Prediction of ecological and geological niches of Salvadora oleoides in arid zones of India: causes and consequences of global warming.

Autor: 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
Zdroj: Arabian Journal of Geosciences; Mar2021, Vol. 14 Issue 6, p1-18, 18p
Abstrakt: 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 km2), which ranged from 2102.90 (RCP 2.6_70) to 5494.23 km2 (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 FROM AUTHOR]
Databáze: Complementary Index
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