Maximum Entropy machine learning model output using all digitized downed trees with three variables and regularization multiplier of 6.0
This dataset (multiple outputs files in a compressed folder) is output from a run of the Maxent ML Model (Phillips et.al., 2006, url: https://biodiversityinformatics.amnh.org/open_source/maxent/) using all digitized downed trees (N=9505) and includes the three environmental variables with most contr...
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| Format: | Dataset |
| Language: | unknown |
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Texas Data Repository Dataverse
2020
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| Online Access: | https://dx.doi.org/10.18738/t8/whs3br https://dataverse.tdl.org/citation?persistentId=doi:10.18738/T8/WHS3BR |
| Summary: | This dataset (multiple outputs files in a compressed folder) is output from a run of the Maxent ML Model (Phillips et.al., 2006, url: https://biodiversityinformatics.amnh.org/open_source/maxent/) using all digitized downed trees (N=9505) and includes the three environmental variables with most contribution according to MaxentVariableSelection (Jueterbock, et.al. 2016) (vegetation community, inundation probability, and soil classification) and implemented as hinge features with a regularization multiplier (beta parameter) of 6.0. The value of 0p000 in the dataset file name refers to the fact that it uses all digitized downed trees. Phillips, S.J., Anderson, R.P., Schapire, R.E. (2006). Maximum Entropy modelling of species geographic distribution. Ecological Modelling, 190, 231-259. Jueterbock, A. Smolina, I., Cover, J.A., Hoaru, C. (2016) The fate of arctic seaweed Fucus disticus under climate change: an ecological niche modelling approach. Ecology and Evolution, 6(6), 1712-1724. |
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