Professor Ben Sheldon

<jats:p> In many species, individuals gather information about their environment both through direct experience and through information obtained from others. Social learning, or the acquisition of information from others, can occur both within and between species and may facilitate the rapid spread of antipredator behaviour. Within birds, acoustic signals are frequently used to alert others to the presence of predators, and individuals can quickly learn to associate novel acoustic cues with predation risk. However, few studies have addressed whether such learning occurs only though direct experience or whether it has a social component, nor whether such learning can occur between species. We investigate these questions in two sympatric species of Parids: blue tits ( <jats:italic>Cyanistes caeruleus</jats:italic> ) and great tits ( <jats:italic>Parus major</jats:italic> ). Using playbacks of unfamiliar bird vocalizations paired with a predator model in a controlled aviary setting, we find that blue tits can learn to associate a novel sound with predation risk via direct experience, and that antipredator response to the sound can be socially transmitted to heterospecific observers, despite lack of first-hand experience. Our results suggest that social learning of acoustic cues can occur between species. Such interspecific social information transmission may help to mediate the formation of mixed-species aggregations. </jats:p>

Changes in the timing of life-history events (phenology) are a widespread consequence of climate change. Predicting population resilience requires knowledge of how phenology is likely to change over time, which can be gained by identifying the specific environmental cues that drive phenological events. Cue identification is often achieved with statistical testing of candidate cues. As the number of methods used to generate predictions increases, assessing the predictive accuracy of different approaches has become necessary. This study aims to (a) provide an empirical illustration of the predictive ability of five commonly applied statistical methods for cue identification (absolute and relative sliding time window analyses, penalized signal regression, climate sensitivity profiles and a growing degree-day model) and (b) discuss approaches for implementing cue identification methods in different systems. Using a dataset of mean clutch initiation timing in wild great tits (Parus major), we explored how the days of the year identified as most important, and the aggregate statistic identified as a cue, differed between statistical methods and with respect to the time span of data used. Each method's predictive capacity was tested using cross-validation and assessed for robustness to varying sample size. We show that the identified critical time window of cue sensitivity was consistent across four of the five methods. The accuracy and precision of predictions differed by method with penalized signal regression resulting in the most accurate and most precise predictions in our case. Accuracy was maximal for near-future predictions and showed a relationship with time. The difference between predictions and observations systematically shifted across the study from preceding observations to lagging. This temporal trend in prediction error suggests that the current statistical tools either fail to capture a key component of the cue-phenology relationship, or the relationship itself is changing through time in our system. These two influences need to be teased apart if we are to generate realistic predictions of phenological change. We recommend future phenological studies to challenge the idea of a static cue-phenology relationship and should cross-validate results across multiple time periods.