Abstract1. Omnivory, feeding at more than one trophic level, is a prevalent feature of freshwater ecosystems. Understanding where and when omnivory is important, its relevance for sustaining diversity, and the effect it may have on ecosystem responses to disturbances, are necessary for effective management of freshwater ecosystems.2. The many theoretical predictions of the effects of omnivory are often contradictory, and empirical studies aimed at understanding omnivory have been difficult and contingent on a number of factors. Here, I synthesise theoretical evidence to generate five predictions of where omnivory will be most important in freshwater ecosystems, how it is maintained and the effect it will have on communities.3. First, theory indicates that, while strong omnivorous interactions are destabilising, weak omnivorous interactions usually enhance stability. Therefore, mechanisms which decrease the strength of omnivorous interactions should favour their occurrence and increase stability of food webs. Secondly, omnivorous interactions which are theoretically unstable may be found in stable food webs due to stabilising features of the web as a whole. Thirdly, omnivory is likely to persist primarily at intermediate productivity levels and be more common in disturbed environments. Fourthly, omnivory is likely to decrease the strength of trophic cascades. Finally, omnivores should generally make more successful invaders.4. These predictions are important for effective freshwater management because actions which decrease the strength of omnivorous interactions, such as maintaining habitat refuges for consumers (e.g. woody debris and aquatic plants), may be essential for sustaining biodiversity. In addition, if omnivores make better invaders, effective invasion management may benefit from focussing resources on omnivorous invaders to limit their spread and impact.5. Overall, this synthesis of theoretical and empirical studies indicates that, while their predictions may be frequently at odds, with deeper investigation they are largely reconcilable and can be used to make practical suggestions for the careful management of omnivory in freshwater food webs. K E Y W O R D Shabitat refuge, intraguild predation, trophic cascade, trophic level, weak interactions

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1. Species traits and environmental conditions determine the occurrence and strength of trophic interactions. If we understand the relationship between these factors and trophic interactions, we can make more accurate predictions and build better trophic-interaction models.2. We can compare traits and conditions by considering their effect on different parts (steps) of a trophic interaction, such as the steps search and pursuit. By linking traits to relevant steps, we can use these relationships to build trophicinteraction models. Currently, this is done ad hoc, defining steps based on the species and traits of interest. This makes it difficult to compare across traits and species and gain an overarching understanding of how traits and the environment drive trophic interactions.3. We present a comprehensive approach for the explicit choice of interaction steps and species traits or environmental conditions, which is readily integrated into existing models. The core of this framework is that it is modular; we present eight steps that occur in all trophic interactions and use them to build a modular, general dynamic model. When applying the framework, one explicitly selects only the most relevant steps and uses those to build a specific model. 4. To build our modular framework, we revisit and expand the functional and numerical response functions, dividing the trophic interaction into eight steps: (1) search, (2) prey detection, (3) attack decision, (4) pursuit, (5) subjugation, (6) ingestion, (7) digestion and (8) nutrient allocation. Together these steps form a general dynamical model where trophic interactions can be explicitly parameterized for multiple traits and environmental factors. We then concretize this approach by outlining how a specific community can be modelled by selecting key modules (steps) and parameterizing them for relevant factors. This we exemplify for a community of terrestrial arthropods using empirical data on body size and temperature responses.5. With species interactions at the core of community dynamics, our modular approach allows for quantification and comparisons of the importance of different steps, traits, and abiotic factors across ecosystems and trophic-interaction types, and provides a powerful tool for trait-based prediction of food-web structure and dynamics.

Successfully applying theoretical models to natural communities and predicting ecosystem behavior under changing conditions is the backbone of predictive ecology. However, the experiments required to test these models are dictated by practical constraints, and models are often opportunistically validated against data for which they were never intended. Alternatively, we can inform and improve experimental design by an in-depth pre-experimental analysis of the model, generating experiments better targeted at testing the validity of a theory. Here, we describe this process for a specific experiment. Starting from food web ecological theory, we formulate a model and design an experiment to optimally test the validity of the theory, supplementing traditional design considerations with model analysis. The experiment itself will be run and described in a separate paper. The theory we test is that trophic population dynamics are dictated by species traits, and we study this in a community of terrestrial arthropods. We depart from the Allometric Trophic Network (ATN) model and hypothesize that including habitat use, in addition to body mass, is necessary to better model trophic interactions. We therefore formulate new terms which account for micro-habitat use as well as intra- and interspecific interference in the ATN model. We design an experiment and an effective sampling regime to test this model and the underlying assumptions about the traits dominating trophic interactions. We arrive at a detailed sampling protocol to maximize information content in the empirical data obtained from the experiment and, relying on theoretical analysis of the proposed model, explore potential shortcomings of our design. Consequently, since this is a “pre-experimental” exercise aimed at improving the links between hypothesis formulation, model construction, experimental design and data collection, we hasten to publish our findings before analyzing data from the actual experiment, thus setting the stage for strong inference.