Comparative performance of generalized additive models and multivariate adaptive regression splines for statistical modelling of species distributions

By J. Leathwick, Jane Elith, Trevor J. Hastie. 2006

453 citations recorded by the source corpus.

Subfields: Ecological Modeling, Ecology, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/f5f50a9dfe2c8cc5f5f377bacdbf8e5844bea799

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In Ecological Modeling

In Ecology

In Nature and Landscape Conservation

Citations in the corpus

A comprehensive evaluation of predictive performance of 33 species distribution models at species and community levels (2019, background): “…of the main sources of variation in SDM performance is the structure of the data (Fielding and Haworth 1995), especially the prevalence of species (Leathwick et al. 2006, Meynard and Quinn 2007, Syphard and Franklin 2009, Santika 2011, Madon et al. 2013) and the strength and shape of the…”
Profile or group discriminative techniques? Generating reliable species distribution models using pseudo‐absences and target‐group absences from natural history collections (2010, methodology): “Maxent and MARS are more recent additions to the modeller’s toolbox, both exhibiting good performance (Muñoz & Felicı́simo, 2004; Elith et al., 2006; Leathwick et al., 2006).”
WHAT MATTERS FOR PREDICTING THE OCCURRENCES OF TREES: TECHNIQUES, DATA, OR SPECIES' CHARACTERISTICS? (2007, methodology): “BRT (boosted regression trees) comprises two algorithms (boosting and regression trees) and computes a sequence of simple regression trees, where each successive tree is fitted to the residuals of the existing set of trees (Leathwick et al. 2006b).”
Pandora's Box Contained Bait : The Global Problem of Introduced Earthworms (2008, methodology): “…rapid advances in computational methods for habitat modeling, including the Genetic Algorithm for Rule Set Production [GARP (e.g., Drake & Bossenbroek 2004)], maximum entropy methods (Phillips et al. 2006), and a wide range of statistical and machine learning methods (e.g., Leathwick et al. 2006).”
Effects of the number of presences on reliability and stability of MARS species distribution models: the importance of regional niche variation and ecological heterogeneity (2010, methodology): “2001) have been applied in previous ecological modelling exercises (Muñoz & Felicı́simo 2004; Leathwick et al. 2006; Elith & Leathwick 2007).”
A strict maximum likelihood explanation of MaxEnt, and some implications for distribution modelling (2013, methodology): “The other top-ranked methods were boosted regression trees (BRT; De´ath 2007, Elith et al. 2008) and multivariate adaptive regression splines (MARS; Friedman 1991, Leathwick et al. 2006).”
Application of a generalized additive model (GAM) to reveal relationships between environmental factors and distributions of pelagic fish and krill: a case study in Sendai Bay, Japan (2009, methodology): “Maravelias (1997) used two-strata GAMs (first stratum: presence and absence; second stratum: number of individuals given presence) to study trends in abundance and geographic distribution of North Sea herring.”
The application of predicted habitat models to investigate the spatial ecology of demersal fish assemblages (2010, background): “greater than 0.7 were excluded from the modelling (Sleeman et al. 2005; Leathwick et al. 2006 ).”