{"id":1524,"date":"2015-05-11T18:23:05","date_gmt":"2015-05-11T18:23:05","guid":{"rendered":"http:\/\/daphnia.ecology.uga.edu\/drakelab\/?p=1524"},"modified":"2015-05-18T17:55:44","modified_gmt":"2015-05-18T17:55:44","slug":"range-bagging-a-new-method-for-ecological-niche-modeling-from-presence-only-data","status":"publish","type":"post","link":"https:\/\/daphnia.ecology.uga.edu\/drakelab\/?p=1524","title":{"rendered":"Range bagging: a new method for ecological niche modeling from presence-only data"},"content":{"rendered":"

Ecological niche modeling concerns the task of identifying the environmental conditions under which species persist. Niche modeling — and the mapping of species distributions it is usually used for — are often complicated by the presence of extraneous information, awkward sampling, and complex interactions. Range bagging is a new method that aims to simplify ecological niche modeling by focusing on the niche itself — viewed as a range of habitable environments. Bagging is an ensemble learning approach that renders the resulting model more stable than a single highly tuned model. In this case, it also solves the\u00a0complexity problem one encounters when trying to\u00a0identify an n<\/em>-dimensional environmental (or\u00a0convex hull) for large n<\/em>. See here<\/a> for the details. R code is available here<\/a>.<\/p>\n