The Baltic Sea scale inventory of benthic faunal communities

Abstract

This study provides an inventory of the recent benthic macrofaunal communities in the entire Baltic Sea. The analyses of soft-bottom benthic invertebrate community data based on over 7000 locations in the Baltic Sea suggested the existence of 10 major communities based on species abundances and 17 communities based on species biomasses, respectively. The low-saline northern Baltic, characterized by silty sediments, is dominated by Monoporeia affinis, Marenzelleria spp., and Macoma balthica. Hydrobiidae, Pygospio elegans, and Cerastoderma glaucum dominate the community in sandy habitats off the Estonian west coast and in the southeastern and southern Baltic Sea. Deep parts of the Gulf of Finland and central Baltic Sea often experience hypoxia, and when oxygen levels in these regions recover, Bylgides sarsi was the first species to colonize. The southwestern Baltic Sea, with high salinity, has higher macrofaunal diversity compared with the northern parts. To spatially interpolate the distribution of the major communities, we used the Random Forest method. Substrate data, bathymetric maps, and modelled hydrographical fields were used as predictors. Model predictions were in good agreement with observations, quantified by Cohen's κ of 0.90 for the abundance and 0.89 in the wet weight-based model. Misclassifications were mainly associated with uncommon classes in regions with high spatial variability. Our analysis provides a detailed baseline map of the distribution of benthic communities in the Baltic Sea to be used both in science and management.

Predicting the Composition of Polychaete Assemblages in the Aegean Coast of Turkey

“…This can reflect how well the individual species' responses are aligned with their respective archetypical response (Woolley et al, 2013); indeed archetype 3 has the largest proportion of indicatively affiliated species (Table S1). Furthermore, uncertainty and predictive performance improve with the number of species represented by each archetype, in agreement with findings that the more observations a group response contains the better it is predicted (Elith and Leathwick, 2007;Gogina et al, 2016). For archetypes 1 and 3 it is possible that the smaller number of included species is not adequate for an accurate characterization of the group response or that these groups have a more heterogeneous species composition.…”

“…With respect to the first step, predictive modeling of community composition has been extensively employed to integrate the biological components of the seabed with physical characteristics in order to define and map benthic habitats. This is usually accomplished with some application of the "assemble first-predict later" approach, sensu Ferrier and Guisan (2006), whereby communities or species groups are usually first delineated through algorithmic multivariate analyses, followed by modeling of these entities against environmental parameters (Degraer et al, 2008;BuhlMortensen et al, 2014BuhlMortensen et al, , 2015Gonzalez-Mirelis and BuhlMortensen, 2015;Gogina et al, 2016;Rubidge et al, 2016). While variations of this strategy have served the modeling community well, they have their shortcomings and limitations.…”

“…For instance, distance-based methods have been criticized for confounding location and dispersion effects by misspecifying the mean-variance relationship in the data with severe consequences in identifying "characteristic" taxa and detecting multivariate effects. As another example, sites that are not well classified in the "assemble" step and are commonly assumed to represent transitional communities result in entities that are either poorly predicted (Gogina et al, 2016) or are discarded from the "predict" step (Degraer et al, 2008), resulting in information loss that can be substantial for limited data sets.…”

Salinity drives meiofaunal community structure dynamics across the Baltic ecosystem

“…These ecological interactions have been shown to have an importance on biogeochemical cycles; however, studies that focus on meiofauna‐macrofauna interactions in situ and over regional and ecologically relevant scales are scarce. Macrofauna diversity is generally higher in more saline regions (Gogina et al, ), and meiofauna‐macrofauna interactions might therefore be more prominent in saline regions with higher diversity and species richness. Gaining such insights will help to elucidate potential trophic interactions in the sediment and how these may be affected by contemporary ecological and environmental pressures.…”

A ‘fuzzy clustering’ approach to conceptual confusion: how to classify natural ecological associations

“…Guillemot et al 2011), environmental relationships (e.g. Leaper et al 2014;Gogina et al 2016) and assembly rules (e.g. Spasojevic & Suding 2012, Ricklefs 2015.…”