A note on the circadian rhythm and feeding behaviour of sheep fitted with a lightweight GPS collar

The circadian rhythm and feeding behaviour (bite rate) of 16 Scottish Blackface ewes of which eight were fitted with a lightweight (863 g) GPS collar were examined. The collar consisted of a leather harness and a 0.5 l aluminium box and represented 2.2% of the average body mass of the study ewes. No differences in circadian rhythm and bite rate were found between ewes fitted with the collars and ewes that were not. The relevance of using collar weight as a proportion of body mass as a guide to gauge its impact on the feeding behaviour of small ruminant (35–75 kg) herbivores is questioned.     

Effects of Global Positioning System Collar Weight on Zebra Behavior and Location Error

ABSTRACT Global Positioning System (GPS) collars are increasingly being used to study fine-scale patterns of animal behavior. Previous studies on GPS collars have tried to determine the causes of location error without attempting to investigate whether the accuracy of fixes provides a correspondingly accurate measure of the animal's natural behavior. When comparing 2 types of GPS collar, we found a significant effect of collar weight and fit on the rate of travel of plains zebra (Equus burchelli antiquorum) females in the Makgadikgadi, Botswana. Although both types of collar were well within accepted norms of collar weight, the slightly heavier collars (0.6% of total body mass [TBM]) reduced rate of travel by >50% when foraging compared with the collar that was 0.4% of TBM. Collar effect was activity specific, particularly interfering with grazing behavior; the effect was less noticeable when zebras crossed larger interpatch distances. We highlight that small differences in collar weight or fit can affect specific behaviors, limiting the extrapolation of fine-scaled GPS data. This has important implications for wildlife biologists, who hitherto have assumed that collars within accepted weight limits have little or no effect on animal movement parameters.     

The development of an intermediate‐duration tag to characterize the diving behavior of large whales

The development of high‐resolution archival tag technologies has revolutionized our understanding of diving behavior in marine taxa such as sharks, turtles, and seals during their wide‐ranging movements. However, similar applications for large whales have lagged behind due to the difficulty of keeping tags on the animals for extended periods of time. Here, we present a novel configuration of a transdermally attached biologging device called the Advanced Dive Behavior (ADB) tag. The ADB tag contains sensors that record hydrostatic pressure, three‐axis accelerometers, magnetometers, water temperature, and light level, all sampled at 1 Hz. The ADB tag also collects Fastloc GPS locations and can send dive summary data through Service Argos, while staying attached to a whale for typical periods of 3–7 weeks before releasing for recovery and subsequent data download. ADB tags were deployed on sperm whales (Physeter macrocephalus; N = 46), blue whales (Balaenoptera musculus; N = 8), and fin whales (B. physalus; N = 5) from 2007 to 2015, resulting in attachment durations from 0 to 49.6 days, and recording 31 to 2,539 GPS locations and 27 to 2,918 dives per deployment. Archived dive profiles matched well with published dive shapes of each species from short‐term records. For blue and fin whales, feeding lunges were detected using peaks in accelerometer data and matched corresponding vertical excursions in the depth record. In sperm whales, rapid orientation changes in the accelerometer data, often during the bottom phase of dives, were likely related to prey pursuit, representing a relative measure of foraging effort. Sperm whales were documented repeatedly diving to, and likely foraging along, the seafloor. Data from the temperature sensor described the vertical structure of the water column in all three species, extending from the surface to depths >1,600 m. In addition to providing information needed to construct multiweek time budgets, the ADB tag is well suited to studying the effects of anthropogenic sound on whales by allowing for pre‐ and post‐exposure monitoring of the whale's dive behavior. This tag begins to bridge the gap between existing long‐duration but low‐data throughput tags, and short‐duration, high‐resolution data loggers.     

Modeling activity patterns of wildlife using time‐series analysis

The study of wildlife activity patterns is an effective approach to understanding fundamental ecological and evolutionary processes. However, traditional statistical approaches used to conduct quantitative analysis have thus far had limited success in revealing underlying mechanisms driving activity patterns. Here, we combine wavelet analysis, a type of frequency‐based time‐series analysis, with high‐resolution activity data from accelerometers embedded in GPS collars to explore the effects of internal states (e.g., pregnancy) and external factors (e.g., seasonal dynamics of resources and weather) on activity patterns of the endangered giant panda (Ailuropoda melanoleuca). Giant pandas exhibited higher frequency cycles during the winter when resources (e.g., water and forage) were relatively poor, as well as during spring, which includes the giant panda's mating season. During the summer and autumn when resources were abundant, pandas exhibited a regular activity pattern with activity peaks every 24 hr. A pregnant individual showed distinct differences in her activity pattern from other giant pandas for several months following parturition. These results indicate that animals adjust activity cycles to adapt to seasonal variation of the resources and unique physiological periods. Wavelet coherency analysis also verified the synchronization of giant panda activity level with air temperature and solar radiation at the 24‐hr band. Our study also shows that wavelet analysis is an effective tool for analyzing high‐resolution activity pattern data and its relationship to internal and external states, an approach that has the potential to inform wildlife conservation and management across species.     

Migratory patterns and connectivity of two North American grassland bird species

Effective management and conservation of migratory bird populations require knowledge and incorporation of their movement patterns and space use throughout the annual cycle. To investigate the little‐known migratory patterns of two grassland bird species, we deployed 180 light‐level geolocators on Grasshopper Sparrows (Ammodramus savannarum) and 29 Argos‐GPS tags on Eastern Meadowlarks (Sturnella magna) at Konza Prairie, Kansas, USA, and six US Department of Defense (DoD) installations distributed across the species' breeding ranges. We analyzed location data from 34 light‐level geolocators and five Argos‐GPS tags attached for 1 year to Grasshopper Sparrows and Eastern Meadowlarks, respectively. Grasshopper Sparrows were present on the breeding grounds from mid‐April through early October, substantially longer than previously estimated, and migrated on average ~2,500 km over ~30 days. Grasshopper Sparrows exhibited strong migratory connectivity only at a continental scale. The North American Great Lakes region likely serves as a migratory divide for Midwest and East Coast Grasshopper Sparrows; Midwest populations (Kansas, Wisconsin, and North Dakota; n = 13) largely wintered in Texas or Mexico, whereas East Coast populations (Maryland and Massachusetts, n = 20) wintered in the northern Caribbean or Florida. Our data from Eastern Meadowlarks provided evidence for a diversity of stationary and short‐ and long‐distance migration strategies. By providing the most extensive examination of the nonbreeding movement ecology for these two North American grassland bird species to date, we refine information gaps and provide key insight for their management and conservation.     

Linking behavioral states to landscape features for improved conservation management

1. A central theme for conservation is understanding how animals differentially use, and are affected by change in, the landscapes they inhabit. However, it has been challenging to develop conservation schemes for habitat‐specific behaviors.
2. Here we use behavioral change point analysis to identify behavioral states of golden eagles (Aquila chrysaetos) in the Sonoran and Mojave Deserts of the southwestern United States, and we identify, for each behavioral state, conservation‐relevant habitat associations.
3. We modeled behavior using 186,859 GPS points from 48 eagles and identified 2,851 distinct segments comprising four behavioral states. Altitude above ground level (AGL) best differentiated behavioral states, with two clusters of short‐distance movement behaviors characterized by low AGL (state 1 AGL = 14 m (median); state 2 AGL = 11 m) and two associated with longer‐distance movement behaviors and characterized by higher AGL (state 3 AGL = 108 m; state 4 AGL = 450 m).
4. Behaviors such as perching and low‐altitude hunting were associated with short‐distance movements in updraft‐poor environments, at higher elevations, and over steeper and more north‐facing terrain. In contrast, medium‐distance movements such as hunting and transiting were over gentle and south‐facing slopes. Long‐distance transiting occurred over the desert habitats that generate the best updraft.
5. This information can guide management of this species, and our approach provides a template for behavior‐specific habitat associations for other species of management concern.