Abstract

Sensors, such as accelerometers, in tracking devices allow for detailed bio-logging to understand animal behaviour, even in remote places where direct observation is difficult. To study breeding in birds remotely, one needs to understand how to recognise a breeding event from tracking data, and ideally validate this by direct observation. We tagged 49 adult female pink-footed geese (Anser brachyrhynchus) with transmitter neckbands in Finland in spring of 2018 and 2019, and in Svalbard in summer 2018, and validated inferences from tracking by field observations of nesting sites and family status in 2018–2020 (54 spring–summer tracks). We estimated nesting locations by taking the median coordinates of GPS-fixes at which the goose was motionless (overall dynamic body acceleration, ODBA < 1) on days with a daily median ODBA < 1, which approached the real nesting locations closely (within 1.6–3.7 m, n = 6). The start of nesting was defined as the first day on which the goose spent > 75% of time within 50 m of the nest, because nest site attendances steeply increased within one day to above this threshold. Nesting duration (number of consecutive days with > 75% nest site attendance) ranged between 3 and 44 days (n = 28), but was 30–34 days in confirmed successful nests (n = 9). The prolonged nesting of 39–44 days (n = 3) suggested incubation on unhatchable egg(s). Nest losses before hatching time occurred mostly in day 3–10 and 23–29 of nesting, periods with an increased frequency of nest site recesses. As alternative method, allowing for non-simultaneous GPS and accelerometer data, we show that nesting days were classified with 98.6% success by two general characteristics of breeding: low body motion (daily median ODBA) and low geographic mobility (daily SD of latitude). Median coordinates on nesting days approached real nest sites closely (within 0.8–3.6 m, n = 6). When considering only geographic mobility (allowing for GPS data only) nesting locations were similarly accurate, but some short nesting attempts were undetected and non-breeding tracks misclassified. We show that nesting attempts, as short as 3 days, and nesting success can be detected remotely with good precision using GPS-tracking and accelerometry. Our method may be generalised to other (precocial) bird species with similar incubation behaviour.

Highlights

  • The tracking of individual breeding attempts allows us to estimate population growth parameters and study individual variation in reproductive decisions which influence these parameters

  • We focus on the migratory pink-footed goose (Anser brachyrhynchus), which breeds in remote arctic regions and exhibits similar behaviour before, during and after the breeding season as many other goose species [23, 24]

  • Extracting nesting locations and nest attendance The overall dynamic body acceleration (ODBA) threshold was defined at 1.0, because the daily median ODBA was below this value on 75% of days during prolonged time windows of motionlessness, typical for nesting (283 out of 377 days, n = 13 nesting geese, Figs. 3a, c; 4a), During such time windows, geographic mobility was consistently low as well (Figs. 3b; 4a)

Read more

Summary

Introduction

The tracking of individual breeding attempts allows us to estimate population growth parameters and study individual variation in reproductive decisions which influence these parameters. Arctic-breeding migratory birds may adjust their arrival to the breeding grounds and timing of breeding to keep up with this environmental change [6], or may adjust their breeding location to a place or habitat with a more favourable phenology [15]. A remote region does not allow for direct observations of breeding birds. Migratory birds can be tagged in a non-breeding location and followed without direct observation to their remote breeding areas such as the Arctic [e.g., 16], while additional sensors such as accelerometers allow for detailed bio-logging to understand the birds’ behaviour [e.g., 17, 18]. To enable the study of breeding biology in such birds, one needs to understand how to recognise a breeding event from tracking data, and ideally validate this with individuals that were observed directly, which is challenging in remote areas

Objectives
Methods
Results
Conclusion
Full Text
Published version (Free)

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call