The Aedes aegypti mosquito is the main vector for several diseases of global importance, such as dengue and yellow fever. This species was first identified on Madeira Island in 2005, and between 2012 and 2013 was responsible for an outbreak of dengue that affected several thousand people. However, the potential distribution of the species on the island remains poorly investigated. Here we assess the suitability of current and future climatic conditions to the species on the island and complement this assessment with estimates of the suitability of land use and human settlement conditions. We used four modelling algorithms (boosted regression trees, generalized additive models, generalized linear models and random forest) and data on the distribution of the species worldwide and across the island. For both climatic and non-climatic factors, suitability estimates predicted the current distribution of the species with good accuracy (mean area under the Receiver Operating Characteristic curve = 0.88 ±0.06, mean true skill statistic = 0.72 ±0.1). Minimum temperature of coldest month was the most influential climatic predictor, while human population density, residential housing density and public spaces were the most influential predictors describing land use and human settlement conditions. Suitable areas under current climates are predicted to occur mainly in the warmer and densely inhabited coastal areas of the southern part of the island, where the species is already established. By mid-century (2041–2060), the extent of climatically suitable areas is expected to increase, mainly towards higher altitudes and in the eastern part of the island. Our work shows that ongoing efforts to monitor and prevent the spread of Ae. aegypti on Madeira Island will have to increasingly consider the effects of climate change.

The perfect storm model that was elaborated for the HIV-1M pandemic has also been used to explain the emergence of HIV-2, a second human immunodeficiency virus-acquired immunodeficiency syndrome (HIV-AIDS) that became an epidemic in Guinea-Bissau, West Africa. The use of this model creates epidemiological generalizations, ecological oversimplifications and historical misunderstandings as its assumptions—an urban center with explosive population growth, a high level of commercial sex and a surge in STDs, a network of mechanical transport and country-wide, en masse mobile campaigns—are absent from the historical record. This model fails to explain how the HIV-2 epidemic actually came about. This is the first study to conduct an exhaustive examination of sociohistorical contextual developments and align them with environmental, virological and epidemiological data. The interdisciplinary dialogue indicates that the emergence of the HIV-2 epidemic piggybacked on local sociopolitical transformations. The war’s indirect effects on ecological relations, mobility and sociability were acute in rural areas and are a key to the HIV-2 epidemic. This setting had the natural host of the virus, the population numbers, the mobility trends and the use of technology on a scale needed to foster viral adaptation and amplification. The present analysis suggests new reflections on the processes of zoonotic spillovers and disease emergence.

Background: Mosquitoes from the Aedes (Ae.) genus are vectors of dengue, Zika, chikungunya, and other arboviruses, posing a significant public health threat. In 2005, Aedes aegypti was detected for the first time in Madeira Island, Portugal, in the city of Funchal, and has since become established in the region. In 2017, Aedes albopictus was detected for the first time in mainland Portugal. These invasion events require targeted entomological surveillance, which demands substantial human resources and a high management capacity for traditional vector monitoring. Following promising results obtained in laboratory conditions, a field-deployable model of a bioacoustic sensor for the automatic classification of mosquitoes integrated with a Biogents Sentinel trap as part of the VECTRACK system was tested in three regions in Portugal. Methods: The VECTRACK system was deployed in three locations: Funchal on Madeira Island, and Palmela and Algarve on mainland Portugal. Catch bags were manually inspected at intervals ranging from daily to weekly, resulting in a total of 38 captures in Madeira, 10 in Palmela, and 7 in the Algarve. Manual identifications were compared with those generated by the VECTRACK system, and the degree of correlation between the two datasets was assessed using Spearman’s rank correlation coefficient. Results: A total of 176 mosquitoes were captured in Madeira, 732 in Palmela, and 143 in the Algarve. Both manual and sensor-based identifications demonstrated similar performance, with high correlation observed between the two methods. Spearman’s rank correlation coefficients indicated high agreement for both female and male mosquitoes across all sites: Madeira: females = 0.84, males = 0.92, Palmela: females = 0.99, males = 0.84, Algarve: females = 0.98, and males = 0.99, all with p-values < 0.001. Conclusions: The VECTRACK system demonstrated strong performance in accurately distinguishing mosquitoes from non-mosquitoes, differentiating between Aedes and Culex genera, and identifying the sex of individual specimens. These promising results provide a solid foundation for the development of automated early warning systems and enhance mosquito surveillance strategies, which are critical for timely responses to potential vector-borne disease outbreaks.

Ecosystems with a naturally accumulated peat layer at the surface and wetlands with or without a peat layer dominated by a vegetation that may produce peat (hereafter 'peatlands' for simplicity) are critical assets in global change due to their large carbon content storage capacity and essential ecosystem services. A Global Peatland Assessment was initiated during the UNFCCC COP22; however, many countries still lack accurate peatland maps. This is particularly concerning in the Western Mediterranean, where these ecosystems are frequently overlooked or misclassified. To help address this issue, we developed predictive distribution models for vascular plant and bryophyte species typical of peatland-type systems in the Western Mediterranean. We identified 28 'indicator' mire-typical plant taxa and created a database using filtered data from six different data sources, including our own field surveys. Thirteen high-resolution spatial predictor variables, covering topographic, soil, and hydroclimatic factors, were selected to reflect potential relationships with species presence. For each species, a potential distribution model was built using MaxEnt software. The resulting potential-distribution maps were cross-validated, and models with an AUC score of ≥0.75 were classified into suitable or unsuitable areas. The species richness map aligned well with areas known to support peatlands. The indicator species-based mapping also suggests a broad potential for yet unrecorded peatlands in the Western Mediterranean, including small areas, particularly along the western and southwestern sandy coastlines and in the southern mountains.