SPATIAL ECOLOGY OF DEEP-SEA BENTHOS FOR MANAGEMENT OF MARINE PROTECTED AREAS

Abstract

Deep-water corals, sponges, and other animals play functional roles, such as providing habitat, promoting diversity in seafloor communities in the deep sea. Little is known about the spatial organization of these vulnerable marine ecosystems, limiting our ability to link patterns to ecological processes for effective management. I quantified spatial patterns of invertebrates (> 2 cm) on soft sediments from imagery, focusing on the Laurentian Channel Marine Protected Area in the Canadian Northwest Atlantic. At broad scales (100s m – 100s km), three types of assemblages of varying composition, diversity, and abundance were associated with benthoscape class (environmental mosaic), a potential proxy for different habitats. At fine scales (0 – 100s m), I recorded taxon-specific local aggregations and variation in patchiness. For broad-scale patterns, potential spatial drivers included benthoscape classes [incorporating depth, pockmarks (fluid/gas-created pits), ice scours, and sediment composition)] and food quantity/quality; for fine-scale patterns, drivers likely included bathymetric position index (local changes in depth), pockmarks, and biological relationships. My results illustrate that sampling designs that ignore spatial patterns can result in the misrepresentation of diversity and abundance, impacting follow-up analyses and scientific conclusions. Further, different sampling tools [remotely operated vehicle (ROV), drop camera, and trawl] and designs (e.g. number and spacing of images and transects) had trade-offs and biases. For example, in some instances, the drop camera captured higher abundance and diversity than ROV. Sampling by ROV was advantageous for spatial and species association analyses, because of high spatial resolution, maneuverability, and minimal disturbance. Recommendations for developing deep-sea monitoring frameworks include optimized sampling designs for scales relevant to taxa and processes of interest, and high spatial resolution, replication, and multiple spatial lags to ensure representation of assemblages. My novel application of spatial statistics is applicable to other areas to quantify spatial patterns (abundance or different variables) at various scales e.g. transect, station, regional, or network levels. The thesis advances the field of spatial ecology in deep-sea ecosystems, to better understand changes in species distributions and comparisons at different spatial scales.