Botanical richness and endemicity patterns of Borneo derived from species distribution models

By Niels Raes, M. Roos, J. W. F. Slik, Emiel van Loon, H. ter Steege. 2009

198 citations recorded by the source corpus.

Subfields: Ecological Modeling, Ecology, Evolution, Behavior and Systematics, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/55cb9887af1fb3472c6e793b4917bc42818a03c1

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In Ecological Modeling

In Ecology, Evolution, Behavior and Systematics

In Nature and Landscape Conservation

Citations in the corpus

Using multi-scale modelling to predict habitat suitability for species of conservation concern: The grey long-eared bat as a case study (2011, methodology): “This is a conservative value that is commonly used in species distribution modelling studies (e.g., Raes et al., 2009; Rebelo and Jones, 2010).”
Applications and limitations of museum data for conservation and ecology, with particular attention to species distribution models (2010, methodology): “This approach has been used a number of times to model patterns of species richness in parts of the world where distribution data are patchy (Loiselle et al., 2003; Garcia, 2006; Pineda and Lobo, 2009; Raes et al., 2009; Newbold et al., 2009).”
Extent and fragmentation of suitable leopard habitat in South Africa (2013, methodology): “To classify our model predictions into suitable and unsuitable habitat, we assumed that at least 10% of the presence points suffered from spatial error (Raes et al., 2009).”
Protected areas network is not adequate to protect a critically endangered East Africa Chelonian: Modelling distribution of pancake tortoise, Malacochersus tornieri under current and future climates (2020, background): “This is particularly problematic when trying to delimit a range boundary or the likelihood of finding an individual, and is compounded when trying to combine predictions for multiple species to obtain estimates of diversity (cf. Raes et al. 2009).”
Predicting species distribution at range margins: testing the effects of study area extent, resolution and threshold selection in the Sahara–Sahel transition zone (2014, background): “…limited to fragmented habitats, more restrictive thresholds increased the balance between correct classification of both validation data and absences, although maximizing the fraction of true positives (Liu et al., 2005; Jim enez-Valverde & Lobo, 2007; Raes et al., 2009; Braunisch & Suchant, 2010).”