The uncertain nature of absences and their importance in species distribution modelling

By Jorge M. Lobo, A. Jiménez‐Valverde, Joaquín Hortal. 2010

656 citations recorded by the source corpus.

Subfields: Ecological Modeling, Genetics, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/2096ca8c66b23549b554475c30fd7cf78e2397ab

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Citations in the corpus

Variation in niche and distribution model performance: The need for a priori assessment of key causal factors (2012, background): “Known presences in relation to probable absences define the occupied area of a species, making crucial an explicit understanding of the type of absence data that are available (Lobo et al., 2010).”
A framework for species distribution modelling with improved pseudo-absence generation (2015, methodology): “In these cases, the RS method introduces337 false absences (within both the realized and fundamental niches) introduc-338 ing a major source of uncertainty (Lobo et al., 2010) and resulting in over-339 20 constrained areas of high suitability (Fig.”
Mechanistic and Correlative Models of Ecological Niches (2015, background): “The reduced nature of NF* with respect to NF can be appreciated also in Figure 2.”
A strict maximum likelihood explanation of MaxEnt, and some implications for distribution modelling (2013, background): “DM has proliferated strongly in recent years, with respect to the diversity of available approaches [see Franklin (2009) and Peterson et al. (2011)] and the rate by which new papers are published (Lobo et al. 2010).”
Accounting for uncertainty when mapping species distributions: The need for maps of ignorance (2011, background): “…and historical events (for detailed at Circolo Giuridico on April 1, 2011ppg.sagepub.comDownloaded from discussion see Colwell and Rangel, 2009; Lobo et al., 2010; Jiménez-Valverde et al., 2008b; Soberón and Nakamura, 2009; Soberón, 2007, 2010), most of which are unknown and difficult to…”
Species Distribution Modeling and Ecological Niche Modeling: Getting the Concepts Right (2012, neutral): “Key words: Geographic Range, Niche, Species Distributions, Macroecology, Biogeography.”
Applying a spread model to identify the entry points from which the pine wood nematode, the vector of pine wilt disease, would spread most rapidly across Europe (2011, methodology): “Although true absence data are essential in distribution models (Václavı́k and Meentemeyer 2009), they are sometimes not available and generating pseudo-absence data can induce some errors in the models (e.g., Elith and Leathwick 2009, Lobo et al. 2010).”
No silver bullets in correlative ecological niche modelling: insights from testing among many potential algorithms for niche estimation (2015, background): “Empirical studies of geographic and environmental distributions of species have seen massive research interest in recent years (Lobo, Jim enez-Valverde & Hortal 2010), generally under the rubric of ‘species distribution modelling’ (SDM) or ‘ecological niche modelling’ (ENM), although conceptual conf”
Using species distribution models in paleobiogeography: A matter of data, predictors and concepts (2011, methodology): “should be used with caution (Lobo and Tognelli, 2011) because the way they are selected has a great influence on the geographic representation of the distribution of the species that is finally obtained (Chefaoui and Lobo, 2008; Anderson and Raza, 2010; Lobo et al., 2010).”
Climatic Risk and Distribution Atlas of European Bumblebees (2015, methodology): “1) but we did not use grids with no data in the species distribution models (Lobo et al., 2010), thus the influence of false absences are expected to be rather low for most species even if some species that are difficult to identify or recently separated taxonomicly will have a less well known distr”
GIFT – A Global Inventory of Floras and Traits for macroecology and biogeography (2019, background): “They aim at providing (near-)complete floristic inventories for a given region and thus provide information on species presences and their floristic status, and additionally allow for the inference of local species absences (Lobo et al., 2010; Jetz et al., 2012).”
Species distribution models that do not incorporate global data misrepresent potential distributions: a case study using Iberian diving beetles (2011, methodology): “…envelope procedure (MDE) to estimate potential species distributions because this procedure allowed us to represent regions with favourable conditions based on observed occurrences (see JiménezValverde et al., 2008; Lobo et al., 2010 and Soberón, 2010 for the conceptual basis of this approach).”
Predicting distributions of invasive species (2013, background): “The species modelling literature (for both equilibrium and invasive species) includes several suggestions about how to establish sensible locations for pseudo-absences or to define reliable absences in the absence of surveyed absences (Le Maitre et al., 2008; Lobo et al., 2010), and new papers with ”