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Learning is a taxonomically widespread process by which animals change their behavioural responses to stimuli as a result of experience. In this way, it plays a crucial role in the development of individual behaviour and underpins substantial phenotypic variation within populations. Nevertheless, the impact of learning in social contexts on evolutionary change is not well understood. Here, we develop game theoretical models of competition for resources in small groups (e.g. producer–scrounger and hawk–dove games) in which actions are controlled by reinforcement learning and show that biases in the subjective valuation of different actions readily evolve. Moreover, in many cases, the convergence stable levels of bias exist at fitness minima and therefore lead to disruptive selection on learning rules and, potentially, to the evolution of genetic polymorphisms. Thus, we show how reinforcement learning in social contexts can be a driver of evolutionary diversification. In addition, we consider the evolution of ability in our games, showing that learning can also drive disruptive selection on the ability to perform a task. [ABSTRACT FROM AUTHOR]

Aim: Geographical patterns of migrant species arrival have been little studied, despite their relevance to global change responses. Here, we quantify continent‐wide interspecific variation in spatiotemporal patterns of spring arrival of 30 common migrant bird species and relate these to species characteristics and environmental conditions. Location: Europe. Time Period: 2010–2019. Major Taxa Studied: Birds, 30 species. Methods: Using citizen science data from EuroBirdPortal, we modelled arrival phenology for 30 Afro‐Palaearctic migrant species across Europe to extract start and duration of species arrival at a 400 km square resolution. We related inter and intraspecific variation in arrival and duration to species characteristics and temperature at the start of the growing season (green‐up). Results: Spatial variation in start of arrival times indicates that it took, on average, 1.6 days for the leading migratory front to move northwards by 100 km (range: 0.6–2.5 days). There was a major gradient in arrival phenology, from species which arrived earlier, least synchronously, in colder temperatures and progressed slowly northwards to species which arrived later, most synchronously and in warmer temperatures and advanced quickly through Europe. The slow progress of early arrivers suggests that temperature limits their northward advance; this group included Aerial Insectivores and species wintering north of the Sahel. For the late arrivers, which included species wintering further south, seasonal resource availability in Africa may delay their arrival into Europe. Main Conclusions: We found support for the green‐wave hypothesis applying widely to migratory landbirds. Species arrival phenologies are linked to ecological differences between taxa, such as diet, and wintering location. Understanding these differences informs predictions of species' sensitivity to global change. Publishing these arrival phenologies will facilitate further research and have additional conservation benefits such as informing designation of hunting seasons. Our methods are applicable to any taxa with repeated occurrence data across large scales. [ABSTRACT FROM AUTHOR]

Foraging is risk sensitive if choices depend on the variability of returns from the options as well as their mean return. Risk‐sensitive foraging is important in behavioural ecology, psychology and neurophysiology. It has been explained both in terms of mechanisms and in terms of evolutionary advantage. We provide a critical review, evaluating both mechanistic and evolutionary accounts. Some derivations of risk sensitivity from mechanistic models based on psychophysics are not convincing because they depend on an inappropriate use of Jensen's inequality. Attempts have been made to link risk sensitivity to the ecology of a species, but again these are not convincing. The field of risk‐sensitive foraging has provided a focus for theoretical and empirical work and has yielded important insights, but we lack a simple and empirically defendable general account of it in either mechanistic or evolutionary terms. However, empirical analysis of choice sequences under theoretically motivated experimental designs and environmental settings appears a promising avenue for mapping the scope and relative merits of existing theories. Simply put, the devil is in the sequence. [ABSTRACT FROM AUTHOR]

An animal's behaviour can influence many variables, such as its energy reserves, its risk of injury or mortality, and its rate of reproduction. To identify the optimal action in a given situation, these various effects can be compared in the common currency of reproductive value. While this idea has been widely used to study trade‐offs between pairs of variables, e.g. between energy gain versus survival, here we present a unified framework that makes explicit how these various trade‐offs fit together. This unification covers a wide range of biological phenomena, highlighting similarities in their logical structure and helping to identify knowledge gaps. To fill one such gap, we present a new model of foraging under the risk of predation and damage accumulation. We conclude by discussing the use and limitations of state‐dependent optimisation theory in predicting biological observations. [ABSTRACT FROM AUTHOR]