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Detection parameters for managing invasive rats in urban environments

Effective mitigation of the impacts of invasive ship rats (Rattus rattus) requires a good understanding of their ecology, but this knowledge is very sparse for urban and peri-urban areas. We radiomarked ship rats in Wellington, New Zealand, to estimate detection parameters (σ, ε(0), θ, and g(0)) tha...

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Autores principales: Mackenzie, Henry R., Latham, M. Cecilia, Anderson, Dean P., Hartley, Stephen, Norbury, Grant L., Latham, A. David M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9530159/
https://www.ncbi.nlm.nih.gov/pubmed/36192476
http://dx.doi.org/10.1038/s41598-022-20677-8
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author Mackenzie, Henry R.
Latham, M. Cecilia
Anderson, Dean P.
Hartley, Stephen
Norbury, Grant L.
Latham, A. David M.
author_facet Mackenzie, Henry R.
Latham, M. Cecilia
Anderson, Dean P.
Hartley, Stephen
Norbury, Grant L.
Latham, A. David M.
author_sort Mackenzie, Henry R.
collection PubMed
description Effective mitigation of the impacts of invasive ship rats (Rattus rattus) requires a good understanding of their ecology, but this knowledge is very sparse for urban and peri-urban areas. We radiomarked ship rats in Wellington, New Zealand, to estimate detection parameters (σ, ε(0), θ, and g(0)) that describe the process of an animal encountering a device (bait stations, chew cards and WaxTags) from a distance, and then approaching it and deciding whether to interact with it. We used this information in simulation models to estimate optimal device spacing for eradicating ship rats from Wellington, and for confirming eradication. Mean σ was 25.37 m (SD = 11.63), which equates to a circular home range of 1.21 ha. The mean nightly probability of an individual encountering a device at its home range center (ε(0)) was 0.38 (SD = 0.11), whereas the probability of interacting with the encountered device (θ) was 0.34 (SD = 0.12). The derived mean nightly probability of an individual interacting with a device at its home range center (g(0)) was 0.13 (SD = 0.08). Importantly, σ and g(0) are intrinsically linked through a negative relationship, thus g(0) should be derived from σ using a predictive model including individual variability. Simulations using this approach showed that bait stations deployed for about 500 days using a 25 m × 25 m grid consistently achieved eradication, and that a surveillance network of 3.25 chew cards ha(−1) or 3.75 WaxTags ha(−1) active for 14 nights would be required to confidently declare eradication. This density could be halved if the surveillance network was deployed for 28 nights or if the prior confidence in eradication was high (0.85). These recommendations take no account of differences in detection parameters between habitats. Therefore, if surveillance suggests that individuals are not encountering devices in certain habitats, device density should be adaptively revised. This approach applies to initiatives globally that aim to optimise eradication with limited funding.
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spelling pubmed-95301592022-10-05 Detection parameters for managing invasive rats in urban environments Mackenzie, Henry R. Latham, M. Cecilia Anderson, Dean P. Hartley, Stephen Norbury, Grant L. Latham, A. David M. Sci Rep Article Effective mitigation of the impacts of invasive ship rats (Rattus rattus) requires a good understanding of their ecology, but this knowledge is very sparse for urban and peri-urban areas. We radiomarked ship rats in Wellington, New Zealand, to estimate detection parameters (σ, ε(0), θ, and g(0)) that describe the process of an animal encountering a device (bait stations, chew cards and WaxTags) from a distance, and then approaching it and deciding whether to interact with it. We used this information in simulation models to estimate optimal device spacing for eradicating ship rats from Wellington, and for confirming eradication. Mean σ was 25.37 m (SD = 11.63), which equates to a circular home range of 1.21 ha. The mean nightly probability of an individual encountering a device at its home range center (ε(0)) was 0.38 (SD = 0.11), whereas the probability of interacting with the encountered device (θ) was 0.34 (SD = 0.12). The derived mean nightly probability of an individual interacting with a device at its home range center (g(0)) was 0.13 (SD = 0.08). Importantly, σ and g(0) are intrinsically linked through a negative relationship, thus g(0) should be derived from σ using a predictive model including individual variability. Simulations using this approach showed that bait stations deployed for about 500 days using a 25 m × 25 m grid consistently achieved eradication, and that a surveillance network of 3.25 chew cards ha(−1) or 3.75 WaxTags ha(−1) active for 14 nights would be required to confidently declare eradication. This density could be halved if the surveillance network was deployed for 28 nights or if the prior confidence in eradication was high (0.85). These recommendations take no account of differences in detection parameters between habitats. Therefore, if surveillance suggests that individuals are not encountering devices in certain habitats, device density should be adaptively revised. This approach applies to initiatives globally that aim to optimise eradication with limited funding. Nature Publishing Group UK 2022-10-03 /pmc/articles/PMC9530159/ /pubmed/36192476 http://dx.doi.org/10.1038/s41598-022-20677-8 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Mackenzie, Henry R.
Latham, M. Cecilia
Anderson, Dean P.
Hartley, Stephen
Norbury, Grant L.
Latham, A. David M.
Detection parameters for managing invasive rats in urban environments
title Detection parameters for managing invasive rats in urban environments
title_full Detection parameters for managing invasive rats in urban environments
title_fullStr Detection parameters for managing invasive rats in urban environments
title_full_unstemmed Detection parameters for managing invasive rats in urban environments
title_short Detection parameters for managing invasive rats in urban environments
title_sort detection parameters for managing invasive rats in urban environments
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9530159/
https://www.ncbi.nlm.nih.gov/pubmed/36192476
http://dx.doi.org/10.1038/s41598-022-20677-8
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