RESUMO
Visceral leishmaniasis (VL) is the second most common protozoosis that affects people around the world. The aim of this study is to understand how environmental and socioeconomic factors, as well as VL control and surveillance interventions, influence the spread and detection of VL cases in Pernambuco state (Brazil). A novel model was developed to analyze cases of VL between 2007 and 2018, enabling the quantification of the association of these variables with two processes: the probability of "invasion" (emergence of new cases) at municipalities by VL, and the probability of detecting cases not reported in municipalities that have already been invaded. Pernambuco state identified 1,410 cases of VL between 2007 and 2018, with an average of 128 cases per year and average incidence of 1.28/100 thousand people. These cases were distributed in 77.1% (142/184) of the municipalities, and 54.8% (773/1,410) of them were autochthonous. Our model reveals that the proportion of agriculture was positively associated with VL invasion probability. We also find that municipalities that are closer to notification centers and/or that have received technical training and support tend to have higher detection rates of VL cases. Taken together, these results suggest that a municipality with almost no agriculture and that received technical training, located close to a notification center, is unlikely to be invaded if no cases have ever been detected. On the other hand, a municipality that is far from the notification center, with no technical training, with a large agricultural area might have already been invaded but the surveillance system might have routinely failed to detect VL cases due to low detection probability. By disentangling the processes of invasion and detection, we were able to generate insights that are likely to be useful for the strategic allocation of VL prevention and control interventions.
Assuntos
Leishmaniose Visceral , Humanos , Leishmaniose Visceral/epidemiologia , Brasil/epidemiologia , Cidades , Incidência , ProbabilidadeRESUMO
Advances in biologging have increased the understanding of how animals interact with their environment, especially for cryptic species. For example, giant armadillos (Priodontes maximus) are the largest extant species of armadillo but are rarely encountered due to their fossorial and nocturnal behavior. Through the analysis of speed, turning angles, and accelerometer activity counts, we estimated behavioral states, characterized activity budgets, and investigated the state-habitat associations exhibited by individuals monitored with GPS telemetry in the Brazilian Pantanal from 2019 to 2020. This methodology is proposed as a useful framework for the identification of priority habitat. Using the non-parametric Bayesian mixture model for movement (M3), we estimated four latent behavioral states that were named 'vigilance-excavation', 'local search', 'exploratory', and 'transit'. These states appeared to correspond with behavior near burrows or termite mounds, foraging, ranging, and rapid movements, respectively. The first and last hours of activity presented relatively high proportions of the vigilance-excavation state, while most of the activity period was dominated by local search and exploratory states. The vigilance-excavation state occurred more frequently in regions between forest and closed savannas, whereas local search was more likely in high proportions of closed savanna. Exploratory behavior probability increased in areas with high proportions of both forest and closed savanna. Our results establish a baseline for behavioral complexity, activity budgets, and habitat associations in a relatively pristine environment that can be used for future work to investigate anthropogenic impacts on giant armadillo behavior and fitness. The integration of accelerometer and GPS-derived movement data through our mixture model has the potential to become a powerful methodological approach for the conservation of other cryptic species.
Assuntos
Tatus , Ecossistema , Animais , Teorema de Bayes , Florestas , BrasilRESUMO
Clustering is a ubiquitous task in ecological and environmental sciences and multiple methods have been developed for this purpose. Because these clustering methods typically require users to a priori specify the number of groups, the standard approach is to run the algorithm for different numbers of groups and then choose the optimal number using a criterion (e.g., AIC or BIC). The problem with this approach is that it can be computationally expensive to run these clustering algorithms multiple times (i.e., for different numbers of groups) and some of these information criteria can lead to an overestimation of the number of groups. To address these concerns, we advocate for the use of sparsity-inducing priors within a Bayesian clustering framework. In particular, we highlight how the truncated stick-breaking (TSB) prior, a prior commonly adopted in Bayesian nonparametrics, can be used to simultaneously determine the number of groups and estimate model parameters for a wide range of Bayesian clustering models without requiring the fitting of multiple models. We illustrate the ability of this prior to successfully recover the true number of groups for three clustering models (two types of mixture models, applied to GPS movement data and species occurrence data, as well as the species archetype model) using simulated data in the context of movement ecology and community ecology. We then apply these models to armadillo movement data in Brazil, plant occurrence data from Alberta (Canada), and bird occurrence data from North America. We believe that many ecological and environmental sciences applications will benefit from Bayesian clustering methods with sparsity-inducing priors given the ubiquity of clustering and the associated challenge of determining the number of groups. Two R packages, EcoCluster and bayesmove, are provided that enable the straightforward fitting of these models with the TSB prior.
Assuntos
Algoritmos , Alberta , Teorema de Bayes , Brasil , Análise por ConglomeradosRESUMO
The illegal use of natural resources, manifested in activities like illegal logging, poaching, and illegal wildlife trade, poses a global threat to biodiversity. Addressing them will require an understanding of the magnitude of and factors influencing these activities. However, assessing such behaviors is challenging because of their illegal nature, making participants less willing to admit engaging in them. We compared how indirect (randomized response technique) and direct questioning techniques performed when assessing non-sensitive (fish consumption, used as negative control) and sensitive (illegal consumption of wild animals) behaviors across an urban gradient (small towns, large towns, and the large city of Manaus) in the Brazilian Amazon. We conducted 1,366 surveys of randomly selected households to assess the magnitude of consumption of meat from wild animals (i.e., wild meat) and its socioeconomic drivers, which included years the head of household lived in urban areas, age of the head of household, household size, presence of children, and poverty. The indirect method revealed higher rates of wildlife consumption in larger towns than did the direct method. Results for small towns were similar between the two methods. The indirect method also revealed socioeconomic factors influencing wild meat consumption that were not detected with direct methods. For instance, the indirect method showed that wild meat consumption increased with age of the head of household, and decreased with poverty and years the head of household lived in urban areas. Simultaneously, when responding to direct questioning, households with characteristics associated with higher wild meat consumption, as estimated from indirect questioning, tended to underreport consumption to a larger degree than households with lower wild meat consumption. Results for fish consumption, used as negative control, were similar for both methods. Our findings suggest that people edit their answers to varying degrees when responding to direct questioning, potentially biasing conclusions, and indirect methods can improve researchers' ability to identify patterns of illegal activities when the sensitivity of such activities varies across spatial (e.g., urban gradient) or social (e.g., as a function of age) contexts. This work is broadly applicable to other geographical regions and disciplines that deal with sensitive human behaviors.
Assuntos
Animais Selvagens , Conservação dos Recursos Naturais , Animais , Biodiversidade , Brasil , Cidades , HumanosRESUMO
There has been substantial interest on the effect of large-scale environmental change, such as deforestation, on human health. An important and relatively recent development has been the use of causal-inference approaches (e.g., instrumental variables [IVs]) to more properly analyze this type of observational data. Here, we discuss an important study that attempted to disentangle the effect of malaria on deforestation from the effect of deforestation on malaria using an IV approach. The authors found that deforestation increases malaria (e.g., they estimate that a 10% increase in deforestation leads to a 3.3% increase in malaria incidence) through ecological mechanisms, whereas malaria reduces deforestation through socioeconomic mechanisms. An important characteristic of causal-inference approaches is that they are critically dependent on the plausibility of the underlying assumptions and that, differently from standard statistical models, many of these assumptions are not testable. In particular, we show how important assumptions of the IV approach adopted in the study described earlier were not met and that, as a result, it is possible that the correct conclusion could have been the opposite of that reported by the authors (e.g., deforestation decreases, rather than increasing, malaria through ecological mechanisms). Causal-inference approaches may be critical to characterize the relationship between environmental change and disease risk, but conclusions based on these methods can be even more unreliable than those from traditional methods if careful attention is not given to the plausibility of the underlying assumptions.
Assuntos
Meio Ambiente , Medicina Tropical/métodos , Brasil , Conservação dos Recursos Naturais , Microbiologia Ambiental , Humanos , Incidência , TemperaturaRESUMO
We examine deforestation processes in Apuí, a deforestation hotspot in Brazil's state of Amazonas and present processes of land-use change on this Amazonian development frontier. Settlement projects attract agents whose clearing reflects land accumulation and the economic importance of deforestation. We used a mixed-method approach in the Rio Juma Settlement to examine colonization and deforestation trajectories for 35 years at three scales of analysis: the entire landscape, cohorts of settlement lots divided by occupation periods, and lots grouped by landholding size per household. All sizes of landholdings are deforesting much more than before, and current political and economic forces favoring the agribusiness sector foreshadow increasing rates of forest clearing for pasture establishment in Apuí. The area cleared per year over the 2013-2018 period in Apuí grew by a percentage more than twice the corresponding percentage for the Brazilian Amazon as a whole. With the national congress and presidential administration signaling impunity for illegal deforestation, wealthy actors, and groups are investing resources in land grabbing and land accumulation, with land speculation being a crucial deforestation factor. This paper is unique in providing causal explanations at the decision-maker's level on how deforestation trajectories are linked to economic and political events (period effects) at the larger scales, adding to the literature by showing that such effects were more important than aging and cohort effects as explanations for deforestation trajectories. Additional research is needed to deepen our understanding of relations between land speculation, illegal possession of public lands, and the expansion of agricultural frontiers in Amazonia.
Assuntos
Conservação dos Recursos Naturais , Florestas , Agricultura , Brasil , Humanos , PolíticasRESUMO
Count data commonly arise in natural sciences but adequately modeling these data is challenging due to zero-inflation and over-dispersion. While multiple parametric modeling approaches have been proposed, unfortunately there is no consensus regarding how to choose the best model. In this article, we propose a ordinal regression model (MN) as a default model for count data given that this model is shown to fit well data that arise from several types of discrete distributions. We extend this model to allow for automatic model selection (MN-MS) and show that the MN-MS model generates superior inference when compared to using the full model or more traditional model selection approaches. The MN-MS model is used to determine how human biting rate of mosquitoes, known to be able to transmit malaria, are influenced by environmental factors in the Peruvian Amazon. The MN-MS model had one of the best fit and out-of-sample predictive skill amongst all models. While A. darlingi is strongly associated with highly anthropized landscapes, all the other mosquito species had higher mean biting rates in landscapes with a lower fraction of exposed soil and urban area, revealing a striking shift in species composition. We believe that the MN and MN-MS models are valuable additions to the modelling toolkit employed by environmental modelers and quantitative ecologists.
Assuntos
Anopheles/parasitologia , Monitorização de Parâmetros Ecológicos/estatística & dados numéricos , Mordeduras e Picadas de Insetos/epidemiologia , Malária/epidemiologia , Modelos Estatísticos , Distribuição Animal , Animais , Humanos , Mordeduras e Picadas de Insetos/parasitologia , Malária/parasitologia , Malária/transmissão , Peru/epidemiologia , Distribuição de Poisson , Análise de RegressãoRESUMO
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'.
Assuntos
Conservação dos Recursos Naturais , Florestas , Malária/transmissão , Mosquitos Vetores/fisiologia , Brasil/epidemiologia , Malária/epidemiologia , Peru/epidemiologia , Risco , América do Sul/epidemiologiaRESUMO
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.
Assuntos
Doenças do Cão/epidemiologia , Leishmaniose Visceral/parasitologia , Leishmaniose Visceral/veterinária , Animais , Brasil/epidemiologia , Doenças do Cão/parasitologia , Doenças do Cão/transmissão , Cães , Humanos , Insetos Vetores/parasitologia , Insetos Vetores/fisiologia , Leishmaniose Visceral/epidemiologia , Leishmaniose Visceral/transmissão , Psychodidae/parasitologia , Psychodidae/fisiologia , Medição de Risco , Fatores de Risco , Análise EspacialRESUMO
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.