Little information exists on the environmental requirements of sponges from the Canadian Arctic, increasing the necessity to establish baseline distribution data on sponge assemblages to predict their susceptibility to climate change. Here we describe the sponge taxa of Hudson Strait, Ungava Bay, Western Davis Strait and Western Baffin Bay collected by Canadian research vessel trawl surveys. A total of 2026 sponge specimens were examined, and 93 different taxa were identified with 79% identified to species, of which 2 are new to science, 1 recorded for the first time in the North Atlantic, 16 are new records for the northwest Atlantic, and 10 are new records for the Baffin Bay, Davis Strait and Hudson Strait sponge fauna. Taxonomic distinctness was higher north of Cape Dyer and south of Davis Strait, whereas the number of species reached a maximum in Davis Strait, which represents the southern distribution limit of the arctic sponge fauna along the slope in this region. Five sponge species assemblages were identified, some of which have been observed elsewhere, suggesting that they may be common to the North Atlantic and at the generic level to the global oceans. Two of the Baffin Bay−Davis Strait assemblages were characterized by large structure-forming astrophorids: one, with arctic species, found at midwater depths in Baffin Bay and the other, characterized by boreal species, was found deeper, south of Davis Strait. Another assemblage characterized by glass and carnivorous sponges was found along the continental slope of western Baffin Bay. Candidate target indicator species are provided for future sponge community monitoring.

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Citizen science schemes enable ecological data collection over very large spatial and temporal scales, producing datasets of high value for both pure and applied research. However, the accuracy of citizen science data is often questioned, owing to issues surrounding data quality and verification, the process by which records are checked after submission for correctness. Verification is a critical process for ensuring data quality and for increasing trust in such datasets, but verification approaches vary considerably between schemes. Here, we systematically review approaches to verification across ecological citizen science schemes that feature in published research, aiming to identify the options available for verification, and to examine factors that influence the approaches used. We reviewed 259 schemes and were able to locate verification information for 142 of those. Expert verification was most widely used, especially among longer-running schemes, followed by community consensus and automated approaches. Expert verification has been the default approach for schemes in the past, but as the volume of data collected through citizen science schemes grows and the potential of automated approaches develops, many schemes might be able to implement approaches that verify data more efficiently. We present an idealised system for data verification, identifying schemes where this system could be applied and the requirements for implementation. We propose a hierarchical approach in which the bulk of records are verified by automation or community consensus, and any flagged records can then undergo additional levels of verification by experts.

On the cover: Various underwater photographs taken from within the NAFO Convention Area. Coral on top (Paragorgia arborea); Sponge on bottom: rock wall with several sponge and coral taxa.

Citizen science schemes (projects) enable ecological data collection over very large spatial and temporal scales, producing datasets of high value for both pure and applied research. However, the accuracy of citizen science data is often questioned, owing to issues surrounding data quality and verification, the process by which records are checked after submission for correctness. Verification is a critical process for ensuring data quality and for increasing trust in such datasets, but verification approaches vary considerably among schemes. Here, we systematically review approaches to verification across ecological citizen science schemes, which feature in published research, aiming to identify the options available for verification, and to examine factors that influence the approaches used (Baker et al. 2021). We reviewed 259 schemes and were able to locate verification information for 142 of those. Expert verification was most widely used, especially among longer-running schemes. Community consensus was the second most common verification approach, used by schemes such as Snapshot Serengeti (Swanson et al. 2016) and MammalWeb (Hsing et al. 2018). It was more common among schemes with a larger number of participants and where photos or video had to be submitted with each record. Automated verification was not widely used among the schemes reviewed. Schemes that used automation, such as eBird (Kelling et al. 2011) and Project FeederWatch (Bonter and Cooper 2012) did so in conjunction with other methods such as expert verification. Expert verification has been the default approach for schemes in the past, but as the volume of data collected through citizen science schemes grows and the potential of automated approaches develops, many schemes might be able to implement approaches that verify data more efficiently. We present an idealised system for data verification, identifying schemes where this hierachical system could be applied and the requirements for implementation. We propose a hierarchical approach in which the bulk of records are verified by automation or community consensus, and any flagged records can then undergo additional levels of verification by experts.