The art of modelling range‐shifting species

By Jane Elith, Michael R. Kearney, Steven J. Phillips. 2010

2,621 citations recorded by the source corpus.

Subfields: Ecological Modeling, Ecology, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/57fbdc5ff136d10620ea5204db024583249f9550

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Citations in the corpus

MaxEnt versus MaxLike: empirical comparisons with ant species distributions (2013, methodology): “See Ellison et al. (2012) for additional details on the natural history of these six ant species and the broader dataset.”
On the brink of extinction? How climate change may affect global chelonian species richness and distribution (2012, methodology): “2b), which are outside the current potential niches of the species by 2080 assuming the IPCC SRES A1b (a), A2a (b) and B2a (c) as quantified via multivariate environmental similarity surfaces (MESS; Elith et al., 2010).”
Effects of climate change on species distribution, community structure, and conservation of birds in protected areas in Colombia (2013, background): “Furthermore, projections of species distributions into future climate present the additional challenge of inferring occurrence in areas with combinations of climate and species composition for which we have no current analog (Elith et al. 2010).”
A statistical explanation of MaxEnt for ecologists (2011, methodology): “Hinge features tend to make linear and threshold features redundant, and one way to form a model with relatively smooth fitted functions, more like a GAM, is to use only hinge features (e.g., Elith et al., 2010 and case study 1).”
Spatial-temporal dynamics of a Lotka-Volterra competition model with nonlocal dispersal under shifting environment (2019, background): “For example, to understand how species transfer their distribution over time and to predict whether the species can keep pace with the climate-induced range shifts in future, [6,17,43,20,28] adopted a practical approach of characterizing the habitats “on the move” by considering the growth rate r(t,”
Predictor complexity and feature selection affect Maxent model transferability: Evidence from global freshwater invasive species (2020, methodology): “…of previous studies showing that Maxent model performance frequently peaks at RM values higher than the default of 1, which is often insufficient to reduce model overparameterization and overfitting to acceptable levels (Elith et al., 2010; Radosavljevic & Anderson, 2014; Warren & Seifert, 2011).”
Plant extinction risk under climate change: are forecast range shifts alone a good indicator of species vulnerability to global warming? (2012, methodology): “, 2006), and for forecasting changes under climate change (Araújo & Rahbek, 2006; Elith et al., 2010).”
Exploring consensus in 21st century projections of climatically suitable areas for African vertebrates (2011, background): “De-coupling of patterns of covariance between predictor and proximal variables may undermine the value of BEMs when extrapolating in time (Jackson et al. , 2009; Elith et al. , 2010).”
A 40‐year, continent‐wide, multispecies assessment of relevant climate predictors for species distribution modelling (2014, background): “Although many studies previously discussed the various sources of uncertainty in SDMs (Ara ujo et al., 2005; Buisson et al., 2010; Elith et al., 2010), the focus has mainly been on algorithms and climate scenarios.”
Drawbacks to palaeodistribution modelling: the case of South American seasonally dry forests (2013, methodology): “…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).”
Here be dragons: a tool for quantifying novelty due to covariate range and correlation change when projecting species distribution models (2014, methodology): “A good example is the Multivariate Environmental Similarity Surface feature (or MESS map, sensu Elith et al., 2010) implemented in recent versions of MAXENT (3.3.2 and later; Phillips et al., 2006).”
Opening the black box: an open-source release of Maxent (2017, neutral): “Species distribution models based on Maxent and IPP remain an active area of research, as new methods are developed to accommodate the challenging nature of occurrence data and species distributions (Fithian et al. 2015, Merow et al. 2016). The open-source release of the Maxent Java code, together w”
Hierarchical Species Distribution Models (2016, background): “It is even more challenging to reliably predict how species’ distributions will change due to threats such as climate change and habitat destruction [25, 26, 93].”
Selecting predictors to maximize the transferability of species distribution models: lessons from cross‐continental plant invasions (2017, methodology): “A Multivariate Environmental Similarity Surface (MESS, Elith et al., 2010) was computed for each climate layer (instead of using composite MESS layers) to select eight variables with the lowest number of non-analog sites in the invaded range (based on all individual MESS layers).”