Modelling Betula utilis distribution in response to climate-warming scenarios in Hindu-Kush Himalaya using random forest
Globally, the increase in the climatic variability has led to adverse effects on the treeline species in the high-elevation mountain landscapes. Identifying the geographical space that supports the treeline species survival over time is essential for conservation biogeography. Increase in the global...
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Veröffentlicht in: | Biodiversity and conservation 2019-07, Vol.28 (8-9), p.2295-2317 |
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creator | Mohapatra, Jakesh Singh, Chandra Prakash Hamid, Maroof Verma, Anirudh Semwal, Sudeep Chandra Gajmer, Bandan Khuroo, Anzar A. Kumar, Amit Nautiyal, Mohan C. Sharma, Narpati Pandya, Himanshu A. |
description | Globally, the increase in the climatic variability has led to adverse effects on the treeline species in the high-elevation mountain landscapes. Identifying the geographical space that supports the treeline species survival over time is essential for conservation biogeography. Increase in the global warming and snowmelt has made available the treeline species favourable niches in the higher elevations. Random Forest algorithm assuming non-parametric distribution was employed to predict the potential distribution of
Betula utilis
niche in the Hindu-Kush Himalayan (HKH) region. The potential distributions were simulated in the Last Inter-Glaciation (LIG), present (the year 1970–2000) and future (the year 2061–2080) environmental conditions. The actual distribution of the species in the current time was modelled and evaluated. The model sensitivity with reference to independent evaluation dataset for highly suitable
B. utilis
niche was 0.78. The model statistics of the current time was further applied to both the LIG and future (2061–2080) scenarios in order to get a fundamental niche of
B. utilis
. The treeline species,
B. utilis
was projected to become vulnerable to 21st century climate changes. The high suitability of
B. utilis
occurrence in the LIG, current and the future scenario were more likely in the elevation ranges 2601–2800 m, 3801–4000 m, and 4201–4400 m, respectively. The magnitude of advancement was relatively more along elevation and longitude, compared to the latitudinal gradient. The present study provides scientific evidence to conclude that the treeline species potential distribution in HKH is climate driven. |
doi_str_mv | 10.1007/s10531-019-01731-w |
format | Article |
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Betula utilis
niche in the Hindu-Kush Himalayan (HKH) region. The potential distributions were simulated in the Last Inter-Glaciation (LIG), present (the year 1970–2000) and future (the year 2061–2080) environmental conditions. The actual distribution of the species in the current time was modelled and evaluated. The model sensitivity with reference to independent evaluation dataset for highly suitable
B. utilis
niche was 0.78. The model statistics of the current time was further applied to both the LIG and future (2061–2080) scenarios in order to get a fundamental niche of
B. utilis
. The treeline species,
B. utilis
was projected to become vulnerable to 21st century climate changes. The high suitability of
B. utilis
occurrence in the LIG, current and the future scenario were more likely in the elevation ranges 2601–2800 m, 3801–4000 m, and 4201–4400 m, respectively. The magnitude of advancement was relatively more along elevation and longitude, compared to the latitudinal gradient. The present study provides scientific evidence to conclude that the treeline species potential distribution in HKH is climate driven.</description><identifier>ISSN: 0960-3115</identifier><identifier>EISSN: 1572-9710</identifier><identifier>DOI: 10.1007/s10531-019-01731-w</identifier><language>eng</language><publisher>Dordrecht: Springer Netherlands</publisher><subject>21st century ; Algorithms ; Analysis ; Betula utilis ; Biodiversity ; Biogeography ; Biomedical and Life Sciences ; Climate ; Climate change ; Climate Change/Climate Change Impacts ; Climate models ; Climate variability ; Computer simulation ; Conservation Biology/Ecology ; Decision trees ; Distribution ; Ecology ; Elevation ; Emissions ; Environmental conditions ; Evaluation ; Generalized linear models ; Glaciation ; Glaciers ; Glaciology ; Global warming ; Greenhouse gases ; Landscape ; Latitudinal variations ; Life Sciences ; Machine learning ; Modelling ; Niches ; Original Paper ; Sensitivity analysis ; Snowmelt ; Species ; Statistical methods ; Survival ; Treeline ; Wildlife conservation</subject><ispartof>Biodiversity and conservation, 2019-07, Vol.28 (8-9), p.2295-2317</ispartof><rights>Springer Nature B.V. 2019</rights><rights>COPYRIGHT 2019 Springer</rights><rights>Biodiversity and Conservation is a copyright of Springer, (2019). All Rights Reserved.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c358t-59f96c1080c8813102247bebed43f5eb772a4ec00aefbd828f5d752d0c4031ca3</citedby><cites>FETCH-LOGICAL-c358t-59f96c1080c8813102247bebed43f5eb772a4ec00aefbd828f5d752d0c4031ca3</cites><orcidid>0000-0002-7490-2619 ; 0000-0002-7412-057X</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s10531-019-01731-w$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s10531-019-01731-w$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,777,781,27905,27906,41469,42538,51300</link.rule.ids></links><search><creatorcontrib>Mohapatra, Jakesh</creatorcontrib><creatorcontrib>Singh, Chandra Prakash</creatorcontrib><creatorcontrib>Hamid, Maroof</creatorcontrib><creatorcontrib>Verma, Anirudh</creatorcontrib><creatorcontrib>Semwal, Sudeep Chandra</creatorcontrib><creatorcontrib>Gajmer, Bandan</creatorcontrib><creatorcontrib>Khuroo, Anzar A.</creatorcontrib><creatorcontrib>Kumar, Amit</creatorcontrib><creatorcontrib>Nautiyal, Mohan C.</creatorcontrib><creatorcontrib>Sharma, Narpati</creatorcontrib><creatorcontrib>Pandya, Himanshu A.</creatorcontrib><title>Modelling Betula utilis distribution in response to climate-warming scenarios in Hindu-Kush Himalaya using random forest</title><title>Biodiversity and conservation</title><addtitle>Biodivers Conserv</addtitle><description>Globally, the increase in the climatic variability has led to adverse effects on the treeline species in the high-elevation mountain landscapes. Identifying the geographical space that supports the treeline species survival over time is essential for conservation biogeography. Increase in the global warming and snowmelt has made available the treeline species favourable niches in the higher elevations. Random Forest algorithm assuming non-parametric distribution was employed to predict the potential distribution of
Betula utilis
niche in the Hindu-Kush Himalayan (HKH) region. The potential distributions were simulated in the Last Inter-Glaciation (LIG), present (the year 1970–2000) and future (the year 2061–2080) environmental conditions. The actual distribution of the species in the current time was modelled and evaluated. The model sensitivity with reference to independent evaluation dataset for highly suitable
B. utilis
niche was 0.78. The model statistics of the current time was further applied to both the LIG and future (2061–2080) scenarios in order to get a fundamental niche of
B. utilis
. The treeline species,
B. utilis
was projected to become vulnerable to 21st century climate changes. The high suitability of
B. utilis
occurrence in the LIG, current and the future scenario were more likely in the elevation ranges 2601–2800 m, 3801–4000 m, and 4201–4400 m, respectively. The magnitude of advancement was relatively more along elevation and longitude, compared to the latitudinal gradient. The present study provides scientific evidence to conclude that the treeline species potential distribution in HKH is climate driven.</description><subject>21st century</subject><subject>Algorithms</subject><subject>Analysis</subject><subject>Betula utilis</subject><subject>Biodiversity</subject><subject>Biogeography</subject><subject>Biomedical and Life Sciences</subject><subject>Climate</subject><subject>Climate change</subject><subject>Climate Change/Climate Change Impacts</subject><subject>Climate models</subject><subject>Climate variability</subject><subject>Computer simulation</subject><subject>Conservation Biology/Ecology</subject><subject>Decision trees</subject><subject>Distribution</subject><subject>Ecology</subject><subject>Elevation</subject><subject>Emissions</subject><subject>Environmental conditions</subject><subject>Evaluation</subject><subject>Generalized linear models</subject><subject>Glaciation</subject><subject>Glaciers</subject><subject>Glaciology</subject><subject>Global warming</subject><subject>Greenhouse gases</subject><subject>Landscape</subject><subject>Latitudinal variations</subject><subject>Life Sciences</subject><subject>Machine learning</subject><subject>Modelling</subject><subject>Niches</subject><subject>Original Paper</subject><subject>Sensitivity analysis</subject><subject>Snowmelt</subject><subject>Species</subject><subject>Statistical methods</subject><subject>Survival</subject><subject>Treeline</subject><subject>Wildlife conservation</subject><issn>0960-3115</issn><issn>1572-9710</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNp9kUGPFCEQhYnRxHH1D3jqxHOvBQwDfVw36hrXeNEzoaEY2XTDCN0Z999bY5t4M4RQkPe9KvIYe83hmgPot42DkrwHPtDWVJ2fsB1XWvSD5vCU7WA4QC85V8_Zi9YegCB14Dv260sJOE0pH7t3uKyT69YlTal1IbWlppFuJXcpdxXbqeSG3VI6P6XZLdifXZ0vZPOYXU2lXYR3KYe1_7y2H1TObnKP5NkusupyKHMXC3ktL9mz6KaGr_6eV-z7h_ffbu_6-68fP93e3PdeKrP0aojDwXMw4I3hkoMQez3iiGEvo8JRa-H26AEcxjEYYaIKWokAfg-Seyev2JvN91TLz5Ua24ey1kwtreDmMIhBCkmq6011dBPalGNZqvO0As7Jl4wx0fuNFlrKQRlDgNgAX0trFaM9VfptfbQc7CUSu0ViKRL7JxJ7JkhuUCNxPmL9N8t_qN9ru5Ea</recordid><startdate>20190730</startdate><enddate>20190730</enddate><creator>Mohapatra, Jakesh</creator><creator>Singh, Chandra 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Betula utilis distribution in response to climate-warming scenarios in Hindu-Kush Himalaya using random forest</title><author>Mohapatra, Jakesh ; Singh, Chandra Prakash ; Hamid, Maroof ; Verma, Anirudh ; Semwal, Sudeep Chandra ; Gajmer, Bandan ; Khuroo, Anzar A. ; Kumar, Amit ; Nautiyal, Mohan C. ; Sharma, Narpati ; Pandya, Himanshu A.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c358t-59f96c1080c8813102247bebed43f5eb772a4ec00aefbd828f5d752d0c4031ca3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>21st century</topic><topic>Algorithms</topic><topic>Analysis</topic><topic>Betula utilis</topic><topic>Biodiversity</topic><topic>Biogeography</topic><topic>Biomedical and Life Sciences</topic><topic>Climate</topic><topic>Climate change</topic><topic>Climate Change/Climate Change Impacts</topic><topic>Climate models</topic><topic>Climate variability</topic><topic>Computer simulation</topic><topic>Conservation Biology/Ecology</topic><topic>Decision trees</topic><topic>Distribution</topic><topic>Ecology</topic><topic>Elevation</topic><topic>Emissions</topic><topic>Environmental conditions</topic><topic>Evaluation</topic><topic>Generalized linear models</topic><topic>Glaciation</topic><topic>Glaciers</topic><topic>Glaciology</topic><topic>Global warming</topic><topic>Greenhouse gases</topic><topic>Landscape</topic><topic>Latitudinal variations</topic><topic>Life Sciences</topic><topic>Machine learning</topic><topic>Modelling</topic><topic>Niches</topic><topic>Original Paper</topic><topic>Sensitivity analysis</topic><topic>Snowmelt</topic><topic>Species</topic><topic>Statistical methods</topic><topic>Survival</topic><topic>Treeline</topic><topic>Wildlife conservation</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Mohapatra, Jakesh</creatorcontrib><creatorcontrib>Singh, Chandra Prakash</creatorcontrib><creatorcontrib>Hamid, Maroof</creatorcontrib><creatorcontrib>Verma, Anirudh</creatorcontrib><creatorcontrib>Semwal, Sudeep Chandra</creatorcontrib><creatorcontrib>Gajmer, Bandan</creatorcontrib><creatorcontrib>Khuroo, Anzar A.</creatorcontrib><creatorcontrib>Kumar, Amit</creatorcontrib><creatorcontrib>Nautiyal, Mohan C.</creatorcontrib><creatorcontrib>Sharma, Narpati</creatorcontrib><creatorcontrib>Pandya, Himanshu A.</creatorcontrib><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Ecology Abstracts</collection><collection>Entomology Abstracts (Full archive)</collection><collection>Environment Abstracts</collection><collection>Sustainability Science Abstracts</collection><collection>Agricultural Science Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Science Database (Alumni Edition)</collection><collection>ProQuest Pharma 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Conserv</stitle><date>2019-07-30</date><risdate>2019</risdate><volume>28</volume><issue>8-9</issue><spage>2295</spage><epage>2317</epage><pages>2295-2317</pages><issn>0960-3115</issn><eissn>1572-9710</eissn><abstract>Globally, the increase in the climatic variability has led to adverse effects on the treeline species in the high-elevation mountain landscapes. Identifying the geographical space that supports the treeline species survival over time is essential for conservation biogeography. Increase in the global warming and snowmelt has made available the treeline species favourable niches in the higher elevations. Random Forest algorithm assuming non-parametric distribution was employed to predict the potential distribution of
Betula utilis
niche in the Hindu-Kush Himalayan (HKH) region. The potential distributions were simulated in the Last Inter-Glaciation (LIG), present (the year 1970–2000) and future (the year 2061–2080) environmental conditions. The actual distribution of the species in the current time was modelled and evaluated. The model sensitivity with reference to independent evaluation dataset for highly suitable
B. utilis
niche was 0.78. The model statistics of the current time was further applied to both the LIG and future (2061–2080) scenarios in order to get a fundamental niche of
B. utilis
. The treeline species,
B. utilis
was projected to become vulnerable to 21st century climate changes. The high suitability of
B. utilis
occurrence in the LIG, current and the future scenario were more likely in the elevation ranges 2601–2800 m, 3801–4000 m, and 4201–4400 m, respectively. The magnitude of advancement was relatively more along elevation and longitude, compared to the latitudinal gradient. The present study provides scientific evidence to conclude that the treeline species potential distribution in HKH is climate driven.</abstract><cop>Dordrecht</cop><pub>Springer Netherlands</pub><doi>10.1007/s10531-019-01731-w</doi><tpages>23</tpages><orcidid>https://orcid.org/0000-0002-7490-2619</orcidid><orcidid>https://orcid.org/0000-0002-7412-057X</orcidid></addata></record> |
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subjects | 21st century Algorithms Analysis Betula utilis Biodiversity Biogeography Biomedical and Life Sciences Climate Climate change Climate Change/Climate Change Impacts Climate models Climate variability Computer simulation Conservation Biology/Ecology Decision trees Distribution Ecology Elevation Emissions Environmental conditions Evaluation Generalized linear models Glaciation Glaciers Glaciology Global warming Greenhouse gases Landscape Latitudinal variations Life Sciences Machine learning Modelling Niches Original Paper Sensitivity analysis Snowmelt Species Statistical methods Survival Treeline Wildlife conservation |
title | Modelling Betula utilis distribution in response to climate-warming scenarios in Hindu-Kush Himalaya using random forest |
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