INTERPRETATION OF MODELS OF FUNDAMENTAL ECOLOGICAL NICHES AND SPECIES' DISTRIBUTIONAL AREAS

By Jorge Soberón, A. Townsend Peterson. 2005

2,082 citations recorded by the source corpus.

Estimation of the dimensions of fundamental ecological niches of species to predict their geographic distributions is increasingly being attempted in systematics, ecology, conservation, public health, etc. This technique is often (of necessity) based on data comprising records of presences only. In recent years, modeling approaches have been devised to estimate these interrelated expressions of a species' ecology, distributional biology, and evolutionary history—nevertheless, a formal basis in ecological and evolutionary theory has largely been lacking. In this paper, we outline such a formal basis to clarify the use of techniques applied to the challenge of estimating 'ecological niches;' we analyze example situations that can be modeled using these techniques, and clarify interpretation of results.

Subfields: Ecological Modeling, Ecology, Genetics

View Paper: https://www.semanticscholar.org/paper/676ab86cfdd2c361c0350c78d8c46e2d178fbd64

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Citations in the corpus

Consensual predictions of potential distributional areas for invasive species: a case study of Argentine ants in the Iberian Peninsula (2009, methodology): “…algorithms search for non-random associations between known occurrences of the species and relevant ecological/environmental parameters in the form of digital maps; these niche models are then used to identify areas fitting the ecological requirements of the species (Soberón and Peterson 2005).”
Uses and requirements of ecological niche models and related distributional models (2006, background): “…in some portion of the range, and good (dense, representative) sampling will usually reveal that potential (Araújo and Guisan 2006): a worked example is provided in a recent publication (Anderson et al. 2002b), and the theoretical issues have also been reviewed recently (Soberón and Peterson 2005).”
A statistical explanation of MaxEnt for ecologists (2011, result): “…the sorts of distributions (e.g., potential vs. realized) that can be modelled with presence-only data in contrast to presenceabsence data (e.g., Soberón & Peterson, 2005; Chefaoui & Lobo, 2007; Hirzel & Le Lay, 2008; Jiménez-Valverde et al., 2008; Soberón & Nakamura, 2009; Lobo et al.,…”
Mechanistic and Correlative Models of Ecological Niches (2015, background): “…among abiotic, biotic, and dispersal effects can be rather complex: interpretation of correlational models will always be compromised by this mixture, as joint effects will frequently not be able to be teased apart, distinguished, and interpreted completely and clearly (Soberón & Peterson 2005).”
The uncertain nature of absences and their importance in species distribution modelling (2010, background): “…which, in a Grinellian sense, can be considered as the set of abiotic requirements in which a species can maintain a net positive rate of population increase without immigration (Grinnell 1917, Soberón and Peterson 2005, Soberón 2007, 2010, Colwell and Rangel 2009, Soberón and Nakamura 2009).”
Species Distribution Modeling and Ecological Niche Modeling: Getting the Concepts Right (2012, methodology): “We use the Biotic-Abiotic-Mobility (BAM; Figure 1) framework (Soberón & Peterson 2005) to contemplate the suites of factors that determine geographic distributions of species, and that have been known and discussed for many decades (Grinnell 1924; Good 1931).”
Grinnellian and Eltonian niches and geographic distributions of species. (2007, background): “The need for clarification of terminology and concepts is now pressing (Guisan & Thuiller 2005; Soberón & Peterson 2005; Araújo & Guisan 2006; Kearny 2006).”
No silver bullets in correlative ecological niche modelling: insights from testing among many potential algorithms for niche estimation (2015, methodology): “…the vastly different outcomes that are obtained in SDM/ENM analyses under different configurations of a Venn diagram that summarizes abiotic requirements andmovement considerations in structuring species’ distributions (portions of the ‘BAM’ diagram; Sober on & Peterson 2005; Peterson et al. 2011).”
Delimiting the geographical background in species distribution modelling (2012, background): “This can be done by controlling for the broad-scale geographical structure of the data that may be caused by numerous factors such as dispersal limitation, geographical characteristics or historical events, among others (McGlone, 1996; Soberón & Peterson, 2005; Svenning & Skov, 2005).”
Species distribution models that do not incorporate global data misrepresent potential distributions: a case study using Iberian diving beetles (2011, background): “Understanding the environmental limits of a species is fundamental to predicting its potential distributional area (Thuiller et al., 2004; Soberón & Peterson, 2005; Araújo & Guisan, 2006).”
Ecological niches and potential geographical distributions of Mediterranean fruit fly (Ceratitis capitata) and Natal fruit fly (Ceratitis rosa) (2007, background): “…situation, for two reasons: (1) ROC requires absence data, which are not available in the present case; (2) ROC weights type 1 and type 2 errors equally, but the present conceptual basis for niche modelling (Soberón & Peterson, 2005) would weight omission error more heavily than commission error.”