Climate change and sugarcane expansion increase Hantavirus infection risk

Hantavirus Cardiopulmonary Syndrome (HCPS) is a disease caused by Hantavirus, which is highly virulent for humans. High temperatures and conversion of native vegetation to agriculture, particularly sugarcane cultivation can alter abundance of rodent generalist species that serve as the principal reservoir host for HCPS, but our understanding of the compound effects of land use and climate on HCPS incidence remains limited, particularly in tropical regions. Here we rely on a Bayesian model to fill this research gap and to predict the effects of sugarcane expansion and expected changes in temperature on Hantavirus infection risk in the state of São Paulo, Brazil. The sugarcane expansion scenario was based on historical data between 2000 and 2010 combined with an agro-environment zoning guideline for the sugar and ethanol industry. Future evolution of temperature anomalies was derived using 32 general circulation models from scenarios RCP4.5 and RCP8.5 (Representative greenhouse gases Concentration Pathways adopted by IPCC). Currently, the state of São Paulo has an average Hantavirus risk of 1.3%, with 6% of the 645 municipalities of the state being classified as high risk (HCPS risk ≥ 5%). Our results indicate that sugarcane expansion alone will increase average HCPS risk to 1.5%, placing 20% more people at HCPS risk. Temperature anomalies alone increase HCPS risk even more (1.6% for RCP4.5 and 1.7%, for RCP8.5), and place 31% and 34% more people at risk. Combined sugarcane and temperature increases led to the same predictions as scenarios that only included temperature. Our results demonstrate that climate change effects are likely to be more severe than those from sugarcane expansion. Forecasting disease is critical for the timely and efficient planning of operational control programs that can address the expected effects of sugarcane expansion and climate change on HCPS infection risk. The predicted spatial location of HCPS infection risks obtained here can be used to prioritize management actions and develop educational campaigns

Spatiotemporal Dynamics of Hantavirus Cardiopulmonary Syndrome Transmission Risk in Brazil

Background: Hantavirus disease in humans is rare but frequently lethal in the Neotropics. Several abundant and widely distributed Sigmodontinae rodents are the primary hosts of Orthohantavirus and, in combination with other factors, these rodents can shape hantavirus disease. Here, we assessed the influence of host diversity, climate, social vulnerability and land use change on the risk of hantavirus disease in Brazil over 24 years. Methods: Landscape variables (native forest, forestry, sugarcane, maize and pasture), climate (temperature and precipitation), and host biodiversity (derived through niche models) were used in spatiotemporal models, using the 5570 Brazilian municipalities as units of analysis. Results: Amounts of native forest and sugarcane, combined with temperature, were the most important factors influencing the increase of disease risk. Population at risk (rural workers) and rodent host diversity also had a positive effect on disease risk. Conclusions: Land use change—especially the conversion of native areas to sugarcane fields—can have a significant impact on hantavirus disease risk, likely by promoting the interaction between the people and the infected rodents. Our results demonstrate the importance of understanding the interactions between landscape change, rodent diversity, and hantavirus disease incidence, and suggest that land use policy should consider disease risk. Meanwhile, our risk map can be used to help allocate preventive measures to avoid disease.publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time, and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space. While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes, vast areas of the tropics remain understudied. In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity, but it remains among the least known forests in America and is often underrepresented in biodiversity databases. To worsen this situation, human-induced modifications may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge, it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

O risco de transmissão da Hantavirose em função do clima e da estrutura da paisagem

Hantavirus Cardiopulmonary Syndrome (HCPS) is a disease caused by Hantavirus, which are negative-sense RNA viruses in the family Bunyaviridae. These viruses are highly virulent to humans, taking about 50% of infected people to death. The main Hantavirus reservoir is constituded by generalist rodents species, which increase in abundance in agricultural and fragmented landscapes, potencially augmenting the transmission risk of the disease. Climate can also affect rodent population dynamics and the virus survival in the environment, as well as the time it remains virulent, while social factors may regulate the processes of transmitting viruses from reservoirs to humans. However, despite the high virulence of these viruses and the lack of vaccine is not yet well established how these different factors linked to landscape structure, climate and social conditions affect the dynamics of transmission of the disease. Thus, this study aimed to: 1) identify which social and ecological factors affect the transmission of HCPS, identifying the areas of greatest risk in the state of São Paulo and 2) predict how climate change (RCP4.5 and RCP8.5) and expansion of sugarcane scenarios influence the transmission of HCPS. To answer these questions the study system corresponded to the 645 municipalities that compose the state of São Paulo. To achieve our goals, in a first chapter, we conducted a literature review to understand how landscape structure and climate variables affect the risk of HCPS. In a second chapter we used a Bayesian model to quantify the association between HCPS annual incidence in the state of São Paulo, obtained by the number of cases confirmed by the Ministry of Health, between the years 1993-2012, and climate variables (total annual precipitation and mean annual temperature), landscape structure (percentage of native vegetation, number of fragments and percentage of area occupied with sugarcane), chosen in the literature review, and social factors (number of rural men over 14 years - risk population, and the Human Development Index - HDI). We build separate models for the Atlantic Forest and the Cerrado. In both biomes, the risk of HCPS increased mainly with the proportion of land cultivated with sugarcane and the HDI, but the proportion of native habitat, mean annual temperatures and risk population also showed positive relationships to Atlantic Forest. The average risk of HCPS for the state of São Paulo was 1.3%, with 6% of the municipalities being classified as medium to high risk (>= 5%). In a third chapter we used sugarcane expansion and extracted temperature anomalies of RCP4.5 and RCP8.5 scenarios of general circulation models (GCMs) of IPCC5 to predict HCPS risk. With sugarcane expansion, average risk for HCPS increases from 1.3 to 1.5%, while RCP4.5 and RCP8.5 scenarios increased the risk to 1.6% and 1.7%, respectively. RCP4.5 and RCP8.5 scenarios alone are responsible for the largest increase in the maximum risk of infection (46.1% to 51.4% and 51.7%), while the sugarcane expansion combined with climate scenarios are causing the larger expansion in the number of municipalities at high risk, which goes to 7%. Our analyzes provide the first evidence on the action of landscape, climate and social factors in HCPS incidence in the Neotropics. Moreover, our risk maps can be used to optimize the correct allocation of resources, allowing actions to be taken to reduce the impacts of sugarcane expansion and climate change over this disease propagationA Síndrome Cardiopulmonar por Hantavirose (HCPS) é uma doença causada por Hantavírus, um conjunto de vírus com RNA negativo pertencentes à família Bunyaviridae. Esses vírus são altamente virulentos para os seres humanos, levando cerca de 50% dos infectados a óbito. O principal reservatório de HCPS é constituído por espécies de roedores generalistas, que aumentam em abundância em paisagens agrícolas e fragmentadas, potencialmente elevando o risco de transmissão dessa doença. O clima também pode afetar a dinâmica populacional dos roedores e a sobrevivência do vírus no ambiente, assim como o tempo em que este se mantém virulento, enquanto que fatores sociais podem regular os processos de transmissão dos vírus dos reservatórios para os seres humanos. No entanto, apesar da alta virulência destes vírus e da falta de vacina, não está ainda bem estabelecido como esses diferentes fatores ligados à estrutura da paisagem, ao clima e às condições sociais afetam a dinâmica de transmissão dessa doença. O presente trabalho teve assim como objetivos: 1) identificar quais fatores ecológicos e sociais afetam a transmissão de HCPS, identificando as áreas de maior risco no estado de São Paulo e 2) prever como cenários de mudanças climáticas (RCP4.5 e RCP8.5) e de expansão de cana-de-açúcar influenciam a transmissão de HCPS. Para responder aos nossos objetivos, o sistema de estudo compreendeu os 645 municípios que compõe o estado de São Paulo. Num primeiro capítulo, realizamos uma revisão bibliográfica para entender como as variáveis de paisagem e de clima afetam o risco de HCPS. Num segundo capítulo, utilizamos um modelo Bayesiano para quantificar a associação entre a incidência anual de HCPS no estado de São Paulo, obtida através do número de casos confirmados pelo Ministério da Saúde, entre os anos de 1993 a 2012, e as variáveis de clima (precipitação total anual e temperatura anual média), estrutura da paisagem (porcentagem de vegetação nativa, número de fragmentos e porcentagem de área ocupada com cana-de-açúcar), escolhidas na revisão bibliográfica, além de fatores sociais (número de homens rurais acima de 14 anos - população de risco, e o Índice de Desenvolvimento Humano - IDH). Construimos modelos separados para a Mata Atlântica e o Cerrado. Em ambos os biomas, o risco de HCPS aumentou principalmente com a proporção de terra cultivada com cana-de-açúcar e com o IDH, mas a proporção de habitat nativo, temperatura anual média e população de risco também mostraram relações positivas para Mata Atlântica. O risco médio de HCPS para o estado de São Paulo foi de 1.3%, com 6% dos municípios sendo classificados como de médio a alto risco (>= 5%). Num terceiro capítulo, utilizamos cenários de expansão de cana-de-açúcar e anomalias de temperatura extraidas dos cenários RCP4.5 e RCP8.5 de 32 modelos de circulação geral (GCMs) do IPCC5 para prever os riscos futuros de HCPS. Com a expansão de cana-de-açúcar, o risco médio de HCPS para o estado aumenta de 1.3 para 1.5%, enquanto que os cenários RCP4.5 e RCP8.5 aumentam o risco para 1.6% e 1.7%, respectivamente. RCP4.5 e RCP8.5 sozinhos são os cenários que mais aumentam o risco máximo de infecção (46.1% para 51.4% e 51.7%), enquanto que a expansão de cana-de-açúcar combinada com os cenários climáticos são os que mais provocam o aumento da expansão do risco no estado de São Paulo, expandindo o número de municípios em alto risco para 7%. Nossas análises fornecem as primeiras evidências sobre a ação de fatores da paisagem, climáticos e sociais na incidência de HCPS nos Neotrópicos. Também, nossos mapas de risco podem ser utilizados para otimizar a correta alocação de recursos, permitindo que ações sejam tomadas para reduzir os impactos da expansão da cana e das mudanças climáticas sobre a propagação da doenç

Spatial analysis of midsized and large-bodied vertebrates according to two typical deforestation patterns of the Amazon forest in Alta Floresta region - MT State

A Amazônia Brasileira possui diversos tipos de padrão de desmatamento, variando do típico padrão \"espinha-de-peixe\", comum em pequenas propriedades, para grandes áreas desmatadas (padrão grandes propriedades), resultando em paisagens com diferentes estruturas, configuração e nível de perturbação. A teoria sugere que uma perda desproporcional de espécies ocorre quando a cobertura total de habitat cai para menos de 30% da paisagem, e a configuração passa então a ter um maior efeito sobre as espécies. Para analisar o efeito da configuração de habitat na persistência e riqueza de vertebrados de médio e grande porte (aves e mamíferos) foram amostradas 21 paisagens (4 x 4 km) do sul da Amazônia com quantidade similar de habitat (~25%), mas configurações de paisagem contrastantes. Entrevistas (n = 150) foram aplicadas de Fevereiro a Julho de 2009 para registrar a ocorrência de vertebrados, e o nível de perturbação das 21 paisagens, compostas de sete áreas controle (áreas não perturbadas de floresta contínua), sete paisagens com padrão de grandes propriedades e sete de espinhas-de-peixe. Métricas de paisagem foram extraídas de uma imagem Landsat-TM de 2009 e de 14 imagens Landsat-TM bianuais, para determinar o melhor preditor para a persistência das espécies. Existiu uma diferença significativa na riqueza de espécies entre os padrões espinha-de-peixe, grandes propriedades e as áreas controle, com uma média de 29.28 (SD=4.6), 38.8 (SD=5.2), e 43.5 (SD=2.2), respectivamente. Nós também encontramos um maior número de espécies especialistas nas áreas controle (média ± SD = 13.7 ± 0.95) e grandes propriedades (média ± SD = 11.71 ± 2.2), quando comparadas ao padrão espinha-de-peixe (média ± SD = 5.14 ± 2.6). Os resultados da NMDS mostram que a comunidade de vertebrados de médio e grande porte das áreas controle é muito similar à comunidade encontrada nas unidades de grande propriedade, além de todas as unidades de área controle e grande propriedade serem homogêneas entre si. Por outro lado, as unidades espinha-de-peixe, além de apresentarem uma maior heterogeneidade entre suas unidades, também se mostrou muito dissimilar em relação às outras paisagens, tanto para a comunidade de vertebrados quando para a comunidade de espécies especialistas. O padrão espinha-de-peixe também apresentou uma alta intensidade de queimadas, retirada de madeira e pressão de caça, enquanto que o padrão grandes propriedades apresentou uma leve intensidade de queimada e uma alta pressão de caça, e as áreas controle não apresentaram nenhum sinal de perturbação. O número de espécies e o número de espécies especialistas foram negativamente afetados pelo número de fragmentos e, secundariamente, pela idade de isolamento. Assim, quanto maior o número de fragmentos na paisagem e maior o tempo de isolamento, menor será a riqueza de espécies e o número de espécies especialistas. Nossos resultados demonstram que o padrão grandes propriedades leva a uma estrutura de paisagem mais favorável para a biodiversidade. Este tipo de paisagem pode manter um alto número de espécies e uma comunidade de vertebrados de médio e grande porte mais diversa, incluindo predadores de topo e grandes cracídeos, considerados fundamentais para a integridade do ecossistema, sendo mais similar às áreas controle. Por outro lado, o padrão espinha-de-peixe leva a uma paisagem mais fragmentada, com uma comunidade de vertebrados mais pobre e dominada por espécies generalistas.The Brazilian Amazon has several types of deforestation patterns, varying from the typical \"fishbone pattern\" common in small properties, to large deforested areas (large-property pattern), resulting in landscapes with different structure, configuration and disturbance levels. Theory suggests that a disproportionate loss of species occurs when total habitat cover decreases to less than 30% of the landscape, and the landscape configuration starts to have a large effect over species. To analyse the effects of the habitat configuration on the persistence and richness of mid-sized and large-bodied vertebrates (mammals and birds), we have sampled 21 landscapes in the southern Amazonia with similar amounts of habitat (~25%) but contrasting configuration. Interviews (n = 150) were used from February to July 2009 to record the occurrence of vertebrates and the disturbance degree in the 21 landscapes, composed of seven control areas (undisturbed areas of continuous forest), seven large-properties and seven fishbone deforestation patterns. Forest-patch metrics were extracted from a 2009 Landsat-TM image and from 14 bi-annual Landsat-TM images to examine the best predictor to species persistence. There was a significant difference in species richness between fishbone, large-property and control areas with an average of 29.28 (SD=4.6), 38.8 (SD=5.2), and 43.5 (SD=2.2), respectively. We also found a higher number of specialist species in control areas (mean ± SD = 13.7 ± 0.95) and large-properties (mean ± SD = 11.71 ± 2.2), when compared with fish-bone pattern (mean ± SD = 5.14 ± 2.6). NMDS results show vertebrate community in control areas are very similar to the ones found in large-property sites, beyond all landscapes (control areas and large properties) are homogeneous among themselves. On the other hand the fish-bone landscapes are very dissimilar from them and heterogeneous among each other, concerning both the large vertebrate community and the specialist species community. The fish-bone pattern also shows a heavy intensity of fire, selective logging and hunting pressure while the large-property pattern shows a light intensity of fire and a heavy hunting pressure whereas the control areas show no sign of disturbance. The number of species and the number of specialist species were negatively affected by the number of fragments and secondarily by the isolation age. Therefore the greater the number of fragments in the landscape unit and the older is the isolation process, the fewer is the species richness as well as the number of specialist species. Our results demonstrated that large-property pattern leads to a landscape structure that is better for biodiversity. This type of landscape can maintain a higher number of species and a more diverse community of large vertebrates, including top predators and large cracids, considered fundamental for the integrity of the ecosystem, being more similar to the control areas. On the other hand, the fish-bone pattern leads to a more fragmented landscape with a poorest vertebrate community and dominated by generalist species