Ecosystem-Based MSP for Enhanced Fisheries Sustainability: An Example from the Northern Adriatic (Chioggia—Venice and Rovigo, Italy)
Abstract
:1. Introduction
2. Materials and Methods
2.1. Case Study: Environmental Features, Human Activities, Existing Environmental, and Resources Protection Measures
2.2. Analyses
2.2.1. AIS Data Processing
- Raw Data Cleaning: identifying and editing/deleting outliers, such as pings in land and/or far away from preceding and following transmissions;
- Fishing trip identification: partitioning strings of consecutive AIS records for each unique Maritime Mobile Service Identity (MMSI) into individual trips, from the time vessels leave a port to the time they return to a port. An ad-hoc algorithm was developed in Matlab software to identify individual trips, requiring a number of nuanced steps to fix data issues, such as missing AIS transmissions in close proximity to ports and vessels steaming near the coast. Fishing trips were stored in the spatial database along with inherited geographical attributes (ports, countries, and GSAs of departure and landing) and additional trip metrics were computed such as duration and percentage of data gaps due either to intentional switch off by fishermen or transmission issues (i.e., in regions with high maritime traffic or missing terrestrial receivers along the coastlines).
- Assignment of the fishing gear: a rule-based machine learning was used to characterize single trips and to identify the type of fishing, based on GPS values and profiles of speed and course and additional gear-specific movement patterns (e.g., duration of the fishing trip and operations, course values during the hauling, and time gap between subsequent hauls). A set of algorithms were developed using Matlab software environment to incorporate prior domain knowledge for different gear types and executed in sequence to label single trips according to predefined gear classes: bottom otter trawl, beam trawl, pelagic trawl, purse seine, longline, and “other” fishing (including nets and traps).
- The algorithms were designed for each of the predefined gear classes and then a rule-based decision was taken to uniquely assign a gear type to each month on the basis of single answers (given by algorithms for each trip). Decisions were taken on a monthly basis and weighting only “stable” fishing trips that were more robustly classified, such as those with at least 30 transmissions and with a percentage of data gaps non-exceeding 90%. Given the above, the current rule-based system was able to question trip-level outliers (i.e., erroneous gear detection due to a poor data coverage), impose the predominant monthly gear type in use and/or validate real changes in fishing behavior, such as when vessels rotate fishing gears.
- Fishing segment identification: once trips had been labeled according to the gear type, hauls were extracted when fishing vessels were likely engaging in fishing operations. Extracted fishing segments were stored in the single spatial database and ready to be aggregated and binned into grid cells. Segments were stored along with their attributes (MMSI, number of pings, speed class, distances, and duration) and related parent tables (trip ID and raw pings).
- Finally, the intensity of fishing operations was mapped on a monthly and yearly basis and the output was represented on 1 km × 1 km grid cells, and populated with different metrics of fishing effort (i.e., fishing time and swept length). Annual/monthly fishing effort estimates were quantified in each grid cell spatial joining overlapping portions of hauls, and summing relative durations and lengths.
2.2.2. Data Collection and Preparation for DST
2.2.3. Maritime Use Conflict (MUC) Analysis
2.2.4. Cumulative Effect (CEA) Analysis
2.2.5. Stakeholder Engagement
2.3. Management Framework Proposal
3. Results
3.1. Overall Results
3.1.1. Fishing Activity from AIS Data
3.1.2. Maritime Use Conflict (MUC) Analysis
3.1.3. Cumulative Effect (CEA) Analysis
3.2. Management Framework Proposal
3.2.1. Priorities for Long-Term Sustainable Fisheries in the Area
3.2.2. Measure Typologies
3.2.3. Possible Management Measures
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A. Management Measures
Appendix A.1. Issue A. Conflicts between Fishing Segments
Appendix A.2. Issue B. Sustainability of Fishing Effort
Appendix A.3. Issue C. Rules Enforcement
Appendix A.4. Issue D. Impacts of Fisheries on Vulnerable Species and Valuable Environmental Components
Appendix A.5. Issue E. Fragmentation of the Sector
Appendix A.6. Issue F. Relationships with Other National and Foreign Fisheries and Common Rules
Appendix A.7. Issue G. Synergies between Fishing, Aquaculture and Other Uses of the Sea
Appendix A.8. Issue H. Need to Increase the Whole Product Value Chain
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Human Use | Description | Unit | Source |
---|---|---|---|
Aquaculture | Aquaculture (Veneto Region—2015) | P/A | Veneto Region—2015 |
Cables and Pipelines | LNG submarine pipelines | P/A | Veneto Region—2015 |
Coastal and Maritime Tourism | Bathing waters | P/A | SHAPE Project |
Marinas—Italy | Nr of berth | PagineAzzurre.com (elaboration Tools4MSP) | |
Land Based Activities | Modeled land based pressures from rivers into the Adriatic Sea | Index (1—high contribution to 0—no contribution) | SHYFEM (Shallow water Hydrodynamic Finite Model) |
Liquefied natural gas plant (LNG) | LNG terminal “Adriatic LNG” | P/A | Veneto Region—2015 |
Maritime Transport | Adriatic traffic density July 2014—June 2015 | Ship passages/cell | BALMAS project (www.balmas.eu), elaboration Tools4MSP |
Naval base activities | Cruise Ports 2015 | Nr of cruises | [47] |
Main Ports (Total shipped goods) 2015–2016 | Tons | [47] | |
Oil & Gas extraction | EMODNET Hydrocarbon Extraction—Offshore Installations | P/A | [47] |
Small scale fishery | Small scale fisheries (e.g., Set gillnets, purse seines, traps, etc.) | Total operations | FAO-ADRIAMED data, 2015 |
Hydraulic dredges | Hydraulic dredges for clam fisheries | Total operations | [49] |
Mid-water pelagic trawling (PTM) | Pelagic trawl from AIS 2015 | Swept length (km) | See Section 2.2.1 |
Bottom trawling (OTB) | Bottom otter trawl from AIS 2015 | Swept length (km) | See Section 2.2.1 |
Beam trawling (TBB) | Beam trawl from AIS 2015 | Swept length (km) | See Section 2.2.1 |
Environmental Component | Description | Unit | Source |
---|---|---|---|
Marine seabed habitats | EUNIS A4.26—Mediterranean coralligenous communities | P/A | [48] |
EUNIS A5.23—Infralittoral fine sands | |||
EUNIS A5.25—Circalittoral fine sands | |||
EUNIS A5.26—Circalittoral muddy sand | |||
EUNIS A5.33—Infralittoral sandy mud | |||
EUNIS A5.35—Circalittoral sandy mud | |||
EUNIS A5.36—Circalittoral fine mud | |||
MM—Marine mammals | Tursiops truncatus predicted density in the Adriatic Sea (2010–2013) | Density (nr of animals/100 km2) categories | [51] |
EFH—Essential Fish Habitats | Engraulis encrasicolus (European anchovy) spawners | Probability of occurrence | MAREA-MEDISEH Project [52] |
Pagellus erythrinus (Common Pandora) recruits | |||
Solea solea (Common sole) recruits | |||
Trachurus mediterraneus (Mediterranean horse mackerel) recruits | |||
Trachurus trachurus (Atlantic horse mackerel) recruits | |||
Sardina pilchardus (European pilchard) spawners | |||
Scomber colias (Atlantic chub mackerel) spawners | |||
Scomber colias (Atlantic chub mackerel) recruits | |||
Scomber scombrus (Atlantic mackerel) recruits | |||
Mullus barbatus (Red mullet) recruits | |||
Engraulis encrasicolus (European anchovy) recruits | |||
Sardina pilchardus (European pilchard) recruits | |||
Solea solea (Common sole) spawners | |||
TU—Turtles | Caretta caretta predicted density in the Adriatic Sea (2010–2013) | Density (nr of animals/100 km2) categories | [51] |
Title | Description | |
---|---|---|
A | Conflicts between fishing segments | Conflicts exist between small-scale fishing and bottom trawling and between different types of bottom trawling. |
B | Sustainability of fishing effort | Spatial, temporal, and space-time measures for the sustainable management of fishing activities are already in place. These actions need to be updated, based on results achieved so far and new policies. |
C | Rules enforcement | Effectiveness of the measures in place is compromised by infringements. |
D | Impacts of fisheries on vulnerable species and valuable environmental components | Techniques and practices can be developed and tested, also with the contribution of operators, to minimize the negative effects of fishing on some species in particular (e.g., bottlenose dolphins, turtles, and non-target fish species), on habitats (e.g., bioconstructions, bottom communities) or ecosystems. |
E | Fragmentation of the sector | Fragmentation in the sector impairs innovation and the diversification capacities, makes it less representative in decision-making contexts, and increases internal conflicts. |
F | Lack of common rules | The study area is used not only by local fisheries but also by fisheries from neighboring fishing ports, such as Rimini and Cesenatico (Emilia-Romagna Region) and Ancona (Marche Region). Furthermore, there is competition for resources and shared markets with foreign fisheries, in particular with the Croatian one. When the space-time regulations in place are different, disadvantages—and consequently conflicts—may arise for one or the other part. |
G | Synergies between fishing, aquaculture and other uses of the sea | In a context of lower and lower profitability in the sector, developing innovative activities, complementary to fishing, represents an opportunity for integration of income. The environmental, historical and cultural richness of the case study area creates a favorable context for the development of synergies between the fishing sector and other uses of the sea such as tourism and recreation. |
H | Need to increase the whole product value chain | The current organization of product distribution—including competition from abroad—determines the race for lower and lower prices. With higher value of product, catches could be reduced because fishermen could get the same income with a lower number of fishing days. |
Title | Description | |
---|---|---|
1 | Spatial measures | Measures regulating fishing with reference to specific areas |
2 | Spatial-Temporal measures | Measures regulating fishing with reference to specific areas and periods |
3 | Behavioral measures | Measures sustaining good fishing practices and minimizing environmental impacts |
4 | Technical and technological improvements | Measures concerning boats and their components, methods of navigation and fishing, tools, devices, products, processes, and any element useful to improve the sustainability of fishing activities |
5 | Knowledge measures | Aimed at increasing knowledge on the characteristics of the sea and marine ecosystems in the study area, on fishing practices and their effects, on other coastal and maritime activities that interact with fishing |
6 | Monitoring, control and surveillance | Aimed at measuring the trend of parameters relating to fishing activities and the characteristics of marine ecosystems; and monitoring compliance with rules and regulations relating to fishing activities in force in the study area. |
7 | Multi-level governance | Measures aimed at improving the organization and management of fishing activities; |
8 | Regulations and administrative measures | Measures aimed at the optimization and rationalization of regulations and bureaucracy of the sector |
9 | Economic and financial measures | Measures dealing with economic aspects (including taxation) and aimed to identify financial resources to support the performance and the start-up of activities, including innovative ones (e.g., incentives, financing, and indemnities) |
10 | Entrepreneurial development and marketing | Measures aimed at increasing the entrepreneurial capacity of operators, including organizational and representative aspects, and the ability to commercialize the product |
11 | Training and engagement of operators | Measures aimed at training of fishing operators on specific technical topics and encouraging the active participation in decision-making and management processes |
12 | Education and awareness raising of the public | Measures aimed at informing consumers about fishing activities and their products, with particular reference to the seasonality of species, lesser known species, the need to prefer local products, and fished with sustainable techniques. |
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Farella, G.; Tassetti, A.N.; Menegon, S.; Bocci, M.; Ferrà, C.; Grati, F.; Fadini, A.; Giovanardi, O.; Fabi, G.; Raicevich, S.; et al. Ecosystem-Based MSP for Enhanced Fisheries Sustainability: An Example from the Northern Adriatic (Chioggia—Venice and Rovigo, Italy). Sustainability 2021, 13, 1211. https://doi.org/10.3390/su13031211
Farella G, Tassetti AN, Menegon S, Bocci M, Ferrà C, Grati F, Fadini A, Giovanardi O, Fabi G, Raicevich S, et al. Ecosystem-Based MSP for Enhanced Fisheries Sustainability: An Example from the Northern Adriatic (Chioggia—Venice and Rovigo, Italy). Sustainability. 2021; 13(3):1211. https://doi.org/10.3390/su13031211
Chicago/Turabian StyleFarella, Giulio, Anna Nora Tassetti, Stefano Menegon, Martina Bocci, Carmen Ferrà, Fabio Grati, Amedeo Fadini, Otello Giovanardi, Gianna Fabi, Saša Raicevich, and et al. 2021. "Ecosystem-Based MSP for Enhanced Fisheries Sustainability: An Example from the Northern Adriatic (Chioggia—Venice and Rovigo, Italy)" Sustainability 13, no. 3: 1211. https://doi.org/10.3390/su13031211
APA StyleFarella, G., Tassetti, A. N., Menegon, S., Bocci, M., Ferrà, C., Grati, F., Fadini, A., Giovanardi, O., Fabi, G., Raicevich, S., & Barbanti, A. (2021). Ecosystem-Based MSP for Enhanced Fisheries Sustainability: An Example from the Northern Adriatic (Chioggia—Venice and Rovigo, Italy). Sustainability, 13(3), 1211. https://doi.org/10.3390/su13031211