Habitat selection by Chinese pangolin (Manis pentadactyla) in winter was studied in Dawuling Natural Reserve, Maoming City of Guangdong Province, China, from December 1999 through February 2001.The objective of this study was to determine the pangolin's habitat requirement in winter during poor environmental conditions. The dominant habitat of this Reserve was classified into 4 types, i.e. mixed coniferous and broadleaf forest (MCBF), evergreen broadleaf forest (EBF), coniferous forest (CF) and shrub forest (SF). The results showed that the ranking of vegetation types selected by pangolins in winter was MCBF > SF > EBF > CF. Pangolins preferred MCBF, and avoided CF. The environmental factors preferred by pangolins in winter were 30°-60° steep slopes, middle of slopes and bottom of slopes, sunny slopes, dis-KEY WORDS Dawuling Natural Reserve, pangolin, Manis pentadactyla, habitat selection. MAMMALIA, t. 67. n° 4 · 2003 · 493-501 493 Brought to you by | University of Georgia Libraries Authenticated Download Date | 6/1/15 1:07 AM Wu S.B. andai.tance from human disturbance source exceeding 1 000 m with a minor disturbance degree, heavy (81 % -100 0/ o) undergrowth with good shelter conditions, moderate (760 -1 500 m) elevation, and medium (31 "o -70 %) closure of arbor canopy. The surroundings factors avoided by pangolins were sharp slopes steeper than 60° or gentle slopes less than 30°, shady slopes, distance from human disturbance source within 1 000 m, dense (71 % -100 %) or sparse (0 % -30 %) closure of arbor canopy, medium or lower coverage (0 % -50 %) of undergrowth, and the top of the slope. Pangolins preferred south-facing burrow entrances with thick cover, and avoided north-facing burrow entrances with bare or poor shelter. The thick layer of shrub and herbs growing under the tree canopy appeared to be especially important to pangolins during winter.

The alpine tree line ecotone, reflecting interactions between climate and ecology, is very sensitive to climate change. To identify tree line responses to climate change, including intensity and local variations in tree line advancement, the use of Landsat images with long-term data series and fine spatial resolution is an option. However, it is a challenge to extract tree line data from Landsat images due to classification issues with outliers and temporal inconsistency. More importantly, direct classification results in sharp boundaries between forest and non-forest pixels/segments instead of representing the tree line ecotone (three ecological regions—tree species line, tree line, and timber line—are closely related to the tree line ecotone and are all significant for ecological processes). Therefore, it is important to develop a method that is able to accurately extract the tree line from Landsat images with a high temporal consistency and to identify the appropriate ecological boundary. In this study, a new methodology was developed based on the concept of a local indicator of spatial autocorrelation (LISA) to extract the tree line automatically from Landsat images. Tree line responses to climate change from 1987 to 2018 in Wuyishan National Park, China, were evaluated, and topographic effects on local variations in tree line advancement were explored. The findings supported the methodology based on the LISA concept as a valuable classifier for assessing the local spatial clusters of alpine meadows from images acquired in nongrowing seasons. The results showed that the automatically extracted line from Landsat images was the timber line due to the restriction in spatial autocorrelation. The results also indicate that parts of the tree line in the study area shifted upward vertically by 50 m under a 1 °C temperature increase during the period from 1987 to 2018, with local variations influenced by slope, elevation, and interactions with aspect. Our study contributes a novel result regarding the response of the alpine tree line to global warming in a subtropical region. Our method for automatic tree line extraction can provide fundamental information for ecosystem managers.

No abstract

Various faults of photovoltaic (PV) modules inevitably occur in the work process, since PV modules are installed in hostile situation. To obtain the types of failure, a novel fault diagnosis method based on back propagation (BP) neural network with Levenberg-Marquardt (L-M) algorithm for PV modules is proposed. Through the in-depth analysis the output of PV modules under normal and fault conditions, the input variables of the diagnosis model are acquired. The high-speed and real-time fault diagnosis model for PV modules is first designed based on TMS320VC5402 DSP and long-distance wireless fault diagnosis is realized by Zigbee technology. The simulation and experimental results show that the fault diagnosis method for PV modules based on BP network with L-M algorithm can effectively detect four types of fault for PV modules such as open circuit, short circuit, partial shading and abnormal degradation. The numerical results verify the effectiveness and correctness of the proposed method, which can provide a great educational benefit of PV operation technology.

Bikes are among the healthiest, greenest, and most affordable means of transportation for a better future city, but mobility patterns of riders with different income were rarely studied due to limitation on collecting data. Newly emergent dockless bike-sharing platforms that record detailed information regarding each trip provide us a unique opportunity. Attribute to its better usage flexibility and accessibility, dockless bike-sharing platforms are booming over the past a few years worldwide and reviving the riding fashion in cities. In this work, by exploiting massive riding records in two megacities from a dockless bike-sharing platform, we reveal that individual mobility patterns, including radius of gyration and average travel distance, are similar among users with different income, which indicates that human beings all follow similar physical rules. However, collective mobility patterns, including average range and diversity of visitation, and commuting directions, all exhibit different behaviors and spatial patterns across income categories. Hotspot locations that attract more cycling activities are quite different over groups, and locations where users reside are of a low user ratio for both higher and lower income groups. Lower income groups are inclined to visit less flourishing locations, and commute towards the direction to the city center in both cities, and of a smaller mobility diversity in Beijing but a larger diversity in Shanghai. In addition, differences on mobility patterns among socioeconomic categories are more evident in Beijing than in Shanghai. Our findings would be helpful on designing better promotion strategies for dockless bike-sharing platforms and towards the transition to a more sustainable green transportation.