BIOMOD – optimizing predictions of species distributions and projecting potential future shifts under global change

By Wilfried Thuiller. 2003

1,057 citations recorded by the source corpus.

Subfields: Ecological Modeling, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/daca38cf99ae99bdab1206efddd2ca560989d860

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In Ecological Modeling

In Nature and Landscape Conservation

Citations in the corpus

Endemic species and ecosystem sensitivity to climate change in Namibia (2006, methodology): “For each species, the model with the highest AUC was retained to project future distributions (Thuiller, 2003).”
Species distribution modelling of marine benthos: a North Sea case study (2011, methodology): “…with the BIOMOD package 1.1-5 for GLMs, generalised boosting models (GBM), MARS, random forests (RF) and mixture discriminant analysis (MDA = FDA) (Thuiller 2003, R Development Core Team 2009), and the openModeller 1.0.9 framework for the Bioclimatic Envelope (BIOCLIM), the genetic algorithm for…”
A review of evidence about use and performance of species distribution modelling ensembles like BIOMOD (2019, methodology): “We directly searched for all citing articles (up to October 2016 when this review was initiated) of Thuiller (2003) and Thuiller et al. (2009) in the Web of Science database, yield‐ ing 443 and 475 results respectively (787 in total after remov‐ ing duplicates).”
Climate warming and the decline of amphibians and reptiles in Europe (2006, methodology): “Details on model parameterization are provided in Thuiller (2003).”
Potential impacts of climatic change upon geographical distributions of birds (2006, positive): “2002, 2004, Peterson 2003); faced with this diversity one worker has advocated applying a suite of alternatives and choosing that model that performs ‘best’ according to ‘expert opinion’ (Thuiller 2003).”
Species distribution models and ecological theory: A critical assessment and some possible new approaches (2007, methodology): “Current best practice for assessing model success for presence/absence data is AUC (Pearce and Ferrier, 2000; Rushton et al., 2004; Thuiller, 2003), while a number of different measures are used for quantitative data (Moisen and Frescino, 2002; Moisen et al., 2006).”
The effect of species response form on species distribution model prediction and inference (2009, neutral): “…in fitting the relationship between the probability of presences and a set of predictors, a higher order polynomial fitting function was introduced in LR (Ferrer-Castan et al., 1995; Guisan et al., 1999; Thuiller, 2003), i.e. log ( p 1 − p ) = ˇ + f (X1) + · · · + f (Xn) (5) where f(X1),. .”
Why does phenology drive species distribution? (2010, methodology): “fication methods (Thuiller 2003; Thuiller et al 2003, 2005).”
Ditch the niche – is the niche a useful concept in ecology or species distribution modelling? (2012, background): “Not according to the original papers (Thuiller, 2003; Thuiller et al., 2009), where neutral terminology is used.”
Quarantine arthropod invasions in Europe: the role of climate, hosts and propagule pressure (2014, methodology): “…software based on temperature and precipitation parameters, such as CLIMEX (Version 3, Hearne Scientific Software, Melbourne 2007) and Biomod (Thuiller, 2003), have been used to predict the potential distribution ranges of alien insects based on climatic conditions in their existing and…”
Distribution models predict large contractions of habitat‐forming seaweeds in response to ocean warming (2018, methodology): “Model accuracy of supplementary model algorithms used to confirm variable selection was additionally assessed by calculating AUC values for GLMs and GAMs with the R package BIOMOD2 (Thuiller et al. 2016).”
Soil nutritional factors improve models of plant species distribution: an illustration with Acer campestre (L.) in France (2006, methodology): “Most of these climatic variables have been used in recent studies dealing with plant species distribution modelling (e.g. Leathwick & Rogers, 1996; Leathwick & Whitehead, 2001; Pearson et al., 2002; Thuiller, 2003).”
Response of British lichens to climate change scenarios: Trends and uncertainties in the projected impact for contrasting biogeographic groups (2007, background): “Advances in the bioclimatic envelope approach have been largely methodological, and have tested variations in statistical design and modelling (Thuiller, 2003; Thuiller et al., 2003; Segurado and Araújo, 2004; Luoto et al., 2005; Thuiller et al., 2004; Araújo et al., 2005a; Araújo and Guisan,…”
Climate increases regional tree‐growth variability in Iberian pine forests (2007, background): “Moreover, special caution was recommended for species growing close to their distribution limits due to the failure of models to collect the complete suitable environmental range (Thuiller, 2003).”
Improving species distribution models: the value of data on abundance (2014, methodology): “1a,b; paired t-tests, kappa t344 = 13 23, P < 0 01, AUC t344 = 3 72, P < 0 01).”
Climate‐induced changes in the distribution of freshwater fish: observed and predicted trends (2013, methodology): “Indeed, forecasting potential distributional shifts in freshwater fish in response to projected climate scenarios has become a popular conservation tool, favoured by the recent development of many statistical methods that are now applied routinely (e.g. Thuiller, 2003).”
Modelling exploration of the future of European beech (Fagus sylvatica L.) under climate change—Range, abundance, genetic diversity and adaptive response (2010, methodology): “Weused the BIOMOD framework (Thuiller, 2003, 2004; Thuiller et al., 2009) implemented under the R-software.”
A physiological analogy of the niche for projecting the potential distribution of plants (2012, methodology): “In fact, recent attempts to model the distribution of plant species rely almost exclusively on the basis of (increasingly sophisticated) correlative methods, which although useful for hypothesis generation (Guisan & Zimmermann, 2000; Thuiller, 2003; Elith et al., 2006) are far removed from the physi”