No abstract

Evolutionary questions regarding aging address patterns of within-individual change in traits during a lifetime. However, most studies report associations between age and, for example, reproduction based on cross-sectional comparisons, which may be confounded with progressive changes in phenotypic population composition. Unbiased estimation of patterns of age-dependent reproduction (or other traits) requires disentanglement of within-individual change (improvement, senescence) and between-individual change (selective appearance and disappearance). We introduce a new statistical model that allows patterns of variance and covariance to differ between levels of aggregation. Our approach is simpler than alternative methods and can quantify the relative contributions of within- and between-individual changes in one framework. We illustrate our model using data on a long-lived bird species, the oystercatcher (Haematopus ostralegus). We show that for different reproductive traits (timing of breeding and egg size), either within-individual improvement or selective appearance can result in a positive association between age and reproductive traits at the population level. Potential applications of our methodology are manifold because within- and between-individual patterns are likely to differ in many biological situations.

Species' responses to environmental changes such as global warming are affected not only by trends in mean conditions, but also by natural and human-induced environmental fluctuations. Methods are needed to predict how such environmental variation affects ecological and evolutionary processes, in order to design effective strategies to conserve biodiversity under global change. Here, we review recent theoretical and empirical studies to assess: (1) how populations respond to changes in environmental variance, and (2) how environmental variance affects population responses to changes in mean conditions. Contrary to frequent claims, empirical studies show that increases in environmental variance can increase as well as decrease long-term population growth rates. Moreover, environmental variance can alter and even reverse the effects of changes in the mean environment, such that even if environmental variance remains constant, omitting it from population models compromises their ability to predict species' responses to changes in mean conditions. Drawing on theory relating these effects of environmental variance to the curvatures of population growth responses to the environment, we outline how species' traits such as phylogenetic history and body mass could be used to predict their responses to global change under future environmental variability.

Summary1. Ecologists and many evolutionary biologists relate the variation in physiological, behavioural, life-history, demographic, population and community traits to the variation in weather, a key environmental driver. However, identifying which weather variables (e.g. rain, temperature, El Niño index), over which time period (e.g. recent weather, spring or year-round weather) and in what ways (e.g. mean, threshold of temperature) they affect biological responses is by no means trivial, particularly when traits are expressed at different times among individuals.2. A literature review shows that a systematic approach for identifying weather signals is lacking and that the majority of studies select weather variables from a small number of competing hypotheses that are founded on unverified a priori assumptions. This is worrying because studies that investigate the nature of weather signals in detail suggest that signals can be complex. Using suboptimal or wrongly identified weather signals may lead to unreliable projections and management decisions. 3. We propose a four-step approach that allows for more rigorous identification and quantification of weather signals (or any other predictor variable for which data are available at high temporal resolution), easily implementable with our new R package 'climwin'. We compare our approach with conventional approaches and provide worked examples. 4. Although our more exploratory approach also has some drawbacks, such as the risk of overfitting and bias that our simulations show can occur at low sample and effect sizes, these issues can be addressed with the right knowledge and tools. 5. By developing both the methods to fit critical weather windows to a wide range of biological responses and the tools to validate them and determine sample size requirements, our approach facilitates the exploration and quantification of the biological effects of weather in a rigorous, replicable and comparable way, while also providing a benchmark performance to compare other approaches to.