Modelling of Arctic gelatinous zooplankton distribution and abundance based on data from pelagic video transects in Fram Strait

• Main Authors: Pantiukhin, Dmitrii, Hoving, Henk-Jan T., Verhaegen, Gerlien, Havermans, Charlotte
• Other Authors: Schadewell, Yvonne, Heel, Lena
• Format: Conference Object
• Language: unknown
• Published: 2021
• Subjects: Arctic; Climate change; Fram Strait; Global warming; North Atlantic; Zooplankton
• Online Access: https://epic.awi.de/id/eprint/56354/; https://hdl.handle.net/10013/epic.296df55d-1a86-4b49-a0fd-735ea03bd7a1

Global warming in the Arctic region causes alterations in the composition and structure of marine communities. Such changes are particularly pronounced in the transitional zones such as Fram Strait where the increased inflow of warm Atlantic waters accelerates this process. In other ecoregions of the world’s oceans, warming has caused an increase in the biomass of gelatinous zooplankton (or jellies). Jellies are versatile predators in diverse marine ecosystems. Despite the potential impact of jelly communities on the Arctic food webs, their ecological roles have been poorly studied. We hypothesise that the Arctic pelagic community consists of a significant component of gelatinous fauna, some of which are expanding from the North Atlantic. To test this hypothesis, we obtained baseline data on vertical distribution and diversity of Arctic jellies using towed camera video transects during expeditions to the HAUSGARTEN LTER in Fram Strait in 2019, 2020 and 2021. The data include the abundance of 17 groups of gelatinous zooplankton, among which the most abundant were the hydrozoan family of Rhopalonematidae, mainly consisting of Aglantha and Rhopalonema, and the siphonophore suborders Physonectae and Calycophorae. Based on the obtained abundance data, we fitted bayesian joint species distribution models (JSDMs) to understand current patterns of species distributions at different depth layers and to provide predictive insights into community assembly processes. Variance partitioning over the explanatory variables showed that depth and temperature explained a substantial amount of variation for most of the taxa. The trained models were later coupled with climate change scenarios, which allowed us to forecast spatial niche range shifts in ecosystems.