Based on the data collected on the taxonomically diverse communities monitored in NEON (National Ecological Observatory Network), BBS (Breeding Bird Survey) and FIA (Forest Inventory & Analysis), we have forecasted community change and reorganization resulting from climate change across the coterminous USA. Predictive sensitivity analysis under climate change anticipates community reorganization and the habitats that will be most critical for new species assemblages. Our new semi-automated platform (pbgjam.org) enables revised forecasts as updated climate, remote sensing and biodiversity data come available. Our future work will build upon this modeling and platform by developing a dynamic, bio-physical framework to quantify the environment-species interactions that structure ecological communities and determine how they respond to environmental change.

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.

Understanding the impact of climate and land-use on forest ecosystems involves modeling and simulating complex spatial interactions at many different scales. With this goal in mind, we have developed an individual-based, spatially explicit forest simulator, which incorporates fine-scale processes that influence forest dynamics. In this paper we present new, faster algorithms for computing understory light and for dispersal of seeds --- the two most computationally intensive submodules in our simulator. By exploiting temporal coherence, we circumvent the problem of doing the entire simulation at each step. We provide experimental results that support the efficiency and efficacy of our approach.

Models of forest ecosystems are needed to understand how climate and land-use change can impact biodiversity. In this paper we describe an individual-based, spatially-explicit forest simulator with full accounting of both landscape context and the fine-scale processes that influence forest dynamics. Unfortunately, performing realistic forest simulations of such models is computationally infeasible. We design efficient algorithms for computing seed dispersal and light, using a plethora of techniques. These include hierarchical spatial decomposition, monopole approximation and utilizing the graphics hardware for fast geometric computations. These algorithms allow us to simulate large landscapes for long periods of time.