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. 2018 Jul 20:6:e5276.
doi: 10.7717/peerj.5276. eCollection 2018.

The influence of different deep-sea coral habitats on sediment macrofaunal community structure and function

Affiliations
Free PMC article

The influence of different deep-sea coral habitats on sediment macrofaunal community structure and function

Jill R Bourque et al. PeerJ. .
Free PMC article

Abstract

Deep-sea corals can create a highly complex, three-dimensional structure that facilitates sediment accumulation and influences adjacent sediment environments through altered hydrodynamic regimes. Infaunal communities adjacent to different coral types, including reef-building scleractinian corals and individual colonies of octocorals, are known to exhibit higher macrofaunal densities and distinct community structure when compared to non-coral soft-sediment communities. However, the coral types have different morphologies, which may modify the adjacent sediment communities in discrete ways. Here we address: (1) how infaunal communities and their associated sediment geochemistry compare among deep-sea coral types (Lophelia pertusa, Madrepora oculata, and octocorals) and (2) do infaunal communities adjacent to coral habitats exhibit typical regional and depth-related patterns observed in the Gulf of Mexico (GOM). Sediment push cores were collected to assess diversity, composition, numerical abundance, and functional traits of macrofauna (>300 µm) across 450 kilometers in the GOM at depths ranging from 263-1,095 m. Macrofaunal density was highest in L. pertusa habitats, but similar between M. oculata and octocorals habitats. Density overall exhibited a unimodal relationship with depth, with maximum densities between 600 and 800 m. Diversity and evenness were highest in octocoral habitats; however, there was no relationship between diversity and depth. Infaunal assemblages and functional traits differed among coral habitats, with L. pertusa habitats the most distinct from both M. oculata and octocorals. These patterns could relate to differences in sediment geochemistry as L. pertusa habitats contained high organic carbon content but low proportions of mud compared to both M. oculata and octocoral habitats. Distance-based linear modeling revealed depth, mud content, and organic carbon as the primary factors in driving coral infaunal community structure, while geographic location (longitude) was the primary factor in functional trait composition, highlighting both the location and ecological differences of L. pertusa habitats from other coral habitats. Enhanced habitat structural complexity associated with L. pertusa and differences in localized hydrodynamic flow may contribute to the dissimilarities in the communities found among the coral types. Our results suggest a decoupling for infaunal coral communities from the typical depth-related density and diversity patterns present throughout soft-sediment habitats in the GOM, highlighting the importance of deep-sea corals in structuring unique communities in the nearby benthos.

Keywords: Community ecology; Functional traits; Gulf of Mexico; Lophelia pertusa; Madrepora oculata; Octocorals; Sediment macrofauna.

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Conflict of interest statement

The authors declare there are no competing interests.

Figures

Figure 1
Figure 1. Coral habitat types.
(A) Lophelia pertusa reef at VK906, Image credit: USGS/DISCOVRE. (B) Madrepora oculata colonies at AT357, Image credit: C. Fisher. (C) Paramuricea (Octocoral) colonies at MC203, Image credit: C. Fisher.
Figure 2
Figure 2. Map of study locations in the northern Gulf of Mexico, with soft-sediment deep-sea infaunal community zonation from Wei et al. (2010).
Bathymetric intervals are 50 m starting at 150 m depth. Base map data from NOAA, GCOOS.
Figure 3
Figure 3. Macrofaunal density at deep-sea coral habitats.
(A) Mean macrofaunal density (individuals m−2 ±  1 S.E.) near coral (solid bars) and background (open bars) soft-sediment habitats in the GOM. (B) Macrofaunal density (individuals m−2) of near-coral cores with depth, with polynomial trendline delineated (y =  − 0.0475x2 + 63.81x − 1579, R2 = 0.101).
Figure 4
Figure 4. Rarefaction via estimated number of taxa (ES[n]).
(A) Near-coral habitats pooled by coral type and all near-coral samples, with total number of taxa including colonial taxa indicated. (B) Rarefaction compared with depth for multiple levels of ES(n). ES(5): y = 2e−06x2 − 0.0021x + 4.8524, R2 = 0.041; ES(10): y = 3e−06x2 − 0.0044x + 8.5654, R2 = 0.025; ES(20): y = 8e−06x2 − 0.0109x + 14.726, R2 = 0.036.
Figure 5
Figure 5. Taxonomic composition of dominant macrofauna at near-coral and background habitats.
Other Taxa includes Halacaridae, Callipallenidae, Cnidaria, Echinodermata, Nemertea, Urochordata, Chaetognatha, Sipuncula, Echiura, and Turbellaria.
Figure 6
Figure 6. Non-metric multidimensional scaling (nMDS) of infaunal community composition and functional trait composition of near-coral and background habitats.
(A) Infaunal community composition near coral habitats and in nearby background soft-sediments, based on Bray–Curtis similarities of square-root transformed abundance data from sediment core. (B) Functional trait composition of near-coral habitats and in nearby background soft-sediments, based on Bray–Curtis similarities of square-root transformed trait-weighted abundance data from sediment cores. Bubble size represents sample depth, ranging 263–1,095 m.
Figure 7
Figure 7. Functional trait composition of near-coral and background habitats.
(A) Feeding type. (B) Feeding location. (C) Taxa motility. (D) Life habit.
Figure 8
Figure 8. Sediment geochemistry for near-coral habitats.
(A) Grain size composition. (B) Mean organic carbon content (% ± 1 S.E.). (C) Organic carbon content (%) with depth.
Figure 9
Figure 9. Distance-based redundancy analysis of the best two-variable model from distance-based linear modeling of sampling locations near coral habitats where sediment geochemistry data were available.
(A) Based on Bray–Curtis similarities of square-root transformed abundance data averaged for individual sampling locations. (B) Based on Bray–Curtis similarities of square-root transformed functional trait weighted abundances averaged for individual sampling locations.

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Grants and funding

Funding was provided to A. Demopoulos from the US Geological Survey Outer Continental Shelf Environments Program and NOAA’s Office of Response and Restoration as part of the Natural Resource Damage Assessment for the Deepwater Horizon oil spill. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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