Presence-absence versus presence-only modelling methods for predicting bird habitat suitability

By Lluís Brotons, Wilfried Thuiller, Miguel B. Araújo, A. Hirzel. 2004

896 citations recorded by the source corpus.

Subfields: Ecological Modeling, Ecology, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/3da546f2285c24d28b4e70b6a1f21a4e3ec5e715

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In Ecological Modeling

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Citations in the corpus

Modelling geographic distribution and detecting conservation gaps in Italy for the threatened beetle Rosalia alpina (2013, background): “Hence, it is more likely that a smaller dataset may cover uch of the species’ niche (Brotons et al. 2004).”
Modelling distribution and abundance with presence‐only data (2006, methodology): “ uses factor analysis to quantify the environmental conditions of the presence sites by comparing them to the environmental conditions of the entire region of interest, and predictions are provided as a habitat suitability index (Hirzel et al. 2002; Dettki, Löfstrand & Edenius 2003; Reutter et a”
Five (or so) challenges for species distribution modelling (2006, neutral): “…techniques yield different results, even when models are calibrated with the same response and predictor variables (e.g. Elith, 2000; Olden & Jackson, 2002; Thuiller et al., 2003; Brotons et al., 2004; Segurado & Araújo, 2004; Araújo et al., 2006; Elith et al., 2006; Pearson et al., 2006).”
Species diversification and phylogeographical patterns of birds in response to the uplift of the Qinghai-Tibet Plateau and Quaternary glaciations (2014, neutral): “…such as integration with geographic information systems (GISs) and ecological niche modeling (ENM), have recently been used in phylogeography to better understand the ecological causes of population history (Brotons et al., 2004; Kozak et al., 2008; Smith et al., 2011; Zhao et al., 2012).”
Ocean‐scale prediction of whale shark distribution (2012, background): “, 2005) or generalized linear or additive models (Nelder & Wedderburn, 1972; Hastie & Tibshirani, 1986) tend to provide more realistic projections when using reliable and accurate absence data (Zaniewski et al., 2002; Brotons et al., 2004).”
Vulnerability of African mammals to anthropogenic climate change under conservative land transformation assumptions (2006, background): “Extensive habitat alteration could create large gaps between suitable patches (Brown & Lomolino, 1998; Tokeshi, 1999) and, thus prevent free migration of species to keep pace with CC.”
Habitat suitability for marine fishes using presence-only modelling and multibeam sonar (2010, background): “…the same data, and the majority of these have focused on terrestrial, freshwater or simulated datasets (Elith & Burgman 2002, Ferrier et al. 2002b, Olden & Jackson 2002, Farber & Kadmon 2003, Loiselle et al. 2003, Brotons et al. 2004, Segurado & Araújo 2004, Elith et al. 2006, Tsoar et al. 2007).”
The performance of state-of-the-art modelling techniques depends on geographical distribution of species. (2009, background): “A number of studies (Kadmon et al., 2003; Brotons et al., 2004; McPherson et al., 2004; Segurado and Araújo, 2004; Luoto et al., 2005) have shown that these factors may affect the modelling accuracy.”
Predicting global habitat suitability for stony corals on seamounts (2009, neutral): “Although it is preferable to use both presence and absence data where available, because of the additional information that absence records contain (Brotons et al. , 2004), presence-only statistical models have been developed for when absence data are unavailable (such as here).”
The effect of prevalence and its interaction with sample size on the reliability of species distribution models (2009, background): “Given that the relative occurrence area of the species is also known to influence the values of model performance measures (Lobo et al. 2008, Jiménez-Valverde et al. 2008b), it has been difficult to separate its effect from the true effect of prevalence (e.g., Brotons et al. 2004).”
Consensual predictions of potential distributional areas for invasive species: a case study of Argentine ants in the Iberian Peninsula (2009, methodology): “Since absence data were unavailable, we generated pseudoabsence data for constructing the models despite being aware of their limitations (Brotons et al. 2004; Pearce and Boyce 2006).”