Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation

By Steven J. Phillips, Miroslav Dud'ik. 2008

6,939 citations recorded by the source corpus.

Subfields: Ecological Modeling, Ecology, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/1517e9695cb0bf98ad69ad7fcbff720dd4f79d7d

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Citations in the corpus

Comparing sample bias correction methods for species distribution modeling using virtual species (2021, methodology): “…2021 v Volume 12(3) v Article e03422 observations of multiple species within a target group of similar taxa whose locality data were collected in the same way as the modeled species, and has shown promise for reducing sampling bias (Phillips and Dudik 2008, Elith et al. 2011, Ranc et al. 2016).”
Mechanisms in macroecology: AWOL or purloined letter? Towards a pragmatic view of mechanism (2010, background): “Th e last few years have seen a spate of papers importing the statistical mechanics approach of maximum entropy to biogeography (Phillips et al. 2006, Phillips and Dudik 2008) and ecology (McGill 2006, Shipley et al. 2006, Pueyo et al. 2007, Dewar and Porté 2008; see also Table 1).”
Optimizing resiliency of reserve networks to climate change: multispecies conservation planning in the Pacific Northwest, USA (2010, methodology): “Recent efforts have combined new robust habitat modeling methods for presence-only data (MAXENT; Phillips et al., 2006; Phillips & Dudik, 2008) with reserve selection software adapted to process the large numbers of resultant habitat suitability surfaces (ZONATION; Moilanen et al., 2005; Moilanen &…”
Keep collecting: accurate species distribution modelling requires more collections than previously thought (2011, methodology): “As a result of its strong performance, availability and ease of implementation, maxent is one of the most popular SDMs currently being employed to estimate species ranges in relation to environmental predictors (Phillips & Dudı́k, 2008; Franklin, 2009).”
Paintings predict the distribution of species, or the challenge of selecting environmental predictors and evaluation statistics (2018, methodology): “Specifically, we restrained our analysis to articles that cited at least one of the two main references for MAXENT, the most widely used SDM method: Phillips et al. (2006) and Phillips and Dudík (2008).”
Niches, models, and climate change: Assessing the assumptions and uncertainties (2009, background): “1 (70)] and generalized additive models [GAM (52)], to project future bird distributions based on modeled associations with climate and vegetation (71) (methodological details are provided in SI Text).”
Feeding habitat of the whale shark Rhincodon typus in the northern Gulf of Mexico determined using species distribution modelling (2012, methodology): “…is likely due to the fact that MaxEnt can create a spatial prediction with a higher fit (as indicated by higher test scores) by im plementing different feature classes into more complex functions (i.e. linear, quadratic, product, threshold, hinge, and category indicator) (Phillips & Dudík 2008).”
The art of modelling range‐shifting species (2010, methodology): “MaxEnt v 3.3.1 (Phillips & Dudik 2008) used with default settings except: only hinge features allowed, and 10-fold cross-validation; either (a) defaults for regularization, or (b) multiplier of 2Æ5 to fit more general models (Appendix S2) , 1, 330–342 2010 The Authors.”
Partitioning and mapping uncertainties in ensembles of forecasts of species turnover under climate change (2009, neutral): “…Linear Models (GLM) (McCullagh and Nelder 1989) and more complex machine learning approaches such as Random Forest (RF) (Breiman 2001), Genetic Algorithm for Rule Set Production (GARP) (Stockwell and Noble 1992), and Maximum Entropy (MAXENT) (Phillips et al. 2006, Phillips and Dudı́k 2008).”