Predicting patterns of plant species richness in megadiverse South Africa

By Wilfried Thuiller, Guy F. Midgley, Mathieu Rouget, Richard M. Cowling. 2006

145 citations recorded by the source corpus.

Subfields: Ecological Modeling, Ecology, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/0a0f737907baed69727cff695bf5f32dfe78f00e

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In Ecological Modeling

In Ecology

In Nature and Landscape Conservation

Citations in the corpus

Climate change hastens the turnover of stream fish assemblages (2008, methodology): “Taking into account ecological characteristics of species should be helpful to identify similar responses across contrasted assemblages (Thuiller et al., 2004, 2006b) and should thus enhance our understanding of potential impacts of climate change on stream fish assemblages.”
Consensual predictions of potential distributional areas for invasive species: a case study of Argentine ants in the Iberian Peninsula (2009, methodology): “We used a permutation method, which randomly resamples each predictor variable independently and computes the associated reduction in predictive performance (Thuiller et al. 2006).”
SESAM – a new framework integrating macroecological and species distribution models for predicting spatio‐temporal patterns of species assemblages (2011, methodology): “These approaches have been widely used in the last two decades (e.g. Pausas & Austin, 2001; Currie et al., 2004; Thuiller et al., 2006; Rahbek et al., 2007; Currie & Kerr, 2008; Algar et al., 2009).”
Vulnerability of global biodiversity hotspots to climate change (2021, background): “…in shaping biodiversity patterns, particularly in low- energy, low- rainfall hotspots outside of the tropics, such as those with mediterranean climates, where other conventional drivers of diversity do not offer sufficient explanation (O'Brien, 1993; O'Brien et al., 2000; Thuiller et al., 2006).”
Ecology of butterflies in Europe (2009, methodology): “Boosted regression trees (BRT, Friedman, 2002; Thuiller et al., 2006; Elith et al., 2008) are a combination of the boosting algorithm and regression trees.”
Using species distribution models to inform IUCN Red List assessments (2014, background): “AET is the amount of water loss given existing evaporative energy in a system and the available water provided by precipitation and storage in the soil (Frank and Inouye 1994), while AET/PET is the index of humidity (Thuiller et al. 2006), which estimates the drought stress as evaporative demand tha”
Predicting spatial patterns of plant species richness: a comparison of direct macroecological and species stacking modelling approaches (2011, background): “These models can also help to improve our understanding of biodiversity patterns and community assembly processes (Gioia & Pigott, 2000; Gould, 2000; Moser et al., 2005; Thuiller et al., 2006; NoguesBravo et al., 2008).”
A comparison of predictive methods in modelling the distribution of periglacial landforms in Finnish Lapland (2008, methodology): “The boosted classifier’s prediction is based on an accuracy weighted vote across the estimated classifiers (Thuiller et al., 2006).”