Ocean Data Sets Research Articles

A See‐Saw of Subduction Rate in the North Pacific Western and Eastern Subtropical Mode Water Formation Areas Induced by the Aleutian Low

AbstractSubduction of water masses serves as a critical linkage between the surface and subsurface ocean, playing a crucial role in redistributing heat, carbon, and nutrients in the ocean. This process significantly influences global climate and marine ecosystem dynamics. Analyzing five ocean reanalysis data sets, we reveal a distinctive see‐saw pattern in anomalous subduction rates between western and eastern Subtropical Mode Water (STMW) formation areas. This pattern is predominantly driven by variations in latent heat flux, which modulates the late‐winter mixed layer depth. We demonstrate that late‐winter Aleutian Low (AL) activity induces these anti‐phase variations through its impact on latent heat flux. Notably, the Pacific Decadal Oscillation (PDO) significantly influences this subduction see‐saw pattern. During PDO warm phases, AL southward shift intensifies the anti‐correlated variations. Conversely, during cold phases, AL northward shift weakens its influence on ESTMW area, resulting in a less stable anti‐phase relationship.

Subsurface Marine Heatwaves in the South China Sea

AbstractMarine heatwaves (MHWs), extreme warm ocean temperature events, greatly impact marine ecosystems. While most MHW studies in the South China Sea (SCS) focus on the sea surface, subsurface characteristics remain less explored. This study uses high‐resolution (1/12°) ocean reanalysis data sets to assess MHW properties with depth in the SCS from 1993 to 2022. We find that MHW intensities are typically stronger at subsurface levels (30–150 m) than at the surface, with significant spatial variations. The vertical structures of MHW maximum and cumulative intensities peak between 70 and 100 m with 2.7°C and 63°C days, respectively; MHW annual days and duration increase gradually from 10 to 2,000 m with duration peaks at 2,000 m (∼60 days), four times longer than that at the surface. The SCS experienced two major periods of extensive El Niño‐related subsurface MHWs from 1997 to 2002 and 2008 to 2014, with MHWs reaching depths of 150 m and covering 70% of the upper 40 m. Strong El Niño events strengthen the western Pacific subtropical high, reducing summer monsoon winds, weakening coastal upwelling east of Vietnam, and increasing water temperatures. Regions of elevated eddy kinetic energy in the SCS suggest that significant mesoscale eddy activities are situated east of Vietnam. In addition, notable eddy heat transport was observed in the above region and west of the Luzon Strait in the upper 1,000 m. This may help to explain the high maximum and cumulative intensities of MHWs exceeding 100 m in the northern SCS.

Multivariate statistical “unmixing” of Indian and Pacific Ocean sediment provenance

AbstractThe geochemistry of marine sediment is a massive archive of (paleo)oceanographic information. Accessing that information requires “unmixing” the various influences on marine sediment geochemistry to understand individual sources and marine geochemical processes. Q‐mode factor analysis (QFA) and independent component analysis (ICA) are multivariate statistical techniques that have successfully been applied to large datasets of marine sediment element concentrations to identify the number and composition of marine sediment sources or end‐members. In this study, we apply both techniques to two datasets of marine sediment geochemistry, compare the output, and discuss the advantages of each approach. In both datasets, ICA identified a mixing trend between carbonates and dust, whereas QFA represented the end‐members as two separate factors. In the Pacific and Indian Oceans dataset, both techniques produced three factors or independent components involving rare earth elements, but two of the QFA factors explained a small, almost negligible, amount of the variability of the dataset. Also, QFA identified more aluminosilicate end‐members (dust or volcanic ash) than ICA. In the Indian Ocean Sites 738 and 752 dataset, ICA identified two processes affecting Sr and Ba concentrations as separate independent components, while QFA created a factor representing the covariation of Sr and Ba over intervals of the site's paleoceanographic history. The results of this study exemplify that QFA identifies covariances and finds discrete end‐members contributing to the bulk mass of sediment. ICA works best with non‐Gaussian element distributions and finds geochemical signals and mixing trends that constitute the characteristic structure of the multielemental data.

Enhancing the Resolution of Satellite Ocean Data Using Discretized Satellite Gridding Neural Networks

Ocean satellite data are often impeded by intrinsic limitations in resolution and accuracy. However, conventional data reconstruction approaches encounter substantial challenges when facing the nonlinear oceanic system and high-resolution fusion of variables. This research presents a Discrete Satellite Gridding Neural Network (DSGNN), a new machine learning method that processes satellite data within a discrete grid framework. By transforming the positional information of grid elements into a standardized vector format, the DSGNN significantly elevates the accuracy and resolution of data fusion through a neural network model. This method’s innovative aspect lies in its discretization and fusion technique, which not only enhances the spatial resolution of oceanic data but also, through the integration of multi-element datasets, better reflects the true physical state of the ocean. A comprehensive analysis of the reconstructed datasets indicates the DSGNN’s consistency and reliability across different seasons and oceanic regions, especially in its adept handling of complex nonlinear interactions and small-scale oceanic features. The DSGNN method has demonstrated exceptional competence in reconstructing global ocean datasets, maintaining small error variance, and achieving high congruence with in situ observations, which is almost equivalent to 1/12° hybrid coordinate ocean model (HYCOM) data. This study offers a novel and potent strategy for the high-resolution reconstruction and fusion of ocean satellite datasets.

Nitrite-oxidizing bacteria adapted to low-oxygen conditions dominate nitrite oxidation in marine oxygen minimum zones.

Nitrite is a central molecule in the nitrogen cycle because nitrite oxidation to nitrate (an aerobic process) retains fixed nitrogen in a system and its reduction to dinitrogen gas (anaerobic) reduces the fixed nitrogen inventory. Despite its acknowledged requirement for oxygen, nitrite oxidation is observed in oxygen-depleted layers of the ocean's oxygen minimum zones (OMZs), challenging the current understanding of OMZ nitrogen cycling. Previous attempts to determine whether nitrite-oxidizing bacteria in the anoxic layer differ from known nitrite oxidizers in the open ocean were limited by cultivation difficulties and sequencing depth. Here, we construct 31 draft genomes of nitrite-oxidizing bacteria from global OMZs. The distribution of nitrite oxidation rates, abundance and expression of nitrite oxidoreductase genes, and relative abundance of nitrite-oxidizing bacterial draft genomes from the same samples all show peaks in the core of the oxygen-depleted zone (ODZ) and are all highly correlated in depth profiles within the major ocean oxygen minimum zones. The ODZ nitrite oxidizers are not found in the Tara Oceans global dataset (the most complete oxic ocean dataset), and the major nitrite oxidizers found in the oxygenated ocean do not occur in ODZ waters. A pangenomic analysis shows the ODZ nitrite oxidizers have distinct gene clusters compared to oxic nitrite oxidizers and are microaerophilic. These findings all indicate the existence of nitrite oxidizers whose niche is oxygen-deficient seawater. Thus, specialist nitrite-oxidizing bacteria are responsible for fixed nitrogen retention in marine oxygen minimum zones, with implications for control of the ocean's fixed nitrogen inventory.

AttGAN: attention gated generative adversarial network for spatio-temporal super-resolution of ocean phenomena

ABSTRACT This study proposes an innovative deep learning-aided approach based on generative adversarial networks named AttGAN, which is specialized for solving the spatio-temporal super-resolution problem of ocean datasets with complex waveforms and turbulent features. The proposed method can efficiently restore the desired fine high-frequency details while maintaining high efficiency. The key idea of the proposed approach is to incorporate an attention gate in the generator, which allows for emphasizing salient features for super-resolution tasks. In addition, residual convolutional blocks are used in the generator and discriminator to extract features. By establishing regression loss, physical constraints, and adversarial loss into a comprehensive loss function, a generator that exhibits high fidelity and strong generalization capabilities is trained. The proposed AttGAN is evaluated by experiments conducted on two datasets, and the experimental results validate its superiority over state-of-the-art physical constraint models in both qualitative and quantitative metrics. Moreover, the AttGAN has faster processing times than the corresponding ocean numerical models and better quality of results. Impact statement

Mitigating Masked Pixels in a Climate-Critical Ocean Dataset

Clouds and other data artefacts frequently limit the retrieval of key variables from remotely sensed Earth observations. We train a natural language processing (NLP)-inspired algorithm with high-fidelity ocean simulations to accurately reconstruct masked or missing data in sea surface temperature (SST) fields—one of 54 essential climate variables identified by the Global Climate Observing System. We demonstrate that the resulting model, referred to as Enki, repeatedly outperforms previously adopted inpainting techniques by up to an order of magnitude in reconstruction error, while displaying exceptional performance even in circumstances where the majority of pixels are masked. Furthermore, experiments on real infrared sensor data with masked percentages of at least 40% show reconstruction errors of less than the known uncertainty of this sensor (root mean square error (RMSE) ≲0.1 K). We attribute Enki’s success to the attentive nature of NLP combined with realistic SST model outputs—an approach that could be extended to other remotely sensed variables. This study demonstrates that systems built upon Enki—or other advanced systems like it—may therefore yield the optimal solution to mitigating masked pixels in in climate-critical ocean datasets sampling a rapidly changing Earth.

Entourage: all-in-one sequence analysis software for genome assembly, virus detection, virus discovery, and intrasample variation profiling

BackgroundPan-virus detection, and virome investigation in general, can be challenging, mainly due to the lack of universally conserved genetic elements in viruses. Metagenomic next-generation sequencing can offer a promising solution to this problem by providing an unbiased overview of the microbial community, enabling detection of any viruses without prior target selection. However, a major challenge in utilising metagenomic next-generation sequencing for virome investigation is that data analysis can be highly complex, involving numerous data processing steps.ResultsHere, we present Entourage to address this challenge. Entourage enables short-read sequence assembly, viral sequence search with or without reference virus targets using contig-based approaches, and intrasample sequence variation quantification. Several workflows are implemented in Entourage to facilitate end-to-end virus sequence detection analysis through a single command line, from read cleaning, sequence assembly, to virus sequence searching. The results generated are comprehensive, allowing for thorough quality control, reliability assessment, and interpretation. We illustrate Entourage's utility as a streamlined workflow for virus detection by employing it to comprehensively search for target virus sequences and beyond in raw sequence read data generated from HeLa cell culture samples spiked with viruses. Furthermore, we showcase its flexibility and performance on a real-world dataset by analysing a preassembled Tara Oceans dataset. Overall, our results show that Entourage performs well even with low virus sequencing depth in single digits, and it can be used to discover novel viruses effectively. Additionally, by using sequence data generated from a patient with chronic SARS-CoV-2 infection, we demonstrate Entourage's capability to quantify virus intrasample genetic variations, and generate publication-quality figures illustrating the results.ConclusionsEntourage is an all-in-one, versatile, and streamlined bioinformatics software for virome investigation, developed with a focus on ease of use. Entourage is available at https://codeberg.org/CENMIG/Entourage under the MIT license.

DSE-NN: Discretized Spatial Encoding Neural Network for Ocean Temperature and Salinity Interpolation in the North Atlantic

The precise interpolation of oceanic temperature and salinity is crucial for comprehending the dynamics of marine systems and the implications of global climate change. Prior neural network-based interpolation methods face constraints related to their capacity to delineate the intricate spatio-temporal patterns that are intrinsic to ocean data. This research presents an innovative approach, known as the Discretized Spatial Encoding Neural Network (DSE-NN), comprising an encoder–decoder model designed on the basis of deep supervision, network visualization, and hyperparameter optimization. Through the discretization of input latitude and longitude data into specialized vectors, the DSE-NN adeptly captures temporal trends and augments the precision of reconstruction, concurrently addressing the complexity and fragmentation characteristic of oceanic data sets. Employing the North Atlantic as a case study, this investigation shows that the DSE-NN presents enhanced interpolation accuracy in comparison with a traditional neural network. The outcomes demonstrate its quicker convergence and lower loss function values, as well as the ability of the model to reflect the spatial and temporal distribution characteristics and physical laws of temperature and salinity. This research emphasizes the potential of the DSE-NN in providing a robust tool for three-dimensional ocean temperature and salinity reconstruction.

Anomalous trends in global ocean carbon concentrations following the 2022 eruptions of Hunga Tonga-Hunga Ha’apai

We report on observed trend anomalies in climate-relevant global ocean biogeochemical properties, as derived from satellite ocean color measurements, that show a substantial decline in phytoplankton carbon concentrations following eruptions of the submarine volcano Hunga Tonga-Hunga Ha’apai in January 2022. The anomalies are seen in remotely-sensed ocean color data sets from multiple satellite missions, but not in situ observations, thus suggesting that the observed anomalies are a result of ocean color retrieval errors rather than indicators of a major shift in phytoplankton carbon concentrations. The enhanced concentration of aerosols in the stratosphere following the eruptions results in a violation of some fundamental assumptions in the processing algorithms used to obtain marine biogeochemical properties from satellite radiometric observations, and it is demonstrated through radiative transfer simulations that this is the likely cause of the anomalous trends. We note that any future stratospheric aerosol disturbances, either natural or geoengineered, may lead to similar artifacts in satellite ocean color and other remote-sensing measurements of the marine environment, thus confounding our ability to track the impact of such events on ocean ecosystems.

Extraction of comprehensive feature for active target detection using two hand-crafted acoustic features

We propose a model for creating a new feature suitable for classifying targets and non-targets using a small active sonar data. The proposed model extracts a comprehensive feature from two different hand-crafted features using deep-learning (DL). To extract comprehensive feature, we contrived a complementary learning module (CLM), which consists of two stages. In the first stage, a transformer encoder was adopted to reinforce the input features and then complementarily learned through attention mechanism in the following stage. The output features from the CLM are passed through a shallow CNN to classify targets and non-targets. In addition, an uncertainty quantification method is proposed using two CLMs to quickly add new samples acquired in the real ocean dataset. Using the decisions from two CLMs, we calculate the variance and determine which samples from the entire dataset are worthy of prioritized addition. Two real-ocean datasets were used to examine the model generalization and compare the classification results with conventional DL models. The generalization performance of the proposed model was comparable or superior to other DL models and uncertainty quantificaion method improved the generalization performance. [Work supported by Korea Research Institute for defense Technology planning and advancement(KRIT)—Grant funded by the Korea government (DAPA—Defense Acquisition Program Administration) (No. KRIT-CT-23-026, Integrated Underwater Surveillance Research Center for Adapting Future Technologies, 2024)]

Assessment and prediction of significant wave height using hybrid CNN-BiLSTM deep learning model for sustainable wave energy in Australia

Wave energy is regarded as one of the powerful renewable energy sources and depends on the assessment of significant wave height (Hs) for feasibility. Hence, this study explores the potential of wave energy by assessing and predicting Hs for two study sites in Queensland (Emu Park and Townsville), Australia. Assessment and prediction of Hs is extremely important for reliable planning, cost management and implementation of wave energy projects. The study utilized oceanic datasets based on wave measurements obtained from buoys along coastal regions of Queensland that are transmitted to nearby receiver stations. The parameters of the datasets include maximum wave height, zero up crossing wave period, peak energy wave period and sea surface temperature to accurately predict Hs. A new hybrid Convolutional Neural Network (CNN) and Bidirectional Long Short Term (BiLSTM) deep learning model with Multivariate Variational Mode Decomposition (MVMD) is developed which is benchmarked by Multi-Layer Perceptron (MLP), Random Forest (RF) and Categorical Boosting (CatBoost) to compare the performance. All models attain relatively high-performance results. The MVMD-CNN-BiLSTM attains slightly better performance values for both study sites among all developed models with highest correlation values of 0.9957 and 0.9986 for Emu Park and Townsville, respectively. Other performance evaluation metrics were also higher for MVMD-CNN-BiLSTM with lowest error values in comparison to the benchmark models. The annual mean of Hs is also computed to compare and obtain an insight with a linear projection. There is a greater ocean wave energy potential for Emu Park for a 10-year period with a projected mean Hs of 0.865 m in comparison to Townsville where the projected mean was of 0.665 m.

Indexing and real-time user-friendly queries in terabyte-sized complex genomic datasets with kmindex and ORA.

Public sequencing databases contain vast amounts of biological information, yet they are largely underutilized as it is challenging to efficiently search them for any sequence(s) of interest. We present kmindex, an approach that can index thousands of metagenomes and perform sequence searches in a fraction of a second. The index construction is an order of magnitude faster than previous methods, while search times are two orders of magnitude faster. With negligible false positive rates below 0.01%, kmindex outperforms the precision of existing approaches by four orders of magnitude. Here we demonstrate the scalability of kmindex by successfully indexing 1,393 marine seawater metagenome samples from the Tara Oceans project. Additionally, we introduce the publicly accessible web server Ocean Read Atlas, which enables real-time queries on the Tara Oceans dataset.

Study On Marine Boundary Layer Aerosol Structure based on Satellite Observations

The aerosol vertical distribution in the boundary layer is heavily influenced by the boundary layer thermal structure. This paper studied on marine boundary layer (MBL) aerosol and its structure by using satellite lidar measurements. The lidar methodology was improved and applied to the 3-year satellite MBL aerosol observations over Oceans, and a new global ocean dataset was built. The global MBL structure were examined and discussed with the new dataset. Results showed that the BLH generally increases and MLH generally decreases when away from the coast. Further analyze shows that the MBL tends to be more well-mixed with lower BLH, and the aerosol loading shows weaker relationships with U10m under overcast conditions than partly cloudy conditions. This study demonstrated that satellite lidar measurements have the great potential in evaluating and constraining both modeled MBL aerosol optical properties and modeled MBL structure.

Active prophages in coral-associated Halomonas capable of lateral transduction.

Temperate phages can interact with bacterial hosts through lytic and lysogenic cycles via different mechanisms. Lysogeny has been identified as the major form of bacteria-phage interaction in the coral-associated microbiome. However, the lysogenic-to-lytic switch of temperate phages in ecologically important coral-associated bacteria and its ecological impact have not been extensively investigated. By studying the prophages in coral-associated Halomonas meridiana, we found that two prophages, Phm1 and Phm3, are inducible by the DNA-damaging agent mitomycin C and that Phm3 is spontaneously activated under normal cultivation conditions. Furthermore, Phm3 undergoes an atypical lytic pathway that can amplify and package adjacent host DNA, potentially resulting in lateral transduction. The induction of Phm3 triggered a process of cell lysis accompanied by the formation of outer membrane vesicles (OMVs) and Phm3 attached to OMVs. This unique cell-lysis process was controlled by a four-gene lytic module within Phm3. Further analysis of the Tara Ocean dataset revealed that Phm3 represents a new group of temperate phages that are widely distributed and transcriptionally active in the ocean. Therefore, the combination of lateral transduction mediated by temperate phages and OMV transmission offers a versatile strategy for host-phage coevolution in marine ecosystems.

A time-varying copula approach for describing seasonality in multivariate ocean data

Characterizing multivariate ocean variables is quite critical for reliability design and risk assessment of marine structures. A robust, precise, and practical multivariate statistic model is necessary for comprehending ocean characteristics. As the time-varying characteristics exist in the ocean data, it is unreasonable to employ a simple constant statistical model to characterize all the multivariate data at one time. Therefore, in this paper, a time-varying copula approach is developed for modeling time-varying multivariate ocean data. Considering climate variations, a time-varying formula for return period and environment contour is also derived. The developed approach is demonstrated based on a site-specific ocean dataset collected from a buoy on the US coast. The climate effects associated with the multivariate ocean variables are characterized. The developed time-varying copula approach is also compared to the conventional copula and the conditional model in estimating the return period. The results showed that the time-varying model is helpful to explore the most critical environmental conditions for marine structures.

Coherence-enhancing anisotropic diffusion filter for 3D high-resolution reconstruction of P-wave velocity and density using full-waveform inversion: Application to a North Sea ocean bottom cable data set

Regularization is a central topic in the study of the solutions of ill-posed inverse problems. High-resolution seismic imaging using full-waveform inversion (FWI) belongs to this category of problems. Regularization through anisotropic diffusion, a technique that emerged in the field of image processing, is an interesting alternative to conventional regularization strategies. Exploiting the structural information of a given image, it has the capability to smooth this image along its main structures. The main difficulty is how to design the anisotropic diffusion operator. The concept of coherence enhancing proposed in 2D is extended in 3D and applied so as to filter and enhance the structural coherence of the model updates within an FWI algorithm. The benefits of this strategy are investigated on a 2D synthetic experiment before considering the multiparameter inversion of a 3D field data set from the North Sea up to 10 Hz. From this data, the vertical velocity and the density are simultaneously reconstructed. Compared with a conventional nonstationary Gaussian regularization strategy, the models obtained using the coherence-enhancing anisotropic diffusion strategy indicate an enhanced resolution, especially for the density model. The high-resolution reflectivity image computed from the impedance volume clearly illustrates the benefit this filtering approach can deliver in terms of structural interpretation.

Disentangling Biological Transformations and Photodegradation Processes from Marine Dissolved Organic Matter Composition in the Global Ocean.

Dissolved organic matter (DOM) holds the largest amount of organic carbon in the ocean, with most of it residing in the deep for millennia. Specific mechanisms and environmental conditions responsible for its longevity are still unknown. Microbial transformations and photochemical degradation of DOM in the surface layers are two processes that shape its molecular composition. We used molecular data (via Fourier transform ion cyclotron resonance mass spectrometry) from two laboratory experiments that focused on (1) microbial processing of fresh DOM and (2) photodegradation of deep-sea DOM to derive independent process-related molecular indices for biological formation and transformation (Ibio) and photodegradation (Iphoto). Both indices were applied to a global ocean data set of DOM composition. The distributions of Iphoto and Ibio were consistent with increased photodegradation and biological reworking of DOM in sunlit surface waters, and traces of these surface processes were evident at depth. Increased Ibio values in the deep Southern Ocean and South Atlantic implied export of microbially reworked DOM. Photodegraded DOM (increased Iphoto) in the deep subtropical gyres of Atlantic and Pacific oceans suggested advective transport in warm-core eddies. The simultaneous application of Iphoto and Ibio disentangled and assessed two processes that left unique molecular signatures in the global ocean.

Constraining an eddy energy dissipation rate due to relative wind stress for use in energy budget-based eddy parameterisations

Abstract. A geostrophic eddy energy dissipation rate due to the interaction of the large-scale wind field and mesoscale ocean currents, or relative wind stress, is derived here for use in eddy energy budget-based eddy parameterisations. We begin this work by analytically deriving a relative wind stress damping term and a baroclinic geostrophic eddy energy equation. The time evolution of this analytical eddy energy in response to relative wind stress damping is compared directly with a baroclinic eddy in a general circulation model for both anticyclones and cyclones. The dissipation of eddy energy is comparable between each model and eddy type, although the numerical model diverges from the analytical model at around day 150, likely due to the presence of non-linear baroclinic processes. A constrained dissipation rate due to relative wind stress is then proposed using terms from the analytical eddy energy budget. This dissipation rate depends on the potential energy of the eddy thermocline displacement, which also depends on eddy length scale. Using an array of ocean datasets, and computing two forms for the eddy length scale, a range of values for the dissipation rate are presented. The analytical dissipation rate is found to vary from 0.25 to 4 times that of a constant dissipation rate employed in previous studies. The dissipation rates are generally enhanced in the Southern Ocean but smaller in the western boundaries. This proposed dissipation rate offers a tool to parameterise the damping of total eddy energy in coarse resolution global climate models and may have implications for a wide range of climate processes.

Concentration and condition of American lobster postlarvae in small‐scale convergences

AbstractInvertebrate larvae are often abundant in the surface ocean, which plays a key role in their dispersal and connectivity. Pelagic microhabitats characterized by small‐scale hydrographic variability are complex and ubiquitous in the coastal ocean, but their study is challenging, and they have been largely neglected in meroplankton ecology. Surface convergences, i.e., surface microhabitats featuring convergent horizontal currents, may aggregate the last larval stage of the American lobster and could provide shelter and food for Stage IV postlarvae and thus enhance their condition. We tested these hypotheses by conducting a series of cruises in the southwestern Gulf of Maine in summer 2021, sampling 15 paired sets of potential convergences and off‐convergence unstructured habitat. We measured postlarval abundance, surface hydrography, acoustic backscatter, and circulation. Experiments and image analysis compared condition, color, and morphology of postlarvae sampled inside and outside potential convergences. Potential convergences varied in near‐surface hydrographic patterns, with most displaying consistency among two transects and diverse patterns in salinity and temperature (e.g., across‐convergence gradients with equal or different signs). While the highest postlarval abundances were found in convergences, abundance patterns on and off convergences were not consistent, and another analysis indicated higher abundance in convergences than in a 7‐year untargeted surface ocean data set. Experiments indicated no survivorship differences among convergence and non‐convergence individuals at two temperatures, while image analyses revealed differences in color and size. Physical measurements and qualitative neuston community analyses indicated substantial heterogeneity among potential convergences. Our results reinforce that small‐scale heterogeneities are highly variable but important to the ecology of meroplankton, including the pelagic and neustonic habitats where lobster postlarvae are abundant.