High dispersal ability versus migratory traditions: Fine-scale population structure and post-glacial colonisation in bar-tailed godwits.

In migratory animals, high mobility may reduce population structure through increased dispersal and enable adaptive responses to environmental change, whereas rigid migratory routines predict low dispersal, increased structure, and limited flexibility to respond to change. We explore the global population structure and phylogeographic history of the bar-tailed godwit, Limosa lapponica, a migratory shorebird known for making the longest non-stop flights of any landbird. Using nextRAD sequencing of 14,318 single-nucleotide polymorphisms and scenario-testing in an Approximate Bayesian Computation framework, we infer that bar-tailed godwits existed in two main lineages at the last glacial maximum, when much of their present-day breeding range persisted in a vast, unglaciated Siberian-Beringian refugium, followed by admixture of these lineages in the eastern Palearctic. Subsequently, population structure developed at both longitudinal extremes: in the east, a genetic cline exists across latitude in the Alaska breeding range of subspecies L. l. baueri; in the west, one lineage diversified into three extant subspecies L. l. lapponica, taymyrensis, and yamalensis, the former two of which migrate through previously glaciated western Europe. In the global range of this long-distance migrant, we found evidence of both (1) fidelity to rigid behavioural routines promoting fine-scale geographic population structure (in the east) and (2) flexibility to colonise recently available migratory flyways and non-breeding areas (in the west). Our results suggest that cultural traditions in highly mobile vertebrates can override the expected effects of high dispersal ability on population structure, and provide insights for the evolution and flexibility of some of the world's longest migrations.

Global temperature homogenization can obliterate temporal isolation in migratory animals with potential loss of population structure.

Climate change is expected to increase the spatial autocorrelation of temperature, resulting in greater synchronization of climate variables worldwide. Possibly such 'homogenization of the world' leads to elevated risks of extinction and loss of biodiversity. In this study, we develop an empirical example on how increasing synchrony of global temperatures can affect population structure in migratory animals. We studied two subspecies of bar-tailed godwits Limosa lapponica breeding in tundra regions in Siberia: yamalensis in the west and taymyrensis further east and north. These subspecies share pre- and post-breeding stopover areas, thus being partially sympatric, but exhibiting temporal segregation. The latter is believed to facilitate reproductive isolation. Using satellite tracking data, we show that migration timing of both subspecies is correlated with the date of snowmelt in their respective breeding sites (later at the taymyrensis breeding range). Snow-cover satellite images demonstrate that the breeding ranges are on different climate trajectories and become more synchronized over time: between 1997 and 2020, the date of snowmelt advanced on average by 0.5 days/year in the taymyrensis breeding range, while it remained stable in the yamalensis breeding range. Previous findings showed how taymyrensis responded to earlier snowmelt by advancing arrival and clutch initiation. In the predicted absence of such advancements in yamalensis, we expect that the two populations will be synchronized by 2036-2040. Since bar-tailed godwits are social migrants, this raises the possibility of population exchange and prompts the question whether the two subspecies can maintain their geographic and morphological differences and population-specific migratory routines. The proposed scenario may apply to a wide range of (social) migrants as temporal segregation is crucial for promoting and maintaining reproductive isolation in many (partially sympatric) migratory populations. Homogenization of previously isolated populations could be an important consequence of increasing synchronized environments and hence climate change.

GPS tracking data of Eurasian oystercatchers (Haematopusostralegus) from the Netherlands and Belgium.

We describe six datasets that contain GPS and accelerometer data of 202 Eurasian oystercatchers (Haematopusostralegus) spanning the period 2008-2021. Birds were equipped with GPS trackers in breeding and wintering areas in the Netherlands and Belgium. We used GPS trackers from the University of Amsterdam Bird Tracking System (UvA-BiTS) for several study purposes, including the study of space use during the breeding season, habitat use and foraging behaviour in the winter season, and impacts of human disturbance. To enable broader usage, all data have now been made open access. Combined, the datasets contain 6.0 million GPS positions, 164 million acceleration measurements and 7.0 million classified behaviour events (i.e., flying, walking, foraging, preening, and inactive). The datasets are deposited on the research repository Zenodo, but are also accessible on Movebank and as down-sampled occurrence datasets on the Global Biodiversity Information Facility (GBIF) and Ocean Biodiversity Information System (OBIS).

Extreme altitude changes between night and day during marathon flights of great snipes.

Several factors affect the flight altitude of migratory birds, such as topography, ambient temperature, wind conditions, air humidity, predation avoidance, landmark orientation, and avoiding over-heating from direct sunlight.1-6 Recent tracking of migratory birds over long distances has shown that migrants change flight altitude more commonly and dramatically than previously thought.4-8 The reasons behind these altitude changes are not well understood. In their seasonal migrations between Sweden and sub-Saharan Africa, great snipes Gallinago media make non-stop flights of 4,000-7,000 km, lasting 60-90 h.9,10 Activity and air pressure data from multisensor dataloggers showed that great snipes repeatedly changed altitudes around dawn and dusk, between average cruising heights about 2,000 m (above sea level) at night and around 4,000 m during daytime. Frequency and autocorrelation analyses corroborated a conspicuous diel cycle in flight altitude. Most birds regularly flew at 6,000 m and one bird reached 8,700 m, possibly the highest altitude ever recorded for an identified migrating bird. The diel altitude changes took place independently of climate zone, topography, and habitat overflown. Ambient temperature, wind condition, and humidity have no important diel variation at the high altitudes chosen by great snipes. Instead, improved view for orientation by landmarks, predator avoidance, and not least, seeking cold altitudes at day to counteract heating from direct sunlight are the most plausible explanations for the diel altitude cycle. Together with similar recent findings for a small songbird,6 the great snipes' altitudinal performance sheds new light on the complexity and challenges of migratory flights.

Stomach fullness shapes prey choice decisions in crab plovers (Dromas ardeola).

Foragers whose energy intake rate is constrained by search and handling time should, according to the contingency model (CM), select prey items whose profitability exceeds or equals the forager's long-term average energy intake rate. This rule does not apply when prey items are found and ingested at a higher rate than the digestive system can process them. According to the digestive rate model (DRM), foragers in such situations should prefer prey with the highest digestive quality, instead of the highest profitability. As the digestive system fills up, the limiting constraint switches from ingestion rate to digestion rate, and prey choice is expected to change accordingly for foragers making decisions over a relative short time window. We use these models to understand prey choice in crab plovers (Dromas ardeola), preying on either small burrowing crabs that are swallowed whole (high profitability, but potentially inducing a digestive constraint) or on larger swimming crabs that are opened to consume only the flesh (low profitability, but easier to digest). To parameterize the CM and DRM, we measured energy content, ballast mass and handling times for different sized prey, and the birds' digestive capacity in three captive individuals. Subsequently, these birds were used in ad libitum experiments to test if they obeyed the rules of the CM or DRM. We found that crab plovers with an empty stomach mainly chose the most profitable prey, matching the CM. When stomach fullness increased, the birds switched their preference from the most profitable prey to the highest-quality prey, matching the predictions of the DRM. This shows that prey choice is context dependent, affected by the stomach fullness of an animal. Our results suggest that prey choice experiments should be carefully interpreted, especially under captive conditions as foragers often 'fill up' in the course of feeding trials.

Optimizing acceleration-based ethograms: the use of variable-time versus fixed-time segmentation.

BACKGROUND: Animal-borne accelerometers measure body orientation and movement and can thus be used to classify animal behaviour. To univocally and automatically analyse the large volume of data generated, we need classification models. An important step in the process of classification is the segmentation of acceleration data, i.e. the assignment of the boundaries between different behavioural classes in a time series. So far, analysts have worked with fixed-time segments, but this may weaken the strength of the derived classification models because transitions of behaviour do not necessarily coincide with boundaries of the segments. Here we develop random forest automated supervised classification models either built on variable-time segments generated with a so-called 'change-point model', or on fixed-time segments, and compare for eight behavioural classes the classification performance. The approach makes use of acceleration data measured in eight free-ranging crab plovers Dromas ardeola. RESULTS: Useful classification was achieved by both the variable-time and fixed-time approach for flying (89% vs. 91%, respectively), walking (88% vs. 87%) and body care (68% vs. 72%). By using the variable-time segment approach, significant gains in classification performance were obtained for inactive behaviours (95% vs. 92%) and for two major foraging activities, i.e. handling (84% vs. 77%) and searching (78% vs. 67%). Attacking a prey and pecking were never accurately classified by either method. CONCLUSION: Acceleration-based behavioural classification can be optimized using a variable-time segmentation approach. After implementing variable-time segments to our sample data, we achieved useful levels of classification performance for almost all behavioural classes. This enables behaviour, including motion, to be set in known spatial contexts, and the measurement of behavioural time-budgets of free-living birds with unprecedented coverage and precision. The methods developed here can be easily adopted in other studies, but we emphasize that for each species and set of questions, the presented string of work steps should be run through.

From sensor data to animal behaviour: an oystercatcher example.

Animal-borne sensors enable researchers to remotely track animals, their physiological state and body movements. Accelerometers, for example, have been used in several studies to measure body movement, posture, and energy expenditure, although predominantly in marine animals. In many studies, behaviour is often inferred from expert interpretation of sensor data and not validated with direct observations of the animal. The aim of this study was to derive models that could be used to classify oystercatcher (Haematopus ostralegus) behaviour based on sensor data. We measured the location, speed, and tri-axial acceleration of three oystercatchers using a flexible GPS tracking system and conducted simultaneous visual observations of the behaviour of these birds in their natural environment. We then used these data to develop three supervised classification trees of behaviour and finally applied one of the models to calculate time-activity budgets. The model based on accelerometer data developed to classify three behaviours (fly, terrestrial locomotion, and no movement) was much more accurate (cross-validation error = 0.14) than the model based on GPS-speed alone (cross-validation error = 0.35). The most parsimonious acceleration model designed to classify eight behaviours could distinguish five: fly, forage, body care, stand, and sit (cross-validation error = 0.28); other behaviours that were observed, such as aggression or handling of prey, could not be distinguished. Model limitations and potential improvements are discussed. The workflow design presented in this study can facilitate model development, be adapted to a wide range of species, and together with the appropriate measurements, can foster the study of behaviour and habitat use of free living animals throughout their annual routine.