Coevolutionary interactions between farmers and mafia induce host acceptance of avian brood parasites

Brood parasites exploit their host in order to increase their own fitness. Typically, this results in an arms race between parasite trickery and host defence. Thus, it is puzzling to observe hosts that accept parasitism without any resistance. The ‘mafia’ hypothesis suggests that these hosts accept parasitism to avoid retaliation. Retaliation has been shown to evolve when the hosts condition their response to mafia parasites, who use depredation as a targeted response to rejection. However, it is unclear if acceptance would also emerge when ‘farming’ parasites are present in the population. Farming parasites use depredation to synchronize the timing with the host, destroying mature clutches to force the host to re-nest. Herein, we develop an evolutionary model to analyse the interaction between depredatory parasites and their hosts. We show that coevolutionary cycles between farmers and mafia can still induce host acceptance of brood parasites. However, this equilibrium is unstable and in the long-run the dynamics of this host–parasite interaction exhibits strong oscillations: when farmers are the majority, accepters conditional to mafia (the host will reject first and only accept after retaliation by the parasite) have a higher fitness than unconditional accepters (the host always accepts parasitism). This leads to an increase in mafia parasites’ fitness and in turn induce an optimal environment for accepter hosts.


MAC, 0000-0002-4895-954X
Brood parasites exploit their host in order to increase their own fitness. Typically, this results in an arms race between parasite trickery and host defence. Thus, it is puzzling to observe hosts that accept parasitism without any resistance. The 'mafia' hypothesis suggests that these hosts accept parasitism to avoid retaliation. Retaliation has been shown to evolve when the hosts condition their response to mafia parasites, who use depredation as a targeted response to rejection. However, it is unclear if acceptance would also emerge when 'farming' parasites are present in the population. Farming parasites use depredation to synchronize the timing with the host, destroying mature clutches to force the host to re-nest. Herein, we develop an evolutionary model to analyse the interaction between depredatory parasites and their hosts. We show that coevolutionary cycles between farmers and mafia can still induce host acceptance of brood parasites. However, this equilibrium is unstable and in the long-run the dynamics of this host-parasite interaction exhibits strong oscillations: when farmers are the majority, accepters conditional to mafia (the host will reject first and only accept after retaliation by the parasite) have a higher fitness than unconditional accepters (the host always accepts parasitism). This leads to an increase in mafia parasites' fitness and in turn induce an optimal environment for accepter hosts.  Figure 1. Game-tree for the host-parasite interaction. The host lays b h eggs in a clutch, which may become parasitized (at a cost c p ) or depredated. Depredation forces the host to re-nest, costing c s . Hosts may reject (at cost c r ) or accept the parasitic egg and incur nestling cost c n . The parasite gains the accepted egg.
several stages. Initially, the host lays b h eggs in a clutch that can either be parasitized or depredated by a parasite. After parasitism, the host may accept the parasitic egg incurring a parasitism cost c p and a nestling cost c n . Alternatively, the host may reject the parasitic egg at a cost c r (the rejection cost encompasses a host's ability to recognize and eject the foreign egg) and risk retaliation. If the nest is depredated, either by a farmer (before parasite eggs are laid) or by a retaliator (after her egg was rejected), hosts are forced to re-nest at a cost c s . For the evolutionary model, we consider four types of hosts behaviours: two unconditional behaviours, which are to always accept or reject parasitism, and two conditional behaviours, which are to accept only after depredation or only after retaliation. The conditional behaviours mimic a host's ability to learn and change as a response to repeated interactions with the parasite [21]. For the parasites, we consider three types of behaviour: (i) non-depredating parasites; (ii) mafia retaliators that depredate the nest after rejection. They re-parasitize the same host, and they destroy all future clutches unless acceptance occurs; and (iii) farmers depredate the host's nest in the first stage to create an opportunity for parasitism. For simplicity, we neglect the possibility of parasites that engage in both farming and retaliation simultaneously-in particular, farmers do not return to the host after parasitism.
The fitness of each type is calculated at the end of a season. Non-depredatory parasites N lay a total of β N eggs per season, whereas mafia and farming parasites, M and F, lay a total of β M and β F eggs, respectively. We assume β M ≤ β F and β M ≤ β N to incorporate that depredation may be costly for the mafia parasite (for example, because of cognitive and energy costs, [16]). We do not presume any particular relationship between between β F and β N . The case β F < β N may be justified because farming requires more efforts than non-depredating behaviour. On the other hand, β F ≥ β N may be justified because farmers are able to create additional parasitism opportunities. The three types of parasites have frequencies denoted by x N , x M and x F , such that x N + x M + x F = 1. Likewise, the four types of hosts, accepters A, conditional to mafia CM, conditional to depredation CD, and rejecters R have respective frequencies y A , y CM , y CD and y R such that y A + y CM + y CD + y R = 1.
We calculate the average fitness of each parasite by considering the average number of accepted eggs reared by the host. To this end, we also need to consider the average number of laid eggs per host interaction. Non-depredatory parasites lay a single egg per parasitized host nest, which only becomes accepted if the host happens to be an accepter. For a mafia parasite, the average number of eggs laid per parasitized host nest is y A + 2y CM + 2y CD + 2y R = 2 − y A , whereas the expected number of accepted eggs per parasitized host nest is y A + y CM + y CD . Finally, farmer parasites lay a single egg per nest, and the expected number of accepted eggs per parasitized host is y A + y CD . The average fitness of a parasite is thus given by π N = y A · β N , To derive the average fitness of the hosts, we first need to calculate the probability with which a host is visited by each parasite type. Farmers and non-depredatory parasites visit β F and β N different hosts, respectively, whereas mafia parasites require on average β M /(2 − y A ) different hosts. Thus, the probability that a foreign egg in a host's nest comes from a non-depredatory parasite, farmer, or mafia parasite is proportional to , respectively. As a consequence, the average fitness of a parasitized host becomes To model the population dynamics, we assume that the frequencies of each type change according to the replicator equation,ẋ i = x i (π i −π) for the parasites withπ = j π j x j andẏ i = y i (π i −π) for the hosts withπ = j π j y j [22]. That is, strategies that yield a fitness above the population average are expected to spread, whereas strategies that yield a fitness below the population average decrease over time.

Results and discussion
We fist explore the strategy dynamics of two different situations, one which assumes β M < β F = β N and the other β M < β F < β N , as shown in figure 2. When farmers lay at least as many eggs as nondepredating parasites, we observe coevolutionary cycles between mafia and farmer parasites, and between accepter hosts (always accepting) and conditional accepter hosts (reject first and accept after retaliation). Conversely, when farmers have a disadvantage compared with the N type, we observe coevolutionary cycles between non-depredatory parasites and mafia parasites, and between accepter hosts and conditional accepter hosts. Both examples illustrate that accepter hosts can have a temporary fitness advantage over other host types.
These evolutionary outcomes can be understood explicitly. First, unconditional rejection is never optimal because conditional behaviour leads to a higher fitness, π CM ≥ π R . Similarly, given that c r < c n , it is better for hosts to condition their behaviours on retaliation rather than on depredation, as π CM ≥ π CD . Hence, the abundance of CD is expected to drop in any mixed population, and eventually y CD ≈ 0. Furthermore, the laid eggs per season determine whether farmers or non-depredating parasites gain more offspring. When β F ≥ β N , farming is more profitable, π F ≥ π N , and we can simplify our model to a parasite population composed of just depredators (mafia and farmers) and a host population composed of unconditional accepters and accepters conditional to mafia. For this reduced system, the replicator dynamics simplifies toẋ Where the remaining abundances are x F = 1 − x M and y CM = 1 − y A . For c r > c n , the dynamics has no interior equilibrium and mafia behaviour will vanish. For c r < c n , the dynamics has an equilibrium (x * M , y * A ) in the interior of the state space, which is from π M = π F and π A = π CM ,  (a) When β F = β N , we find oscillations between mafia and farmer parasites, and between conditional accepter and accepter hosts. (b) When β F < β N , the non-predatory parasites displace the farmers, leading to oscillations between mafia and non-depredatory parasites, and between conditional to mafia and unconditional accepter hosts. (c) For a simplified model with only two strategies present in each population, we can show the existence of a mixed equilibrium. However, that equilibrium is unstable; nearby initial populations follow cycles with increasing amplitude. The insets shows the fitness table arising from a single host-parasite interaction: however, owing to the nonlinear interaction, this cannot be directly interpreted as a game-theoretical pay-off matrix, see equations (2.1) and (2.2).
However, this equilibrium is unstable (appendix A), and in the long-run the dynamics of this hostparasite interaction exhibits strong oscillations (as depicted in figure 2). If most parasites engage in farming, hosts that condition acceptance to just after retaliation have a higher fitness than unconditional accepters. As the frequency of conditional accepters increases, the mafia parasites' fitness supersedes the fitness of farmers. In turn, as mafia parasites increase in frequency, they induce an optimal environment for unconditional accepter hosts, eventually leading the parasite population back to the farmer strategy. Interestingly, one can observe exactly the same dynamics when exploring the competition between non-depredating parasites and mafia parasites [11]. Intuitively, for explaining the evolution of acceptance among hosts, the distinction between farming behaviour and non-depredating parasites becomes irrelevant. Both types evoke the same response among hosts, making it optimal for hosts to reject first attempts. Thus, our model suggests that if accepting behaviour is observed in a host population, it needs to be owing to the presence of retaliating parasites. So far we have been able to show that even when both farmers and mafia interact, hosts will evolve to accept some degree of parasitism. While our theory predicts cyclical dynamics, it is still debated whether host-parasite interactions display any cycles at all [1,4,23,24]. In our model, there must be at least two types of hosts and parasites available for cycles to occur, which in turn depends on the costs incurred when hosts and parasites interact. So far we have assumed that the costs of raising a nestling c n outweigh the rejection cost c r (i.e. the cost for the host's ability to recognize and eject a foreign egg without errors). To address whether accepter hosts are able to evolve when rejection costs are low, our numerical examples so far have assumed that the host incurs no extra costs when rejecting a foreign egg. Once we relax this assumption and consider the case c r > 0, we identified that cycles tend to only occur when rejection costs are less than nestling costs, c r < c n (see figure 3a, for an example with c r > 0). In that case, one can show from the fitness equations in (2.2) that for hosts it is optimal always to accept parasitism, as π A ≥ π CM , π A ≥ π CD and π A ≥ π R . The dynamics leads to a steady state in which hosts accept, and in which the parasite with the highest number of eggs β i succeeds such as the example shown in figure 3b. These regimes resemble the two competing hypothesis put forward to explain the lack of consensus seen experimentally: the first is the evolutionary lag hypothesis, which states that hosts would naturally evolve to defend against the current parasites which will then bring about change for the parasite who must evolve to survive. The problem is time-we may not catch the next step of the arms race, because there is a lag in their response and insufficient amount of time has passed for the adaptation to occur. The second is the equilibrium hypothesis, which states that what we observe is a stable state where hosts are behaving as they should [3,4]. Thus, our model suggests that if we assume low rejection costs relative to nestling costs, cycles will exist leading to an evolutionary lag (experimentally, it can be shown that raising a parasite's egg incurs fitness loss for many hosts [2,4,23]). This prediction is in line with experimental results of a system where the rejection costs of a host are zero [24]. However, once rejection costs exceed nestling costs, the outcome will depend on the overall costs and competition within species. Future empirical work could aim to disentangle the costs of the hosts, and generalize the patterns observed in the model to the natural systems.
The general dynamical pattern discussed herein is consistent for both the simplified and complex model. However, these models proposed still involve a number of simplifications. In particular, we did not explicitly keep track of the time within a season (which is certainly an important variable to model the effects of farming). Instead, the advantages of farming were incorporated indirectly, by giving farmers additional opportunities to parasitize hosts. However, we expect our basic insights to be robust also for models that include more detail: depending on environmental conditions, farming may be a profitable strategy for brood parasites, but it does not induce hosts to accept parasitism. By contrast, acceptance can readily evolve when parasites engage in retaliatory behaviour, and if depredation is specifically targeted at hosts who have previously rejected the parasite's eggs. Our model also highlights that there may be no optimal behaviour in these multispecies systems. Instead, hosts and parasites will typically remain in a recurring process of mutual adaptation.