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Individual-based models (IBMs) are increasingly used to link the dynamics of individuals to higher levels of biological organization. Still, many IBMs are data hungry, species specific, and time-consuming to develop and analyze. Many of these issues would be resolved by using general theories of individual dynamics as the basis for IBMs. While such theories have frequently been examined at the individual level, few cross-level tests exist that also try to predict population dynamics. Here we performed a cross-level test of dynamic energy budget (DEB) theory by parameterizing an individualbased model using individual-level data of the water flea, Daphnia magna, and comparing the emerging population dynamics to independent data from population experiments. We found that DEB theory successfully predicted population growth rates and peak densities but failed to capture the decline phase. Further assumptions on food-dependent mortality of juveniles were needed to capture the population dynamics after the initial population peak. The resulting model then predicted, without further calibration, characteristic switches between small- and large-amplitude cycles, which have been observed for Daphnia. We conclude that cross-level tests help detect gaps in current individual-level theories and ultimately will lead to theory development and the establishment of a generic basis for individual-based models and ecology. [ABSTRACT FROM AUTHOR]

The removal of individuals from an infected population (culling) is a common strategy used to eradicate wildlife diseases. The manipulation of host density can impose strong selective pressures on pathogen virulence by changing the ecological conditions, thus affecting the effectiveness of eradication programs. We present an analysis of the effect of virulence evolution on culling by extending a susceptible-infected model to the case of competing strains with superinfection. To assess both short- and long-term effects, we first carried out the analysis on an ecological timescale, with a two-strain competition model; then we explore the dynamics of a continuum of pathogenic strains on evolutionary timescales using a quantitative genetics approach (when infection and evolutionary processes occur on comparable timescales) and a game-theoretic approach (when evolutionary processes occur on a slower scale).We demonstrate that the competition among pathogenic variants in the presence of superinfection affects outcome of culling campaigns, since increased host mortality may select for less virulent strains able to establish in sparser populations. This can lead to the counterintuitive result that disease abundance and prevalence may even increase with culling, thus making the eradication of infections considerably less likely. This is particularly relevant in the case of zoonoses where higher prevalence and abundance of pathogens in wild reservoirs may increase the risk of spillover in livestock and humans. [ABSTRACT FROM AUTHOR]

Pathogen transmission can involve direct and/or indirect pathways. Using theoretical models, in this study we ask, "do directly and indirectly transmitted pathogens yield different population-level epidemiological dynamics?" and "can the transmission pathway be inferred from population-level epidemiological data?" Our approach involves comparing the continuous-time dynamics of a class of compartmental epidemiological models with direct versus environmentally mediated indirect transmission pathways. Combing analytical theory and numerical simulations we show that models with direct and indirect transmission can produce quantitatively similar time series when the pathogen cannot reproduce in the environment, particularly when the environmental pathogen dynamics are fast. We apply these results to a previous study on chronic wasting disease and show that identifying the transmission pathway is more difficult than previously acknowledged. Our analysis and simulations also yield conditions under which numerical differences can potentially identify the transmission route in oscillating endemic systems and systems where the environmental pathogen dynamics are not fast. This work begins to identify how differences in the transmission pathway can result in quantitatively different epidemiological dynamics and how those differences can be used to identify the transmission pathway from population level time series. [ABSTRACT FROM AUTHOR]

Plant pathogens often respond sensitively to changes in their environmental conditions and consequently represent a potentially important ecological response to global change. Although several studies have considered the effects of increased temperature and CO2 concentrations on plant pathogen risk, the effects of changing precipitation regimes have drawn less attention. Many classes of plant pathogen, however, are sensitive to changes in the water potential of their local environment. This study applied existing ecohydrological frameworks to connect precipitation, soil, and host properties with scenarios of pathogen risk, focusing on two water-sensitive pathogens: Phytophthora cinnamomi and Botryosphaeria doithidea. Simple models were developed to link the dynamics of these pathogens to water potentials. Model results demonstrated that the risk of host plants being colonized by the pathogens varied sensitively with soil and climate. The model was used to predict the distribution of Phytophthora in Western Australia and the severity of disease in horticultural blueberry trials with variable irrigation rates, illustrating potential applications of the framework. Extending the modeling framework to include spatial variation in hydrology, epidemic progression, and feedbacks between pathogens and soil moisture conditions may be needed to reproduce detailed spatial patterns of disease. At regional scales, the proposed modeling approach provides a tractable framework for coupling climatic drivers to ecosystem response while accounting for the probabilistic and variable nature of disease. [ABSTRACT FROM AUTHOR]