Correlative and mechanistic models of species distribution provide congruent forecasts under climate change

By Michael R. Kearney, Brendan A. Wintle, Warren P. Porter. 2010

506 citations recorded by the source corpus.

Subfields: Ecological Modeling, Ecology, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/aaeb0add4993fcd7782c72a414b5084176ddd387

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Citations in the corpus

Predicting organismal vulnerability to climate warming: roles of behaviour, physiology and adaptation (2012, background): “Perhaps the ﬁrst step is developing a full mechanistic understanding of factors that increase endotherm vulnerability to warming [26,27].”
Biotic interactions influence the projected distribution of a specialist mammal under climate change (2012, background): “, 2007), and it performs well under current, past and future climate scenarios, matching closely the predictions of models produced by mechanistic approaches (Hijmans & Graham, 2006; Kearney et al., 2010).”
Exploring consensus in 21st century projections of climatically suitable areas for African vertebrates (2011, background): “Research is ongoing to develop more biologically realistic models (Keith et al. , 2008; Anderson et al. , 2009; Brook et al. , 2009; Huntley et al. , 2010; Kearney et al. , 2010), but the breath of information and data required to appropriately parameterize them is large.”
Comparing sample bias correction methods for species distribution modeling using virtual species (2021, background): “…variable selection) may be better accomplished with unbiased presence and absence data (Guisan and Zimmermann 2000) or in combination with mechanistic models incorporating biophysical ecology and functional traits (Kearney et al. 2010a, b) rather than PB SDM, even with bias correction.”
Extinction risk in cloud forest fragments under climate change and habitat loss (2013, background): “It has also been shown to be of great utility when projecting models into novel environments (i.e. future scenarios; Hijmans & Graham, 2006; Kearney et al., 2010; Ponce-Reyes et al., 2012).”
Modelling climate-change-induced shifts in the distribution of the koala (2011, methodology): “It is widely used for modelling shifts in species’ distributions (e.g. Fitzpatrick et al. 2008; Carroll et al. 2010; Kearney et al. 2010) and has shown good correspondence with mechanistic models of Australian mammal physiological responses under projected changes in climate (Kearney et al.”
Managing for change: wetland transitions under sea‐level rise and outcomes for threatened species (2011, methodology): “The spatial, quantitative output of SLAMM simulations allows coupling with demographic models, providing insight into outcomes for species conservation that may not have been immediately apparent (Kearney et al., 2010).”
Predicting distributions of invasive species (2013, background): “These comparisons often show congruence of predictions in the regions in which the correlative model was trained, and a range of outcomes (from congruence to dissimilarity) for predictions for novel times or places (Kearney et al. 2010; Morin & Thuiller 2009).”
Analysing and mapping species range dynamics using occupancy models (2013, background): “In this context, predictive models of species distributions are even more critical in the face of climate change, because they allow an understanding of how the distributions of species might change under different projected climatic scenarios (e.g. Hannah et al., 2005; Kearney et al., 2010).”
Guidance on quantitative pest risk assessment (2018, background): “Correlative SDMs convey practical advantages over more mechanistic modelling methods due to the simplicity and flexibility of their data requirements, their relative ease of use within freeware packages, and the range of the interactions (biotic and abiotic) they can detect and characterise (Kearney”
Comparison of Forecasting Energy Consumption in Shandong, China Using the ARIMA Model, GM Model, and ARIMA-GM Model (2017, background): “The principle of the Gray Metabolic Forecast Model is: After the initial sequence X(0) = (x(0)(1), x(0)(2), · · · , x(0)(n)) has been modeled and the predicted data has been calculated, the latest information x(0)(n + 1) is inserted, while the oldest information x(0)(1) is removed [63,67].”
Evaluating correlative and mechanistic niche models for assessing the risk of pest establishment (2014, methodology): “The process-based or mechanistic niche models use species’ functional traits and physiological tolerances for model fitting (Kearney et al. 2010).”