Abstract
Benthic fauna form spatial patterns which are the result of both biotic and abiotic processes, which can be quantified with a range of landscape ecology descriptors. Fine- to medium-scale spatial patterns (<1–10 m) have seldom been quantified in deep-sea habitats, but can provide fundamental ecological insights into species’ niches and interactions. Cold-water coral reefs formed by Desmophyllum pertusum (syn. Lophelia pertusa) and Madrepora oculata are traditionally mapped and surveyed with multibeam echosounders and video transects, which limit the ability to achieve the resolution and/or coverage to undertake fine-scale, centimetric quantification of spatial patterns. However, photomosaics constructed from imagery collected with remotely operated vehicles (ROVs) are becoming a prevalent research tool and can reveal novel information at the scale of individual coral colonies. A survey using a downward facing camera mounted on a ROV traversed the Piddington Mound (Belgica Mound Province, NE Atlantic) in a lawnmower pattern in order to create 3D reconstructions of the reef with Structure-from-Motion techniques. Three high resolution orthorectified photomosaics and digital elevation models (DEM) >200 m2 were created and all organisms were geotagged in order to illustrate their point pattern. The pair correlation function was used to establish whether organisms demonstrated a clustered pattern (CP) at various scales. We further applied a point pattern modelling approach to identify four potential point patterns: complete spatial randomness (CSR), an inhomogeneous pattern influenced by environmental drivers, random clustered point pattern indicating biologically driven clustering and an inhomogeneous clustered point pattern driven by a combination of environmental drivers and biological effects. Reef framework presence and structural complexity determined inhabitant distribution with most organisms showing a departure from CSR. These CPs are likely caused by an affinity to local environmental drivers, growth patterns and restricted dispersion reproductive strategies within the habitat across a range of fine to medium scales. These data provide novel and detailed insights into fine-scale habitat heterogeneity, showing that non-random distributions are apparent and detectable at these fine scales in deep-sea habitats.
Highlights
Aggregations of animals and single celled organisms have been recognised and studied for some time (Allee, 1927), in both mobile (e.g., Parrish and Edelstein-Keshet, 1999) and sessile species (e.g., Condit et al, 2000)
This work was undertaken as part of the VENTuRE survey in Wheeler and Shipboard Party (2011), and the mound was photomosaiced using a scale invariant feature transformation algorithm (SIFT), to create a high-resolution habitat map classified into the categories “hemipelagic sediment”, “hemipelagic sediment with dropstones”, “live coral framework”, “dead coral framework” and “coral rubble” (Lim et al, 2017)
Non-reef substrate vector ruggedness measure (VRM) represented over 80%, over 70%, and over 80% of the pixels with VRM values between 0 and 0.05 VRM, the lowest bin, at sites A, B, and C, respectively (Figure 5)
Summary
Aggregations of animals and single celled organisms have been recognised and studied for some time (Allee, 1927), in both mobile (e.g., Parrish and Edelstein-Keshet, 1999) and sessile species (e.g., Condit et al, 2000). A range of first and second order statistical tests have been developed to assess spatial patterns as being clustered, randomly or overdispersed distributions (Baddeley et al, 2015). These types of analyses using discrete point data have typically been applied to terrestrial systems, for example, to understand tree distributions in forests (Condit et al, 2000; Woodall and Graham, 2004; Law et al, 2009), plant distribution in deserts (Eccles et al, 1999), deer distribution (Plante et al, 2004), and bird nest distributions (Melles et al, 2009; McDowall and Lynch, 2017). In the marine environment, quantifying spatial faunal patterns remains more challenging due to the technical difficulties of collecting precise positional data of biological observations
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