2020
DOI: 10.1016/j.scitotenv.2020.139543
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Species range shifts in response to climate change and human pressure for the world's largest amphibian

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Cited by 23 publications
(15 citation statements)
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“…In addition to local and regional climate, species distributional responses are also a result of species‐specific physiological, behavioural, ecological and evolutionary responses (William et al, 2009), along with biological interactions (Ling, 2008; Wisz et al, 2013), and the extent and nature of additional stressors (Zhang et al, 2020). In general, at warmer range edges where elevated temperatures exceed physiological tolerances, species distribution edges are typically contracting, while at cooler range edges, where there is an increasing availability of suitable conditions, species distributions are typically extending.…”
Section: Introductionmentioning
confidence: 99%
“…In addition to local and regional climate, species distributional responses are also a result of species‐specific physiological, behavioural, ecological and evolutionary responses (William et al, 2009), along with biological interactions (Ling, 2008; Wisz et al, 2013), and the extent and nature of additional stressors (Zhang et al, 2020). In general, at warmer range edges where elevated temperatures exceed physiological tolerances, species distribution edges are typically contracting, while at cooler range edges, where there is an increasing availability of suitable conditions, species distributions are typically extending.…”
Section: Introductionmentioning
confidence: 99%
“…The map scales and coordinate grids are the same as Figure 2 salamanders, including the Chinese Giant Salamander (Andrias davidianus; Zhang, Mammola, et al, 2020;Zhang, Dong, et al, 2020), the Korean Crevice Salamander (Karsenia koreana; Borzée, Andersen, et al, 2019), and the Lorestan Mountain Newt (Neurergus kaiseri; Ashrafzadeh et al, 2019).…”
Section: Discussionmentioning
confidence: 99%
“…An ensemble of species distribution models (Araújo & Guisan, 2006 ) was used to model potential suitable habitat for D. involucrata using the biomod2 package in the R platform (v. 4.0.4; http://cran.r‐project.org ). We chose the ensemble modeling approach because of its ability to create a consensus of the predictions of multiple algorithms and reduce the predictive uncertainty of single algorithm (Kanagaraj et al., 2019 ; Thuiller et al., 2014 ; Zhang, Dong, et al., 2020 ; Zhang, Mammola, et al., 2020 ). Ten algorithms were considered in the ensemble model: artificial neural network (ANN; Ripley, 1996 ), classification tree analysis (CTA; Breiman et al., 1984 ), flexible discriminant analysis (FDA; Hastie et al., 1994 ), generalized additive model (GAM; Hastie & Tibshirani, 1990 ), generalized boosting model (GBM; Ridgeway, 1999 ), generalized linear model (GLM; McCullagh & Nelder, 1989 ), multiple adaptive regression splines (MARS; Friedman, 1991 ), maximum entropy (MAXENT; Phillips et al., 2006 ), random forest (RF; Breiman, 2001 ), and surface range envelope (SRE; Busby, 1991 ).…”
Section: Methodsmentioning
confidence: 99%
“…By using ensemble modeling approaches, more robust projections can be produced with reasonable interpretation (Araújo & Guisan, 2006 ). Consequently, these approaches have been widely used to estimate the distributions of species under future climate change scenarios for plants (Forester et al., 2013 ), amphibians (Zhang, Dong, et al., 2020 ; Zhang, Mammola, et al., 2020 ), insects (Marshall et al., 2018 ), and mammals (Ahmad et al., 2020 ; Yen et al., 2011 ).…”
Section: Introductionmentioning
confidence: 99%