Investigation of interrelations between sediment and near-bottom environmental parameters and macrozoobenthic distribution patterns for the Baltic Sea

• Main Author: Gogina, Mayya
• Format: Doctoral or Postdoctoral Thesis
• Language: English
• Published: 2010
• Subjects: Zoobenthos; Ostsee; ddc:910; Arctica islandica
• Online Access: https://epub.ub.uni-greifswald.de/frontdoor/index/index/docId/630; https://nbn-resolving.org/urn:nbn:de:gbv:9-000803-1; https://epub.ub.uni-greifswald.de/files/630/Gogina_dissertation_2010.pdf

The objectives of the present work are to relate the spatial distribution of benthic macrofauna in the Baltic Sea to patterns in environmental variables describing near-bottom hydrographical conditions and sediment characteristics, analyzing the data for two various spatial extents. The first case study is devoted to an exploratory statistical description of the prevailing ecological structure within the limited area attached to the region of the Mecklenburg Bight. Key environmental descriptors of spatial distribution of macrofaunal communities were disclosed within the area of investigation: water depth, regarded as a proxy for other environmental factors, and total organic content. Distinct benthic assemblages that are discriminated by particular species (Hydrobia ulvae–Scoloplos armiger, Lagis koreni–Mysella bidentata and Capitella capitata–Halicryptus spinulosus) were defined. Each assemblage is related to different spatial subarea and is characterized by a certain variability of environmental factors. This study represented the basis for the predictive modelling of species distribution in the selected investigation area, which constituted the next part of the investigation. Species-specific models predicting the probability of occurrence relative to environmental and sedimentological characteristics were developed for 29 representative macrofaunal species using a logistic regression modelling approach. Subsequently, the technique for a predictive modelling of species distributions in response to abiotic parameters based on single-factor logistic regression models, utilizing Akaike’s information criterion (AIC) and Akaike weights for multimodel inference, was used. Thus, probabilities of occurrence for selected exemplary species (Arctica islandica, Hediste diversicolor, Pygospio elegans, Tubificoides benedii and Scoloplos armiger) were modelled and mapped. Finally, the investigation proceeded on a large spatial scale. The discriminating ability of such factors as salinity, bathymetry, and sediment ...