Not as good as they seem: the importance of concepts in species distribution modelling

By A. Jiménez‐Valverde, Jorge M. Lobo, Joaquín Hortal. 2008

665 citations recorded by the source corpus.

Subfields: Ecological Modeling, Ecology, Genetics

View Paper: https://www.semanticscholar.org/paper/6cd1650975f00b727bb6820d166b4d250657797b

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Citations in the corpus

Ground validation of presence‐only modelling with rare species: a case study on barbastelles Barbastella barbastellus (Chiroptera: Vespertilionidae) (2010, methodology): “Additionally, by calculating sensitivity and specificity we evaluate how well models identify presences and absences respectively (Jiménez-Valverde et al. 2008a; Lobo et al. 2008).”
Two alternative evaluation metrics to replace the true skill statistic in the assessment of species distribution models (2019, background): “Unfortunately, the relative ease of characterising narrowly confined species (Jiménez-Valverde et al. 2008) and consequent high model performance scores, may not be due to biological causes, such as ecological specialisation, i.e. dependence on specific environmental conditions.”
Using species distribution models in paleobiogeography: A matter of data, predictors and concepts (2011, methodology): “…past changes in species distributions quantitatively, the adequacy of SDMs to attain certain research questions and the accuracy and reliability of their results are currently a matter of discussion (e.g. Jimenez-Valverde et al., 2008; Colwell and Rangel, 2009; Godsoe, 2010; Sinclair et al., 2010).”
Using species distribution modelling to disentangle realised versus potential distributions for rare species conservation (2013, background): “Yet, when no absence data is available, what we get are distributions close to potential ranges for the species (Jiménez-Valverde et al., 2008; Lobo, 2008).”
Defying the curse of ignorance: perspectives in insect macroecology and conservation biogeography (2010, background): “Niche modelling and their applications Niche modelling is becoming a very popular approach for generating models of geographic range and is being used to address a large set of ecological and evolutionary questions (e.g., Pearson & Dawson, 2003; Jiménez-Valverde et al., 2008).”
Delimiting the geographical background in species distribution modelling (2012, methodology): “In this way, it is easy to obtain models with high discriminatory power but with little informational content (Jiménez-Valverde et al., 2008).”
Evaluating correlative and mechanistic niche models for assessing the risk of pest establishment (2014, methodology): “The niche models are based on classical concept of ‘‘niche’’ in ecology, and model potential or realized distribution of a species depending on the modeling algorithm used (Jimenez-Valverde et al. 2008, Franklin 2009, Peterson et al. 2011).”
The effect of the extent of the study region on GIS models of species geographic distributions and estimates of niche evolution: preliminary tests with montane rodents (genus Nephelomys) in Venezuela (2010, methodology): “Among other factors, the modelling technique employed and the nature of the data can affect results (Chefaoui & Lobo, 2008; Jiménez-Valverde et al., 2008).”
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).”
The effect of prevalence and its interaction with sample size on the reliability of species distribution models (2009, methodology): “Therefore, if species distribution models are going to be commonly used as a tool in ecological (e.g., Jiménez-Valverde et al. 2008a), biogeographical (e.g., Lobo et al. 2006) or paleoecological (e.g., Nogués-Bravo et al. 2008) studies as well as in Conservation Biogeography (sensu Whittaker et al.…”
Variation in niche and distribution model performance: The need for a priori assessment of key causal factors (2012, background): “This limitation causes confusion as to whether model output should be interpreted as GO or as A, which is central in many of the arguments regarding “overprediction” (Jiménez-Valverde et al., 2008) and whether the object of modeling is a niche or a distribution.”
Potential range of the invasive fish rotan (Perccottus glenii) in the Holarctic (2011, background): “In principle, the fundamental niche would be more relevant to the potential distribution, but is more difficult to define (Jeschke and Strayer 2008; Jiménez-Valverde et al. 2008; Kearney and Porter 2009; Medley 2010).”
Predicting distributions of invasive species (2013, methodology): “The important problem with surveybased absence records stems from imperfect detection (i.e. false negative records; Hirzel & Le Lay, 2008; Jiménez-Valverde et al., 2008), but there are now a number of methods available for dealing with imperfect detection in correlative SDMs (e.g. Eraud et al.,…”
A statistical explanation of MaxEnt for ecologists (2011, background): “…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., 2010).”