Journal Articles

Permanent URI for this collectionhttps://mro.massey.ac.nz/handle/10179/7915

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    Towards an Abundance Estimate for New Zealand Fur Seal in New Zealand
    (John Wiley and Sons Ltd, Hoboken, USA, 2025-04) Hall A; Chilvers BL; Weir JS; Burdett H
    A lack of population abundance and trajectory data is a conservation and management issue relevant to numerous pinniped species, many of which are exposed to a variety of threats. New Zealand fur seal (Arctocephalus forsteri; ‘NZFS’) populations in different parts of New Zealand have experienced both substantial increases and decreases to their abundance over the last 50 years, since the last nationwide census. Here, existing data and stage-structured matrix modelling were used to provide a contemporary nationwide estimate of NZFS abundance. Graphical depictions demonstrate the spatial inconsistencies in NZFS monitoring in New Zealand through time. A minimum population estimate of 131,338–168,269 NZFS was calculated by combining the most recently available pup production data from around New Zealand and using established multipliers. A second estimate of 181,646–239,473 NZFS was calculated using stage-structured matrix models to project contemporary abundance. Inconsistent NZFS population monitoring and sparse vital rate data for New Zealand's NZFS limited this study, and both population ranges are likely underestimates. However, they still represent substantial increases on the most cited nationwide abundance figure (100,000 NZFS). From these findings, we suggest that a regularised program of monitoring is adopted for New Zealand's NZFS, as has been achieved for similar species in other countries. This would both aid in the management of NZFS in the face of emerging risks, such as H5N1 avian influenza, and enable their use as a sentinel for the health of New Zealand's marine ecosystems.
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    Combining prior and post-release data while accounting for dispersal to improve predictions for reintroduction populations
    (John Wiley & Sons, Inc. on behalf of Zoological Society of London., 2024-05-24) Armstrong DP; Stone ZL; Parlato EH; Ngametua G; King E; Gibson S; Zieltjes S; Parker KA; Ewen J; Canessa S
    Attempts 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.