Predicting species distributions across the Amazonian and Andean regions using remote sensing data

By W. Buermann, Sassan S. Saatchi, T. Smith, Brian R Zutta, Jaime A Chaves, Borja Milá, Catherine H. Graham. 2008

241 citations recorded by the source corpus.

Subfields: Ecological Modeling, Ecology, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/c927720c196909d47dd491565df02e383e903095

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In Ecological Modeling

In Ecology

In Nature and Landscape Conservation

Citations in the corpus

Phytoplankton niches estimated from field data (2012, methodology): “produced two sets of models: one containing all of the environmental predictors, for the purpose of predicting species presence probabilities and assessing model utility, and a second set of models using just one environmental predictor at a time for the purpose of characterizing the species respons”
Species distribution modelling for the people: unclassified landsat TM imagery predicts bird occurrence at fine resolutions (2013, background): “Because mean bird counts per station tended to be low (< 2) for most species and we were interested in estimating probability of occurrence in a way that is consistent with other species distribution modelling efforts (e.g. Guisan & Thuiller, 2005; Buermann et al., 2008), we reduced relative abundan”
Beyond climate control on species range: The importance of soil data to predict distribution of Amazonian plant species (2018, background): “Several studies have used spatial correlative models to predict current species distributions (Buermann et al., 2008; Moscoso, Albernaz, & Salom~ao, 2013; Vedel-Sørensen, Tovaranonte, Bøcher, Balslev, & Barfod, 2013) and potential range shifts under climate change for Amazonian species (Miles, Grain”
ENMTools: a toolbox for comparative studies of environmental niche models (2010, background): “studies compare predicted habitat suitabilities generated by ENMs from different species or populations to aid in species delimitation or test hypotheses about niche conservation (Buermann et al. 2008, Lentz et al. 2008, Warren et al. 2008). Comparative analyses of ENMs also permit studies of niche ”
A strict maximum likelihood explanation of MaxEnt, and some implications for distribution modelling (2013, background): “Simpler models in terms of number of parameters have the additional advantage of being more easily interpretable than more complex models (Buermann et al. 2008, Parolo et al. 2008, Wollan et al. 2008, Warren & Seifert 2011) and may therefore be more useful for understanding which factors are…”
Observing Changing Ecological Diversity in the Anthropocene (2013, background): “Global coverage allows analyses and models based on complete coverage, rather than on sparse statistical samples (Buermann et al 2008).”
Measuring and modelling biodiversity from space (2008, methodology): “However, a number of recent studies have used radar backscatter from QSCAT (Buermann et al., 2008; Saatchi et al., 2008) and SIR (Bergen et al., 2007) to improve species distribution models Source: Pearson and Dawson (2003). by providing information on vegetation structure.”