Seabed Sediment Type Research Articles

Relating Geotechnical Sediment Properties and Low Frequency CHIRP Sonar Measurements

Low frequency acoustic methods are a common tool for seabed stratigraphy mapping. Due to the efficiency in seabed mapping compared to geotechnical methods, estimating geotechnical sediment properties from acoustic surveying is attractive for many applications. In this study, co-located geotechnical and geoacoustic measurements of different seabed sediment types in shallow water environments (<5 m of water depth) are analyzed. Acoustic impedance estimated from sediment properties based on laboratory testing of physical samples is compared to acoustic impedance deduced from CHIRP sonar measurements using an inversion approach. Portable free fall penetrometer measurements provided in situ sediment strength. The results show that acoustic impedance values deduced from acoustic data through inversion fall within a range of ±25% of acoustic impedance estimated from porosity and bulk density. The acoustic measurements reflect variations in shallow sediment properties such as porosity and bulk density (~10 cm below seabed surface), even for very soft sediments (su < 3 kPa) and loose sands (~20% relative density). This is a step towards validating the ability of acoustic methods to capture geotechnical properties in the topmost seabed layers.

Multitemporal Acoustic Backscatter Data Analysis to Monitor the Dynamics of Seabed Surface Sediments

Backscatter strength is a product of underwater acoustic remote sensing. This study used a multibeam echosounder as an acoustic remote sensing instrument to collect backscatter strength data. These data are then used to classify the surficial sediment distribution. To monitor the difference in seabed sediment distribution, a time-series survey was performed to obtain multitemporal acoustic backscatter data. An EM 304 multibeam system from Kongsberg was mounted on the Research Vessel Baruna Jaya III from the Indonesia National Research and Innovation Agency (BRIN). It was used to collect backscatter data in the waters of Raja Ampat, Indonesia. The data were collected at different times, April and July 2021. Geometric and radiometric corrections were applied to these backscatter data. Based on the angular response curve analysis from acoustic backscatter strength, the research area can be classified into four seabed sediment types: boulder, gravel, sand, and mud. A comparison of both time series backscatter data shows that the boulder and gravel areas increased by 13.6% and 19.0%, respectively. Elsewhere, the area with sediment types of sand and mud diminished by 30.5% and 2.0%. The change in the sediment type area occurred as much as 50.5%, while the remaining 49.5% area remained unchanged. This resulting value is apparently derived from the steep topography that rapidly changes sediment distribution. One such suggestion is that sediment sampling should be performed to confirm the model from angular response curve analysis.

Multitemporal Acoustic Backscatter Data Analysis to Monitor the Dynamics of Seabed Surface Sediments

Backscatter strength is a product of underwater acoustic remote sensing. This study used a multibeam echosounder as an acoustic remote sensing instrument to collect backscatter strength data. These data are then used to classify the surficial sediment distribution. To monitor the difference in seabed sediment distribution, a time-series survey was performed to obtain multitemporal acoustic backscatter data. An EM 304 multibeam system from Kongsberg was mounted on the Research Vessel Baruna Jaya III from the Indonesia National Research and Innovation Agency (BRIN). It was used to collect backscatter data in the waters of Raja Ampat, Indonesia. The data were collected at different times, April and July 2021. Geometric and radiometric corrections were applied to these backscatter data. Based on the angular response curve analysis from acoustic backscatter strength, the research area can be classified into four seabed sediment types: boulder, gravel, sand, and mud. A comparison of both time series backscatter data shows that the boulder and gravel areas increased by 13.6% and 19.0%, respectively. Elsewhere, the area with sediment types of sand and mud diminished by 30.5% and 2.0%. The change in the sediment type area occurred as much as 50.5%, while the remaining 49.5% area remained unchanged. This resulting value is apparently derived from the steep topography that rapidly changes sediment distribution. One such suggestion is that sediment sampling should be performed to confirm the model from angular response curve analysis.

Combining Two Classification Methods for Predicting Jakarta Bay Seabed Type Using Multibeam Echosounder Data

Classification of seabed types from multibeam echosounder data using machine learning techniques has been widely used in recent decades, such as Random Forest (RF), Artificial Neural Network (ANN), Support Vector Machine (SVM), and Nearest Neighbor (NN). This study combines the two most frequently used machine learning techniques to classify and map the seabed sediment types from multibeam echosounder data. The classification model developed in this study is a combination of two machine learning classification techniques, namely Support Vector Machine (SVM) and K-Nearest Neighbor (K-NN). This classification technique is called SV-KNN. Simply, SV-KNN adopts these two techniques to carry out the classification process. The SV-KNN technique begins with determining test data by specifying support vectors and hyperplanes, as was done on the SVM method, and executes the classification process using the K-NN. Clay, fine silt, medium silt, coarse silt, and fine sand are the five main classes produced by SVKNN. The SV-KNN method has an overall accuracy value of 87.38% and a Kappa coefficient of 0.3093.

Modelling the distribution of vulnerable skate from fisheries dependent data using imperfect detection

Little is still known about the biology and ecology of many elasmobranchs which often inhibits species specific management measures from being implemented. The primary aim of this study was to improve the knowledge on the distribution and habitat use of the threatened and data deficient shagreen ray, Leucoraja fullonica, using fisheries dependent data. To model its distribution, we used Bayesian hierarchical modelling, taking into consideration imperfect capture from the non-random nature of fishing gear type and spatial autocorrelation. Our second objective was to identify the potential functional role of the high occurrence area by analysing spatial length segregation using a generalised additive mixed model.From five environmental variables, depth, distance to coast, and seabed sediment type were used to model its habitat. L. fullonica was found to mainly inhabit an area of high concentration between the southern Celtic Seas and the northern Bay of Biscay. Within this area, smaller individuals were found in the deeper south-western part and larger individuals in shallower waters, closer to the coast, suggesting ontogenetic shift or spawning migration. L. fullonica were mainly caught by bottom trawl fishing gears. The isolated habitat occupancy of this species may increase its vulnerability, particularly since high fishing pressure has been observed in this area. These results highlight the importance of fisheries-dependent data for data-poor species and provide valuable new information on its ecology when considering management or conservation measures at a species level.

Deep-Sea Seabed Sediment Classification Using Finely Processed Multibeam Backscatter Intensity Data in the Southwest Indian Ridge

In 2007, China discovered a hydrothermal anomaly in the Longqi hydrothermal area of the Southwest Indian Ridge. It was the first seabed hydrothermal area discovered in the ultraslow spreading ocean ridge in the world. Understanding the types of seabed sediments in this area is critical for studying the typical topography and geological characteristics of deep-sea seabed hydrothermal areas. The traditional classification of deep-seabed sediments adopts box sampling or gravity column sampling and identifies the types of seabed sediments through laboratory analysis. However, this classification method has some shortcomings, such as the presence of discrete sampling data points and the failure of full-coverage detection. The geological sampling in deep-sea areas is particularly inefficient. Hence, in this study, the EM122 multibeam sonar data collected in the Longqi hydrothermal area, Southwest Indian Ridge, in April 2019 are used to analyze multibeam backscatter intensity. Considering various errors in the complex deep-sea environment, obtaining backscatter intensity data can truly reflect seabed sediment types. Through unsupervised and supervised classification, the seabed sediment classification in the Longqi hydrothermal area was studied. The results showed that the accuracy of supervised classification is higher than that of unsupervised classification. Thus, unsupervised classification is primarily used for roughly classifying sediment types without on-site geological sampling. Combining the genetic algorithm (GA) and the support vector machine (SVM) neural network, deep-sea sediment types, such as deep-sea clay and calcareous ooze, can be identified rapidly and efficiently. Based on comparative analysis results, the classification accuracy of the GA-SVM neural network is higher than that of the SVM neural network, and it can be effectively applied to the high-precision classification and recognition of deep-sea sediments. In this paper, we demonstrate the fine-scale morphology and surface sediment structure characteristics of the deep-sea seafloor by finely processing high-precision deep-sea multibeam backscatter intensity data. This research can provide accurate seabed topography and sediment data for the exploration of deep-sea hydrothermal resources and the assessment of benthic habitats in deep-sea hydrothermal areas.

DNN-based seabed classification using differently weighted MBES multifeatures

Seabed sediment classification has significance for the utilization of marine resources and marine scientific research. Currently, the multibeam echo sounder (MBES) is increasingly becoming the tool of choice for large-scale seabed sediment classification. To further explore the technology of seabed sediment classification, this paper proposes a new classification method. In addition to backscatter mosaic, the method also integrates three other different types of features, including texture features of backscatter mosaic, MBES bathymetry features, and backscatter angular response (AR) features, which are given different weights in the classification process. First, geographically weighted regression (GWR) analysis is performed between different types of features and seabed sediment types, and the normalized coefficient of determination (R2) is employed as the weight coefficient for the different types of features. Second, the backscatter mosaic is combined with features from different types to predict the seabed sediment types using a deep neural network (DNN) classifier. Third, the classification residuals of the features from these three different types are acquired through the above classification results. Last, the classification residuals of features from different types are added to the classification results of the backscatter mosaic according to the weights, thereby achieving seabed sediment classification based on MBES multifeatures with different weights. The results show that the overall classification accuracy of the seabed sediments can be significantly improved from 88.98%/85.14% to 93.43% when using the DNN classification model based on MBES multifeatures with different weights compared with the other two models (DNN classification model based on MBES multifeatures with equal weights and DNN classification model based on principal component analysis (PCA) dimensionality reduction). The kappa coefficient can also be significantly improved from approximately 0.85/0.80 to 0.91. Via analysis, the proposed method can reasonably assign the weights of the different features and take advantage of integrating MBES multifeatures for seabed sediment classification. This approach also provides an important reference for future research on seabed sediment classification.

Using seabed acoustic imagery to characterize and classify seabed sediment types in the pockmark area of the North Yellow Sea, China

Pockmarks are circular or elliptical depressions in the seabed that are geomorphologically similar but otherwise distinct from volcanic craters. Pockmarks are found at various depths in the ocean including on the continental shelf, the continental slope and the deep-sea plain. This paper presents data covering a large-scale pockmark group in the middle part of the shallow sea sedimentary plain of the North Yellow Sea at a water depth of 51–58 m. The distribution of pockmarks within this group is very dense, and pockmark diameters range from several hundred meters to several kilometers. Based on multibeam echo sounder data, a high-resolution image of the pockmark area is obtained by efficient image processing methods. The textural features of the seabed surface layer are then described and analyzed. Multibeam echo sounder backscatter data are combined with seabed sediment sampling data to develop a statistical model linking seabed backscatter sonar strength and sediment grain-size characteristics. Additionally, an improved BP neural network is used to realize rapid and automatic classification and identification of seabed sediment types. Fine-scale processing analysis of sonar images and acoustic classification of seabed sediments as presented in this paper are useful for determining seabed surface textures and local sediment distribution laws. This study thus provides a basis for further study of the formation and evolution of seabed pockmarks.

Seabed sediment classification using multibeam backscatter data based on the selecting optimal random forest model

Seabed sediment classification using acoustic remote sensing technique is an attractive approach due to its high coverage capabilities and limited costs compared to taking samples of the seafloor. This paper focuses on backscatter intensity correction, sonar image quality improvement, and classifier construction, which aims to improve the accuracy of seabed sediment classification. The details are as follows. 1) A series of multibeam echosounder backscatter intensity correction model is constructed, including time-varying gains (TVG), transmission loss, actual area of insonification, source level, transmitting and receiving beam patterns, specular area correction, etc., to obtain accurate intensity values that accurately reflect seabed sediment types. 2) The pulse coupled neural network (PCNN) image enhancement model is established to improve the quality of sonar images, and 40 dimensional features are included to enrich the intensity description. 3) Selecting optimal random forest (SORF) seabed sediment automatic classification models that can select the input feature vectors and optimize the model parameters automatically are established. 4) Taking multibeam backscatter intensity data collected in Jiaozhou Bay as an example, the effectiveness and advantages of SORF are verified by comparing with support vector machine (SVM) and random forest (RF) classifiers.

A Method of the Voronoi Seabed Sedimentary Domain Boundary Reconstruction

Seabed sediment point data is the data basis for expressing the sediment elements. The determination of the extent of the seabed sediment type is the data base of the geometric model and the seabed sediment thematic chart. For the existing point element selection method cannot meet the application requirements. This paper uses the weighted idea to different data density and complex marine environment. Taking into account the trend of the sediment size and charting compensation effect, the influence weight of the point-like bottom data is analyzed, and the range of influence is reasonably determined. It provides a certain reference for the production of seabed sediment thematic chart and has high use value and broad application prospects.

Where’s the Carbon: Exploring the Spatial Heterogeneity of Sedimentary Carbon in Mid-Latitude Fjords

Fjords are recognized as globally significant hotspots for the burial and long-term storage of marine and terrestrially derived organic carbon (OC). By trapping and locking away OC over geological timescales, fjord sediments provide a potentially important yet largely overlooked climate regulation service. Currently, our understanding of the spatial distribution of OC within the surficial sediments of fjords is limited and this potentially implies an overestimation in the global estimates of OC buried in fjords as current calculation methods assume a homogeneous seabed. Using the mid-latitude fjords of Scotland and Ireland as a natural laboratory, we have developed a multi-tiered methodological approach utilizing a spectrum of data ranging from freely available chart data to the latest multibeam geophysics to determine and map the seabed sediment type. Targeted sampling of fjord sediments was undertaken to establish a calibration of sediment type against OC content. The results show that fjord sediments are highly heterogeneous both in sediment type and OC content. Utilizing the tiered mapping outputs, first order estimates of the surficial (top 10 cm) sediment OC stock within Scottish fjords (4.16 ± 0.5 Mt OC) and Irish systems (2.09 ± 0.26 Mt OC), when normalized for area the surficial sediments of Scottish and Irish fjords hold 2027 ± 367 and 1844 ± 94 respectively far exceed estimates for the continental shelf, again highlighting fjord sediments as hotspots for the capture of OC. This tiered approach to mapping sediment type is ideally suited to areas of the marine environment where data availability and quality is a limiting issue. Further understanding of the spatial heterogeneity of these sediments provides a foundation to reevaluate global fjord OC burial rates and to better understand the role of fjord sediments in regulating the global climate.

Rapid response of the Changjiang (Yangtze) River and East China Sea source-to-sink conveying system to human induced catchment perturbations

In order to investigate human impact in the Changjiang – East China Sea source to sink conveying system, we analyze the siltation and erosion state of the estuary-shelf deposition systems, and discuss the response intensity and timing of the depositional system of the Changjiang subaqueous delta and the related Changjiang distal mud. Results show that after the Three Gorges Dam was put into operation in 2003, the abrupt reduction of the sediment discharge to the estuary disturbed the equilibrium between the estuary-coast-shelf deposition systems, modifying the siltation-erosion balance and seabed sediment types. From the Changjiang subaqueous delta to its distal mud areas, the degree of response to catchment changes diminishes, with a larger response lag from north to south of the distal muds. Influenced by dam emplacements, the change of the Changjiang – East China Sea source to sink conveying system can be divided into three stages, i.e., pre-1950, 1950–2002 and post-2003. Before 1950, the source to sink system remained relatively stable. Between 1950 and 2002, man-made fluvial impoundments created an important sink for terrigenous sediment of the upstream regions, but without profound system changes. In contrast, after 2003, the morphodynamic patterns for the entire mid-lower reaches of the Changjiang River, the subaqueous delta and the distal muds shifted rapidly from siltation to erosion within a short period of time. Furthermore, the upper reaches of the river became disconnected from the source to sink conveying system, and the mid-lower reaches of the river, together with its subaqueous delta and northern distal mud area were converted into net sediment sources. As such, the sink area has shrunk while the source area expanded for the Changjiang – East China Sea source to sink conveying system.

Seafloor Characterization and Backscatter Variability of Coral Reef by Different Acoustic Techniques, El-Gouna, Red Sea, Egypt

Seabed sediment classification is an important research study for marine geology, marine environment and other research areas. The study area located at 25 km North of Hurghada. This study aims at identification of sea bed sediments and coral reef habitat of El-Gouna Area. Side scan sonar and QTC system used for surveying the area. Detection of the destroyed coral reef areas depends on the acoustic backscatter and diving survey along the study area. The sea floor sediment types, features and the biological habitats were the main causes that control the acoustic signature in the study area. The main seabed sediment types in the study area were hard bottom representing the coral reef patches, coarse and fine sand fractions. Overall, the acoustic approach showed consistent results for the assessment and mapping of the benthic habitats in this shallow-water area. Three different acoustic patterns were recognized: pattern of strong backscatter related to coarse sand, while Pattern of weak backscatter confined to silt and fine sand, patches of strong and weak backscatter are Patterns with isolated reflections belong to hard and soft coral reefs.

Modelling the sensitivity of suspended sediment profiles to tidal current and wave conditions

Seawater turbidity due to suspended particulate material (SPM) is an important property of a marine ecosystem, determining the underwater light environment and many aspects of biological production and ecology. SPM concentrations are largely determined by patterns of sediment resuspension from the seabed due to shear stress caused by waves and currents. Hence planning for the construction of large scale offshore structures which will alter regional hydrodynamics needs to consider the consequences for SPM concentrations. Here we develop a one-dimensional (vertical) model of SPM dynamics which can be used to scope the effects of changes in wave and tidal current properties at a site. We implement the model for a number of sites off the east coast of Scotland where we have extensive data sets to enable numerical parameter optimisation. The model performs well at simulating fluctuations in turbidity varying from flood-ebb tidal cycles, spring-neap cycles, storm wave events, and an annual cycle of SPM concentration which is attributed to seasonal consolidation of seabed sediments. Sensitivity analysis shows that, for the range of seabed sediment types in the study (water depth 16–50 m; mud content 0.006–0.380 proportion by weight), relatively large (50%) attenuations of tidal current speed are required to produce changes in water column turbidity which would be detectable by observations given the variability in measurements. The model has potential for application to map the large scale sensitivity of turbidity distributions to the installation of wave and tidal energy extraction arrays.

Influence of suspended kelp culture on seabed sediment composition in Heini Bay, China

Kelp aquaculture activities occupy large nearshore areas with significant effects on sediment properties, primarily caused by the influence of the suspended kelp on local hydrodynamics. Changes in sediment composition and grain-size distributions were investigated prior to and following the commencement of kelp aquaculture activities in Heini Bay in eastern China. Seabed sediment types and the particulate matter in suspension during the kelp seeding and harvesting periods, and in sediment cores, were analyzed. While suspended sediment in the kelp aquaculture area was up to 20% organic material, sediment organic content on the seabed remained at similar levels as areas lacking aquaculture. The composition of the seabed sediment in the kelp aquaculture area became finer-grained by the capture of fine particles. Within the kelp aquaculture area, the sediments are poorly sorted and positively skewed, whereas at the shoreward and seaward of the aquaculture area the sediments are relatively coarse-grained, well-sorted and nearly symmetrically distributed. Therefore, the kelp aquaculture activities not only increase the fine particulate fraction in the sediments within the aquaculture area, but also result in similar deposits seaward of it, indicating that seabed erosion and accretion is also controlled by the sediment source and the hydrodynamic conditions. The analysis of sediment cores showed that kelp culturing refines the sediment by preferentially capturing particles in the 38–40 μm size class, while having no effect on the <32 μm fractions, as evidenced by the positive skew of the surficial sediments. The captured particle size class became well mixed into the sediment, thereby changing the composition of the sediment in the uppermost layer of the core, indicating the existence of continuous and stable hydrodynamic conditions within the kelp aquaculture area. The same effect was observed in the seabed sediments seaward of the aquaculture area.

Classifying seabed sediment type using simulated tidal-induced bed shear stress

Abstract An ability to estimate the large-scale spatial variability of seabed sediment type in the absence of extensive observational data is valuable for many applications. In some physical (e.g., morphodynamic) models, knowledge of seabed sediment type is important for inputting spatially-varying bed roughness, and in biological studies, an ability to estimate the distribution of seabed sediment benefits habitat mapping (e.g., scallop dredging). Although shelf sea sediment motion is complex, driven by a combination of tidal currents, waves, and wind-driven currents, in many tidally energetic seas, such as the Irish Sea, long-term seabed sediment transport is dominated by tidal currents. We compare observations of seabed sediment grain size from 242 Irish Sea seabed samples with simulated tidal-induced bed shear stress from a three-dimensional tidal model (ROMS) to quantitatively define the relationship between observed grain size and simulated bed shear stress. With focus on the median grain size of well-sorted seabed sediment samples, we present predictive maps of the distribution of seabed sediment classes in the Irish Sea, ranging from mud to gravel. When compared with the distribution of well-sorted sediment classifications (mud, sand and gravel) from the British Geological Survey digital seabed sediment map of Irish Sea sediments (DigSBS250), this ‘grain size tidal current proxy’ (GSTCP) correctly estimates the observed seabed sediment classification in over 73% of the area.

Seabed Mixed Sediment Classification with Multi-beam Echo Sounder Backscatter Data in Jiaozhou Bay

The multi-beam echo sounder system can not only obtain high-precision seabed bathymetry data, but also obtain high-resolution seabed backscatter strength data. A number of studies have applied acoustic remote sensing method to classify seabed sediment type with multi-beam backscatter strength data, and obtained better classification results than the traditional sediment sampling method. However, these studies mainly focus on the single type sediment classification or seabed mixed sediment classification using single beam data, not multi-beam echo sounder data. Based on backscatter strength data by a high-frequency (300 kHz) Simrad EM3000 multi-beam echo sounder and seabed sediment sampling data of Jiaozhou Bay in Qingdao, China, we establish the relation model between seabed backscatter strength and sediment type characteristics after data processing and corrections. The purpose of data processing is to diminish or weaken the influence of local bottom slope and near nadir reflection on backscatter strength data. Processed backscatter strength data through corrections and compensations can better characterize the features of the seabed sediments. Applying the back propagation neural network method based on genetic algorithm, we present a fast and accurate seabed classification method in this paper which could identify not just a single type of sediment-like rock, sand but also mixed types of sediment like sand gravel, clayey silt and sand-silt-clay.

Delineating recurrent fish spawning habitats in the North Sea

The functional value of spawning habitats makes them critically important for the completion of fish life cycles and spawning grounds are now considered to be “essential habitats”. Inter-annual fluctuations in spawning ground distributions of dab (Limanda Limanda), plaice (Pleuronectes platessa), cod (Gadus morhua) and whiting (Merlangius merlangus) were investigated in the southern North Sea and eastern English Channel, from 2006 to 2009. The preferential spawning habitats of these species were modelled using generalised linear models, with egg distribution being used as proxy of spawners' location. Egg spatial and temporal distributions were explored based on six environmental variables: sea surface temperature and salinity, chlorophyll a concentration, depth, bedstress and seabed sediment types. In most cases, egg density was found to be strongly related to these environmental variables. Egg densities were positively correlated with shallow to intermediate depths having low temperature and relatively high salinity. Habitat models were used to map annual, i.e. 2006 to 2009, winter spatial distributions of eggs, for each species separately. Then, annual maps were combined to explore the spatial variability of each species' spawning grounds, and define recurrent, occasional, rare and unfavourable spawning areas. The recurrent spawning grounds of all four species were located in the south-eastern part of the study area, mainly along the Dutch and German coasts. This study contributes knowledge necessary to the spatial management of fishery resources in the area, and may also be used to identify marine areas with particular habitat features that need to be preserved.

Modelled distributions of ten demersal elasmobranchs of the eastern English Channel in relation to the environment

Generalised Linear Models, combined with a Geographic Information System, were used to map the habitats of ten demersal elasmobranch taxa (five skates, four sharks and a stingray) in the eastern English Channel, based on an annual fishery-independent bottom trawl survey conducted each October, and covering 21years. Habitat models showing mean density levels were developed for the most frequently occurring species, whilst binomial occurrence habitat models were built for less frequent species. The main predictors of elasmobranch habitats in the eastern English Channel were depth, bed shear stress and salinity, followed by seabed sediment type and temperature. Habitat maps highlighted contrasting habitat utilisation across species. Raja clavata, and to a lesser extent Scyliorhinus canicula, showed ontogenetic differences in habitat utilisation, with early life history stages found in shallow coastal waters, sheltered from strong tidal currents and typically with softer sediments. Spatial segregation by sex was also evident for S. canicula, with females found at higher densities on some harder grounds in the study area. An improved knowledge of habitat utilisation by elasmobranchs (particularly for important life history stages and all seasons) is required if spatial management is to be considered for these species.

What influences European plaice (Pleuronectes platessa) distribution in the eastern English Channel? Using habitat modelling and GIS to predict habitat utilization

Abstract Lauria, V., Vaz, S., Martin, C. S., Mackinson, S., and Carpentier, A. 2011. What influences European plaice (Pleuronectes platessa) distribution in the eastern English Channel? Using habitat modelling and GIS to predict habitat utilization. – ICES Journal of Marine Science, 68: 1500–1510. Conservation of fish habitat requires knowledge of how spatial distributions of species are related to environmental factors. Habitat modelling and mapping are useful in predicting species–environment relationships. Species abundance is modelled as a function of environmental parameters to understand species habitat utilization better. The influence of environmental factors on plaice (Pleuronectes platessa) distribution was investigated for two life stages and over two seasons. Generalized linear modelling and quantile regression modelling were used to relate the relative abundance of (juvenile and adult) fish to environmental predictors (seawater temperature, salinity, water column depth, bed-shear stress, and sediment type) in autumn and summer. The resulting regression parameters were used to map preferential and potential habitat distributions within a geographic information system. Models were evaluated by comparing predicted against observed abundances. Seabed sediment type was the main significant predictor of plaice preferential and potential habitats, whereas other factors did not show such a clear influence. The results contribute to a better understanding of the spatial ecology of the species.