摘要：Net primary productivity (NPP) of the vegetation in an oasis can reflect the productivity capacity of a plant community under natural environmental conditions. Owing to the extreme arid climate conditions and scarce precipitation in the arid oasis regions, groundwater plays a key role in restricting the development of the vegetation. The Qira Oasis is located on the southern margin of the Taklimakan Desert (Tarim Basin, China) that is one of the most vulnerable regions regarding vegetation growth and water scarcity in the world. Based on remote sensing images of the Qira Oasis and daily meteorological data measured by the ground stations during the period 2006–2019, this study analyzed the temporal and spatial patterns of NPP in the oasis as well as its relation with the variation of groundwater depth using a modified Carnegie Ames Stanford Approach (CASA) model. At the spatial scale, NPP of the vegetation decreased from the interior of the Qira Oasis to the margin; at the temporal scale, NPP of the vegetation in the oasis fluctuated significantly (ranging from 29.80 to 50.07 g C/(m2•month)) but generally showed an increasing trend, with the average increase rate of 0.07 g C/(m2•month). The regions with decreasing NPP occupied 64% of the total area of the oasis. During the study period, NPP of both farmland and grassland showed an increasing trend, while that of forest showed a decreasing trend. The depth of groundwater was deep in the south of the oasis and shallow in the north, showing a gradual increasing trend from south to north. Groundwater, as one of the key factors in the surface change and evolution of the arid oasis, determines the succession direction of the vegetation in the Qira Oasis. With the increase of groundwater depth, grassland coverage and vegetation NPP decreased. During the period 2008–2015, with the recovery of groundwater level, NPP values of all types of vegetation with different coverages increased. This study will provide a scientific basis for the rational utilization and sustainable management of groundwater resources in the oasis.
摘要：Central Asia is located in the hinterland of Eurasia, comprising Kazakhstan, Uzbekistan, Kyrgyzstan, Turkmenistan, and Tajikistan; over 93.00% of the total area is dryland. Temperature rise and human activities have severe impacts on the fragile ecosystems. Since the 1970s, nearly half the great lakes in Central Asia have shrunk and rivers are drying rapidly owing to climate changes and human activities. Water shortage and ecological crisis have attracted extensive international attention. In general, ecosystem services in Central Asia are declining, particularly with respect to biodiversity, water, and soil conservation. Furthermore, the annual average temperature and annual precipitation in Central Asia increased by 0.30°C/decade and 6.9 mm/decade in recent decades, respectively. Temperature rise significantly affected glacier retreat in the Tianshan Mountains and Pamir Mountains, which may intensify water shortage in the 21st century. The increase in precipitation cannot counterbalance the aggravation of water shortage caused by the temperature rise and human activities in Central Asia. The population of Central Asia is growing gradually, and its economy is increasing steadily. Moreover, the agricultural land has not been expended in the last two decades. Thus, water and ecological crises, such as the Aral Sea shrinkage in the 21st century, cannot be attributed to agriculture extension any longer. Unbalanced regional development and water interception/transfer have led to the irrational exploitation of water resources in some watersheds, inducing downstream water shortage and ecological degradation. In addition, accelerated industrialization and urbanization have intensified this process. Therefore, all Central Asian countries must urgently reach a consensus and adopt common measures for water and ecological protection.
摘要：Odor detection applications are needed by human societies in various circumstances. Rodent offers unique advantages in developing biologic odor detection systems. This report outlines a novel apparatus designed to train maximum 5 mice automatically to detect odors using a new olfactory, relative go no-go, operant conditioning paradigm. The new paradigm offers the chance to measure real-time reliability of individual animal's detection behavior with changing responses. All of 15 water-deprivation mice were able to learn to respond to unpredictable delivering of the target odor with higher touch frequencies via a touch sensor. The mice were continually trained with decreasing concentrations of the target odor (n-butanol), the average correct percent significantly dropped when training at 0.01% solution concentration; the alarm algorithm showed excellent recognition of odor detection behavior of qualified mice group through training. Then, the alarm algorithm was repeatedly tested against simulated scenario for 4 blocks. The mice acted comparable to the training period during the tests, and provided total of 58 warnings for the target odor out of 59 random deliveries and 0 false alarm. The results suggest this odor detection method is promising for further development in respect to various types of odor detection applications.