Beyond single mechanism studies
Each of the individual mechanisms for predator-spreading described above requires substantial additional study to understand both their relative importance across natural systems and the factors that contribute to the strength of the effect. While much can be learned by studying a single mechanism in a single predator-prey-parasite system, a broad understanding of the importance of predator-spreading to disease dynamics in wildlife requires inference that spans mechanisms and study systems (Duffy et al. 2021). This type of inference necessitates both the study of multiple predator-spreading mechanisms within a single system and the comparison of predator-spreading across a wide range of biological systems.
Although we have noted the characteristics of systems that may make them especially suited to studying a particular type of predator spreading, it is vital that a variety of predator spreading mechanisms are tested in a single system. It is natural for a particular system to be used repeatedly to investigate a particular mechanism of predator spreading due to demonstrated feasibility. For example, behavioral predator spreading has been well studied in aquatic frog tadpole prey because tadpoles can readily be exposed to caged predators or predator kairomones to provoke behavioral responses, and because methods are well-established (Han et al. 2011; Szuroczki & Richardson 2012; Koprivnikar & Urichuk 2017; Buss & Hua 2018). It is, however, plausible that other predator-spreading mechanisms such as sloppy-predation, selective predation, and parasites passing through predators could prove important in some tadpole-parasite systems. By comparing the relative magnitude of effect sizes across multiple predator-spreader mechanisms or by manipulating multiple mechanisms within a single experiment (e.g., by manipulating predator kairomone presence and the presence of shredded infected tadpoles), we can come to understand whether predator-spreading is important to disease dynamics; this would also allow us to understand which mechanisms are key to driving this phenomenon in a particular system and whether or not there are synergistic effects between mechanisms.
In addition to studying multiple mechanisms in a single system, we must study the same mechanism in multiple systems – an approach known as “horizontal integration” (Travis 2006). Horizontal integration requires a range of systems that are amenable to study (Duffy et al. 2021); even without considering predation, dominant model systems in the study of disease ecology and evolution have major gaps that likely impede our understanding (Wale & Duffy 2021). Despite these challenges, a comprehensive understanding of the factors that promote predator spreading will require studies that use prey from a range of taxa, predators that differ in key traits (e.g., related to how prey are detected and captured), with a variety of parasites (including microparasites, macroparasites, and ectoparasites), and in a variety of habitats (including terrestrial and aquatic).
Comparative analyses of predator-spreading mechanisms across taxa and study systems will require substantial scientific coordination. At present, quite a few labs across the world have conducted or regularly conduct experiments or observational studies of predator-prey-parasite interactions. Some of these labs have a primary focus on predator-prey or parasite-host ecology and use a study system as a way of asking fundamental ecological questions. However, many predator-prey-parasite experiments are conducted with an aim towards better pest management in agriculture or other applied outcomes (e.g., Chacón et al. 2008; Chailleux et al. 2017; de Lourdes Ramírez-Ahuja et al.2017). Unfortunately few of these studies share common reporting standards and many suffer from similar experimental design limitations. In addition to placing all data in a publicly accessible repository, we encourage future researchers measuring predator spreader effects to follow these general guidelines:
  1. Report all measurable parasitism outcomes; ideally at least prevalence and prey population density, and intensity if feasible
  2. Measure as many proximate predator effects as is practical (e.g., prey demography, prey physiology, prey immunity, prey space-use or grouping behavior)
  3. Test a range of predation pressures instead of presence/absence of predators
Most studies of predator-parasite interactions report only a single parasite outcome and rarely report prey population density (though studies on agricultural pests are a notable exception to the latter (Kaneko 2006; Agboton et al. 2013; Chailleux et al.2017)). The choice of parasite outcome, typically prevalence or intensity, is at times motivated by underlying theory or parasite biology, but frequently the reasons for the choice are unclear. Measuring intensity is more common in studies of macroparasites, but we suggest that it could be interesting to measure this for microparasites as well (since, for example, predators may shift age or size structure in a way that alters the average burden of infection). We also note that the term “intensity” can denote different types of parasite quantification in different systems (similar to the case for the term “virulence”), so it will be important to clearly define how intensity is being quantified when reporting the results. Mechanisms of predator spreading affect prevalence and intensity in different ways (Richardset al. 2022). For example, if predators have the largest physiological effect on prey already susceptible to infection then individuals who are likely to be infected may become more heavily infected at a higher rate than healthy individuals become newly infected. The combination of effects on prevalence and intensity may also help to identify the specifics of predator spreading mechanisms. We also suggest the reporting of prey population density as an outcome because of the important role it plays in the original healthy herds hypothesis and our general understanding of the predator-prey-parasite interaction, and because this metric can be of particular interest (e.g., when the prey is of conservation concern) (Packer et al.2003; Duffy et al. 2019). If predators increase parasitism but fail to have persistent effects on prey population densities or prey fitness, the predator-spreader interaction would be of limited use for understanding prey population dynamics. Studies on agricultural arthropod pests which manipulate both predators and parasites typically focus on prey density as the key outcome of interest (Kaneko 2006; Vance-Chalcraft et al. 2007; Agboton et al. 2013; Linet al. 2019); these studies often focus on whether predator and parasite effects on pest species are additive, substitutive, antagonistic, or synergistic in order to best accomplish biological control of pest species (Roy et al. 2001; Zhang et al.2015; Lin et al. 2019). Measuring effects on population density does generally require longer-term studies than those simply reporting parasite outcomes, but the amount of additional time required varies substantially with prey life history. The reporting of all three of these outcome variables (prevalence, intensity, and density) will both improve understanding of mechanisms in individual studies and facilitate future synthetic and meta-analytic work on the subject.
In addition to measuring and reporting multiple parasitism outcome variables, we also encourage researchers to measure and report as many proximate predator effects as feasible. These proximate predator effects include many of the intermediary mechanistic steps we detail above: prey demography, prey immune function, and prey space-use behavior. Very few predator-parasite studies report any proximate predator effects but those that do are able to tell the clearest and most convincing stories of predator-spreading mechanisms (e.g., Navarro et al. 2004; Cáceres et al. 2009; Szuroczki & Richardson 2012). If investigating a particular mechanism of predator-spreading, we hold it is essential to measure the proximate predator effects that mediate that mechanism. Moreover, because multiple predator-spreading mechanisms are likely at play in any given system, future research would greatly benefit from casting a wide net of measured proximate predator effects when logistically feasible.
Nearly all predator-parasite studies include just two levels of predation, typically presence/absence or high/low (Richards et al. 2022), but it is well known that reducing a continuous spectrum of a predictor variable to a binary is dangerous for inference (Inouye 2001). What may appear to be a clear positive or negative effect when considered at just two levels may in fact be a complex non-monotonic relationship. Predator-parasite studies that consider a broader range of predation pressure levels have, in fact, found hump-shaped relationships between predation and parasitism (Hawlena et al. 2010), as has been predicted by theory (Holt & Roy 2007). It is also likely that some predator-spreading mechanisms may operate most strongly at different points on the predator-pressure spectrum. For example, behavioral effects may respond strongly to the introduction of predators but weakly to increases in predation pressure thereafter, whereas consumptive effects may respond more linearly with increasing predation pressure. In such a situation, multiple interacting predator-spreader effects may produce unexpectedly non-linear relationships between predation pressure and parasitism over the full spectrum of predation.