Aguila, G. and Waqa-Sakiti, Hilda F.V. and Winder, L (2016) 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. [Professional and Technical Reports]
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Abstract
Several 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.
| Item Type: | Professional and Technical Reports |
|---|---|
| Subjects: | Q Science > QL Zoology |
| Divisions: | Faculty of Science, Technology and Environment (FSTE) > Institute of Applied Science |
| Depositing User: | Hilda Sakiti-Waqa |
| Date Deposited: | 24 Jul 2017 02:12 |
| Last Modified: | 24 Jul 2017 03:22 |
| URI: | https://repository.usp.ac.fj/id/eprint/10046 |
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