Modelling ecological niches from low numbers of occurrences: assessment of the conservation status of poorly known viverrids (Mammalia, Carnivora) across two continents

By Monica Papes, Philippe Gaubert. 2007

326 citations recorded by the source corpus.

Subfields: Ecological Modeling, Ecology, Genetics

View Paper: https://www.semanticscholar.org/paper/d0f5e5d4f32a8923b78f8879eb91795bceb89b3a

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Citations in the corpus

Environmental Niche Equivalency versus Conservatism: Quantitative Approaches to Niche Evolution (2008, unknown): “…of algorithm (e.g., Elith et al. 2006; Guisan and Zimmerman 2000), choice of environmental variables (e.g., Parra et al. 2004; Peterson and Nakazawa 2008), spatial resolution (e.g., Engler et al. 2004; Guisan et al. 2007a,b), and sample size (e.g., Guisan et al. 2007b; Papes and Gaubert 2007).”
Estimating optimal complexity for ecological niche models: A jackknife approach for species with small sample sizes (2013, background): “…niche models (ENMs) and species distribution models SDMs) based on presence-only occurrence data constitute widely sed tools for many areas of biogeographic research, as well as for onservation planning (Papeş and Gaubert, 2007; Wilting et al., 010; Lawler et al., 2011; Anderson, 2013).”
Biogeography and conservation of taxa from remote regions: An application of ecological-niche based models and GIS to North-African canids (2009, methodology): “…performed well in comparison to other methods (Elith et al., 2006), especially at low samples sizes (Hernandez et al., 2006), and it has been used successfully in ecological-niche based modelling (e.g. Papes and Gaubert, 2007; Brito et al., 2008; Martínez-Freiría et al., 2008; Torres et al., 2009).”
The challenge of modeling niches and distributions for data‐poor species: a comprehensive approach to model complexity (2018, background): “More generally, including data-poor species is necessary for comprehensive analyses identifying areas for preservation (i.e. reserve selection; Papeş and Gaubert 2007, Lawler et al. 2011), understanding macroecological patterns, and synthetic studies of the effect of past environmental change on…”
Applications and limitations of museum data for conservation and ecology, with particular attention to species distribution models (2010, background): “Several studies have used distribution models to assess the protection afforded to threatened species by existing protected areas networks and to propose additions to these networks that would benefit the species concerned (Papeş and Gaubert, 2007; Solano and Feria, 2007; Thorn et al., 2009).”
The effects of small sample size and sample bias on threshold selection and accuracy assessment of species distribution models (2012, background): “…for most species, small datasets (generally considered 100 occurrence points (Hernandez et al. 2006), due either to species rarity or incomplete historical collections, are increasingly being used in suitability modeling (Gibson et al. 2007, Papes and Gaubert 2007, DeMatteo and Loiselle 2008).”
Minimum required number of specimen records to develop accurate species distribution models (2016, background): “Model performance is known to rapidly decrease for sample sizes smaller than 20 (Stockwell and Peterson 2002) or 15 (Papeş and Gaubert 2007), and is dramatically poor for samples sizes smaller than 5 records (Pearson et al.”
Maxent modeling for predicting suitable habitat for threatened and endangered tree Canacomyrica monticola in New Caledonia (2009, background): “…which has been found to perform best among many different modeling methods (Elith et al., 2006; Ortega-Huerta and Peterson, 2008), and may remain effective despite small sample sizes (Hernandez et al., 2006; Pearson et al., 2007; Papes and Gaubert, 2007; Wisz et al., 2008; Benito et al., 2009).”
Ecological niche modelling as a technique for assessing threats and setting conservation priorities for Asian slow lorises (Primates: Nycticebus) (2009, methodology): “Reducing the minimum number of occurrences required to produce useful predictions of species distribution will greatly increase the number of species that can be studied (Pearson et al., 2007).”
Effects of the number of presences on reliability and stability of MARS species distribution models: the importance of regional niche variation and ecological heterogeneity (2010, methodology): “If working with a limited number of presences, other methods, especially Maxent, can generate better models (Hernandez et al. 2006; Phillips et al. 2006; Papeş & Gaubert 2007; Pearson et al. 2007; Wisz et al. 2008).”
Selecting thresholds for the prediction of species occurrence with presence‐only data (2013, methodology): “In this situation, it is commonly accepted that the methods developed for presence/absence data are no longer applicable (Jim enez-Valverde & Lobo, 2007; Pearson et al., 2007), and that selecting an appropriate threshold is problematic (Papes & Gaubert, 2007; Rebelo & Jones, 2010).”
MaxEnt versus MaxLike: empirical comparisons with ant species distributions (2013, background): “…open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. http://creativecommons.org/licenses/by/3.0/ E-mail: mfitzpatrick@umces.edu”
Using species distribution models to inform IUCN Red List assessments (2014, methodology): “Recent studies have used SDMs to estimate species ranges and occupied areas for the purposes of informing IUCN Red List assessments (e.g. Cardoso et al. 2011; Jiménez-Alfaro et al. 2012; Papes and Gaubert 2007; Pena et al. 2014; Sergio et al. 2007).”
Profile or group discriminative techniques? Generating reliable species distribution models using pseudo‐absences and target‐group absences from natural history collections (2010, background): “Maxent, on the contrary, can automatically generate its own pseudo-absences and seems to produce stable models with very good predictive performance for small sample sizes (Hernandez et al., 2006; Phillips et al., 2006; Papeş & Gaubert, 2007; Pearson et al., 2007).”