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Ecology can be studied at multiple scales (e.g. population ecology vs. behavioural ecology), yet studies that focus on the relationships between dynamics at different scales are critical for understanding the functions and processes of ecosystems (Levin 1992). Fine-scale variables, such as behavioural and physiological traits of individual organisms, can have major effects on broader-scale dynamics, such as population and evolutionary processes. For example, the foraging activities of, and prey selection by, predators serve as selective filters that can alter the distribution of phenotypic traits within their prey populations. Such predator-driven natural selection is widely acknowledged to increase the fitness of prey herds by weeding out the sicker and weaker animals, resulting in a ‘healthy herd’ (Packer et al. 2003). At broader scales, predation can also alter the composition of and interactions within prey assemblages if some species are more impacted by predation than others (Chase et al. 2002). Mechanisms such as these provide insights into the important connections between behavioural ecology and evolutionary patterns, and understanding such interactions is necessary to inform management and conservation efforts in the face of broad-scale changes in ecosystems worldwide.

However, the complexity of ecosystems raises the question of how variation in simple, fine-scale dynamics, such as the filter of selective predation, translates upward to dynamics at higher ecological scales. As the number of species in an ecological community change, perhaps driven by anthropogenic change, the web of interactions among these species will likewise change, and the direct and indirect effects of these species on one another will take new forms. Indirect effects, such as predator-induced shifts in pathogen–host interactions, are difficult to observe or predict but are ubiquitous in natural communities (Wootton 1994; Werner & Peacor 2003). In this volume, Duffy et al. (2011) present a study that highlights the complexities of, and vagaries associated with, interaction webs in ecological communities. They demonstrate a counterexample to the aforementioned ‘healthy herds’ hypothesis of predator-mediated population dynamics, a fact that has important consequences for our understanding of many ecological processes. They show that the predator-avoidance mechanism of an herbivore renders it more susceptible to a pathogen, demonstrating that, contrary to the classical hypothesis, predators can have negative effects on the health of prey populations. Daphnia dentifera, an herbivorous cladoceran, allocated more resources to growth when in the presence of a gape-limited predator, the larva of Chaoborus phantom midges. This increase in growth allows Daphia to more quickly outgrow the susceptible size classes. However, increased growth corresponded with increased food intake, which also increased the uptake and spore yield of a pathogenic yeast, Metschnikowia, thereby increasing the spread of the pathogen in the Daphnia population. Thus, the net effect of the predator in this system is actually a decrease in the average health of its prey population. This presents a serious challenge to the ‘healthy herd’ hypothesis and calls for a more dynamic hypothesis to explain the effects of predators on prey populations.

The ‘healthy herd’ hypothesis can be regarded as prey-centric. That is, it views the health or fitness of herbivore populations as the consequences of a range of phenotypes within the population (e.g. susceptibility to disease), among which predation is the agent of natural selection. However, if herbivore health is determined by the action of a pathogen, rather than by the phenotype of the host alone, a positive predator–pathogen interaction hypothesis may be a more accurate way to model the resulting dynamics. In this light, the Chaoborus-Daphnia-Metschnikowia system of Duffy et al. (2011) is analogous to examples of interactions and emergent effects among predators, of which there is a sizable literature available (Soluk & Collins 1988; Losey & Denno 1998; Nyström et al. 2001; Cardinale et al. 2003). This literature contains examples of predator-avoidance mechanisms that effectively reduce the likelihood of being consumed by one predator, only to increase vulnerability to a second predator (Soluk & Collins 1988; Losey & Denno 1998). Such interactive effects of predators on prey populations are termed ‘risk-enhancing’ because of the increased risk of mortality for the prey (Sih, Englund & Wooster 1998; Schmitz 2007), and ‘trait-mediated’ because it is a phenotypic trait that modulates the interaction among organisms. The study of Duffy et al. (2011) fits in this same category and is particularly noteworthy because, in addition to highlighting the analogies between predator–prey and pathogen–host dynamics, it provides a critical, mechanistic link between the effects of the predator and pathogen and demonstrates that trait-mediated indirect effects can be disseminated through changes in physiological, and not just behavioural, traits.

While trait-mediated interactions are undoubtedly important to ecosystem functions, indirect effects are also mediated via higher-order mechanisms. For example, indirect effects can also be mediated by changes in the densities of intermediate species (e.g. Cardinale et al. 2003; Ohba & Nakasuji 2007). Such density-mediated effects feature prominently in predator–prey and community ecology studies; however, trait-mediated effects may be just as important for understanding community dynamics (Werner & Peacor 2003). As Duffy et al. (2011) have shown, the effects of indirect interactions can be diverse, and care must be taken to study interaction webs thoroughly to gain a clear picture of the dynamics of species interactions and their influences on ecosystem functions. Whereas synergistic indirect effects of predators and pathogens may be beneficial in the context of ecosystem services provided in agriculture, they also have the potential to complicate conservation efforts by reducing the predictability of management effects. It is therefore critical to thoroughly examine the potential mechanisms and consequences of ecological interactions to predict, manage and balance future environmental and societal goals. Duffy et al. (2011) have helped set an important standard for studying ecological interactions among organisms and understanding the dynamics of ecosystems across multiple scales of study.

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