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Prediction of ecological and geological niches of Salvadora oleoides in arid zones of India: causes and consequences of global warming
Salvadora oleoides is a keystone and economically important species in the desert biome of the Indian subcontinent. However, species decline has been observed during field surveys (2016–2019) conducted in the Indian states of Punjab, Haryana, Rajasthan and Gujarat. For species distribution mapping,...
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Published in: | Arabian journal of geosciences 2021-03, Vol.14 (6), Article 524 |
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creator | Bhandari, Maneesh S. Shankhwar, Rajeev Maikhuri, Sandeep Pandey, Shailesh Meena, Rajendra K. Ginwal, Harish S. Kant, Rama Rawat, Parveen S. Martins-Ferreira, Marco Antonio Caçador Silveira, Lívia Helena Carrera |
description | Salvadora oleoides
is a keystone and economically important species in the desert biome of the Indian subcontinent. However, species decline has been observed during field surveys (2016–2019) conducted in the Indian states of Punjab, Haryana, Rajasthan and Gujarat. For species distribution mapping, geospatial data of 683 trees were recorded, out of which 70.28% were used for training and the rest to validate the MaxEnt model. The statistically significant AUC value ranged from 0.92 ± 0.02 (LGM) to 0.93 ± 0.01 (RCP 2.6_70) with an average of 0.92 ± 0.00. The bioclimatic variables, namely precipitation of warmest quarter (Bio 18), annual precipitation (Bio 12), altitude (Alt), precipitation of wettest month (Bio 13), precipitation of coldest quarter (Bio 19) and maximum temperature of warmest month (Bio 5), contributed significantly for predicting the species distribution, which ranged from 60.3% (Last Glacial Maximum, LGM) to 85.5% (Current) with an average of 75.82%. For the climate change scenario (2050s and 2070s), a sharp decline in the species distribution area was observed for all RCPs (when compared with the current estimate of 8638.01 km
2
), which ranged from 2102.90 (RCP 2.6_70) to 5494.23 km
2
(RCP 8.5_70). The model output revealed that the distribution is in accordance with the Köppen-Geiger climate classification system and the geological map of northwestern India. In relation to the geological variables, distribution of
S. oleoides
occurrence is conditioned to the presence of calcium-rich bedrock, which is used as a mechanism of compensation for the high salinity of desert soils. Overall, the study provides an eco-geo-distribution map and the potential use of MaxEnt in prediction modelling of the species. Furthermore, the research led to insights for suitable conservation and management plans for
S. oleoides
in arid zones of south Asian sub-continental climatic conditions. |
doi_str_mv | 10.1007/s12517-020-06384-6 |
format | article |
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is a keystone and economically important species in the desert biome of the Indian subcontinent. However, species decline has been observed during field surveys (2016–2019) conducted in the Indian states of Punjab, Haryana, Rajasthan and Gujarat. For species distribution mapping, geospatial data of 683 trees were recorded, out of which 70.28% were used for training and the rest to validate the MaxEnt model. The statistically significant AUC value ranged from 0.92 ± 0.02 (LGM) to 0.93 ± 0.01 (RCP 2.6_70) with an average of 0.92 ± 0.00. The bioclimatic variables, namely precipitation of warmest quarter (Bio 18), annual precipitation (Bio 12), altitude (Alt), precipitation of wettest month (Bio 13), precipitation of coldest quarter (Bio 19) and maximum temperature of warmest month (Bio 5), contributed significantly for predicting the species distribution, which ranged from 60.3% (Last Glacial Maximum, LGM) to 85.5% (Current) with an average of 75.82%. For the climate change scenario (2050s and 2070s), a sharp decline in the species distribution area was observed for all RCPs (when compared with the current estimate of 8638.01 km
2
), which ranged from 2102.90 (RCP 2.6_70) to 5494.23 km
2
(RCP 8.5_70). The model output revealed that the distribution is in accordance with the Köppen-Geiger climate classification system and the geological map of northwestern India. In relation to the geological variables, distribution of
S. oleoides
occurrence is conditioned to the presence of calcium-rich bedrock, which is used as a mechanism of compensation for the high salinity of desert soils. Overall, the study provides an eco-geo-distribution map and the potential use of MaxEnt in prediction modelling of the species. Furthermore, the research led to insights for suitable conservation and management plans for
S. oleoides
in arid zones of south Asian sub-continental climatic conditions.</description><identifier>ISSN: 1866-7511</identifier><identifier>EISSN: 1866-7538</identifier><identifier>DOI: 10.1007/s12517-020-06384-6</identifier><language>eng</language><publisher>Cham: Springer International Publishing</publisher><subject>2nd CAJG 2019 ; Annual precipitation ; Arid zones ; Aridity ; Bedrock ; Bioclimatology ; Calcium ; Climate change ; Climatic conditions ; Desert soils ; Deserts ; Distribution ; Earth and Environmental Science ; Earth science ; Earth Sciences ; Geiger counters ; Geographical distribution ; Geologic mapping ; Geological mapping ; Geological maps ; Geology ; Global warming ; Population decline ; Precipitation ; Prediction models ; Sandy soils ; Soil ; Spatial data ; Species ; Statistical analysis ; Surveys ; Training</subject><ispartof>Arabian journal of geosciences, 2021-03, Vol.14 (6), Article 524</ispartof><rights>Saudi Society for Geosciences 2021</rights><rights>Saudi Society for Geosciences 2021.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c2708-426172c0d7ab07cb33126499763a441405c60e807d121492d75e49708ba639af3</citedby><cites>FETCH-LOGICAL-c2708-426172c0d7ab07cb33126499763a441405c60e807d121492d75e49708ba639af3</cites><orcidid>0000-0002-7069-7048</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27924,27925</link.rule.ids></links><search><creatorcontrib>Bhandari, Maneesh S.</creatorcontrib><creatorcontrib>Shankhwar, Rajeev</creatorcontrib><creatorcontrib>Maikhuri, Sandeep</creatorcontrib><creatorcontrib>Pandey, Shailesh</creatorcontrib><creatorcontrib>Meena, Rajendra K.</creatorcontrib><creatorcontrib>Ginwal, Harish S.</creatorcontrib><creatorcontrib>Kant, Rama</creatorcontrib><creatorcontrib>Rawat, Parveen S.</creatorcontrib><creatorcontrib>Martins-Ferreira, Marco Antonio Caçador</creatorcontrib><creatorcontrib>Silveira, Lívia Helena Carrera</creatorcontrib><title>Prediction of ecological and geological niches of Salvadora oleoides in arid zones of India: causes and consequences of global warming</title><title>Arabian journal of geosciences</title><addtitle>Arab J Geosci</addtitle><description>Salvadora oleoides
is a keystone and economically important species in the desert biome of the Indian subcontinent. However, species decline has been observed during field surveys (2016–2019) conducted in the Indian states of Punjab, Haryana, Rajasthan and Gujarat. For species distribution mapping, geospatial data of 683 trees were recorded, out of which 70.28% were used for training and the rest to validate the MaxEnt model. The statistically significant AUC value ranged from 0.92 ± 0.02 (LGM) to 0.93 ± 0.01 (RCP 2.6_70) with an average of 0.92 ± 0.00. The bioclimatic variables, namely precipitation of warmest quarter (Bio 18), annual precipitation (Bio 12), altitude (Alt), precipitation of wettest month (Bio 13), precipitation of coldest quarter (Bio 19) and maximum temperature of warmest month (Bio 5), contributed significantly for predicting the species distribution, which ranged from 60.3% (Last Glacial Maximum, LGM) to 85.5% (Current) with an average of 75.82%. For the climate change scenario (2050s and 2070s), a sharp decline in the species distribution area was observed for all RCPs (when compared with the current estimate of 8638.01 km
2
), which ranged from 2102.90 (RCP 2.6_70) to 5494.23 km
2
(RCP 8.5_70). The model output revealed that the distribution is in accordance with the Köppen-Geiger climate classification system and the geological map of northwestern India. In relation to the geological variables, distribution of
S. oleoides
occurrence is conditioned to the presence of calcium-rich bedrock, which is used as a mechanism of compensation for the high salinity of desert soils. Overall, the study provides an eco-geo-distribution map and the potential use of MaxEnt in prediction modelling of the species. Furthermore, the research led to insights for suitable conservation and management plans for
S. oleoides
in arid zones of south Asian sub-continental climatic conditions.</description><subject>2nd CAJG 2019</subject><subject>Annual precipitation</subject><subject>Arid zones</subject><subject>Aridity</subject><subject>Bedrock</subject><subject>Bioclimatology</subject><subject>Calcium</subject><subject>Climate change</subject><subject>Climatic conditions</subject><subject>Desert soils</subject><subject>Deserts</subject><subject>Distribution</subject><subject>Earth and Environmental Science</subject><subject>Earth science</subject><subject>Earth Sciences</subject><subject>Geiger counters</subject><subject>Geographical distribution</subject><subject>Geologic mapping</subject><subject>Geological mapping</subject><subject>Geological maps</subject><subject>Geology</subject><subject>Global warming</subject><subject>Population decline</subject><subject>Precipitation</subject><subject>Prediction models</subject><subject>Sandy soils</subject><subject>Soil</subject><subject>Spatial data</subject><subject>Species</subject><subject>Statistical analysis</subject><subject>Surveys</subject><subject>Training</subject><issn>1866-7511</issn><issn>1866-7538</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><recordid>eNp9kN1OwyAYhonRxDm9AY-aeFz9oBRaz8ziz5IlmqjHhAKtLB1M2DR6AV631Jp55hHw5Xle4EXoFMM5BuAXEZMS8xwI5MCKiuZsD01wxVjOy6La3-0xPkRHMS4BWAW8mqCvh2C0VRvrXebbzCjf-84q2WfS6awzu6Oz6sXEgXmU_ZvUPsjM98ZbnabWZTJYnX16NzJzp628zJTcxjQYopR30bxujVMj0fW-SbHvMqys647RQSv7aE5-1yl6vrl-mt3li_vb-exqkSvCocopYZgTBZrLBrhqigITRuuas0JSiimUioFJP9OYYFoTzUtD62Q2khW1bIspOhtz18Gnx8SNWPptcOlKQUpICjDKEkVGSgUfYzCtWAe7kuFDYBBD32LsW6S-xU_fYpCKUYoJdp0Jf9H_WN8QL4Lq</recordid><startdate>20210301</startdate><enddate>20210301</enddate><creator>Bhandari, Maneesh S.</creator><creator>Shankhwar, Rajeev</creator><creator>Maikhuri, Sandeep</creator><creator>Pandey, Shailesh</creator><creator>Meena, Rajendra K.</creator><creator>Ginwal, Harish S.</creator><creator>Kant, Rama</creator><creator>Rawat, Parveen S.</creator><creator>Martins-Ferreira, Marco Antonio Caçador</creator><creator>Silveira, Lívia Helena Carrera</creator><general>Springer International Publishing</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7UA</scope><scope>C1K</scope><scope>F1W</scope><scope>H96</scope><scope>L.G</scope><orcidid>https://orcid.org/0000-0002-7069-7048</orcidid></search><sort><creationdate>20210301</creationdate><title>Prediction of ecological and geological niches of Salvadora oleoides in arid zones of India: causes and consequences of global warming</title><author>Bhandari, Maneesh S. ; Shankhwar, Rajeev ; Maikhuri, Sandeep ; Pandey, Shailesh ; Meena, Rajendra K. ; Ginwal, Harish S. ; Kant, Rama ; Rawat, Parveen S. ; Martins-Ferreira, Marco Antonio Caçador ; Silveira, Lívia Helena Carrera</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c2708-426172c0d7ab07cb33126499763a441405c60e807d121492d75e49708ba639af3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>2nd CAJG 2019</topic><topic>Annual precipitation</topic><topic>Arid zones</topic><topic>Aridity</topic><topic>Bedrock</topic><topic>Bioclimatology</topic><topic>Calcium</topic><topic>Climate change</topic><topic>Climatic conditions</topic><topic>Desert soils</topic><topic>Deserts</topic><topic>Distribution</topic><topic>Earth and Environmental Science</topic><topic>Earth science</topic><topic>Earth Sciences</topic><topic>Geiger counters</topic><topic>Geographical distribution</topic><topic>Geologic mapping</topic><topic>Geological mapping</topic><topic>Geological maps</topic><topic>Geology</topic><topic>Global warming</topic><topic>Population decline</topic><topic>Precipitation</topic><topic>Prediction models</topic><topic>Sandy soils</topic><topic>Soil</topic><topic>Spatial data</topic><topic>Species</topic><topic>Statistical analysis</topic><topic>Surveys</topic><topic>Training</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Bhandari, Maneesh S.</creatorcontrib><creatorcontrib>Shankhwar, Rajeev</creatorcontrib><creatorcontrib>Maikhuri, Sandeep</creatorcontrib><creatorcontrib>Pandey, Shailesh</creatorcontrib><creatorcontrib>Meena, Rajendra K.</creatorcontrib><creatorcontrib>Ginwal, Harish S.</creatorcontrib><creatorcontrib>Kant, Rama</creatorcontrib><creatorcontrib>Rawat, Parveen S.</creatorcontrib><creatorcontrib>Martins-Ferreira, Marco Antonio Caçador</creatorcontrib><creatorcontrib>Silveira, Lívia Helena Carrera</creatorcontrib><collection>CrossRef</collection><collection>Water Resources Abstracts</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ASFA: Aquatic Sciences and Fisheries Abstracts</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy & Non-Living Resources</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) Professional</collection><jtitle>Arabian journal of geosciences</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Bhandari, Maneesh S.</au><au>Shankhwar, Rajeev</au><au>Maikhuri, Sandeep</au><au>Pandey, Shailesh</au><au>Meena, Rajendra K.</au><au>Ginwal, Harish S.</au><au>Kant, Rama</au><au>Rawat, Parveen S.</au><au>Martins-Ferreira, Marco Antonio Caçador</au><au>Silveira, Lívia Helena Carrera</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Prediction of ecological and geological niches of Salvadora oleoides in arid zones of India: causes and consequences of global warming</atitle><jtitle>Arabian journal of geosciences</jtitle><stitle>Arab J Geosci</stitle><date>2021-03-01</date><risdate>2021</risdate><volume>14</volume><issue>6</issue><artnum>524</artnum><issn>1866-7511</issn><eissn>1866-7538</eissn><abstract>Salvadora oleoides
is a keystone and economically important species in the desert biome of the Indian subcontinent. However, species decline has been observed during field surveys (2016–2019) conducted in the Indian states of Punjab, Haryana, Rajasthan and Gujarat. For species distribution mapping, geospatial data of 683 trees were recorded, out of which 70.28% were used for training and the rest to validate the MaxEnt model. The statistically significant AUC value ranged from 0.92 ± 0.02 (LGM) to 0.93 ± 0.01 (RCP 2.6_70) with an average of 0.92 ± 0.00. The bioclimatic variables, namely precipitation of warmest quarter (Bio 18), annual precipitation (Bio 12), altitude (Alt), precipitation of wettest month (Bio 13), precipitation of coldest quarter (Bio 19) and maximum temperature of warmest month (Bio 5), contributed significantly for predicting the species distribution, which ranged from 60.3% (Last Glacial Maximum, LGM) to 85.5% (Current) with an average of 75.82%. For the climate change scenario (2050s and 2070s), a sharp decline in the species distribution area was observed for all RCPs (when compared with the current estimate of 8638.01 km
2
), which ranged from 2102.90 (RCP 2.6_70) to 5494.23 km
2
(RCP 8.5_70). The model output revealed that the distribution is in accordance with the Köppen-Geiger climate classification system and the geological map of northwestern India. In relation to the geological variables, distribution of
S. oleoides
occurrence is conditioned to the presence of calcium-rich bedrock, which is used as a mechanism of compensation for the high salinity of desert soils. Overall, the study provides an eco-geo-distribution map and the potential use of MaxEnt in prediction modelling of the species. Furthermore, the research led to insights for suitable conservation and management plans for
S. oleoides
in arid zones of south Asian sub-continental climatic conditions.</abstract><cop>Cham</cop><pub>Springer International Publishing</pub><doi>10.1007/s12517-020-06384-6</doi><orcidid>https://orcid.org/0000-0002-7069-7048</orcidid></addata></record> |
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subjects | 2nd CAJG 2019 Annual precipitation Arid zones Aridity Bedrock Bioclimatology Calcium Climate change Climatic conditions Desert soils Deserts Distribution Earth and Environmental Science Earth science Earth Sciences Geiger counters Geographical distribution Geologic mapping Geological mapping Geological maps Geology Global warming Population decline Precipitation Prediction models Sandy soils Soil Spatial data Species Statistical analysis Surveys Training |
title | Prediction of ecological and geological niches of Salvadora oleoides in arid zones of India: causes and consequences of global warming |
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