Combining prior and post-release data while accounting for dispersal to improve predictions for reintroduction populations

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dc.citation.volumeEarly View
dc.contributor.authorArmstrong DP
dc.contributor.authorStone ZL
dc.contributor.authorParlato EH
dc.contributor.authorNgametua G
dc.contributor.authorKing E
dc.contributor.authorGibson S
dc.contributor.authorZieltjes S
dc.contributor.authorParker KA
dc.contributor.editorEwen J
dc.contributor.editorCanessa S
dc.date.accessioned2024-07-17T01:36:29Z
dc.date.available2024-07-17T01:36:29Z
dc.date.issued2024-05-24
dc.description.abstractAttempts to reintroduce species to managed areas may be compromised by dispersal into the surrounding landscape. Therefore, decisions regarding the selection and ongoing management of reintroduction areas require predicting dispersal as well as the survival and reproduction rates of the species to be reintroduced. Dispersal can potentially be measured directly by tracking animals, but this is often impractical. However, dispersal can also be inferred from re-sighting surveys done within reintroduction areas if such data are available from multiple areas with varying connectivity to the surrounding landscape, allowing apparent survival and recruitment to be modelled as a function of connectivity metrics. Here, we show how data from 10 previous reintroductions of a New Zealand passerine, the toutouwai (Petroica longipes), were used to predict population dynamics at a predator-controlled reintroduction area with high connectivity, and predictions then updated using post-release data. Bayesian hierarchical modelling of the previous data produced prior distributions for productivity, adult survival and apparent juvenile survival rates that accounted for random variation among areas as well as rat density and connectivity. The modelling of apparent juvenile survival as a function of connectivity allowed it to be partitioned into estimates of survival and fidelity. Bayesian updating based on post-release data produced posterior distributions for parameters that were consistent with the priors but much more precise. The prior data also allowed the recruitment rate estimated in the new area to be partitioned into separate estimates for productivity, juvenile survival and juvenile fidelity. Consequently, it was possible to not only estimate population growth under current management, but also predict the consequences of reducing the scale or intensity of predator control, facilitating adaptive management. The updated model could then be used to predict population growth as a function of the connectivity and predator control regime at proposed reintroduction areas while accounting for random variation among areas.
dc.description.confidentialfalse
dc.edition.edition2024
dc.identifier.citationArmstrong DP, Stone ZL, Parlato EH, Ngametua G, King E, Gibson S, Zieltjes S, Parker KA. (2024). Combining prior and post-release data while accounting for dispersal to improve predictions for reintroduction populations. Animal Conservation. Early View.
dc.identifier.doi10.1111/acv.12949
dc.identifier.eissn1469-1795
dc.identifier.elements-typejournal-article
dc.identifier.issn1367-9430
dc.identifier.urihttps://mro.massey.ac.nz/handle/10179/70207
dc.languageEnglish
dc.publisherJohn Wiley & Sons, Inc. on behalf of Zoological Society of London.
dc.publisher.urihttps://zslpublications.onlinelibrary.wiley.com/doi/10.1111/acv.12949
dc.relation.isPartOfAnimal Conservation
dc.rights(c) 2024 The Author/s
dc.rightsCC BY 4.0
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectAdaptive management
dc.subjectBayesian updating
dc.subjectdispersal
dc.subjectpopulation modelling
dc.subjectpopulation viability analysis
dc.subjectreintroduction
dc.subjecttranslocation
dc.titleCombining prior and post-release data while accounting for dispersal to improve predictions for reintroduction populations
dc.typeJournal article
pubs.elements-id489152
pubs.organisational-groupOther
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