Species distribution models (SDMs) have been criticized for involving assumptions that ignore or categorize many ecologically relevant factors such as dispersal ability and biotic interactions. Another potential source of model error is the assumption that species are ecologically uniform in their climatic tolerances across their range. Typically, SDMs treat a species as a single entity, although populations of many species differ due to local adaptation or other genetic differentiation. Not taking local adaptation into account may lead to incorrect range prediction and therefore misplaced conservation efforts. A constraint is that we often do not know the degree to which populations are locally adapted. Lacking experimental evidence, we still can evaluate niche differentiation within a species' range to promote better conservation decisions. We explore possible conservation implications of making type I or type II errors in this context. For each of two species, we construct three separate Max-Ent models, one considering the species as a single population and two of disjunct populations. Principal component analyses and response curves indicate different climate characteristics in the current environments of the populations. Model projections into future climates indicate minimal overlap between areas predicted to be climatically suitable by the whole species vs. population-based models. We present a workflow for addressing uncertainty surrounding local adaptation in SDM application and illustrate the value of conducting population-based models to compare with whole-species models. These comparisons might result in more cautious management actions when alternative range outcomes are considered.

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Local adaptation and species interactions have been shown to affect geographic ranges; therefore, we need models of climate impact that include both factors. To identify possible dynamics of species when including these factors, we ran simulations of two competing species using an individual-based, coupled map-lattice model using a linear climatic gradient that varies across latitude and is warmed over time. Reproductive success is governed by an individual's adaptation to local climate as well as its location relative to global constraints. In exploratory experiments varying the strength of adaptation and competition, competition reduces genetic diversity and slows range change, although the two species can coexist in the absence of climate change and shift in the absence of competitors. We also found that one species can drive the other to extinction, sometimes long after climate change ends. Weak selection on local adaptation and poor dispersal ability also caused surfing of cooler-adapted phenotypes from the expanding margin backwards, causing loss of warmer-adapted phenotypes. Finally, geographic ranges can become disjointed, losing centrally-adapted genotypes. These initial results suggest that the interplay between local adaptation and interspecific competition can significantly influence species' responses to climate change, in a way that demands future research.

Understanding the historical trends and driving mechanism of China's soil moisture change is an important step in combating climate change. Using the time series satellite-derived Essential Climate Variable Soil Moisture (ECV_SM) product, we detected a significant decrease trend in land surface soil moisture in eastern China over a 32 year period . Theoretical sensitivity analysis suggested that soil moisture is regulated collectively by precipitation (P), potential evapotranspiration (PET), land surface conditions such as land cover/use changes, landscape features, irrigation and urban expansion, (m), and the water balance between input and output water supplies O (the input water supplies minus the output). The change in spatial pattern and temporal trend of P/PET is highly consistent with the corresponding change in soil moisture. The magnitude of soil moisture variation is also well correlated with that of P/PET (R 2 = 0.43; p < 0.001). Therefore, P/PET is believed to be the dominant factor in determining the temporal trends of soil moisture change. Among the 29 drainage basins with significant decreasing trend of soil moisture change, the areas of forest cover increased by 36.08% and the average topographic slope was twice steeper than that of other regions. Therefore, besides the climate factor (P/PET variable), land surface conditions (such as land cover changes and topographic) also played important roles in regulating the trend of regional soil moisture change. 1998;Uitdewilligen et al., 2003;Chen et al., 2012]. Due to its longer wavelength, the microwave can penetrate deeper into the media (i.e., soil and vegetation canopy) than the optical electromagnetic wave, and the CHEN ET AL.CHINA'S 32 YEAR SOIL MOISTURE 5177

BackgroundLeptospirosis is a water-borne and widespread spirochetal zoonosis caused by pathogenic bacteria called leptospires. Human leptospirosis is an important zoonotic infectious disease with frequent outbreaks in recent years in China. Leptospirosis’s emergence has been linked to many environmental and ecological drivers of disease transmission. In this paper, we identified the environmental and socioeconomic factors associated with leptospirosis in China, and predict potential risk area of leptospirosis using predictive models.MethodsLeptospirosis incidence data were derived from the database of China’s web-based infectious disease reporting system, a national surveillance network maintained by Chinese Center for Disease Control and Prevention. We built statistical relationship between occurrence of leptospirosis and nine environmental and socioeconomic risk factors using logistic regression model and Maxent model.ResultsBoth logistic regression model and Maxent model have high performance in predicting the occurrence of leptospirosis, with AUC value of 0.95 and 0.96, respectively. Annual mean temperature (Bio1) and annual total precipitation (Bio12) are two most important variables governing the geographic distribution of leptospirosis in China. The geographic distributions of areas at risk of leptospirosis predicted from both models show high agreement. The risk areas are located mainly in seven provinces of China: Sichuan Province, Chongqing Municipality, Hunan Province, Jiangxi Province, Guangdong Province, Guangxi Province, and Hainan Province, where surveillance and control programs are urgently needed. Logistic regression model and Maxent model predicted that 403 and 464 counties are at very high risk of leptospirosis, respectively.ConclusionsOur results highlight the importance of socioeconomic and environmental variables and predictive models in identifying risk areas for leptospirosis in China. The values of Geographic Information System and predictive models were demonstrated for investigating the geographic distribution, estimating socioeconomic and environmental risk factors, and enhancing our understanding of leptospirosis in China.

The nighttime stable light (NSL) images on board the Operational Line-scan System (OLS) of the Defense Meteorological Satellite Program (DMSP) are useful for extracting large-scale built-up urban areas. However, most NSL-based studies are presently empirical threshold-based approaches. They always overestimate the areas of built-up land in urban regions because of the 'blooming' effect of NSL; and overlook small patches in developing towns where the NSL is much lower. In this study, a neighborhood statistics analysis (NSA) method is developed on the basis of the relative spatial variations between neighboring built-up and non-built-up pixels in DMSP-OLS images. It is applied to extract the built-up areas of eight cities in the Pearl River Delta in 1996Delta in , 2000Delta in , 2005Delta in , and 2009. The validations indicate that the total areas of the NSA-mapped results are highly correlated with those from Landsat TM/ETM+ data (R 2 = 0.94; p < 0.001). The comparison results, which are evaluated by landscape indices (the landscape shape index (LSI), the contiguity index (CONTIG), and the perimeter area ratio (PARA)), also show good correlations (R 2 > 0.46; p < 0.001). In addition, the total NSL of the built-up urban areas is significantly correlated with the statistical population data (R 2 = 0.62; p < 0.001), which indirectly confirms the effectiveness of our proposed method.Keywords: DMSP-OLS nighttime stable light (NSL); built-up urban area; urban expansion; remote sensing IntroductionThirty years of rapid urbanization in China have resulted in a 3.37-fold increase in builtup areas (Fang 2009;Lu et al. 2008 Lu et al. , 2012. Large amounts of natural lands in cities are being replaced by artificial surfaces, which cause numerous environmental and ecological problems (Milesi et al. 2003;Imhoff et al. 2004;Su et al. 2010;Chen et al. 2012). Therefore, accurately monitoring the regional expansion of built-up areas in China is essential (He et al. 2006;Liu et al. 2012 (Cao et al. 2009;Liu et al. 2012;Bhatti and Tripathia 2014), are commonly used. However, because of the limited geographic coverage of medium-and high-resolution images, these methods require a large number of scenes to cover the regional and national scales (Liu et al. 2003(Liu et al. , 2005(Liu et al. , 2010Zhang, He, and Liu 2014).Nighttime stable light (NSL) images on board the Operational Line-scan System (OLS) of the Defense Meteorological Satellite Program (DMSP), which uses a low-light detecting sensor to detect city night lights (Elvidge et al. 1997(Elvidge et al. , 1999, were first demonstrated to be capable of mapping built-up urban areas by Croft (1978) and Kramer (1994). Subsequent studies demonstrated the superiority of DMSP-OLS NSL in extracting the built-up urban areas at global (Elvidge et al. 1997(Elvidge et al. , 1999(Elvidge et al. , 2007 (Imhoff et al. 1997a(Imhoff et al. , 1997bSmall, Pozzi, and Elvidge 2005;Lu et al. 2008;Cao et al. 2009;Shi et al. 2014;Wei et al. 2014). However, most NSL-based studies presently use empir...