Maximum entropy modeling of species geographic distributions

By Steven J. Phillips, R. P. Anderson, Robert E. Schapire. 2006

17,266 citations recorded by the source corpus.

Subfields: Ecological Modeling, Ecology, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/9d7b1ed4b707ce278b44191c9ba885385c2aa688

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In Ecological Modeling

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In Nature and Landscape Conservation

Citations in the corpus

Diverse Mycorrhizal Associations Enhance Terrestrial C Storage in a Global Model (2019, methodology): “Niche models were created using theMaxent algorithm (Phillips et al., 2006) and validated against overfitting and accuracy via the ENMEval R package (Muscarella et al., 2014) following Kivlin et al. (2017).”
Integrated research methods in watershed science (2015, neutral): “However, in simulations of the biogeographical distribution of vegetation and the evolution of ecosystems, concepts based on Darwinism have achieved certain successes (e.g., Phillips et al., 2006).”
Can species distribution modelling provide estimates of population densities? A case study with jaguars in the Neotropics (2012, methodology): “…have been widely used in many recent studies, and detailed descriptions of their implementation and comparison of their performance can be found elsewhere (Segurado & Araújo, 2004; Elith et al., 2006; Phillips et al., 2006; Tsoar et al., 2007; Phillips & Dudı́k, 2008; Elith & Graham, 2009).”
Not as good as they seem: the importance of concepts in species distribution modelling (2008, neutral): “On the other, a wide variety of modelling techniques specially designed to model species distributions are currently available (see Guisan & Thuiller, 2005), including a number of presence- only freeware modelling tools (e.g. Bioclim and Domain, Busby, 1991; Carpenter et al ., 1993, respectively; ht”
The projected effects of climatic and vegetation changes on the distribution and diversity of Southeast Asian bats (2012, methodology): “Output rasters from Maxent were reclassified [for each species in each future scenario, plus baseline present scenarios for each of the two groups (Table 1)].”
Maxent modeling for predicting suitable habitat for threatened and endangered tree Canacomyrica monticola in New Caledonia (2009, methodology): “Maxent is a maximum entropy based machine learning program that estimates the probability distribution for a species' occurrence based on environmental constraints (Phillips et al., 2006).”
Can we derive macroecological patterns from primary Global Biodiversity Information Facility data (2015, methodology): “We modelled the distribution of all species using the recommended (Elith et al., 2006) and widely used MaxEnt application with default options (version 3.3.3; see Phillips et al., 2006) in which 10,000 background absences are randomly selected across the entire world.”
Climate‐based models of spatial patterns of species richness in Egypt’s butterfly and mammal fauna (2009, methodology): “Full details are given in Phillips et al. (2006).”