Niche and area of distribution modeling: a population ecology perspective

By Jorge Soberón. 2010

345 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/ddad78c1c9ca0c60fccfa94c96c3e4f9cb46ad50

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Citations in the corpus

Hypervolume concepts in niche‐ and trait‐based ecology (2018, background): “For example, the growing body of research on demographic models based on niches, traits, and species interactions (reviewed by Soberón 2010, Zurell et al. 2016, Anderson 2017) suggests that such extensions will be important.”
Uncertainty associated with survey design in Species Distribution Models (2014, background): “Despite their increasing use during the last 15 years (Thuiller et al., 2009; Lobo et al., 2010; Varela et al., 2014), SDMs pose many conceptual problems (Ara ujo & Guisan, 2006; Sober on, 2007, 2010; Jim enez-Valverde et al., 2008; Colwell & Rangel, 2009; Sober on & Nakamura, 2009; Hortal et al., 2”
When and how should biotic interactions be considered in models of species niches and distributions? (2017, background): “Relevant interactors whose populations are affected by the focal species (i.e. are linked to it) must be taken into account in other ways, rather than being used as predictor variables in a niche/distributional model of suitability or presence (Grinnellian niches of Sober on, 2007, 2010; Fig.”
Plant pathogens, insect pests and weeds in a changing global climate: a review of approaches, challenges, research gaps, key studies and concepts (2012, background): “Theoretical frameworks for climate change research with respect to multi-trophic interactions of insects have been proposed by many authors (e.g. Harrington et al. 2001; Bale et al. 2002; Helmuth 2009; Berg et al. 2010; Gilman et al. 2010; Pelini et al. 2010; Soberon 2010).”
The crucial role of the accessible area in ecological niche modeling and species distribution modeling (2011, methodology): “of Warren et al. (2008) uses two similarity indices, Hellinger’s I and Schoener’s D, to quantify the level of overlap between models, and compares this observed overlap to a null distribution of values generated from models associated with random points from within the “area of interest.”
Ecological niche shifts and environmental space anisotropy: a cautionary note (2011, neutral): “Unfortunately when presence-only data is used, without extra information it is impossible to assess where exactly the position of the estimate lies (Soberón, 2010).”
Uses and misuses of bioclimatic envelope modeling. (2012, background): “…bioclimatic envelope models estimate potential distribu- tions (i.e., the geographic projection of the estimated realized niches of species), rather than the occupied distributional area of the species (Araújo and Guisan 2006, Soberón 2007, 2010, Hirzel and Lay 2008, Soberón and Nakamura 2009).”
Integrating ensemble species distribution modelling and statistical phylogeography to inform projections of climate change impacts on species distributions (2013, background): “dynamics, which typically occur at finer scales (Pearson and Dawson 2003; Soberón 2010).”
Species Distribution Modeling and Ecological Niche Modeling: Getting the Concepts Right (2012, background): “…world has emphasized that their focus is on a subset of all ecological niche dimensions that is defined in coarse-resolution dimensions that are relatively unlinked to (i.e., not affected by) the population processes of the species in question (Soberón 2007; Soberón & Nakamura 2009; Soberón 2010).”
Understanding (insect) species distributions across spatial scales (2010, methodology): “Sobero´n (2010) presents the most recent account of his theoretical framework merging the Grinnellian and Eltonian views of the niche (i.e. abiotic and biotic, respectively; Sobero´n 2007, Sobero´n and Nakamura 2009).”
Creating individual accessible area hypotheses improves stacked species distribution model performance (2018, methodology): “Nevertheless, stacked SDMs constrained a priori in their geographical extent perform better in anticipating geographic patterns of observed species richness than models that lack dispersal-based constraints (Barve et al., 2011; Guisan et al., 2006; Sober on, 2010; Sober on & Peterson, 2005).”
The geographic scaling of biotic interactions (2013, background): “In practice, several biotic and abiotic factors can simultaneously affect the distributions of species (Soberón 2010, Peterson et al. 2011) and, therefore, co-occurrence (Cohen 1971, Leibold 1997, Amarasekare et al. 2004, Ovaskainen et al. 2010, Araújo et al. 2011).”
Insights into the area under the receiver operating characteristic curve (AUC) as a discrimination measure in species distribution modelling (2012, methodology): “In this paper, by the general term SDMs I refer to both potential and realized distribution models sensu Jiménez-Valverde et al. (2008) and the corresponding ecological niche models (ENMs) and distribution models sensu Soberón (2010).”
Using species distribution models in paleobiogeography: A matter of data, predictors and concepts (2011, background): “The ideal variables should have the adequate spatial and temporal resolution to describe the response of the studied species, while at the same time be descriptors or proxies of the causal factors that truly determine the occurrence of the species (Austin, 2002) and/ or have the greatest impact on t”
Modelling horses for novel climate courses: insights from projecting potential distributions of native and alien Australian acacias with correlative and mechanistic models (2011, background): “Current consensus suggests that correlative model outputs align more closely with species’ realized distributions, while mechanistic models more closely approximate their fundamental climate niche and therefore robustly project species ranges into novel climates (Soberón, 2010; Rodda et al., 2011).”