Predicting distributions of Mexican birds using ecological niche modelling methods

By A. Townsend Peterson, Lisa G. Ball, Kevin P. Cohoon. 2002

171 citations recorded by the source corpus.

Subfields: Ecological Modeling, Ecology, Genetics

View Paper: https://www.semanticscholar.org/paper/36288d6247a356b3fa83d29d70e21451a2af07e8

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Citations in the corpus

Reconstructing the Pleistocene geography of the Aphelocoma jays (Corvidae) (2004, methodology): “…for both past and present, tests of ecological niche conservatism across significant periods of time and drastic climate changes can greatly inform future projections of species’ distributional potential in future changing climates (Peterson et al., 2002b; Peterson, 2003b; Thomas et al., 2004).”
Using niche-based GIS modeling to test geographic predictions of competitive exclusion and competitive release in South American pocket mice (2002, background): “…distributions (Peterson and Cohoon 1999, Peterson et al. 1999, Chen and Peterson 2000, Godown and Peterson 2000, SánchezCordero and Martı́nez-Meyer 2000, Peterson 2001, Peterson and Vieglais 2001, Peterson et al. 2001, Anderson et al. 2002, Stockwell and Peterson 2002, Peterson et al. 2002).”
Ecological niches as stable distributional constraints on mammal species, with implications for Pleistocene extinctions and climate change projections for biodiversity (2004, background): “Previous such model tests have shown that small test sample sizes strongly affect ability to discern between good and bad models (Peterson et al., 2002a), and indeed our tests of the adequacy of models or model tests based on 5 points suggest that these species would likely show intertemporal…”
Can species distribution modelling provide estimates of population densities? A case study with jaguars in the Neotropics (2012, background): “The use of models to predict species’ potential distribution can be useful for several purposes, such as indicating where to establish new conservation units, guiding field research more efficiently, predicting potential climate change impacts and indicating areas for habitat restoration or species ”
Invasion dynamics and potential spread of the invasive alien plant species Ageratina adenophora (Asteraceae) in China (2006, background): “GARP’s predictive abilities have been tested and proven under diverse circumstances (Peterson & Cohoon, 1999; Peterson et al ., 1999, 2002, 2003; Peterson & Vieglais, 2001; Peterson, 2001; Anderson et al ., 2002; Oberhauser & Peterson, 2003; Rice et al ., 2003).”
Urban flood risk mapping using the GARP and QUEST models: A comparative study of machine learning techniques (2019, background): “A complete mathematical and technical description of GARP model can be found in Peterson et al. (2002b) and Fitzpatrick et al. (2007).”
SEASONAL NICHES OF NEARCTIC-NEOTROPICAL MIGRATORY BIRDS: IMPLICATIONS FOR THE EVOLUTION OF MIGRATION (2004, methodology): “That is, on the basis of 50% random splits of input data, the models predicted the distribution of the independent test data set signifi cantly be er than random models, as has been found in previous analyses using the same data sets (Peterson 2001, Peterson et al. 2002a).”
An evaluation of a GARP model as an approach to predicting the spatial distribution of non‐vagile invertebrate species (2006, methodology): “…BIOCLIM models because GARP includes logistic regression and range envelopes as two of the four methods employed by default in the algorithm (Anderson et al ., 2002; Stockwell et al. , 1999; Peterson et al ., 2002b); however, few comparative analyses have been published to support this postulate.”
Evaluating predictive models of species’ distributions: criteria for selecting optimal models (2003, background): “GARP has proven especially successful in predicting species' potential distributions under a wide variety of situations (Peterson and Cohoon, 1999; Peterson et al., 1999, 2001, 2002a,b,c; Godown and Peterson, 2000; Sánchez-Cordero and Martínez-Meyer, 2000; Peterson, 2001; Elith and Burgman, 2002;…”
Modeling species’ geographic distributions for preliminary conservation assessments: an implementation with the spiny pocket mice (Heteromys) of Ecuador (2004, background): “Because true absence data are lacking for most species (Funk et al., 1999; Ponder et al., 2001; Stockwell and Peterson, 2002a), drawbacks exist to the most common portrayals of geographic ranges in field guides and taxonomic revisions (Rapoport, 1982; Ceballos and Ehrlich, 2002).”
Real vs. artefactual absences in species distributions: tests for Oryzomys albigularis (Rodentia: Muridae) in Venezuela (2003, background): “…a wide variety of conditions and is especially suited to use with presence-only occurrence data (Stockwell & Noble, 1992; Peterson & Cohoon, 1999; Peterson et al., 1999, 2001, 2002a,b,c,d; Stockwell & Peters, 1999; Chen & Peterson, 2000; Elith, 2000; Godown & Peterson, 2000; Sánchez-Cordero &…”
The importance of biotic interactions for modelling species distributions under climate change (2007, background): “…‘envelope’ modelling approach, whereby present-day distributions of species are combined with environmental variables to enable projected distributions of species under future climate scenarios (e.g. Berry et al ., 2002; Peterson et al ., 2002; Thuiller et al ., 2005; Araújo et al ., 2006).”
Reducing uncertainty in projections of extinction risk from climate change (2005, background): “Erasmus et al., 2002; Midgley et al., 2002; Peterson et al., 2002; Thomas et al., 2004).”
Predicting the Distribution of the Amphibian Pathogen Batrachochytrium dendrobatidis in the New World 1 (2005, result): “These results parallel those of other investigations that have shown a high predictive power of distribution ranges from niche modeling (e.g., Anderson et al. 2002b, 2003; Peterson et al. 2002a, 2003, 2004; Peterson 2003; Ortega-Huerta & Peterson 2004).”
Modelling spatial patterns of biodiversity for conservation prioritization in North‐eastern Mexico (2004, methodology): “GARP was selected because of ample testing of its predictive ability (Peterson & Cohoon, 1999; Peterson, 2001), particularly in Mexico (Peterson et al ., 2002), and its demonstrated robustness in modelling species’ distributions even at relatively low sample sizes (Peterson & Cohoon, 1999;…”