The Retrospective Analysis of Antarctic Tracking (Standardised) Data from the Scientific Committee on Antarctic Research

データ レコード

この sampling event リソース内のデータは、1 つまたは複数のデータ テーブルとして生物多様性データを共有するための標準化された形式であるダーウィン コア アーカイブ (DwC-A) として公開されています。 コア データ テーブルには、4,060 レコードが含まれています。

拡張データ テーブルは1 件存在しています。拡張レコードは、コアのレコードについての追加情報を提供するものです。 各拡張データ テーブル内のレコード数を以下に示します。

この IPT はデータをアーカイブし、データ リポジトリとして機能します。データとリソースのメタデータは、 ダウンロード セクションからダウンロードできます。 バージョン テーブルから公開可能な他のバージョンを閲覧でき、リソースに加えられた変更を知ることができます。

バージョン

次の表は、公にアクセス可能な公開バージョンのリソースのみ表示しています。

引用方法

研究者はこの研究内容を以下のように引用する必要があります。:

Ropert-Coudert Y, Van de Putte A P, Bornemann H, Charrassin J, Costa D P, Danis B, Hückstädt L A, Jonsen I D, Lea M, Reisinger R R, Thompson D, Torres L G, Trathan P N, Wotherspoon S, Ainley D G, Alderman R, Andrews-Goff V, Arthur B, Ballard G, Bengtson J, Bester M N, Boehme L, Bost C, Boveng P, Cleeland J, Constantine R, Crawford R J M, Dalla Rosa L, de Bruyn P N, Delord K, Descamps S, Double M, Emmerson L, Fedak M, Friedlander A, Gales N, Goebel M, Goetz K T, Guinet C, Goldsworthy S D, Harcourt R, Hinke J, Jerosch K, Kato A, Kerry K R, Kirkwood R, Kooyma G L, Kovacs K M, Lawton K, Lowther A D, Lydersen C, Lyver P O, Makhado A B, Márquez M E I, McDonald B, McMahon C, Muelbert M, Nachtsheim D, Nicholls K W, Nordøy E S, Olmastroni S, Phillips R A, Pistorius P, Plötz J, Pütz K, Ratcliffe N, Ryan P G, Santos M, Schytte Blix A, Southwell C, Staniland I, Takahashi A, Tarroux A, Trivelpiece W, Wakefield E, Weimerskirch H, Wienecke B, Xavier J C, Raymond B, Hindell M A, Van de Putte A (2024). The Retrospective Analysis of Antarctic Tracking (Standardised) Data from the Scientific Committee on Antarctic Research. Version 1.5. SCAR - AntOBIS. Samplingevent dataset. https://ipt.biodiversity.aq/resource?r=scar_raatd_trackingdata&v=1.5

権利

研究者は権利に関する下記ステートメントを尊重する必要があります。:

パブリッシャーとライセンス保持者権利者は SCAR - AntOBIS。 This work is licensed under a Creative Commons Attribution (CC-BY 4.0) License.

GBIF登録

このリソースをはGBIF と登録されており GBIF UUID: b86fe411-8e62-4cd0-aab2-914d75401598が割り当てられています。   Ocean Biodiversity Information System によって承認されたデータ パブリッシャーとして GBIF に登録されているSCAR - AntOBIS が、このリソースをパブリッシュしました。

キーワード

Occurrence; ANIMAL ECOLOGY AND BEHAVIOR BIRDS ALBATROSSES/PETRELS AND ALLIES PENGUINS MAMMALS SEALS/SEA LIONS/WALRUSES BALEEN WHALES; Samplingevent

外部データ

リソース データは他の形式で入手可能です。

連絡先

地理的範囲

The dataset is restricted to the Southern Ocean, in a broad sense. All species considered in this dataset have circumpolar distributions with a longitudinal range spanning 180°W to 180°E. The species breed either on the coast of the Antarctic continent or on the sub-Antarctic islands to the north

生物分類学的範囲

Seventeen species of meso- and top predators were included in the dataset, five marine mammals (one baleen whale, one otariid and three phocid seals) and twelve seabirds (five penguin, five albatross, and two petrels). The species cover a diverse range of ecological niches and life history traits and include dietary specialists (e.g. crabeater seals), deep divers (e.g. elephant seal Mirounga leonina and emperor penguin Aptenodytes forsteri), wide ranging, highly migratory species (e.g. wandering albatross Diomedea exulans), nearshore foragers (e.g. Adélie penguin Pygoscelis adeliae) and capital (e.g. Weddell seal) versus income (e.g. Antarctic fur seal Arctocephalus gazella) breeders.

時間的範囲

プロジェクトデータ

The Retrospective Analysis of Antarctic Tracking Data (RAATD) was initiated in 2010 by 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). RAATD aims to advance our understanding of both fundamental and applied questions in a data-driven way, matching research priorities already identified by the SCAR Horizon Scanand key questions in animal movement ecology. For these reasons, we worked on the collation, validation and preparation of tracking data collected south of 45° S. Data from over 70 contributors were collated. This database includes information from 17 predator species, including 4,060 individuals and over 2.9 million at-sea locations. To exploit this unique dataset, RAATD undertook a multi-species assessment of habitat use for higher predators in the Southern Ocean (Hindell et al. in prep.).

プロジェクトに携わる要員:

収集方法

Original deployment of tracking devices The RAATD core group (Fig. 1) aggregated data from three types of tracking device used by individual research teams. In increasing order of precision these are light recording Global Location Sensors (GLS loggers or geolocators), satellite-relayed Platform Terminal Transmitters (PTTs), and Global Positioning System devices (GPS). Typically, GLS and GPS devices record data in internal memory, and so must be physically recovered in order to download the data. PTTs transmit a carrier signal to satellites, and so can deliver data remotely and in near-real time. Some modern devices now combine the capabilities of PTT and GPS (or other) devices, relaying data to satellites. A GLS device, which is the smallest thus allowing deployment on the smaller predators, typically records ambient light levels through the day from which relatively coarse estimates of latitude and longitude can be calculated (~100–200 km) using day length and timing of local noon. Some GLS units can also record sea surface temperature, and this can help refine position estimates. GLS locations were estimated by the data contributors using five methods (Phillips et al. 2004, Sumner et al. 2009, Lisovski & Hahn 2012, Bindoff 2017, Wotherspoon 2017) (Supplementary File S2). With very small batteries, the data are usually archived and thus animals must be recaptured to download the data. GPS tags make use of global navigation satellite systems and provide very high resolution (~10 m) location fixes and time information. PTT tags transmit signals to ARGOS satellites which transfer the received signals and their frequencies to a receiving station at the Collecte de Localisation Satellites (CLS) in Toulouse, France, to estimate locations based on Doppler shifts in the received signals to a medium level of accuracy(~1,000 m). Processing by CLS involved a least-squares filtering method up to 2008, thereafter using Kalman filters (Lopez et al. 2014). Different models of GLS, PTT, and GPS devices from different manufactures have been used throughout the years, each of these having specific characteristics (size, operating modes, etc.) that may influence accuracy of the locations, but as device type was not always provided by the data providers, we applied standard corrections (see below). Device attachment to animals was also species-specific. When loggers are small enough, like GLS, they are mounted on leg or flipper bands, while larger data-loggers and transmitters are often attached to the plumage or fur on the back or head of the animal, a position that optimizes data communication with satellites. Modes of attachment on the back varied from using harnesses, glue or marine tape. For whales, transmitters with cutaneous anchors were attached to the back, using poles, cross bows or air guns. Scientists limited handling time and stress as much as possible during attachment and retrieval of devices (e.g. Field et al. 2012), following established animal handling guidelines, and institutional ethical review. However, it should be noted that our dataset contains tracking data that span almost three decades during which time substantial progress has been made in terms of miniaturization and advances in electronic components. Any adverse effects of devices on animals are therefore likely to be less acute in recent years than in the earlier years of tracking. Consideration of adverse reaction on study animals has been reviewed in the past (e.g. Phillips et al. 2003, Vandenabeele et al. 2012, Bannasch et al. 1994).

Method step description:

1. Data Collection Starting from 2010, the core group of RAATD compiled a catalogue of existing (both published and unpublished) tracking data by contacting international experts and asking them to contribute data. The data collection phase ended in 2016. Besides directly contacting researchers, the team also harvested data from existing repositories, including the Australian Antarctic Data Center (https://data.aad.gov.au/), the Integrated Marine Observing System (http://imos.org.au/), PANGAEA (https://www.pangaea.de/), BirdLife International (http://www.seabirdtracking.org/), the Antarctic Biodiversity Portal (http://www.biodiversity.aq/), Ocean Biogeographic Information System (http://www.iobis.org/), and the Global Biodiversity Information Facility (http://www.gbif.org/).
2. Associated metadata Where available, information on the deployment site and relevant characteristics of the animal at the time of deployment was standardized by the data editors. Where age class and sex were known, these were included in the metadata
3. Data standardization Location dates and times were converted to UTC (Coordinated Universal Time). Records with missing latitude or longitude values were removed, and all longitudes were transformed to lie between 180° W and 180° E. Data files were row-ordered by individual, with rows within an individual in their correct temporal sequence. Near-duplicate positions were removed, those positions defined as having occurred 3 seconds or less after an existing position fix from the same animal, and which had identical longitude and latitude values (for GPS devices) or longitude and latitude values that differed by less than 1e-05 and which had the same location quality value (for PTT devices). Entries in the age class, breeding stage, device type, location quality, scientific, common, and abbreviated name, sex, and deployment site columns were validated against controlled vocabularies. Mandatory entries (e.g. deployment date, device type, individual animal identifier) were checked for missing values. Deployment locations were recorded by the original field team either at the individual animal level (using e.g. a hand-held GPS device) or at the deployment-site level (i.e. one deployment location per group of animals). The latter was common for deployments at colonies, whereas the former was most common for non-colony deployments (e.g. on seals and whales). Where deployment locations were not recorded by the field team, the first location estimate(s) in the tracking data were used. Deployment site names were standardized to colony granularity wherever possible (e.g. to the beach-on-island level). Periods at the start or end of deployments were identified and discarded if there was evidence that location data during these periods did not represent the animals’ at-sea movement. For example, tags may have been turned on early (thereby recording locations prior to their deployment on animals) or animals may have remained at the deployment site, e.g. the breeding colony, for an extended period at the start or end of the tag deployment. Some tracks also showed a marked deterioration in the frequency and quality (for PTTs) of location estimates near the end of a track. Such locations were visually identified based on maps of each track in conjunction with plots of location distance from deployment site against time. This information is captured in the location_to_keep column appended to each species’ raw data file (1 = keep, 0 = discard).
4. Data filtering The trimmed data were subjected to a number of automated quality control checks to remove individual deployments that: 1) were flagged for removal by the Data Editor Group (using the keepornot column in the metadata file); 2) had fewer than 20 location records; and 3) had deployments lasting less than 1 day. Additionally, individual deployments were checked to ensure that: 1) duplicate records in PTTs (locations occurring within 2 min of each other) were removed; 2) PTT Argos Z-class locations were reclassified as B-class locations (the least precise Argos location quality class that has an associated error variance; Jonsen et al. 2005); and 3) locations implying unrealistic travel rates (> 10 m s-1 for penguins and marine mammals and > 30 m s-1 for flying seabirds) were removed. Note that the definition of “duplicate locations” in this filtering context is more aggressive than that used during data standardization: for standardization purposes, the intention was to keep the data as close to original as possible, whereas for filtering the presence of multiple positions in a short period of time (< 2 min) has a negative effect on the filter performance. A state-space model (SSM) was used to estimate locations at regular time intervals (1 h for GPS data; 2 h for Argos data; 12 h for GLS data) and account for measurement error in the original observations (Jonsen et al. 2005, Block et al. 2011). The data were SSM-filtered and subject to a final quality control where tracks that failed to converge, as judged by nlminb convergence criteria (Nash 2014), were re-fit using different initial values. If re-fit tracks continually failed to converge they were removed from the final filtered dataset. For converged tracks, longitude and latitude residuals were examined for systematic trends indicative of lack of fit. Tracks that failed this inspection were removed from the final filtered dataset.
5. Data publication The core working team of RAATD established a data sharing and publication agreement with all data providers in 2017. The standardized (trimmed) data are held here. The filtered data are published in international repositories (see details below, in the ‘Filtered Data’ section).

追加のメタデータ