Like all examples of conditional reciprocity (‘I help you if you helped me’), egg trading has a bootstrapping problem (Andr´e, 2014). Conditionality evolves to target helping behaviour towards individuals who are likely to reciprocate (Taborsky et al., 2016). Consequently, it is predicted to be under positive frequency-dependent selection. If conditionality is rare in a population, then helping a partner usually has no influence on the partner’s behaviour. Target- ing helping behaviour towards partners who help is pointless in this case: the partner would help (or not) regardless. On the other hand, if conditionality is common, then helping partners who reciprocate is an efficient way of max- imising the help an individual receives.
Recent theory has emphasised that separate mechanisms are required to ex- plain the initial origins of conditional reciprocity, which perhaps explains why it is so rare (Andr´e, 2014; 2015; Bernard et al., 2016). Few plausible mech- anisms are known, however. In egg-trading fishes, I see three processes that might kick-start conditionality. They are not mutually exclusive, and none has been formally modelled.
First, egg release could provide information about a mate’s fecundity (Lan- dolfa, 2002). Fecund partners might be preferred as fathers due to indirect genetic benefits, which could select for conditional egg release. One problem with this explanation is that other traits, like body size, might be more reliable indicators of long-term fecundity than any isolated release of eggs. Indeed, size- assortative mating is common in egg-trading fishes (Fischer & Petersen, 1987).
Second, egg release might indicate that an individual has not mated recently, and consequently has an adequate supply of sperm. Egg-trading fishes release gametes into the water column, so there are no second chances if fertilisation is incomplete. Preferentially releasing eggs to partners that have not mated recently might increase the chances that the eggs are fertilised.
Third, ancestral mating may have already involved multiple bouts of egg re- lease, but without any conditionality. Staggering the release of eggs may have originally served another purpose, such as increasing the surface area of egg
that has run out of eggs would cost time, which could be better spent courting a new partner that might still have eggs. Time costs could be exacerbated by the high population-level synchronicity of mating in some egg-trading species (Friedman & Hammerstein, 1991).
Many other cases of conditional reciprocity occur in species that live in kin- based social groups (e.g. food-sharing in vampire bats), in which helping may be targeted to both kin and non-kin (Wilkinson et al., 2016; Taborsky et al., 2016). Helping in these species may have evolved originally as an unconditional behaviour towards close kin (e.g. between mothers and their daughters). Older individuals can easily infer kin relationships behaviourally (e.g. the bat being suckled by your sister is probably a niece or nephew). However, younger individuals might evolve to infer kinship from the helping behaviour itself. This would select for a rudimentary kind of conditionality (‘If you help me then you are kin, so I help you later’) that could be elaborated into more nuanced reciprocity, including between non-kin, over evolutionary time.
Concluding remarks
All theory must compromise between simplicity, generality, and precision. The spectacular diversity of the natural world means that biologists feel this tension particularly keenly. It is present throughout this thesis, where I have sought unifying models to explain and quantify mating system evolution across a be- wildering variety of taxa. Many nuances are unfortunately, but inevitably, lost in this search for broad patterns and general tools.
The ‘simplicity’ of models should be understood as relative to human cognitive capabilities. We are all limited in our ability to visualise and process models of the world. Models that are too complex do not forward our understanding of the underlying phenomena. This is especially important in organismal biology, where accurate prediction is rarely a feasible scientific aim, and so explanatory models and rule-of-thumb predictions play a relatively greater role.
Happily, a model’s simplicity also depends on the conceptual and computa- tional tools available to interpret it, as well as the educational background of
its users. The rise of statistical software has greatly increased the range of models that are accessible to applied scientists, because only the software’s interface and outputs need be simple to users, and mathematical details can often be left to specialists. Similarly, numerical and simulation models have provided new ways to interrogate and visualise complex ideas. Many models that appeared formidable to their first users are now routinely integrated into scientific practice. I hope that some of the quantitative tools in this thesis will be so lucky.
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