Climate change ranks among the most important issues globally, affecting geographic distributions of vectors and pathogens, and inducing losses in livestock production among many other damaging effects. We characterized the potential geographic distribution of the ticksRhipicephalus(Boophilus)microplus, an important vector of babesiosis and anaplasmosis globally. We evaluated potential geographic shifts in suitability patterns for this species in two periods (2050 and 2070) and under two emissions scenarios (RCPs 4.5 and 8.5). Our results anticipate increases in suitability worldwide, particularly in the highest production areas for cattle. The Indo-Malayan region resulted in the highest cattle exposure under both climate change projections (2050), with increases in suitability of > 30%. This study illustrates how ecological niche modeling can be used to explore probable effects of climate change on disease vectors, and the possible consequences on economic dimensions.

Ecological niche models have seen intensive exploration as a tool in biodiversity conservation and evaluation of areas for designing protected natural areas systems, including projections of potential distributions under future conditions. Cloud forest is the most endangered ecosystem in Mexico, and yet ranks high in terms of diversity and endemism. This study focuses on 12 endangered and range-restricted tree species in Mexican cloud forests, exploring patterns of distribution and diversity under 2 future emissions scenarios (representative concentration pathways 4.5 and 8.5) as anticipated by 20 general circulation models. Our results indicate a likely strong reduction in species’ distributional areas and —consequently— species diversity manifested in different cloud forest patches across the country. The genus Quercus resulted the most sensitive to climate change. We identified cloud forest patches that are most vulnerable to climate change effects, which can and should focus priorities for protection of this ecosystem, particularly in the Sierra Madre Oriental, where cloud forest is presently lacking any protection.

The reemergence of the New World screwworm (Cochliomyia hominivorax) poses a significant threat to animal and public health with minimal regulatory oversight. This study analyzes the potential distribution and reemergence of this pest, which is endemic to South America but was previously eradicated in North America. We first developed bioclimatic suitability models, and then incorporated these findings along with reemergence records and inspection point data to simulate possible dispersal routes into Mexico and the United States. Our results document the historical distribution of C. hominivorax across the Americas and recent reemergence events in Panama (2023) and Mexico (2024–2025). Findings indicate high invasion potential from Central America, with significant risk along Mexico’s Pacific and Atlantic coasts and the Yucatan Peninsula. In the United States, Texas and Florida face the highest risk. Regions with high livestock density in both countries demonstrate considerable climatic suitability for the pest. Our simulations identify Chiapas, Campeche, Tabasco, and Veracruz as critical northern dispersal points. The invadable areas contain substantial populations of domestic hosts, primarily cattle and horses. These findings will enable governmental authorities to develop comprehensive prevention and control strategies to address this emerging threat.

Mosquito-borne diseases account for human morbidity and mortality worldwide, caused by the parasites (e.g., malaria) or viruses (e.g., dengue, Zika) transmitted through bites of infected female mosquitoes. Globally, billions of people are at risk of infection, imposing significant economic and public health burdens. As such, efficient methods to monitor mosquito populations and prevent the spread of these diseases are at a premium. One proposed technique is to apply acoustic monitoring to the challenge of identifying wingbeats of individual mosquitoes. Although researchers have successfully used wingbeats to survey mosquito populations, implementation of these techniques in areas most affected by mosquito-borne diseases remains challenging. Here, methods utilizing easily accessible equipment and encouraging community-scientist participation are more likely to provide sufficient monitoring. We present a practical, community-science-based method of monitoring mosquito populations using smartphones. We applied deep-learning algorithms (TensorFlow Inception v3) to spectrogram images generated from smartphone recordings associated with six mosquito species to develop a multiclass mosquito identification system, and flag potential invasive vectors not present in our sound reference library. Though TensorFlow did not flag potential invasive species with high accuracy, it was able to identify species present in the reference library at an 85% correct identification rate, an identification rate markedly higher than similar studies employing expensive recording devices. Given that we used smartphone recordings with limited sample sizes, these results are promising. With further optimization, we propose this novel technique as a way to accurately and efficiently monitor mosquito populations in areas where doing so is most critical.

Climate change is a global phenomenon that presents diverse threats to global food security. Of the avocados (Persea americana Mill), Hass is the most commonly cultivated variety in the world, representing an important source of nutrition in numerous countries, yet its potential risks in the face of climate change are unknown. Here, we characterise current and future potential distributional areas for Hass avocado under different scenarios of climate change across the Americas. We use ecological-niche modelling approaches to explore implications of changes in climate, considering 22 general circulation models, two emissions scenarios, and six model parameterisations. The current potential distribution of Hass avocado extends across tropical America (excluding most of Amazonia), including some areas at higher latitudes. Future projections show stability in potential distribution. Range expansions are expected mainly in temperate areas, and range contractions are related to temperature and precipitation increases, mostly in Amazonia. Model parametrisations contributed the most to overall variation in future projections, followed by climate models, and then emissions scenarios. Our conclusion of relative stability for the crop’s potential distribution is still subject to effects on other components of avocado production systems, and may be vulnerable to extreme phenomena.