Development in many organisms appears to show evidence of sensitive windows—periods or stages in ontogeny in which individual experience has a particularly strong influence on the phenotype (compared to other periods or stages). Despite great interest in sensitive windows from both fundamental and applied perspectives, the functional (adaptive) reasons why they have evolved are unclear. Here we outline a conceptual framework for understanding when natural selection should favour changes in plasticity across development. Our approach builds on previous theory on the evolution of phenotypic plasticity, which relates individual and population differences in plasticity to two factors: the degree of uncertainty about the environmental conditions and the extent to which experiences during development (‘cues’) provide information about those conditions. We argue that systematic variation in these two factors often occurs within the lifetime of a single individual, which will select for developmental changes in plasticity. Of central importance is how informational properties of the environment interact with the life history of the organism. Phenotypes may be more or less sensitive to environmental cues at different points in development because of systematic changes in (i) the frequency of cues, (ii) the informativeness of cues, (iii) the fitness benefits of information and/or (iv) the constraints on plasticity. In relatively stable environments, a sensible null expectation is that plasticity will gradually decline with age as the developing individual gathers information. We review recent models on the evolution of developmental changes in plasticity and explain how they fit into our conceptual framework. Our aim is to encourage an adaptive perspective on sensitive windows in development.

Models and experiments on adaptive decision-making typically consider highly simplified environments that bear little resemblance to the complex, heterogeneous world in which animals (including humans) have evolved. These studies reveal an array of so-called cognitive biases and puzzling features of behaviour that seem irrational in the specific situation presented to the decision-maker. Here we review an emerging body of work that highlights spatiotemporal heterogeneity and autocorrelation as key properties of most real-world environments that may help us understand why these biases evolved. Ecologically rational decision rules adapted to such environments can lead to apparently maladaptive behaviour in artificial experimental settings. We encourage researchers to consider environments with greater complexity to understand better how evolution has shaped our cognitive systems.

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In the past decade there has been a profusion of studies highlighting covariation between individual differences in stress physiology and behavioural profiles, here called personalities. Such individual differences in ways of coping with stress are relevant both in biomedicine, since different personalities may experience a different stress and disease vulnerability, and in behavioural ecology, since their adaptive value and evolutionary maintenance are the subject of debate. However, the precise way in which individual stress differences and personalities are linked is unclear. Here we provide an updated overview of this covariation across different species and taxa, consider its functional significance and present working hypotheses for how behavioural and physiological responses to stress might be causally linked, affecting life-history traits such as dispersal and life-span.

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Most research in biology is empirical, yet empirical studies rely fundamentally on theoretical work for generating testable predictions and interpreting observations. Despite this interdependence, many empirical studies build largely on other empirical studies with little direct reference to relevant theory, suggesting a failure of communication that may hinder scientific progress. To investigate the extent of this problem, we analyzed how the use of mathematical equations affects the scientific impact of studies in ecology and evolution. The density of equations in an article has a significant negative impact on citation rates, with papers receiving 28% fewer citations overall for each additional equation per page in the main text. Long, equation-dense papers tend to be more frequently cited by other theoretical papers, but this increase is outweighed by a sharp drop in citations from nontheoretical papers (35% fewer citations for each additional equation per page in the main text). In contrast, equations presented in an accompanying appendix do not lessen a paper's impact. Our analysis suggests possible strategies for enhancing the presentation of mathematical models to facilitate progress in disciplines that rely on the tight integration of theoretical and empirical work.impact factor | mathematical formula | mathematical literacy | theoretical biology T he efficient exchange of new findings and insights between empirical and theoretical approaches is critical to a range of scientific disciplines, including nuclear physics (1), physical chemistry (2), neuroscience (3), epidemiology (4), ecology (5), and atmospheric science (6). In evolutionary biology, for example, the integration of empirical and theoretical work is essential for understanding how natural selection shapes organisms and their interactions (7-16). Most biological research is empirical, yet empirical studies rely fundamentally on theory for generating testable predictions and interpreting observations. In return, empirical data provide both tests of established theory and guidance in the development of new models.However, the importance of presenting theory in sufficient technical detail can sometimes conflict with the need to communicate the essence of a model in a clear, accessible manner. Concise and precise description of the structure of a mathematical model demands the use of equations, but such technical details might deter a broad audience of scientists doing largely empirical research. A cursory reading of the biological literature reveals that many empirical studies build largely on other empirical studies, with little direct reference to relevant theory. This observation suggests a breakdown of communication that may impede scientific progress.To explore the extent of this problem, we systematically investigated how the use of mathematical equations affects the scientific impact of studies in ecology and evolution. We examined the use of equations and obtained citation data for all papers (total n = 649; Dataset S1) published in 1998...

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