Dengue is the most important viral disease transmitted by mosquitoes, predominantly Aedes (Stegomyia) aegypti (L.) (Diptera:Culicidae). Forty percent of the world’s population is at risk of contracting the disease, and a large area of Mexico presents suitable environmental conditions for the life cycle of Ae. aegypti. In particular, the Central Mexican Highlands have a high population density, increasing the risk of transmission and propagation of dengue. In the present study, the potential distribution of Ae. aegypti was modeled under an ecological niche approach using the maximum entropy technique with the aim of determining the spatial risk distribution of dengue. The final model of five variables (minimum temperature of the coldest month |Bio6|, precipitation of the wettest month |Bio13|, precipitation seasonality |Bio15|, the normalized difference vegetation index (NDVI), and relative humidity) contributed to more than 90% of the model’s performance. The results of the potential distribution model were then compared with the number of dengue cases per locality during the 2009–2015 period considering four suitability of presence categories. Category 4 corresponded with the highest suitability of presence (0.747 to 1) and the greatest risk of dengue (odds ratio [OR] = 103.27; P < 0.001). In conclusion, the present ecological niche model represents an important tool for the monitoring of dengue and the identification of high-risk areas.

This paper shows the effects of changes in the spatial-temporal behavior and phase shift of climate variables on rainfed agriculture in the Lerma-Chapala-Santiago Basin in central Mexico. Specifically, changes in rainfall (R), maximum temperature (Tmax), and minimum temperature (Tmin) were analyzed over two 25-year periods (1960 to 1985 and 1986 to 2010). Climate surfaces were generated by interpolation using the thin-plate smoothing spline algorithm in the software ANUSPLIN. Climate data were Fourier-transformed and fitted to a sinusoidal curve model, and changes in amplitude (increase) and phase were analyzed. The temporal behavior (1960–2010) indicated that rainfall was the most stable variable at the monthly level and presented no significant changes. However, Tmax increased by 2°C in the final period, and Tmin increased by 0.7°C at the end of the final period. The basin was discretized into ten rainfed crop areas (RCAs) according to the extent of changes in the amplitude and phase of the climate variables. The central and southern portions (55% of the area) presented more significant changes in amplitude, mainly in Tmin and Tmax. The remaining RCAs were smaller (14.6%) but presented greater variation: the amplitude of the Tmin decreased in addition to showing a phase shift, whereas Tmax increased in addition to showing a phase shift. These results translate into a delay in the characteristic temperatures of the spring and summer seasons, which can impact the rainfed crop cycle. Additionally, rainfall showed an annual decrease of approximately 50 mm in all RCAs, which can affect the phenological development of crops during critical stages (emergence through flowering). These changes represent a significant threat to the regional economy and food security of Mexico.

The current study presents a method for automating the Köppen–Garcia climate classification using a GIS module. This method was then applied in a case study of the Lerma-Chapala-Santiago watershed to compare time series data on climate from 1960 to 1989, 1981 to 2010, and 1960 to 2010. The kappa statistic indicated that the climate classifications of the generated model had a perfect degree of agreement with those of a prior nonautomated study. The climate data from the period 1960 to 2010 were used to create a climate map for the watershed. Overall, the dominant climates were dry, semiarid, temperate, and semiwarm temperate with a summer rainfall pattern. A comparative analysis of climate behavior between 1960 and 1989 and between 1981 and 2010 showed changes in temperature and extreme temperatures over 13.6% and 9.9%, respectively, of the watershed; the presence or absence of mid-summer drought also changed over 0.8% of the watershed. The module developed herein can be used to classify climates across all of Mexico, and data of varying spatial resolution and coverage can be inputted to the module. Finally, this module can be used to automate the creation of climate maps or to update climate maps at diverse spatial-temporal scales.