The performance of state-of-the-art modelling techniques depends on geographical distribution of species.

By Mathieu Marmion, Miska Luoto, Risto Heikkinen, Wilfried Thuiller. 2009

195 citations recorded by the source corpus.

Subfields: Ecological Modeling, Ecology, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/eccc9ce622035cc8147ee740a62c9c0cf8cf761c

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In Ecological Modeling

In Ecology

In Nature and Landscape Conservation

Citations in the corpus

Assessing the vulnerability of European butterflies to climate change using multiple criteria (2010, methodology): “Recent applications in species distribution modelling have shown that RF provides high classification accuracy, and it often outperforms other modelling techniques (Lawler et al. 2006; Prasad et al. 2006; Cutler et al. 2007; Marmion et al. 2009b).”
Climatic niche shifts between species' native and naturalized ranges raise concern for ecological forecasts during invasions and climate change (2014, methodology): “Conversely, when tested using current native data alone, SDMs appear to be accurate for species with small native ranges, suggesting they are climate specialists (Franklin et al., 2009; Marmion et al., 2009).”
Species distribution modelling of marine benthos: a North Sea case study (2011, background): “Given the need for ecosystem-based fisheries management, most recent studies using SDMs in marine ecosystems have focussed on the distribution of commercial fish species (Venables & Dichmont 2004, Maxwell et al. 2009, Moore et al. 2010, Lenoir et al. 2011).”
Identifying priority areas for reducing species vulnerability to climate change (2012, background): “Furthermore, consistent with other studies (Marmion et al., 2009a), the current climate ensemble forecast outperformed all of the individual models.”
Comparing species distribution models constructed with different subsets of environmental predictors (2015, methodology): “All algorithms were implemented using the biomod2 package (Thuiller et al.,2013) in R. Detailed descriptions of the GLM, MARS, GBM and RF algorithms can be found in Marmion et al. (2009a), and in Phillips et al. (2006) for Maxent.”
A strict maximum likelihood explanation of MaxEnt, and some implications for distribution modelling (2013, background): “Other studies in which relationships between AUC and prevalence have been addressed, include Luoto et al. (2005), Franklin et al. (2009), and Marmion et al. (2009).”
The 'dirty dozen': socio-economic factors amplify the invasion potential of 12 high-risk aquatic invasive species in Great Britain and Ireland. (2013, background): “Species with a large number of occurrences spread over several continents, some far from the core of the species’ distribution, have a higher prevalence but are likely to yield low suitability scores (Marmion et al. 2009), which explains low minimum training suitability reported for species like A.”