Erica F. Stuber

Perceived predation risk affects sleep behaviour in free-living great tits, Parus major

Sleep is of major importance to most organisms but insights into how sleep is affected by ecological processes are largely lacking. Perceived predation risk constitutes a major factor that should shape adaptive phenotypic plasticity in sleep but it is unclear to what degree an individual can tailor sleep to different types of risk. If animals base behavioural decisions on the predation landscape then we would expect individuals to adjust their sleep behaviour when exposed to changes in predation risk. Here we investigated the plasticity of phenotypic sleep in wild great tits roosting in nestboxes and exposed to different types of predation risk. Following our prediction, when exposed to experimentally increased perceived predation risk from owls, Strix aluco (a bird that can prey on birds solely outside their roosting cavity), individuals increased total sleep duration. Contrary to our prediction, when exposed to experimentally increased perceived predation risk from martens, Martes martes (a mammal that can prey on birds inside cavities), individuals woke up less often during the night, but otherwise did not change their sleep behaviour. Birds did not alter total time spent awake during the night in response to predator exposure. Our findings demonstrate that individual great tits modify their sleep behaviour in response to changes in predation risk. Ecological factors including exposure to predators, resource availability and reproductive competition may act as significant constraints on natural sleep patterns and warrant further investigation with free-living individuals.

Genetic Correlates of Individual Differences in Sleep Behavior of Free-Living Great Tits (Parus major)

Within populations, free-living birds display considerable variation in observable sleep behaviors, reflecting dynamic interactions between individuals and their environment. Genes are expected to contribute to repeatable between-individual differences in sleep behaviors, which may be associated with individual fitness. We identified and genotyped polymorphisms in nine candidate genes for sleep, and measured five repeatable sleep behaviors in free-living great tits (Parus major), partly replicating a previous study in blue tits (Cyanistes caeruleus). Microsatellites in the CLOCK and NPAS2 clock genes exhibited an association with sleep duration relative to night length, and morning latency to exit the nest box, respectively. Furthermore, microsatellites in the NPSR1 and PCSK2 genes associated with relative sleep duration and proportion of time spent awake at night, respectively. Given the detection rate of associations in the same models run with random markers instead of candidate genes, we expected two associations to arise by chance. The detection of four associations between candidate genes and sleep, however, suggests that clock genes, a clock-related gene, or a gene involved in the melanocortin system, could play key roles in maintaining phenotypic variation in sleep behavior in avian populations. Knowledge of the genetic architecture underlying sleep behavior in the wild is important because it will enable ecologists to assess the evolution of sleep in response to selection.

Sex-specific association between sleep and basal metabolic rate in great tits

Differences in animal behaviour can arise from individual variation in energy resource allocation decisions. Because energy is essential to fuel all processes that permit behaviour, it is necessary to consider metabolism for a more complete understanding of behavioural ecology. Although many studies have explored interspecific relationships between metabolic rate and behaviour, few studies have evaluated within-species relationships between metabolism and sleep. We investigated the relationship between basal metabolic rate (BMR) and components of sleep behaviour measured in wild great tits, Parus major, during winter and the prebreeding period. Individuals with higher metabolic rates may partially offset their costs by using sleep as an energy conservation strategy, where individuals with higher BMR may sleep more. On the other hand, the energetic savings of longer sleep may not be worth the lost foraging opportunities and therefore higher BMR individuals may sleep less. Our results suggest that the relationship between BMR and sleep behaviours may depend on sex. Female great tits displayed a positive relationship between metabolic rate and sleep duration consistent with energy conservation, or protection, while male great tits displayed a negative relationship. Differences in sleep duration came about largely due to a sex-specific interaction between BMR and sleep onset time; we found no relationship between BMR and time of awakening in either sex. Nor did it appear that individuals compensated for changes in duration of sleep with changes to quality of sleep, measured as frequency of night-time awakenings. This suggests that male and female great tits use different sleep strategies based on their metabolic requirements which may contribute to variation in sleep behaviour within a species. Sex-specific differences in metabolic physiology, or fitness-enhancing behaviours throughout the circannual cycle may contribute to variation in energy-balancing strategies within and between the sexes.

Translating statistical species-habitat models to interactive decision support tools

Understanding species-habitat relationships is vital to successful conservation, but the tools used to communicate species-habitat relationships are often poorly suited to the information needs of conservation practitioners. Here we present a novel method for translating a statistical species-habitat model, a regression analysis relating ring-necked pheasant abundance to landcover, into an interactive online tool. The Pheasant Habitat Simulator combines the analytical power of the R programming environment with the user-friendly Shiny web interface to create an online platform in which wildlife professionals can explore the effects of variation in local landcover on relative pheasant habitat suitability within spatial scales relevant to individual wildlife managers. Our tool allows users to virtually manipulate the landcover composition of a simulated space to explore how changes in landcover may affect pheasant relative habitat suitability, and guides users through the economic tradeoffs of landscape changes. We offer suggestions for development of similar interactive applications and demonstrate their potential as innovative science delivery tools for diverse professional and public audiences.

A Bayesian method for assessing multi-scale species-habitat relationships

ContextScientists face several theoretical and methodological challenges in appropriately describing fundamental wildlife-habitat relationships in models. The spatial scales of habitat relationships are often unknown, and are expected to follow a multi-scale hierarchy. Typical frequentist or information theoretic approaches often suffer under collinearity in multi-scale studies, fail to converge when models are complex or represent an intractable computational burden when candidate model sets are large.ObjectivesOur objective was to implement an automated, Bayesian method for inference on the spatial scales of habitat variables that best predict animal abundance.MethodsWe introduce Bayesian latent indicator scale selection (BLISS), a Bayesian method to select spatial scales of predictors using latent scale indicator variables that are estimated with reversible-jump Markov chain Monte Carlo sampling. BLISS does not suffer from collinearity, and substantially reduces computation time of studies. We present a simulation study to validate our method and apply our method to a case-study of land cover predictors for ring-necked pheasant (Phasianus colchicus) abundance in Nebraska, USA.ResultsOur method returns accurate descriptions of the explanatory power of multiple spatial scales, and unbiased and precise parameter estimates under commonly encountered data limitations including spatial scale autocorrelation, effect size, and sample size. BLISS outperforms commonly used model selection methods including stepwise and AIC, and reduces runtime by 90%.ConclusionsGiven the pervasiveness of scale-dependency in ecology, and the implications of mismatches between the scales of analyses and ecological processes, identifying the spatial scales over which species are integrating habitat information is an important step in understanding species-habitat relationships. BLISS is a widely applicable method for identifying important spatial scales, propagating scale uncertainty, and testing hypotheses of scaling relationships.

No relationship between brain size and risk of being shot in hunted birds: a comment on Møller & Erritzøe (2016)

In a provocative paper, Moller & Erritzoe (M&E) [[1][1]] claim that Danish birds with relatively larger brains have a lower risk of being shot by hunters. They point out that this result is consistent with studies showing that brain size is positively associated with behavioural fitness traits

Predicting species-habitat relationships: does body size matter?

ContextAllometric scaling laws are foundational to structuring processes from cellular to ecosystem levels. The idea that allometric relationships underlie species characteristic selection scales, the spatial scales at which species respond to landscape features, has recently been investigated, however, supporting empirical evidence is scarce.ObjectivesLack of pattern can be explained by inaccurate estimation, low power, confounding factors, or absence of a relationship. In this paper, we evaluate the relationship between body size and species characteristic selection scales after overcoming limitations of previous study designs.MethodsWe conducted 1328 avian point counts across the state of Nebraska using the robust sampling design to account for imperfect detection. We used Bayesian latent indicator scale selection with N-mixture models to estimate species’ characteristic selection scales of six habitat features for 86 species. We propagated the uncertainty associated with assigning characteristic scales to a model of the relationship between body size and characteristic spatial scales.ResultsSpecies characteristic scales varied across habitat predictors, and varied in the uncertainty associated with selecting single characteristic scales. After propagating uncertainty our results do not support a relationship between species’ body size and the spatial scales at which they respond to landscape features.ConclusionsAs species abundance integrates birth, death, immigration, and emigration processes, each of which are influenced by ecological processes manifesting at various scales, we question whether a general allometric relationship should be expected. Our results suggest that selection may act on responses to specific environmental features, rather than responses to spatial scale per se.

Does perceived predation risk affect patterns of extra‐pair paternity? A field experiment in a passerine bird

Non-consumptive predator effects have been shown to influence a wide range of behavioural, life history and morphological traits. Extra-pair reproduction is widespread among socially monogamous birds and may incur predation costs. Consequently, altered rates of extra-pair reproduction are expected in circumstances characterized by increased adult perceived predation risk. In addition, extra-pair reproduction is expected to be most affected for birds with phenotypes that generally increase predation risk (such as more active individuals). In two consecutive years, perceived predation risk was manipulated for great tits Parus major breeding in 12 nest-box plots by broadcasting sounds of their main predator (European sparrowhawk Accipiter nisus;six plots). As a control treatment, sounds of a sympatric, avian non-predator species were broadcast (Eurasian blackbird Turdus merula;six plots). Levels of extra-pair paternity did not differ between plots with different predation risk treatments. Males that moved more in a novel environment (more active or faster exploring) tended to have offspring with fewer partners, but this effect did not vary with predation risk treatment. From an adaptive viewpoint, predation costs associated with extra-pair reproduction may be small and may not outweigh the benefits of extra-pair behaviour. Research on a broader range of taxa with different mating strategies is now needed to confirm the generality of our findings.