BACKGOUND: Biological invasions are a global concern in agriculture, food production and biodiversity. Among the invasive species, some hornets are known to have serious effects on honey bees, as found during the invasion of Vespa velutina in Europe. The recent findings of Vespa mandarinia individuals in Washington state in the west coast of the USA have raised alarm in the whole country. Here we estimate the potential spread of V. mandarinia in the USA, analyzing its potential impacts on honey bee colonies, economic losses in the honey bee industry and bee-pollinated croplands. RESULTS: We found that V. mandarinia could colonize Washington and Oregon states in the west coast and a significant proportion of the east coast. If this species spread across the country, it could threaten 95 216 ± 5551 honey bee colonies, threatening an estimated income of US$11.9 and 101.8 million for hive derived products and bee-pollinated crops production, respectively, while colonizing 60 837.8 km 2 of bee-pollinated croplands. CONCLUSION: Our results suggest that V. mandarinia will have serious effects in the USA, raising the need for prompt monitoring actions and planning at different administrative levels to avoid its potential spread.

Zika virus (ZIKV) is an arbovirus transmitted mainly by Aedes aegypti mosquitoes. Recent scientific evidence on Culex quinquefasciatus has suggested its potential as a vector for ZIKV, which may change the current risk zones. We aimed to quantify the world population potentially exposed to ZIKV in a spatially explicit way, considering the primary vector (A. aegypti) and the potential vector (C. quinquefasciatus). Our model combined species distribution modelling of mosquito species with spatially explicit human population data to estimate ZIKV exposure risk. We estimated the potential global distribution of C. quinquefasciatus and estimated its potential interaction zones with A. aegypti. Then we evaluated the risk zones for ZIKV considering both vectors. Finally, we quantified and compared the people under risk associated with each vector by risk level, country and continent. We found that C. quinquefasciatus had a more temperate distribution until 42° in both hemispheres, while the risk involving A. aegypti is concentrated mainly in tropical latitudes until 35° in both hemispheres. Globally, 4.2 billion people are under risk associated with ZIKV. Around 2.6 billon people are under very high risk associated with C. quinquefasciatus and 1 billion people associated with A. aegypti. Several countries could be exposed to ZIKV, which emphasises the need to clarify the competence of C. quinquefasciatus as a potential vector as soon as possible. The models presented here represent a tool for risk management, public health planning, mosquito control and preventive actions, especially to focus efforts on the most affected areas.

Our results provide a spatially explicit baseline from which to focus efforts on the prevention, management and control of this pest worldwide. © 2018 Society of Chemical Industry.

Here we present a global time‐series of global forest above ground biomass from 2000 to 2019, analyzing spatiotemporal patterns of carbon balance, accounting for losses and gains. We generated a global Above‐Ground Biomass (AGB) map for the year 2000 and assessed its correlation with different satellite products. We generated a multi‐year analysis of AGB changes at the pixel level was generated, estimating carbon (C) loss and gain. Finally, we estimated the C losses due to forest clearing and wildfires analyzing their trends across biomes and countries. Our results show that the global mean annual loss was 2.88 ± 0.33 PgC yr−1, while global mean C gain was 2.95 ± 0.43 PgC yr−1, resulting in a neutral to sink behavior of −0.06 ± 0.58 PgC yr−1. The mean annual C loss by forest clearing was 1.04 ± 0.03 PgC yr−1, with an increasing trend of +0.03 ± 0.01 PgC yr−1. Eight biomes and 54 countries showed a significant increasing trend of C loss by forest clearing. Wildfires C losses reached 0.351 ± 0.02 Pg C yr−1, representing the 33.71% of forest clearing C losses. Boreal forest presented the highest C losses from wildfires, while significant increasing trends were evidenced in five biomes. We also find increasing trends of wildfire C loss in 20 countries while decreasing trends were identified in 10 countries. Our findings highlight the importance of designing strong policies to halt deforestation as agreed in the recent COP26 and provide information to identify priority areas to start implementing these policies in the short term.

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Cryptococcus is an important fungal pathogen worldwide, causing serious clinical manifestations that can affect immunocompetent patients and can be particularly severe for immunocompromised patients. The Cryptococcus gattii s.s. (AFLP4/VGI), Cryptococcus tetragattii (AFLP/VGIV), Cryptococcus neoformans, and Cryptococcus deneoformans have been isolated from both clinical and environmental sources in Europe. We aim to quantify the people in Europe and the entire Mediterranean area who are under risk associated with each of the three fungal pathogens in a spatially explicit way, generating a series of maps and population statistics per country. Niche modeling was applied to estimate the potential distribution of each fungal pathogen, then these models were overlapped with a map of population density to estimate risk levels. The potential number of people per risk level and per country was quantified using a map of population count per pixel. Prevalence of HIV per country was also included in the analysis to quantify the HIV‐infected population under potential risk. People under risk associated with exposure to C. gattii species (C. gattii s.s. and C. tetragattii) reached 137.65 million, whereas those exposed to C. neoformans and C. deneoformans were 268.58 and 360.78 million people, respectively. More than a half million HIV‐infected patients are exposed to each of the two species of the C. neoformans species complex, and more than 200,000 to the C. gattii species complex. The present results can be useful for public health planning by European governments, focusing on the provision of inputs for a “screen‐and‐treat” approach, availability of medical resources, and continuous monitoring programs in risk zones.

AimBiogeographic approaches usually have been developed apart from population ecology, resulting in predictive models without key parameters needed to account for reproductive and behavioural limitations on dispersal. Our aim was to incorporate fully spatially explicit population traits into a classic species distribution model (SDM) using Geographic Information Systems (GIS), aiming at conservation purposes.LocationSouthern South America.MethodsOur analysis incorporates the effects of habitat loss and fragmentation on population viability and therefore provides insights into how much spatially explicit population traits can improve the SDM prediction of habitable habitat. We utilized a well‐studied focal endemic bird of South American temperate rainforests (Scelorchilus rubecula). First, at a large scale, we assessed the historical extent habitat based on climate envelopes in an SDM. Second, we used a land cover change analysis at a regional scale to account for recent habitat loss and fragmentation. Third, we used empirically derived criteria to predict population responses to fragmented forest landscapes to identify actual losses of habitat and population. Then we selected three sites of high conservation value in southern Chile and applied our population model. Finally, we discuss the degree to which spatially explicit population traits can improve the SDM output without intervening in the modelling process itself.ResultsWe found a historical habitat loss of 39.12% and an additional forest cover loss of 3.03% during 2000–2014; the latter occurred with a high degree of fragmentation, reducing the overall estimation of (1) carrying capacity by −82.4%, −33.1% and −45.1% and (2) estimated number of pairs on viable populations by −84.1%, −33.0% and −54.6% on the three selected sites.Main conclusionWe conclude that our approach sharpened the SDM prediction on environmental suitability by 54.4%, adjusting the habitable area by adding population parameters through GIS, and allowing to incorporate other phenomena as fragmentation and habitat loss.