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dc.contributor.authorAguilar, Glenn
dc.contributor.authorWaqa-Sakiti, Hilda
dc.contributor.authorWinder, Linton
dc.date.accessioned2017-01-19T19:53:38Z
dc.date.available2017-01-19T19:53:38Z
dc.date.issued2016-12-09
dc.identifier.isbn9781927214213
dc.identifier.urihttps://hdl.handle.net/10652/3618
dc.description.abstractSeveral modelling tools were utilised to develop maps predicting the suitability of the Fiji Islands for longhorned beetles (Cerambycidae) that include endemic and endangered species such as the Giant Fijian Beetle Xixuthrus heros. This was part of an effort to derive spatially relevant knowledge for characterising an important taxonomic group in an area with relatively few biodiversity studies. Occurrence data from the Global Biodiversity Information Facility (GBIF) and bioclimatic variables from the WorldClim database were used as input for species distribution modelling (SDM). Due to the low number of available occurrence data resulting in inconsistent performance of different tools, several algorithms implemented in the DISMO package in R (Bioclim, Domain, GLM, Mahalanobis, SVM, RF and MaxEnt) were tested to determine which provide the best performance. Occurrence sets at several distribution densities were tested to determine which algorithm and sample size combination provided the best model results. The machine learning algorithms RF, SVM and MaxEnt consistently provided the best performance as evaluated by the True Skill Statistic (TSS), Kappa and Area Under Curve (AUC) metrics. The occurrence set with a density distribution of one sampling point per 10km2 provided the best performance and was used for the final prediction model. An ensemble of the best-performing algorithms generated the final suitability predictive map. The results can serve as a basis for additional studies and provide initial information that will eventually support decision-making processes supporting conservation in the archipelago.en_NZ
dc.language.isoenen_NZ
dc.publisherUnitec ePressen_NZ
dc.relation.urihttp://www.unitec.ac.nz/GisForConservation/en_NZ
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/nz/*
dc.subjectFijien_NZ
dc.subjectLong-horned beetles (Cerambycidae)en_NZ
dc.subjectGIS for conservationen_NZ
dc.subjectGiant Fijian Beetle (Xixuthrus heros)en_NZ
dc.subjectGIS mappingen_NZ
dc.titleUsing predicted locations and an ensemble approach to address sparse data sets for species distribution modelling : Long-horned beetles (Cerambycidae) of the Fiji islandsen_NZ
dc.title.alternativeGIS For Conservation. Using predicted locations and an ensemble approach to address sparse data sets for species distribution modelling : Long-horned beetles (Cerambycidae) of the Fiji islandsen_NZ
dc.typeJournal Articleen_NZ
dc.rights.holderUnitec ePressen_NZ
dc.subject.marsden050202 Conservation and Biodiversityen_NZ
dc.identifier.bibliographicCitationAguilar, G. D., Waqa-Sakiti, H. & Winder, L. (2016). Using predicted locations and an ensemble approach to address sparse data sets for species distribution modelling : Long-horned beetles (Cerambycidae) of the Fiji islands. Research report & emedia teaching resource. Unitec Institute of Technology. Unitec ePress. Retrieved from: http://www.unitec.ac.nz/epressen_NZ
unitec.institutionUnitec Institute of Technologyen_NZ
unitec.publication.spage1en_NZ
unitec.publication.lpage18en_NZ
unitec.peerreviewedyesen_NZ
unitec.identifier.roms59825
unitec.relation.epresshttp://www.unitec.ac.nz/epress/index.php/gis-for-conservation-using-predicted-locations-and-an-ensemble-approach-to-address-sparse-data-sets-for-species-distribution-modelling-long-horned-beetles-cerambycidae-of-the-fiji-islands/en_NZ


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