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dc.contributor.authorHollings, T
dc.contributor.authorRobinson, A
dc.contributor.authorvan Andel, M
dc.contributor.authorJewell, C
dc.contributor.authorBurgman, M
dc.date.accessioned2020-12-21T01:54:17Z
dc.date.available2020-12-21T01:54:17Z
dc.date.issued2017-08-24
dc.identifierpii: PONE-D-17-20181
dc.identifier.citationHollings, T., Robinson, A., van Andel, M., Jewell, C. & Burgman, M. (2017). Species distribution models: A comparison of statistical approaches for livestock and disease epidemics. PLOS ONE, 12 (8), https://doi.org/10.1371/journal.pone.0183626.
dc.identifier.issn1932-6203
dc.identifier.urihttp://hdl.handle.net/11343/256708
dc.description.abstractIn livestock industries, reliable up-to-date spatial distribution and abundance records for animals and farms are critical for governments to manage and respond to risks. Yet few, if any, countries can afford to maintain comprehensive, up-to-date agricultural census data. Statistical modelling can be used as a proxy for such data but comparative modelling studies have rarely been undertaken for livestock populations. Widespread species, including livestock, can be difficult to model effectively due to complex spatial distributions that do not respond predictably to environmental gradients. We assessed three machine learning species distribution models (SDM) for their capacity to estimate national-level farm animal population numbers within property boundaries: boosted regression trees (BRT), random forests (RF) and K-nearest neighbour (K-NN). The models were built from a commercial livestock database and environmental and socio-economic predictor data for New Zealand. We used two spatial data stratifications to test (i) support for decision making in an emergency response situation, and (ii) the ability for the models to predict to new geographic regions. The performance of the three model types varied substantially, but the best performing models showed very high accuracy. BRTs had the best performance overall, but RF performed equally well or better in many simulations; RFs were superior at predicting livestock numbers for all but very large commercial farms. K-NN performed poorly relative to both RF and BRT in all simulations. The predictions of both multi species and single species models for farms and within hypothetical quarantine zones were very close to observed data. These models are generally applicable for livestock estimation with broad applications in disease risk modelling, biosecurity, policy and planning.
dc.languageEnglish
dc.publisherPUBLIC LIBRARY SCIENCE
dc.rights.urihttps://creativecommons.org/licenses/by/4.0
dc.titleSpecies distribution models: A comparison of statistical approaches for livestock and disease epidemics
dc.typeJournal Article
dc.identifier.doi10.1371/journal.pone.0183626
melbourne.affiliation.departmentSchool of BioSciences
melbourne.source.titlePLoS One
melbourne.source.volume12
melbourne.source.issue8
dc.rights.licenseCC BY
melbourne.elementsid1229581
melbourne.contributor.authorRobinson, Andrew
melbourne.contributor.authorBurgman, Mark
melbourne.contributor.authorHollings, Tracey
dc.identifier.eissn1932-6203
melbourne.accessrightsOpen Access


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