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Virtual species are often used in ecology to test modelling methods. In my research, I am using virtual species to test species distribution models, but some of the initial work that I am doing involves understanding methods for implementing these simulations. A lot of the work that I do uses Cellular Automata and Individual-Based Models to incorporate processes such as dispersal and population dynamics. However, when developing my own models I quickly found that computational processing time is a big bottleneck.
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Published in Ecological Modelling, 2020
Using virtual species we test how spatial predictions of presence-background species distribution models vary with sample size. Of the four algorithms tested, Maxent was the most stable with spatial predictions varying much less in comparison to the algorithms. Machine learning methods Random Forest and Support Vector Machines are sensitive to background sample size, with significant differences in spatial predictions observed.
Recommended citation: Grimmett, L., Whitsed, R., & Horta, A. (2020). Presence-only species distribution models are sensitive to sample prevalence: Evaluating models using spatial prediction stability and accuracy metrics. Ecological Modelling, 431(June), 109194. https://doi.org/10.1016/j.ecolmodel.2020.109194
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Undergraduate course, University 1, Department, 2014
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Workshop, University 1, Department, 2015
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