COVID-19 lockdown allows researchers to quantify the effects of human activity on wildlife Reduced human mobility during the pandemic will reveal critical aspects of our impact on animals, providing important guidance on how best to share space on this crowded planet.

No abstract

How do seabirds deal with intra-specific competition for food? We addressed this question in a study of the foraging behaviour of 91 Cape gannets Morus capensis from 2 South African colonies, situated 110 km apart, using GPS and time-depth recorders. Theoretically birds should have widely overlapping foraging areas and comparable foraging characteristics. Surprisingly, the foraging areas only overlapped by 13 and 14%, and birds from the 2 colonies also showed marked differences in their foraging patterns. Birds from the larger colony foraged more intensively; their foraging trips lasted longer (22.6 vs 8.5 h), involving longer total flight time (7.8 vs 5.9 h), longer foraging path length (293 vs 228 km), and greater maximum distance from the breeding site (104 vs 67 km). They also travelled faster (50 vs 44 km h -1 ), and had a larger number of foraging locations during each trip (252 vs 121), with more sinuous foraging paths (1.4 vs 1.1). However, there were no significant differences in the number of dives per foraging trip (68 vs 66), the average maximum depth attained (3.4 vs 3.6 m), nor the average or total dive duration per foraging trip (4.3 vs 4.3 s and 5.7 vs 4.3 min, respectively). We conclude that gannets from these 2 colonies are spatially segregated and experience different foraging conditions. We speculate that wind patterns and group feeding could generate such foraging asymmetries. Foraging site fidelity and memory effects could consolidate these asymmetries, and generate 'cultural' differences in foraging patterns.

These datasets and accompanying syntheses provide a greater understanding of fundamental ecosystem processes in the Southern Ocean, support modelling of predator distributions under future climate scenarios and create inputs that can be incorporated into decision making processes by management authorities. In this data paper, we present the compiled tracking data from research groups that have worked in the Antarctic since the 1990s. The data are publicly available through biodiversity.aq and the Ocean Biogeographic Information System. The archive includes tracking data from over 70 contributors across 12 national Antarctic programs, and includes data from 17 predator species, 4060 individual animals, and over 2.9 million observed locations.Scientific Data | (2020) 7:94 | https://doi.org/10.1038/s41597-020-0406-x www.nature.com/scientificdata www.nature.com/scientificdata/ circum-Antarctic synthesis yet exists that crosses species boundaries. This deficiency prompted the Expert Group on Birds and Marine Mammals (EG-BAMM) and the Expert Group on Antarctic Biodiversity Informatics (EGABI) of the Scientific Committee on Antarctic Research (SCAR; www.scar.org) to initiate in 2010 the Retrospective Analysis of Antarctic Tracking Data (RAATD). RAATD aims to advance our understanding of fundamental and applied questions in a data-driven way, matching research priorities already identified by the SCAR Horizon Scan 9,21 and key questions in animal movement ecology 22 . For these reasons, we worked on the collation, validation and preparation of tracking data collected south of 45 °S. Data from over seventy contributors (Data Contacts and Citations 23 ) were collated. This database includes information from seventeen predator species, 4,060 individuals and over 2.9 million at-sea locations. To exploit this unique dataset, RAATD is undertaking a multi-species assessment of habitat use for higher predators in the Southern Ocean 24 .RAATD will provide a greater understanding of predator distributions under varying climate regimes, and provide outputs that can inform spatial management and planning decisions by management authorities such as the Commission for the Conservation of Antarctic Marine Living Resources (CCAMLR; www.ccamlr.org). Our synopsis and analysis of multi-predator tracking data will also highlight regional or seasonal data-gaps.Scientific Data | (2020) 7:94 | https://doi.

T here are two primary methods of studying animals in the wild: observation from a distance, and observation of the animals from their own perspective. The former is the standard choice; this reflects our bias towards vision, our primary sense, and is illustrated by visual observation studies of nature going all the way back to Aristotle. This approach is common even today, although now the shortcomings in our visual capacity can be enhanced by technologies ranging from photography through infrared cameras, videos, and night vision devices to radar, echolocation, and hyperspectral scanners (Amlaner and McDonald 1980). Irrespective of the type of aids used to "observe" animals remotely, these studies are always hampered by elements that can come between the observer and the study animals (eg undergrowth, clouds, water, etc); in each case, the effects are exacerbated by distance and ultimately lead to range limitations.Telemetry (from the Greek tele, far, and metros, measure-ment) is a branch of science that seeks to eliminate such limitations, although in reality the first classic telemetry studies (using radio telemetry; Amlaner and McDonald 1980) were also range limited. In its ultimate form, however, this approach has no range limits, since both the sensory and recording systems are attached to the animal itself. This form of animal-attached remote sensing has recently been termed "bio-logging" (Naito 2004), a combination of the terms "biology" and "logging", the latter being derived from the old term "ship's log", where data were stored. The physical contact between the logging device, or recording tag, and the study animal allows the sensors to collect dataon a multitude of parameters, including heart beat frequency, skin humidity, and breathing rates, none of which are accessible by visual observation. Given the huge datastorage capacity available today, multiple variables can now be assessed simultaneously at rates of many times per second, to acquire millions of data points describing the biology of free-living animals over a wide range of time periods. In other words, bio-logging allows scientists in the field to record complex quantitative measurements from animals that are behaving completely naturally. First stepsHistorically, there have been four important conceptual stages in the development of bio-logging. The first stage involved the realization that animals can carry foreign objects attached to their bodies. This probably dates as far back as the origin of domestication, to the time when pack animals were first used. The second stage was reached when, for the first time, a device capable of transmitting information was attached to an animal to monitor something related to the animal itself. To the best of our knowledge, this occurred when 437 Animal-attached remote sensing, or bio-logging, refers to the deployment of autonomous recording tags on free-living animals, so that multiple variables can be monitored at rates of many times per second, thereby generating millions of data points over period...

International audienceSatellite telemetry data are a key source of animal distribution information for marine ecosystem management and conservation activities. We used two decades of telemetry data from the East Antarctic sector of the Southern Ocean. Habitat utilization models for the spring/summer period were developed for six highly abundant, wide-ranging meso- and top-predator species: Adélie Pygoscelisadeliae and emperor Aptenodytes forsteri penguins, light-mantled albatross Phoebetria palpebrata , Antarctic fur seals Arctocephalus gazella , southern elephant seals Mirounga leonina , and Weddell seals Leptony-chotes weddellii . The regional predictions from these models were combined to identify areas utilized by multiple species, and therefore likely to be of particular ecological significance. These areas were distributed across the longitudinal breadth of the East Antarctic sector, and were characterized by proximity to breeding colonies, both on the Antarctic continent and on subantarctic islands to the north, and by sea-ice dynamics, particularly locations of winter polynyas. These areas of important habitat were also congruent with many of the areas reported to be showing the strongest regional trends in sea ice seasonality. Th e results emphasize the importance of on-shore and sea-ice processes to Antarctic marine ecosystems. Our study provides ocean-basin-scale predictions of predator habitat utilization, an assessment of contemporary habitat use against which future changes can be assessed, and is of direct relevance to current conservation planning and spatial management efforts

Animal‐attached remote sensing, or bio‐logging, refers to the deployment of autonomous recording tags on free‐living animals, so that multiple variables can be monitored at rates of many times per second, thereby generating millions of data points over periods ranging from hours to years. Rapid advances in technology are allowing scientists to use data‐recording units to acquire huge, quantitative datasets of behavior from animals moving freely in their natural environment. In other words, scientists can examine wild animals in the field, behaving normally, with the same rigor that is normally used in the laboratory. The flexibility of such recording systems means that bio‐logging science operates at the interface of several biological disciplines, looking at a wide array of aquatic, airborne, and terrestrial species, monitoring not only the physical characteristics of the environment, but also the animal's reactions to it. This approach is critically important in an era when global change threatens the survival of species and where habitat loss is leading to widespread extinctions.

Animal ecology is shaped by energy costs, yet it is difficult to measure finescale energy expenditure in the wild. Because metabolism is often closely correlated with mechanical work, accelerometers have the potential to provide detailed information on energy expenditure of wild animals over fine temporal scales. Nonetheless, accelerometry needs to be validated on wild animals, especially across different locomotory modes. We merged data collected on 20 thick-billed murres (Uria lomvia) from miniature accelerometers with measurements of daily energy expenditure over 24 h using doubly labelled water. Across three different locomotory modes (swimming, flying and movement on land), dynamic body acceleration was a good predictor of daily energy expenditure as measured independently by doubly labelled water (R 2 ¼ 0.73).The most parsimonious model suggested that different equations were needed to predict energy expenditure from accelerometry for flying than for surface swimming or activity on land (R 2 ¼ 0.81). Our results demonstrate that accelerometers can provide an accurate integrated measure of energy expenditure in wild animals using many different locomotory modes.