Description: Machine learning spatial modeling is used for mapping the distribution of deep-sea polymetallic nodules (PMN). However, the presence and influence of spatial autocorrelation (SAC) have not been extensively studied. SAC can provide information regarding the variable selection before modeling, and it results in erroneous validation performance when ignored. ML models are also problematic when applied in areas far away from the initial training locations, especially if the (new) area to be predicted covers another feature space. Here, we study the spatial distribution of PMN in a geomorphologically heterogeneous area of the Peru Basin, where SAC of PMN exists. The local Moran’s I analysis showed that there are areas with a significantly higher or lower number of PMN, associated with different backscatter values, aspect orientation, and seafloor geomorphological characteristics. A quantile regression forests (QRF) model is used using three cross-validation (CV) techniques (random-, spatial-, and cluster-blocking). We used the recently proposed “Area of Applicability” method to quantify the geographical areas where feature space extrapolation occurs. The results show that QRF predicts well in morphologically similar areas, with spatial block cross-validation being the least unbiased method. Conversely, random-CV overestimates the prediction performance. Under new conditions, the model transferability is reduced even on local scales, highlighting the need for spatial model-based dissimilarity analysis and transferability assessment in new areas.

Description: Marine heatwaves (MHWs) are extreme events characterized by abnormally high sea surface temperatures, and they have significant impacts on marine ecosystems and human society. The rapid and accurate forecasting of MHWs is crucial for preventing and responding to the impacts they can lead to. However, the research on relevant forecasting methods is limited, and a dedicated forecasting system specifically tailored for the South China Sea (SCS) region has yet to be reported. This study proposes a novel forecasting system utilizing U-Net and ConvLSTM models to predict MHWs in the SCS. Specifically, the U-Net model is used to forecast the intensity of MHWs, while the ConvLSTM model is employed to predict the probability of their occurrence. The indication of an MHW relies on both the intensity forecasted by the U-Net model exceeding threshold T and the occurrence probability predicted by the ConvLSTM model surpassing threshold P. Incorporating sensitivity analysis, optimal thresholds for T are determined as 0.9 °C, 0.8 °C, 1.0 °C, and 1.0 °C for 1-, 3-, 5-, and 7-day forecast lead times, respectively. Similarly, optimal thresholds for P are identified as 0.29, 0.30, 0.20, and 0.28. Employing these thresholds yields the highest forecast accuracy rates of 0.92, 0.89, 0.88, and 0.87 for the corresponding forecast lead times. This innovative approach gives better predictions of MHWs in the SCS, providing invaluable reference information for marine management authorities to make well-informed decisions and issue timely MHW warnings.

Description: Cyanobacterial blooms are the most important and best studied type of harmful algal blooms in fresh waters and brackish coastal seas. We here review how and to which extent they resist grazing by zooplankton, how zooplankton responds to cyanobacterial blooms and how these effects are further transmitted to fish. Size, toxicity and poor nutritional value are widespread mechanisms of grazing defense by cyanobacteria. In some cases, defenses are inducible, in some they are obligate. However, to some extent zooplankton overcome grazing resistance, partly after evolutionary adaptation. Cyanotoxins are also harmful to fish and may cause fish kills. However, some fish species feed on Cyanobacteria, are able to reduce their abundance, and grow on a cyanobacterial diet. While reduced edibility for crustacean zooplankton tends to elongate the food chain from primary producers to fish, direct feeding by fish tends to shorten it. The few available comparative studies relating fish yield to nutrients or phytoplankton provide no indication that cyanobacteria should reduce the ratio fish production: primary production.

Description: The brown algal genus Fucus provides essential ecosystem services crucial for marine environments. Macroalgae (seaweeds) release dissolved organic matter, hence, are under strong settlement pressure from micro- and macrofoulers. Seaweeds are able to control surface epibionts directly by releasing antimicrobial compounds onto their surfaces, and indirectly by recruiting beneficial microorganisms that produce antimicrobial/antifouling metabolites. In the Kiel Fjord, in the German Baltic Sea, three distinct Fucus species coexist: F. vesiculosus, F. serratus, and F. distichus subsp. evanescens. Despite sharing the same habitat, they show varying fouling levels; F. distichus subsp. evanescens is the least fouled, while F. vesiculosus is the most fouled. The present study explored the surface metabolomes and epiphytic microbiota of these three Fucus spp., aiming to uncover the factors that contribute to the differences in the fouling intensity on their surfaces. Towards this aim, algal surface metabolomes were analyzed using comparative untargeted LC-MS/MS-based metabolomics, to identify the marker metabolites influencing surface fouling. Their epiphytic microbial communities were also comparatively characterized using high-throughput amplicon sequencing, to pinpoint the differences in the surface microbiomes of the algae. Our results show that the surface of the least fouling species, F. distichus subsp. evanescens, is enriched with bioactive compounds, such as betaine lipids MGTA, 4-pyridoxic acid, and ulvaline, which are absent from the other species. Additionally, it exhibits a high abundance of the fungal genera Mucor and Alternaria, along with the bacterial genus Yoonia-Loktanella. These taxa are known for producing antimicrobial/antifouling compounds, suggesting their potential role in the observed fouling resistance on the surface of the F. distichus subsp. evanescens compared to F. serratus and F. vesiculosus. These findings provide valuable clues on the differential surface fouling intensity of Fucus spp., and their importance in marine chemical defense and fouling dynamics.

Description: The use of pesticides in agricultural systems may have deleterious effects on surrounding environments. Aquatic systems are no exception and are increasingly polluted through the leaching of pesticides from agricultural activities. However, the pesticide pollution effects on key aquatic species have not been studied in many regions. In southern Africa, increasing pesticide use associated with macadamia tree Macadamia integrifolia farming presents a growing risk to surrounding aquatic ecosystems. This study assessed behavioural responses of an important and widely-distributed freshwater fish, Mozambique tilapia Oreochromis mossambicus, following exposure to three commonly used macadamia pesticides (i.e., Karate Zeon 10 CS, Mulan 20 SP, Pyrinex 250 CS) at different concentrations (0.7–200 µL, 0.3–1000 mg, and 0.7–8750 µL, respectively) over 24 h. Behavioural responses, i.e., swimming erratically, surfacing, vertical positioning, loss of equilibrium, being motionless and mortality were observed after pesticides exposure. Lethal dose 50 (LD50) values of Karate Zeon 10 CS, Mulan 20 SP and Pyrinex 250 CS were 2.1 µL (per water litre dilution—WLD), 5.2 mg (WLD) and 21.5 µL (WLD), respectively. These concentrations are therefore expressed as a maximal threshold usage in the environment around macadamia farms and a minimum distance of the plantations to water systems should be considered. Further studies should examine effects on other fish species and aquatic invertebrates to inform on pesticide pollution threats and mitigation plans for the region.

Description: Imaging technologies are being deployed on cabled observatory networks worldwide. They allow for the monitoring of the biological activity of deep-sea organisms on temporal scales that were never attained before. In this paper, we customized Convolutional Neural Network image processing to track behavioral activities in an iconic conservation deep-sea species—the bubblegum coral Paragorgia arborea—in response to ambient oceanographic conditions at the Lofoten-Vesterålen observatory. Images and concomitant oceanographic data were taken hourly from February to June 2018. We considered coral activity in terms of bloated, semi-bloated and non-bloated surfaces, as proxy for polyp filtering, retraction and transient activity, respectively. A test accuracy of 90.47% was obtained. Chronobiology-oriented statistics and advanced Artificial Neural Network (ANN) multivariate regression modeling proved that a daily coral filtering rhythm occurs within one major dusk phase, being independent from tides. Polyp activity, in particular extrusion, increased from March to June, and was able to cope with an increase in chlorophyll concentration, indicating the existence of seasonality. Our study shows that it is possible to establish a model for the development of automated pipelines that are able to extract biological information from times series of images. These are helpful to obtain multidisciplinary information from cabled observatory infrastructures.

Description: In the context of forecasted climate change scenarios, the growth of forest tree species at their distribution margin is crucial to adapt current forest management strategies. Analyses of beech (Fagus sylvatica L.) growth have shown high plasticity, but easternmost beech populations have been rarely studied. To describe the response of the marginal beech population to the climate in the far east sites of its distribution, we first compiled new tree ring width chronologies. Then we analyzed climate–growth relationships for three marginal beech populations in the Republic of Moldova. We observed a relatively high growth rate in the marginal populations compared to core distribution sites. Our analyses further revealed a distinct and significant response of beech growth to all climatic variables, assessing for the first time the relationship between growth and vapor pressure deficit (VPD) which described how plant growth responds to drought. These results highlight that accumulated water deficit is an essential limiting factor of beech growth in this region. In conclusion, beech growth in the easternmost marginal population is drought-limited, and the sensitivity to VPD will need to be considered in future studies to update the forest management of other economic and ecologically important species.

Description: The utilization of stationary underwater cameras is a modern and well-adapted approach to provide a continuous and cost-effective long-term solution to monitor underwater habitats of particular interest. A common goal of such monitoring systems is to gain better insight into the dynamics and condition of populations of various marine organisms, such as migratory or commercially relevant fish taxa. This paper describes a complete processing pipeline to automatically determine the abundance, type and estimate the size of biological taxa from stereoscopic video data captured by the stereo camera of a stationary Underwater Fish Observatory (UFO). A calibration of the recording system was carried out in situ and, afterward, validated using the synchronously recorded sonar data. The video data were recorded continuously for nearly one year in the Kiel Fjord, an inlet of the Baltic Sea in northern Germany. It shows underwater organisms in their natural behavior, as passive low-light cameras were used instead of active lighting to dampen attraction effects and allow for the least invasive recording possible. The recorded raw data are pre-filtered by an adaptive background estimation to extract sequences with activity, which are then processed by a deep detection network, i.e., Yolov5. This provides the location and type of organisms detected in each video frame of both cameras, which are used to calculate stereo correspondences following a basic matching scheme. In a subsequent step, the size and distance of the depicted organisms are approximated using the corner coordinates of the matched bounding boxes. The Yolov5 model employed in this study was trained on a novel dataset comprising 73,144 images and 92,899 bounding box annotations for 10 categories of marine animals. The model achieved a mean detection accuracy of 92.4%, a mean average precision (mAP) of 94.8% and an F1 score of 93%.

Description: Natural Products (NP) are essential for the discovery of novel drugs and products for numerous biotechnological applications. The NP discovery process is expensive and time-consuming, having as major hurdles dereplication (early identification of known compounds) and structure elucidation, particularly the determination of the absolute configuration of metabolites with stereogenic centers. This review comprehensively focuses on recent technological and instrumental advances, highlighting the development of methods that alleviate these obstacles, paving the way for accelerating NP discovery towards biotechnological applications. Herein, we emphasize the most innovative high-throughput tools and methods for advancing bioactivity screening, NP chemical analysis, dereplication, metabolite profiling, metabolomics, genome sequencing and/or genomics approaches, databases, bioinformatics, chemoinformatics, and three-dimensional NP structure elucidation.