Periodic Reporting for period 1 - SSIAS (Sexual Selection and Sleep in Arctic Shorebirds)
Reporting period: 2021-02-15 to 2023-02-14
What is the best way to attract and pursue a mate? This question is central for understanding how animal behaviour evolves through sexual selection. In the Arctic, male pectoral sandpipers (Calidris melanotos) almost entirely forgo sleep during the breeding season, and males that sleep least have the most offspring. These findings defy our understanding of the importance of sleep for optimal performance. So which specific behaviours are key to the success of these birds?
We used state-of-the-art accelerometers and machine learning models, together with conventional field methods, to explore how male behaviour affects reproductive success in pectoral sandpipers. Specifically, we investigated (1) whether the duration, frequency or intensity of courtship and territorial displays predicted the number of sired offspring, and (2) whether the frequency of these behaviours varied with time spent awake. Our research had three main objectives. First, we aimed to determine whether overall time spent active is most important for reproductive success, as implied by previous research on this species, or whether specific behaviours are key. Second, we aimed to learn how extreme sleep loss relates to behaviour in wild birds, which might offer insights into the functions of sleep. Finally, we aimed to test the extent to which accelerometry can be used to classify and distinguish mating behaviours.
We used state-of-the-art accelerometers and machine learning models, together with conventional field methods, to explore how male behaviour affects reproductive success in pectoral sandpipers. Specifically, we investigated (1) whether the duration, frequency or intensity of courtship and territorial displays predicted the number of sired offspring, and (2) whether the frequency of these behaviours varied with time spent awake. Our research had three main objectives. First, we aimed to determine whether overall time spent active is most important for reproductive success, as implied by previous research on this species, or whether specific behaviours are key. Second, we aimed to learn how extreme sleep loss relates to behaviour in wild birds, which might offer insights into the functions of sleep. Finally, we aimed to test the extent to which accelerometry can be used to classify and distinguish mating behaviours.
To achieve our objectives, the project was divided into two major components. For the first component of the project, we tested various algorithms and methods for classifying mating behaviours from accelerometry, using captive male ruffs (Calidris pugnax). For the second component of the project, we recorded and analysed the behaviour and reproductive success of pectoral sandpipers in their natural habitat.
Ruffs are a polymorphic, lekking shorebird with highly skewed mating success. We fitted 20 captive males with accelerometers and recorded their behaviour using video cameras. We then trained machine learning algorithms to identify behaviours from accelerometry. We compared the classification performance of three supervised machine learning methods: Random Forest, Hidden Markov Models and Neural Networks. During this process, we also identified issues that can cause overestimation of model performance. A full description of the methods and results, including the associated code, is being prepared for publication. Our findings will also be presented at the European Ornithologists’ Union conference (Lund, Sweden) in August 2023.
Like many other projects, our research was disrupted by the COVID-19 pandemic. We were unable to travel to our field site in 2021, which caused some delays to the field component of our study. In 2022, we fitted 100 male pectoral sandpipers with accelerometers at our study site near Utqiagvik, Alaska. As with the ruffs, we also recorded male behaviour using video cameras. To determine reproductive success, we caught adults and searched nests throughout the study site and collected a small blood sample from all males and females, as well as from all offspring. Our analysis is still ongoing, but so far our results replicate a previous study that found that more active (sleepless) males sired more offspring. We are now using the accelerometry data and machine learning methods tested with the ruffs to quantify how much time and energy the males spent on various courtship and territorial behaviours. We expect that these results will be published and made publicly available later this year.
Ruffs are a polymorphic, lekking shorebird with highly skewed mating success. We fitted 20 captive males with accelerometers and recorded their behaviour using video cameras. We then trained machine learning algorithms to identify behaviours from accelerometry. We compared the classification performance of three supervised machine learning methods: Random Forest, Hidden Markov Models and Neural Networks. During this process, we also identified issues that can cause overestimation of model performance. A full description of the methods and results, including the associated code, is being prepared for publication. Our findings will also be presented at the European Ornithologists’ Union conference (Lund, Sweden) in August 2023.
Like many other projects, our research was disrupted by the COVID-19 pandemic. We were unable to travel to our field site in 2021, which caused some delays to the field component of our study. In 2022, we fitted 100 male pectoral sandpipers with accelerometers at our study site near Utqiagvik, Alaska. As with the ruffs, we also recorded male behaviour using video cameras. To determine reproductive success, we caught adults and searched nests throughout the study site and collected a small blood sample from all males and females, as well as from all offspring. Our analysis is still ongoing, but so far our results replicate a previous study that found that more active (sleepless) males sired more offspring. We are now using the accelerometry data and machine learning methods tested with the ruffs to quantify how much time and energy the males spent on various courtship and territorial behaviours. We expect that these results will be published and made publicly available later this year.
Our project has advanced and tested the limits of biologging for behavioural research. To record the behaviour of free-living pectoral sandpipers, we worked with Druid Technology to develop customised devices. Each device recorded acceleration data at a high temporal resolution and sent the data via bluetooth to specialised base stations. These devices represent an important breakthrough in our capacity to collect around-the-clock behavioural data from many individuals. Accelerometry and machine learning have rarely been used to quantify mating behaviours, such as competition, courtship and copulation. In part, this is likely due to the inherent challenges of classifying behaviours that can be brief and infrequent, and that are not necessarily exhibited by all individuals. Our research helped to identify multiple challenges, potential pitfalls and opportunities associated with these methods. We anticipate that our findings will assist other researchers and advance future projects.