strandCet: R package for estimating natural and non-natural mortality-at-age of cetaceans from age-structured strandings.

作者: Camilo Saavedra

DOI: 10.7717/PEERJ.5768

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摘要: Mortality is one of the most important parameters for study population dynamics. One main sources information to calculate mortality cetaceans arises from observed age-structure stranded animals. A method based on an adaptation a Heligman-Pollard model proposed. freely accessible package functions (strandCet) has been created apply this in statistical software R. Total, natural, and anthropogenic mortality-at-age estimated using only data whose age known. Bayesian melding estimation with Incremental Mixture Importance Sampling used fitting model. This characteristic, which accounts uncertainty, further eases credible intervals. The also includes perform life tables, Siler models total Leslie matrices derive projections. Estimated mortalities can be tested under different scenarios. Population as growth, net production or generation time derived strandCet R provides new analytical framework assess cetacean populations explore consequences management decisions stranding-derived data.

参考文章(26)
David Sharrow, Samuel J. Clark, Mark Collinson, Kathleen Kahn, Stephen Tollman, The Age Pattern of Increases in Mortality Affected by HIV: Bayesian Fit of the Heligman-Pollard Model to Data from the Agincourt HDSS Field Site in Rural Northeast South Africa. Demographic Research. ,vol. 29, pp. 1039- 1096 ,(2013) , 10.4054/DEMRES.2013.29.39
P. H. LESLIE, On the Use of Matrices in Certain Population Mathematics Biometrika. ,vol. 33, pp. 183- 212 ,(1945) , 10.1093/BIOMET/33.3.183
Graeme Caughley, Mortality Patterns in Mammals Ecology. ,vol. 47, pp. 906- 918 ,(1966) , 10.2307/1935638
Petros Dellaportas, Adrian F. M. Smith, Photis Stavropoulos, Bayesian analysis of mortality data Journal of The Royal Statistical Society Series A-statistics in Society. ,vol. 164, pp. 275- 291 ,(2001) , 10.1111/1467-985X.00202
Sylvia Tippmann, Programming tools: Adventures with R Nature. ,vol. 517, pp. 109- 110 ,(2015) , 10.1038/517109A
David Poole, Adrian E. Raftery, Inference for Deterministic Simulation Models: The Bayesian Melding Approach Journal of the American Statistical Association. ,vol. 95, pp. 1244- 1255 ,(2000) , 10.1080/01621459.2000.10474324