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Global Climate Change and Tropical Forest Genetic Resources 总被引：4，自引：0，他引：4
Global climate change may have a serious impact on genetic resources in tropical forest trees. Genetic diversity plays a critical role in the survival of populations in rapidly changing environments. Furthermore, most tropical plant species are known to have unique ecological niches, and therefore changes in climate may directly affect the distribution of biomes, ecosystems, and constituent species. Climate change may also indirectly affect plant genetic resources through effects on phenology, breeding systems, and plant-pollinator and plant seed disperser interactions, and may reduce genetic diversity and reproductive output. As a consequence, population densities may be reduced leading to reduction in genetic diversity through genetic drift and inbreeding. Tropical forest plants may respond to climate change through phenotypic plasticity, adaptive evolution, migration to suitable site, or extinction. However, the potential to respond is limited by a rapid pace of change and the non-availability of alternate habitats due to past and present trends of deforestation. Thus climate change may result in extinction of many populations and species. Our ability to estimate the precise response of tropical forest ecosystems to climate change is limited by lack of long-term data on parameters that might be affected by climate change. Collection of correlative data from long-term monitoring of climate as well as population and community responses at selected sites offer the most cost-effective way to understand the effects of climate change on tropical tree populations. However, mitigation strategies need to be implemented immediately. Because many effects of climate change may be similar to the effects of habitat alteration and fragmentation, protected areas and buffer zones should be enlarged, with an emphasis on connectivity among conserved landscapes. Taxa that are likely to become extinct should be identified and protected through ex situ conservation programs. 相似文献
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Kamaljit Singh Rajkumar Megha Maheshwari Jayaprasad Pallipad Dhani Ram Rajak Raj Kumar 《Marine Geodesy》2015,38(3):497-509
Radar altimetry provides an important geophysical parameter, backscatter coefficient (σ0), which is useful in studying target surface characteristics. Ku-band (Oceansat-2 scatterometer- OSCAT) and Ka-band (SARAL-AltiKa altimeter) data are concurrently used to characterize polar surface features over the Antarctic region. Maximum-likelihood classification has been employed to classify combined data set (AltiKa and OSCAT) for discrimination among sea ice, open water, and ice sheet (interior and exterior). The sea ice region obtained using the current approach has been compared with sea ice boundary derived from passive microwave data. 相似文献
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Shufen Pan Guangsheng Chen Wei Ren Shree R. S. Dangal Kamaljit Banger Jia Yang 《International Journal of Digital Earth》2018,11(6):558-582
Terrestrial ecosystems play a significant role in global carbon and water cycles because of the substantial amount of carbon assimilated through net primary production and large amount of water loss through evapotranspiration (ET). Using a process-based ecosystem model, we investigate the potential effects of climate change and rising atmospheric CO2 concentration on global terrestrial ecosystem water use efficiency (WUE) during the twenty-first century. Future climate change would reduce global WUE by 16.3% under high-emission climate change scenario (A2) and 2.2% under low-emission climate scenario (B1) during 2010–2099. However, the combination of rising atmospheric CO2 concentration and climate change would increase global WUE by 7.9% and 9.4% under A2 and B1 climate scenarios, respectively. This suggests that rising atmospheric CO2 concentration could ameliorate climate change-induced WUE decline. Future WUE would increase significantly at the high-latitude regions but decrease at the low-latitude regions under combined changes in climate and atmospheric CO2. The largest increase of WUE would occur in tundra and boreal needleleaf deciduous forest under the combined A2 climate and atmospheric CO2 scenario. More accurate prediction of WUE requires deeper understanding on the responses of ET to rising atmospheric CO2 concentrations and its interactions with climate. 相似文献
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Megha Maheshwari C. Mahesh Kamaljit Singh Rajkumar J. Pallipad Sandip R. Oza 《Marine Geodesy》2015,38(3):487-496
An attempt has been made to derive sea ice freeboard from Ka-band Altimeter (SARAL/AltiKa) over Arctic region for 15 March–15 April 2013 (spring) and 15 September–15 October 2013 (autumn). A waveform template matching technique is employed for classification of leads and floe pixels. The estimated sea ice freeboards were found in close agreement with “Operation IceBridge quick look” freeboards (RMSD = 0.30 m). The differences between the two freeboards were largely due to snow layer over sea ice (R = 0.8). The estimated freeboards were of the order of 0.08–0.15 m during the two seasons. 相似文献
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In this study, we used 30 years of an operational sea surface temperature (SST) product, the NOAA Optimum Interpolation (OI) SST Version 2 dataset, to examine variations in Arctic SSTs during the period December 1981–October 2011. We computed annual SST anomalies and interannual trends in SST variations for the period 1982–2010; during this period, marginal (though statistically significant) increases in SSTs were observed in oceanic regions poleward of 60°N. A warming trend is evident over most of the Arctic region, the Beaufort Sea, the Chuckchi Sea, Hudson Bay, the Labrador Sea, the Iceland Sea, the Norwegian Sea, Bering Strait, etc.; Labrador Sea experienced higher temperature anomalies than those observed in other regions. However, cooling trends were observed in the central Arctic, some parts of Baffin Bay, the Kara Sea (south of Novaya Zemlya), the Laptev Sea, the Siberian Sea, and Fram Strait. The central Arctic region experienced a cooling trend only during 1992–2001; warming trends were observed during 1982–1991 and 2002–2010. We also examined a 30-yr (1982–2011) record of summer season (June–July–August) SST variations and a 29-yr (1982–2010) record of September SST variations, the results of which are discussed. 相似文献
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