Seasonal population dynamics of the primary yellow fever vector Haemagogus leucocelaenus (Dyar & Shannon) (Diptera: Culicidae) is mainly influenced by temperature in the Atlantic Forest, southeast Brazil.

BACKGROUND: Southeast Brazil has recently experienced a Yellow Fever virus (YFV) outbreak where the mosquito Haemagogus leucocelaenus was a primary vector. Climatic factors influence the abundance of mosquito vectors and arbovirus transmission. OBJECTIVES: We aimed at describing the population dynamics of Hg. leucocelaenus in a county touched by the recent YFV outbreak. METHODS: Fortnightly egg collections with ovitraps were performed from November 2012 to February 2017 in a forest in Nova Iguaçu, Rio de Janeiro, Brazil. The effects of mean temperature and rainfall on the Hg. leucocelaenus population dynamics were explored. FINDINGS: Hg. leucocelaenus eggs were continuously collected throughout the study, with a peak in the warmer months (December-March). The climatic variables had a time-lagged effect and four weeks before sampling was the best predictor for the positivity of ovitraps and total number of eggs collected. The probability of finding > 50% positive ovitraps increased when the mean temperature was above 24ºC. The number of Hg. leucocelaenus eggs expressively increase when the mean temperature and accumulated precipitation surpassed 27ºC and 100 mm, respectively, although the effect of rainfall was less pronounced. MAIN CONCLUSIONS: Monitoring population dynamics of Hg. leucocelaenus and climatic factors in YFV risk areas, especially mean temperature, may assist in developing climate-based surveillance procedures to timely strengthening prophylaxis and control.

Detecting the macroevolutionary signal of species interactions.

Species interactions lie at the heart of many theories of macroevolution, from adaptive radiation to the Red Queen. Although some theories describe the imprint that interactions will have over long timescales, we are still missing a comprehensive understanding of the effects of interactions on macroevolution. Current research shows strong evidence for the impact of interactions on macroevolutionary patterns of trait evolution and diversification, yet many macroevolutionary studies have only a tenuous relationship to ecological studies of interactions over shorter timescales. We review current research in this area, highlighting approaches that explicitly model species interactions and connect them to broad-scale macroevolutionary patterns. We also suggest that progress has been made by taking an integrative interdisciplinary look at individual clades. We focus on African cichlids as a case study of how this approach can be fruitful. Overall, although the evidence for species interactions shaping macroevolution is strong, further work using integrative and model-based approaches is needed to spur progress towards understanding the complex dynamics that structure communities over time and space.

Network Structure and Selection Asymmetry Drive Coevolution in Species-Rich Antagonistic Interactions.

Ecological interactions shape and are shaped by the evolution of interacting species. Mathematical models and empirical work have explored the multiple ways coevolution could occur in small sets of species, revealing that the addition of even one species can change the coevolutionary dynamics of a pairwise interaction. As a consequence, one of the current challenges in evolutionary biology is to understand how species-rich assemblages evolve and coevolve as networks of interacting species. We combined an adaptive network framework, a trait evolutionary model, and data on network structure to study how network organization affects and is affected by selection in antagonistic interactions such as parasitism, predation, and herbivory. We explored how selection imposed by interactions shapes the evolution of attack and defense traits, parameterizing our models with structural information from 31 empirical assemblages of antagonistic species. In the simulations, the form of coevolution in antagonistic interactions is affected by the intensity and asymmetry of the selection imposed by the interacting partners. Transient escalation in attack and defensive traits was the most prevalent form of coevolutionary dynamics, especially in networks formed by modules of highly interacting species. Fluctuating evolution of traits was observed when the intensity of selection was higher in exploiters than in victims and was especially favored in nested networks. At the species level, highly connected species experienced higher temporal variation in selection regardless of the network structure, resulting in high trait mismatching with their partners. The mismatched patterns of highly connected species, in turn, may explain the emergence of modularity in antagonistic interactions in which selection is stronger on exploiters than on their victims. Our results highlight the roles of different aspects of network structure on antagonistic coevolution: nestedness shapes coevolutionary dynamics, whereas modularity emerges as one outcome of coevolutionary dynamics.

Integrating databases for spatial analysis of parasite-host associations and the novel Brazilian dataset.

Incomplete information on parasites, their associated hosts, and their precise geographical location hampers the ability to predict disease emergence in Brazil, a continental-sized country characterised by significant regional disparities. Here, we demonstrate how the NCBI Nucleotide and GBIF databases can be used as complementary databases to study spatially georeferenced parasite-host associations. We also provide a comprehensive dataset of parasites associated with mammal species that occur in Brazil, the Brazilian Mammal Parasite Occurrence Data (BMPO). This dataset integrates wild mammal species' morphological and life-history traits, zoonotic parasite status, and zoonotic microparasite transmission modes. Through meta-networks, comprising interconnected host species linked by shared zoonotic microparasites, we elucidate patterns of zoonotic microparasite dissemination. This approach contributes to wild animal and zoonoses surveillance, identifying and targeting host species accountable for disproportionate levels of parasite sharing within distinct biomes. Moreover, our novel dataset contributes to the refinement of models concerning disease emergence and parasite distribution among host species.

Trajetorias: a dataset of environmental, epidemiological, and economic indicators for the Brazilian Amazon.

The Trajetorias dataset is a harmonized set of environmental, epidemiological, and poverty indicators for all municipalities of the Brazilian Legal Amazon (BLA). This dataset is the result of a scientific synthesis research initiative conducted by scientists from several natural and social sciences fields, consolidating multidisciplinary indicators into a coherent dataset for integrated and interdisciplinary studies of the Brazilian Amazon. The dataset allows the investigation of the association between the Amazonian agrarian systems and their impacts on environmental and epidemiological changes, furthermore enhancing the possibilities for understanding, in a more integrated and consistent way, the scenarios that affect the Amazonian biome and its inhabitants.

Socioecological vulnerability and the risk of zoonotic disease emergence in Brazil.

In developing countries, outbreaks of zoonotic diseases (ZDs) result from intertwined ecological, socioeconomic, and demographic processes that shape conditions for (i) increased contact between vulnerable human population and wildlife in areas undergoing environmental degradation and (ii) the rapid geographic spread of infections across socially vulnerable regions. In Brazil, recent increases in environmental and social vulnerabilities, amplified by economic and political crises, are potential triggers for outbreaks. We discuss Brazilian features that favor outbreaks and show a novel quantitative method for zoonotic risk assessment. Using data on nine ZDs from 2001 to 2019, we found that the most significant causal variables were vegetation cover and city remoteness. Furthermore, 8 of 27 states presented low-level risk of ZD outbreaks. Given the ZD-bushmeat connection, we identified central hunted mammals that should be surveilled to prevent spillover events. The current challenge is to coordinate intersectoral collaboration for effective One Health management in megadiverse countries with high social vulnerability and growing environmental degradation like Brazil.

The impact of super-spreader cities, highways, and intensive care availability in the early stages of the COVID-19 epidemic in Brazil.

Although international airports served as main entry points for SARS-CoV-2, the factors driving the uneven geographic spread of COVID-19 cases and deaths in Brazil remain mostly unknown. Here we show that three major factors influenced the early macro-geographical dynamics of COVID-19 in Brazil. Mathematical modeling revealed that the "super-spreading city" of São Paulo initially accounted for more than 85% of the case spread in the entire country. By adding only 16 other spreading cities, we accounted for 98-99% of the cases reported during the first 3 months of the pandemic in Brazil. Moreover, 26 federal highways accounted for about 30% of SARS-CoV-2's case spread. As cases increased in the Brazilian interior, the distribution of COVID-19 deaths began to correlate with the allocation of the country's intensive care units (ICUs), which is heavily weighted towards state capitals. Thus, severely ill patients living in the countryside had to be transported to state capitals to access ICU beds, creating a "boomerang effect" that contributed to skew the distribution of COVID-19 deaths. Therefore, if (i) a lockdown had been imposed earlier on in spreader-capitals, (ii) mandatory road traffic restrictions had been enforced, and (iii) a more equitable geographic distribution of ICU beds existed, the impact of COVID-19 in Brazil would be significantly lower.

Epidemiology, Biodiversity, and Technological Trajectories in the Brazilian Amazon: From Malaria to COVID-19.

The Amazon biome is under severe threat due to increasing deforestation rates and loss of biodiversity and ecosystem services while sustaining a high burden of neglected tropical diseases. Approximately two thirds of this biome are located within Brazilian territory. There, socio-economic and environmental landscape transformations are linked to the regional agrarian economy dynamics, which has developed into six techno-productive trajectories (TTs). These TTs are the product of the historical interaction between Peasant and Farmer and Rancher practices, technologies and rationalities. This article investigates the distribution of the dominant Brazilian Amazon TTs and their association with environmental degradation and vulnerability to neglected tropical diseases. The goal is to provide a framework for the joint debate of the local economic, environmental and health dimensions. We calculated the dominant TT for each municipality in 2017. Peasant trajectories (TT1, TT2, and TT3) are dominant in ca. fifty percent of the Amazon territory, mostly concentrated in areas covered by continuous forest where malaria is an important morbidity and mortality cause. Cattle raising trajectories are associated with higher deforestation rates. Meanwhile, Farmer and Rancher economies are becoming dominant trajectories, comprising large scale cattle and grain production. These trajectories are associated with rapid biodiversity loss and a high prevalence of neglected tropical diseases, such as leishmaniasis, Aedes-borne diseases and Chagas disease. Overall, these results defy simplistic views that the dominant development trajectory for the Amazon will optimize economic, health and environmental indicators. This approach lays the groundwork for a more integrated narrative consistent with the economic history of the Brazilian Amazon.

Ecological multiplex interactions determine the role of species for parasite spread amplification.

Despite their potential interplay, multiple routes of many disease transmissions are often investigated separately. As a unifying framework for understanding parasite spread through interdependent transmission paths, we present the 'ecomultiplex' model, where the multiple transmission paths among a diverse community of interacting hosts are represented as a spatially explicit multiplex network. We adopt this framework for designing and testing potential control strategies for Trypanosoma cruzi spread in two empirical host communities. We show that the ecomultiplex model is an efficient and low data-demanding method to identify which species enhances parasite spread and should thus be a target for control strategies. We also find that the interplay between predator-prey and host-parasite interactions leads to a phenomenon of parasite amplification, in which top predators facilitate T. cruzi spread, offering a mechanistic interpretation of previous empirical findings. Our approach can provide novel insights in understanding and controlling parasite spreading in real-world complex systems.

Deciphering the Interdependence between Ecological and Evolutionary Networks.

Biological systems consist of elements that interact within and across hierarchical levels. For example, interactions among genes determine traits of individuals, competitive and cooperative interactions among individuals influence population dynamics, and interactions among species affect the dynamics of communities and ecosystem processes. Such systems can be represented as hierarchical networks, but can have complex dynamics when interdependencies among levels of the hierarchy occur. We propose integrating ecological and evolutionary processes in hierarchical networks to explore interdependencies in biological systems. We connect gene networks underlying predator-prey trait distributions to food webs. Our approach addresses longstanding questions about how complex traits and intraspecific trait variation affect the interdependencies among biological levels and the stability of meta-ecosystems.