2019
DOI: 10.1111/ddi.12898
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Predicting range shifts of Asian elephants under global change

Abstract: Aim: Climate change alters the water cycle, potentially affecting the distribution of species. Using an ensemble of species distribution models (SDMs), we predicted changes in distribution of the Asian elephant in South Asia due to increasing climatic variability under warming climate and human pressures. Location: India and Nepal.Methods: We compiled a comprehensive geodatabase of 115 predictor variables, which included climatic, topographic, human pressures and land use, at a resolution | 823 KANAGARAJ et Al. Show more

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Cited by 67 publications
(42 citation statements)
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References 73 publications
(100 reference statements)
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“…To date, a large number of connectivity models and metrics have been proposed, such as those based on metapopulation theory (Hanski andOvaskainen 2000, Moilanen andNieminen 2002), network theory (Urban and Keitt 2001, Bodin and Norberg 2006, Saura and Pascual-Hortal 2007, and on circuit theory (McRae et al 2008). These approaches have also been employed to evaluate the impacts of climate change on habitat connectivity and species persistence (Dilts et al 2016, Albert et al 2017, Rehnus et al 2018, Kanagaraj et al 2019. Most connectivity models rely on spatial snap-shots of landscape structure to generate static connectivity estimates; however, some species predicted to experience ARH decrease or increase do not actually have immediate population declines or growth as expected (Metzger et al 2009, Semper-Pascual et al 2018, Lira et al 2019).…”
Section: Introductionmentioning
confidence: 99%
“…To date, a large number of connectivity models and metrics have been proposed, such as those based on metapopulation theory (Hanski andOvaskainen 2000, Moilanen andNieminen 2002), network theory (Urban and Keitt 2001, Bodin and Norberg 2006, Saura and Pascual-Hortal 2007, and on circuit theory (McRae et al 2008). These approaches have also been employed to evaluate the impacts of climate change on habitat connectivity and species persistence (Dilts et al 2016, Albert et al 2017, Rehnus et al 2018, Kanagaraj et al 2019. Most connectivity models rely on spatial snap-shots of landscape structure to generate static connectivity estimates; however, some species predicted to experience ARH decrease or increase do not actually have immediate population declines or growth as expected (Metzger et al 2009, Semper-Pascual et al 2018, Lira et al 2019).…”
Section: Introductionmentioning
confidence: 99%
“…Incorporating land cover into species distribution models in dynamic geographic regions such as Southeast Asia presents a considerable challenge for model accuracy, because of temporal mismatches between (historical) occurrence records and (contemporary) land cover data. Indeed, previous research investigating changes in suitable habitat under climate change typically did not include land cover 11 , 104 or included it as a variable in the model itself 12 , 105 , 106 . Here, we incorporated land cover post-hoc, which allowed us to leverage the maximum amount of occurrence-environment data without incorporating errors generated from those temporal mismatches 46 , 47 .…”
Section: Discussionmentioning
confidence: 99%
“…Links between climate change, habitat loss and increasing conservation conflicts have been reported (e.g. Kanagaraj et al, 2019), and climate change is likely to be one of the main threats facing people and wildlife within the next decades (Nyhus, 2016). Further in-depth socio-ecological studies are needed to determine the possible connection between climate change and the intensification of future conflicts.…”
Section: Discussionmentioning
confidence: 99%