Profile or group discriminative techniques? Generating reliable species distribution models using pseudo‐absences and target‐group absences from natural history collections

By R. Mateo, T. Croat, Á. M. Felicísimo, Jesús Muñoz. 2010

169 citations recorded by the source corpus.

Subfields: Ecological Modeling, Genetics, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/9b4c0be6c08e7f5fad62ef0a28f0457c2eb9e642

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In Ecological Modeling

In Genetics

In Nature and Landscape Conservation

Citations in the corpus

Using species distribution models to inform IUCN Red List assessments (2014, methodology): “MaxEnt MaxEnt is among the best-performing of the different presence-only correlative SDM approaches available (Elith et al. 2006, Mateo et al. 2010, Williams et al. 2009).”
A framework for species distribution modelling with improved pseudo-absence generation (2015, methodology): “Group discrimi-10 2 nation techniques have gained popularity in recent years, as they have been11 reported to yield better results than profile techniques (Engler et al., 2004;12 Chefaoui and Lobo, 2008; Elith and et al, 2006; Mateo et al., 2010).”
A strict maximum likelihood explanation of MaxEnt, and some implications for distribution modelling (2013, background): “While there is growing consensus about which methods generally give the best SPM models ( e.g., Elith et al. 2006, Mateo et al. 2010, Rebelo & Jones 2010, Rupprecht et al. 2011), choosing strategy for transformation of explanatory variables (Step 5,ii) and model selection (Step 8,i) have remained…”
Predicting the distribution of vulnerable marine ecosystems in the deep sea using presence-background models (2014, methodology): “Application of this technique has been shown to improve the accuracy of modelled predictions when using species data containing sample selection bias (Mateo et al., 2010; Phillips et al., 2009).”
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): “Models of 1, 2, 4, 8, 16 and 32 km pixel size basically contain the same information per pixel, and so we decided to set the buffer size to 30 km (Mateo et al. 2010).”
The Effects of Sampling Bias and Model Complexity on the Predictive Performance of MaxEnt Species Distribution Models (2013, background): “A number of other studies have shown the importance of the background data selection method through a variety of SDM techniques [19,20,21,22,23,52] and studies have begun to also evaluate MaxEnt [12,14,53,54,55].”
Global mapping of Jatropha curcas yield based on response of fitness to present and future climate (2010, background): “Pseudo-absence locations are sampled within the training area following the ‘target-group absences’ approach in order to minimize sampling biases and increase SDM predictive performances (Mateo et al., 2010).”