Ensemble forecasting of species distributions.

By Miguel B. Araújo, M. New. Trends in Ecology & Evolution, 2007

3,430 citations recorded by the source corpus.

Subfields: Ecological Modeling, Global and Planetary Change, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/6d5faca2dc2613067d0399451c0a91c05ed5f5bd

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Citations in the corpus

Evidence of climatic niche shift during biological invasion. (2007, methodology): “As recently suggested, we use a combination of these techniques to adjust for the inherent uncertainty from these models and to find the optimal solution from an ensemble of predictions (Thuiller 2004; Araujo & New 2007 ).”
A standard protocol for reporting species distribution models (2020, methodology): “…for exploring the range of predictions given the different uncertainties (initial conditions, model classes, model parameters, boundary conditions, Araújo and New 2007, Thuiller et al. 2019), but it is also increasingly used for mapping, e.g. to model rare species (Breiner et al. 2015, 2018, Hao…”
Predicting the past distribution of species climatic niches. (2009, methodology): “We can achieve better hindcasting by using ensemble procedures ( Araujo & New, 2007 ).”
Correlative and mechanistic models of species distribution provide congruent forecasts under climate change (2010, background): “There are attempts to reduce this problem through ensemble methods that average across many different modeling approaches and data treatments (Araújo & New 2007).”
Barriers to globally invasive species are weakening across the Antarctic (2017, methodology): “This provides a more robust methodology than using singular SDMs and reduces the impact of individual model biases (Araújo & New, 2007).”
Global Amphibian Extinction Risk Assessment for the Panzootic Chytrid Fungus (2009, background): “Methodical inherent uncertainties can be assessed comparing multiple SDMs in ensembles of models providing more robust predictions [34].”
From climate change predictions to actions – conserving vulnerable animal groups in hotspots at a regional scale (2010, methodology): “…caution given all the uncertainties in the process: the statistical methods used for modelling species distributions, the GCMs used to predict future climate conditions (e.g. Araújo & New, 2007; Beaumont et al., 2008), the scale of the analysis (Seo et al., 2009), and specific dispersal abilities.”
Partitioning and mapping uncertainties in ensembles of forecasts of species turnover under climate change (2009, methodology): “…establish which models are more adequate to a particular problem, a potential solution to take inter-model variability into account is to fit multiple models, or ensembles, and combine them into some sort of consensus forecast (for reviews see Araújo and New 2007, Leutbecher and Palmer 2008).”
Adaptive phenotypic plasticity and resilience of vertebrates to increasing climatic unpredictability. (2010, background): “Ecoclimatic niche models also link the occurrence of phenotypes to ecoclimatic variables, such as temperature, or indicators of primary production (Pettorelli et al. 2005, Araújo & New 2007).”
The effect of sample size on the accuracy of species distribution models: considering both presences and pseudo‐absences or background sites (2019, methodology): “Ensemble models, especially with consensus methods, typically refer to approaches that employ multiple modelling techniques (Araújo and New 2007, Marmion et al. 2009, Thuiller et al. 2009, Grenouillet et al. 2011). In this study, they have been used within modelling techniques. We have shown that ”
Pest Risk Maps for Invasive Alien Species: A Roadmap for Improvement (2010, background): “While imperfect knowledge of complex ecological systems is well recognized, recent studies that compared species-distribution models illustrate model uncertainty well, showing clearly that different models can give divergent results (Elith et al. 2006, Pearson et al. 2006, Araújo and New 2007).”
BIOMOD – a platform for ensemble forecasting of species distributions (2009, methodology): “There are a range of approaches to do this (for review see Araújo and New 2007), but in BIOMOD weights are currently calculated on the basis of models’ predictive accuracy on test data (i.e. a form of ‘‘stacking’’).”