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.
Tools and 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.
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.