Modeling species’ geographic distributions for preliminary conservation assessments: an implementation with the spiny pocket mice (Heteromys) of Ecuador

By R. P. Anderson, E. Martínez‐Meyer. 2004

254 citations recorded by the source corpus.

Subfields: Ecological Modeling, Ecology, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/b12080958ff95c23a6e08fffba457f33db878e31

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In Ecological Modeling

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In Nature and Landscape Conservation

Citations in the corpus

Ecological niche modelling as a technique for assessing threats and setting conservation priorities for Asian slow lorises (Primates: Nycticebus) (2009, background): “Data on geographical ranges are therefore of paramount importance when delineating the conservation status of a species (Anderson & Martinez-Meyer, 2004; Hernandez et al ., 2006) and in evaluating current levels of threat and protection (Fuller et al ., 2006).”
A maximum entropy approach to species distribution modeling (2004, methodology): “These are the most-often used GARP thresholds (Anderson & Martı́nez-Meyer, 2004).”
The challenge of modeling niches and distributions for data‐poor species: a comprehensive approach to model complexity (2018, background): “Critically, ENM predictions inform conservation assessments, which are especially important for data-poor species (Anderson and Martínez-Meyer 2004, Franklin 2010).”
Climate Change and Biodiversity: Some Considerations in Forecasting Shifts in Species' Potential Distributions (2005, background): “…can be used as “cookiecutters” to trim potential distribution maps under the assumption that the species’ distribution is well-sampled at the level of ecoregions, and that disjunctions among such regions represent distributional barriers for the target species (Anderson and Martínez-Meyer 2004).”
Modelling ecological niches from low numbers of occurrences: assessment of the conservation status of poorly known viverrids (Mammalia, Carnivora) across two continents (2007, background): “Second, temporal correspondence between series of occurrence data and environmental variables like current land-cover classifications is often poor (Anderson & Martinez-Meyer , 2004; Gaubert et al ., 2006).”
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, background): “…been necessary to clarify the degree to which, and under what circumstances, various correlative techniques can estimate a species’ potential distribution (rather than its realized distribution; e.g. Anderson & Martı́nez-Meyer, 2004; Araújo & Guisan, 2006; Soberón, 2007; Morin & Thuiller, 2009).”
Estimating optimal complexity for ecological niche models: A jackknife approach for species with small sample sizes (2013, methodology): “…r t o r h o e i e c e s 2 s r p w t c t t M. Shcheglovitova, R.P. Anderson ost known localities of H. teleus (although the northern portion hat holds records of H. australis is unseasonal evergreen forest; ee Anderson and Martínez-Meyer (2004) for possible competitive xclusion between the species).”
Maxent modeling for predicting impacts of climate change on the potential distribution of Thuja sutchuenensis Franch., an extremely endangered conifer from southwestern China (2017, background): “…range of a species given presence-only occurrence data and environmental variables assumed to influence its distribution (Raxworthy et al., 2003; Anderson and Martinez-Meyer, 2004; Franklin andMiller, 2009; Thorn et al., 2009; Elith and Leathwick, 2009; Peterson et al., 2011;Wilson et al.,…”
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, background): “(2006), or the seven to 12 presences used by Anderson & Martinez-Meyer (2004). Others have merely mentioned the potential drawbacks of small data sets (Stockwell & Peters 1999; Reese et al.”
Species-specific tuning increases robustness to sampling bias in models of species distributions: An implementation with Maxent (2011, background): “…the need for transferring a model to another time period or place, conservation assessments, macroecological analyses, distributional studies of zoonotic diseases, and many other uses need models with an optimal level of complexity (Anderson and Martínez-Meyer, 2004; Peterson et al., in press).”
Predicting species distributions from herbarium collections: does climate bias in collection sampling influence model outcomes? (2007, background): “In such cases, the potential geographical distributions of species can be ‘corrected’ to reflect true distributions more accurately (e.g. Loiselle et al., 2003; Anderson & Martı́nez-Meyer, 2004).”