Articles | Volume 16, issue 3
https://doi.org/10.5194/essd-16-1177-2024
https://doi.org/10.5194/essd-16-1177-2024
Data description paper
 | 
05 Mar 2024
Data description paper |  | 05 Mar 2024

PANABIO: a point-referenced PAN-Arctic data collection of benthic BIOtas

Dieter Piepenburg, Thomas Brey, Katharina Teschke, Jennifer Dannheim, Paul Kloss, Marianne Rehage, Miriam L. S. Hansen, and Casper Kraan
Abstract

Profound environmental changes, such as drastic sea-ice decline, leave large-scale ecological footprints on the distribution and composition of marine biota in the Arctic. Currently, the impact of such stressors is not sufficiently understood due to the lack of pan-Arctic data that allow for estimating ecological baselines as well as modelling current and forecast potential changes in benthic biodiversity and ecosystem functioning. Here, we introduce the PAN-Arctic data collection of benthic BIOtas (PANABIO) and discuss its timeliness, potential, and details of its further development. The data collection contains individual datasets with records (presence, counts, abundance, or biomass) of benthic fauna, usually at genus level or species level, which were identified in field samples obtained at point-referenced locations (stations) by means of grabs, towed gear, or seabed imaging. The data cover the entire pan-Arctic realm, i.e. the central Arctic Ocean, Chukchi Sea, East Siberian Sea, Laptev Sea, Kara Sea, Barents Sea (including the White Sea), Svalbard waters, Greenland Sea, Norwegian Sea, Canadian Archipelago, Beaufort Sea, and Bering Sea as well as some adjacent sub-Arctic regions (Sea of Japan, Gulf of Okhotsk). Currently (as of 14 December 2023), PANABIO includes 27 datasets with a total of 126 388 records of 2978 taxa collected from 11 555 samples taken at 10 596 stations during 1095 cruises between 1800 and 2014. These numbers will increase with more data becoming available over time through contributions from PANABIO users. The data collection is available in a PostgreSQL-based data warehouse that can be accessed and queried through an open-access front-end web service at https://critterbase.awi.de/panabio (last access: 27 February 2024). A snapshot of the current data collection and its 27 individual datasets is also available from the data publisher PANGAEA (https://doi.org/10.1594/PANGAEA.963640, Piepenburg et al., 2023).

1 Introduction

Climate change progresses faster and stronger in the Arctic than elsewhere (Meredith et al., 2019; Constable et al., 2022). Therefore, Arctic marine organisms and ecosystems are particularly affected and potentially threatened at large scales by accelerating environmental change, such as ocean warming and acidification, sea-ice decline, and increased riverine input (Kedra et al., 2015), albeit with distinct regional differences. Moreover, expanding human activities, such as exploration and exploitation of natural resources, ship traffic, and tourism, add further pressures. Accordingly, there is an urgent need for a thorough and rapid assessment of how environmental changes may alter ecosystems and their functioning in polar latitudes (Degen et al., 2018). However, research on footprints and implications of climate change and direct anthropogenic impacts – such as range shifts, changes in abundance, declines in growth and condition, and community and regime shifts – has been hampered by the problems of accessing sound data, which is unevenly distributed among regions and taxonomic groups (Wassmann et al., 2011).

Recent research emphasised the critical importance of the direct and indirect ecological effects of sea-ice decline, which is one of the most striking and far-reaching footprints of climate change in polar regions (Macias-Fauria and Post, 2018). Sea ice is a dominant ecological driver in Arctic seas, as it not only represents a specifically polar marine habitat but also controls the light, stratification, and, hence, productivity regime of the underlying water column (Bluhm and Gradinger, 2008). Both pelagic and benthic secondary production depend directly on sea ice and pelagic primary production. Ultimately, due to the importance of the cryo-pelagic–benthic coupling in polar seas, the loss of sea ice will very likely have profound consequences for the diversity, structure, and functioning of benthic fauna across pan-Arctic seascapes (Piepenburg, 2005; Hinzman et al., 2013; Macias-Fauria and Post, 2018; Wassmann et al., 2011; Brandt et al., 2023).

However, the challenge of quantifying, understanding, modelling, and forecasting the impact of climate change and anthropogenic pressures on Arctic benthic species and assemblages has rarely been addressed (e.g. Renaud et al., 2019; Pantiukhin et al., 2021). Indeed, it is commonly acknowledged that currently neither the direction nor mode of ongoing or future ecological regime shifts are sufficiently investigated and understood to soundly predict forthcoming changes in Arctic marine ecosystem functions (Wassmann et al., 2011; Post et al., 2013; Meredith et al., 2019; Constable et al., 2022; Brandt et al., 2023). This gap can be attributed to the difficulty of obtaining solid data from the Arctic due to, amongst others, its remoteness and hostile environmental conditions, the tremendous costs of sea-going polar research, and a lack of synergistic research spanning multiple Arctic ecoregions.

Consequently, the urgently needed integrative approach can only be achieved through an upscaling from local to large scales, but this would require a comprehensive data source representative of the whole Arctic. Here, we present PANABIO, the PAN-Arctic data collection of benthic BIOtas within the CRITTERBASE data warehouse (Teschke et al., 2022). It provides standardised open access to point-referenced quantitative ecological data by integrating data from various sources and of various formats through a generic data–ingest interface and offering versatile exploration tools for data filtering and mapping provided by the overarching CRITTERBASE system.

https://essd.copernicus.org/articles/16/1177/2024/essd-16-1177-2024-f01

Figure 1Major Arctic sea regions, embedded in five Major Fishing Areas of the Food and Agriculture Organization (FAO) (orange: no. 18 – Arctic Sea; yellow: no. 21 – northwest Atlantic; blue: no. 27 – northeast Atlantic; green: no. 61 – northwest Pacific; purple: no. 67 – northeast Pacific). The map was created with ArcGIS Earth (Earthstar Geographics Esri).

Table 1Summary of the data contained within the pan-Arctic data collection of benthic biota (PANABIO), grouped by region: number of stations, samples, records, and taxa by the FAO (Food and Agriculture Organization of the United Nations) areas (each encompassing various Arctic sea regions). Please note that the number of taxa in the five FAO areas does not sum up because taxa can occur in two or more areas.

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2 Material and methods

2.1 Definition of study area

We used the common definition of Arctic seas proposed by the Arctic Monitoring and Assessment Programme (AMAP; https://www.amap.no, last access: 27 February 2024) to identify our pan-Arctic study area, which also includes some adjacent sub-Arctic regions, such as the Sea of Okhotsk and the Sea of Japan (Fig. 1; Table 1), since the distribution ranges of many species occurring in Arctic seas extend into the bordering areas. In the global CRITTERBASE data warehouse, the PANABIO data collection and its individual datasets can be filtered by the statistical areas used by the Food and Agriculture Organization of the United Nations (FAO).

Table 2Information about individual datasets currently (as of 14 December 2023) contained within the pan-Arctic data collection of benthic biota (PANABIO), including the names of datasets, reference persons and their institutions as well as the numbers of cruises, years (ranges), stations, water depths (ranges), samples, records, and taxa. Please note that the number of taxa in the 27 datasets does not sum up because taxa can occur in two or more datasets.

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2.2 Data sources

The PANABIO data collection currently contains 27 point-referenced datasets of benthic taxa from various sources, a total of 17 of which have previously been compiled during the Arctic Ocean Diversity (ArcOD) project (see Piepenburg et al., 2011) and/or are published in the PANGAEA data repository (https://www.pangaea.de, last access: 27 February 2024) and the Arctic OBIS node (https://obis.org/node/da50007b-7871-46cf-8530-441b5836d2c1, last access: 27 February 2024). The individual datasets vary in origin as well as in spatial, temporal, and taxonomic coverage (Piepenburg et al., 2023). The provenance of each dataset is indicated in PANABIO, including contact person and DOIs of a related peer-reviewed article and/or data publication (Table 2). Note that the number of datasets represents the current stock. We anticipate that additional sets of further historical or novel data will be added over time, by us and other data contributors, using the Collector App of CRITTERBASE (Teschke et al., 2022), resulting in a steady growth of the data collection in the number of datasets, records, and samples and thus also in spatial and temporal resolution and taxonomic coverage.

2.3 Data compilation

Following the workflow suggested by Piepenburg et al. (2011), we first harmonised and validated the nomenclature by consistently using the valid taxon name according to the World Registry of Marine Species (WoRMS; https://www.marinespecies.org, last access: 27 February 2024). This prevents confounding cross-data comparisons and prevents the inflation of diversity estimates due to the use of synonyms or unaccepted species names. Therefore, to the best of our knowledge, each record in the validated data collection represents a single taxonomic unit (mostly species or genera, only in some cases at higher levels if a sound and reliable identification at species or genus level was not possible).

Data records provide information about the occurrence (presence) or, if available, counts of specimens, abundance (numbers of individuals per area), and/or biomass (wet mass or ash-free dry mass) of each taxon encountered and identified in a sample taken at a specific place at a specific time. Metadata inform about geographic location, region, water depth (m), and date of sampling as well as the sampling gear used (such as towed gear, grabs, corers, or seafloor imaging). The latter determines which part of the benthic community is generally represented in the samples; e.g. trawl catches contain mainly megabenthos, grab samples macrobenthos, and seafloor images epibenthos. Metadata also provide information about the taxonomic coverage of the dataset to indicate it comprises only a certain taxonomic subset of the entire benthic fauna (e.g. “polychaetes” because only this group has been analysed in the samples on a species or genus level). Furthermore, the full taxonomic tree is given for each taxon to allow for summarising and scaling information from species to kingdom level. For more detailed information about the data model and data quality control, see Teschke et al. (2022) and https://critterbase.awi.de/#qc (last access: 27 February 2024).

https://essd.copernicus.org/articles/16/1177/2024/essd-16-1177-2024-f02

Figure 2Locations of point-referenced data in the PANABIO data collection, embedded in five Major Fishing Areas of the Food and Agriculture Organization (FAO) (orange: no. 18 – Arctic Sea; yellow: no. 21 – northwest Atlantic; blue: no. 27 – northeast Atlantic; green: no. 61 – northwest Pacific; purple: no. 67 – northeast Pacific). The map was created with ArcGIS Earth (Earthstar Geographics Esri).

3 Data availability

Currently (as of 14 December 2023), PANABIO contains 126 389 records from 11 555 samples taken at 10 597 stations during 1094 cruises (Table 2; Fig. 2) and 2968 taxonomic entities, collected between 1800 and 2014. These circumpolar data on Arctic benthic biodiversity, comprising mainly Echinodermata, Arthropoda, Mollusca, and Annelida, are hosted as the Arctic regional component of the PostgreSQL-based global data warehouse CRITTERBASE (Teschke et al., 2022) and can be accessed via a web portal at https://critterbase.awi.de/#map-panabio (last access: 27 February 2024, Piepenburg et al., 2024). Here, the entire data collection or, if required, only excerpts of it can be downloaded as CSV and/or Excel files, to be used for further analysis. Moreover, a snapshot of the current version of the PANABIO data collection is available from the data publisher PANGAEA via https://doi.org/10.1594/PANGAEA.963640 (Piepenburg et al., 2023), from where each individual dataset can be accessed separately through its own DOI link.

4 Discussion

4.1 Timeliness

Open-access data collections on pan-Arctic benthic biota, such as PANABIO, are needed to explore and forecast potential impacts of climate change on benthic diversity and food-web complexity and its consequences for higher trophic levels, such as marine mammals and seabirds, relying on benthic fauna for food (Macias-Fauria and Post, 2018; Post et al., 2013; Wassmann et al., 2011). Moreover, they are also needed to obtain a scientifically sound baseline of current diversity patterns in Arctic benthic systems, from which further change can be assessed. PANABIO does not aim to replace but rather complement the services offered by common and well-established data repositories, such as the Global Biodiversity Information Facility (GBIF; https://www.gbif.org/, last access: 27 February 2024), the Ocean Biodiversity Information System (OBIS; https://obis.org/, last access: 27 February 2024), and PANGAEA. There is one crucial difference: while such repositories focus on long-term data storage and/or on individual datasets, PANABIO provides a “ready-to-use”, standardised, and quality-checked compilation of many individual datasets. Moreover, it has a distinct focus on Arctic benthos, facilitating instant and low-level access to relevant data for the Arctic benthic research community as well as providing an attractive tool for cooperation and networking. It is currently used as a tool for managing and exploring the first comprehensive bottom grab-sample survey of the Marine Benthos Expert Network of the Circumpolar Biodiversity Monitoring Program (CBMP) of the Arctic Council working group Conservation of Arctic Flora and Fauna (CAFF) (https://caff.is/work/projects/circumpolar-biodiversity-monitoring-program-cbmp/cbmp-marine-biodiversity-monitoring/benthos-expert-network/ (last access: 27 February 2024). Such results are also important for informing decision-makers and the general public, who are concerned about the vulnerability of Arctic environments due to emerging and rapidly increasing economic and geopolitical interests.

4.2 Outlook

Arctic marine biotic data are still fragmented into a dazzling array of databases and files, most of which are not public. This situation seriously hampers progress in Arctic marine ecological research, as it prevents us from coupling environmental dynamics with ecological dynamics across large spatial and temporal scales. Currently this problem is tackled from various perspectives; e.g. WoRMS addresses marine taxonomic inconsistencies at a global scale (WoRMS Editorial Board, 2023), AquaMaps (Kaschner et al., 2016) provides ecological and biological information on marine species, and OBIS (De Pooter et al., 2017) allows us to explore marine species occurrences in relation to environmental conditions. In the context of these ongoing and planned efforts, PANABIO represents a regional data collection of the biological data warehouse CRITTERBASE with an open-access web service that allows on-the-fly exploration, selection, and download of geo-referenced and validated Arctic benthic biodiversity data. What is still lacking in the PANABIO data collection and what we currently are investing in is the development of an interface and a workflow to link the biotic data in PANABIO to ecological data layers from Arctic regions, such as raster information on bottom topography, sea-ice and ocean dynamics, or chlorophyll a distribution patterns, to support analysis and modelling work in day-to-day operations. In addition, the free availability of the PANABIO data collection via CRITTERBASE and PANGAEA (where static snapshots of the collection will be published at regular time intervals) will be complemented through an interface of the two global biodiversity networks GBIF and OBIS.

Competing interests

The contact author has declared that none of the authors has any competing interests.

Author contributions

All authors contributed to information system design; data collection, curation and analysis; and writing and reviewing the paper. CK and DP wrote the first version of the paper.

Disclaimer

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this paper. While Copernicus Publications makes every effort to include appropriate place names, the final responsibility lies with the authors.

Acknowledgements

We greatly appreciate all researchers who contributed their data to the open-access PANABIO project. CRITTERBASE is supported by the DFG project “NFDI4Biodiversity”.

Financial support

Casper Kraan was funded by a Marie Skłodowska-Curie action (grant no. 700796). Katharina Teschke was financially supported by the German Federal Ministry of Food and Agriculture (BMEL) through the Federal Office for Agriculture and Food (BLE) (grant no. 2819HS015).

The article processing charges for this open-access publication were covered by the Alfred-Wegener-Institut Helmholtz-Zentrum für Polar- und Meeresforschung.

Review statement

This paper was edited by Giuseppe M. R. Manzella and reviewed by Paul E. Renaud and two anonymous referees.

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Short summary
Research on ecological footprints of climate change and human impacts in Arctic seas is still hampered by problems in accessing sound data, which is unevenly distributed among regions and faunal groups. To address this issue, we present the PAN-Arctic data collection of benthic BIOtas (PANABIO). It provides open access to valuable biodiversity information by integrating data from various sources and of various formats and offers versatile exploration tools for data filtering and mapping.
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