Data from: Why time-limited individuals can make populations more vulnerable to disturbance

Abstract

Oystercatchers are long‐lived shorebirds that winter in large numbers in the Wadden Sea. They forage on intertidal flats on shellfish (e.g. Cerastoderma edule and Mytilus edulis) and soft‐bodied prey (e.g. Polychaeta) during low tide. During high tide, birds roost or can forage on alternative feeding areas where intake rates are lower, for example inland fields. Most individuals show high site fidelity and return each winter to the same site. Most birds arrive in the winter areas in July and depart to the breeding areas in February.

The field study was conducted during the non‐breeding season in the western Dutch Wadden Sea, on the barrier island Vlieland. The western half of the island is used as a military air force training area. Jets, helicopters and small civil airplanes frequently occur in the study area, but their disturbance impact is normally low, eliciting no or relatively short flight responses. In contrast, low‐flying transport airplanes occur rarely (three times per year in 2017–2019) but evoke strong flight responses likely because of their exceptionally large size, even when the transport airplane flies far away (10 km) from the birds.

Oystercatcher GPS data

Tidal foraging time and flight time of oystercatchers was quantified using solar powered UvA‐BiTS GPS trackers. Forty oystercatchers were equipped with GPS trackers on the Vliehors (53°23'N, 04°93'E): twenty non‐local breeders were caught with mist nets in winter (December 2016–January 2017) and 20 local breeders were caught on the nest (May–July 2017). GPS fixes were taken in 5‐ or 60‐min intervals and a 0.35 s sample (seven measurements) of a 20 Hz tri‐axial accelerometer was taken at least every 10 min. We used a training dataset containing annotated accelerometer samples to train a Random Forest model to classify foraging, flying and other (inactive and preening) behaviour (details in van der Kolk et al. 2020a). Using an independent testing dataset with 8178 samples, we confirmed the high prediction accuracy of the Random Forest model (precision: forage 98%, fly 98%, other 100%; recall: forage 99%, fly 95%, other 99%).

Aircraft and environmental data

Timings of transport airplane exercises were provided by the Royal Netherlands Air Force. The exact times when minimum and maximum water levels occurred at low and high tide, respectively, and water heights for every 10 min were provided for Vlieland harbour (53°29'5''N, 05°09'1''E) by Rijkswaterstaat. A tidal period was defined as the period between two consecutive high tide maxima at Vlieland harbour. Each tidal period thus included a single low tide and was approximately 12.4 h long. Timings of sunrise and sunset were obtained via the sunrise.set function in the StreamMetabolism package in R. Daily moon illumination values were obtained via the lunar.illumination function in the lunar package in R.

Empirical study: individual variation in additional flight time and foraging time loss

Data of GPS‐tagged oystercatchers were used to empirically study how individuals (which varied in their average foraging time in undisturbed tidal periods) altered flight time and foraging time in response to disturbances. We first compared flight time and foraging time between disturbed tidal periods and undisturbed tidal periods. We then quantified whether individuals compensated for costs of disturbance by increasing their foraging time in subsequent tidal periods. All analyses were performed in R ver. 3.5.3 (<www.r‐project.org>).

We studied three disturbed tidal periods on 9, 10 and 16 August 2017 (henceforth event 1, 2 and 3 respectively) with large disturbances by one Lockheed C‐130 Hercules transport aircraft. The aircraft entered the study area 3, 5 and 6.5 h before low tide, respectively, and circled for approximately one hour in the study area. During the first two disturbance events, all GPS tagged oystercatchers present on the Vliehors were disturbed (i.e. took flight), whereas during the third event a subset of the birds were disturbed, which we confirmed by visual inspection of the GPS data. We collected data from 18 individuals during all three disturbed tidal periods, three individuals during two disturbed tidal periods and one individual during one disturbed tidal period (nbird = 22, nbird‐disturbance = 61). Data from another 18 oystercatchers was not available because the tracker malfunctioned, the individual had died or the individual was outside the study area.

Tidal time budgets: flight time and foraging time

The total time spent flying, foraging or other behaviour was quantified for each bird for each tidal period. First, each annotated accelerometer measurement within a tidal period was weighed based on the time interval until the next annotated accelerometer measurement. This time interval was typically ten minutes, the default interval at which GPS trackers took accelerometer measurements, but sometimes longer if an accelerometer measurement was interrupted and could not be annotated. The time intervals from all annotated flight behaviours or annotated foraging behaviours within one tidal period were then summed to acquire the total tidal flight time or tidal foraging time in hours, respectively. To ensure sufficient accuracy, tidal flight and foraging time estimates were omitted if there were less than 70 behavioural measurements or if the maximum interval exceeded 20 min.

Average foraging time

The average foraging time in undisturbed tidal periods and standard error was calculated for each individual over the whole winter season (1 August 2017–31 March 2018). The tidal foraging time of 17 out of 22 individuals was measured during at least 100 tidal periods in this period. We used the average foraging time as explanatory variable in statistical models estimating the effect of disturbance on flight time and foraging time.