection based on Markov chain Monte Carlo algorithms are presented. The stochastic selection of the explanatory variables which are associated with a reduced chance of finding a local mussel population provides new evidence for the causes of the deterioration of a highly threatened species.

Keywords: Bayesian inference; Margaritifera margaritifera; Non-homogeneous Markov chain; River Dee; Variable selection (search for similar items in EconPapers)
Date: 2014
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0167947313003411
Full text for ScienceDirect subscribers only.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:71:y:2014:i:c:p:599-614

DOI: 10.1016/j.csda.2013.09.017

Access Statistics for this article

Computational Statistics & Data Analysis is currently edited by S.P. Azen

More articles in Computational Statistics & Data Analysis from Elsevier
Bibliographic data for series maintained by Catherine Liu ().