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Title: A comparison of absolute performance of different correlative and mechanistic species distribution models in an independent area
Contributor(s): Shabani, Farzin  (author); Kumar, Lalit  (author)orcid ; Ahmadi, Mohsen (author)
Publication Date: 2016
Open Access: Yes
DOI: 10.1002/ece3.2332Open Access Link
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Abstract: To investigate the comparative abilities of six different bioclimatic models in an independent area, utilizing the distribution of eight different species available at a global scale and in Australia. Global scale and Australia. We tested a variety of bioclimatic models for eight different plant species employing five discriminatory correlative species distribution models (SDMs) including Generalized Linear Model (GLM), MaxEnt, Random Forest (RF), Boosted Regression Tree (BRT), Bioclim, together with CLIMEX (CL) as a mechanistic niche model. These models were fitted using a training dataset of available global data, but with the exclusion of Australian locations. The capabilities of these techniques in projecting suitable climate, based on independent records for these species in Australia, were compared. Thus, Australia is not used to calibrate the models and therefore it is as an independent area regarding geographic locations. To assess and compare performance, we utilized the area under the receiver operating characteristic (ROC) curves (AUC), true skill statistic (TSS), and fractional predicted areas for all SDMs. In addition, we assessed satisfactory agreements between the outputs of the six different bioclimatic models, for all eight species in Australia. The modeling method impacted on potential distribution predictions under current climate. However, the utilization of sensitivity and the fractional predicted areas showed that GLM, MaxEnt, Bioclim, and CL had the highest sensitivity for Australian climate conditions. Bioclim calculated the highest fractional predicted area of an independent area, while RF and BRT were poor. For many applications, it is difficult to decide which bioclimatic model to use. This research shows that variable results are obtained using different SDMs in an independent area. This research also shows that the SDMs produce different results for different species; for example, Bioclim may not be good for one species but works better for other species. Also, when projecting a "large" number of species into novel environments or in an independent area, the selection of the "best" model/technique is often less reliable than an ensemble modeling approach. In addition, it is vital to understand the accuracy of SDMs' predictions. Further, while TSS, together with fractional predicted areas, are appropriate tools for the measurement of accuracy between model results, particularly when undertaking projections on an independent area, AUC has been proved not to be. Our study highlights that each one of these models (CL, Bioclim, GLM, MaxEnt, BRT, and RF) provides slightly different results on projections and that it may be safer to use an ensemble of models.
Publication Type: Journal Article
Source of Publication: Ecology and Evolution, 6(16), p. 5973-5986
Publisher: John Wiley & Sons Ltd
Place of Publication: United Kingdom
ISSN: 2045-7758
Field of Research (FOR): 050104 Landscape Ecology
050206 Environmental Monitoring
050101 Ecological Impacts of Climate Change
Socio-Economic Outcome Codes: 960305 Ecosystem Adaptation to Climate Change
960501 Ecosystem Assessment and Management at Regional or Larger Scales
960805 Flora, Fauna and Biodiversity at Regional or Larger Scales
Peer Reviewed: Yes
HERDC Category Description: C1 Refereed Article in a Scholarly Journal
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Appears in Collections:Journal Article
School of Environmental and Rural Science

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