Pre-roost murmuration displays by European starlings Sturnus vulgaris are a spectacular example of collective animal behaviour. To date, empirical research has focussed largely on flock movement and biomechanics whereas research on possible causal mechanisms that affect flock size and murmuration duration has been limited and restricted to a small number of sites. Possible explanations for this behaviour include reducing predation through the dilution, detection or predator confusion effects (the “safer together” hypotheses) or recruiting more birds to create larger (warmer) roosts (the “warmer together” hypothesis). We collected data on size, duration, habitat, temperature and predators from >3,000 murmurations using citizen science. Sightings were submitted from 23 countries but UK records predominated. Murmurations occurred across a range of habitats but there was no association between habitat and size/duration. Size increased significantly from October to early February, followed by a decrease until the end of the season in March (overall mean 30,082 birds; maximum 750,000 birds). Mean duration was 26 minutes (± 44 seconds SEM). Displays were longest at the start/end of the season, probably due to a significant positive relationship with day length. Birds of prey were recorded at 29.6% of murmurations. The presence of predators including harrier Circus, peregrine Falco peregrinus, and sparrowhawk Accipiter nisus was positively correlated with murmuration size (R2 = 0.401) and duration (R2 = 0.258), especially when these species were flying near to, or actively engaging with, starlings. Temperature was negatively correlated with duration but the effect was much weaker than that of day length. When predators were present, murmurations were statistically more likely to end with all birds going down en masse to roost rather than dispersing from the site. Our findings suggest that starling murmurations are primarily an anti-predator adaptation rather than being undertaken to attract larger numbers of individuals to increase roost warmth.
Social media provides unique opportunities for data collection. Retrospective analysis of social media posts has been used in seismology, political science and public risk perception studies but has not been used extensively in ecological research. There is currently no assessment of whether such data are valid and robust in ecological contexts.
We used “Twitter mining” methods to search Twitter (a microblogging site) for terms relevant to three nationwide UK ecological phenomena: winged ant emergence; autumnal house spider sightings; and starling murmurations. To determine the extent to which Twitter‐mined data were reliable and suitable for answering specific ecological questions the data so gathered were analysed and the results directly compared to the findings of three published studies based on primary data collected by citizen scientists during the same time period.
Twitter‐mined data proved robust for quantifying temporal ecological patterns. There was striking similarity in the temporal patterns of winged ant emergence between previously published work and our analysis of Twitter‐mined data at national scales; this was also the case for house spider sightings. Spatial data were less available but analysis of Twitter‐mined data was able to replicate most spatial findings from all three studies. Baseline ecological findings, such as the sex ratio of house spider sightings, could also be replicated. Where Twitter mining was less successful was answering specific questions and testing hypotheses. Thus, we were unable to determine the influence of microhabitat on winged ants or test predation and weather hypotheses for initiation of murmuration behaviour.
Twitter mining clearly has great potential to generate spatiotemporal ecological data and to answer specific ecological questions. However, we found that the types and usefulness of data differed substantially between the three phenomena. Consequently, we suggest that understanding users' behaviour when posting on ecological topics would be useful if using social media is to generate ecological data.
Monitoring animal populations often relies on direct visual observations. This is problematic at night when spotlighting can cause misidentification and inaccurate counting. Using infrared thermography (IRT) could potentially solve these difficulties, but reliability is uncertain. Here, we test the accuracy of 24 observers, differing in experience and skill levels, in identifying antelope species from IRT photographs taken in the African bush. Overall, 38% of identifications were correct to species level, and 50% were correct to genus/subfamily level. Identification accuracy depended on the confidence and skill of the observer (positive relationship), the number of animals present (positive relationship), and the distance at which it was taken (negative relationship). Species with characteristic features, horn morphology, or posture were identified with ~80% accuracy (e.g. wildebeest, kudu and impala) while others were considerably lower (e.g. blesbok and waterbuck). Experience significantly improved identification accuracy but the effect was not consistent between species and even experienced observers struggled to identify red hartebeest, reedbuck and eland. Counting inaccuracies were commonplace, particularly when group size was large. We conclude that thermal characteristics of species and experience of observers can pose challenges for African field ecologists, but IRT can be used to identify and count some species accurately, especially <100 m.
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