Traits and drivers: Functioning of macrobenthic communities across the deep Fram Strait (Arctic Ocean)
Deep-sea regions provide vast ecosystem services such as biological habitat and nutrient cycling. Even though being threatened by climate change and facing possible biodiversity loss, these deep-sea ecosystems are poorly understood. So are macrobenthic communities and their functions within these ec...
| Published in: | Ecological Indicators |
|---|---|
| Main Authors: | , , , , |
| Format: | Article in Journal/Newspaper |
| Language: | English |
| Published: |
Elsevier
2021
|
| Subjects: | |
| Online Access: | https://doi.org/10.1016/j.ecolind.2020.107324 https://doaj.org/article/fe0b59e65dd44b549cb272d7e486cc6f |
| Summary: | Deep-sea regions provide vast ecosystem services such as biological habitat and nutrient cycling. Even though being threatened by climate change and facing possible biodiversity loss, these deep-sea ecosystems are poorly understood. So are macrobenthic communities and their functions within these ecosystems. Biodiversity and ecosystem function relationships as well as their link to environmental drivers can be assessed with the biological trait analysis. We used this approach for the first time for macrofauna assemblages across the deep Fram Strait between Greenland and Svalbard (1000–5500 m water depth) to evaluate their community-specific function from the upper continental slope down to the deepest known Arctic depression, the Molloy Deep. We aimed to investigate whether there are changes in benthic functioning along the bathymetric gradient and if so, which environmental stressors may drive these changes.In total, 16 stations were sampled with a giant box corer (0.25 m2) in 2016 and 2018. Sediments were sieved through a 0.5 mm mesh size sieve and fauna was identified to lowest possible taxonomic entity. Functions of species were characterized by using six traits split in 24 modalities gathered in a fuzzy coded species × traits array. Environmental parameters shaping the benthic habitat and reflecting food availability were gathered from remote sensing, mooring deployments, and sediment sampling.A distance-based redundancy analysis indicated near-bottom water temperature, seabed inclination, water depth as well as phytodetritial matter at the sea surface and seafloor (indicating food availability) to be the best variables explaining the trait and station distribution. Stations clustered into three groups based on their trait composition. Shallower stations characterized by high chlorophyll a concentration with large organisms, living within the sediment as well as predating specimens clustered in one group. A second group was characterized by stations with low chlorophyll a concentration and medium-sized, ... |
|---|