Considerable interest in the relationship between biodiversity and disease has recently captured the attention of the research community, with important public policy implications. In particular, malaria in the Amazon region is often cited as an example of how forest conservation can improve public health outcomes. However, despite a growing body of literature and an increased understanding of the relationship between malaria and land use / land cover change (LULC) in Amazonia, contradictions have emerged. While some studies report that deforestation increases malaria risk, others claim the opposite. Assessing malaria risk requires examination of dynamic processes among three main components: (i) the environment (i.e. LULC and landscape transformations), (ii) vector biology (e.g. mosquito species distributions, vector activity and life cycle, plasmodium infection rates), and (iii) human populations (e.g. forest-related activity, host susceptibility, movement patterns). In this paper, we conduct a systematic literature review on malaria risk and deforestation in the Amazon focusing on these three components. We explore key features that are likely to generate these contrasting results using the reviewed articles and our own data from Brazil and Peru, and conclude with suggestions for productive avenues in future research.This article is part of the themed issue ‘Conservation, biodiversity and infectious disease: scientific evidence and policy implications'.

Visceral leishmaniasis (VL) is an important neglected disease caused by a protozoan parasite, and represents a serious public health problem in many parts of the world. It is zoonotic in Europe and Latin America, where infected dogs constitute the main domestic reservoir for the parasite and play a key role in VL transmission to humans. In Brazil this disease is caused by the protozoan Leishmania infantum chagasi, and is transmitted by the sand fly Lutzomyia longipalpis. Despite programs aimed at eliminating infection sources, the disease continues to spread throughout the Country. VL in São Paulo State, Brazil, first appeared in the northwestern region, spreading in a southeasterly direction over time. We integrate data on the VL vector, infected dogs and infected human dispersion from 1999 to 2013 through an innovative spatial temporal Bayesian model in conjunction with geographic information system. This model is used to infer the drivers of the invasion process and predict the future progression of VL through the State. We found that vector dispersion was influenced by vector presence in nearby municipalities at the previous time step, proximity to the Bolívia-Brazil gas pipeline, and high temperatures (i.e., annual average between 20 and 23°C). Key factors affecting infected dog dispersion included proximity to the Marechal Rondon Highway, high temperatures, and presence of the competent vector within the same municipality. Finally, vector presence, presence of infected dogs, and rainfall (approx. 270 to 540mm/year) drove the dispersion of human VL cases. Surprisingly, economic factors exhibited no noticeable influence on disease dispersion. Based on these drivers and stochastic simulations, we identified which municipalities are most likely to be invaded by vectors and infected hosts in the future. Prioritizing prevention and control strategies within the identified municipalities may help halt the spread of VL while reducing monitoring costs. Our results contribute important knowledge to public and animal health policy planning, and suggest that prevention and control strategies should focus on vector control and on blocking contact between vectors and hosts in the priority areas identified to be at risk.

BackgroundMost of the malaria burden in the Americas is concentrated in the Brazilian Amazon but a detailed spatial characterization of malaria risk has yet to be undertaken.MethodsUtilizing 2004-2008 malaria incidence data collected from six Brazilian Amazon states, large-scale spatial patterns of malaria risk were characterized with a novel Bayesian multi-pathogen geospatial model. Data included 2.4 million malaria cases spread across 3.6 million sq km. Remotely sensed variables (deforestation rate, forest cover, rainfall, dry season length, and proximity to large water bodies), socio-economic variables (rural population size, income, and literacy rate, mortality rate for children age under five, and migration patterns), and GIS variables (proximity to roads, hydro-electric dams and gold mining operations) were incorporated as covariates.ResultsBorrowing information across pathogens allowed for better spatial predictions of malaria caused by Plasmodium falciparum, as evidenced by a ten-fold cross-validation. Malaria incidence for both Plasmodium vivax and P. falciparum tended to be higher in areas with greater forest cover. Proximity to gold mining operations was another important risk factor, corroborated by a positive association between migration rates and malaria incidence. Finally, areas with a longer dry season and areas with higher average rural income tended to have higher malaria risk. Risk maps reveal striking spatial heterogeneity in malaria risk across the region, yet these mean disease risk surface maps can be misleading if uncertainty is ignored. By combining mean spatial predictions with their associated uncertainty, several sites were consistently classified as hotspots, suggesting their importance as priority areas for malaria prevention and control.ConclusionThis article provides several contributions. From a methodological perspective, the benefits of jointly modelling multiple pathogens for spatial predictions were illustrated. In addition, maps of mean disease risk were contrasted with that of statistically significant disease clusters, highlighting the critical importance of uncertainty in determining disease hotspots. From an epidemiological perspective, forest cover and proximity to gold mining operations were important large-scale drivers of disease risk in the region. Finally, the hotspot in Western Acre was identified as the area that should receive highest priority from the Brazilian national malaria prevention and control programme.Electronic supplementary materialThe online version of this article (doi:10.1186/1475-2875-13-443) contains supplementary material, which is available to authorized users.

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O presente estudo compara a composição e estrutura das comunidades de palmeiras da Área de Proteção Ambiental Raimundo Irineu Serra - APARIS, localizada no perímetro urbano do Município de Rio Branco-Acre. Foram selecionadas três áreas de floresta secundária em estágios sucessionais distintos: 7,5 anos, 27,5 anos, 37,5 anos de idade, e um fragmento de floresta primária. Em cada área foram instaladas cinco parcelas de 20 X 20m, onde foram analisadas a composição florística, estrutura horizontal e estrutura populacional das palmeiras. Foram identificados 1.034 indivíduos, incluídos em 12 gêneros e 19 espécies de palmeiras. A área de floresta primária apresentou maior diversidade. Na análise da estrutura populacional de cada área, comprovamos a existência de uma escassez de plântulas (≤ 50 cm de altura) e adultos reprodutivos. A fragmentação alterou a composição e diminuiu a riqueza e a diversidade de palmeiras na área da APARIS, enquanto, está favorecendo a dominância de certas espécies como A. phalerata.

In this study, we examined the distribution and conservation status of understory palms in the Brazilian state of Santa Catarina using data collected by the Floristic and Forest Inventory of Santa Catarina (IFFSC). Understory palms were systematically sampled within sampling units (SU) distributed over a state-wide 10 × 10 km grid. Among the 206 total SU monitored by IFFSC within Pluvial and Coastal Forest, 86% (n=177 SU) contained understory palms, comprising 1738 individuals from the following species: Bactris setosa, Geonoma elegans, G. gamiova, and G. schottiana. To explore the conservation status of understory palm populations in Santa Catarina, we overlaid a map of federal priority conservation areas on top of IFFSC distribution data for understory palms. Conservation priority levels defined by the Brazilian Environmental Agency (MMA) are rated Extremely High, Very High, High and Insufficiently Known. Ninety-four percent of the SU containing understory palms overlapped priority conservation areas, highlighting the centrality of understory palms in biodiversity conservation. Despite the wide distribution of understory palm communities in Santa Catarina, intense scrutiny of forest remnants is necessary in view of sensitivity to environmental disturbance, so as to guarantee the maintenance of understory palm populations and their ecosystem services.

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Recognized as one of the world’s most vital natural and cultural resources, the Amazon faces a wide variety of threats from natural resource and infrastructure development. Within this context, rigorous scientific study of the region’s complex social-ecological system is critical to inform and direct decision-making toward more sustainable environmental and social outcomes. Given the Amazon’s tightly linked social and ecological components and the scope of potential development impacts, effective study of this system requires an easily accessible resource that provides a broad and reliable data baseline. This paper brings together multiple datasets from diverse disciplines (including human health, socio-economics, environment, hydrology, and energy) to provide investigators with a variety of baseline data to explore the multiple long-term effects of infrastructure development in the Brazilian Amazon.