Parasitoids
An REM of parasitoid effect sizes detected a statistically significant,
negative, overall effect of predation on parasitoid abundance in prey (z
= -6.919, p < 0.001; Figure 1c), with a smaller amount of
heterogeneity between studies as compared to the analyses of parasite
responses (I2 = 35.47%). While there was evidence of
significant publication bias (đťž˝ = -0.227, p = 0.032), the inclusion of 9
missing positive effect sizes estimated by the trim and fill method did
not eliminate the overall significant negative effect of predators on
parasitoids (z = -4.630, p < 0.001).
DISCUSSION
The healthy herds hypothesis (HHH)
(Packer et al.2003) predicts that predators should have negative effects on parasites
in their prey, but empirical studies testing this hypothesis have
reported a variety of different effects. We hypothesized that this
variation is a result of nuances in predator-prey-parasite interactions,
including transmission strategy of the parasite studied and the type of
predator interaction manipulated. Specifically, we hypothesized that the
negative effect predicted by the HHH would be larger for macroparasites
and parasitoids than for microparasites and would only hold when
consumptive interactions are manipulated and when those predators are
not “predator-spreaders”. Using a meta-analytic approach that
accounted for potential sources of variation in observed
predator-prey-parasite interaction outcomes, we found that the main
effect of predators on parasites in prey differed between parasites and
parasitoids but not between conventional macro- and microparasites, with
a net negative effect only present for parasitoids. Additionally, we
found that interaction type (all vs. non-consumptive), and its subset of
predator-spreader interactions, were most important in predicting the
effect of predators on parasites in prey. These findings provide clear
evidence that the HHH prediction is not universal. The degree to which
it holds in a given system is both parasite- and context-dependent, but
also predictable with limited information.
We observed significant heterogeneity across studies of the HHH
resulting from substantial variation in the magnitude and direction of
the main effect of predators on parasites in prey. We, therefore, sought
to determine if there were factors that explained this variation in
effects. First, we found that the difference between consumptive and
non-consumptive interactions can explain variation in the effect of
predators on parasites, but specific mechanisms of those interactions
are also very important. In studies that measured intensity variables,
the effect size significantly differed between interactions involving
consumptive and non-consumptive interactions, with non-consumptive
interactions having generally more positive effects. This result aligns
with our prediction that consumptive interactions will have more
negative effects on parasites compared with non-consumptive
interactions. We note that our studies involving consumptive
interactions typically were open to all sorts of interactions including
non-consumptive, suggesting that this result may, in fact, be
conservative. Our result for studies measuring prevalence variables
contradicts this finding as consumptive and non-consumptive interactions
were estimated to be nearly identical on average. We suggest that the
difference between these two response variables is an artifact of the
significant residual heterogeneity even in our best fit models. Most of
this variation is likely hidden in unexplored mechanisms within these
studies. Duffyet al. (2019) outlined 7 independent mechanisms whereby
consumption can directly or indirectly impact disease in prey. For
example, predators can selectively prey on uninfected individuals, shift
host population structure toward more susceptible or heavily infected
classes, and suppress competition between hosts allowing them to support
more parasites. Unfortunately few studies provide sufficient information
to assess which mechanisms are at play. Nonetheless, we were able to
directly test this idea by including one of these mechanisms
(predator-spreaders;
(Cáceres et al.2009)) as a moderator variable since researchers typically identified
this attribute of predators in their studies. As expected,
predator-spreader identity was highly important for predicting the
parasite outcome in the prevalence dataset, generally increasing
parasite prevalence. The difference in the number of predator-spreader
effect sizes between prevalence (n = 25) and intensity (n= 0) responses explains why we saw this effect emerge in the prevalence
but not intensity dataset. Ultimately, the lack of universal support for
the HHH is a result of the conflicting negative effects in studies of
typical consumptive interactions versus positive effects in studies of
consumptive predator-spreader interactions and certain non-consumptive
interactions,
Second, unlike predator interaction type, we failed to detect an effect
of parasite type in our analysis. We hypothesized that differences in
the aggregation patterns of micro- and macroparasites would result in
macroparasites having a stronger and more negative response to predator
pressure than microparasites, but found no evidence for a difference
between parasite types in either intensity or prevalence effect sizes
and this variable was generally of less importance for explaining
variation. This lack of an effect may be due to a number of factors.
While one might expect random predation, or predation on infected
individuals, to decrease parasitism more when parasites are aggregated
(Packer et al.2003), the opposite is also true. Gape limited predators, such as many
piscivorous fish and carnivorous snakes
(Nilsson & Brönmark
2000; King 2002) that selectively prey on smaller and younger
individuals may cause population demographics to shift towards larger,
older and more heavily infected hosts
(Dobson 1989; Nilsson
& Brönmark 2000; Byers et al. 2015; Duffy et al. 2019).
Alternatively, our assumption that high aggregation among macroparasites
makes them more vulnerable to predation may be countered by the
existence of significant aggregation in microparasite systems as well
(Lord et al. 1999; Grogan et al. 2016).
Third, while there may not be a significant difference between micro-
and macroparasites we saw a clear difference between parasites and
parasitoids. Even when controlling for publication bias, predators had a
significant negative effect on parasitoids as compared to the lack of
any overall effect on parasites. Our ability to detect a strong
directional effect for parasitoids is perhaps partly due to the
uniformity across the studies in the parasitoid analysis, also supported
by the more limited heterogeneity in the parasitoid REM. The negative
direction of the effect may be due to the fact that consumptive effects
of predators on parasitoids rarely include mechanisms that could produce
positive effects. Predators rarely act in a “spreader” role for
parasitoids in their prey because the larval life-cycle of the
parasitoid is typically interrupted by predation
(Naselli et al.2017). Perhaps most non-consumptive effects of predators on parasitoids
concern free-living adult life stages, which may avoid areas with
predators due to direct intraguild predation of predators on adult
parasitoids (Heimpelet al. 1997; Brodeur & Rosenheim 2000). As a result, it is
conceivable that parasitoids would display the a stronger negative
response to predator addition than other parasitic organisms.
One of the main limitations of this study, as with all quantitative
synthesis, is the selection bias in the field being synthesized. We
detected significant publication bias in the literature in multiple
directions. Particularly, our analysis of prevalence showed a
significant bias towards publication of positive effect sizes, probably
due to the abundance of predator-spreader associated effect sizes. In
the case of parasitoids, however, there was significant evidence of
publication bias for negative effect sizes. While correction for these
biases did not influence qualitative conclusions, their presence does
suggest the need for additional attention to the types of results
published. Besides publication bias in effect sizes, we noted a number
of important imbalances in study characteristics, particularly the lack
of observational studies that inspected non-consumptive effects. We also
found that studies which identified predators as predator-spreaders were
largely limited to studies of microparasite prevalence. This finding
suggests that the empirical dissection of consumptive effect mechanisms
is not only limited to cases that are easy to characterize (like
predator-spreaders), but also limited in taxonomic coverage. Given these
gaps in the literature, we suggest the following priorities for future
work: (i) examining the effect of non-consumptive predator interactions
on parasites in non-manipulative field observations and (ii) further
dissecting the effect of predator-spreaders and other types of
consumptive interactions on both micro- and macroparasites.
Overall, we found that the healthy herds hypothesis is not broadly
supported by the current literature. Instead, the average effect of
predators on parasites in prey varies significantly according to the
type of interaction being studied and whether the focus is on parasites
or parasitoids. Our findings provide the first quantitative analysis
supporting the growing consensus
(Hethcote et
al. 2004; Choisy & Rohani 2006; Holt & Roy 2007; Roy & Holt 2008;
Duffy et al.2019) that predator effects on parasites are context dependent. Our
results further suggest that the mechanistic basis of predator-prey
interactions strongly influences parasite outcomes and that these
effects are predictable.