Abundance of small individuals influences the effectiveness of processing techniques for deep-sea nematodes

Nematodes are the most abundant metazoans of deep-sea benthic communities, but knowledge of their distribution is limited relative to larger organisms. Whilst some aspects of nematode processing techniques, such as extraction, have been extensively studied, other key elements have attracted little attention. We compared the effect of (1) mesh size (63, 45, and 32 μm) on estimates of nematode abundance, biomass, and body size, and (2) microscope magnification (50 and 100×) on estimates of nematode abundance at bathyal sites (250-3100 m water depth) on the Challenger Plateau and Chatham Rise, south-west Pacific Ocean. Variation in the effectiveness of these techniques was assessed in relation to nematode body size and environmental parameters (water depth, sediment organic matter content, %silt/clay, and chloroplastic pigments). The 63-μm mesh retained a relatively low proportion of total nematode abundance (mean ±SD = 55 ±9%), but most of nematode biomass (90 ± 4%). The proportion of nematode abundance retained on the 45-μm mesh in surface (0-1 cm) and subsurface (1-5 cm) sediment was significantly correlated (P < 0.01) with %silt/clay (R² = 0.39) and chloroplastic pigments (R² = 0.29), respectively. Variation in median nematode body weight showed similar trends, but relationships between mean nematode body weight and environmental parameters were either relatively weak (subsurface sediment) or not significant (surface sediment). Using a low magnification led to significantly lower (on average by 43%) nematode abundance estimates relative to high magnification (P < 0.001), and the magnitude of this difference was significantly correlated (P < 0.05) with total nematode abundance (R²p = 0.53) and the number of small (≤ 250 μm length) individuals (R²p = 0.05). Our results suggest that organic matter input and sediment characteristics influence the abundance of small nematodes in bathyal communities. The abundance of small individuals can, in turn, influence abundance estimates obtained using different mesh sizes and microscope magnifications

Lugworm (Abarenicola affinis) in seagrass and unvegetated habitats

Abstract In Otago, southern New Zealand, the lugworm Abarenicola affinis resides in neighbouring tidal inlets with and without seagrass (Zostera muelleri). A comparison of abundance, body size and biomass of A. affinis between seagrass habitat (Papanui Inlet) and unvegetated habitat (Hoopers Inlet) showed little seasonal variation of these parameters in each habitat and relatively similar abundances between both habitats. In contrast, lugworm biomass was considerably lower in the seagrass habitat due to the lack of large individuals compared with unvegetated habitat. In the seagrass habitat, there was a significant negative influence of Z. muelleri below-ground biomass on abundance and biomass of A. affinis, indicating that seagrass affected lugworm burrowing and/or feeding processes. In contrast to the unvegetated habitat, where lugworms spread relatively evenly across the intertidal area, lugworms were mostly restricted to the upper intertidal zone in the seagrass habitat. The findings suggest that the extensive seagrass bed in the mid and low intertidal zones of Papanui Inlet limited lugworm distribution in an otherwise suitable habitat. Whereas small lugworms colonised seagrass areas, the largest individuals occurred only in unvegetated sediment and seemed to be more hampered by the presence of seagrass than smaller individuals. The findings highlight negative feedback between antagonistic ecosystem engineers, with the potential of seagrass physical structures (autogenic engineering) to impact negatively on lugworm activity (allogenic engineering)

Is there a link between deep-sea biodiversity and ecosystem function?

Studying the diversity-ecosystem function relationship in the deep sea is of primary importance in the face of biodiversity loss and for our understanding of how the deep sea functions. Results from the first study of diversity-ecosystem function relationships in the deep sea (Danovaro et al. 2008; Current Biology, 18, 1–8) are unexpected and show an exponential relationship between deep-sea nematode diversity and ecosystem function and efficiency, although this relationship appears largely restricted to relatively low diversities [ES(51) <25]. Here, we investigate the relationship between nematode diversity and several independent measures/proxies of ecosystem function (sediment community oxygen consumption, bacterial biomass, bacterial extracellular enzyme activity) and efficiency (ratio of bacterial/nematode carbon to organic C content of the sediment) on the New Zealand continental slope. Nematode diversity at our study sites was relatively high [ES(51) = 30–42], and there was no relationship between species/functional diversity and ecosystem function/efficiency after accounting for the effects of water depth and food availability. Our results are consistent with a breakdown of the exponential diversity-function relationship at high levels of diversity, which may be due to increased competition or greater functional redundancy. Future studies need to take into account as many environmental factors and as wide a range of diversities as possible to provide further insights into the diversity-ecosystem function relationship in the largest ecosystem on Earth

Differences in meiofauna communities with sediment depth are greater than habitat effects on the New Zealand continental margin: implications for vulnerability to anthropogenic disturbance

Studies of deep-sea benthic communities have largely focused on particular (macro) habitats in isolation, with few studies considering multiple habitats simultaneously in a comparable manner. Compared to mega-epifauna and macrofauna, much less is known about habitat-related variation in meiofaunal community attributes (abundance, diversity and community structure). Here, we investigated meiofaunal community attributes in slope, canyon, seamount, and seep habitats in two regions on the continental slope of New Zealand (Hikurangi Margin and Bay of Plenty) at four water depths (700, 1,000, 1,200 and 1,500 m). We found that patterns were not the same for each community attribute. Significant differences in abundance were consistent across regions, habitats, water and sediment depths, while diversity and community structure only differed between sediment depths. Abundance was higher in canyon and seep habitats compared with other habitats, while between sediment layer, abundance and diversity were higher at the sediment surface. Our findings suggest that meiofaunal community attributes are affected by environmental factors that operate on micro- (cm) to meso- (0.1–10 km), and regional scales (> 100 km). We also found a weak, but significant, correlation between trawling intensity and surface sediment diversity. Overall, our results indicate that variability in meiofaunal communities was greater at small scale than at habitat or regional scale. These findings provide new insights into the factors controlling meiofauna in these deep-sea habitats and their potential vulnerability to anthropogenic activities

Unimodal relationship between biomass and species richness of deep-sea nematodes: implications for the link between productivity and diversity

Describing large-scale patterns of biological diversity is a first step towards understanding the mechanisms that generate and maintain diversity. The highly diverse deep-sea floor is the largest ecosystem on Earth, but the productivity-diversity relationship in this biome is not well characterized. We investigated this relationship by using biomass of nematodes as a proxy for productivity (particulate organic carbon flux to the seabed). We used sample data collected from the New Zealand and Antarctic regions and combined these with published data from around the globe for broader analyses. There was a significant unimodal relationship between nematode biomass and diversity, i.e. expected number of species, ES(51) both within the New Zealand region and across ocean basins. This relationship remained significant after accounting for the effects of both water depth and nematode abundance. These findings support earlier suggestions of a unimodal productivity-diversity relationship in the deep sea that were based on other proxies (e.g. water depth, modelled particulate organic carbon flux). We argue that the 'productivity context' is of primary importance when determining the strength and nature of the relationship between other environmental factors and diversity. Studies that include either or both extremes of the productivity scale are likely to find that productivity is the main factor limiting deep-sea diversity, whereas those focusing on the intermediate productivity range are more likely to find that other factors (e.g. disturbance, habitat heterogeneity) play a rol

Habitat-Forming Bryozoans in New Zealand: Their Known and Predicted Distribution in Relation to Broad-Scale Environmental Variables and Fishing Effort

<div><p>Frame-building bryozoans occasionally occur in sufficient densities in New Zealand waters to generate habitat for other macrofauna. The environmental conditions necessary for bryozoans to generate such habitat, and the distributions of these species, are poorly known. Bryozoan-generated habitats are vulnerable to bottom fishing, so knowledge of species’ distributions is essential for management purposes. To better understand these distributions, presence records were collated and mapped, and habitat suitability models were generated (Maxent, 1 km² grid) for the 11 most common habitat-forming bryozoan species: <i>Arachnopusia</i><i>unicornis</i>, <i>Cellaria</i><i>immersa</i>, <i>Cellaria</i><i>tenuirostris</i>, <i>Celleporariaagglutinans</i>, <i>Celleporinagrandis</i>, <i>Cinctipora</i><i>elegans</i>, <i>Diaperoecia</i><i>purpurascens</i>, <i>Galeopsis</i><i>porcellanicus</i>, <i>Hippomenella</i><i>vellicata</i>, <i>Hornerafoliacea</i>, and <i>Smittoideamaunganuiensis</i>. The models confirmed known areas of habitat, and indicated other areas as potentially suitable. Water depth, vertical water mixing, tidal currents, and water temperature were useful for describing the distribution of the bryozoan species at broad scales. Areas predicted as suitable for multiple species were identified, and these ‘hotspots’ were compared to fishing effort data. This showed a potential conflict between fishing and the conservation of bryozoan-generated habitat. Fishing impacts are known from some sites, but damage to large areas of habitat-forming bryozoans is likely to have occurred throughout the study area. In the present study, spatial error associated with the use of historic records and the coarse native resolution of the environmental variables limited both the resolution at which the models could be interpreted and our understanding of the ecological requirements of the study species. However, these models show species distribution modelling has potential to further our understanding of habitat-forming bryozoan ecology and distribution. Importantly, comparisons between hotspots of suitable habitat and the distribution of bottom fishing in the study area highlight the need for management measures designed to mitigate the impact of seafloor disturbance on bryozoan-generated habitat in New Zealand waters.</p> </div

<i>Hornerafoliacea</i> known distribution (left), predicted suitable habitat (right), and fitted responses curves (marginal).

<p>For the predicted distribution, logistic probabilities less than the 10^th percentile presence value indicated cells were unsuitable habitat. Probabilities of 0.4–0.6 indicated habitat suitability typical of the presence records, values of 0.6–0.8 and 0.8–1 indicated favourable and highly suitable habitat, respectively. Black cells had missing data in one or more environmental layer. Independent records are layed over the predictions. Marginal response curves show how the prediction changes for different values of each variable when all other variables were at their average sample value. Individual response curves are shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0075160#pone.0075160.s003" target="_blank">Figure S3</a>. For the categorical variable Sediment type: 1 = deep ocean clays; 2 = calcareous gravel; 3 = volcanic; 4 = calcareous mud; 5 = gravel; 6 = mud; 7 = sand; 8 = calcareous sand.</p

<i>Diaperoecia</i><i>purpurascens</i> known distribution (left), predicted suitable habitat (right), and fitted responses curves (marginal).

<p>For the predicted distribution, logistic probabilities less than the 10^th percentile presence value indicated cells were unsuitable habitat. Probabilities of 0.4–0.6 indicated habitat suitability typical of the presence records, values of 0.6–0.8 and 0.8–1 indicated favourable and highly suitable habitat, respectively. Black cells had missing data in one or more environmental layer. Independent records are layed over the predictions. Marginal response curves show how the prediction changes for different values of each variable when all other variables were at their average sample value. Individual response curves are shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0075160#pone.0075160.s003" target="_blank">Figure S3</a>. For the categorical variable Sediment type: 1 = deep ocean clays; 2 = calcareous gravel; 3 = volcanic; 4 = calcareous mud; 5 = gravel; 6 = mud; 7 = sand; 8 = calcareous sand.</p

<i>Galeopsis</i><i>porcellanicus</i> known distribution (left), predicted suitable habitat (right), and fitted responses curves (marginal).

<p>For the predicted distribution, logistic probabilities less than the 10^th percentile presence value indicated cells were unsuitable habitat. Probabilities of 0.4–0.6 indicated habitat suitability typical of the presence records, values of 0.6–0.8 and 0.8–1 indicated favourable and highly suitable habitat, respectively. Black cells had missing data in one or more environmental layer. Independent records are layed over the predictions. Marginal response curves show how the prediction changes for different values of each variable when all other variables were at their average sample value. Individual response curves are shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0075160#pone.0075160.s003" target="_blank">Figure S3</a>. For the categorical variable Sediment type: 1 = deep ocean clays; 2 = calcareous gravel; 3 = volcanic; 4 = calcareous mud; 5 = gravel; 6 = mud; 7 = sand; 8 = calcareous sand.</p