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Animals, and mammals in particular, vary widely in their “pace of life,” with some species living long lives and reproducing infrequently (slow life histories) and others living short lives and reproducing often (fast life histories). These species also vary in the importance of maternal care in offspring fitness: In some species, offspring are fully independent of their mothers following a brief period of nutritional input, while others display a long period of continued dependence on mothers well after nutritional dependence. Here, we hypothesize that these two axes of variation are causally related to each other, such that extended dependence of offspring on maternal presence leads to the evolution of longer lives at the expense of reproduction. We use a combination of deterministic modeling and stochastic agent-based modeling to explore how empirically observed links between maternal survival and offspring fitness are likely to shape the evolution of mortality and fertility. Each of our modeling approaches leads to the same conclusion: When maternal survival has a strong impact on the survival of offspring and grandoffspring, populations evolve longer lives with less frequent reproduction. Our results suggest that the slow life histories of humans and other primates as well as other long-lived, highly social animals such as hyenas, whales, and elephants are partially the result of the strong maternal care that these animals display. We have designed our models to be readily parameterized with demographic data that are routinely collected by long-term researchers, which will facilitate more thorough testing of our hypothesis. 

Many animals are able to perform recognition feats that astound us—such as a rodent recognizing kin it has never met. Yet in other contexts, animals appear clueless as when reed warblers rear cuckoo chicks that bear no resemblance to their own species. Failures of recognition when it would seem adaptive have been especially puzzling. Here, we present a simple tug-of-war game theory model examining how individuals should optimally invest in affecting the accuracy of discrimination between desirable and undesirable recipients. In the game, discriminating individuals (operators) and desirable and undesirable recipients (targets and mimics, respectively) can all invest effort into their own preferred outcome. We demonstrate that stable inaccurate recognition will arise when undesirable recipients have large fitness gains from inaccurate recognition relative to the pay-offs that the other two parties receive from accurate recognition. The probability of accurate recognition is often determined by just the relative pay-offs to the desirable and undesirable recipients, rather than to the discriminator. Our results provide a new lens on long-standing puzzles including a lack of nepotism in social insect colonies, tolerance of brood parasites and male birds caring for extra-pair young in their nests, which our model suggests should often lack accurate discrimination. This article is part of the theme issue ‘Signal detection theory in recognition systems: from evolving models to experimental tests'. 

Social interactions are mediated by recognition systems, meaning that the cognitive abilities or phenotypic diversity that facilitate recognition may be common targets of social selection. Recognition occurs when a receiver compares the phenotypes produced by a sender with a template. Coevolution between sender and receiver traits has been empirically reported in multiple species and sensory modalities, though the dynamics and relative exaggeration of traits from senders versus receivers have received little attention. Here, we present a coevolutionary dynamic model that examines the conditions under which senders and receivers should invest effort in facilitating individual recognition. The model predicts coevolution of sender and receiver traits, with the equilibrium investment dependent on the relative costs of signal production versus cognition. In order for recognition to evolve, initial sender and receiver trait values must be above a threshold, suggesting that recognition requires some degree of pre-existing diversity and cognitive abilities. The analysis of selection gradients demonstrates that the strength of selection on sender signals and receiver cognition is strongest when the trait values are furthest from the optima. The model provides new insights into the expected strength and dynamics of selection during the origin and elaboration of individual recognition, an important feature of social cognition in many taxa. This article is part of the theme issue ‘Signal detection theory in recognition systems: from evolving models to experimental tests’.