The unexpected influence of legacy conspecific density dependence.

When plants die, neighbours escape competition. Living conspecifics could disproportionately benefit because they are freed from negative intraspecific processes; however, if the negative effects of past conspecific neighbours persist, other species might be advantaged, and diversity might be maintained through legacy effects. We examined legacy effects in a mapped forest by modelling the survival of 37,212 trees of 23 species using four neighbourhood properties: living conspecific, living heterospecific, legacy conspecific (dead conspecifics) and legacy heterospecific densities. Legacy conspecific effects proved nearly four times stronger than living conspecific effects; changes in annual survival associated with legacy conspecific density were 1.5% greater than living conspecific effects. Over 90% of species were negatively impacted by legacy conspecific density, compared to 47% by living conspecific density. Our results emphasize that legacies of trees alter community dynamics, revealing that prior research may have underestimated the strength of density dependent interactions by not considering legacy effects.

Oaks enhance early life stage longleaf pine growth and density in a subtropical xeric savanna.

The interplay of positive and negative species interactions controls species assembly in communities. Dryland plant communities, such as savannas, are important to global biodiversity and ecosystem functioning. Sandhill oaks in xeric savannas of the southeastern United States can facilitate longleaf pine by enhancing seedling survival, but the effects of oaks on recruitment and growth of longleaf pine have not been examined. We censused, mapped, and monitored nine contiguous hectares of longleaf pine in a xeric savanna to quantify oak-pine facilitation, and to examine other factors impacting recruitment, such as vegetation cover and longleaf pine tree density. We found that newly recruited seedlings and grass stage longleaf pines were more abundant in oak-dominated areas where densities were 230% (newly recruited seedlings) and 360% (grass stage) greater from lowest to highest oak neighborhood densities. Longleaf pine also grew faster under higher oak density. Longleaf pine recruitment was lowest under longleaf pine canopies. Mortality of grass stage and bolt stage longleaf pine was low (~1.0% yr-1) in the census interval without fire. Overall, our findings highlight the complex interactions between pines and oaks-two economically and ecologically important genera globally. Xeric oaks should be incorporated as a management option for conservation and restoration of longleaf pine ecosystems.

Ten simple rules for creating a scientific web application.

The use of scientific web applications (SWApps) across biological and environmental sciences has grown exponentially over the past decade or so. Although quantitative evidence for such increased use in practice is scant, collectively, we have observed that these tools become more commonplace in teaching, outreach, and in science coproduction (e.g., as decision support tools). Despite the increased popularity of SWApps, researchers often receive little or no training in creating such tools. Although rolling out SWApps can be a relatively simple and quick process using modern, popular platforms like R shiny apps or Tableau dashboards, making them useful, usable, and sustainable is not. These 10 simple rules for creating a SWApp provide a foundation upon which researchers with little to no experience in web application design and development can consider, plan, and carry out SWApp projects.

Automatic selection of the number of clusters using Bayesian clustering and sparsity-inducing priors.

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.

Guiding placement of health facilities using multiple malaria criteria and an interactive tool.

BACKGROUND: Access to healthcare is important in controlling malaria burden and, as a result, distance or travel time to health facilities is often a significant predictor in modelling malaria prevalence. Adding new health facilities may reduce overall travel time to health facilities and may decrease malaria transmission. To help guide local decision-makers as they scale up community-based accessibility, the influence of the spatial allocation of new health facilities on malaria prevalence is evaluated in Bunkpurugu-Yunyoo district in northern Ghana. A location-allocation analysis is performed to find optimal locations of new health facilities by separately minimizing three district-wide objectives: malaria prevalence, malaria incidence, and average travel time to health facilities. METHODS: Generalized additive models was used to estimate the relationship between malaria prevalence and travel time to the nearest health facility and other geospatial covariates. The model predictions are then used to calculate the optimisation criteria for the location-allocation analysis. This analysis was performed for two scenarios: adding new health facilities to the existing ones, and a hypothetical scenario in which the community-based healthcare facilities would be allocated anew. An interactive web application was created to facilitate efficient presentation of this analysis and allow users to experiment with their choice of health facility location and optimisation criteria. RESULTS: Using malaria prevalence and travel time as optimisation criteria, two locations that would benefit from new health facilities were identified, regardless of scenarios. Due to the non-linear relationship between malaria incidence and prevalence, the optimal locations chosen based on the incidence criterion tended to be inequitable and was different from those based on the other optimisation criteria. CONCLUSIONS: This study findings underscore the importance of using multiple optimisation criteria in the decision-making process. This analysis and the interactive application can be repurposed for other regions and criteria, bridging the gap between science, models and decisions.

Investigating illegal activities that affect biodiversity: the case of wildlife consumption in the Brazilian Amazon.

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.

Impacts of rural to urban migration, urbanization, and generational change on consumption of wild animals in the Amazon.

For the first time in history, more people live in urban areas than in rural areas. This trend is likely to continue, driven largely by rural-to-urban migration. We investigated how rural-to-urban migration, urbanization, and generational change affect the consumption of wild animals. We used chelonian (tortoises and freshwater turtles), one of the most hunted taxa in the Amazon, as a model. We surveyed 1356 households and 2776 school children across 10 urban areas of the Brazilian Amazon (6 small towns, 3 large towns, and Manaus, the largest city in the Amazon Basin) with a randomized response technique and anonymous questionnaires. Urban demand for wild meat (i.e., meat from wild animals) was alarmingly high. Approximately 1.7 million turtles and tortoises were consumed in urban areas of Amazonas during 2018. Consumption rates declined as size of the urban area increased and were greater for adults than children. Furthermore, the longer rural-to-urban migrants lived in urban areas, the lower their consumption rates. These results suggest that wild meat consumption is a rural-related tradition that decreases as urbanization increases and over time after people move to urban areas. However, it is unclear whether the observed decline will be fast enough to conserve hunted species, or whether children's consumption rate will remain the same as they become adults. Thus, conservation actions in urban areas are still needed. Current conservation efforts in the Amazon do not address urban demand for wildlife and may be insufficient to ensure the survival of traded species in the face of urbanization and human population growth. Our results suggest that conservation interventions must target the urban demand for wildlife, especially by focusing on young people and recent rural to urban migrants. Article impact statement: Amazon urbanite consumption of wildlife is high but decreases with urbanization, over time for rural to urban migrants, and between generations. Impactos de la Migración del Campo a la Ciudad, la Urbanización y del Cambio Generacional sobre el Consumo de Animales Silvestres en el Amazonas.

Por primera vez en la historia, la población urbana es mayor que la rural. Es muy probable que esta tendencia continúe debido a la migración del campo a la ciudad. Investigamos el efecto de la migración del campo a la ciudad, la urbanización y el cambio generacional sobre el consumo de animales silvestres. Utilizamos como modelo a los quelonios (tortugas acuáticas y terrestres), uno de los taxa más cazados en el Amazonas. Aplicamos encuestas en 1,356 casas y a 2,776 niños en edad escolar en 10 áreas urbanas de la Amazonía brasileña (6 poblados pequeños, 3 poblados grandes y Manaos, la mayor ciudad en la Cuenca del Amazonas) mediante una técnica de respuesta aleatoria y cuestionarios anónimos. La demanda urbana de carne silvestre (i.e., carne de animales silvestres) fue alarmantemente alta. Aproximadamente 1.7 millones de tortugas acuáticas y terrestres fueron consumidas en áreas urbanas del Amazonas durante 2018. Las tasas de consumo declinaron a medida que incrementó la superficie urbana y fueron mayores en adultos que en niños. Más aun, entre más tiempo viviendo en áreas urbanas, las tasas de consumo fueron menores en los migrantes del campo a la ciudad. Estos resultados sugieren que el consumo de carne silvestre es una tradición rural que disminuye a medida que aumenta la urbanización y el tiempo desde que los habitantes se mueven a la ciudad. Sin embargo, no es claro si la declinación observada será lo suficientemente rápida para conservar a las especies cazadas, o si la tasa de consumo de los niños permanecerá igual cuando sean adultos. Por lo tanto, aun se requieren acciones de conservación en áreas urbanas. Los actuales esfuerzos de conservación en el Amazonas no abordan la demanda urbana de carne de monte y pueden ser insuficientes para asegurar la supervivencia de especies comercializadas ante la urbanización y el crecimiento de la población humana. Nuestros resultados sugieren que las intervenciones de conservación deben atender la demanda de fauna silvestre, con énfasis en los jóvenes y los migrantes recientes.

Isolating the roles of movement and reproduction on effective connectivity alters conservation priorities for an endangered bird.

Movement is important for ecological and evolutionary theory as well as connectivity conservation, which is increasingly critical for species responding to environmental change. Key ecological and evolutionary outcomes of movement, such as population growth and gene flow, require effective dispersal: movement that is followed by successful reproduction. However, the relative roles of movement and postmovement reproduction for effective dispersal and connectivity remain unclear. Here we isolate the contributions of movement and immigrant reproduction to effective dispersal and connectivity across the entire breeding range of an endangered raptor, the snail kite (Rostrhamus sociabilis plumbeus). To do so, we unite mark-resight data on movement and reproduction across 9 years and 27 breeding patches with an integrated model that decomposes effective dispersal into its hierarchical levels of movement, postmovement breeding attempt, and postmovement reproductive success. We found that immigrant reproduction limits effective dispersal more than movement for this endangered species, demonstrating that even highly mobile species may have limited effective connectivity due to reduced immigrant reproduction. We found different environmental limitations for the reproductive component of effective dispersal compared with movement, indicating that different conservation strategies may be needed when promoting effective dispersal rather than movement alone. We also demonstrate that considering immigrant reproduction, rather than movement alone, alters which patches are the most essential for connectivity, thereby changing conservation priorities. These results challenge the assumption that understanding movement alone is sufficient to infer connectivity and highlight that connectivity conservation may require not only fostering movement but also successful reproduction of immigrants.

Predict or describe? How learning analytics dashboard design influences motivation and statistics anxiety in an online statistics course.

Based on the achievement goal theory, this experimental study explored the influence of predictive and descriptive learning analytics dashboards on graduate students' motivation and statistics anxiety in an online graduate-level statistics course. Participants were randomly assigned into one of three groups: (a) predictive dashboard, (b) descriptive dashboard, or (c) control (i.e., no dashboard). Measures of motivation and statistical anxiety were collected in the beginning and the end of the semester via the Motivated Strategies for Learning Questionnaire and Statistical Anxiety Rating Scale. Individual semi-structured interviews were used to understand learners' perceptions of the course and whether the use of the dashboards influenced the meaning of their learning experiences. Results indicate that, compared to the control group, the predictive dashboard significantly reduced learners' interpretation anxiety and had an effect on intrinsic goal orientation that depended on learners' lower or higher initial levels of intrinsic goal orientation. In comparison to the control group, both predictive and descriptive dashboards reduced worth of anxiety (negative attitudes towards statistics) for learners who started the course with higher levels of worth anxiety. Thematic analysis revealed that learners who adopted a more performance-avoidance goal orientation approach demonstrated higher levels of anxiety regardless of the dashboard used. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11423-021-09998-z.

To screen or not to screen: an interactive framework for comparing costs of mass malaria treatment interventions.

BACKGROUND: Mass drug administration and mass-screen-and-treat interventions have been used to interrupt malaria transmission and reduce burden in sub-Saharan Africa. Determining which strategy will reduce costs is an important challenge for implementers; however, model-based simulations and field studies have yet to develop consensus guidelines. Moreover, there is often no way for decision-makers to directly interact with these data and/or models, incorporate local knowledge and expertise, and re-fit parameters to guide their specific goals. METHODS: We propose a general framework for comparing costs associated with mass drug administrations and mass screen and treat based on the possible outcomes of each intervention and the costs associated with each outcome. We then used publicly available data from six countries in western Africa to develop spatial-explicit probabilistic models to estimate intervention costs based on baseline malaria prevalence, diagnostic performance, and sociodemographic factors (age and urbanicity). In addition to comparing specific scenarios, we also develop interactive web applications which allow managers to select data sources and model parameters, and directly input their own cost values. RESULTS: The regional-level models revealed substantial spatial heterogeneity in malaria prevalence and diagnostic test sensitivity and specificity, indicating that a "one-size-fits-all" approach is unlikely to maximize resource allocation. For instance, urban communities in Burkina Faso typically had lower prevalence rates compared to rural communities (0.151 versus 0.383, respectively) as well as lower diagnostic sensitivity (0.699 versus 0.862, respectively); however, there was still substantial regional variation. Adjusting the cost associated with false negative diagnostic results to included additional costs, such as delayed treated and potential lost wages, undermined the overall costs associated with MSAT. CONCLUSIONS: The observed spatial variability and dependence on specified cost values support not only the need for location-specific intervention approaches but also the need to move beyond standard modeling approaches and towards interactive tools which allow implementers to engage directly with data and models. We believe that the framework demonstrated in this article will help connect modeling efforts and stakeholders in order to promote data-driven decision-making for the effective management of malaria, as well as other diseases.

Deforestation Trajectories on a Development Frontier in the Brazilian Amazon: 35 Years of Settlement Colonization, Policy and Economic Shifts, and Land Accumulation.

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.

Characterizing local-scale heterogeneity of malaria risk: a case study in Bunkpurugu-Yunyoo district in northern Ghana.

BACKGROUND: Bayesian methods have been used to generate country-level and global maps of malaria prevalence. With increasing availability of detailed malaria surveillance data, these methodologies can also be used to identify fine-scale heterogeneity of malaria parasitaemia for operational prevention and control of malaria. METHODS: In this article, a Bayesian geostatistical model was applied to six malaria parasitaemia surveys conducted during rainy and dry seasons between November 2010 and 2013 to characterize the micro-scale spatial heterogeneity of malaria risk in northern Ghana. RESULTS: The geostatistical model showed substantial spatial heterogeneity, with malaria parasite prevalence varying between 19 and 90%, and revealing a northeast to southwest gradient of predicted risk. The spatial distribution of prevalence was heavily influenced by two modest urban centres, with a substantially lower prevalence in urban centres compared to rural areas. Although strong seasonal variations were observed, spatial malaria prevalence patterns did not change substantially from year to year. Furthermore, independent surveillance data suggested that the model had a relatively good predictive performance when extrapolated to a neighbouring district. CONCLUSIONS: This high variability in malaria prevalence is striking, given that this small area (approximately 30 km × 40 km) was purportedly homogeneous based on country-level spatial analysis, suggesting that fine-scale parasitaemia data might be critical to guide district-level programmatic efforts to prevent and control malaria. Extrapolations results suggest that fine-scale parasitaemia data can be useful for spatial predictions in neighbouring unsampled districts and does not have to be collected every year to aid district-level operations, helping to alleviate concerns regarding the cost of fine-scale data collection.

Extending the Latent Dirichlet Allocation model to presence/absence data: A case study on North American breeding birds and biogeographical shifts expected from climate change.

Understanding how species composition varies across space and time is fundamental to ecology. While multiple methods having been created to characterize this variation through the identification of groups of species that tend to co-occur, most of these methods unfortunately are not able to represent gradual variation in species composition. The Latent Dirichlet Allocation (LDA) model is a mixed-membership method that can represent gradual changes in community structure by delineating overlapping groups of species, but its use has been limited because it requires abundance data and requires users to a priori set the number of groups. We substantially extend LDA to accommodate widely available presence/absence data and to simultaneously determine the optimal number of groups. Using simulated data, we show that this model is able to accurately determine the true number of groups, estimate the underlying parameters, and fit with the data. We illustrate this method with data from the North American Breeding Bird Survey (BBS). Overall, our model identified 18 main bird groups, revealing striking spatial patterns for each group, many of which were closely associated with temperature and precipitation gradients. Furthermore, by comparing the estimated proportion of each group for two time periods (1997-2002 and 2010-2015), our results indicate that nine (of 18) breeding bird groups exhibited an expansion northward and contraction southward of their ranges, revealing subtle but important community-level biodiversity changes at a continental scale that are consistent with those expected under climate change. Our proposed method is likely to find multiple uses in ecology, being a valuable addition to the toolkit of ecologists.

Detecting local risk factors for residual malaria in northern Ghana using Bayesian model averaging.

BACKGROUND: There is a need for comprehensive evaluations of the underlying local factors that contribute to residual malaria in sub-Saharan Africa. However, it is difficult to compare the wide array of demographic, socio-economic, and environmental variables associated with malaria transmission using standard statistical approaches while accounting for seasonal differences and nonlinear relationships. This article uses a Bayesian model averaging (BMA) approach for identifying and comparing potential risk and protective factors associated with residual malaria. RESULTS: The relative influence of a comprehensive set of demographic, socio-economic, environmental, and malaria intervention variables on malaria prevalence were modelled using BMA for variable selection. Data were collected in Bunkpurugu-Yunyoo, a rural district in northeast Ghana that experiences holoendemic seasonal malaria transmission, over six biannual surveys from 2010 to 2013. A total of 10,022 children between the ages 6 to 59 months were used in the analysis. Multiple models were developed to identify important risk and protective factors, accounting for seasonal patterns and nonlinear relationships. These models revealed pronounced nonlinear associations between malaria risk and distance from the nearest urban centre and health facility. Furthermore, the association between malaria risk and age and some ethnic groups was significantly different in the rainy and dry seasons. BMA outperformed other commonly used regression approaches in out-of-sample predictive ability using a season-to-season validation approach. CONCLUSIONS: This modelling framework offers an alternative approach to disease risk factor analysis that generates interpretable models, can reveal complex, nonlinear relationships, incorporates uncertainty in model selection, and produces accurate predictions. Certain modelling applications, such as designing targeted local interventions, require more sophisticated statistical methods which are capable of handling a wide range of relevant data while maintaining interpretability and predictive performance, and directly characterize uncertainty. To this end, BMA represents a valuable tool for constructing more informative models for understanding risk factors for malaria, as well as other vector-borne and environmentally mediated diseases.

Spatial heterogeneity can undermine the effectiveness of country-wide test and treat policy for malaria: a case study from Burkina Faso.

BACKGROUND: Considerable debate has arisen regarding the appropriateness of the test and treat malaria policy broadly recommended by the World Health Organization. While presumptive treatment has important drawbacks, the effectiveness of the test and treat policy can vary considerably across regions, depending on several factors such as baseline malaria prevalence and rapid diagnostic test (RDT) performance. METHODS: To compare presumptive treatment with test and treat, generalized linear mixed effects models were fitted to data from 6510 children under five years of age from Burkina Faso's 2010 Demographic and Health Survey. RESULTS: The statistical model results revealed substantial regional variation in baseline malaria prevalence (i.e., pre-test prevalence) and RDT performance. As a result, a child with a positive RDT result in one region can have the same malaria infection probability as a demographically similar child with a negative RDT result in another region. These findings indicate that a test and treat policy might be reasonable in some settings, but may be undermined in others due to the high proportion of false negatives. CONCLUSIONS: High spatial variability can substantially reduce the effectiveness of a national level test and treat malaria policy. In these cases, region-specific guidelines for malaria diagnosis and treatment may need to be formulated. Based on the statistical model results, proof-of-concept, web-based tools were created that can aid in the development of these region-specific guidelines and may improve current malaria-related policy in Burkina Faso.

Bias in logistic regression due to imperfect diagnostic test results and practical correction approaches.

BACKGROUND: Logistic regression is a statistical model widely used in cross-sectional and cohort studies to identify and quantify the effects of potential disease risk factors. However, the impact of imperfect tests on adjusted odds ratios (and thus on the identification of risk factors) is under-appreciated. The purpose of this article is to draw attention to the problem associated with modelling imperfect diagnostic tests, and propose simple Bayesian models to adequately address this issue. METHODS: A systematic literature review was conducted to determine the proportion of malaria studies that appropriately accounted for false-negatives/false-positives in a logistic regression setting. Inference from the standard logistic regression was also compared with that from three proposed Bayesian models using simulations and malaria data from the western Brazilian Amazon. RESULTS: A systematic literature review suggests that malaria epidemiologists are largely unaware of the problem of using logistic regression to model imperfect diagnostic test results. Simulation results reveal that statistical inference can be substantially improved when using the proposed Bayesian models versus the standard logistic regression. Finally, analysis of original malaria data with one of the proposed Bayesian models reveals that microscopy sensitivity is strongly influenced by how long people have lived in the study region, and an important risk factor (i.e., participation in forest extractivism) is identified that would have been missed by standard logistic regression. CONCLUSION: Given the numerous diagnostic methods employed by malaria researchers and the ubiquitous use of logistic regression to model the results of these diagnostic tests, this paper provides critical guidelines to improve data analysis practice in the presence of misclassification error. Easy-to-use code that can be readily adapted to WinBUGS is provided, enabling straightforward implementation of the proposed Bayesian models.

Decomposing biodiversity data using the Latent Dirichlet Allocation model, a probabilistic multivariate statistical method.

We propose a novel multivariate method to analyse biodiversity data based on the Latent Dirichlet Allocation (LDA) model. LDA, a probabilistic model, reduces assemblages to sets of distinct component communities. It produces easily interpretable results, can represent abrupt and gradual changes in composition, accommodates missing data and allows for coherent estimates of uncertainty. We illustrate our method using tree data for the eastern United States and from a tropical successional chronosequence. The model is able to detect pervasive declines in the oak community in Minnesota and Indiana, potentially due to fire suppression, increased growing season precipitation and herbivory. The chronosequence analysis is able to delineate clear successional trends in species composition, while also revealing that site-specific factors significantly impact these successional trajectories. The proposed method provides a means to decompose and track the dynamics of species assemblages along temporal and spatial gradients, including effects of global change and forest disturbances.

Improving the modeling of disease data from the government surveillance system: a case study on malaria in the Brazilian Amazon.

The study of the effect of large-scale drivers (e.g., climate) of human diseases typically relies on aggregate disease data collected by the government surveillance network. The usual approach to analyze these data, however, often ignores a) changes in the total number of individuals examined, b) the bias towards symptomatic individuals in routine government surveillance, and; c) the influence that observations can have on disease dynamics. Here, we highlight the consequences of ignoring the problems listed above and develop a novel modeling framework to circumvent them, which is illustrated using simulations and real malaria data. Our simulations reveal that trends in the number of disease cases do not necessarily imply similar trends in infection prevalence or incidence, due to the strong influence of concurrent changes in sampling effort. We also show that ignoring decreases in the pool of infected individuals due to the treatment of part of these individuals can hamper reliable inference on infection incidence. We propose a model that avoids these problems, being a compromise between phenomenological statistical models and mechanistic disease dynamics models; in particular, a cross-validation exercise reveals that it has better out-of-sample predictive performance than both of these alternative models. Our case study in the Brazilian Amazon reveals that infection prevalence was high in 2004-2008 (prevalence of 4% with 95% CI of 3-5%), with outbreaks (prevalence up to 18%) occurring during the dry season of the year. After this period, infection prevalence decreased substantially (0.9% with 95% CI of 0.8-1.1%), which is due to a large reduction in infection incidence (i.e., incidence in 2008-2010 was approximately one fifth of the incidence in 2004-2008).We believe that our approach to modeling government surveillance disease data will be useful to advance current understanding of large-scale drivers of several diseases.

Large-scale drivers of malaria and priority areas for prevention and control in the Brazilian Amazon region using a novel multi-pathogen geospatial model.

BACKGROUND: Most 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. METHODS: Utilizing 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. RESULTS: Borrowing 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. CONCLUSION: This 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.