Biogeographic classification of the Caspian Sea

Abstract

Abstract. Like other inland seas, the Caspian Sea (CS) has been influenced by climate change and anthropogenic disturbance during recent decades, yet the scientific understanding of this water body remains poor. In this study, an eco-geographical classification of the CS based on physical information derived from space and in situ data is developed and tested against a set of biological observations. We used a two-step classification procedure, consisting of (i) a data reduction with self-organizing maps (SOMs) and (ii) a synthesis of the most relevant features into a reduced number of marine ecoregions using the hierarchical agglomerative clustering (HAC) method. From an initial set of 12 potential physical variables, 6 independent variables were selected for the classification algorithm, i.e., sea surface temperature (SST), bathymetry, sea ice, seasonal variation of sea surface salinity (DSSS), total suspended matter (TSM) and its seasonal variation (DTSM). The classification results reveal a robust separation between the northern and the middle/southern basins as well as a separation of the shallow nearshore waters from those offshore. The observed patterns in ecoregions can be attributed to differences in climate and geochemical factors such as distance from river, water depth and currents. A comparison of the annual and monthly mean Chl a concentrations between the different ecoregions shows significant differences (one-way ANOVA, P < 0.05). In particular, we found differences in phytoplankton phenology, with differences in the date of bloom initiation, its duration and amplitude between ecoregions. A first qualitative evaluation of differences in community composition based on recorded presence–absence patterns of 25 different species of plankton, fish and benthic invertebrate also confirms the relevance of the ecoregions as proxies for habitats with common biological characteristics.

Mapping of oceanographic properties along a middle Adriatic transect using Self-Organising Maps

“…It has been successfully applied to a variety of problems in which a reduction of dimensionality (Murtagh and Hernandez Pajares, 1995), extraction of patterns (Kopp et al, 2010) or detecting outliers (Munoz and Murazabal, 1998) was performed. Although the method prefers large datasets, which are provided via remote sensing techniques (e.g., satellites, Richardson et al, 2003) or climate studies (e.g., Morioka et al, 2010), SOM has been successfully applied to limited datasets, such as in situ oceanographic data, from which patterns have been extracted and connected to driving processes and forces (e.g., Fendereski et al, 2014).…”

Differential impact of anthropogenic pressures on Caspian Sea ecoregions

“…A potential solution to this problem arises from the fact that the Caspian Sea has been divided into ten ecoregions based on ecologically relevant environmental variables (Fendereski et al, 2014). These ecoregions generally correspond to the distribution ranges of many endemic, native and invasive species (Fendereski et al, 2014), and they allow for a coarse latitudinal and vertical (bathymetrical) assessment of anthropogenic pressures. Thus, ecoregion-specific environmental analyses might be a suitable approach to obtain a basic understanding of the spatially-explicit impact of anthropogenic pressures on Caspian Sea biota.…”

On the Art of Classification in Spatial Ecology: Fuzziness as an Alternative for Mapping Uncertainty

“…In spatial ecology, for example, grouping locations with similar features may help the detection of areas driven by the same ecological processes and occupied by same species (Fortin and Dale, 2005;Elith et al, 2006), which can support conservation actions. In fact, classifications have been used with the aim of investigating the spatial distribution of target categories such as habitats (Coggan and Diesing, 2011), ecoregions (Fendereski et al, 2014), sediment classes (Hass et al, 2017), or biotopes (Schiele et al, 2015). Sometimes such classifications were found to act as surrogates for biodiversity in data-poor regions (e.g., Lucieer and Lucieer, 2009;Huang et al, 2012), some class being known for supporting higher biodiversity.…”