Journal ArticleDOI
Evaluation of consensus methods in predictive species distribution modelling
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TLDR
In this article, the authors tested the predictive accuracies of five consensus methods, namely Weighted Average (WA), Mean(All), Median(All, Median(PCA), and Best, for 28 threatened plant species.Abstract:
Aim Spatial modelling techniques are increasingly used in species distribution modelling. However, the implemented techniques differ in their modelling performance, and some consensus methods are needed to reduce the uncertainty of predictions. In this study, we tested the predictive accuracies of five consensus methods, namely Weighted Average (WA), Mean(All), Median(All), Median(PCA), and Best, for 28 threatened plant species.
Location North-eastern Finland, Europe.
Methods The spatial distributions of the plant species were forecasted using eight state-of-the-art single-modelling techniques providing an ensemble of predictions. The probability values of occurrence were then combined using five consensus algorithms. The predictive accuracies of the single-model and consensus methods were assessed by computing the area under the curve (AUC) of the receiver-operating characteristic plot.
Results The mean AUC values varied between 0.697 (classification tree analysis) and 0.813 (random forest) for the single-models, and from 0.757 to 0.850 for the consensus methods. WA and Mean(All) consensus methods provided significantly more robust predictions than all the single-models and the other consensus methods.
Main conclusions Consensus methods based on average function algorithms may increase significantly the accuracy of species distribution forecasts, and thus they show considerable promise for different conservation biological and biogeographical applications.read more
Citations
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References
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Random Forests
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Measuring the accuracy of diagnostic systems
TL;DR: For diagnostic systems used to distinguish between two classes of events, analysis in terms of the "relative operating characteristic" of signal detection theory provides a precise and valid measure of diagnostic accuracy.
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TL;DR: This work compared 16 modelling methods over 226 species from 6 regions of the world, creating the most comprehensive set of model comparisons to date and found that presence-only data were effective for modelling species' distributions for many species and regions.
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Predictive habitat distribution models in ecology
TL;DR: A review of predictive habitat distribution modeling is presented, which shows that a wide array of models has been developed to cover aspects as diverse as biogeography, conservation biology, climate change research, and habitat or species management.
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