Browsing by Author "Canessa S"

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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.
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    Simulating Demography, Monitoring, and Management Decisions to Evaluate Adaptive Management Strategies for Endangered Species
    (Wiley, 2025-04-02) Canessa S; Converse SJ; Adams L; Armstrong DP; Makan T; McCready M; Parker KA; Parlato EH; Sipe HA; Ewen JG
    Adaptive management (AM) remains underused in conservation, partly because optimization-based approaches require real-world problems to be substantially simplified. We present an approach to AM based in management strategy evaluation, a method used largely in fisheries. Managers define objectives and nominate alternative adaptive strategies, whose future performance is simulated by integrating ecological, learning and decision processes. We applied this approach to conservation of hihi (Notiomystis cincta) across Aotearoa-New Zealand. For multiple extant and prospective hihi populations, we jointly simulated demographic trends, monitoring, estimation, and decisions including translocations and supplementary feeding. Results confirmed that food supplementation assisted recovery, but was more intensive and expensive. Over 20 years, actively pursuing learning, for example by removing food from populations, provided little benefit. Recovery group members supported continuing current management or increasing priority on existing populations before reintroducing new populations. Our simulation-based approach can complement formal optimization-based approaches and improve AM uptake, particularly for programs involving many complex and coordinated decisions.
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    Using citizen data to understand earthquake impacts: Aotearoa New Zealand’s earthquake Felt Reports
    (Massey University, 2021-12) Goded T; Tan ML; Becker JS; Horspool N; Canessa S; Huso R; Jonathan H; Johnston D
    Aotearoa New Zealand's national seismic network, GeoNet, administers Felt Reports, including the Felt RAPID and Felt Detailed databases, which are being collected at present. NZ has a long tradition of using earthquake Felt Reports provided by the public to analyse the damage caused by moderate to large earthquakes. From traditional paper-based Felt Reports to current online reports (using the GeoNet website or a mobile app), researchers have been using such data to obtain a geographical distribution of the damage caused by an earthquake and to assess what actions people take during shaking. Felt Reports include questions on people's reactions, indoor and outdoor effects of earthquake shaking, building damage, and tsunami evacuation. The database of long online Felt Reports (Felt Classic between 2004 and 2016 and Felt Detailed from 2016 to the present) comprises over 930,000 reports from more than 30,000 earthquakes. Current research being carried out using this data includes: 1) updating of the NZ Ground Motion to Intensity Conversion Equation and Intensity Prediction Equation, 2) understanding human behaviour for earthquakes and related hazards such as tsunami, 3) developing a predictive model of human behaviour in earthquakes to estimate injuries and fatalities, and 4) improving public education. This paper summarises the history of NZ earthquake Felt Reports as well as the research currently being carried out using this data. Finally, we discuss how citizen science helps in the understanding of earthquake impacts and contributes to the aim of improving Aotearoa New Zealand's resilience to future events.