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1. Most scenarios for future climate change predict increased variability and thus increased frequency of extreme weather events. To predict impacts of climate change on wild populations, we need to understand whether this translates into increased variability in demographic parameters, which would lead to reduced population growth rates even without a change in mean parameter values. This requires robust estimates of temporal process variance, for example in survival, and identification of weather covariates linked to interannual variability. 2. The European shag Phalacrocorax aristotelis (L.) shows unusually large variability in population size, and large-scale mortality events have been linked to winter gales. We estimated first-year, second-year and adult survival based on 43 years of ringing and dead recovery data from the Isle of May, Scotland, using recent methods to quantify temporal process variance and identify aspects of winter weather linked to survival. 3. Survival was highly variable for all age groups, and for second-year and adult birds process variance declined strongly when the most extreme year was excluded. Survival in these age groups was low in winters with strong onshore winds and high rainfall. Variation in first-year survival was not related to winter weather, and process variance, although high, was less affected by extreme years. A stochastic population model showed that increasing process variance in survival would lead to reduced population growth rate and increasing probability of extinction. 4. As in other cormorants, shag plumage is only partially waterproof, presumably an adaptation to highly efficient underwater foraging. We speculate that this adaptation may make individuals vulnerable to rough winter weather, leading to boom-and-bust dynamics, where rapid population growth under favourable conditions allows recovery from periodic large-scale weather-related mortality. 5. Given that extreme weather events are predicted to become more frequent, species such as shags that are vulnerable to such events are likely to exhibit stronger reductions in population growth than would be expected from changes in mean climate. Vulnerability to extreme events thus needs to be accounted for when predicting the ecological impacts of climate change.

Many seabirds in the North Sea feed on lesser sandeelsAmmodytes marinusduring the breeding season. Unprecedented breeding failures were recorded at many seabird colonies on the east coast of Britain in 2004. We used demographic, dietary and behavioural data from a long-term study of a colony of common guillemotsUria aalge, the most abundant seabird species in the North Sea, to set the 2004 season in context. Birds at this colony showed greatly reduced breeding success and those chicks that did survive left the colony in very poor condition. The main prey item fed to chicks in 2004 was spratSprattus sprattusrather than sandeels, and parents increased the amount of time spent foraging, frequently leaving chicks unattended in order to maintain a normal feeding rate. The calculated daily food intake of chicks derived from these values did not differ markedly from previous years and therefore the magnitude of the impact on chick growth and breeding success appeared disproportionately large. However, nutrient analyses of fish collected from birds in 2004 revealed them to be of significantly lower energy value than expected. Poor food quality therefore appeared to be the proximate cause of seabird breeding failure in 2004 giving support to the ’junk-food’ hypothesis. Single-prey loaders such as guillemots will be particularly sensitive to reductions in the energy value of food items. The reasons for the poor fish condition in this part of the North Sea are currently unknown, but the results provide further evidence of major changes in the North Sea food web.

Seabirds are undergoing drastic declines globally and spend the non-breeding season at sea, making it challenging to study the drivers of their survival. Harsh weather and changes in climate conditions can have large impacts on seabird population dynamics through increased mortality. The intensity and persistence of extreme events are forecasted to increase with global warming. As shared conditions can induce population synchrony, multi-population studies of key demographic parameters are imperative to explore the influence of climate change. We used long-term mark-recapture data and position data to determine non-breeding stop-over areas of 5 Atlantic puffin (Fratercula arctica) populations over a latitudinal gradient in the Northeast Atlantic (56°11’–70°23’ N). We investigated synchrony in adult survival in relation to shared stop-over areas. We quantified effects of extreme extra-tropical cyclones (ETCs) specific to populations’ stop-over areas and the North Atlantic Oscillation on adult survival. Populations with overlapping stop-over areas exhibited temporal synchrony in survival rates. Winter ETCs negatively influenced survival in 1 population, which was the one most exposed to extreme weather, but did not directly influence adult survival in the other 4 populations. Synchrony among populations with shared stop-over areas highlights the importance of these areas for adult survival, a key life-history rate. However, extreme weather was not identified as a driving factor for the majority of study populations. This suggests other factors in these areas, likely related to bottom-up trophic interactions, as environmental drivers of synchrony in the survival of Atlantic puffins.

Summary Fishes are wonderfully diverse. This variety is a result of the ability of ray‐finned fishes to adapt to a wide range of environments, and has made them more specious than the rest of vertebrates combined. With such diversity it is easy to dismiss comparisons between distantly related fishes in efforts to understand the biology of a particular fish species. However, shared ancestry and the conservation of developmental mechanisms, morphological features and physiology provide the ability to use comparative analyses between different organisms to understand mechanisms of development and physiology. The use of species that are amenable to experimental investigation provides tools to approach questions that would not be feasible in other ‘non‐model’ organisms. For example, the use of small teleost fishes such as zebrafish and medaka has been powerful for analysis of gene function and mechanisms of disease in humans, including skeletal diseases. However, use of these fish to aid in understanding variation and disease in other fishes has been largely unexplored. This is especially evident in aquaculture research. Here we highlight the utility of these small laboratory fishes to study genetic and developmental factors that underlie skeletal malformations that occur under farming conditions. We highlight several areas in which model species can serve as a resource for identifying the causes of variation in economically important fish species as well as to assess strategies to alleviate the expression of the variant phenotypes in farmed fish. We focus on genetic causes of skeletal deformities in the zebrafish and medaka that closely resemble phenotypes observed both in farmed as well as natural populations of fishes.

1. An increase in average breeding performance with age and experience among younger age classes has been recorded in numerous studies of iteroparous breeders. An important component of this pattern is thought to be improvements in foraging performance, resulting in delivery of more or better quality food to offspring by older, more experienced individuals. 2. Young, inexperienced breeders may exhibit lower foraging efficiency or foraging effort, and it has been predicted that differences in foraging performance with age and experience will be more marked when environmental conditions are poor. However, as the timing of breeding generally differs with age and experience, intrinsic differences in foraging abilities are typically confounded by variation in extrinsic conditions, and hence food availability. 3. To disentangle these effects, we experimentally manipulated the timing of breeding in European shags, Phalacrocorax aristotelis Linnaeus. We used a cross-fostering protocol, such that naive, young birds reared their chicks at the same time as older, experienced individuals. Our design produced simultaneous chick rearing during two periods in the same breeding season that differed markedly in environmental conditions: early, when conditions were good; and late, when conditions were poorer. We examined foraging efficiency, foraging effort and amount of food delivered to offspring by the two classes of breeder. We predicted that any differences in foraging performance would be more marked under the poorer conditions later in the season. 4. We found that experienced parents delivered more food than naive parents, irrespective of the time of season. This was due partly to the consistently higher foraging efficiency of the experienced parents. In addition, experienced parents adjusted their foraging effort to the environmental conditions. Early in the breeding season, they made less foraging effort than naive parents. Under the poorer foraging conditions, experienced parents increased their foraging effort but naive parents did not, being either unable or unwilling to do so. 5. Our results suggest that an increase in foraging efficiency, and the capacity to adjust foraging effort in response to food availability, are important components of the observed improvements in breeding performance with age and experience.

The demography of vertebrate populations is governed in part by processes operating at large spatial scales that have synchronizing effects on demographic parameters over large geographic areas, and in part, by local processes that generate fluctuations that are independent across populations. We describe a statistical model for the analysis of individual monitoring data at the multi-population scale that allows us to (1) split up temporal variation in survival into two components that account for these two types of processes and (2) evaluate the role of environmental factors in generating these two components. We derive from this model an index of synchrony among populations in the pattern of temporal variation in survival, and we evaluate the extent to which environmental factors contribute to synchronize or desynchronize survival variation among populations. When applied to individual monitoring data from four colonies of the Atlantic Puffin (Fratercula arctica), 67% of between-year variance in adult survival was accounted for by a global spatial-scale component, indicating substantial synchrony among colonies. Local sea surface temperature (SST) accounted for 40% of the global spatial-scale component but also for an equally large fraction of the local-scale component. SST thus acted at the same time as both a synchronizing and a desynchronizing agent. Between-year variation in adult survival not explained by the effect of local SST was as synchronized as total between-year variation, suggesting that other unknown environmental factors acted as synchronizing agents. Our approach, which focuses on demographic mechanisms at the multi-population scale, ideally should be combined with investigations of population size time series in order to characterize thoroughly the processes that underlie patterns of multi-population dynamics and, ultimately, range dynamics.

Summary This review addresses the use of comparative studies of development to help understand the mechanisms underlying the evolution of morphological and physiological diversity. Mutation analysis in experimental model organisms is at the core of our understanding of how development works, and these findings have proven important in the identification and functional support of genetic variations associated with character change in natural populations. However, it has been argued that the essential and pleiotropic effect of many developmentally important genes limits the utility of such mutational study of development toward understanding evolution. Here, I argue that an analysis of the genetic regulation of postembryonic development will refine such comparative analysis. By focusing on late developmental events, one limits the type of genes as well as the types of genetic and developmental changes that may underlie evolutionary change. This may refine the predictive value of such comparative analysis. In this review, I discuss the use of fish as experimental and natural models to address these questions. Fish have several established, experimental models for genetic analysis. Additionally, the morphological and genetic diversity among fish provides a wealth of new models in which to look at the genetic and developmental basis of character change. This broadening of comparative genetic analysis may resolve the bias against genetic ‘monsters’ and what they can tell us about evolutionary change.

Demographic parameters and population growth rates vary among populations of the same species. Such variation can be extensive, as shown by a comparison of nine published studies of black-legged kittiwakes Rissa tridactyla. We argue that rather than being a nuisance, inter-population variation can improve our understanding of population dynamics. Analysis of data from several geographically dispersed studies can aid detection of causal relationships between demographic parameters and environmental factors and thus improve our understanding of impacts of e.g. climate change, and help elucidate mechanisms of population regulation. Extensive inter-population variation also adds another perspective to studies of life history evolution and should be taken into account in across-species comparisons.

Many seabirds nesting in areas bordering the North Sea have recently experienced large annual variation in breeding success, including reproductive failures in some cases. In contrast, the breeding success of northern gannetsMorus bassanushas remained remarkably stable. The present study examines data from the large gannet colony at the Bass Rock (southeast Scotland) across 3 years, to assess the extent to which such stability may reflect both flexibility and consistency in diets and foraging behaviour. Adults exhibited great flexibility both in the species and sizes of prey consumed and in foraging trip durations, ranges and total distances travelled. They also showed a high degree of consistency in bearings of foraging trips and in behaviour at sea; the sinuosity of foraging tracks and average speed of travel was very similar each year and birds in all years spent about half their time at sea in flight. Adults returned to the nest at higher speeds from more distant foraging locations up to ca. 300 km from the colony, but speeds decreased for the farthest destinations (>ca. 400 km). Moreover, the relationship between trip duration and distances travelled at sea was asymptotic beyond ca. 60 h. These non-linear relationships probably reflected constraints on energy expenditure during flight. As a result, nest attendance was low in years with long average trip durations and chicks were left unattended and vulnerable to attack by conspecifics. These data suggest that while adults have so far been able to maintain high reproductive success in years of low prey availability, they may not be able to do so in future years if providing sufficient food for chicks entails any further increases in trip duration or foraging effort.

The electrocardiogram (ECG) is a widespread diagnostic tool in healthcare and supports the diagnosis of cardiovascular disorders. Deep learning methods are a successful and popular technique to detect indications of disorders from an ECG signal. However, there are open questions around the robustness of these methods to various factors, including physiological ECG noise. In this study, we generate clean and noisy versions of an ECG dataset before applying symmetric projection attractor reconstruction (SPAR) and scalogram image transformations. A convolutional neural network is used to classify these image transforms. For the clean ECG dataset, F1 scores for SPAR attractor and scalogram transforms were 0.70 and 0.79, respectively. Scores decreased by less than 0.05 for the noisy ECG datasets. Notably, when the network trained on clean data was used to classify the noisy datasets, performance decreases of up to 0.18 in F1 scores were seen. However, when the network trained on the noisy data was used to classify the clean dataset, the decrease was less than 0.05. We conclude that physiological ECG noise impacts classification using deep learning methods and careful consideration should be given to the inclusion of noisy ECG signals in the training data when developing supervised networks for ECG classification. This article is part of the theme issue ‘Advanced computation in cardiovascular physiology: new challenges and opportunities’.