Ecological Niches and Geographic Distributions

By A. Townsend Peterson, Jorge Soberón, Richard G. Pearson, R. P. Anderson, E. Martínez‐Meyer, Miguel Nakamura, Miguel B. Araújo. 2011

1,887 citations recorded by the source corpus.

Subfields: Ecological Modeling, Ecology

View Paper: https://www.semanticscholar.org/paper/f73fd9c74f274a49b7973784ee951d10b112e630

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Citations in the corpus

Plant extinction risk under climate change: are forecast range shifts alone a good indicator of species vulnerability to global warming? (2012, background): “…factors constraining those distributions; (ii) current climatic and geographic constraints that define a species distribution reflect its biophysical limits; and (iii) a model fitted to current data will project sensibly to new environmental conditions (Franklin, 2010; Peterson et al., 2011).”
bioclim: the first species distribution modelling package, its early applications and relevance to most current MaxEnt studies (2014, methodology): “There has been a rapid increase in species distribution and ecological niche modelling in recent years (see books by Franklin, 2009 and Peterson et al., 2011 as well as reviews by Guisan & Zimmermann, 2000; Guisan & Thuiller, 2005; Elith & Leathwick, 2009).”
Using district-level occurrences in MaxEnt for predicting the invasion potential of an exotic insect pest in India (2014, background): “…(Soberon and Peterson, 2005; Menke et al., 2009) ENM/SDM are increasingly used to predict potential distributions of species of concern (Franklin, 2009; Elith et al., 2010; Peterson et al., 2011) because resource managers need accurate maps of species distribution and abundance for risk analysis.”
Species Distribution Modeling and Ecological Niche Modeling: Getting the Concepts Right (2012, background): “2010), but also many confusions and inappropriate conclusions have been reached for lack of conceptual rigor (Peterson 2011; Soberón & Peterson 2011; Araújo & Peterson 2012).”
Mechanistic and Correlative Models of Ecological Niches (2015, methodology): “2013), we are working within a reductive framework that has been outlined in a recent book co-authored by two of us and colleagues (Peterson et al. 2011).”
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).”
Variation in niche and distribution model performance: The need for a priori assessment of key causal factors (2012, background): “Here and elsewhere (Peterson et al., 2011), we emphasize the critical need for much more careful use of terminology and concepts: a clear separation between the two ideas is that of focus on estimation of GO (species distribution modeling) versus focus on estimation of the environments of A (ecologi”
Ditch the niche – is the niche a useful concept in ecology or species distribution modelling? (2012, background): “In summary, while there are supporters of the niche (e.g. Peterson et al., 2011; de Souza Muñoz et al., 2011), there are important components of the literature that do not suggest that SDM methods rely on niche terminology (e.g. Elith & Leathwick, 2009; Thuiller et al., 2009).”
Accounting for spatially biased sampling effort in presence‐only species distribution modelling (2015, methodology): “…effort bias in species distribution modelling Analytical techniques are needed to account for sample selection bias in presence-only modelling of species’ distributions providing greater utility of increasingly accessible online presence data (Graham et al., 2004; Peterson et al., 2011).”
Climate change in the Eastern Amazon: crop-pollinator and occurrence-restricted bees are potentially more affected (2020, methodology): “…best possible representative of each bee species distribution, since it compiles all updated available data; although there may be biases in the surveys conducted in the various regions encompassed by our analysis, SDM is considered a robust tool to be used in such a case (Peterson et al. 2011).”
Spatial filtering to reduce sampling bias can improve the performance of ecological niche models (2014, methodology): “…models (ENMs) are a correlative approach iming to approximate the abiotically suitable area of a species y comparing environmental conditions at localities where the pecies occurs with the overall conditions available in the study egion (see Peterson et al., 2011; Anderson, 2012 for terminolgy).”
Tools for integrating range change, extinction risk and climate change information into conservation management (2013, methodology): “ENMs are first used to quantify the importance of environmental conditions that define present-day patterns of occurrence (Peterson et al. 2011).”
Uncertainty associated with survey design in Species Distribution Models (2014, background): “…incomplete information about the occurrence of species (and sometimes about their absence) with environmental predictors, to generate spatially explicit predictions about their geographic distributions (Ara ujo & Guisan, 2006; Franklin & Miller, 2009; Peterson et al., 2011; Hortal et al., 2012).”
Mapping the Global Potential Geographical Distribution of Black Locust (Robinia Pseudoacacia L.) Using Herbarium Data and a Maximum Entropy Model (2014, background): “[18] as the occupied distributional area and the invadable distributional area, respectively.”