Transmission potential of human schistosomes can be driven by resource competition among snail intermediate hosts.

Predicting and disrupting transmission of human parasites from wildlife hosts or vectors remains challenging because ecological interactions can influence their epidemiological traits. Human schistosomes, parasitic flatworms that cycle between freshwater snails and humans, typify this challenge. Human exposure risk, given water contact, is driven by the production of free-living cercariae by snail populations. Conventional epidemiological models and management focus on the density of infected snails under the assumption that all snails are equally infectious. However, individual-level experiments contradict this assumption, showing increased production of schistosome cercariae with greater access to food resources. We built bioenergetics theory to predict how resource competition among snails drives the temporal dynamics of transmission potential to humans and tested these predictions with experimental epidemics and demonstrated consistency with field observations. This resource-explicit approach predicted an intense pulse of transmission potential when snail populations grow from low densities, i.e., when per capita access to resources is greatest, due to the resource-dependence of cercarial production. The experiment confirmed this prediction, identifying a strong effect of infected host size and the biomass of competitors on per capita cercarial production. A field survey of 109 waterbodies also found that per capita cercarial production decreased as competitor biomass increased. Further quantification of snail densities, sizes, cercarial production, and resources in diverse transmission sites is needed to assess the epidemiological importance of resource competition and support snail-based disruption of schistosome transmission. More broadly, this work illustrates how resource competition can sever the correspondence between infectious host density and transmission potential.

A time-since-infection model for populations with two pathogens.

The pioneering work of Kermack and McKendrick (1927, 1932, 1933) is now most known for introducing the SIR model, which divides a population into discrete compartments for susceptible, infected and removed individuals. The SIR model is the archetype of widely used compartmental models for epidemics. It is sometimes forgotten, that Kermack and McKendrick introduced the SIR model as a special case of a more general framework. This general framework distinguishes individuals not only by whether they are susceptible, infected or removed, but additionally tracks the time passed since they got infected. Such time-since-infection models can mechanistically link within-host dynamics to the population level. This allows the models to account for more details of the disease dynamics, such as delays of infectiousness and symptoms during the onset of an infection. Details like this can be vital for interpreting epidemiological data. The time-since-infection framework was originally formulated for a host population with a single pathogen. However, the interactions of multiple pathogens within hosts and within a population can be crucial for understanding the severity and spread of diseases. Current models for multiple pathogens mostly rely on compartmental models. While such models are relatively easy to set up, they do not have the same mechanistic underpinning as time-since-infection models. To approach this gap of connecting within-host dynamics of multiple pathogens to the population level, we here extend the time-since-infection framework of Kermack and McKendrick for two pathogens. We derive formulas for the basic reproduction numbers in the system. Those numbers determine whether a pathogen can invade a population, potentially depending on whether the other pathogen is present or not. We then demonstrate use of the framework by setting up a simple within-host model that we connect to the population model. The example illustrates the context-specific information required for this type of model, and shows how the system can be simulated numerically. We verify that the formulas for the basic reproduction numbers correctly specify the invasibility conditions.

Fertilization by coral-dwelling fish promotes coral growth but can exacerbate bleaching response.

Many corals form close associations with a diverse assortment of coral-dwelling fishes and other fauna. As coral reefs around the world are increasingly threatened by mass bleaching events, it is important to understand how these biotic interactions influence corals' susceptibility to bleaching. We used dynamic energy budget modeling to explore how nitrogen excreted by coral-dwelling fish affects the physiological performance of host corals. In our model, fish presence influenced the functioning of the coral-Symbiodiniaceae symbiosis by altering nitrogen availability, and the magnitude and sign of these effects depended on environmental conditions. Although our model predicted that fish-derived nitrogen can promote coral growth, the relationship between fish presence and coral tolerance of photo-oxidative stress was non-linear. Fish excretions supported denser symbiont populations that provided protection from incident light through self-shading. However, these symbionts also used more of their photosynthetic products for their own growth, rather than sharing with the coral host, putting the coral holobiont at a higher risk of becoming carbon-limited and bleaching. The balance between the benefits of increased symbiont shading and costs of reduced carbon sharing depended on environmental conditions. Thus, while there were some scenarios under which fish presence increased corals' tolerance of light stress, fish could also exacerbate bleaching and slow or prevent subsequent recovery. We discuss how the contrast between the potentially harmful effects of fish predicted by our model and results of empirical studies may relate to key model assumptions that warrant further investigation. Overall, this study provides a foundation for future work on how coral-associated fauna influence the bioenergetics of their host corals, which in turn has implications for how these corals respond to bleaching-inducing stressors.

Extrapolating Contaminant Effects from Individuals to Populations: A Case Study on Nanoparticle Toxicity to Daphnia Fed Environmentally Relevant Food Levels.

Ecological risk assessment (ERA) is charged with assessing the likelihood a chemical will have adverse environmental or ecological effects. When assessing the risk of a potential contaminant to biological organisms, ecologists are most concerned with the sustainability of populations of organisms, rather than protecting every individual. However, ERA most commonly relies on data on the effect of a potential contaminant on individuals because these experiments are more feasible than costly population-level exposures. In this work, we address the challenge of extrapolating these individual-level results to predict population-level effects. Previous per-capita population growth rate estimates calculated from individual-level exposures of Daphnia pulicaria to silver nanoparticles (AgNPs) at different food rations predict a critical daily food requirement for daphnid populations exposed to 200 µg/L AgNPs to avoid extinction. To test this, we exposed daphnid populations to the same AgNP concentration at three different food inputs, with the lowest ration close to the extinction threshold predicted from data on individuals. The two populations with the higher food inputs persisted, and the population with the lowest food input went extinct after 50 days but did persist through two generations. We demonstrate that we can extrapolate between these levels of biological organization by parameterizing an individual-level biomass model with data on individuals' response to AgNPs and using these parameters to predict the outcome for control and AgNP-exposed populations. Key to successful extrapolation is careful modeling of temporal changes in resource density, driven by both the experimental protocols and feedback from the consumer. The implication for ecotoxicology is that estimates of extinction thresholds based on studies of individuals may be reliable predictors of population outcomes, but only with careful treatment of resource dynamics.

Bioenergetic theory predicts infection dynamics of human schistosomes in intermediate host snails across ecological gradients.

Epidemiological dynamics depend on the traits of hosts and parasites, but hosts and parasites are heterogeneous entities that exist in dynamic environments. Resource availability is a particularly dynamic and potent environmental driver of within-host infection dynamics (temporal patterns of growth, reproduction, parasite production and survival). We developed, parameterised and validated a model for resource-explicit infection dynamics by incorporating a parasitism module into dynamic energy budget theory. The model mechanistically explained the dynamic multivariate responses of the human parasite Schistosoma mansoni and its intermediate host snail to variation in resources and host density. At the population level, feedbacks mediated by resource competition could create a unimodal relationship between snail density and human risk of exposure to schistosomes. Consequently, weak snail control could backfire if reductions in snail density release remaining hosts from resource competition. If resource competition is strong and relevant to schistosome production in nature, it could inform control strategies.

A dynamic bioenergetic model for coral-Symbiodinium symbioses and coral bleaching as an alternate stable state.

Coral reef ecosystems owe their ecological success - and vulnerability to climate change - to the symbiotic metabolism of corals and Symbiodinium spp. The urgency to understand and predict the stability and breakdown of these symbioses (i.e., coral 'bleaching') demands the development and application of theoretical tools. Here, we develop a dynamic bioenergetic model of coral-Symbiodinium symbioses that demonstrates realistic steady-state patterns in coral growth and symbiont abundance across gradients of light, nutrients, and feeding. Furthermore, by including a mechanistic treatment of photo-oxidative stress, the model displays dynamics of bleaching and recovery that can be explained as transitions between alternate stable states. These dynamics reveal that "healthy" and "bleached" states correspond broadly to nitrogen- and carbon-limitation in the system, with transitions between them occurring as integrated responses to multiple environmental factors. Indeed, a suite of complex emergent behaviors reproduced by the model (e.g., bleaching is exacerbated by nutrients and attenuated by feeding) suggests it captures many important attributes of the system; meanwhile, its modular framework and open source R code are designed to facilitate further problem-specific development. We see significant potential for this modeling framework to generate testable hypotheses and predict integrated, mechanistic responses of corals to environmental change, with important implications for understanding the performance and maintenance of symbiotic systems.

Integrating lipid storage into general representations of fish energetics.

Fish, even of the same species, can exhibit substantial variation in energy density (energy per unit wet weight). Most of this variation is due to differences in the amount of storage lipids. In addition to their importance as energy reserves for reproduction and for survival during unfavourable conditions, the accumulation of lipids represents a large energetic flux for many species, so figuring out how this energy flux is integrated with other major energy fluxes (growth, reproduction) is critical for any general theory of organismal energetics. Here, we synthesize data from a wide range of fish species and identify patterns of intraspecific variation in energy storage, and use these patterns to formulate a general model of energy allocation between growth, lipid storage and reproduction in fishes. From the compiled data we identified two patterns: (1) energy density increases with body size during the juvenile period, but is invariant with body size within the adult size range for most species, and (2) energy density changes across seasons, with depletion over winter, but increases fastest in periods of transition between favourable and unfavourable conditions for growth (i.e. fall). Based on these patterns we propose DEBlipid, a simple, general model of energy allocation that is closely related to a simplified version of Dynamic Energy Budget theory, DEBkiss. The crux of the model is that assimilated energy is partitioned, with κ fraction of energy allocated to pay maintenance costs first, and the surplus allocated to growth, and 1 - κ fraction of assimilated energy is allocated to accumulating storage lipids during the juvenile phase, and later to reproduction as adults. This mechanism, in addition to capturing the two patterns that motivated the model, was able to predict lipid dynamics in a novel context, the migration of anadromous fish from low-food freshwater to high-food marine environments. Furthermore, the model was used to explain intra and interspecific variation in reproductive output based on patterns of lipid accumulation as juveniles. Our results suggest that many seemingly complex, adaptive energy allocation strategies in response to ontogeny, seasonality and habitat quality can emerge from a simple physiological heuristic.

Host-Symbiont Interaction Model Explains Non-monotonic Response of Soybean Growth and Seed Production to Nano-CeO2 Exposure.

Recent nanotoxicity studies have demonstrated non-monotonic dose-response mechanisms for planted soybean that have a symbiotic relationship with bacteroids in their root nodules: reduction of growth and seed production was greater for low, as compared to high, exposures. To investigate mechanistic underpinnings of the observed patterns, we formulated an energy budget model coupled to a toxicokinetic module describing bioaccumulation, and two toxicodynamic modules describing toxic effects on host plant and symbionts. By fitting data on plants exposed to engineered CeO2 nanoparticles to the newly formulated model, we show that the non-monotonic patterns can be explained as the interaction of two, individually monotonic, dose-response processes: one for the plant and the other for the symbiont. We further validate the newly formulated model by showing that, without the need for additional parameters, the model successfully predicts changes in dinitrogen fixation potential as a function of exposure (dinitrogen fixation potential data not used in model fitting). The symbiont buffers overall toxicity only when, in the absence of exposure to a toxicant, it has a parasitic interaction with the host plant. If the interaction is mutualistic or commensal, there is no buffering and only monotonic toxic responses are possible. Because the model is based on general biological principles, we expect it to be applicable to other similar symbiotic systems, especially other nodule-forming legumes.

The pros and cons of ecological risk assessment based on data from different levels of biological organization.

Ecological risk assessment (ERA) is the process used to evaluate the safety of manufactured chemicals to the environment. Here we review the pros and cons of ERA across levels of biological organization, including suborganismal (e.g., biomarkers), individual, population, community, ecosystem and landscapes levels. Our review revealed that level of biological organization is often related negatively with ease at assessing cause-effect relationships, ease of high-throughput screening of large numbers of chemicals (it is especially easier for suborganismal endpoints), and uncertainty of the ERA because low levels of biological organization tend to have a large distance between their measurement (what is quantified) and assessment endpoints (what is to be protected). In contrast, level of biological organization is often related positively with sensitivity to important negative and positive feedbacks and context dependencies within biological systems, and ease at capturing recovery from adverse contaminant effects. Some endpoints did not show obvious trends across levels of biological organization, such as the use of vertebrate animals in chemical testing and ease at screening large numbers of species, and other factors lacked sufficient data across levels of biological organization, such as repeatability, variability, cost per study and cost per species of effects assessment, the latter of which might be a more defensible way to compare costs of ERAs than cost per study. To compensate for weaknesses of ERA at any particular level of biological organization, we also review mathematical modeling approaches commonly used to extrapolate effects across levels of organization. Finally, we provide recommendations for next generation ERA, submitting that if there is an ideal level of biological organization to conduct ERA, it will only emerge if ERA is approached simultaneously from the bottom of biological organization up as well as from the top down, all while employing mathematical modeling approaches where possible to enhance ERA. Because top-down ERA is unconventional, we also offer some suggestions for how it might be implemented efficaciously. We hope this review helps researchers in the field of ERA fill key information gaps and helps risk assessors identify the best levels of biological organization to conduct ERAs with differing goals.

The implications of reduced metabolic rate in resource-limited corals.

Many organisms exhibit depressed metabolism when resources are limited, a change that makes it possible to balance an energy budget. For symbiotic reef corals, daily cycles of light and periods of intense cloud cover can be chronic causes of food limitation through reduced photosynthesis. Furthermore, coral bleaching is common in present-day reefs, creating a context in which metabolic depression could have beneficial value to corals. In the present study, corals (massive Porites spp.) were exposed to an extreme case of resource limitation by starving them of food and light for 20 days. When resources were limited, the corals depressed area-normalized respiration to 37% of initial rates, and coral biomass declined to 64% of initial amounts, yet the corals continued to produce skeletal mass. However, the declines in biomass cannot account for the declines in area-normalized respiration, as mass-specific respiration declined to 30% of the first recorded time point. Thus, these corals appear to be capable of metabolic depression. It is possible that some coral species are better able to depress metabolic rates than others; such variation could explain differential survival during conditions that limit resources (e.g. shading). Furthermore, we found that maintenance of existing biomass, in part, supports the production of skeletal mass. This association could be explained if maintenance supplies needed energy (e.g. ATP) or inorganic carbon (i.e. CO2) that otherwise limits the production of skeletal mass. Finally, the observed metabolic depression can be explained as a change in pool sizes, and does not require a change in metabolic rules.

Feedbacks and tipping points in organismal response to oxidative stress.

Biological feedbacks play a crucial role in determining effects of toxicants, radiation, and other environmental stressors on organisms. Focusing on reactive oxygen species (ROS) that are increasingly recognized as a crucial mediator of many stressor effects, we investigate how feedback strength affects the ability of organisms to control negative effects of exposure. We do this by developing a general theoretical framework for describing effects of a wide range of stressors and species. The framework accounts for positive and negative feedbacks representing cellular processes: (i) production of ROS due to metabolism and the stressor, (ii) ROS reactions with cellular compounds that cause damage, and (iii) cellular control of both ROS and damage. We suggest functional forms that capture generic properties of cellular control mechanisms constituting the feedbacks, assess stability of equilibrium states in the resulting models, and investigate tipping points at which cellular control breaks down causing unregulated increase of ROS and damage. Depending on the chosen functional forms, the models can have zero, one, or two positive steady states; except in one singular case, the steady state with lowest values of ROS and damage is locally stable. We found two types of tipping points: those induced by changes in the parameters (including the stressor intensity), and those induced by the history of exposure, i.e. ROS and damage levels. The relatively simple models effectively describe several patterns of cellular responses to stress, such as the covariation of ROS and damage, the break-down of cellular control leading to explosion of ROS and/or damage, increase in damage even when ROS is (near)-constant, and the effects of exposure history on the ability of the cell to handle additional stress. The models quantify dynamics of cellular control, and could therefore be used to estimate the metabolic costs of stress and help integrate them into models that use energetic considerations to model organism's response to the environment. Although developed with unicellular organisms in mind, our models can be applied to all multicellular organisms that utilize similar feedbacks when dealing with stress.

Influence of Phytoplankton on Fate and Effects of Modified Zerovalent Iron Nanoparticles.

Nanoscale zerovalent iron (nZVI) and its derivatives hold promise for remediation of several pollutants but their environmental implications are not completely clear. In this study, the physicochemical properties and aggregation kinetics of sulfide/silica-modified nZVI (FeSSi) were compared in algal media in which Chlamydomonas reinhardtii had been cultured for 1, 2, or 11 days in order to elicit the effects of organic matter produced by the freshwater algae. Furthermore, transformation of FeSSi particles were investigated in C. reinhardtii cultures in exponential (1-d) and slowing growth (11-d) phases while monitoring the response of algae. We found evidence for steric stabilization of FeSSi by algal organic matter, which led to a decrease in the particles' attachment efficiency. Transformation of FeSSi was slower in 11-d cultures as determined via inductively coupled plasma and X-ray analyses. High concentrations of FeSSi caused a lag in algal growth, and reduction in steady state population size, especially in cultures in exponential phase. The different outcomes are well described by a dynamic model describing algal growth, organic carbon production, and FeSSi transformations. This study shows that feedback from algae may play important roles in the environmental implications of engineered nanomaterials.

Considerations of Environmentally Relevant Test Conditions for Improved Evaluation of Ecological Hazards of Engineered Nanomaterials.

Engineered nanomaterials (ENMs) are increasingly entering the environment with uncertain consequences including potential ecological effects. Various research communities view differently whether ecotoxicological testing of ENMs should be conducted using environmentally relevant concentrations-where observing outcomes is difficult-versus higher ENM doses, where responses are observable. What exposure conditions are typically used in assessing ENM hazards to populations? What conditions are used to test ecosystem-scale hazards? What is known regarding actual ENMs in the environment, via measurements or modeling simulations? How should exposure conditions, ENM transformation, dose, and body burden be used in interpreting biological and computational findings for assessing risks? These questions were addressed in the context of this critical review. As a result, three main recommendations emerged. First, researchers should improve ecotoxicology of ENMs by choosing test end points, duration, and study conditions-including ENM test concentrations-that align with realistic exposure scenarios. Second, testing should proceed via tiers with iterative feedback that informs experiments at other levels of biological organization. Finally, environmental realism in ENM hazard assessments should involve greater coordination among ENM quantitative analysts, exposure modelers, and ecotoxicologists, across government, industry, and academia.

Daphnia magna's sense of competition: intra-specific interactions (ISI) alter life history strategies and increase metals toxicity.

This work investigates whether the scale-up to multi-animal exposures that is commonly applied in genomics studies provides equivalent toxicity outcomes to single-animal experiments of standard Daphnia magna toxicity assays. Specifically, we tested the null hypothesis that intraspecific interactions (ISI) among D. magna have neither effect on the life history strategies of this species, nor impact toxicological outcomes in exposure experiments with Cu and Pb. The results show that ISI significantly increased mortality of D. magna in both Cu and Pb exposure experiments, decreasing 14 day LC50 s and 95 % confidence intervals from 14.5 (10.9-148.3) to 8.4 (8.2-8.7) µg Cu/L and from 232 (156-4810) to 68 (63-73) µg Pb/L. Additionally, ISI potentiated Pb impacts on reproduction eliciting a nearly 10-fold decrease in the no-observed effect concentration (from 236 to 25 µg/L). As an indication of environmental relevance, the effects of ISI on both mortality and reproduction in Pb exposures were sustained at both high and low food rations. Furthermore, even with a single pair of Daphnia, ISI significantly increased (p < 0.05) neonate production in control conditions, demonstrating that ISI can affect life history strategy. Given these results we reject the null hypothesis and conclude that results from scale-up assays cannot be directly applied to observations from single-animal assessments in D. magna. We postulate that D. magna senses chemical signatures of conspecifics which elicits changes in life history strategies that ultimately increase susceptibility to metal toxicity.

Relating suborganismal processes to ecotoxicological and population level endpoints using a bioenergetic model.

Ecological effects of environmental stressors are commonly evaluated using organismal or suborganismal data, such as standardized toxicity tests that characterize responses of individuals (e.g., mortality and reproduction) and a rapidly growing body of "omics" data. A key challenge for environmental risk assessment is relating such information to population dynamics. One approach uses dynamic energy budget (DEB) models that relate growth and reproduction of individuals to underlying flows of energy and elemental matter. We hypothesize that suborganismal information identifies DEB parameters that are most likely impacted by a particular stressor and that the DEB model can then project suborganismal effects on life history and population endpoints. We formulate and parameterize a model of growth and reproduction for the water flea Daphnia magna. Our model resembles previous generic bioenergetic models, but has explicit representation of discrete molts, an important feature of Daphnia life history. We test its ability to predict six endpoints commonly used in chronic toxicity studies in specified food environments. With just one adjustable parameter, the model successfully predicts growth and reproduction of individuals from a wide array of experiments performed in multiple laboratories using different clones of D. magna raised on different food sources. Fecundity is the most sensitive endpoint, and there is broad correlation between the sensitivities of fecundity and long-run growth rate, as is desirable for the default metric used in chronic toxicity tests. Under some assumptions, we can combine our DEB model with the Euler-Lotka equation to estimate longrun population growth rates at different food levels. A review of Daphnia gene-expression experiments on the effects of contaminant exposure reveals several connections to model parameters, in particular a general trend of increased transcript expression of genes involved in energy assimilation and utilization at concentrations affecting growth and reproduction. The sensitivity of fecundity to many model parameters was consistent with frequent generalized observations of decreased expression of genes involved in reproductive physiology, but interpretation of these observations requires further mechanistic modeling. We thus propose an approach based on generic DEB models incorporating few essential species-specific features for rapid extrapolation of ecotoxicogenomic assays for Daphnia-based population risk assessment.

Quantitative Adverse Outcome Pathway Analysis of Hatching in Zebrafish with CuO Nanoparticles.

This study develops and evaluates a mechanistic model of the hatching of zebrafish eggs that were exposed to CuO engineered nanoparticles (ENP) in a high-throughput screening system and places this model in an adverse outcome pathway (AOP) that also includes CuO ENP dissolution and Cu bioaccumulation. Cu(2+) inhibits the proteolytic activity of Zebrafish Hatching Enzyme 1 and thereby delay or impair hatching success. This study demonstrates that noncompetitive inhibition kinetics describe the impact of dissolved Cu on hatching; it is estimated that indefinitely long exposure to 1.88 µM dissolved Cu in the environment reduces hatching enzyme activity by 50%. The complexity arising from CuO ENP dissolution and CuO ENP assisted bioaccumulation of Cu has led to apparently contradictory findings about ion versus "nano" effects on hatching. Model-mediated data analyses indicate that, relative to copper salts, CuO ENPs increase the uptake rates of Cu into the perivitelline space up to 8 times. The toxicity assessment framework in this study can be adapted to accommodate other types of toxicant, environmental samples and other aquatic oviparous species.

Soybean susceptibility to manufactured nanomaterials with evidence for food quality and soil fertility interruption.

Based on previously published hydroponic plant, planktonic bacterial, and soil microbial community research, manufactured nanomaterial (MNM) environmental buildup could profoundly alter soil-based food crop quality and yield. However, thus far, no single study has at once examined the full implications, as no studies have involved growing plants to full maturity in MNM-contaminated field soil. We have done so for soybean, a major global commodity crop, using farm soil amended with two high-production metal oxide MNMs (nano-CeO(2) and -ZnO). The results provide a clear, but unfortunate, view of what could arise over the long term: (i) for nano-ZnO, component metal was taken up and distributed throughout edible plant tissues; (ii) for nano-CeO(2), plant growth and yield diminished, but also (iii) nitrogen fixation--a major ecosystem service of leguminous crops--was shut down at high nano-CeO(2) concentration. Juxtaposed against widespread land application of wastewater treatment biosolids to food crops, these findings forewarn of agriculturally associated human and environmental risks from the accelerating use of MNMs.

Ecological nanotoxicology: integrating nanomaterial hazard considerations across the subcellular, population, community, and ecosystems levels.

Research into the health and environmental safety of nanotechnology has seriously lagged behind its emergence in industry. While humans have often adopted synthetic chemicals without considering ancillary consequences, the lessons learned from worldwide pollution should motivate making nanotechnology compatible with environmental concerns. Researchers and policymakers need to understand exposure and harm of engineered nanomaterials (ENMs), currently nanotechnology's main products, to influence the ENM industry toward sustainable growth. Yet, how should research proceed? Standard toxicity testing anchored in single-organism, dose-response characterizations does not adequately represent real-world exposure and receptor scenarios and their complexities. Our approach is different: it derives from ecology, the study of organisms' interactions with each other and their environments. Our approach involves the characterization of ENMs and the mechanistic assessment of their property-based effects. Using high throughput/content screening (HTS/HCS) with cells or environmentally-relevant organisms, we measure the effects of ENMs on a subcellular or population level. We then relate those effects to mechanisms within dynamic energy budget (DEB) models of growth and reproduction. We reconcile DEB model predictions with experimental data on organism and population responses. Finally, we use microcosm studies to measure the potential for community- or ecosystem-level effects by ENMs that are likely to be produced in large quantities and for which either HTS/HCS or DEB modeling suggest their potential to harm populations and ecosystems. Our approach accounts for ecological interactions across scales, from within organisms to whole ecosystems. Organismal ENM effects, if propagated through populations, can alter communities comprising multiple populations (e.g., plant, fish, bacteria) within food webs. Altered communities can change ecosystem services: processes that cycle carbon, nutrients, and energy, and regulate Earth's waters and atmosphere. We have shown ENM effects on populations, communities, and ecosystems, including transfer and concentration of ENMs through food chains, for a range of exposure scenarios; in many cases, we have identified subcellular ENM effects mechanisms. To keep pace with ENM development, rapid assessment of the mechanisms of ENM effects and modeling are needed. DEB models provide a method for mathematically representing effects such as the generation of reactive oxygen species and their associated damage. These models account for organism-level effects on metabolism and reproduction and can predict outcomes of ENM-organism combinations on populations; those predictions can then suggest ENM characteristics to be avoided. HTS/HCS provides a rapid assessment tool of the ENM chemical characteristics that affect biological systems; such results guide and expand DEB model expressions of hazard. Our approach addresses ecological processes in both natural and managed ecosystems (agriculture) and has the potential to deliver timely and meaningful understanding towards environmentally sustainable nanotechnology.

Dynamic energy budget modeling reveals the potential of future growth and calcification for the coccolithophore Emiliania huxleyi in an acidified ocean.

Ocean acidification is likely to impact the calcification potential of marine organisms. In part due to the covarying nature of the ocean carbonate system components, including pH and CO2 and CO3(2-) levels, it remains largely unclear how each of these components may affect calcification rates quantitatively. We develop a process-based bioenergetic model that explains how several components of the ocean carbonate system collectively affect growth and calcification rates in Emiliania huxleyi, which plays a major role in marine primary production and biogeochemical carbon cycling. The model predicts that under the IPCC A2 emission scenario, its growth and calcification potential will have decreased by the end of the century, although those reductions are relatively modest. We anticipate that our model will be relevant for many other marine calcifying organisms, and that it can be used to improve our understanding of the impact of climate change on marine systems.

Limitations of extrapolating toxic effects on reproduction to the population level.

For the ecological risk assessment of toxic chemicals, standardized tests on individuals are often used as proxies for population-level effects. Here, we address the utility of one commonly used metric, reproductive output, as a proxy for population-level effects. Because reproduction integrates the outcome of many interacting processes (e.g., feeding, growth, allocation of energy to reproduction), the observed toxic effects in a reproduction test could be due to stress on one of many processes. Although this makes reproduction a robust endpoint for detecting stress, it may mask important population-level consequences if the different physiological processes stress affects are associated with different feedback mechanisms at the population level. We therefore evaluated how an observed reduction in reproduction found in a standard reproduction test translates to effects at the population level if it is caused by hypothetical toxicants affecting different physiological processes (physiological modes of action; PMoA). For this we used two consumer­resource models: the Yodzis-Innes (YI) model, which is mathematically tractable, but requires strong assumptions of energetic equivalence among individuals as they progress through ontogeny, and an individual-based implementation of dynamic energy budget theory (DEB-IBM), which relaxes these assumptions at the expense of tractability. We identified two important feedback mechanisms controlling the link between individual- and population-level stress in the YI model. These mechanisms turned out to also be important for interpreting some of the individual-based model results; for two PMoAs, they determined the population response to stress in both models. In contrast, others stress types involved more complex feedbacks, because they asymmetrically stressed the production efficiency of reproduction and somatic growth. The feedbacks associated with different PMoAs drastically altered the link between individual- and population-level effects. For example, hypothetical stressors with different PMoAs that had equal effects on reproduction had effects ranging from a negligible decline in biomass to population extinction. Thus, reproduction tests alone are of little use for extrapolating toxicity to the population level, but we showed that the ecological relevance of standard tests could easily be improved if growth is measured along with reproduction.