Species Distribution Models: Ecological Explanation and Prediction Across Space and Time

By Jane Elith, J. Leathwick. 2009

1,234 citations recorded by the source corpus.

Subfields: Ecological Modeling, Ecology, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/101454fb124d0084569b4ce4f0f1608bbdca1b31

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Citations in the corpus

Climate change threatens European conservation areas (2011, neutral): “Implementations of these methods as well as their uncertainties have been extensively reviewed (e.g. Araújo & Guisan 2006; Heikkinen et al. 2006; Elith & Leathwick 2009).”
Comparing species distribution models constructed with different subsets of environmental predictors (2015, positive): “Because of the potential usefulness of their outputs, (i.e. predictive suitability maps) climate-only SDMs have been identified as important tools for guiding future conservation efforts (Elith & Leathwick, 2009).”
Linking earth observation and taxonomic, structural and functional biodiversity: local to ecosystem perspectives (2016, neutral): “The im of these algorithms is not to classify the landscape into certain ategories as in the habitat quantifying approach, but to predict he probabilities of species occurrence as the single target variable Elith and Leathwick, 2009).”
Drawbacks to palaeodistribution modelling: the case of South American seasonally dry forests (2013, neutral): “…generalized additive modelling (GAM), generalized linear modelling (GLM), multiple adaptive regression splines (MARS), neural network (NNET) and random forest (RF) (for general descriptions of the methods see Elith & Leathwick, 2009; Franklin, 2009; Peterson et al., 2011; Elith et al., 2011).”
Machine learning and deep learning—A review for ecologists (2022, neutral): “…traditional mechanistic models (Rammer & Seidl, 2019 ; Reichstein et al., 2019), for approximating differential equations (Chen et al., 2019; Rackauckas et al., 2021 ), or for species distribution models (Chen et al., 2018 ; Elith & Leathwick, 2009 ; Harris et al., 2018 ; Wilkinson et al., 2019).”
Modeling Species and Community Responses to Past, Present, and Future Episodes of Climatic and Ecological Change (2015, neutral): “…variables such as temperature or precipitation, physical environmental variables such as soil characteristics, or even biotic variables such as the presence of competitors), thereby allowing models to be fit in and/or projected to past, present, or future time periods (Elith & Leathwick 2009).”
Modelling climate change impacts on marine fish populations: process-based integration of ocean warming, acidification and other environmental drivers (2016, neutral): “…(SDMs, also termed niche-based models, climate envelope 439 models, or predictive habitat distribution models) link observed geographical species 440 distributions to environmental parameters, classically through regression analysis (Guisan 441 and Zimmermann, 2000, Elith and Leathwick, 2009).”
Potential species distribution modeling and the use of principal component analysis as predictor variables (2014, neutral): “Recibido: 02 abril 2013; aceptado: 07 octubre 2013 190 Cruz-Cárdenas et al.- Distribution modeling and PCA as predictor variables the environmental conditions where a species could survive indefinitely (Pulliam, 2000; Guisan and Thuiller, 2005; Elith and Leathwick, 2009b).”
Assessing transferability of ecological models: an underappreciated aspect of statistical validation (2012, neutral): “The issue of transferability has been the subject of intermittent ecological interest for a number of years, but this greatly increasedwith the rise of the field of species distributionmodelling (Elith & Leathwick 2009) in the 2000s.”