Uncertainty of bioclimate envelope models based on the geographical distribution of species

By Miska Luoto, J. Pöyry, Risto Heikkinen, K. Saarinen. 2005

238 citations recorded by the source corpus.

Subfields: Ecological Modeling, Ecology, Evolution, Behavior and Systematics, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/67f328bdccf721e48fb2cf6207235e2b196565da

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In Ecology, Evolution, Behavior and Systematics

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Citations in the corpus

Species traits explain recent range shifts of Finnish butterflies (2009, methodology): “The NAFI is based on observations made by voluntary amateur and professional lepidopterists using the same 10 km 10 km grid system as used in the climatic data (Saarinen et al., 2003; Luoto et al., 2005).”
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).”
Determinants of the biogeographical distribution of butterflies in boreal regions (2006, background): “…area have a clear negative correlation (rp ¼ )0.252, P 0.012), i.e. species with a scattered distribution pattern (often with low density) have a greater likelihood of observation in cells with a high recording intensity (local over-sampling in urban/semi-urban areas) (see Luoto et al., 2005).”
Ecology of butterflies in Europe (2009, methodology): “maps of predicted occurrence probabilities (Osborne et al., 2001; Austin, 2002; Joy & Death, 2004; Luoto et al., 2005; Strauss & Biedermann, 2005).”
Modelling present and climate-shifted distribution of marine fishes and invertebrates. (2008, methodology): “…envelope is generally identified by studying the relationships between current species occurrences and biogeographical attributes using statistical methods (e.g. generalized additive model, Luoto et al. 2005) or artificial intelligence models (e.g. artificial neural network, Pearson et al. 2002).”
Assessing the vulnerability of European butterflies to climate change using multiple criteria (2010, methodology): “…based on the modelling technique adopted (Lawler et al. 2006; Pearson et al. 2006), the choice of future climate scenarios (Beaumont et al. 2007), and the life-history traits and geographical characteristics of the modelled species (Luoto et al. 2005; McPherson and Jetz 2007; Pöyry et al. 2008).”
The effect of prevalence and its interaction with sample size on the reliability of species distribution models (2009, background): “Luoto et al. 2005), and others found the best models at prevalence values around 0.5 (McPherson et al. 2004).”
Accounting for uncertainty when mapping species distributions: The need for maps of ignorance (2011, methodology): “discussed and/or highlighted some of the problems of SDM (e.g. Araújo and Guisan, 2006; Heikkinen et al., 2006; Hortal and Lobo, 2006; Hortal et al., 2007, 2008; Jiménez-Valverde et al., 2008b; Kadmon et al., 2003, 2004; Lobo, 2008b; Lobo et al., 2007; Loiselle et al., 2003, 2008; Luoto et al., 2005”
Remotely sensed spectral heterogeneity as a proxy of species diversity: Recent advances and open challenges (2010, background): “Moreover, they allow considering land use change which has been proven to be one of the major drivers of species diversity together with climate change (e.g. Luoto et al., 2005).”
Potential impacts of climate change on the distributions and diversity patterns of European mammals (2007, background): “Luoto et al. (2005) suggest that the effect of range size on model performance is caused by the fact that more is known about the ranges of rare species than of many common ones, and that areas containing rare species may be more attractive and therefore experience local over-sampling.”
Species Distribution Modeling in the Tropics: Problems, Potentialities, and the Role of Biological Data for Effective Species Conservation (2009, methodology): “Examples included Araújo et al. (2005b), Heikkinen et al. (2006), García et al. (2007) and Luoto et al. (2005, 2007).”
Exploring the effects of quantity and location of pseudo-absences and sampling biases on the performance of distribution models with limited point occurrence data (2011, unknown): “All rights reserved. doi:10.1016/j.jnc.2010.03.002 1999; Olden et al. 2002; Vaughan & Ormerod 2003; McPherson et al. 2004; Luoto et al. 2005).”