Benthic Foraminifera as an Environmental Indicators in Mediterranean Marine Caves: A Review

Marine caves are characterized by wide environmental variability for the interaction between marine and continental processes. Their conditions may be defined as extreme for inhabiting organisms due to the enclosed morphology, lack of light, and scarcity of nutrients. Therefore, it is necessary to identify reliable ecological indicators for describing and assessing environmental conditions in these habitats even more than elsewhere. This review aims to provide the state of art related to the application of benthic foraminifera as proxies in the (paleo)ecological characterization of different habitats of marine caves. Special attention was addressed to a research project focused on Mediterranean marine caves with different characteristics, such as extent, morphology, freshwater influence, salinity, sediment type, oxygenation, and organic matter supply. This review aims to illustrate the reliability of foraminifera as an ecological and paleoecological indicator in these habitats. They respond to various environmental conditions with different assemblages corresponding to a very detailed habitat partitioning. Because marine caves may be considered natural laboratories for environmental variability, the results of these studies may be interpreted in the perspective of the global variability to understand the environmental drivers of future changes in marine systems.

A Study on the Effect of Soil and Sediment Types on the Fugacity Based Multimedia Partitioning of a Contact Fungicide Fluopyram: An Equilibrium Quality Criterion (EQC) Level 1 Approach

Equilibrium Quality Criterion (EQC) Level I calculations have been performed with Standard Equilibrium Quality Criterion (EQC) environment to study the environmental partitioning of a fungicide Fluopyram. Equilibrium Quality Criterion (EQC) Level I calculation assumes no degradation of the chemical, steady-state, and equilibrium conditions between the environmental compartments. The results reveal that the concentration of Fluopyram is expected to be maximum in the sediment compartment, followed by soil and water compartments. The effect of soil and sediment types on partitioning has been studied by systematically varying the densities of these two compartments. In the sediment compartment, the Fluopyram concentration is predicted to be highest if the sediment type is ‘sandy’ and the soil type is ‘clay’.

Improving Biological Reefing Viability Index as a Predictive Tool for Assessment of Potential Reefing Site in the Tropical Shallow Offshore Environment

The current study improved the predictive capability of the biological reefing viability index (BRVI) calibrated using local data. The prediction capability of the BRVI improved from 61% to 76% accuracy out of the 181 locations where the underwater videos available for verification. The BRVI includes corals larvae density, age of larvae at site, sea current, sea temperature, chlorophyll-a, water depth and sediment type to predict biological productivity of an area. Among the parameters, corals larvae density and age of the larvae are the most critical parameters that influent establishment of new biological ecosystem. The BRVI uses settlement of corals larvae as the precursors for the establishment of new habitats in the offshore environment because scleractinian corals is known to be able to form backbone of a new habitat in the environment. In this approach, the BRVI focuses on habitat creation instead of just being a fish aggregating device (FAD) when an artificial reef is deployed in an area. The BRVI can be used as a rapid screening tool to identified potential area for deployment of artificial reefs. The BRVI could reduce the chances of artificial reefs deployment that failed to address its objectives and intended outcomes.

Understanding the Spatial and Temporal Distribution and Environmental Characteristics of Polychaete Assemblages in the Coastal Waters of Ulleungdo, East Sea of Korea

The coastal area of Ulleungdo in the East Sea has experienced large climate and environmental changes. However, research on marine benthic animals in this area has been very limited. In the present study, we investigated the spatial and temporal distribution of benthic polychaetes to determine their seasonal adaptability to environmental changes in the coastal waters of Ulleungdo in 2019. In total, 116 species (34 families) of polychaetes were identified with an average of 25 species per site. The average density was 772.8 individuals m−2, with the highest density in August and the lowest in February. The dominant species were Pseudobranchiomma zebuensis (15.6%), Scolelepis sp. (8.6%), Haplosyllis spongiphila (7.3%), and Lumbrineris nipponica (6.3%). The main factors affecting polychaete community structure were water depth and sediment type (gravel, sand, silt, clay). Based on cluster analysis, the polychaetes tended to group mainly in winter and summer with P. zebuensis and Syllis sp. contributing to the grouping. The dominant species was the suspension feeder, which correlated highly with habitat sediment type and was substantially consistent with the coast of Dokdo. Some species overlapped in the East Sea coast, but the number and diversity of species were higher in Ulleungdo. Our study results confirm the ecological characteristics of benthic polychaetes of Ulleungdo and provide information for future monitoring of the environmental and biological changes in the East Sea.

Constructing the Resistivity-to-Sediment-Type Transform for the Interpretation of Airborne EM Data

A novel Markov Chain Monte Carlo (MCMC) based methodology was developed for the transformation of resistivity, derived from airborne electromagnetic (AEM) data, into sediment type. This methodology was developed and tested using AEM data and well sediment type and resistivity logs from Butte and Glenn Counties in the Californian Central Valley. Our methodology accounts for the spatially varying sensitivity of the AEM method by constructing different transforms separated based on the sensitivity of the AEM method. The large spatial separation that typically exists between the AEM data and the wells with sediment type logs was avoided by planning the acquisition of AEM data so as to fly as close as possible to the well locations. We had 55 locations with sediment type logs and AEM data separated by 100 m, determined to be the maximum acceptable separation distance. Differences in vertical resolution between the AEM method and the sediment type logs were addressed by modeling the physics of the AEM measurement, allowing for a comparison of field and AEM data generated during the MCMC process. The influence of saturation state was captured by creating one set of transforms for the region above the top of the saturated zone and another for below. Using the set of transforms developed at the 55 locations, an inverse distance weighting scheme that included a well quality ranking was used to construct a set of 12 (six sensitivity bins, and two saturation states) resistivity-to-sediment-type transforms at every AEM data location. These represent a set of transforms that accommodate the variation in AEM sensitivity and are independent of the inversion used to retrieve the resistivity model. These transforms thus avoid two of the significant limitations common to resistivity-to-sediment-type transforms used to interpret AEM data.

Sounding Out the Carbon: The Potential of Acoustic Backscatter Data to Yield Improved Spatial Predictions of Organic Carbon in Marine Sediments

Marine sediments hold vast stores of organic carbon (OC). Techniques to spatially map sedimentary OC must develop to form the basis of seabed management tools that consider carbon-rich sediments. While the natural burial of carbon (C) provides a climate regulation service, the disturbance of buried C could present a significant positive feedback mechanism to atmospheric greenhouse gas concentrations. We present a regional Scottish case study that explores the suitability of integrating archived seafloor acoustic data (i.e., multibeam echosounder bathymetry and backscatter) with physical samples toward improved spatial mapping of surface OC in a dynamic coastal environment. Acoustic backscatter is a proxy for seabed sediments and can be collected over extensive areas at high resolutions. Sediment type is also an important predictor of OC. We test the potential of backscatter as a proxy for OC which may prove useful in the absence of exhaustive sediment data. Overall, although statistically significant, correlations between the variables OC, sediment type, and backscatter are relatively weak, likely reflecting a combination of limited and asynchronous data, sediment mobility over time, and complex environmental processing of OC in shelf sediments. We estimate linear mixed models to predict OC using backscatter and Folk sediment type as covariates. Our results show that incorporating backscatter in the model improves the precision of OC predictions by 14%. Backscatter discriminates between coarse and fine sediments, and therefore low and high OC regimes; however, was not able to discriminate amongst finer sediments. Although sediment type is a stronger predictor of OC, these data are available at a much lower spatial resolution and do not account for fine-scale variability. The resulting maps display varying spatial distributions of OC reflecting the different scales of the predictor variables, demonstrating a need for further methodological development. Backscatter shows considerable promise as a high-resolution predictor variable to improve the precision of surface OC maps, or to reduce the number of OC measurements required to achieve a specified precision. Applications of such maps have potential in improved C-stock estimates and the design of conservation and management strategies that consider marine sediments as valuable C stores.

The development of a machine learning approach to construct a field-scale rock physics transform

The ability to relate geophysical measurements to the material properties of the subsurface is fundamental to the successful application of geophysical methods. Estimating the electrical resistivity from material properties can be challenging at many hydrogeologic field sites, which typically lack the spatial density and resolution of the measurements needed to develop an accurate rock physics relationship. We developed rock physics transforms using the machine learning method of gradient-boosted decision trees (GBDT). We adopted as our study area the coastal Salinas Valley, where saltwater intrusion results in changes in resistivity. We used measurements available in boreholes, including salinity and sediment type, to predict the resistivity. In some transforms, we included as predictors in the GBDT algorithm the location of each measurement and the aquifer corresponding to each measurement. We also explored incorporating the predictions of a baseline rock physics transform as a prior term within the objective function of the GBDT algorithm to guide the predictions made by the machine learning algorithm. The use of location and aquifer information improved the predictions of the GBDT transform by 28% compared to when location and aquifer information were not included. After the salinity, the easting of each measurement was the most important predictor, due to the spatial pattern of salinity changes in the area. The next most important predictor was the aquifer corresponding to each measurement. The benefit of including the baseline transform in the objective function was greatest for small datasets and when the accuracy of the baseline transform was already high. Finally, using the resistivity predicted by the GBDT, we generated 1-D resistivity models, which we used to simulate the acquisition of airborne electromagnetic (AEM) data. In most cases, the 1-D resistivity models and corresponding AEM data matched well with the models and data corresponding to the resistivity measured in boreholes.

Who lives where? Macrobenthic species distribution over sediment types and depth classes in the eastern North Sea

An extensive data set of macrozoobenthos from the eastern North Sea was evaluated in order to describe the distribution of infaunal species with respect to water depth and median grain size of the sediment. The resulting data are presented for 134 species, in 104 species abundance correlated significantly with water depth, with most species decreasing in abundance towards the hydrographically turbulent shallow areas. This suggests hydrography is a limiting factor for most of the species in this area while very few species only seem to favour the turbulent side of the gradient. With respect to median grain size, two thirds of the species showed significant abundance variations and one third did not. Most of the latter species were either represented by rather poor data or are known to be highly motile. Thus, true sediment generalists seem to be few. Conversely, no species was exclusively restricted to a single sediment type. Significant depth × sediment type interaction terms in a regression model indicate that abundance distribution over sediment types varied with depth level in 60% of the species, indicating that these populations were restricted to a part of their species-specific tolerated ranges in the study area. Hence, the distributional patterns over water depth and sediment types shown here represent realised niches. These data may help to locate populations and, in the light of global change, may serve as a baseline for future comparisons.