From Sensor Data to Animal Behaviour: An Oystercatcher Example

Animal-borne sensors enable researchers to remotely track animals, their physiological state and body movements. Accelerometers, for example, have been used in several studies to measure body movement, posture, and energy expenditure, although predominantly in marine animals. In many studies, behaviour is often inferred from expert interpretation of sensor data and not validated with direct observations of the animal. The aim of this study was to derive models that could be used to classify oystercatcher (Haematopus ostralegus) behaviour based on sensor data. We measured the location, speed, and tri-axial acceleration of three oystercatchers using a flexible GPS tracking system and conducted simultaneous visual observations of the behaviour of these birds in their natural environment. We then used these data to develop three supervised classification trees of behaviour and finally applied one of the models to calculate time-activity budgets. The model based on accelerometer data developed to classify three behaviours (fly, terrestrial locomotion, and no movement) was much more accurate (cross-validation error = 0.14) than the model based on GPS-speed alone (cross-validation error = 0.35). The most parsimonious acceleration model designed to classify eight behaviours could distinguish five: fly, forage, body care, stand, and sit (cross-validation error = 0.28); other behaviours that were observed, such as aggression or handling of prey, could not be distinguished. Model limitations and potential improvements are discussed. The workflow design presented in this study can facilitate model development, be adapted to a wide range of species, and together with the appropriate measurements, can foster the study of behaviour and habitat use of free living animals throughout their annual routine

Nivolumab versus docetaxel in previously treated advanced non-small-cell lung cancer (CheckMate 017 and CheckMate 057): 3-year update and outcomes in patients with liver metastases

Abstract Background Long-term data with immune checkpoint inhibitors in non-small-cell lung cancer (NSCLC) are limited. Two phase III trials demonstrated improved overall survival (OS) and a favorable safety profile with the anti-programmed death-1 antibody nivolumab versus docetaxel in patients with previously treated advanced squamous (CheckMate 017) and nonsquamous (CheckMate 057) NSCLC. We report results from ≥3 years' follow-up, including subgroup analyses of patients with liver metastases, who historically have poorer prognosis among patients with NSCLC. Patients and methods Patients were randomized 1 : 1 to nivolumab (3 mg/kg every 2 weeks) or docetaxel (75 mg/m2 every 3 weeks) until progression or discontinuation. The primary end point of each study was OS. Patients with baseline liver metastases were pooled across studies by treatment for subgroup analyses. Results After 40.3 months' minimum follow-up in CheckMate 017 and 057, nivolumab continued to show an OS benefit versus docetaxel: estimated 3-year OS rates were 17% [95% confidence interval (CI), 14% to 21%] versus 8% (95% CI, 6% to 11%) in the pooled population with squamous or nonsquamous NSCLC. Nivolumab was generally well tolerated, with no new safety concerns identified. Of 854 randomized patients across both studies, 193 had baseline liver metastases. Nivolumab resulted in improved OS compared with docetaxel in patients with liver metastases (hazard ratio, 0.68; 95% CI, 0.50–0.91), consistent with findings from the overall pooled study population (hazard ratio, 0.70; 95% CI, 0.61–0.81). Rates of treatment-related hepatic adverse events (primarily grade 1–2 liver enzyme elevations) were slightly higher in nivolumab-treated patients with liver metastases (10%) than in the overall pooled population (6%). Conclusions After 3 years' minimum follow-up, nivolumab continued to demonstrate an OS benefit versus docetaxel in patients with advanced NSCLC. Similarly, nivolumab demonstrated an OS benefit versus docetaxel in patients with liver metastases, and remained well tolerated. Clinical trial registration CheckMate 017: NCT01642004; CheckMate 057: NCT01673867

Predator-derived bioregions in the Southern Ocean: Characteristics, drivers and representation in marine protected areas

Multiple initiatives have called for large-scale representative networks of marine protected areas (MPAs). MPAs should be ecologically representative to be effective, but in large, remote regions this can be difficult to quantify and assess. We present a novel bioregionalization for the Southern Ocean, which uses the modelled circumpolar habitat importance of 17 marine bird and mammal species. The habitat-use of these predators indicates biodiversity patterns that require representation in Southern Ocean conservation and management planning. In the predator habitat importance predictions, we identified 17 statistical clusters, falling into four larger groups. We characterized and contrasted these clusters based on their predator, prey and oceanographic characteristics. Under the existing Southern Ocean MPA network, some clusters fall short of 10 % representation, yet others meet or exceed these targets. Implementation of currently proposed MPAs can in some cases contribute to meeting even 30 % spatial coverage conservation targets. However, the effectiveness of mixed-use versus no-take MPAs should be taken into consideration, since some clusters are not adequately represented by no-take MPAs. These results, combined with previous studies in the Southern Ocean, can help inform the continued design, implementation, and evaluation of a representative system of MPAs for Southern Ocean conservation and management

The retrospective analysis of Antarctic tracking data project

The Retrospective Analysis of Antarctic Tracking Data (RAATD) is a Scientific Committee for Antarctic Research project led jointly by the Expert Groups on Birds and Marine Mammals and Antarctic Biodiversity Informatics, and endorsed by the Commission for the Conservation of Antarctic Marine Living Resources. RAATD consolidated tracking data for multiple species of Antarctic meso- and top-predators to identify Areas of Ecological Significance. 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

The retrospective analysis of Antarctic tracking data project

The Retrospective Analysis of Antarctic Tracking Data (RAATD) is a Scientific Committee for Antarctic Research project led jointly by the Expert Groups on Birds and Marine Mammals and Antarctic Biodiversity Informatics, and endorsed by the Commission for the Conservation of Antarctic Marine Living Resources. RAATD consolidated tracking data for multiple species of Antarctic meso- and top-predators to identify Areas of Ecological Significance. 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.Supplementary Figure S1: Filtered location data (black) and tag deployment locations (red) for each species. Maps are Lambert Azimuthal projections extending from 90° S to 20° S.Supplementary Table S1: Names and coordinates of the major study sites in the Southern Ocean and on the Antarctic Continent where tracking devices were deployed on the selected species (indicated by their 4-letter codes in the last column).Online Table 1: Description of fields (column names) in the metadata and data files.Supranational committees and organisations including the Scientific Committee on Antarctic Research Life Science Group and BirdLife International. National institutions and foundations, including but not limited to Argentina (Dirección Nacional del Antártico), Australia (Australian Antarctic program; Australian Research Council; Sea World Research and Rescue Foundation Inc., IMOS is a national collaborative research infrastructure, supported by the Australian Government and operated by a consortium of institutions as an unincorporated joint venture, with the University of Tasmania as Lead Agent), Belgium (Belgian Science Policy Office, EU Lifewatch ERIC), Brazil (Brazilian Antarctic Programme; Brazilian National Research Council (CNPq/MCTI) and CAPES), France (Agence Nationale de la Recherche; Centre National d’Etudes Spatiales; Centre National de la Recherche Scientifique; the French Foundation for Research on Biodiversity (FRB; www.fondationbiodiversite.fr) in the context of the CESAB project “RAATD”; Fondation Total; Institut Paul-Emile Victor; Programme Zone Atelier de Recherches sur l’Environnement Antarctique et Subantarctique; Terres Australes et Antarctiques Françaises), Germany (Deutsche Forschungsgemeinschaft, Hanse-Wissenschaftskolleg - Institute for Advanced Study), Italy (Italian National Antarctic Research Program; Ministry for Education University and Research), Japan (Japanese Antarctic Research Expedition; JSPS Kakenhi grant), Monaco (Fondation Prince Albert II de Monaco), New Zealand (Ministry for Primary Industries - BRAG; Pew Charitable Trusts), Norway (Norwegian Antarctic Research Expeditions; Norwegian Research Council), Portugal (Foundation for Science and Technology), South Africa (Department of Environmental Affairs; National Research Foundation; South African National Antarctic Programme), UK (Darwin Plus; Ecosystems Programme at the British Antarctic Survey; Natural Environment Research Council; WWF), and USA (U.S. AMLR Program of NOAA Fisheries; US Office of Polar Programs).http://www.nature.com/sdataam2021Mammal Research Institut