Identifying waterfowl habitat suitability under changing environments, especially land-use change, is crucial to make waterfowl habitat conservation planning. We took Wetland Nature Reserve of Liaohe Estuary, the largest breeding area of Saunders’s Gulls (Larus saundersi) in the world, as our study area, generated land-use-type maps through interpretation of satellite images from four different years (1988, 2000, 2009, 2017), and predicted the potential breeding habitat of Saunders’s Gulls by MaxEnt model based on the land-use map, along with other environmental variables (NDVI, distance to roads and artificial facilities, distance to rivers and water bodies, DEM and distance to shoreline) for the four years, respectively. The models were evaluated using the area under the curve (AUC). We analyzed the changes of the breeding habitat from 1988 to 2017 and utilized RDA to explore the relationships among the changes of suitable habitat of Larus saundersi and the dynamics of land uses. Our results showed that the most suitable habitat decreased by 1286.46 ha during 1988-2009 and increased by 363.51 ha from 2009 to 2017. The suitable habitat decreased by 582.48 ha from 1988 to 2009 and then increased to 1848.96 ha in 2017, while the unsuitable habitat increased by 2793.87 ha during 1988–2009 and then decreased by 178.83 ha from 2009 to 2017. We also found that land use, distance to the coastline, distance to artificial facilities, distance to rivers, distance to roads, and NDVI had certain degrees of impact on the Larus saundersi distribution. The contribution of land use ranged from 16.4% to 40.3%, distance to coastline from 34.7% to 48.0%, distance to artificial facilities from 5.9% to 11.1%, distance to rivers from 5.5% to 11.0%, distance to roads from 3.9% to 12.5%, and NDVI from 0.3% to 6.3%. The change in suitable habitat of Larus saundersi has a positive relationship with the change of seepweed marsh. Human-induced changes in seepweed marsh and coastline position are the main factors influencing the potential breeding habitat of Saunders’s Gulls. We suggest strict conservation of seepweed marsh and implementation of habitat management practices to better protect Saunders’ Gull’s breeding habitat.

Fires greatly threaten the grassland ecosystem, human life, and economic development. However, since limited research focuses on grassland fire prediction, it is necessary to find a better method to predict the probability of grassland-fire occurrence. Multiple environmental variables impact fire occurrence. After selecting natural variables based on remote sensing data and anthropogenic variables, we built regression models of grassland fire probability, taking into account historical fire points and variables in Inner Mongolia. We arrived at three methods to identify grassland fire drivers and predict fire probability: global logistic regression, geographically weighted logistic regression, and random forest. According to the results, the random forest model had the best predictive effect. Nine variables selected by a geographically weighted logistic regression model exercised a spatially unbalanced influence on grassland fires. The three models all showed that meteorological factors and a normalized difference vegetation index (NDVI) were of great importance to grassland fire occurrence. In Inner Mongolia, grassland fires occurring in different areas indicated varying responses to the influencing drivers, and areas that differed in their natural and geographical conditions had different fire-prevention periods. Thus, a grassland fire management strategy based on local conditions should be advocated, and existing fire-monitoring systems based on original meteorological factors should be improved by adding remote sensing data of grassland fuels to increase accuracy.

This study aims to observe the effect of physical quality, staff behavior, ideal self-congruence, brand identification, and lifestyle congruence on consumer satisfaction and consumer satisfaction on brand loyalty. The research was conducted by distributing questionnaires. The objects studied were consumers from local coffee shops (Caturra, Caliber, Monopole, and Volks). The respondent obtained were 130 and 109 data were processed using the PLS technique with the help of the SmartPLS application. The results showed that physical quality and staff behavior had a significant effect on consumer satisfaction, while ideal self-congruence, brand identification, and lifestyle congruence had an effect but not significant on consumer satisfaction. In this study it was also found that consumer satisfaction has an effect on brand loyalty. Abstrak Penelitian ini bertujuan untuk mengukur pengaruh kualitas fisik, perilaku karyawan, kesesuaian diri, identifikasi merek, dan kesesuaian gaya hidup terhadap kepuasan konsumen dan kepuasan konsumen terhadap loyalitas merek. Penelitian dilakukan dengan penyebaran kuesioner. Obyek yang diteliti adalah konsumen dari coffee shop lokal (Caturra, Calibre, Monopole, dan Volks). Responden yang diperoleh sebanyak 130 dan sebanyak 109 data diolah menggunakan teknik PLS dengan bantuan aplikasi SmartPLS. Hasil penelitian menunjukkan bahwa kualitas fisik dan perilaku karyawan berpengaruh signifikan terhadap kepuasan kon­sumen, sedangkan kesesuaian diri, identifikasi merek, dan kesesuaian gaya hidup berpengaruh namun tidak signifikan terhadap kepuasan konsumen. Dalam penelitian ini juga didapati bahwa kepuasan konsumen berpengaruh terhadap loyalitas merek.

(2001). Quantitative measurements of undiilator spatial coherence at the ALS. Synchrotron Radiation News: Vol. 14, No. 2, pp. 32-33.

Grassland fires threaten grassland ecosystem, human life and economic development greatly. However, there are limited researches focusing on grassland fire prediction, thereby it is necessary to find a better method to predict probability of grassland fire occurrence. Here we selected 16 environmental variables that may have impacts on fire occurrence, then built regression models of grassland fire probability based on historical fire points and variables in Inner Mongolia by three methods to identify the grassland fire drivers and to predict fire probability. The three methods were global logistic regression, geographically weighted logistic regression and random forest. The results showed that random forest model had the best predictive effect. The influence of 9 variables selected by geographically weighted logistic regression model on grassland fire was unbalanced spatially. The three models all showed that meteorological factors were of great importance to grassland fire occurrence. In Inner Mongolia, grassland fires occurred in different areas had various responses to the influencing drivers, and the areas with different natural and geographical condition had different fire prevention periods. Thus, the grassland fire management strategy based on local conditions should be advocated.