Understanding the relationships between cetaceans and their environment is crucial for conservation. This study examined humpback whales in Bahía de Banderas, Mexico, identifying key calving habitats. From 2018 to 2023, 1066 sightings were recorded, including 242 mother–calf groups, 109 mating groups, and 715 other groups. Spatial analysis revealed a non-random distribution; both the Kruskal–Wallis and Wilcoxon–Mann–Whitney tests detected significant differences (P < 0.05) in site preferences. Calving mothers favoured habitats with a mean depth of 59 m and a distance of 2 km from the coast, while mating groups preferred locations at 126 m and 4 km, and other groups chose areas at 149 m and 4 km. All groups were found in relatively flat areas around 2° seafloor slope. A dispersion test indicated a significant relationship between the location of calving mothers and environmental factors. K-means clustering showed 83.6% of calving mothers' sightings at depths less than 40 m and 2 km from the coast. Ensemble species distribution models identified three critical calving areas: one large area (261.8 km2) along the north coast and two smaller areas (9.5 and 5.4 km2) at the southern end of the bay. This study highlights Bahía de Banderas as a vital breeding habitat for humpback whales, providing insights for conservation strategies to protect calving grounds during the breeding season.

The effective reproduction number $ R $ was widely accepted as a key indicator during the early stages of the COVID-19 pandemic. In the UK, the $ R $ value published on the UK Government Dashboard has been generated as a combined value from an ensemble of epidemiological models via a collaborative initiative between academia and government. In this paper, we outline this collaborative modelling approach and illustrate how, by using an established combination method, a combined $ R $ estimate can be generated from an ensemble of epidemiological models. We analyse the $ R $ values calculated for the period between April 2021 and December 2021, to show that this $ R $ is robust to different model weighting methods and ensemble sizes and that using heterogeneous data sources for validation increases its robustness and reduces the biases and limitations associated with a single source of data. We discuss how $ R $ can be generated from different data sources and show that it is a good summary indicator of the current dynamics in an epidemic.

Vector-borne diseases are exceptionally sensitive to climate change. Predicting vector occurrence in specific regions is a challenge that disease control programs must meet in order to plan and execute control interventions and climate change adaptation measures. Recently, an increasing number of scientific articles have applied ecological niche modelling (ENM) to study medically important insects and ticks. With a myriad of available methods, it is challenging to interpret their results. Here we review the future projections of disease vectors produced by ENM, and assess their trends and limitations. Tropical regions are currently occupied by many vector species; but future projections indicate poleward expansions of suitable climates for their occurrence and, therefore, entomological surveillance must be continuously done in areas projected to become suitable. The most commonly applied methods were the maximum entropy algorithm, generalized linear models, the genetic algorithm for rule set prediction, and discriminant analysis. Lack of consideration of the full-known current distribution of the target species on models with future projections has led to questionable predictions. We conclude that there is no ideal ‘gold standard’ method to model vector distributions; researchers are encouraged to test different methods for the same data. Such practice is becoming common in the field of ENM, but still lags behind in studies of disease vectors.

There is a growing concern regarding the conservation status of amphibian species worldwide; they are more threatened and declining more rapidly than mammals or birds, and Mexico is considered one of the richest countries on Earth in terms of reptile and amphibian species. Composite models of the current distribution patterns of endemic amphibians in western Mexico were used to predict their potential distributional changes as a consequence of expected climatic changes. The models identified the most significant conservation areas within the region (hotspots), considering existing natural protected areas (NPAs) and previously recognized terrestrial priority regions for conservation (TPRCs). Three niche modelling algorithms (Bioclim, GARP and MaxEnt) used 2412 locality records for 29 species to model their climate envelopes under current and future conditions for the years 2020, 2050 and 2080. The models indicated that overall species persistence was 60% for the years 2020 and 2050, but dropped to < 20% by the year 2080. The current network of NPAs included only 8% of the areas that currently possess the greatest predicted potential richness (16–21 species), and, by 2050, the models indicate they will encompass only 3% of these areas. Six TPRCs included 44% of currently predicted areas with the highest potential species richness, but, by 2050, models predicted only 3% of such areas would persist within one TPRC. Higher uncertainty levels and variability among species surrounded the 2080 projections generated by the three algorithms. Recognition of the potential effects of climate change and consideration of the conservation value of the six TPRCs identified in this study may counteract the potential consequences of climate change on biodiversity in Mexico.