Selective predation
Predators select their prey along a number of axes that can influence
parasite loads and prevalences in the prey population. The healthy herds
hypothesis was initially conceived on the basis that preferential
predation on infected prey would lead to a decrease in parasitism at the
population level (Hudson et al. 1992; Packer et al. 2003).
It is reasonable to expect that infected individuals will often be more
easily captured and eaten by predators (Hudson et al. 1992;
Johnson et al. 2006). However, predators sometimes preferentiallyavoid consuming infected prey (Flick et al. 2016) and in
their original formation of the healthy herd hypothesis, Packer et al.
(2003) noted that this type of selective predation should cause
predators to increase parasitism in their prey.
Predators also select prey in a variety of other ways, leading to
effects of consumption on disease that are more indirect but still
potentially important. For example, many predators prey preferentially
on particular sizes or ages of prey (Price 1975; Nilsson & Brönmark
2000; King 2002), and prey sizes/ages commonly differ in their levels of
infection (Dobson 1989). These intersecting patterns can result in
functional predator-spreading via changes in prey population demography.
For example, large intertidal snails (Littorina littorea ) are far
more likely to carry trematode infections than their smaller (and
younger) conspecifics, but these larger snails are also far less likely
to be preyed on by shell-breaking predators such as crabs (Byerset al. 2015). As a result, predator preference for snail traits
not directly related to infection resulted in much higher levels of
parasitism in areas subjected to higher predation pressure (Byerset al. 2015), similar to the red curve in Fig. 3a.
This type of selective predation predator-spreading requires a clear
predation preference (e.g., due to innate or learned behavioral
preferences and/or biomechanics) that is negatively correlated with the
infection rates in prey (as in the red curves in Fig. 3). Size/age may
be the most straightforward version of this pattern, as in the case of
the crab-snail-trematode system discussed above (Byers et al.2015) and systems where prey are attacked by a gape-limited predator and
a parasite that is more likely to infect larger individuals (as in the
case of the Chaoborus -Daphnia -fungal parasite system
(Cáceres et al. 2009)). However, many other possibilities for
traits where there might be a negative correlation between infection
rates and predation risk exist, such as sex (Gwynne 1987; Acharya 1995;
McCurdy et al. 1998; Reimchen & Nosil 2001; Lodé et al.2004; Krasnov et al. 2005; Harrison et al. 2010),
reproductive status (Tait et al. 2008; VanderWaal & Ezenwa
2016), prey food choice (Kester & Barbosa 1994; Geervliet et al.1996; Garvey et al. 2020a, b), species assemblage (de Rijket al. 2013), and personality (Kortet et al. 2010). In any
system where a trait increases the risk of predation but decreases the
risk of parasitism, we expect predator spreading; conversely if there is
a positive relationship between the trait and both infection risk and
predation risk (as in the blue curves in Fig. 3), we expect predation to
have a healthy herds effect.
Much of this logic assumes that the prey classes that are selectively
removed by predators simply disappear without any other effects on prey
population dynamics. However, the removal of large classes of a
population can have immense effects on the growth, development, and
reproduction rates of other groups which can, in turn, influence
infection. For example, the loss of large bodied prey may make resources
available for smaller or younger individuals, increasing their growth,
maturation, and reproduction rates (Abrams & Rowe 1996; Relyea 2007),
potentially influencing population level parasitism rates in unexpected
ways, especially since susceptibility to infection commonly changes with
age, as reviewed in Ben-Ami (2019). Moreover, in addition to the
potential for these ecological dynamics to impact disease, rapid
evolution in response to a predator may also promote parasitism,
particularly if there is a tradeoff between resistance to predation and
parasitism (Buss & Hua 2018).
As selective predators and unevenly distributed parasites are both
ubiquitous, much additional experimental work is required to understand
the interaction between these two processes. We encourage both
predator-prey ecologists and disease ecologists to look at their study
system from the alternative perspective and identify any potentially
interacting patterns of parasite and predator selectivity. There are
also likely extensive datasets on selective predation by human hunters
with which these questions could be addressed (e.g. Chronic Wasting
Disease in white tailed deer (Rivera et al. 2019)). This area
would also benefit greatly from the combined usage of dynamical
population modeling to make predictions for the outcomes of selective
predation in a system and manipulative experiments to test these
predictions (Box 3).