Predicting species distributions in poorly-studied landscapes
By P. Hernández, Irma Franke, Sebastián K. Herzog, Víctor Pacheco, L. Paniagua, Heidi Quintana, A. Soto, Jennifer J. Swenson, C. Tovar, T. Valqui and 2 more. Biodiversity and Conservation, 2008
236 citations recorded by the source corpus.
Subfields: Ecological Modeling, Ecology, Nature and Landscape Conservation
View Paper: https://www.semanticscholar.org/paper/2a032d0c7ad72cdb3c5a3729fe86a1fcb57bec39
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In Ecological Modeling
In Ecology
In Nature and Landscape Conservation
Citations in the corpus
- Using species distribution models to predict new occurrences for rare plants (2009, result): “The Kappa statistic confirmed what the AUC values indicated and what others have also found in SDM comparisons (Hernandez et al., 2008): namely, that RF and ME were the best performing models and had the highest mean correlation with one another.”
- Application of species distribution models and conservation planning software to the design of a reserve network for the riverine fishes of northeastern Mesoamerica (2011, background): “a species’ range, inaccurate georeferencing of sites, or mixing localities sampled using different gears of levels of effort (Phillips et al., 2006; Hernandez et al., 2008).”
- Overview of random forest methodology and practical guidance with emphasis on computational biology and bioinformatics (2012, background): “Garz´on et al. [27], Evans and Cushman [25], Cutler et al. [23] and Hernandez et al. [35] predict the presence of a species from climatic and topographic variables and Peters et al. [60] show that RF performs well in the prediction of vegetation types from environmental variables.”
- Species diversification and phylogeographical patterns of birds in response to the uplift of the Qinghai-Tibet Plateau and Quaternary glaciations (2014, methodology): “New methodological approaches, such as the MAXENT and GARP methods based on current distribution patterns, have been proven to provide better accuracy in the predictions of past distribution (Elith et al., 2006; Hernandez et al., 2008).”
- Correction note: Poisson point process models solve the “pseudo-absence problem” for presence-only data in ecology (2010, methodology): “Instead, choice of the number of pseudo-absences is ad hoc, and a total of 1000–10,000 pseudo-absences is usually used [Elith and Leathwick (2007); Hernandez et al. (2008)], although sometimes even fewer [Zarnetske, Edwards and Moisen (2007)].”
- Mining data with random forests: A survey and results of new tests (2011, background): “RF have been used for large variety of tasks, including: identification of DNA-binding proteins [13], segmentation of video objects [14], classification of hyper-spectral data [15,16], prediction of the vegetation type occurrence in Belgian lowland valley based on spatially distributed measurements ”
- Impact of the quality of climate models for modelling species occurrences in countries with poor climatic documentation: a case study from Bolivia (2010, methodology): “…climate in North America and Europe (Hijmans et al., 2005), and consequently has been used repeatedly used in a wide range of biogeographic studies, including testing the potential effects of climate change on species ranges (Araújo and Rahbek, 2006; Graham et al., 2006; Hernandez et al., 2008).”