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  • 1
    In: Transboundary and Emerging Diseases, Hindawi Limited, Vol. 2023 ( 2023-8-25), p. 1-12
    Abstract: An essential part of any disease containment and eradication policy is the implementation of restricted zones, but determining the appropriate size of these zones can be challenging for managers. We designed a new method, based on animal movement, to help assess how large restricted zones should be after a spontaneous outbreak to successfully control infectious diseases in wildlife. Our approach uses first-passage time (FPT) analysis and Cox proportional hazard (CPH) models to calculate and compare the risk of an animal leaving different-sized areas. We illustrate our approach using the example of the African swine fever (ASF) virus and its wild pig reservoir host species, the wild boar (Sus scrofa), and we investigate the feasibility of applying this method to other systems. Using GPS data from 57 wild boar living in the Hainich National Park, Germany, we calculate the time spent by each individual in areas of different sizes using FPT analysis. We apply CPH models on the derived data to compare the risk of leaving areas of different sizes and to assess the effects of season and the sex of the wild boar on the risk of leaving. We conduct survival analyses to estimate the risk of leaving an area over time. Our results indicate that the risk of leaving an area decreases exponentially by 10% for each 100 m increase in radius size so that the differences were more pronounced for small sizes. Furthermore, the probability of leaving increases exponentially with time. Wild boar had a similar risk of leaving an area of a given size throughout the year, except in spring and winter, when females had a much lower risk of leaving. Our findings are in agreement with the literature on wild boar movement, further validating our method, and repeated analyses with location data resampled at different rates gave similar results. Our results may be applicable only to our study area, but they demonstrate the applicability of the proposed method to any ecosystem where wild boar populations are likely to be infected with ASF and where restricted zones should be established accordingly. The outlined approach relies solely on the analysis of movement data and provides a useful tool to determine the optimal size of restricted zones. It can also be applied to future outbreaks of other diseases.
    Type of Medium: Online Resource
    ISSN: 1865-1682 , 1865-1674
    Language: English
    Publisher: Hindawi Limited
    Publication Date: 2023
    detail.hit.zdb_id: 2414822-2
    SSG: 22
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  • 2
    Online Resource
    Online Resource
    Springer Science and Business Media LLC ; 2020
    In:  European Journal of Wildlife Research Vol. 66, No. 4 ( 2020-08)
    In: European Journal of Wildlife Research, Springer Science and Business Media LLC, Vol. 66, No. 4 ( 2020-08)
    Type of Medium: Online Resource
    ISSN: 1612-4642 , 1439-0574
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2020
    detail.hit.zdb_id: 2140087-8
    SSG: 12
    SSG: 23
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  • 3
    In: The Journal of Wildlife Management, Wiley, Vol. 87, No. 3 ( 2023-04)
    Abstract: Non‐invasive genetic sampling (NGS) methods are becoming a mainstay in wildlife monitoring and can be used with spatial capture‐recapture (SCR) methods to estimate population density. Yet SCR based on NGS remains relatively underused for ungulate population monitoring, despite the importance of robust density estimates for this ecologically and economically important group of species. This may be in part attributed to biological characteristics of ungulate species and data collection methods that lead to violations of SCR model assumptions. We conducted a simulation study to evaluate the robustness of SCR methods to spatially heterogeneous density (i.e., configuration of individuals into groups of variable sizes and composition), individual heterogeneity in space‐use patterns, and adaptive sampling (i.e., variation in detectability across space that correlates with density). We evaluated each violation separately and in combination. We parameterized our simulations based on published information and preliminary analyses of NGS data sets of 3 ungulate species: chamois ( Rupicapra rupicapra ), red deer ( Cervus elaphus ), and wild boar ( Sus scrofa ). While SCR estimates were robust to grouping and adaptive sampling, abundance estimates could be negatively biased (up to 10% in our simulations) in the presence of unaccounted individual heterogeneity in space use. The degree to which abundance estimates were underestimated depended mostly on the amount of variation in space use and detectability among age classes. This bias was also accompanied by a reduction in precision and coverage probability of the SCR estimators. We discuss the implications of these findings, possible approaches to identify problematic violations in available data sets (goodness‐of‐fit tests), and potential further developments of SCR models to ensure reliable abundance estimates for ungulate populations from NGS data.
    Type of Medium: Online Resource
    ISSN: 0022-541X , 1937-2817
    URL: Issue
    Language: English
    Publisher: Wiley
    Publication Date: 2023
    detail.hit.zdb_id: 2066663-9
    SSG: 12
    SSG: 23
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