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.

Bridging the gap between movement data and connectivity analysis using the Time-Explicit Habitat Selection (TEHS) model.

BACKGROUND: Understanding how to connect habitat remnants to facilitate the movement of species is a critical task in an increasingly fragmented world impacted by human activities. The identification of dispersal routes and corridors through connectivity analysis requires measures of landscape resistance but there has been no consensus on how to calculate resistance from habitat characteristics, potentially leading to very different connectivity outcomes. METHODS: We propose a new model, called the Time-Explicit Habitat Selection (TEHS) model, that can be directly used for connectivity analysis. The TEHS model decomposes the movement process in a principled approach into a time and a selection component, providing complementary information regarding space use by separately assessing the drivers of time to traverse the landscape and the drivers of habitat selection. These models are illustrated using GPS-tracking data from giant anteaters (Myrmecophaga tridactyla) in the Pantanal wetlands of Brazil. RESULTS: The time model revealed that the fastest movements tended to occur between 8 p.m. and 5 a.m., suggesting a crepuscular/nocturnal behavior. Giant anteaters moved faster over wetlands while moving much slower over forests and savannas, in comparison to grasslands. We also found that wetlands were consistently avoided whereas forest and savannas tended to be selected. Importantly, this model revealed that selection for forest increased with temperature, suggesting that forests may act as important thermal shelters when temperatures are high. Finally, using the spatial absorbing Markov chain framework, we show that the TEHS model results can be used to simulate movement and connectivity within a fragmented landscape, revealing that giant anteaters will often not use the shortest-distance path to the destination patch due to avoidance of certain habitats. CONCLUSIONS: The proposed approach can be used to characterize how landscape features are perceived by individuals through the decomposition of movement patterns into a time and a habitat selection component. Additionally, this framework can help bridge the gap between movement-based models and connectivity analysis, enabling the generation of time-explicit connectivity results.

Estimation and interpretation problems and solutions when using proportion covariates in linear regression models.

Proportion variables, also known as compositional data, are very common in ecology. Unfortunately, few scientists are aware of how compositional data, when used as covariates, can adversely impact statistical analysis. We describe here how proportion covariates result in multicollinearity and parameter identifiability problems. Using simulated data on bird species richness as a function of land use, we show how these problems manifest when fitting a wide range of models in R, both in a frequentist and Bayesian framework. In particular, we show that similar models can often generate substantially different parameter estimates, leading to very different conclusions. Dropping a covariate or the intercept from the model can solve the multicollinearity and parameter identifiability problems. Unfortunately, these solutions do not fix the inherent challenges associated with interpreting parameter estimates. To this end, we propose focusing the interpretation on the difference of slope parameters to avoid the inherent unidentifiability of individual parameters. We also propose conditional plots with two x-axes and marginal plots as visualization techniques that can help users better interpret their modeling results. We illustrate these problems and proposed solutions using empirical data from the North American Breeding Bird Survey. The practical and straightforward approaches suggested in this article will help the fitting of linear models and interpretation of its results when some of the covariates are proportions.

Environmental drivers and cryptic biodiversity hotspots define endophytes in Earth's largest terrestrial biome.

Understanding how symbiotic associations differ across environmental gradients is key to predicting the fate of symbioses as environments change, and it is vital for detecting global reservoirs of symbiont biodiversity in a changing world.1,2,3 However, sampling of symbiotic partners at the full-biome scale is difficult and rare. As Earth's largest terrestrial biome, boreal forests influence carbon dynamics and climate regulation at a planetary scale. Plants and lichens in this biome host the highest known phylogenetic diversity of fungal endophytes, which occur within healthy photosynthetic tissues and can influence hosts' resilience to stress.4,5 We examined how communities of endophytes are structured across the climate gradient of the boreal biome, focusing on the dominant plant and lichen species occurring across the entire south-to-north span of the boreal zone in eastern North America. Although often invoked for understanding the distribution of biodiversity, neither a latitudinal gradient nor mid-domain effect5,6,7 can explain variation in endophyte diversity at this trans-biome scale. Instead, analyses considering shifts in forest characteristics, Picea biomass and age, and nutrients in host tissues from 46° to 58° N reveal strong and distinctive signatures of climate in defining endophyte assemblages in each host lineage. Host breadth of endophytes varies with climate factors, and biodiversity hotspots can be identified at plant-community transitions across the boreal zone at a global scale. Placed against a backdrop of global circumboreal sampling,4 our study reveals the sensitivity of endophytic fungi, their reservoirs of biodiversity, and their important symbiotic associations, to climate.

Patterns and drivers of Human Visceral Leishmaniasis in Pernambuco (Brazil) from 2007 to 2018.

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.

Biologging as an important tool to uncover behaviors of cryptic species: an analysis of giant armadillos (Priodontes maximus).

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.

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.

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.

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.

The Latent Dirichlet Allocation model with covariates (LDAcov): A case study on the effect of fire on species composition in Amazonian forests.

Understanding and predicting the effect of global change phenomena on biodiversity is challenging given that biodiversity data are highly multivariate, containing information from tens to hundreds of species in any given location and time. The Latent Dirichlet Allocation (LDA) model has been recently proposed to decompose biodiversity data into latent communities. While LDA is a very useful exploratory tool and overcomes several limitations of earlier methods, it has limited inferential and predictive skill given that covariates cannot be included in the model. We introduce a modified LDA model (called LDAcov) which allows the incorporation of covariates, enabling inference on the drivers of change of latent communities, spatial interpolation of results, and prediction based on future environmental change scenarios. We show with simulated data that our approach to fitting LDAcov is able to estimate well the number of groups and all model parameters. We illustrate LDAcov using data from two experimental studies on the long-term effects of fire on southeastern Amazonian forests in Brazil. Our results reveal that repeated fires can have a strong impact on plant assemblages, particularly if fuel is allowed to build up between consecutive fires. The effect of fire is exacerbated as distance to the edge of the forest decreases, with small-sized species and species with thin bark being impacted the most. These results highlight the compounding impacts of multiple fire events and fragmentation, a scenario commonly found across the southern edge of Amazon. We believe that LDAcov will be of wide interest to scientists studying the effect of global change phenomena on biodiversity using high-dimensional datasets. Thus, we developed the R package LDAcov to enable the straightforward use of this model.

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.

A Cautionary Tale Regarding the Use of Causal Inference to Study How Environmental Change Influences Tropical Diseases.

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.

Improving national level spatial mapping of malaria through alternative spatial and spatio-temporal models.

The most common approach to create spatial prediction of malaria in the literature is to approximate a Gaussian process model using stochastic partial differential equation (SPDE). We compared SPDE to computationally faster alternatives, generalized additive model (GAM) and state-of-the-art machine learning method gradient boosted trees (GBM), with respect to their predictive skill for country-level malaria prevalence mapping. We also evaluated the intuition that incorporation of past data and the use of spatio-temporal models may improve predictive accuracy of present spatial distribution of malaria. Model performances varied among the countries and setting with SPDE and GAM performed well generally. The inclusion of past data is beneficial for GAM and GBM, but not for SPDE. We further investigated the weaknesses of SPDE at spatio-temporal setting and GAM at the edges of the countries. Taken together, we believe that spatial/spatio-temporal SPDE models should be evaluated alongside with the alternatives or at least GAM.

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.

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.

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.

Spatial variation in the detection rates of frequently studied pharmaceuticals in Asian, European and North American rivers.

Pharmaceutical consumption has expanded rapidly during the last century and their persistent presence in the environment has become a major concern. Unfortunately, our understanding of the distribution of pharmaceuticals in surface water and their effects on aquatic biota and public health is limited. Here, we explore patterns in the detection rate of the most frequently studied pharmaceuticals in 64 rivers from 22 countries using bi-clustering algorithms and subsequently analyze the results in the context of regional differences in pharmaceutical consumption habits, social and environmental factors, and removal-efficiency of wastewater treatment plants (WWTP). We find that 20% of the pharmaceuticals included in this analysis are pervasively present in all the surface waterbodies. Several pharmaceuticals also display low overall positive detection rates; however, they exhibit significant spatial variability and their detection rates are consistently lower in Western European and North America (WEOG) rivers in comparison to Asian rivers. Our analysis suggests the important role of pharmaceutical consumption and population in governing these patterns, however the role of WWTP efficiency appeared to be limited. We were constrained in our ability to assess the role of hydrology, which most likely also plays an important role in regulating pharmaceuticals in rivers. Most importantly though, we demonstrate the ability of our algorithm to provide probabilistic estimates of the detection rate of pharmaceuticals that were not studied in a river, an exercise that could be useful in prioritizing pharmaceuticals for future study.

Quantifying the impacts of dams on riverine hydrology under non-stationary conditions using incomplete data and Gaussian copula models.

Across the world, the assessment of environmental impacts attributable to infrastructure and development projects often require a comparison between observed post-impact outcomes with what "would have happened" in the absence of the impact (i.e., the counterfactual). Environmental impact assessment (EIA) methods traditionally determine the counterfactual based on strong assumptions of stationarity (e.g., using before and after comparisons) and can be particularly challenging to use in the context of substantial data gaps, a vexing problem when combining several time-series data from different sources. Here we propose and test a widely applicable statistical approach for quantifying environmental impacts that avoids the stationarity assumption and circumvents issues associated with data gaps. Specifically, we used a Gaussian Copula (GC) model to assess the hydrological impacts of the Tucuruí dam on the Tocantins River in the Brazilian Amazon. Using multi-source water level and climate data, GC predictions of pre-dam hydrology for the validation period were excellent (Nash-Sutcliffe coefficients of 0.83 to 0.98 and 93-96% of observations within the 95% predictive intervals). In the post-dam period, the river had higher dry-season water levels both upstream and downstream relative to the predicted counterfactual, and the timing and duration of wet-season drawdown was delayed and extended, substantially altering the flood pulse. These impacts were evident as far as 176 km away from the dam, highlighting widespread hydrological impacts. The GC model outperformed standard multiple regression models in representing predictive uncertainty while also avoiding the stationarity assumption and circumventing the issue of sparse and incomplete data. We thus believe the GC approach has wide utility for integrating disparate time-series data to quantify the impacts of dams and other anthropogenic phenomena on riverine hydrology globally.