Attributing vegetation changes provide fundamental information for ecosystem management,especially in mountainous areas which has vulnerable ecosystems. Based on the Normalized Difference Vegetation Index(NDVI) data, the spatial-temporal change of vegetation was detected in Taihang Mountain(THM) from 2000 to 2014. The topographical factors were introduced to interpret the response of vegetation variation to climate change and human activities. Results showed that the avegaged NDVI during growing season showed a single-peak curve distribution, with the largest value(0.628) among 1600-1800 m. A significant greening trend was detected in THM, with the largest increasing rate(0.0078 yr~(-1)) among the elevation of1600-1800 m and slope gradient between 3~5°. The partial correlation and multiple correlation analyses indicated that vegetation variation in more than81.8% pixels of the THM was mainly impacted by human activities. In the low elevation zones less than1000 m, increasing precipitation is the principle factor promoting vegetation restoration, whereas in the high elevation zones of THM, temperature is the restricted factors impacting vegetation variation.Considering the dramatic climate change in the future,further studies should be conducted to explore inherent mechanism of vegetation growth to dynamic environment changes. 相似文献
Journal of Oceanology and Limnology - A mass mortality often occurs from molting to the megalopa stage during the larval development of the swimming crab Portunus trituberculatus. Larvae with... 相似文献
Self-feeding device is extensively used in aquaculture farms, but for salmonids the individual feeding behavior has seldom been continuously observed. In this article, the individual self-feeding behavior of 10 rainbow trout was continuously monitored with a PIT tag record for 50 days with three replicates. The fish fell into three categories according to their feeding behavior, i.e. high triggering fish (trigger behavior more than 25% of the group, HT), low triggering fish (1%–25%, LT) and zero triggering fish (less than 1%). The results showed that in a group of 10 individual 1–2 HT fish accounted for most of the self-feeding behavior (78.19%–89.14%), which was far more than they could consume. The trigger frequency of the fish was significantly correlated with the initial body weight (P <0.01), however, no significant difference in growth rate among the HT, LT, and ZT fish was observed (P >0.05). Cosinor analysis showed that the two HT fish in the same group had similar acrophase. Though some of the HT fish could be active for 50 d, there were also HT fish decreased triggering behavior around 40 d and the high trigger status was then replaced by other fish, which was first discovered in salimonds. Interestingly, the growth of the group was not affected by the alternation triggering fish. These results provide evidence that in the self-feeding system the HT fish didn’t gain much advantage by their frequent self-feeding behavior, and high trigger status of the HT fish is not only an individual character but also driven by the demand of the group. In the self-feeding system, the critical individual should be closely monitored.
Understanding the spatial scale sensitivity of cellular automata is crucial for improving the accuracy of land use change simulation. We propose a framework based on a response surface method to comprehensively explore spatial scale sensitivity of the cellular automata Markov chain (CA-Markov) model, and present a hybrid evaluation model for expressing simulation accuracy that merges the strengths of the Kappa coefficient and of Contagion index. Three Landsat-Thematic Mapper remote sensing images of Wuhan in 1987, 1996, and 2005 were used to extract land use information. The results demonstrate that the spatial scale sensitivity of the CA-Markov model resulting from individual components and their combinations are both worthy of attention. The utility of our proposed hybrid evaluation model and response surface method to investigate the sensitivity has proven to be more accurate than the single Kappa coefficient method and more efficient than traditional methods. The findings also show that the CA-Markov model is more sensitive to neighborhood size than to cell size or neighborhood type considering individual component effects. Particularly, the bilateral and trilateral interactions between neighborhood and cell size result in a more remarkable scale effect than that of a single cell size. 相似文献