Tracking of marine predators to protect Southern Ocean ecosystems

Abstract

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.

Changes in Phytoplankton Communities Along the Northern Antarctic Peninsula: Causes, Impacts and Research Priorities

“…The projected increase in summer tourism along the NAP (Bender et al, 2016), despite its potential environmental impacts, may also be used as a platform to further monitor biological changes, whether through sensors attached to ships or through citizen science (Brosnan et al, 2015). Moreover, autonomous in situ data sources, such as underwater gliders, floats, drifters or animal-attached instruments, have already shown their potential to further increase the in situ data volume in the Southern Ocean (e.g., Meredith et al, 2013;Roquet et al, 2014;Haëntjens et al, 2017;Thomalla et al, 2017;Hindell et al, 2020). Autonomous data sources allow for the monitoring of the detailed seasonality of phytoplankton biomass and physical and chemical factors (Eriksen et al, 2018).…”

Temporal and spatial differences in the post-breeding behaviour of a ubiquitous Southern Hemisphere seabird, the common diving petrel

“…Spatial segregation is often considered to be a mechanism to reduce competition between species and/or populations [17]. The Polar Front is known to be exploited by a wide range of other seabirds in the region [70][71][72], including the closely related South Georgian diving petrel (Pelecanoides georgicus; A. Fromant et al 2018, unpublished data). Consequently, the observation that post-breeding CDP from Kerguelen do not target the closest Polar Front area, but head farther south, could be owing to intra-and interspecific competition occurring in the densely populated southern Indian Ocean [37].…”

A continuous-time state-space model for rapid quality control of argos locations from animal-borne tags

“…Fitting a state-space model to animal location data is not a panacea. Many ecological analyses of animal tracking data consider remotely sensed or other environmental data at spatial resolutions (2 -10 km; e.g., [32]) approaching the state-space model accuracy limits found here. This highlights the need for researchers to consider the appropriate resolution of their environmental data given their specific questions and the limitations of their location estimates.…”