Diversity, abundance and community structure of benthic macro- and megafauna on the Beaufort shelf and slope.

Diversity and community patterns of macro- and megafauna were compared on the Canadian Beaufort shelf and slope. Faunal sampling collected 247 taxa from 48 stations with box core and trawl gear over the summers of 2009-2011 between 50 and 1,000 m in depth. Of the 80 macrofaunal and 167 megafaunal taxa, 23% were uniques, present at only one station. Rare taxa were found to increase proportional to total taxa richness and differ between the shelf (< 100 m) where they tended to be sparse and the slope where they were relatively abundant. The macrofauna principally comprised polychaetes with nephtyid polychaetes dominant on the shelf and maldanid polychaetes (up to 92% in relative abundance/station) dominant on the slope. The megafauna principally comprised echinoderms with Ophiocten sp. (up to 90% in relative abundance/station) dominant on the shelf and Ophiopleura sp. dominant on the slope. Macro- and megafauna had divergent patterns of abundance, taxa richness (α diversity) and β diversity. A greater degree of macrofaunal than megafaunal variation in abundance, richness and β diversity was explained by confounding factors: location (east-west), sampling year and the timing of sampling with respect to sea-ice conditions. Change in megafaunal abundance, richness and β diversity was greatest across the depth gradient, with total abundance and richness elevated on the shelf compared to the slope. We conclude that megafaunal slope taxa were differentiated from shelf taxa, as faunal replacement not nestedness appears to be the main driver of megafaunal β diversity across the depth gradient

Comparison of macrofaunal (light blue) and megafaunal (dark blue) abundance and taxa richness between shelf and slope stations and sampling years.

<p>Mean total abundance (left panel) and mean taxa richness (right panel). Stations grouped by shelf and slope (top panel) and stations grouped by year on shelf or slope (bottom panels). Bars represent 95% confidence intervals. Sample size (N) is denoted by number on bar. Sample area of macrofauna: 0.125 m² and megafauna: 450 m</p

Sampling stations and ice coverage on the Beaufort shelf and slope from 2009 to 2011.

<p>One box core and trawl sample were collected from each station (left panel). Sample sizes were n = 18 in 2009, n = 18 in 2010 and n = 12 in 2011. Black dotted line outlines the spatial extent of sampling used to calculate the average slope. Ice coverage (white area, right panel) for 2009, 2010 and 2011 benthic sampling periods. Blue coverage area outlines the area over which ice coverage was calculated. Blue lines in plots represent historic ice coverage (median from 1981 to 2010). Green bars indicate when benthic sampling occurred. Ice coverage data courtesy of Canadian Ice Service, Environment Canada.</p

Relative abundance and depth ranges of dominant taxa.

<p>Average relative abundance of dominant taxa by cluster (left). Dominant taxa ordered by their contribution to average relative abundance in clusters A, B, C and D, are highlighted by cluster to illustrate the relative contribution of taxa to each cluster. Colours represent faunal clusters defined in dendrograms (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0101556#pone-0101556-g008" target="_blank">Figure 8</a>). Depth range of corresponding taxa (right); grey circles denote mean depth of samples where taxa were present.</p

Individual-based rarefaction curves for 2009 and 2010 megafaunal datasets.

<p>(A) non-normalized counts and (B) counts normalized to the average trawl area. Curves represent the average of 900 resampling permutations.</p

Relationships of total macro- and megafaunal abundance with depth.

<p>Abundance in number of individuals per sample. Sample area of macrofauna: 0.125 m² and megafauna: 450 m Spearman's rank correlation coefficient denoted by </p

Analysis of variance of macro- and megafaunal abundance and taxa richness with year and depth.

<p>Categorical variables: depth  =  shelf/slope and year  =  2009/2010/2011. Abundance was log transformed to normalize residuals. Significance codes: </p

Map of Beaufort sampling region with georeferenced clusters.

<p>Colours represent macro- and megafaunal clusters defined in dendrograms (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0101556#pone-0101556-g008" target="_blank">Figure 8</a>).</p

Distribution of occurrence.

<p>(A) Distribution of occurrence as percent of sites occupied (binned intervals starting with 1-10%) and (B) mean relative abundance (%) by percent of sites occupied. Relative abundance, a measure of local abundance, was averaged only across sites where taxa were present. Vertical grey line represents rarity cut-off at 10% and horizontal grey line denotes the median average relative abundance. Spearman's rank correlation coefficient denoted by </p

Expert, Crowd, Students or Algorithm: who holds the key to deep-sea imagery ‘big data’ processing?

1.Recent technological development has increased our capacity to study the deep sea and the marine benthic realm, particularly with the development of multidisciplinary seafloor observatories. Since 2006, Ocean Networks Canada cabled observatories, have acquired nearly 65 TB and over 90,000 hours of video data from seafloor cameras and Remotely Operated Vehicles (ROVs). Manual processing of these data is time-consuming and highly labour-intensive, and cannot be comprehensively undertaken by individual researchers. These videos are a crucial source of information for assessing natural variability and ecosystem responses to increasing human activity in the deep sea. 2.We compared the performance of three groups of humans and one computer vision algorithm in counting individuals of the commercially important sablefish (or black cod) Anoplopoma fimbria, in recorded video from a cabled camera platform at 900 m depth in a submarine canyon in the Northeast Pacific. The first group of human observers were untrained volunteers recruited via a crowdsourcing platform and the second were experienced university students, who performed the task for their ichthyology class. Results were validated against counts obtained from a scientific expert. 3.All groups produced relatively accurate results in comparison to the expert and all succeeded in detecting patterns and periodicities in fish abundance data. Trained volunteers displayed the highest accuracy and the algorithm the lowest. 4.As seafloor observatories increase in number around the world, this study demonstrates the value of a hybrid combination of crowdsourcing and computer vision techniques as a tool to help process large volumes of imagery to support basic research and environmental monitoring. Reciprocally, by engaging large numbers of online participants in deep-sea research, this approach can contribute significantly to ocean literacy and informed citizen input to policy development