Parasites steal resources that a host would otherwise direct toward its own growth and reproduction. We use this fundamental notion to explain resource-dependent virulence in a fungal parasite (Metschnikowia)-zooplankton host (Daphnia) system and in a variety of other disease systems with invertebrate hosts. In an experiment, well-fed hosts died faster and produced more parasites than did austerely fed ones. This resource-dependent variation in virulence and other experimental results (involving growth and reproduction rate/timing of hosts) readily emerged from a model based on dynamic energy budgets. This model follows energy flow through the host, from ingestion of food, to internal energy storage, to allocation toward growth and reproduction or to a parasite that consumes these reserves. Acting as a consumer, the parasite catalyzes its own extinction, persistence with an energetically compromised host, or death of the host. In this last case, more resources for the host inadvertently fuels faster parasite growth, thereby accelerating the demise of the host (although the opposite result arises with different resource kinetics of the parasite). Thus, this model can explain how resource supply drives variation in virulence. This ecological dependence of virulence likely rivals and/or interacts with genetic mechanisms that often garner more attention in the literature on disease.

The "dilution effect" concept in disease ecology offers the intriguing possibility that clever manipulation of less competent hosts could reduce disease prevalence in populations of more competent hosts. The basic concept is straightforward: host species vary in suitability (competence) for parasites, and disease transmission decreases when there are more incompetent hosts interacting with vectors or removing free-living stages of a parasite. However, host species also often interact with each other in other ecological ways, e.g., as competitors for resources. The net result of these simultaneous, multiple interactions (disease dilution and resource competition) is challenging to predict. Nonetheless, we see the signature of both roles operating concurrently in a planktonic host-parasite system. We document pronounced spatiotemporal variation in the size of epidemics of a virulent fungus (Metschnikowia bicuspidata) in Midwestern U.S. lake populations of a dominant crustacean grazer (Daphnia dentifera). We show that some of this variation is captured by changes in structure of Daphnia assemblages. Lake-years with smaller epidemics were characterized by assemblages dominated by less suitable hosts ("diluters," D. pulicaria and D. retrocurva, whose suitabilties were determined in lab experiments and field surveys) at the start of epidemics. Furthermore, within a season, less suitable hosts increased as epidemics declined. These observations are consistent with a dilution effect. However, more detailed time series analysis (using multivariate autoregressive models) of three intensively sampled epidemics show the signature of a likely interaction between dilution and resource competition between these Daphnia species. The net outcome of this interaction likely promoted termination of these fungal outbreaks. Should this outcome always arise in "friendly competition" systems where diluting hosts compete with more competent hosts? The answers to this question lie at a frontier of disease ecology.

1. Most forecasts for the future state of ecological systems are conducted once and never updated or assessed. As a result, many available ecological forecasts are not based on the most up-to-date data, and the scientific progress of ecological forecasting models is slowed by a lack of feedback on how well the forecasts perform. Iterative near-term ecological forecasting involves repeated daily to annual scaleforecasts of an ecological system as new data becomes available and regular assessment of the resulting forecasts. We demonstrate how automated iterative near-term forecasting systems for ecology can be constructed by building one to conduct monthly forecasts of rodent abundances at the Portal Project, a longterm study with over 40 years of monthly data. This system automates most aspects of the six stages of converting raw data into new forecasts: data collection, data sharing, data manipulation, modelling and forecasting, archiving, and presentation of the forecasts.3. The forecasting system uses R code for working with data, fitting models, making forecasts, and archiving and presenting these forecasts. The resulting pipeline is automated using continuous integration (a software development tool) to run the entire pipeline once a week. The cyberinfrastructure is designed for long-term maintainability and to allow the easy addition of new models. Constructing this forecasting system required a team with expertise ranging from field site experience to software development.4. Automated near-term iterative forecasting systems will allow the science of ecological forecasting to advance more rapidly and provide the most up-to-date forecasts possible for conservation and management. These forecasting systems will also accelerate basic science by allowing new models of natural systems to be quickly implemented and compared to existing models. Using existing technology, and teams with diverse skill sets, it is possible for ecologists to build automated forecasting systems and use them to advance our understanding of natural systems. K E Y W O R D Sforecasting, iterative forecasting, mammals, Portal Project, predictionThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Although ecology is rife with theory that explores how multiple species co‐occur through space and time, the field lacks robust statistical models to parameterize this theory with empirical data, particularly when species are detected imperfectly and data are collected as a time‐series. We address this need by developing an occupancy model that estimates local colonization and extinction rates for two or more interacting species when data are collected across multiple sampling occasions. This model estimates how community composition at a site may change across sampling occasions by assuming the latent occupancy state is a categorical random variable. We used a multinomial‐logit model to parameterize species specific parameters and pairwise interactions between species, both of which can be made a function of covariates. These transition probabilities between community states can then be converted to occupancy or co‐occurrence probabilities to determine how community composition varies along an environmental gradient or through time. As an example, we estimate patterns of co‐occurrence between coyote Canis latrans, Virginia opossum Didelphis virginiana, and raccoon Procyon lotor in Chicago, Illinois, USA with data from a multiyear camera trapping study. Models with pairwise interactions between species greatly out performed models that assumed independence between species. Opossum and raccoon, for example, were far less likely to go extinct in habitat patches where coyotes were present. Community composition at a site depends on species interactions and the local environment. Our model can separate such effects by estimating the underlying processes that define species occurrence patterns. As a result, our model can more explicitly quantify a wide range of ecological dynamics and therefore be used to empirically test ecological theory, such as estimating priority effects at a site or turnover rates between species, both of which can be made to vary as a function of covariates.