Predicting species distributions from small numbers of occurrence records: A test case using cryptic geckos in Madagascar

By Richard G. Pearson, C. Raxworthy, Miguel Nakamura, A. Townsend Peterson. 2006

3,236 citations recorded by the source corpus.

Subfields: Ecological Modeling, Ecology, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/1a14b1e94a854cf76ef1ba1c2ab1c7baa65db346

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In Ecological Modeling

In Ecology

In Nature and Landscape Conservation

Citations in the corpus

Using species distribution modelling to disentangle realised versus potential distributions for rare species conservation (2013, methodology): “Studies abound that have successfully applied species distribution modelling techniques to existing species occurrence data to predict spatially-explicit species ranges even when data is scarce and presence-only (Elith et al., 2006; Pearson et al., 2007; Phillips and Dudík, 2008; Rebelo and Jones, 2”
Distribution modelling and statistical phylogeography: an integrative framework for generating and testing alternative biogeographical hypotheses (2007, background): “For example, generating an accurate projection of a species’ distribution typically requires samples from at least 20 localities (Stockwell & Peterson, 2002; but see Pearson et al., 2007).”
Influence of bioclimatic variables on tree‐line conifer distribution in the Greater Yellowstone Ecosystem: implications for species of conservation concern (2008, background): “Although constraints exist on the applicability of niche models and the results of such models must be interpreted with caution, species distribution models can provide important biogeographical information about species (Pearson et al., 2007), and the principle behind using a biophysical envelope t”
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”
Knowing the past to predict the future: land‐use change and the distribution of invasive bullfrogs (2010, methodology): “Suitability of each cell was based on the 10th percentile of training presence points (Pearson et al., 2007).”
Dealing with uncertain absences in habitat modelling: a case study of a rare ground‐dwelling parrot (2007, methodology): “not requiring pseudo-absences) such as Domain, GARP, and BIOCLIM (Elith et al., 2006; Hernandez et al., 2006; Pearson et al., 2006; Phillips et al., 2006).”
Modelling horses for novel climate courses: insights from projecting potential distributions of native and alien Australian acacias with correlative and mechanistic models (2011, background): “Model comparison and validation was facilitated by defining a threshold above which model projections are considered to be suitable for the species (Pearson et al., 2007).”
Selecting thresholds for the prediction of species occurrence with presence‐only data (2013, methodology): “In addition, binary predictions are required to evaluate models when using accuracy measures derived from confusion matrices (e.g. Phillips et al., 2006; Pearson et al., 2007).”
Applications of species distribution modeling to paleobiology (2011, methodology): “Jackknife approaches are recommended for validating models based on small numbers of occurrences (Pearson et al., 2007), and are thus particularly useful”