A successful integrated ocean acidification (OA) observing network must include (1) scientists and technicians from a range of disciplines from physics to chemistry to biology to technology development; (2) government, private, and intergovernmental support; (3) regional cohorts working together on regionally specific issues; (4) publicly accessible data from the open ocean to coastal to estuarine systems; (5) close integration with other networks focusing on related measurements or issues including the social and economic consequences of OA; and (6) observation-based informational products useful for decision making such as management of fisheries and aquaculture. The Global Ocean Acidification Observing Network (GOA-ON), a key player in this vision, seeks to expand and enhance geographic extent and availability of coastal and open ocean observing data to ultimately inform adaptive measures and policy action, especially in support of the United Nations 2030 Agenda for Sustainable Development. GOA-ON works to empower and support regional collaborative networks such as the Latin American Ocean Acidification Network, supports new scientists entering the field with training, mentorship, and equipment, refines approaches for tracking biological impacts, and stimulates development of lower-cost methodology and technologies allowing for wider participation of scientists. GOA-ON seeks to collaborate with and complement work done by other observing networks such as those focused on carbon flux into the ocean, tracking of carbon and oxygen in the ocean, observing biological diversity, and determining short- and long-term variability in these and other ocean parameters through space and time.
Tools and Data
Based on fisheries landings data I propose the size-base index (community level) Mean Size of the Landing Catch (MSL). The MSL index was estimated based on high taxonomic resolution data available from auctions (species level) and demographic data acquired during the auction, namely species landed by “size-box” categories, which is assessed mandatorily in all EU members state for fisheries quality and statistic proposes. The MSL was calculated from the average inferred size-box categories of a species weighted by their annual catch. The use of MSL allows determining inter-annual changes in the size of the catch when weighted data is available from the fishery. Using the Portuguese fisheries landing data as an example, the MSL revealed that the landing yield of large fish linearly declined over time while the catch of small fishes increased (i.e., survivors to old age was reduced by fishing). The MSL can be easily used to assess trends in marine exploited commercial communities (community rather than population level) and is fully applicable with any species-size data source (e.g., scientific surveys, visual census data). The MSL can also be applied as a key indicator within the new ecosystem-based Marine Policy Framework Strategy (ecosystem approach to fisheries), which required the use of size-based indicators for the assessment of fisheries trends in exploited marine communities.
- An ecologically representative, well‐connected, and effectively managed system of marine protected areas (MPAs) has positive ecological and environmental effects as well as social and economic benefits. Although progress in expanding the coverage of MPAs has been made, the application of management tools has not yet been implemented in most of these areas.
- In this work, distribution models were applied to nine benthic habitats on a Mediterranean seamount within an MPA for conservation purposes. Benthic habitat occurrences were identified from 55 remotely operated vehicle (ROV) transects, at depths from 76 to 700 m, and data derived from multibeam bathymetry. Generalized additive models (GAMs) were applied to link the presence of each benthic habitat to local environmental proxies (depth, slope, backscatter, aspect, and bathymetric position index, BPI).
- The main environmental drivers of habitat distribution were depth, slope, and BPI. Based on this result, five different geomorphological areas were distinguished. A full coverage map indicating the potential benthic habitat distribution on the seamount was obtained to inform spatial management.
- The distribution of those habitats identified as vulnerable marine ecosystems (VMEs) was used to make recommendations on zonation for developing the management plan of the MPA. This process reveals itself as an appropriate methodological approach that can be developed in other areas of the Natura 2000 marine network.
Remote regions across Alaska are challenging environments for obtaining real-time, operational observations due to lack of power, easy road access, and robust communications. The Alaska Ocean Observing System partners with government agencies, universities, tribes and industry to evaluate innovative observing technologies, infrastructure and applications that address these challenges. These approaches support acquisition of ocean observing data necessary for forecasting and reporting conditions for safe navigation and response to emergencies and coastal hazards. Three applications are now delivering real-time surface current, sea ice, and water level data in areas not possible a mere 10 years ago. One particular challenge in Alaska is providing robust alternative power solutions for shore-based observing. Remote power options have been evolving alongside resilient technologies and are being designed for freeze-up conditions, making it possible to keep remotely deployed operational systems running and easy to maintain year-round. In this paper, three remote observing approaches are reviewed, including use of off-grid power to operate high-frequency (HF) radars for measuring surface currents, a real-time ice detection buoy that remains deployed throughout the freeze-up cycle, and a high-quality water level observing alternative to NOAA’s National Water Level Observing Network (NWLON) installations. These efforts are highly collaborative and require working partnerships and combined funding from other interested groups to make them a reality. Though they respond to Alaska’s needs including Arctic observing, these approaches also have broader applications to other remote coastal regions.
Beaches are economically and socially important to coastal regions. The intensive use of beaches requires active management to mitigate impacts to natural habitats and users. Understanding the patterns of beach use can assist in developing management actions designed to promote sustainable use. We assessed whether remotely piloted aerial systems (commonly known as drones) are an appropriate tool for quantifying beach use, and if beach activities are influenced by environmental conditions. Novel drone-based methods were used to quantify beach use. Drone flights recorded 2 km of beach, capturing video footage of the beach from the dune to water interface and the breaker zone. Flights were undertaken during three school holiday periods at four popular beaches in New South Wales, Australia. These videos were later analysed in the laboratory to categorise beach users. Of the total users sampled, 45.0% were sunbathing, 22.8% swimming, 21.2% walking, 10.6% surfing, and less than 0.5% were fishing. Participation in walking, surfing and fishing was similar throughout the sampling periods. However, sunbathing and swimming significantly increased during the austral spring and summer sampling periods. Usage patterns varied significantly among beaches, and during the different sampling periods, suggesting that adaptive management strategies targeted to specific areas are the most appropriate way to protect beach habitats and users. Furthermore, we demonstrate that drones are an effective assessment tool to improve coastal management decisions.
We propose a fish detection system based on deep network architectures to robustly detect and count fish objects under a variety of benthic background and illumination conditions. The algorithm consists of an ensemble of Region-based Convolutional Neural Networks that are linked in a cascade structure by Long Short-Term Memory networks. The proposed network is efficiently trained as all components are jointly trained by backpropagation. We train and test our system for a dataset of 18 videos taken in the wild. In our dataset, there are around 20 to 100 fish objects per frame with many fish objects having small pixel areas (less than 900 square pixels). From a series of experiments and ablation tests, the proposed system preserves detection accuracy despite multi-scale distortions, cropping and varying background environments. We present analysis that shows how object localization accuracy is increased by an automatic correction mechanism in the deep network's cascaded ensemble structure. The correction mechanism rectifies any errors in the predictions as information progresses through the network cascade. Our findings in this experiment regarding ensemble system architectures can be generalized to other object detection applications.
The Bay of Biscay is being affected by increasing level of marine litter, which is causing a wide variety of adverse environmental, social, public health, safety and economic impacts. The term “beach littering” has been coined to refer to the marine litter that is deposited on beaches. This litter may come from the sea and through land-based pathways, either from remote or adjacent areas. Dirty beaches can derive in loss of aesthetical value, beach cleaning cost, environmental harm or tourism revenue reduction among others. Therefore, local authorities have started to search for cost-effective approaches to understand and reduce litter accumulation in their beaches. A model is presented in this paper, which is based on Bayesian Networks and enables the forecasting of marine litter beaching at seven beaches located on the south-eastern coast of the Bay of Biscay. The model uses 9.5 years of metocean, environmental and beach cleaning data. The class to predict was defined as a variable with two possible values: Low and High accumulation of beach litter. The obtained models reached an average accuracy of 65.3 ± 6.4%, being the river flow, precipitation, wind and wave the most significant predictors and likely drivers of litter accumulation in beaches. These models may provide some insight to local authorities on the drivers affecting the litter beaching and may help to define their strategies for its reduction.
Issues related to protection of the Arctic environment are becoming increasingly urgent, as arctic ecosystems are vulnerable to increasing anthropogenic pressures. The problem of protecting Northern nature from the effects of persistent organic pollutants, which are dangerous for both biota and human health, is particularly acute. This case study analyses the existing normative acts regulating monitoring activities in the Russian Arctic. The paper emphasizes gaps in legal regulation, which are particularly prominent with regard to monitoring the quality of traditional food consumed by indigenous peoples. The author introduces proposals to change the current legislation to improve the efficiency of the state monitoring system in the Russian Arctic. Such changes will also help to harmonize monitoring activities in Russia with other Arctic States and to fill in the gaps in the Global Monitoring Reports and the Arctic Monitoring and Assessment Programme (AMAP) reports on persistent organic pollutants in traditional indigenous food.
Digital photography is widely used by coral reef monitoring programs to assess benthic status and trends. In addition to creating a permanent archive, photographic surveys can be rapidly conducted, which is important in environments where bottom-time is frequently limiting. However, substantial effort is required to manually analyze benthic images; which is expensive and leads to lags before data are available. Using previously analyzed imagery from NOAA’s Pacific Reef Assessment and Monitoring Program, we assessed the capacity of a trained and widely used machine-learning image analysis tool – CoralNet coralnet.ucsd.edu – to generate fully-automated benthic cover estimates for the main Hawaiian Islands (MHI) and American Samoa. CoralNet was able to generate estimates of site-level coral cover for both regions that were highly comparable to those generated by human analysts (Pearson’s r > 0.97, and with bias of 1% or less). CoralNet was generally effective at estimating cover of common coral genera (Pearson’s r > 0.92 and with bias of 2% or less in 6 of 7 cases), but performance was mixed for other groups including algal categories, although generally better for American Samoa than MHI. CoralNet performance was improved by simplifying the classification scheme from genus to functional group and by training within habitat types, i.e., separately for coral-rich, pavement, boulder, or “other” habitats. The close match between human-generated and CoralNet-generated estimates of coral cover pooled to the scale of island and year demonstrates that CoralNet is capable of generating data suitable for assessing spatial and temporal patterns. The imagery we used was gathered from sites randomly located in <30 m hard-bottom at multiple islands and habitat-types per region, suggesting our results are likely to be widely applicable. As image acquisition is relatively straightforward, the capacity of fully-automated image analysis tools to minimize the need for resource intensive human analysts opens possibilities for enormous increases in the quantity and consistency of coral reef benthic data that could become available to researchers and managers.