Multistate capture–recapture models for irregularly sampled data

Mews S, Langrock R, King R, Quick N (2022)
Annals of Applied Statistics 16(2): 982-998.

Zeitschriftenaufsatz | Veröffentlicht | Englisch
 
Download
Es wurden keine Dateien hochgeladen. Nur Publikationsnachweis!
Autor*in
Mews, SinaUniBi; Langrock, RolandUniBi; King, Ruth; Quick, Nicola
Abstract / Bemerkung
Multistate capture-recapture data comprise individual-specific sighting histories, together with information on individuals' states related, for example, to breeding status, infection level, or geographical location. Such data are often analysed using the Arnason-Schwarz model, where transitions between states are modelled using a discrete-time Markov chain, making the model most easily applicable to regular time series. When time intervals between capture occasions are not of equal length, more complex time-dependent constructions may be required, increasing the number of parameters to estimate, decreasing interpretability, and potentially leading to reduced precision. Here we develop a multi-state model based on a state process operating in continuous time, which can be regarded as an analogue of the discrete-time Arnason-Schwarz model for irregularly sampled data. Statistical inference is carried out by regarding the capture-recapture data as realisations from a continuous-time hidden Markov model, which allows the associated efficient algorithms to be used for maximum likelihood estimation and state decoding. To illustrate the feasibility of the modelling framework, we use a long-term survey of bottlenose dolphins where capture occasions are not regularly spaced through time. Here, we are particularly interested in seasonal effects on the movement rates of the dolphins along the Scottish east coast. The results reveal seasonal movement patterns between two core areas of their range, providing information that will inform conservation management.
Stichworte
Arnason-Schwarz model; continuous-time Markov chain; hidden Markov; model; maximum likelihood
Erscheinungsjahr
2022
Zeitschriftentitel
Annals of Applied Statistics
Band
16
Ausgabe
2
Seite(n)
982-998
ISSN
1932-6157
eISSN
1941-7330
Page URI
https://pub.uni-bielefeld.de/record/2964341

Zitieren

Mews S, Langrock R, King R, Quick N. Multistate capture–recapture models for irregularly sampled data. Annals of Applied Statistics. 2022;16(2):982-998.
Mews, S., Langrock, R., King, R., & Quick, N. (2022). Multistate capture–recapture models for irregularly sampled data. Annals of Applied Statistics, 16(2), 982-998. https://doi.org/10.1214/21-AOAS1528
Mews, S., Langrock, R., King, R., and Quick, N. (2022). Multistate capture–recapture models for irregularly sampled data. Annals of Applied Statistics 16, 982-998.
Mews, S., et al., 2022. Multistate capture–recapture models for irregularly sampled data. Annals of Applied Statistics, 16(2), p 982-998.
S. Mews, et al., “Multistate capture–recapture models for irregularly sampled data”, Annals of Applied Statistics, vol. 16, 2022, pp. 982-998.
Mews, S., Langrock, R., King, R., Quick, N.: Multistate capture–recapture models for irregularly sampled data. Annals of Applied Statistics. 16, 982-998 (2022).
Mews, Sina, Langrock, Roland, King, Ruth, and Quick, Nicola. “Multistate capture–recapture models for irregularly sampled data”. Annals of Applied Statistics 16.2 (2022): 982-998.

Export

Markieren/ Markierung löschen
Markierte Publikationen

Open Data PUB

Web of Science

Dieser Datensatz im Web of Science®

Suchen in

Google Scholar