Integrating light‐level geolocation with activity tracking reveals unexpected nocturnal migration patterns of the tawny pipit

Abstract

Migratory birds complete their seasonal journeys between breeding and non-breeding sites with a series of migratory flights that are separated by prolonged stopovers. While songbirds are the most common taxa among migratory birds, empirical data on flight and stopover behaviour along their entire migratory journeys are still rare. Here, we integrate activity and barometric pressure tracking with classical light-level geolocation to describe migration behaviour of tawny pipits Anthus campestris breeding in central Europe. Surprisingly, tracked pipits used, on average, as many as 10 stopover sites during their six week, > 5000 km long autumn migration. This conforms to a typical hop-type pattern of migration. In contrast to common knowledge which considers the tawny pipit as a typical diurnal migrant, our data revealed that more than two thirds of all migratory movements were carried out at night. Nocturnal departure times were highly variable within individuals and spread across the entire night while landing most often took place within the first few hours after sunrise. Consequently, there was a negative relationship between departure timing relative to sunset and flight duration. Short flights of up to 2 h were most common and median flight duration was 4.5 h. There was a hyperbolic relationship between flight duration and maximum flight altitude and flight altitudes during night were two times higher compared to daytime. The overall ratio of flight versus stopover duration during migration was on average 1:6.5. This closely matches predictions from theoretical models. We show that multi-sensor tracking has the potential to provide unprecedented details on migratory behaviour of individual birds along their entire migratory journeys, and it also improves the precision of geographical locations derived from light-level geolocators.

The use of nocturnal flights for barrier crossing in a diurnally migrating songbird

“…Our migration-tracking of purple martins using high precision GPS units support hypotheses for the use of nocturnal flights at barrier crossing by otherwise diurnal migrants [4] and builds upon other recent migration-tracking evidence demonstrating the combination of day and night flights in long-distance migration (e.g. [32]). Our results are consistent with the notion that these primarily diurnal birds use night flight, particularly when making large open-water crossings, to help achieve time-and energy-savings when foraging conditions are suboptimal.…”

pamlr: a toolbox for analysing animal behaviour using pressure, acceleration, temperature, magnetic and light data in R

“…There are a number of other behaviours that are identifiable using multisensory geolocator tags. Though these tags have primarily been used on birds (Bäckman et al, 2017; Dhanjal-Adams et al, 2018; Liechti et al, 2018; Sjöberg et al, 2018; Briedis, Beran, Adamík, & Hahn, 2020; Evens et al, 2020; Barras, Arlettaz, & Liechti, 2021; Sander et al, 2021), they can also be used with bats (Voigt et al, 2020). There are therefore a number of behaviours that might be analysed from these tags.…”

“…The copyright holder for this preprint this version posted August 3, 2021. ; https://doi.org/10.1101/2021.08.02.454456 doi: bioRxiv preprint Though these tags have primarily been used on birds (Bäckman et al, 2017;Dhanjal-Adams et al, 2018;Liechti et al, 2018;Sjöberg et al, 2018;Briedis, Beran, Adamík, & Hahn, 2020;Evens et al, 2020;Barras, Arlettaz, & Liechti, 2021;Sander et al, 2021), they can also be used with bats (Voigt et al, 2020).…”

Complex Day-to-day Movements of an Alpine Passerine May Act as an Insurance Against Environmental Variability

“…More recently, multi-sensor loggers (MSL) that combine GL with other sensors such as barometers and accelerometers have been developed (Liechti et al 2018). Similarly to GL, MSL can collect data at high frequency and over long periods of time, additionally providing much deeper insights into individual spatial behaviour, including activity patterns, migratory schedules and ight altitude (Briedis et al 2020;Dhanjal-Adams et al 2018;Liechti et al 2018). They thus represent a promising tool to better assess the three-dimensional movements of small animals such as passerines.…”