A major challenge for managing impacts and implementing effective mitigation measures and adaptation strategies for coastal zones affected by future sea level (SL) rise is our limited capacity to predict SL change at the coast on relevant spatial and temporal scales. Predicting coastal SL requires the ability to monitor and simulate a multitude of physical processes affecting SL, from local effects of wind waves and river runoff to remote influences of the large-scale ocean circulation on the coast. Here we assess our current understanding of the causes of coastal SL variability on monthly to multi-decadal timescales, including geodetic, oceanographic and atmospheric aspects of the problem, and review available observing systems informing on coastal SL. We also review the ability of existing models and data assimilation systems to estimate coastal SL variations and of atmosphere-ocean global coupled models and related regional downscaling efforts to project future SL changes. We discuss (1) observational gaps and uncertainties, and priorities for the development of an optimal and integrated coastal SL observing system, (2) strategies for advancing model capabilities in forecasting short-term processes and projecting long-term changes affecting coastal SL, and (3) possible future developments of sea level services enabling better connection of scientists and user communities and facilitating assessment and decision making for adaptation to future coastal SL change.
Tools and Data
Due to the recent rapid increase in human activity and economic development, many coastal areas have recently experienced a high degree of land-based pollution. Evaluating the total maximum allocated load (TMAL) of dissolved inorganic nitrogen (DIN) nutrients and the remaining capacity is of importance for improving water quality. A considerable amount of nutrients derived from the coastal watershed can be found in wet seasons, which is non-negligible for the estimation of remaining capacity. Therefore, we use a watershed–coastal ocean coupled model combined with an optimization algorithm to tackle this issue. In contrast with previous studies, this study provides a method to estimate the spatiotemporal variations in TMALs and we then compare it to the current DIN nutrient load, including both point sources and non-point sources. Our results suggest that the TMAL of Daya Bay (DB), which is located in the northern part of the South China Sea, is about 7976 metric tons per year (t/yr) and ranges from 191 metric tons per month (t/month) to 1072 t/month. The increase of non-point source (NPS) DIN input also plays an important role in daily overload events during wet seasons. Moreover, the TMALs show an inverse exponential correlation with the water age, but only about 65% of the variance is explained. This suggests that the variations from the optimization algorithm and from local water function zoning plans are also important. According to our prediction of the DIN input, the TMAL of DB will soon be exhausted in the next several years. Consequently, prompt actions are necessary to consider the distribution of TMALs in urban developments and to decelerate the rapid growth of DIN input. Therefore, the results of this study will be helpful for both local pollution control and future urban planning.
Development of global ocean observing capacity for the biological EOVs is on the cusp of a step-change. Current capacity to automate data collection and processing and to integrate the resulting data streams with complementary data, openly available as FAIR data, is certain to dramatically increase the amount and quality of information and knowledge available to scientists and decision makers into the future. There is little doubt that scientists will continue to expand their understanding of what lives in the ocean, where it lives and how it is changing. However, whether this expanding information stream will inform policy and management or be incorporated into indicators for national reporting is more uncertain. Coordinated data collection including open sharing of data will help produce the consistent evidence-based messages that are valued by managers. The GOOS Biology and Ecosystems Panel is working with other global initiatives to assist this coordination by defining and implementing Essential Ocean Variables. The biological EOVs have been defined, are being updated following community feedback, and their implementation is underway. In 2019, the coverage and precision of a global ocean observing system capable of addressing key questions for the next decade will be quantified, and its potential to support the goals of the UN Decade of Ocean Science for Sustainable Development identified. Developing a global ocean observing system for biology and ecosystems requires parallel efforts in improving evidence-based monitoring of progress against international agreements and the open data, reporting and governance structures that would facilitate the uptake of improved information by decision makers.
The main role of artificial reefs (ARs) is to enhance the productivity and sustainability of coastal fisheries by creating new fish biomass. From a modelling point of view, the creation of new fish biomass would be realized by a shift to a state of higher carrying capacity of the environment (K) for aquatic populations and communities. However, it has not been possible to demonstrate unequivocally rising K as a result of AR deployment because of the difficulty in disentangling enhancements due to simple distributional changes (the attraction hypothesis) versus total abundance rise (the production hypothesis). Here we develop a modelling framework based on simple, inexpensive fisheries data to quantify the impact of ARs, disentangling attraction from production by assessing the rise in regional K. The rationale is that if attraction to ARs from the wider region was the main driver of increased abundance in the ARs then regional K would have remained constant before, during and after deployment of the ARs. Therefore an increase in regional K disproves the hypothesis of attraction. The study case is the fishery for the two-banded seabream Diplodus vulgaris in southern Portugal. Monthly time series of 27 years of landings, 20 years of fishing effort, were available from three small-scale fleets: one was the artisanal fleet operating on the ARs and the other two were semi-industrial fleets operating on the wider continental shelf. The model that we developed and applied incorporated the data from all fleets so it evaluated the change in regional K. We show that regional K for D. vulgaris increased by 35% after final deployment of the ARs and it did so in linear fashion during four years. From a fisheries perspective the result was more nuanced because although the deployment succeeded in raising regional K, stock biomass and thereby enhancing the artisanal fishery, it also led to a substantial rise in total fishing mortality and exploitation rate because the semi-industrial fleets operating offshore increased their harvest rate nearly 3-fold. Our modelling framework has wide applicability in other regions due to the elementary nature of the necessary fishing monitoring data.
Species distribution models (SDMs1) are crucial for guiding management in a changing world. However, limited understanding of algorithm selection, ensemble weights and temporal transferability assessment undermines confidence in their predictions. Transferable predictive models, based on objective and proven selection criteria could therefore provide effective tools for defining species-environment relationships.
This study developed a framework for generating SDMs in the marine environment that improves models’ temporal transferability. The methodological approach steps were: 1) Collection of predictors related to species ecology and their records and species grouping according to their ecological requirements. Twenty-one seaweeds were used as a case study. Environmental and distribution data were divided into two independent periods to evaluate temporal transferability. 2) A model for each species was built in each period with nine algorithms (Generalized Linear Model, Generalized Additive Model, Multivariate Adaptive Regression Spline, Mixture Discriminant Analysis, Classification and Regression Trees, Support Vector Machine, Flexible Discriminant Analysis, Random Forest, MAXENT) and projected into the other period. Predictor contributions to the final models were obtained. 3) Assessment of predictive performance for each model was made using the area under the receiver operating characteristic curve and true skill statistics metric for both models’ accuracy and temporal transferability capabilities. All values were over 0.8 for all groups. In turn, the geographical pattern of all models were shown to be ecologically coherent.
The algorithms and their weights that fit best were used to generate transferable models over time in the marine environment and retained for each species. In general, machine learning algorithms produce models with higher sensitivity than regression-based approaches. This methodology sets the scene for further inquiries in the marine environment when developing consistent practices for model development and transferability.
Results are satisfactory for broad application in marine research, allowing a comparative framework between species predictions and facilitating the use of transferable models, especially in climate change studies across large areas. In addition, the proposed methodological approach is a cost-effective tool for dealing with a high number of species in marine environments. All data are freely available, so the methodology can be reproduced for marine researchers with different objectives.
Seafloor mapping can offer important insights for marine management, spatial planning, and research in marine geology, ecology, and oceanography. Here, we present a method for generating regional bathymetry and geomorphometry maps from crowd-sourced depth soundings (Olex AS) for a small fraction of the cost of multibeam data collection over the same area. Empirical Bayesian Kriging was used to generate a continuous bathymetric surface from incomplete and, in some areas, sparse Olex coverage on the Newfoundland and Labrador shelves of eastern Canada. The result is a 75m bathymetric grid that provides over 100x finer spatial resolution than previously available for the majority of the 672,900 km2 study area. The interpolated bathymetry was tested for accuracy against independent depth data provided by Fisheries and Oceans Canada (Spearman correlation = 0.99, p<0.001). Quantitative terrain attributes were generated to better understand seascape characteristics at multiple spatial scales, including slope, rugosity, aspect, and bathymetric position index. Landform classification was carried out using the geomorphons algorithm and a novel method for the identification of previously unmapped tributary canyons at the continental shelf edge are also presented to illustrate some of many potential benefits of crowd-sourced regional seafloor mapping.
As communities grapple with rising seas and more frequent flooding events, they need improved projections of future rising and flooding over multiple time horizons, to assist in a multitude of planning efforts. There are currently a few different tools available that communities can use to plan, including the Sea Level Report Card and products generated by a United States. Federal interagency task force on sea level rise. These tools are a start, but it is recognized that they are not necessarily enough at present to provide communities with the type of information needed to support decisions that range from seasonal to decadal in nature, generally over relatively small geographic regions. The largest need seems to come from integrated models and tools. Agencies need to work with communities to develop tools that integrate several aspects (rainfall, tides, etc.) that affect their coastal flooding problems. They also need a formalized relationship with end users that allows agency products to be responsive to the various needs of managers and decision makers. Existing boundary organizations can be leveraged to meet this need. Focusing on addressing these needs will allow agencies to create robust solutions to flood risks, leading to truly resilient communities.
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.
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.