The Gulf of California is the most productive fishing region in Mexico; its ecosystems contain a vast diversity of species with exploitation potential, some of them potentially vulnerable to climate change. This research was conducted to analyze, through habitat suitability models, the possible alterations in the distribution of the three shrimp species of the most importance for commercial fishing in the region: Litopenaeus stylirostris, Litopenaeus vannamei, and Farfantepenaeus californiensis. Habitat suitability models were built using the MaxEnt software, primary productivity data, temperature, salinity, bathymetry, substratum, coastal type, and geo-referenced occurrence records of the three species. Of the data, 70% was used on training, while the remaining 30% was used for validation. To make estimates of climate change impact on this fishery, projections on distribution of the three species from environmental forecasts generated by the intergovernmental panel on climate change until 2100 were made. The used model, that is in full development and expansion, could be considered as an applicable tool to other problems and showed efficiency rates above 90%. The species will maintain most of their historical distribution, but L. stylirostris and L. vannamei will have a new distribution area within the zones of the Magdalena-Almejas Bay and the Gulf of Ulloa, with an increase of 80% and 148% respectively; all species will have loss areas in the proportion of 16%, 2%, and 11%, respectively, along the southern Gulf of California.

Climate change will increase the likelihood of adverse events such as droughts, forest fires, and intensification of tropical cyclones, which are known to cause flooding (IPCC, 2014). The effects of these events are a cause of concern for both authorities and citizens, so they prioritize actions that reduce adverse impacts, especially in cities with higher risk. Therefore, the objective of this work was to measure the degree of socio-environmental vulnerability of households to identify the risk areas in the city of La Paz, Baja California Sur, one of the regions with a high degree of incidence of hurricanes in the northwest of Mexico. For this, surveys were carried out with heads of households in 251 homes, and information was aggregated to calculate the vulnerability index through principal components analysis (PCA), which were stratified by the Dalenius–Hodges method, the degree of vulnerability was classified into three categories by the Opiyo method, considering three strata of the Likert scale, 1 = highly vulnerable, 2 = moderately vulnerable, 3 = less vulnerable. The results showed that households that are in the highly vulnerable category are 33% within a range of the index −3.77243 to −0.939141. Moderately vulnerable households constitute 36% with values from −0.929141 to 0.956385. While the least vulnerable represent 31% of households with an index range of 0.966385 to 5.6952. The results have revealed the levels of high and moderate socio-environmental vulnerability by tropical cyclones of 69% homes in La Paz. The above allowed to generate risk maps that will be taken into account in planning and civil protection over adverse events.

Climate change has resulted in severe consequences of hydrometeorological phenomena. The municipality of Los Cabos, Mexico, has been the most affected in the state of Baja California Sur by these hazards due to its location on the southern tip of the peninsula, being exposed with approximately 192 km of coastline; it is an environmental heritage that has made the area a primary tourist attraction in Mexico, which has caused a rapid growth in population with little knowledge about cyclone activity. In addition, there is limited knowledge regarding social indicators that measure vulnerability due to tropical cyclones. Based on the above, the objective of this study was to capture community perceptions about vulnerability related to tropical cyclones and to compare the results with real impacts and their index of socioenvironmental vulnerability, which includes indicators of exposure, sensitivity, and adaptive capacity, to provide useful information to form strategies to mitigate risk. Data were collected through a questionnaire-survey in 335 randomly selected households; we applied a probability model to the perception analysis and calculated an index to categorize vulnerability. We found differences between perceptions and real affectations, with 64% of households categorized as being highly vulnerable to tropical cyclones, and we detected a lower perception about damage suffered to their households. The variables related to knowledge and local or foreigner status were predictors of vulnerability perception. We included georeferenced data on flooding hazard maps as a strategy for adaptation.

Small-scale fishing (SSF) is a relevant economic activity worldwide, so sustainable development will be essential to assure its contributions to food security, poverty alleviation, and healthy ecosystems. However, the wide diversity of fisheries, their complexity, and the lack of information limit the ability to propose/evaluate management measures and plans and their effects on communities and other productive activities. The state of Baja California Sur, Mexico, our study case, ranks as the third place in national fisheries production, possesses SSF fleets, has a wide variety of fisheries that share fishing areas, fishing seasons, and operating units. In this work, assuming SSF as a complex system were proposed deep learning models (DLM) to forecast the catch volumes, evaluate each input variable's importance, and find interactions. Environmental variables and catch fisheries were tested in the DLM to estimate their predictive power. Different DLM structures and parameters to find the optimal model was used. The variables that presented higher predictive power are the environmental variables with R = 0.90. Moreover, when used in combination with the catches from other areas, the performance of R = 0.95 is obtained. Using only the catches, the model has an R = 0.81. This model allows the use of variables that indirectly affect the system and demonstrates a useful tool to assess a complex system's state in the face of disturbances in its variables.