Mechanization of small-scale fisheries in the Nusa Tenggara province was very limited until the 1980s. The adoption of new technologies was constrained by a general lack of credits and government policies directed at facilitating technological transfer (Cribb and Ford, 2009). Other limitations were the availability of new equipment and gear, and the distance to centers of technological dissemination. With changes in infrastructure and communication brought by REPELITA plans in the 1970s and early 1980s, and the operation of large scale fishing fleets in the Eastern regions, exposure to technology gradually increased. As it has been observed for other small-scale fisheries in Southeast Asia and in Africa, the assimilation of new technologies probably modified the composition of fishing effort within communities and created competition between modernized and traditional fishermen (Ahmed, 1992; Andersson and Ngazi, 1998). Higher capture efficiency through gear substitution and motorization can deflate market prices, forcing households to invest in new technologies or to exit the fishery altogether (Bailey et al., 1987; Muhammad and Susilo, 1995). While this reservoir of labor could be absorbed by other sectors in the fishing industry, such as seafood processing or commercialization, in Eastern Indonesia there were very few industries, even less so directed at fisheries (Resosudarmo and Jotzo, 2009). Most likely, additional work effort engrossed the lines of infrastructure, commercial, and government service employment. But, where motorization outpaced both industrial and economic growth or effort concentrated in the hands of few, local fishing communities were most likely becoming poorer (Semedi, 2003).

The process of labor contraction was anticipated in the Western provinces by the evaluation of results from the 1973 national fishing survey by the FAO (Yamamoto, 1980). For example, it was noted that in early 1970s there was an excessive number of small-scale fishermen in the northern coasts of Java, and in other regions like Riau in Sumatra. Increases in efficiency of the fisheries in these regions were to be attained by reducing the numbers of individuals directly engaged in fishing (Yamamoto, 1980). While the creation of a middle fishing sector through technological development was highly desirable as a means to reduce poverty and to improve productivity of nonindustrialized areas, it was also recognized that mechanization alone was not an adequate solution (Muhammad and Susilo, 1995). If the process was not accompanied by the introduction of economic alternatives at the community level, it could exacerbate economic deficiencies and be the "cause of serious social problems" (Yamamoto, 1980, p. 6.1.5; De la Cruz Modino and Pascual-Fernández, 2013; Donkersloot and Menzies, 2015). In fact, dramatic conflicts arose between trawlers and smallscale fishermen in the immediate years throughout the Western Provinces and Irian Jaya (Bailey, 1987; Roch et al., 1995).

In the case of Ende, historical reconstructions showed that the number of active fishermen experienced gradual increases until the1940s, when labor fell due to military conflicts. Activities recovered after the cease of hostilities, and continued to grow steadily into the 1960s. After 1965, a marked decrease of almost 60% in the working force occurred in the lapse of 20 years. High instability and posterior recovery of the number of individuals engaged in the fishery happened between 1987 and 2014. The particular trajectory followed by the labor force in the years in between 1965 and 1984 could be a result of inaccuracies in the process of reconstruction. For example, they may reflect errors in anchoring values due to underestimation of earlier figures. However, it could also be interpreted as evidencing an economic contraction of the number of individuals operating in the fishery given the introduction of technological innovation and mechanization. Additionally, demographic effects might explain a reduction of available labor as the age structure of the population pyramid shifted. While statistical information is scant, there are several pieces of evidence that may support this conclusion.

• For instance, fishermen mentioned that nylon nets began to substitute heavier traditional materials such as cotton or vegetal fibers by the late 1960s. Sailboats remained in use until the mid-1980s, when access to outboard engines became more common. Older fishermen did not recall any government aid directly supporting their activities, but emphasized that improvements were based on individual initiative. Traditional shipping and trade between Ende, Bima, and Ujung Padang, made the circulation of these innovations possible. However, not all fishermen had the means to mechanize, and credits were infrequent. The first mention of provincial support and an increase in fishing effort occurs in 1989 (BPSE). This is not surprising, as REPELITA programs seem to have focused on aquaculture and the development of infrastructure such as ports.

In addition to changes in the composition of the labor force, with the expansion in mechanization in 1970s–1980s, the sustainability of marine resources in the archipelago at large started to decline. In the western Indonesian seas, the central government responded by introducing several measures that sought to protect small-scale fisheries, such as a general ban on all trawling operations. Moreover, during the mid-1980s the general government began to pursue the rationalization and fair allocation of economic growth in the sector by passing several pieces of legislation to protect smaller sectors. This direction took an important role in the quinquennial economic plans, REPELITA V (1989–1994) and REPELITA VI (1994–1999). In 1985, however, the central government modified licensing processes and allowed for the creation of joint ventures. As a result, the fisheries of the Indonesian EZZ became open to the participation of foreign vessels (Sunoko and Huang, 2014).

In Eastern Indonesia, where degradation was not perceived as a threat, the central and the regional government pursued an increase in the number of large vessels fishing for skipjack and tunas operating off ports such as Benoa, Bali and Kupang, Timor. In Ende, modernization continued to prosper with rapid growth in the number of motorized boats. In 1989, through provincial programs, effort quadrupled (BPSE). However, by 1993, the number of boats and landings declined almost 30% suggesting a reduction of fish stocks. Evidence from reconstructions and interviews suggests as signs of overfishing or overexploitation (Reitz, 2004; Carder et al., 2007). It is also in 1992 that Flores suffered a devastating earthquake and tsunami that may also account for the changes in effort. In the following years, landings recovered showing a slow increasing trend due to provincial aid (BPSE). But, this trend was not stable. Throughout the mid-1990s and early 2000s, the level of effort oscillated given new decentralization policies and repercussions of the 1997/1999 El Niño-La Niña events. For example, changes in landings and catch compositions were experienced in other fisheries in Eastern Indonesia in the years before and after 1997 that may have influenced active participation in the fishery (Blaber et al., 2005; Stacey et al., 2011). Tied to success in landings and sustainable aid, the labor force also experienced fluctuations. It is also at this time that immigration to Malaysia and neighboring countries began to rise (Barlow and Gondowarsito, 2009).

At the general level, REPELITA VI marked the beginning of decentralization efforts in natural resource management of fisheries, forestry, and irrigation to provincial and local authorities (Susilowati, 1996). However, the implementation of decentralization policies run into several impediments that include lack of personnel trained in fisheries and lack of trust among different institutions (Satria and Matsuda, 2004; Barlow and Gondowarsito, 2009). All of these limitations seemed to be true in the case of Ende. In 2002, another influx of boats increased effort with landings reaching unprecedented numbers for 2002 and 2003. In 2004, yields fell to 60% of previous reported numbers, remaining at similar or lower levels in comparison to the previous decade. This result may reflect the conflation of incentivization policies with environmental pressures introduced by 2002–2004 El Niño-La Niña events. Changes in ecological conditions probably created new stressors to threatened fishing stocks. The level of effort was incentivized to counterbalance diminishing yields and thus, leading to the 2004 decline.

By 2005, a presidential regulation enacted the National Medium-term Development 2004–2009 plan. The program included an improvement in provision and data accuracy, the development of fishing facilities and handling systems, and the empowerment of the tuna, seaweed, and shrimp fisheries. None of these measures, however, created significant changes in the district of Ende. While informants mentioned the introduction of seaweed programs and training, only a few fishermen were selected to participate and efforts where hindered by favoritism and corruption. When this research was carried out (2009–2012), support from the regional fishery was scant and irregular and mostly directed at intensification through the introduction of lampara boats (middle purse seiners). Going against the longstanding independent and individualistic character of the fishery, fishermen that wanted to access gear or engines needed to form smaller groups of three to four to receive any support. Nowadays, the fishery remains at large of subsistence scale. Lampara boats are growing and encroaching into coastal fishing grounds. With a tonnage that averages 4 of sardines, anchovies, and small pelagic fish, the proliferation of these boats are favoring the concentration of the fishery into a few hands. Ende may be at the brink of a new transition that could mean once again the contraction of the working force and the loss of operational independence by the smallest fishermen.

The future remains unknown for small-scale fishermen in Ende. With the establishment of marine protected areas in the Savu through a presidential decree in 2009, local district offices have taken new steps into addressing environmental concerns (Munasik et al., 2011; Achmad et al., 2013). Whether these new guidelines will be at odds with the pressures toward intensifying the fishery and achieving sufficiency in food production or whether they may be successfully reconciled, remains to be seen.

### CONCLUSION

Through historical ecology and ethnography, this article explored the impact of development and modernization policies among small-scale fisheries in Ende, Eastern Indonesia. Within fishery management, the transformation of local fisheries has often been seen as a result of intensive technological and capital development promoted by government and non-government agencies (MacFadyen and Corcoran, 2002). While true in other contexts, modernization is a relatively recent undertaking in Ende and has followed a rather fragmented and discontinuous process leaving a particular set of tracks. Endenese fisheries have experienced different gradients of transformation that speak of diverse and complex trajectories in the adoption of technological innovation (Ahmed, 1992). Understanding these trajectories is central to advancing fair regimes of resource use.

For example, when mechanization had not been properly regulated, the increasing competition by more efficient boats has resulted in the concentration of resources, the use of damaging fishing practices, and environmental degradation. The opportunistic nature in which policies were implemented has also meant that benefits from advancements were not equally shared among the parts, creating instability in the labor market and further eroding households' economic sufficiency. At present times, the situation has become more critical due continuous pressure toward modernization and stocks experiencing important changes in composition and stability.

As new regimes of governance are being proposed at the provincial level to address resource depletion and damaging fishing practices, it is important to see how small-scale fisheries are not isolated relicts of tradition. Even when considered peripheral in their geographical location, fishing communities like Ende are not disconnected from trade or technological centers (Wolf and Eriksen, 2010). And while they have been relegated in government priorities, they have become very attractive to illegal fishing operations that include shark finning (Christensen and Tull, 2014). In these cases, is through Chinese middlemen that fishermen acquire much desired technological innovations and new fishing gear. Not accounting for these interconnections within policy and governance mechanisms has serious consequences for biodiversity conservation. Ultimately, environmental degradation reverts to the long-term sustainability of local livelihoods within the community and contributes to exacerbate poverty.

Almost 10 years ago, Heazle and Butcher proposed that to prevent further depletion of marine resources, we should pursued management solutions as "integral parts of a broader regional strategy that takes into account the political and economic circumstances of the region" (2007, 282). Historical reconstructions can help us elucidate how different patterns of development take shape. Understanding the outcomes of mechanization at the fringes of more centralized systems is essential to navigate the tradeoffs among poverty reduction, economic growth, and environmental degradation.

### LIMITATIONS OF THE CURRENT STUDY

There are several limitations with the current study. Like other historical reconstructions of small-scale fisheries, this article relies on effort data that may provide an incomplete characterization of the intensity of resource harvesting activities. Given the nature of small-scale fisheries, the large diversity of gears and equipment used makes the systematic consideration of effort an arduous task. Direct monitoring of fishing behavior and landed captures is also difficult due to the flexibility of practices and schedules among individual fishermen. This creates some issues in the estimation procedures that should be acknowledged. Second, while observations and extensive archival and participant observation research were used to contextualized estimates, there are also risks of underestimation given the lack of information in terms of illegal operations in this particular region of the Savu. In addition, it is important to observe that the MSY model suffers from limitations for no biological estimations of growth or survival are available to correct inferences. Calculations might be reflecting thus the intensity of effort instead of real biomass numbers. Finally, environmental fluctuations affecting stocks have not been considered in the projections. The absence of meteorological datasets makes the assessment of the role of environmental fluctuations challenging. Future studies will address these shortcomings.

#### ETHICS STATEMENT

Institutional Review Board from The University of Georgia. Verbal informed consent was used to collect data for this study.

	- (1) Participants were provided with a prior informed consent document informing them of the purpose of the

investigation, the data collection process, benefits, and potential risks.

Observations of actions conducted by individuals were registered by the researcher. Previous authorization to conduct such observations was solicited with the consent document.

#### AVAILABILITY OF DATA AND MATERIALS

The datasets supporting the conclusions of this article are included within the article (and its additional files). All datasets

#### REFERENCES

and sources used if not included in the document are available upon additional request to the author.

### AUTHOR CONTRIBUTIONS

The author confirms being the sole contributor of this work and approved it for publication.

#### FUNDING

Funding for data collection was provided through an NSF DDIG (1022914) dissertation improvement grant and a NOAA/OHH GOHI training grant.

#### SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: http://journal.frontiersin.org/article/10.3389/fmars. 2017.00065/full#supplementary-material

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**Conflict of Interest Statement:** The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2017 Ramenzoni. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# Assessing the Effectiveness of Monitoring Control and Surveillance of Illegal Fishing: The Case of West Africa

Alkaly Doumbouya1 , Ousmane T. Camara1 , Josephus Mamie2 , Jeremias F. Intchama3 , Abdoulie Jarra4 , Salifu Ceesay 5 , Assane Guèye6 , Diène Ndiaye7 , Ely Beibou8 , Allan Padilla9 and Dyhia Belhabib9 \*

1 Centre National des Sciences Halieutiques de Boussoura, Conakry, Guinea, 2 Ministry of Fisheries and Marine Resources, Freetown, Sierra Leone, 3 Centro de Investigaçao Pesqueira Aplicada, Bissau, Guinea Bissau, 4 Ministry of Fisheries, Independant Consultant, Banjul, Gambia, 5 Department of Fisheries, Ministry of Fisheries, Banjul, Gambia, 6 Direction de la Protection et de la Surveillance des Pêches, Dakar, Senegal, 7 Direction des Industries de Transformation de la Pêche, Dakar, Senegal, 8 Institut Mauritanien de Recherches Océanographiques et des Pêches, Nouadhibou, Mauritania, 9 Sea Around Us, University of British Columbia, Vancouver, BC, Canada

#### Edited by:

Maria Lourdes D. Palomares, FishBase Information and Research Group, Philippines

#### Reviewed by:

Dimitrios K. Moutopoulos, Technological Educational Institute of Messolonghi, Greece Cornelia E. Nauen, Mundus Maris-Sciences and Arts for Sustainability, Belgium

\*Correspondence:

Dyhia Belhabib<EMAIL_ADDRESS>
#### Specialty section:

This article was submitted to Marine Fisheries, Aquaculture and Living Resources, a section of the journal Frontiers in Marine Science

Received: 12 November 2016 Accepted: 13 February 2017 Published: 07 March 2017

#### Citation:

Doumbouya A, Camara OT, Mamie J, Intchama JF, Jarra A, Ceesay S, Guèye A, Ndiaye D, Beibou E, Padilla A and Belhabib D (2017) Assessing the Effectiveness of Monitoring Control and Surveillance of Illegal Fishing: The Case of West Africa. Front. Mar. Sci. 4:50. doi: 10.3389/fmars.2017.00050 This paper assesses illegal fishing in West Africa, one of the regions most affected by Illegal, Unreported, and Unregulated fishing (IUU) in the world. The catch, the economic loss and the amount recovered through Monitoring, Control, and Surveillance (MCS) are calculated based on a reconstruction method, and the information made available through national MCS units, between 2010 and 2016 in an effort to assess the effectiveness of surveillance efforts in the region. Results show considerable loss of revenues for Mauritania, Senegal, The Gambia, Guinea Bissau, Guinea, and Sierra Leone, estimated at 2.3 billion USD annually, while a minimal amount of 13 million USD is recovered through MCS. In addition, this paper finds that countries touched by the Ebola crisis (Guinea and Sierra Leone) drive a tremendous increase in the loss generated by illegal fishing. However, further analysis shows that the overall severity of illegal fishing, as defined by a range of types investigated here, declines as the fines against the most severe forms of IUU fishing increase. Finally this study finds that Sierra Leone and The Gambia have the highest scoring MCS systems, and were the countries where the most offenders are caught and charged with the highest fines, while Senegal's new legislations which improved MCS during 2015 does not appear to show on the scoring results. This study finds that illegal fishing amounts the equivalent of 65% of the legal reported catch from West Africa and poses serious concern for food security, and the economy in the region.

Keywords: illegal fishing, IUU, catches, West Africa, economic values, sanctions, offenses, Monitoring Control and Surveillance

# INTRODUCTION

There is growing concern with regards to the health of global fish stocks and the repercussions of their depletion on food security and the economy of most vulnerable countries (FAO, 2016). West African countries rely heavily on fish as a one of the principal sources of protein, but also as a source of income and employment for nearly 7 million people (Belhabib et al., 2015c). This region has seen its fish stocks decline, driven by over-exploitation, overcapacity, and illegal fishing (Daniels et al., 2016). Previous work assessed illegal fishing in the region (Belhabib et al., 2012c, 2016; Belhabib and Pauly, 2015) to nearly 40 per cent of all the fish caught—the highest level worldwide (Agnew et al., 2009). Not only the economy of vulnerable countries is threatened, illegal fishing is estimated to reduce the number of jobs in artisanal sectors by 300,000 (Daniels et al., 2016). Illegal fishing conducted by industrial vessels is very difficult to assess and existing estimates are bound with a high degree of uncertainty, as illegal fishing vessels are highly mobile and develop tedious techniques to escape surveillance, particularly that monitoring is limited in the region.

There are different drivers of illegal fishing, as we look at industrial fishing, we argue that economic gain is the most significant incentive (Le Gallic and Cox, 2006; Sumaila et al., 2006), alongside with the ability of simply doing so (Andrews-Chouicha and Gray, 2005). This is mainly the case in the national waters or exclusive economic zones (EEZs) of the 6 West African countries (The Gambia, Guinea, Guinea-Bissau, Mauritania, Senegal, and Sierra Leone), whose Monitoring Control and Surveillance (MCS) systems are relatively weak.

MCS is often bound with country's indicators such as governance and corruption (Standing, 2006), which are very weak in the region. Poor governance and high corruption combined with high monitoring costs pose a serious concern on the sustainability of West African countries' efforts to combat illegal fishing. Examples in the region show that the presence of international funders helps combatting illegal fishing by adding transparency and increasing surveillance activities. This raises the question of the effectiveness of these systems, and how much of the loss to illegal, unreported, and unregulated fishing is recovered through MCS (fines and sanctions).

MCS efforts are further jeopardized by the use of "detection escape" techniques such as interfering with electronic monitoring systems, the use of different flags to hide vessel identity and escape prosecution, use of multiple boat names, and forgery of registration certificates in the region (J.M. pers. obs.). Several cases illustrate multiple violations of fishery laws within West Africa, and raised important attention on the issue of cost recovery, to allow MCS efforts to become sustainable and independent from foreign "funding" (MRAG, 2005; Greenpeace, 2006). Multiple infractions and low capability of prosecution given high rates of detection escape render MCS particularly vulnerable to lower budgets. This vulnerability limits the ability of West African countries to deter IUU fishing. In addition, losses generated by illegal fishing in the region are barely known which limits knowledge on real economic losses generated by such activity.

This paper seeks to assess economic loss caused by illegal fishing in West Africa and the effectiveness of enforcement in the period between 2010 and 2016. It builds, for the first time, a sanctions and illegal fishing database, and analyses the relationship between sanctions and severity of IUU and draws conclusions on the efforts to make to reduce illegal fishing in the region. It also discusses the implication of illegal fishing on the artisanal fisheries sector, food security, and the economy.

# METHODS

#### Study Area

The study area covers six countries (Mauritania, Senegal, The Gambia, Guinea-Bissau, Guinea, and Sierra Leone). These countries are members of the West African Sub Regional Fisheries Commission (SRFC)1 and lay within the Canary Current Large Marine Ecosystem (CCLME) in the North and the Guinean Current Large Marine Ecosystem (GCLME) in the South. This makes West African waters particularly productive. The coastal zone of West Africa is an area of strategic interest for the socio-economic development and livelihood of 1.4 million people living along the coast, and fisheries therein can contribute up to 38% of the GDP (Belhabib et al., 2015c).

## Building a Comprehensive Database of Illegal Fishing Vessels, Offenses, and Sanctions

We investigate illegal fishing occurrences in West Africa and cover multiple indicators between 2010 and 2016. These are: Vessel name, gear type, country where illegal fishing occurred (or country where a fine was issued), origin of the vessel (flag), amount of the fine paid (or otherwise issued, if not yet paid) converted into USD2015, year of illegal activity, offenses committed per vessel, other sanctions in addition to the fine (such as confiscation of vessel, catch, gear, etc.), and whether a sanction was issued at all. The main data were gathered from various media sources, and observations from various organizations (Environmental Justice Foundation, 2012; INTERPOL, 2014; Greenpeace, 2016a,b), complemented by information from the Department of Surveillance and Protection of Fisheries for Senegal, The MCS units of The Gambia, Sierra Leone and Guinea, and the Ministry of Fisheries of Guinea Bissau, while information for Mauritania were not available. In cases where sanctions were not reported (notably the case of Guinea and Guinea Bissau), the amount of the sanction was assumed to equate the minimum amount that is given under the Fisheries Act of the country for that offense, or the average based on the most recent historical fine amounts available for similar cases.

# Estimation of Illegal Catches

We estimated illegal catches, i.e., catches by foreign fleets as per the definition of illegal fishing (Belhabib et al., 2014a) for each country following different approaches depending on data availability.

#### Senegal

Illegal catches for Senegal for the years 2010 and 2011 were extracted from Belhabib et al. (2014a) and were estimated based on the number of observed illegal fishing boats, their size and a modeled catch per unit of effort (Belhabib et al., 2014b). Given similar MCS efforts between 2011 and 2012, we assumed illegal catches were constant then, and they increased by 20% between 2012 and 2013 after the dismissal of the Russian vessels from Senegalese waters (Belhabib et al., 2014a, 2015a). This particularly applied after Russian vessels obtained licenses in other countries

1Cape Verde, also a member, is not included in this analysis.

in the sub-region (notably, Guinea Bissau and Mauritania), as incursionsto Senegalese waters at night were common (A.G. pers. observation). We then multiplied the illegal catch of 2013 by the variation between 2013 and 2014, and between 2014 and 2015 to estimate the illegal catch for 2014 and 2015. Variation rates were derived from the percentage of infractions (compared to the total observed vessels) found in the World Bank monitoring report (The World Bank, 2016). Fishing vessels observed by aerial/surface patrol or by radar and satellite monitoring that are committing a serious infraction in targeted fisheries represented 86% of the total in 2013, 60% in 2014, and 60% in 2015 (The World Bank, 2016).

#### Guinea Bissau

Two data points were available for Guinea Bissau. Surveillance activities found eight vessels fishing illegally during 1 week in 2014 (Caopa and Rejprao, 2016). Given the conservative nature of this estimate, we extrapolated year long and estimated a number of 52 vessels in 2014. This number was multiplied by a CPUE of 1,200 t•boat−1•year−1 , which is the minimum an illegal trawler catches to cover its operation costs (Pauly et al., 2014; Belhabib et al., 2015b). We assumed the illegal catch was constant between 2014 and 2015 and interpolated linearly between the estimate in 2010 (18,000 t), provided by Belhabib and Pauly (2015) for Guinea Bissau.

#### Guinea

Illegal fishing in Guinea represents the equivalent of 64% of legal reported catches (Belhabib et al., 2012a). We first extracted the estimated illegal catch for Guinea from Belhabib et al. (2012a), and then multiplied the reported catch extracted from the Food and Agriculture Organization (FAO) FishStat database, by 64%. Then we extrapolated the trend forwards to 2015.

#### Sierra Leone

The number of vessels spotted fishing illegally, or estimated, was reported at 30 for 20112 , 10 in 20123 (Finch, 2016), 7 in 2014 (NOAA, 2015), and 80 in 20154 associated with the Ebola crisis. The Ebola crisis along with governance issues related to the cancelation of the World Bank project, a major contributor to the increase in MCS in 2012 and 2013, prompted low to virtually no monitoring after 2014. We interpolated the number of boats between 2012 and 2014 and then multiplied by a minimum CPUE of 446 t•boat−1•year−1 for the industrial fleet operating in Sierra Leone (Seto et al., 2015).

We note that the number of arrests does not imply the total number of illegal fishing vessels. Given that 10 vessels were reported as committing 252 acts of illegal fishing, these numbers are likely very conservative.

#### Mauritania and the Gambia

The baseline illegal catch for Mauritania and The Gambia was extracted from Belhabib et al. (2012b) and Belhabib et al. (2016), respectively. These were then multiplied by the regional trend estimated using the total catch for Senegal, Guinea, Guinea Bissau, and Sierra Leone.

# Estimation of Unreported Foreign Catches

To estimate unreported catches by the foreign fleets legally operating in West Africa, we first estimated the total catch based on the product of the fishing effort (defined as the number of vessels, their GRT, the number of fishing days and their nationality and gear type), and the catch corresponding to that unit of effort (Belhabib et al., 2014a). The number of foreign vessels operating in Senegal, The Gambia, Sierra Leone, Guinea Bissau, and Mauritania were obtained from national governmental organizations during a workshop in 2016, verified and/or complemented with the number of foreign vessels legally operating in the region obtained from official records (e.g., FAO global fishing vessel database5 ) whenever available completed by a literature review (Anon, 2015). The foreign catch from Guinea was extracted as a sub-set of the total catch, to which an underreporting rate of 20% was applied (Belhabib et al., 2012c). The difference between the reported foreign catch (extracted from official statistics provided by MCS units whenever available) and the total estimated catch represents the unreported foreign catch, which is obtained by flag for Senegal, Guinea Bissau, Sierra Leone, and Mauritania.

# Economic Loss

The annual economic loss caused by illegal and unreported fishing was estimated by multiplying the estimated illegal and unreported catch by the ex-vessel price. Ex-vessel prices were obtained from the Sea Around Us ex-vessel price database for 2010 (Swartz et al., 2013) and converted to 2015 USD using Consumer Price Index extracted from the World Bank database (www.worldbank.org).

Although conservative, the values of unregulated catches were also added. The unregulated catch was estimated using the number of vessels that were caught while committing an unregulated fishing act such as illegal transshipment, fishing in a prohibited zone, using illegal mesh size, etc. multiplied by the minimum regional CPUE of 446 t•boat−1•year−1 (Seto et al., 2015). This provides a rather highly conservative estimate since only those who were caught are taken into consideration in the analysis.

#### Assessing the Effectiveness of MCS Using a Scoring System

Sumaila et al. (2006) describe expected penalty drivers (or cheating drivers) as closely related to the effectiveness and efficiency of the surveillance system, the level of nongovernmental or private organizations involvement in detecting offenses, which relate to the likelihood of vessels of being detected; avoidance activities of offenders; and the severity of the penalty which disincentivises illegal fishing when it is accompanied by effective enforcement. Herein, we look at these drivers which are the likelihood of vessels of being detected through the number of offenses and sanctions, the avoidance activities which are represented by the number of offenses

2http://slconcordtimes.com/60-illegal-fishing-in-salone-waters/

3http://blogs.ubc.ca/jdmayer/2014/11/20/from-cannons-to-canon-sinkingpirate-fishing-in-sierra-leone/

4http://cocorioko.info/now-it-is-time-for-sierra-leone-to-turn-attention

5http://www.fao.org/figis/vrmf/finder/search/#.V9BNKfkrKM9

that escape sanctions and the severity of the penalty. We add availability of information as a proxy for transparency and develop a scoring system based on the indicators below:

The total score is then calculated as the sum of the previous scores, where the maximum score is 44 (based on the sum of the maximum of each score).

# RESULTS

# Illegal Fishing Activities and Sanctions

Overall, there were 2306 observed offenses which spread over 23 offense categories, or observed offenses that have been detected and mostly sanctioned in the region (**Table 1**). Around one third of the offenses observed were not sanctioned. Under-reporting of fishing effort (associated with GRT) represents the most recurrent offense. However, this offense is only sanctioned 19% of the time, while it remains undetected by the governments of West Africa the rest of the time. Only Senegal applied sanctions on 12 vessels of Chinese origin of around \$1,000 US each. Gear related offenses (associated with illegal mesh size, illegal gear, improper stowage of fishing gear, etc.) were caught 44 times TABLE 1 | Summary of observed offenses in West Africa, 2009–2016.

overall and were sanctioned entirely, with an average fine of \$137,000 US ranging between \$835 US (Senegal) and \$812,000 US (The Gambia). Fishing in a prohibited zone ranks third with 43 instances and sanctioned 50% of the time only, with an average fine of \$134,000 US, driven by high sanctions in Sierra Leone. Registered vessels fishing without a license were caught 19 times and were fined \$179,260 US on average, with an average fine of \$45,000 US in Senegal (the minimum) and a maximum in The Gambia (\$1.1 million US). Forgery of documents, vessel names and marking fault were severely sanctioned in Sierra Leone with an average fine of \$302,000 US per offense in comparison with Guinea's average (\$30,000 US) and Senegal (\$800 US). Unauthorized entry to or exit from the EEZ of Sierra Leone constituted the sixth offense that was most caught in the region, and was sanctioned with a fine of \$55,100 US on average. Fishing without an authorization ranks seventh and was caught 14 times. Sanctions for this offense vary greatly from withdrawal of fishing license and a fine of \$800 US (Senegal) to around \$2.3 million US (Senegal). The next most recurrent categories of offenses consist of mistreatment (of fishing observers), attempted corruption and/or failure to comply with 14 vessels and an average fine of \$95,600 US per sanction driven by high sanctions in Sierra Leone.

# Value Recovered through MCS (Total Sanctions)

Over the period between 2009 and mid-2016, around \$29 million US were collected or sanctioned in fines.

6We don't obtained information from all countries.

Frontiers in Marine Science | www.frontiersin.org

Total fines collected or to be collected have increased overall from less than \$370,000 US in 2009 to a projected \$13.8 million US in 2016 (**Figure 1**). In contrast, the average fine per sanction has decreased, from the first peak in 2010 (\$362,000 US) to \$137,000 US in 2016 (**Figure 1**). This corresponds to the increase in the number of offenses (**Figure 2**). However, the number of offenses not related to fishing without authorization, such as entry or exit to or from EEZ without authorization, or gear type related offenses have increased which contributed to decrease the average sanction amount per sanction (**Figure 1**).

Overall, the number of caught offenses increased prompting an increase in the total amount recovered, from as low as 2 observed offenses in 2009 to 78 in 2014, declined to 43 observed offenses in 2015, caused by the low MCS in Sierra Leone and Guinea during the Ebola crisis (**Figure 2**), and then increased to a projected 98 caught offenders in 20167 (**Figure 2**).

Comparing the profile of fines per offense in each country allows a better understanding of the effectiveness of the legislative background allowing MCS to recover its operating costs. Hence, both the number of sanctions (or caught offenders) and the average sanction amount are important. The highest number of offenses occurred in Guinea Bissau and Guinea, however the sanction is either low or not enforced (when observed by an NGO for example) with on average \$24,900 US per offense in Guinea and \$81,800 US in Guinea Bissau and a cumulated number of offenses of 109 and 72, respectively. In comparison, The Gambia has the lowest number of caught offenders (11), however charges the highest amount per offense (\$395,000 US), and more efforts need to focus on MCS to effectively sanction the offenders. Senegal and Sierra Leone have both a high number of offenders with 50 and 78 caught offenders, respectively, and a high average sanction per offense (\$181,000 and \$168,000 US, respectively). In addition to high fines and a relatively high number of caught offenders, Sierra Leone MCS detects and fines most categories of offenses (in constraints to detecting the offenders themselves), with the exception of prohibited species, which tends to be merged in the last category, i.e., unspecified violation of fisheries regulations, followed by Senegal (**Figure 3**).

#### Total Reconstructed Illegal Catches

Illegal catches, i.e., catches taken illegally by foreign fleets, were overall constant at around an average of 690,000 t•year−1 between 2010 and 2015. Country estimates vary widely (**Figure 4**). Illegal catches were the highest in Mauritania and Senegal with 268,000 and 261,000 t•year−1 respectively, due to the presence of Eastern European pelagic trawlers targeting small-pelagic fish in high quantities (**Figure 4**). However, improving success of MCS due to the availability of funding (The World Bank, 2016), has prompted illegal catches to decline from around 350,000 to 250,000 t•year−1 between 2010 and 2015 for both countries. Illegal catches in Sierra Leone declined at first from 10,000 t•year−1 in 2010 to less than 3,500 t•year−1 in 2014,

7For Buinea Bissau, 14 and 17 vessels were sanctioned in 2015 and 2016 (January to August), respectively. However, given the lack of information on the nature of the offense, it was not possible to fill in the gap in the fines using the Bissau Guinean legislation. Multiple cases in 2004 illustrate that fines revolve around an average of \$151,611 US (Tribunal International De La Lois Pour La Mer., 2010).

after which they increased drastically due to the Ebola crisis, to reach 35,000 t•year−1 in 2015 (**Figure 4**). Illegal catches in Guinea increased from 40,000 t•year−1 in 2010 to over 150,000 t•year−1 in 2015 (**Figure 4**). Similarly, illegal catches in Guinea Bissau increased from less than 20,000 t•year−1 in 2010 to over 60,000 t•year−1 in 2015 (**Figure 4**). Illegal catches in The Gambia remained overall constant with slight variation, at 2,000 t•year−1 on average between 2010 and 2015 (**Figure 4**).

### Economic Value of IUU Catches

IUU in the waters of west Africa induced, at least, a loss of \$2.3 billion US annually, most of which is caused by illegal fishing, or fishing without an authorization and under-reporting by fleets that are otherwise authorized to fish in West Africa (**Table 2**). IUU losses increased from \$1.8 billion in 2010 (with \$1.1 million recovered through fines) to \$2.2 billion in 2012 (\$0.2 million recovered through fines), decreased to \$2 billion between 2013 (\$0.02 million USD recovered through fines) and 2014 (\$8.5 million recovered through fines), after which they increased to a maximum of \$2.3 billion in 2015 (**Table 2**) during and after the Ebola crisis in Sierra Leone and Guinea, and after the departure of the World Bank and Environmental Justice Foundation from Sierra Leone, whose programs were key in increased monitoring in the country.

Overall, under-reporting alone contributed to a loss of value of \$2 billion over the period from 2010 to 2015, of which 30% is contributed by East European countries and Russia, 20% by Western European countries, which is at the same level than China, and 9% by flag of convenience countries, and 21% by unknown/unidentified countries. This illustrates that the lack of monitoring also reflects upon fleets that are legally entitled to fish in the waters of the West African sub-region. Over a period of 6 years, between 2010 and 2015, West African countries lost a total of \$ 24.6 billion US to IUU fishing, around half of which is taken by vessels that are not authorized to operate in their waters.

### Assessing the Effectiveness of MCS

Using a set of indicators allows to assessing the effectiveness of MCS in West Africa within the regional standard of capabilities. A similar analysis at the global level would warrant a more effective assessment of MCS in the sub-region. This assessment hence takes into account realities of the region, such as governance, human and financial means, corruption, etc. This aims at learning from regional trends lessons that are realistically implementable within a limited means framework.

Of a maximum score of 44, Sierra Leone scores 33.5, followed by The Gambia with 24, Guinea with 23.3, Senegal with 22.7, and Guinea Bissau with 13.4, respectively (**Table 3**). It appears from this ranking that Sierra Leone's MCS ranks first in the region, despite an increased illegal catch during the Ebola crisis. Senegal does not appear to be scoring the highest despite clear efforts at the end of 2015 with the adoption of new historical fine legislations, and a new Fisheries Act. The time series used here may affect the scoring with most vessels being caught during a relatively recent time period.

# DISCUSSIONS

This study estimates that illegal fishing in West Africa is responsible for a loss of over \$2.3 billion US a year, of which only \$13.8 million US/year (2016 baseline) are recovered through MCS. It also sheds light on the types of offenses that are prevalent in the region and the sanctions therein, and through a cross country comparison, illustrates gaps in monitoring. This gap is further illustrated herein by the total economic loss generated by IUU activities amounted 1.8 billion USD in 2010 (<0.1%), when only 1.1 million USD were recovered through fines. The IUU losses then increased to 2.3 billion USD in 2015, when 8.2 million USD was recovered through fines (0.4%).

This study documents 230 observed offenses and ranks them based on the most prevalent ones in the West African subregion. Over the 23 offense categories, 22 are committed by vessels authorized to fish in the waters of Senegal, The Gambia, Guinea Bissau, Guinea, and Sierra Leone. Under-reporting of the fishing effort is the most prevalent offense, which remains mostly unsanctioned. This offense relates to the fact that fishing fees are paid, mainly, based on the total Gross Registered Tonnage (GRT), and vessels from Asia were found under-reporting their GRT (Greenpeace, 2016b) to reduce their fishing fees. This offense alone could add another 520,000 \$ of non-paid GRT fees (Greenpeace, 2016b) to the loss incurred due to IUU fishing. This study also finds that offenses that have a severe impact on small-scale communities were also prevalent with gear related offenses (catching juvenile and prohibited species and fishing in prohibited zones such as artisanal areas). These offense types rank second and third and were more likely to be detected and sanctioned than other offenses. The presence of non-governmental organizations working with small-scale communities enhances the detection of such infractions (Sumaila et al., 2006).

Illegal catches are based on a reconstruction method whose uncertainty is discussed in previous analyses (Belhabib et al., 2016). Over the \$2.3 billion US lost to IUU, only \$13.8 million were recovered through MCS, in 2016. However, encouraging signs illustrate that while the number of detected offenses is increasing, the average sanction per offense is decreasing, alongside with a change in the profile of offenses to less severe offenses (according to national regulations). The rise of the Ebola crisis has slowed down the ability of MCS units in Guinea and Sierra Leone, and hence the total recovered fine, however, this total continues to increase. By developing the first regional database for offenses and sanctions, this study introduces the first scoring system that ranks MCS in West Africa, by looking at the

#### TABLE 3 | Scoring of the MCS system in West Africa8 .

fine amounts, the number of detected offenders, the categories of offenses that are effectively fined, the illegal catch value and the transparency of the information system, which could be reproduced in other regions of the world, and could be adjusted for data availability. Scoring analysis indicates that despite the Ebola crisis, Sierra Leone was the most effective in detecting and sanctioning (with considerably higher fines) illegal fishing both in number and amounts. This is, however, to be taken with caution, as data on the number of offenses relative to the number of operating vessels were not available at the time of this study. Further, such database could serve as a benchmark for fining and sanctioning, particularly with repeat offenders. Examples show that when information with regards to previous offenses by a vessel is available, the fine is inflated, which is in the advantage of the fining country9,10 .

Illegal catches decreased in Senegal, Mauritania, and Guinea Bissau and increased by 3-fold in Guinea and by 4-fold in Sierra Leone, due mainly to the Ebola crisis, and despite drastic measures such as red cards emitted by the EU. In addition, the suspension of the West Africa Regional Fisheries Programme funded by the World Bank which assured major funding for MCS activities, and Environmental Justice Foundation, which played a major role in both training and enhancing MCS capabilities in Sierra Leone, prompted a major increase in illegal catches in the country. These increases drive the trend of illegal fishing upwards, and hence the value from \$1.8 billion in 2010 to \$2.3 billion in 2015, which cumulates to \$12.3 billion during the same time period. Vessels legally operating in the region are under-reporting the equivalent of 13% of this value.

Overall, the results of this study show that countries of West Africa are vulnerable to IUU fishing, which is not news. The contrast in MCS capacity between Mauritania (Beibou, 2015) and Senegal in the north and Guinea to Sierra Leone in south is evident through the trends in illegal catches. However, Sierra Leone scoring highest for MCS is an indicator that—in the absence of major constraints such as Ebola-, encouraging signs of increased efforts to combat illegal fishing are shown. Even though all these countries are part of the Sub-Regional Fisheries Commission which has joint surveillance capabilities, investment in MCS has been much higher in the northern countries whose wide continental shelves and rich waters are more targeted by illegal fishing fleets, than in the south. This study further reveals that most detected offenses escaping sanctions occurs in countries such as Guinea and Guinea Bissau, where the rate of sanction is very low. Hence, it calls for the implementation of regional measures such as the right of pursuit which allows an offended country to pursuit the illegal fishing boat and to catch it in the EEZ of its neighboring country. Other efforts post-detection of offense should also be used, such as AIS tracing of vessel activities, as a proof of infringement in court, increased fines to disincentivize illegal fishing, and increased regional cooperation.

This study also finds that IUU fishing poses a serious threat to populations dependent on fish stocks and to the very safety of artisanal fishers. Among the most common infractions are incursions by trawlers into the zones reserved for artisanal fishers and these tend to occur at night, regularly causing fishers to lose their fishing gear and canoes, and has even resulted in the loss of lives (Doumbouya et al., 2004). In addition, recent analysis indicate that tackling illegal fishing in the region may result in regaining back 300,000 jobs (Daniels et al., 2016).

This analysis shows an important gap between the value of the loss generated by IUU fishing and the amount IUU vessels are effectively fined. It also shows that higher fines contribute into reducing incentives of illegal fishing through a higher capability of catching offenders (increased resources for MCS), and providing higher incentives to avoid being caught. This study recommends, beyond addressing the lack of human and financial resources for MCS efforts:

• Increased sanctions against e.g., repeat offenders and foreign illegal fishing: This can be done through strengthening the legal system.

Indeed, this study further illustrates that this legal framework exists in some countries of the sub-region, and whenever possible is applied appropriately. Both Guinea and Senegal's new Fisheries Acts inflict historically high sanctions for illegal fishing (Daniels et al., 2016).

• Issues of transparency, low governance and high corruption, and hence effective prosecution need to be addressed in the region: This region of the world is particularly targeted by major external funding for its MCS operations (The World Bank, 2016), capacity is focused on building the MCS network,

8Due to the absence of information the Mauritanian MSC was nor considered in this analysis. We note however that this study was done after Mauritania joined the Fisheries Transparency Initiative and hope that similar analysis could be performed for Mauritania in the near future.

9http://www.bbc.com/news/world-africa-25621864

10http://researcherdiaries.com/2016/07/busted-the-gotland-to-pay-1-5-millioneuros-in-fines-to-the-government-of-senegal/

while prosecution for higher fines, or the legislative system that allows for appropriate sanctions may be weak.

Perpetuating lower sanctions and fines makes MCS vulnerable to the availability of funding through external party contributions.

• It is hence very important to use low cost tools such as Automatic Identification System and Vessel Monitoring Systems for effective monitoring, to implement pre-existing legislations, such as the regional right of pursuit that allows countries to either follow or follow up on illegal fishing vessels regionally as illegal fishing takes a form of transnational activity and does not respect national boundaries.

#### AUTHOR CONTRIBUTIONS

AD, OC, and DB gathered, analyzed and interpreted data, discussed the results and co-wrote the manuscript. EB and

#### REFERENCES

AP edited the manuscript and discussed the results, JM, JI, SC, AJ, AG, and DN gathered data and discussed the results.

#### ACKNOWLEDGMENTS

This study is a contribution of West African fisheries specialists who participated in a West Africa capacity Building Workshop at the University of British Columbia Institute for the Oceans (IOF) and Fisheries, and the Sea Around Us toward the project "Marine Conservation Research, Collaboration and Support in West Africa", funded by the MAVA Foundation. The Sea Around Us is a scientific collaboration at the University of British Columbia funded by the Paul G. Allen Family Foundation. The authors thank the IOF staff, faculty and students for their hospitality during the workshop, and colleagues in West Africa for the valued information.

for Sierra Leone, 1950-2010. Vancouver, BC: Fisheries Centre Working Paper, University of British Columbia.

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2017 Doumbouya, Camara, Mamie, Intchama, Jarra, Ceesay, Guèye, Ndiaye, Beibou, Padilla and Belhabib. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# The Marine Fisheries in Bulgaria's Exclusive Economic Zone, 1950–2013

Çetin Keskin1 \*, Aylin Ulman2, 3, Kyrstn Zylich2 , Violin Raykov 4 , Georgi M. Daskalov 5 , Daniel Pauly 2 and Dirk Zeller 2

1 Fisheries Faculty, University of Istanbul, Istanbul, Turkey, 2 Sea Around Us, Global Fisheries Cluster, University of British Columbia, Vancouver, BC, Canada, 3 Department of Earth and Environmental Sciences, University of Pavia, Pavia, Italy, 4 Institute of Oceanology of Bulgarian Academy of Science, Varna, Bulgaria, 5 Laboratory of Marine Ecology, Institute of Biodiversity and Ecosystem Research, Bulgarian Academy of Sciences, Sofia, Bulgaria

This study presents a reconstruction of the total catch of Bulgarian marine fisheries in the Bulgarian Exclusive Economic Zone for the time period 1950–2013, including previously unreported landings, discards, recreational and subsistence catches. The landings data officially reported by Bulgaria to the Food and Agriculture Organization of the United Nations for the Mediterranean and Black Seas (FAO Area 37) were revised in line with all available information. The reconstructed total catch for 1950–2013 was 1.7 times the (adjusted) baseline data reported by Bulgaria to FAO and 1.5 times the unadjusted data as reported by FAO. This study revealed major deficiencies in the officially reported Bulgarian catch data, foremost the large amount of unreported industrial catches, especially for the last two decades. The exclusion of some fisheries sectors, notably the absence of data on the subsistence and recreational fisheries in reported data are also noteworthy.

#### Edited by:

Lyne Morissette, M - Expertise Marine, Canada

#### Reviewed by:

Dimitrios K. Moutopoulos, Technological Educational Institute of Messolonghi, Greece Valeria Mamouridis, Institut de Ciències del Mar (CSIC), Spain

#### \*Correspondence:

Çetin Keskin<EMAIL_ADDRESS>
#### Specialty section:

This article was submitted to Marine Fisheries, Aquaculture and Living Resources, a section of the journal Frontiers in Marine Science

Received: 15 December 2016 Accepted: 15 February 2017 Published: 02 March 2017

#### Citation:

Keskin Ç, Ulman A, Zylich K, Raykov V, Daskalov GM, Pauly D and Zeller D (2017) The Marine Fisheries in Bulgaria's Exclusive Economic Zone, 1950–2013. Front. Mar. Sci. 4:53. doi: 10.3389/fmars.2017.00053 Keywords: Black Sea, invasive species, landing, overfishing, small pelagics, unreported catches

# INTRODUCTION

Global fisheries catches have been decreasing in recent decades (Pauly and Zeller, 2016), which has not only impacted the populations of target species (i.e., population size, demographic and genetic characteristics), but has also changed community structure (biodiversity) and the function of the other components of ecosystems (i.e., trophic levels, Pauly et al., 1998, 2002; Daskalov, 2002; Tsikliras et al., 2015).

The impact fishing has on marine ecosystems can be demonstrated at the broadest scale by initially examining the data documenting extractions of the marine resources. Based on this concept, reconstructing the national fisheries catch data set can provide insights into the historical catch time-series and create a more detailed, comprehensive regional dataset (Pauly and Zeller, 2016 and references therein). The aim of this study is to assemble the total reconstructed catch of Bulgaria in the Black Sea, and to provide a comprehensive dataset which includes all marine fisheries removals, such as landings and unreported catches (discards, subsistence and recreational catches) from the Bulgarian component of the Black Sea ecosystem as a baseline for future studies.

#### Study Area

The Black Sea has a surface area of 422,100 km2 (excluding the Sea of Azov), and a mean and maximum depths of approximately 1,300 and 2,210 m, respectively. The Black Sea is connected to the Aegean and hence Mediterranean Sea through the Bosphorus and the Dardanelles, which themselves are connected by the Sea of Marmara.

The upper layer of the Black Sea has low salinity (averaging around 17–18 psu) and warmer average summer temperatures (up to 30◦C), both of which inhibit the surface layer from mixing with the deeper layer, which has a salinity averaging 22–24 psu and temperatures of approximately 8.5◦C. The majority of the Black Sea water column (about 90%), is deeper than 150–200 m and is anoxic and devoid of multicellular life (Oguz et al., 1998). The contrast between the river runoff (mainly from the Danube, Dniester and Dnieper rivers), and high-salinity waters (from the Mediterranean Sea, entering the Black Sea via the Bosphorus Strait) enhance the stratification, and prevent any mixing between surface and deeper layers. Although the lower 90% of the Black Sea basin is devoid of oxygen and contaminated with hydrogen sulfide, the upper layer is productive and provides suitable habitats for numerous epipelagic and neritic species (Zaitsev, 2008).

The Black Sea ecosystem has suffered from several anthropogenic disturbances, such as eutrophication, the introduction of alien species (Mnemiopsis leidyi) and the overexploitation of large pelagic predators in the mid- to late twentieth century (Prodanov et al., 1997; Zaitsev and Mamaev, 1997; Caddy, 2008). Eutrophication has dramatically altered the base of the marine food web; additionally, the overexploitation and decline of some fish populations, such as large pelagic fishes, contributed to providing the necessary conditions for successful alien species invasions (Daskalov, 2002).

In 1946, a large sea snail, the invasive rapa whelk (Rapana venosa), was first seen in the Black Sea. The rapa whelk was successful in its new environment and became widespread (except in very low salinity areas). It is a notorious predator which feeds on oysters, mussels and other bivalves, and thus exerts a major influence on local populations of malacofauna. In the 1980s, in response to an international demand for sea snails, a massive fishery for the rapa whelk emerged in Turkish waters. Along the Bulgarian coast, a rapa whelk fishery commenced in 1994 (Daskalov and Rätz, 2010), which helped reduce the rapa whelk's impact on its prey species. This may possibly be the only example of a human-induced decline in an introduced species in the Black Sea.

Despite the entire Black Sea ecosystem being affected by these and similar issues, they are all treated as "national" issues, as there is no ecosystem-wide management authority or agreement.

#### Fishing History

The Bulgarian Exclusive Economic Zone (EEZ) is around 35,000 km2 (**Figure 1**, http://www.seaaroundus.org), which corresponds to just under 7% of the total Black Sea area (Popescu, 2011). The Black Sea corresponds to Major Fishing Area 37 of the General Fisheries Commission for the Mediterranean (GFCM), Sub-area 37.4; Division 37.4.2, and Bulgaria's fisheries occur within Geographical Sub-area 29.

Bulgaria's continental shelf (to 100 m depth) along the Bulgarian coast is about 40 km wide; the relatively shallow fishing grounds (down to depth of 100–120 m) range from Cape Kartalburun (near the Romanian border) to the Rezevo River (near the Turkish border). The exploitation of fisheries resources is limited to the upper shelf, since depths below 100–150 m are anoxic.

Bulgarian marine fish catches have exhibited trends similar to other Black Sea countries. In the mid-1960s, Atlantic mackerel (Scomber scombrus), Atlantic bonito (Sarda sarda) and bluefish (Pomatomus saltatrix) were the commercially most important species (Ivanov and Beverton, 1985). In the late 1960s and early 1970s, Atlantic mackerel, Atlantic bonito and bluefish catches dramatically decreased in the Bulgarian Black Sea fisheries. Among demersal species, turbot (Scophthalmus maximus) was one of the most important commercial species, and catches averaged around 330 t·year−1 in the 1960s, but dropped to 12 t·year−1 by the 1980s (Zaitsev and Mamaev, 1997). In the 1970s, the over-exploitation of larger pelagic predators, combined with the increased eutrophication of the north-western Black Sea led to a dramatic increase in the catches of small pelagics such as sprat (Sprattus sprattus), anchovy (Engraulis encrasicolus) and Mediterranean horse mackerel (Trachurus mediterraneus). The sprat population saw a massive increase in biomass from the mid-1970s and 1980s, and its maximum catch was recorded in 1989, after which the stock collapsed, but later rebounded (Radu et al., 2010). In the late 1980s, an alien invasive ctenophore, the warty comb jelly (Mnemiopsis leidyi) reached its maximum abundance in the Black Sea, and thus became a powerful food competitor of adult planktivorous fish, and a significant predator of their eggs and larvae. As a consequence of this and other changes in this Large Marine Ecosystem (LME, Pauly et al., 2008), the rapa whelk has become, since 1995, the most commercially important taxon, followed by sprat.

Modernization of the Bulgarian fishing fleet began just before the 1950s. Industrial or large-scale purse seine and trawl vessels developed in the 1950s. In the 1960s, however, Bulgaria began to buy high-seas fishing and support vessels from the Soviet Union, Poland and East Germany, and began to build infrastructure for fish processing. From 1965 to 1990, Bulgaria owned a large high-seas distant-water fleet (consisting of 30 high-capacity trawlers and 6 transport vessels) that was actively engaged in the Atlantic and in the south-eastern Pacific. This fleet was liquidated in the early 1990s after the collapse of the former USSR, and the Bulgarian fishing fleet refocused their efforts on the Black Sea coastal zone (Popescu, 2011). In the 1970s, approximately 80% of marine catches came from the industrial fisheries, and the remainder came from the artisanal sector, which used mainly passive gears (Kumantsov and Raykov, 2012). In 2008, the Bulgarian fleet consisted of 2,547 vessels with a total gross tonnage (GT) of 8,378 and total kilowatts (kW) of 63,860 (**Table 1**). The small-scale sector represented 96% of the fishing fleet in term of vessel numbers, i.e., 2,440 vessels under 12 m in length, and was responsible for landing around 57% of the Black Sea catch (Radu et al., 2010). Throughout this study, we use the term "industrial" to refer to the large-scale commercial sector, and "artisanal" to refer to the small-scale commercial sector.

Bulgarian fisheries policy is shaped by several international fisheries agreements (i.e., UNCLOS, UNCLOS, CITES) and the European Union Common Fisheries Policy (since its entry into the European Union in 2007). The country is also a member of General Fisheries Commission for the Mediterranean

(GFCM) and the FAO. The National Agency for Fisheries and Aquaculture within the Ministry of Aquaculture and Food is the executive body responsible for national policy on fisheries and aquaculture and implements the Fisheries Legislation in Bulgaria. In this context, Total Allowable Catches (TACs) for sprat and turbot were set in the mid-late 2000s. Some other management implementations include a licensing system for fishers, effort control via limiting fishing gear, engine power and vessels; seasonal closures are imposed to protect some stocks during their reproductive periods; and closed areas and bans of bottom trawling and dredging are imposed. Since 2012, beam trawling is allowed only in selected areas. No permit or licenses are required to participate in the marine recreational fishery.

### "Industrial" (Large-Scale) Fishery

In 2008, the industrial fleet consisted of 108 vessels >12 m in length. Sprat is to this day targeted mainly by large-scale pelagic trawlers seasonally from February to November. Whiting (Merlangius merlangus), turbot, anchovy, shad (Alosa spp.), Mediterranean horse mackerel and red mullet (Mullus barbatus) are incidentally caught as by-catch (Radu et al., 2010), but sold commercially. The bottom trawl fishery began to develop for turbot in the 1950s, but was banned in 1994 to protect declining turbot stocks and beds of Mediterranean mussel, Mytilus galloprovincialis (Konsulova et al., 2001).

Dredges and beam trawlers were used in the rapa whelk fishery, but were also banned in 2001 to protect vulnerable benthic biotic communities such as mussel beds. Note that dredge and beam trawl fisheries may be classified as smallscale fisheries in Bulgaria, as domestic classification is based on vessel size only. However, for the purposes of the Sea TABLE 1 | Composition of the Bulgarian fishing fleet in 2008 (Radu et al., 2010).

Around Us (www.seaaroundus.org), any fishing gears that are actively dragged across the sea-floor or through the water column using engine power are considered "industrial" (i.e., large-scale), following Martín (2012).

# "Artisanal" (Small-scale Commercial) Fishery

The coastal fishery has traditionally been carried out by small vessels (<12 m) which use mainly passive fishing gear, such as trap nets (uncovered pound nets), and beach seines in the inshore area. Here, these vessels/gears are considered "artisanal." Pound nets are deployed in 9 to 12 m depth in coastal inshore waters (Radu et al., 2010) from March to November, and target species vary according to season: sprat is targeted during spring and the beginning of summer, and anchovy and Mediterranean horse mackerel are targeted in summer and autumn. Whiting, turbot, red mullet and other demersal species are incidentally caught as by-catch, but retained for commercial sale (Radu et al., 2010). The set gillnet fishery operates in the coastal and offshore waters of Bulgaria and targets primarily turbot, while dogfish (Squalus acanthias), thornback ray (Raja clavata), common stingray (Dasyatis pastinaca) and sturgeons (Acipenseridae) are often incidentally caught as by-catch (Radu et al., 2010). The number of vessels operating by LOA (length overall) in 2008 is given in **Table 1**.

### METHODS

### Reported Catch Data

The baseline data used for the work presented here are the catch statistics submitted annually by Bulgaria to FAO which are incorporated into the global database (FishStat; www.fao.org/fishery/statistics/software/fishstatj/en), complemented by national Bulgarian data published by Prodanov et al. (1997); Mikhailov and Prodanov (2003), and Panayotova et al. (2012). Tunas and other large, highly migratory pelagic fishes (e.g., swordfish), which were originally abundant in the Black Sea, are not considered here.

According to the data reported by FAO on behalf of Bulgaria in the Mediterranean and Black Seas (FAO Area 37, release date March 2015), total catches appeared very high for the years between 1964 and 1969 (driven by high values for "marine fishes nei," i.e., "marine fish not elsewhere identified"). On closer inspection of Bulgarian FAO data for areas other than 37, it was found that duplicate "marine fishes nei" (or Miscellaneous Marine Fishes, MMF) values had been reported for Bulgaria fishing in three other areas (the central-eastern Atlantic, southeastern Atlantic and north-western Atlantic), i.e., the exact same values were present in all three areas. By comparing FAO data with Northwest Atlantic Fisheries Organization (NAFO) data, it was found that these MMF values were indeed incorrect. It was therefore assumed that the reported catch for FAO Area 37 (Mediterranean and Black Seas) also had these values mistakenly added on to the real reported MMF catch. We therefore subtracted the duplicated MMF tonnage reported in the other areas from the MMF in FAO Area 37 for the years 1964–1969. This resulted in an adjusted FAO baseline, which was used for the rest of the reconstruction as reported baseline data. We suggest Bulgaria formally request a retrospective data correction of FAO data.

Since all taxa were reported as "marine fishes nei" from 1950 to 1963, we disaggregated this category taxonomically by using reports of national data to assign most of the tonnage to specific species or families (Prodanov et al., 1997; Mikhailov and Prodanov, 2003; Panayotova et al., 2012). Any remaining tonnage was kept as "marine fishes nei." These national reports were also used to improve the catch data from 1964 to 1969, as much of these data still remained as MMF and many species/families had rounded, estimated values listed.

#### Unreported Catches

Unreported catches as defined here include unreported commercial, subsistence and recreational catches, as well as discarded catch.

#### Commercial Catches

Sprat has been the main catch for Bulgaria since 1970. However, published reports on Bulgarian fisheries have clearly documented that some commercial sprat catches have gone unreported (Mikhailov and Prodanov, 2003; Daskalov and Rätz, 2010). On average, Daskalov and Rätz (2010) estimated of actual catches for 1992 and 1993 were 55% higher than the reported sprat catches in 1990 and 1991. From 1994 to 1999 sprat catches were assumed to have been under-reported by the same ratio and averaged 1.79 times higher than reported data (Daskalov and Rätz, 2010), and were used to estimated unreported sprat component for 2000 and 2001. The ratio was not applied to the years 2002–2003, as the reported data exhibited a spike in these years and it was assumed that reporting coverage was more complete in this time period. Therefore, in order to remain conservative, we linearly interpolated the unreported tonnage from 2001 to 2004. There was also an expert assessment in 2007 which estimated catches to be 2,985 t as opposed to 2,559 t (EU, 2008), with FAO using the former value. Thus, we assume that catches were fully reported in 2007 and use the ratio (unreported = 0.17∗ reported) between the two 2007 estimates in Daskalov and Rätz (2010) to obtain a conservative estimate of unreported catches in 2008–2013. We also assumed there to be a much lower likelihood of underreporting from 1950 to 1989 (during communist rule), and therefore added a conservative 10% of landings to account for under-reporting of sprat during that period.

In Bulgaria, marine bivalve catches include the striped Venus clam (Chamelea gallina), bean clam (Donax spp.) and mussel. According to information available at the FAO (http://www. fao.org/fi/oldsite/FCP/en/bgr/body.htm), the 2000 FAO reported data for rapa whelk equated to 90% of the total shellfish catch. We considered the remaining 10% to be comprised equally of C. gallina, Donax spp. and miscellaneous marine molluscs ("marine molluscs nei") for the 1994 to 2013 period.

Some sturgeons are anadromous or potamodromous, as in the case of the sterlet sturgeon (Acipenser ruthenus) (Mikhailov and Prodanov, 2003); thus, A. ruthenus was excluded from consideration in the present study. While the fringebarbel sturgeon (Acipenser nudiventris) is commercially extinct, the beluga sturgeon (Huso huso) and the Russian sturgeon (Acipenser guldenstaedti) are still commercially important in the Bulgarian fishery. Wild caviar export data were used to estimate unreported sturgeon catches from 1998 to 2006 (Kecse-Nagy, 2011; **Table 2**). We converted caviar weight to fresh fish weight for H. huso, A. gueldenstaedti and Acipenser stellatus, using gonado-somatic coefficients from Jivkov et al. (2003), and then estimated catches by using the sex ratio of the same three species (**Tables 2, 3**).

Bulgaria became a member of the European Union (EU) in 2007, and intra-EU trade no longer appears in the CITES (Convention on International Trade in Endangered Species of Wild Fauna and Flora) data for caviar exports. It is likely that caviar export to other EU countries have continued after 2007 without being recorded in CITES data, and the estimated unreported catch in 2006 was thus used as estimate for the years 2007 to 2013.

Rapa whelk has become a commercially valuable resource with high demand on the international market. In Bulgaria, this fishery commenced in 1994 and rapa whelk were originally caught by scuba divers. However, shortly thereafter, there was illegal rapa whelk fishing by bottom trawlers, beam trawlers and dredgers (Daskalov and Rätz, 2010). For the period from 2000 to 2010, the bulk of rapa whelk catches were illegally taken by dredges and beam trawls (V. Raykov, pers. obs.). We estimated an unreported catch component for rapa whelk based on export data for the period from 2002 to 2010 (**Table 4**). The same percentage rates for the select processing types of rapa whelk in 2009 (Daskalov and Rätz, 2010) were used and applied to the 2002–2010 period to disaggregate the unreported catch component. Since rapa whelk is exported without its shell, the exported amounts first had to be converted to equivalent weights with shell on, to account for total fishery removals. A rate of 85.8% of total weight was added to both the frozen meat and sweetbread rapa whelk exported amounts to account for this (Düzgüne¸s et al., 1988). Unreported rapa whelk catches were interpolated from zero in 1993 to the estimate of 12,313 t in 2002 that came from the export data. Finally, the ratio of reported to unreported rapa whelk catches in 2010 was applied to the 2011–2013 reported data.

An unreported catch of turbot (Scophthalmus maximus) taken by Bulgaria was estimated from sources stating that Bulgarian fishers also have under-reported their own turbot catches from the Bulgarian EEZ in recent years (total estimated at 300 t·year−1 ; EU, 2009). The total turbot catch was accepted as 250 t·year−1 to avoid over-estimation, and this was used as an anchor point in 2007 (last year of data in the EU, 2009 report). To remain conservative, the unreported catch was then interpolated from zero in 1994 (year before turbot catches started again) to 183 t in 2007 (difference between 250 t and the reported amount). The ratio of reported to unreported data in 2007 was then applied to the reported data from 2008 to 2013. We also assumed some turbot catches to have been unreported throughout the

TABLE 3 | Gonado-somatic coefficients (G) and sex ratio (female: male) for sturgeons in Bulgaria (adapted from Jivkov et al., 2003).

1950–1989 period, but to a much lesser extent. Thus, from 1950 to 1989, an additional 10% of the reported turbot catch amount was estimated to have been unreported, and added to the reported component.

Lastly, there were also data in the national reports used above (Prodanov et al., 1997) from an expert assessment of whiting (Merlangius merlangus) that we incorporated, and which indicated that catches were severely under-reported from 1975 to 1993.

#### Discards

To estimate discards, published reported discard rates by select fisheries from the Black Sea and eastern Mediterranean were sought, which included both industrial (mid-water trawl, bottom trawl, purse seine, dredge) and artisanal fisheries (gill and trammel nets, hand line, long line, fish pound net, beach seine net). These discard rates were then applied to the reported data for each of the target species for each fishery with the help of expert advice (**Table 5**). Given that discard rates were only applied to reported catches (and thus represent minimum estimates of discards), the discards of the rapa whelk fishery are likely considerably underestimated as there was a substantial unreported landings component to that fishery.

Whiting contribute greatly to the trawl catches in the Black Sea, but are not a targeted fishery and are mostly discarded by Bulgarian fishers (Raykov et al., 2008). In neighboring

TABLE 5 | Discard rates applied to select fisheries in Bulgaria.

bKelleher (2005).

c ¸Sahin et al. (2008).

#### TABLE 4 | Exported rapa whelk tonnages (NAFA, Bulgaria for the purposes of National report of Focal point of Bulgaria to AG FOMLR, BCS).

In 2009, the processed category percentage is given in brackets (adapted from Daskalov and Rätz, 2010, Table <IP_ADDRESS>).

Romania, the whiting portion of reported demersal catches was 42% from 2000 to 2006 (Maximov and Staicu, 2008). To account for discarded whiting in Bulgaria, an additional 20% was conservatively assumed to account for this component and was applied to the reported catches of demersal taxa by industrial bottom trawls from 1950 to 1994 (as bottom trawling was banned after 1994).

#### Recreational and Subsistence Catches

Recreational fishing is understood here to mean fishing primarily for leisure or enjoyment, while subsistence fishing is understood to mean fishing for the primary purpose of providing protein for self- or family-consumption (recreational and subsistence fisheries are here assumed not to generate discards) While the two sectors are difficult to separate, it is generally understood that subsistence fishing over time evolved into recreational fishing, as incomes increased and food security was no longer a primary concern.

In Bulgaria, recreational fishing is most popular from April to June, and from September to November. It occurs in inshore waters and targets gobies (Gobiidae), grey mullets (Mugilidae), horse mackerel, bluefish, Atlantic bonito, turbot, Mediterranean horse mackerel and garfish (Belone belone). However, no data on the number of recreational fishers and/or their catch rates or amounts have been collected in Bulgaria.

There has been both recreational and subsistence fishing in Bulgaria for the 1950 to 2010 period. Since no data on this topic exists, estimated catch rates from the Black Sea coast of Turkey were used as a starting point (Ulman et al., 2013) to estimate recreational and subsistence catches, i.e., 0.258 t·fisher−1 ·year−1 in 1950 and 0.129 t·fisher−1 ·year−1 in 2010. To derive the number of recreational/subsistence fishers for Bulgaria, we assumed that in 1950, 2% of the coastal population fished either recreationally and/or for subsistence purposes, and this rate was linearly decreased to 0.95% of the coastal population by 2013 due to the declining availability of larger fish. To derive the coastal population, we started with total population data from Populstat (1950–1959; http://www.populstat.info) and The World Bank (1960–2013; www.data.worldbank.org) and then assumed that only people living within 20 km from the coast were involved in these fisheries. The coastal population data, however, was only available for 100 km from the coastline (CIESIN, 2012); therefore, we conservatively assumed 25% of the population within 100 km of the coast were actually living within 20 km of the coast. The coastal population data was also only available for the years 1990, 2000, and 2010. Therefore, we used the proportion of total population living 100 km from the coastline in 1990 and applied this from 1950 to 1989 and the proportion for 2010 was applied to 2011–2013. We then also interpolated the proportion between the 1990 and 2000 anchor point, as well as the 2000 and 2010 anchor point and applied this to the total population. We then reduced the catch rates used for Bulgaria by 50% from that used for Turkey in 1950, i.e., 0.129 t·fisher−1 ·year−1 , and 20% in 2010, i.e., 0.103 t fisher−1 ·year−1 , since recreational fishing appeared to be less intensive than in Turkey (V. Raykov, pers. obs.); the interpolation was carried forward to 2013. We also made an adjustment to the catch in the early 1990s, as all fisheries were affected by a massive invasion of ctenophore in the Black Sea, which was deemed a "fisheries crisis" and resulted in a temporarily collapse of the pelagic fisheries (Daskalov, 2002). In light of this crisis, from 1989 to 1991, we decreased the recreational/subsistence catch rates by a further 75%. The newly adjusted 1991 value and the 1993 catch amount were then interpolated as there was a quick recovery period for small pelagics. In order to assign the estimated recreational/subsistence catches to the two sectors, we assumed that in 1950, 70% of these catches were taken for subsistence purposes, which was linearly decreased to 30% by 2010 (with decline carried forward to 2013), and the remaining catches were assigned to the recreational fishery (i.e., increasing from 30 to 70%). Sturgeon, Atlantic bonito, Atlantic mackerel, bluefish, turbot, Mediterranean horse mackerel, grey mullet and gobies were the main recreational/subsistence taxa for the 1950–2013 period (**Table 6**). We assumed that the overwhelming majority recreational/subsistence fishers operate from shore.

### RESULTS

The reconstructed total catch for the marine fisheries of Bulgaria for 1950–2013 was estimated to be 1.7 times the (adjusted) data reported by FAO for the Black Sea fisheries of Bulgaria (**Figure 2A**). Total catches were only slightly higher than those reported by the FAO (after adjustment for likely over-reporting) on behalf of Bulgaria up until 1993 (just before the rapa whelk fishery commenced). Total catches increased from an annual average of around 5,800 t·year−1 in the 1950s (only slightly

higher than the 5,100 t·year−1 reported by the FAO on behalf of Bulgaria) to a peak of 25,500 t in 1981 (of which 19,800 t were reported; **Figure 2A**). Catches declined to a low in the early 1990s with an estimated 4,700 t·year−1 and then increased to a second peak of 32,000 t in 2003, before declining to an average of 13,600 t·year−1 at the end of the time period (2010–2013). Total reconstructed catch was on average only 1.2 times the adjusted reported data from 1950 to 1989, and increased to 1.9 times in the 1990s (**Figure 2A**), and were then, on average, 3.1 times for the rest of the time period (**Figure 2A**; Appendix Table 1 in Supplementary Material). The reconstructed total catch consisted of reported industrial landings (54%), unreported industrial landings (34%), industrial discards (3.3%), reported artisanal landings (6.1%), unreported artisanal landings (0.6%), artisanal discards (0.1%), subsistence catches (0.9%), and recreational catches (0.8%).

Total industrial catches increased from 4,100 t·year−1 in the 1950s, to a peak of 24,600 t in 1981. Catches then declined to a low of 3,900 t in 1992. Catches increased in 1994, due to the opening of the rapa whelk fishery, to a second peak of 31,000 t in 2003, before declining to an average of 12,600 t·year−1 at the end of the time period (2010–2013, **Figure 2A**). Industrial unreported catches were 41% of the reconstructed total industrial catch (37.5% unreported landings and 3.6% discards). Unreported industrial landings increased throughout the time period, from 3% of the industrial catch in the 1950s to an average of 20% and 17% in the 1970s and 1980s, respectively. Unreported industrial landings increased rapidly in the mid-1990s due to the rapa whelk fishery and averaged 58% in the 2000s.

The discards from the industrial and artisanal fisheries amounted to 3.3 and 0.1%, respectively, of the reconstructed total catches (**Figure 2A**). Discards increased from 210 t·year−1 in the 1950s to 780 t·year−1 from the late 1970s to late 1980s. Discards then decreased to 130 t·year−1 in the early 1990s but increased again to an average of 570 t·year−1 for the rest of the time period. The main discarded species were sprat (55%), rapa whelk (26%), whiting (8%), turbot (4%) and Mediterranean horse mackerel (1%).

Reconstructed total catches were mostly composed of sprat (47%), rapa whelk (28%), Mediterranean horse mackerel (4%), whiting (4%), Atlantic bonito (3%) and turbot (2%; **Figure 2B**, Appendix Table 2 in Supplementary Material).

#### DISCUSSION

The prospects for the marine fisheries in the Bulgarian EEZ are limited by the specific characteristics and production potential of the Black Sea ecosystem, especially by its limited shelf area. Another constraint is the limited biodiversity, which is under constant threat. There are only 134 fish species recorded in the Bulgarian section of the Black Sea (Stefanov, 2007). During 1960–1970, 26 of these fish species were commercially targeted, which decreased to 5 major target species by the 1980s (Zaitsev and Mamaev, 1997). Our results show three separate periods revealing distinctive catch compositions for the Bulgarian coastal waters: (1) from 1950 to 1969, the major species caught were sprat and Atlantic bonito; (2) from 1970 to the mid-1990s, catches were dominated by sprat; and (3) and from the mid-1990s onwards, catches were dominated by rapa whelk, still with a large contribution of sprat (**Figure 2B**).

The contribution of Bulgarian fisheries catches to total Black Sea catches is low, only slightly over 2% of Turkey's reconstructed total catch from 1950 to 2010 (Ulman et al., 2013). On the other hand, the reconstructed total catch for 1950 to 2013 was 67% higher than the data submitted by Bulgaria to the FAO. Most of the unreported catches were deemed to have occurred after 1990, since reporting and control measures were much stricter in the planned economy of the earlier period, as was the case for neighboring Ukraine (Ulman et al., 2015). Total commercial catches in the Black Sea significantly decreased after the collapse of the Black Sea pelagic fisheries at the end of the 1980s due to overfishing, a trophic cascade and the ctenophore invasion (Daskalov, 2002; Daskalov et al., 2007). The catch dynamics of the most important species in the Bulgarian Black Sea shelf zone illustrate a prominent decreasing trend beginning in the 1990s.

On a sectoral basis, the reconstructed total catch of Bulgaria was similar to that of the Turkish Black Sea (Ulman et al., 2013), both having a small artisanal catch component (7–15%) and a much higher industrial component (75–90%), although, as stated above, the catches of Turkey slightly under of Bulgaria. Bulgaria's reconstructed total catches differed from that of Romania, in terms of its much lower artisanal catch contribution (7% for Bulgaria compared with 66% for Romania) due to the highly popular traditional Romanian fishing method "crawling" (a stationary inshore net deployed in shallow waters) which was the main fishing technique used in Romania until the 1980s (Ulman et al., 2015). The reconstruction for Ukraine demonstrated that the national catch statistics included only commercial largescale landings and failed to include small-scale, recreational or artisanal catches (Ulman et al., 2015), and at present are just over 6 times that of Bulgaria's marine fisheries.

The three distinct periods of catch, characterized by three distinct ecological shifts, can be distinguished in the catch composition of Bulgarian fisheries: during the first and second periods, the catch composition was similar to that of other Black Sea countries in terms of dominance by pelagic fishes, i.e., larger pelagics in the first period and small pelagics in the second. The third period differed from the trends in other Black Sea countries owing to a high rate of rapa whelk catches in Bulgaria. During this third period, targeted species were small pelagic fish (i.e., sprat, Mediterranean horse mackerel, anchovy) and demersal fishes (turbot, gobies, dogfish and most recently red mullet), while the rapa whelk gained the prominent role in the commercial fisheries. Although the introduction of the rapa whelk contributed to the fisheries economy after 1993, high disturbance to the benthic ecosystem from destructive fishing practices (dredging and beam trawling) resulted in negative ecological effects on benthic communities, especially on the Mediterranean mussel beds (Konsulova et al., 2001; Daskalov and Rätz, 2010), as also noticed in neighboring Ukrainian waters where macrobenthos biomass was reduced 20-fold due to intense trawling (Ulman et al., 2015). Taking into consideration the amount of illegal unreported rapa whelk taken from Bulgarian, Ukrainian (Ulman et al., 2015) and Turkish waters (Ulman et al., 2013), habitats and biodiversity are likely much more under threat from illegal mobile bottom-fishing gear than previously assumed.

Russia, Ukraine, Bulgaria, Romania, Turkey and Georgia all share the stocks of migratory Black Sea species. Cold-water small pelagics complete their entire life cycle in the Black Sea, seasonally migrate to reach wintering areas in the south and return to feeding and spawning areas in the following spring in the north (Ivanov and Beverton, 1985). In contrast, larger warmwater pelagics, such as bluefish, Atlantic mackerel and Atlantic bonito are highly migratory, i.e., move from the Sea of Marmara or to the Eastern Aegean Sea through the Bosphorus, then swim westwards and northward along the Bulgarian and Romanian coasts to reach their summer feeding grounds in the western and northwestern Black Sea (Demir, 1957; Türgan, 1959).

The northwest and western region of the Black Sea, with its large shallow continental shelf areas and high nutrient inputs by rivers provide highly productive waters, which are suitable for spawning and feeding. Sprat, a cold-water species, prefers the coldest habitable portions of the Black Sea, as the shoals move toward coastal waters in the northwest in winter and offshore in autumn (Ivanov and Beverton, 1985). Mediterranean horse mackerel are a warm-water migratory species which pass from south to north to spawn along the Bulgarian coast in spring, and from north to south for feeding in autumn. These two species also provide seasonal catches in the Bulgarian EEZ.

Turbot is the main commercial demersal fish species and is mainly found in the western and northwestern shelf of the Black Sea along the coasts of Bulgaria, Romania and Ukraine. Its migration along the shelf links shallow waters (in spring, for spawning) and deeper waters (in winter, for feeding). The previously discussed illegal turbot catch (by Turkish fishers) from Bulgarian and Romanian waters (Ulman et al., 2013; Banaru et al., 2015), points to a need of a common policy between member countries (Bulgaria and Romania), and cooperation with the remaining four non-EU bordering countries to recover turbot stocks to a previous larger-size and population levels.

In the Black Sea, the status of turbot and anchovy stocks were reported as "overexploited" and "in overexploitation," respectively, the Mediterranean horse mackerel stock was reported as "overexploited" and the dogfish population was considered "depleted" at the Black Sea scale. In contrast, Black Sea sprat stocks were deemed as sustainably exploited (GFCM, 2014).

This study illustrates some major deficiencies in the nationally (and hence internationally) reported fisheries data, such as the exclusion of some fisheries sectors, notably the absence of any catches stemming from subsistence and recreational fisheries. We feel that our estimates of total marine fisheries catch for Bulgaria provide a more accurate and comprehensive baseline, which should be further improved through targeted studies of the previously omitted sectors.

The European Union's Marine Strategy Framework Directive, 2008/56/EC (MSFD), the first legislative instrument dedicated to protecting biodiversity for all of Europe's regional seas by 2020, seeks to achieve a Good Environmental Status (GES) in European Seas by protecting the resource base. Although the MSFD seeks to foster the ecosystem approach, environmental protection and sustainable use, if all resource users are not made to fish sustainably, it is highly unlikely that the directive will work. In the Mediterranean and Black Sea basins, the regions in most peril from overexploitation are the Black Sea and the Eastern Mediterranean (Tsikliras et al., 2015). The main driver for the high rate of exploitation is likely the overcapacity of the Turkish large-scale commercial fishery. As long as rebuilding stocks to some optimal former level is not prioritized by all shared users of the Black Sea (Ulman, 2014), it will remain at its current degraded state yielding mainly low value species such as sprat and anchovy, and the future of the fisheries will remain questionable.

This reconstruction of marine fisheries catches for Bulgaria provides an improved baseline for its marine fisheries, to help understand the impact fisheries have had, and to help the implementation of management rules of the MSFD. Because of the many assumptions that were made, some parts of this reconstruction will be very uncertain, however. Thus, readers are welcome to send suggestions for corrections, updates and/or other improvements via www.seaaroundus.org, from which the detailed data underlying this reconstruction can also be downloaded.

## AUTHOR CONTRIBUTIONS

The original methodology was decided upon by DZ. VR and GD provided some of the background data. ÇK, AU, KZ, and DZ helped calculate the methodology. ÇK, AU, KZ, VR, GD, DP, and DZ contributed to both the writing and editing of the paper.

#### REFERENCES

#### ACKNOWLEDGMENTS

ÇK wishes to thank The Scientific and Technological Research Council of Turkey (TÜB˙ITAK) (2219 International Post-Doctoral Research Fellowship Programme); AU, KZ, DP, and DZ acknowledge support from the Sea Around Us, a scientific initiative funded by The Paul G. Allen Family Foundation.

#### SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: http://journal.frontiersin.org/article/10.3389/fmars. 2017.00053/full#supplementary-material

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2017 Keskin, Ulman, Zylich, Raykov, Daskalov, Pauly and Zeller. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# Analysis of Long-Term Changes in a Mediterranean Marine Ecosystem Based on Fishery Landings

Tomaso Fortibuoni 1, 2 \*, Otello Giovanardi 2, 3, Fabio Pranovi 4 , Saša Raicevich2, 3 , Cosimo Solidoro1, 5 and Simone Libralato1

1 ECHO Group, Division of Oceanography, National Institute of Oceanography and Experimental Geophysics, Trieste, Italy, 2 Italian National Institute of Environmental Protection and Research, Chioggia, Italy, 3 Institute of Marine Sciences, National Research Council, Ancona, Italy, 4 Department of Environmental Sciences, Informatics and Statistics, Ca' Foscari University, Venice, Italy, 5 The Abdus Salam International Centre for Theoretical Physics, Trieste, Italy

#### Edited by:

Maria Lourdes D. Palomares, FishBase Information and Research Group, Philippines

#### Reviewed by:

Valeria Mamouridis, Institut de Ciències del Mar (CSIC), Spain Dimitrios K. Moutopoulos, Technological Educational Institute of Messolonghi, Greece

> \*Correspondence: Tomaso Fortibuoni<EMAIL_ADDRESS>
#### Specialty section:

This article was submitted to Marine Fisheries, Aquaculture and Living Resources, a section of the journal Frontiers in Marine Science

Received: 10 November 2016 Accepted: 27 January 2017 Published: 13 February 2017

#### Citation:

Fortibuoni T, Giovanardi O, Pranovi F, Raicevich S, Solidoro C and Libralato S (2017) Analysis of Long-Term Changes in a Mediterranean Marine Ecosystem Based on Fishery Landings. Front. Mar. Sci. 4:33. doi: 10.3389/fmars.2017.00033 In the Mediterranean Sea, structured and standardized monitoring programs of marine resources were set only in the last decades, so the analysis of changes in marine communities over longer time scale has to rely on other sources. In this work, we used seven decades (1945–2014) of disaggregated landings statistics for the Northern Adriatic Sea (Mediterranean) to infer changes in the ecosystem. Analysis of landings composition was enriched with the application of a suite of ecological indicators (e.g., trophodynamic indicators, such as the primary production required to sustain the catches—PPR; size-based indicators, such as the large species indicator—LSI; other indicators, such as the elasmobranchs-bony fish ratio—E/B ratio). Indicators were further compared with main ecosystem drivers, i.e., fishing capacity, nutrient loads and climate change. Species most vulnerable to fishing (i.e., elasmobranchs and large-sized species) dramatically declined at the beginning of the industrialization of fishery that occurred right afterwards World War II, as can be inferred by the negative drop of LSI and E/B ratio in the mid-1950s. However, until the mid-1980s landings and PPR increased due to improvements in fishing activities (e.g., the introduction of more efficient fishing gears) increasing fishing capacity, high productivity of the ecosystem. Overall, the effects of fishing were buffered by an increase in productivity in the period of high nutrient discharge (up to mid-1980s), while significant changes in fish community structure were already occurring. From the mid-1980s, a reduction in nutrient load caused a decline in productivity but the food-web structure was already modified and unable to support, or recover from, such unbalanced situation, resulting in the collapse of landings. This collapse is coherent with alternative stable states hypothesis, typical of complex real systems, that implies drastic interventions that go beyond fisheries management and include regulation of nutrient release for recovery. The work highlights that, despite poor capabilities to track species dynamics, landings and applied indicators might help to shed light on the long-term dynamics of marine communities, thus contributing to place current situation in an historical framework with potential for supporting management.

Keywords: landings, marine historical ecology, ecological indicators, long-term changes, ecosystem drivers, Adriatic Sea

# INTRODUCTION

Long-term analyses of the interactions between human society and the oceans are necessary for understanding the processes that brought the marine ecosystems as we see today, avoiding the so-called "shifting the baseline syndrome" and understanding the magnitude and causes of change (Pauly, 1995; Jackson et al., 2001). In this framework, marine historical ecology (MHE) can bring a significant contribution to present-day management of marine ecosystems, both for conservation and for sustainable exploitation of resources (Engelhard et al., 2015). However, often the analyses of historical changes in marine communities cannot be based on results of structured and standardized monitoring programs, since in most of the cases those were set in the very last decades. The need to bridge the gap between request of knowledge on past status of ecosystems and the available monitoring data, triggered the uses of different approaches to extract information from data coming from other sources, including paleontological, archeological, and historical sources, as well as the use of landings (e.g., Rosenberg et al., 2005; Sàenz-Arroyo et al., 2005; Lotze et al., 2006; Fortibuoni et al., 2010, 2016; Van Beveren et al., 2016).

In this context, detailed and disaggregated fishery statistics represent an important source of information that can be used as proxies to evaluate long-term changes in marine fisheries and communities. Changes in the composition of landings, evaluated through opportune weighting metrics (indicators), showed to reflect changes in the structure of underlying fish communities due to anthropogenic impacts and environmental changes (e.g., Caddy, 1993; Pauly et al., 1998; de Leiva Moreno et al., 2000; Pinnegar et al., 2002; Libralato et al., 2004; Pauly and Watson, 2005; Baeta et al., 2009; Munyandorero and Guenter, 2010; Kleisner et al., 2013). Moreover, catch statistics are recognized to be linked to fishing and environmental pressures and respond selectively to management action (Coll et al., 2016).

Nevertheless, the intrinsic limitations of fishery-dependent data—such as landings—include the lack of standardization, the dependence from fishing fleet activity features as well as market preferences of products that usually change across time and space. All this imposes caution in deriving marine population densities directly from catch statistics (e.g., Essington et al., 2006; Pauly et al., 2013).

The capability to connect modification in landings composition to changes in the community at sea is more robust when local disaggregated landings result from multitarget and multi-gear fisheries (i.e., several distinct métiers), and when changes in fishing activities (e.g., introduction of new technologies, shift from one fishing gear to another, shift in fishing grounds, fishing capacity as number and tonnage of boats) are traceable. In this context an important aspect to be considered is the trend in indices over time, rather than the absolute values they assume, being reference points or limit values for many indicators not yet been established (Shin et al., 2010).

The Northern Adriatic Sea (Mediterranean Sea) represents a valuable case study for MHE, due to the long history of exploitation (Botter et al., 2006; Fortibuoni, 2010), human-induced changes (Lotze et al., 2011), as well as documented long-term modifications of the physical and chemical oceanographic characteristics of the basin due to anthropogenic impact (Mozetic et al., 2009; Solidoro ˇ et al., 2009) and temperature change (Russo et al., 2002).

In this study, a long-term time-series (1945–2014) of landings disaggregated by species (Mazzoldi et al., 2014) for the Northern Adriatic Sea was analyzed to detect changes in total yields and variations in landings composition by functional groups over time. A suite of ecological indicators was applied to landings data to integrate responses to multiple stressors (Fu et al., 2015; Coll et al., 2016). They include trophodynamic indicators (e.g., mean trophic level, primary production required), climatic indicators (mean temperature of the catch), and other indicators, such as elasmobranchs-bony fish ratio. These indicators were compared with independent data describing main ecosystem drivers, in order to corroborate findings.

Our analysis falls within the general need of taking into account ecological processes when considering longterm changes in fishery resources where fishery-independent data are lacking, by testing several ecological indicators, and their responsiveness to fishery and environmental drivers. The approach is suitable to be applied for the purposes of the Ecosystem Approach to Fishery Management (EAFM) and contributes to establishing historical baselines to be used to compare current and future ecosystem status, for instance in the context of the Marine Strategy Framework Directive (MSFD) implementation.

# MATERIALS AND METHODS

#### Area of Study

The Northern Adriatic Sea is the shallowest (average depth of 33.5 m) and northern-most area of the Adriatic and Mediterranean Seas (**Figure 1**). This area is characterized by strong riverine outflows from the Po River (the largest Italian river) that is a primary source of nutrients and organic matter to the basin (Giani et al., 2012). Water circulation is dominated by two counter-clockwise gyres, which confine a large part of the nutrient-enriched riverine inputs along western coastal regions (Zavatarelli et al., 1998). In fact, the western coastal waters were long considered as ones of the most productive of the Mediterranean Sea (Hopkins et al., 1999), and occasionally local hypoxia/anoxia events have been reported especially close to the Po River mouth (Giani et al., 2012). The reduction in phosphorus loads in Italian rivers in the 1980s triggered reversal in the eutrophication trend and was indicated as the start of a (cultural) oligotrophication process for the basin (Mozetic et al., 2009; Solidoro et al., ˇ 2009).

The seabed is characterized by muddy and sandy bottoms, with the presence of few rocky outcrops. Due to the presence of a wide flat trawlable platform coupled with the high productivity, the Northern Adriatic Sea is the Italian basin with the highest fishery pressure, and one of the most exploited areas in the Mediterranean Sea (AdriaMed, 2004).

# History of Fisheries in the Northern Adriatic Sea

The biological resources of the study area have been intensively exploited since centuries, and the port of Chioggia (**Figure 1**) hosts the most important fishing fleet of the Adriatic Sea and one of the most important of the entire Mediterranean basin (Botter et al., 2006; Fortibuoni, 2010; Mion et al., 2015).

Before and immediately after World War II (WWII) the Chioggia fishing fleet adopted a wide variety of artisanal gears, both passive and active and the industrialization of fisheries in the Adriatic Sea gradually became consolidated starting from the 1950s, having begun in the period between the two world wars with the first experiments with engines (Fortibuoni, 2010). The period following WWII was characterized by marked changes in fishing equipment and technologies. There was a great increase in both demand for fish products and technical innovations, which enabled fleets to expand considerably. Consequently, there was a continuous and substantial increase in the fishing capacity (Cataudella and Spagnolo, 2011).

The use of progressively larger ships and engines allowed fishing areas to be expanded as well as larger and heavier gears to be used. As an example, the bottom otter-trawl was previously towed by pair sailing boats (Botter et al., 2006), and after WWII it was increasingly adopted by single mechanized vessels. Most traditional fishing gears were abandoned (e.g., beach seines and longlines, commonly used throughout the nineteenth century) and new and more efficient ones were introduced. However, the geographical characteristics of the Northern Adriatic Sea (shallow and semi-closed basin) constrained the expansion of fisheries, thus fishery grounds exploited by Chioggia's fleets can be considered almost stable throughout the period analyzed (Mazzoldi et al., 2014).

The first of the major innovations in fishing equipment introduced was the "saccaleva" surrounding net aided with a light source for attracting fish. It became widespread in the 1940s in Chioggia and gradually replaced all other methods for fishing small pelagic fish, such as the traditional "menaide" drift net (Fortibuoni, 2010). Successively, the "saccaleva" was substituted in the late 1960s by the more efficient "volante" (mid-water pelagic trawl, towed by paired vessels) that since the 1970s is the main gear used to catch pelagic species (Cingolani et al., 1996). In the mid-1950s the "rapido" trawl (a sort of beam trawl rigged with 10 cm long iron teeth; Pranovi et al., 2001) was introduced targeting flatfish and shellfish (such as the Mediterranean scallop Pecten jacobaeus), but also demersal resources, as spottail mantis shrimp (Squilla mantis) and common cuttlefish (Sepia officinalis). In the early 1970s, the first hydraulic dredge came into use, substituting the traditional hand-maneuvered gears to harvest different species of clams (Romanelli et al., 2009). In relation to this, the clam fishery quickly became one of the most valuable.

Furthermore, in the mid-1980s, the introduction of LORAN (Long Range Navigation) and subsequently the video plotter and GPS (Global Positioning System) greatly improved navigation precision, allowing the exploitation of areas that were previously inaccessible because of their proximity to unsuitable trawling sites, as the presence of rocky outcrops.

As regard fisheries management, a comprehensive scheme in Italy was initiated with the Law 41/1982, establishing that all professional fishing vessels had to possess a license reporting the characteristics of the vessel (e.g., GT), limitations of fishing areas, gear use and spatial licensing (Piroddi et al., 2015). The Italian fisheries management system is actually based on fishing effort/capacity regulation systems, and technical measures. No quotas or TACs (total allowable catch) have been established, except for Atlantic bluefin tuna (Thunnus thynnus) and Striped venus clam (Chamelea gallina). From 1983, the national fishing fleet was subject to reduction constraints in relation to two reference parameters, fleet tonnage and engine power. A further incentive toward fleet capacity/effort reduction was provided by European Structural Funds that financed the voluntary removal of vessels. Moreover, in recent years there has been a voluntary departure from the sector due to the general old age of the fishing fleet and the fisheries crisis (Cataudella and Spagnolo, 2011). However, as regards the Chioggia's fishing fleet, fishing capacity (i.e., total GT) has increased until recent years (Barausse et al., 2011).

#### Data

#### Landings Dataset

Official landings data (1945–2014) from the Chioggia's wholesale fish-market were retrieved from the Clodia database (Clodia database, 2015). Landings of a wide variety of benthic and pelagic species represent the aggregated commercialized quantities caught by the highly diversified fishing gears employed by fishermen of Chioggia. Data do not include any estimate of the discard, and landings disaggregated by gear are not available. Landings refer only to fish and seafood caught by local fishermen from the Chioggia's fleet that operates in the Adriatic Sea. The database was validated by Mazzoldi et al. (2014) to show that landings composition provides reliable indication of fish abundance.

Information on the habitat (pelagic/demersal) and maximum length (Lmax) for each species were taken from FishBase (Froese and Pauly, 2016). Moreover, the thermal preference (median temperature preference, T) was assigned according to Cheung et al. (2013). The trophic level of species (TL), specific for the Northern Adriatic Sea, was obtained from Fortibuoni et al. (2013), from other literature and estimates (e.g., Stergiou and Karpouzi, 2002) or retrieved from FishBase in the case of fish, and SeaLifeBase (Palomares and Pauly, 2016) in the case of invertebrates.

#### Ecosystem Drivers: Fishing Capacity, Nutrient Loads, and Climate Change

Data on Chioggia's fishing fleet capacity in terms of gross registered tonnage (GRT) were gathered from ISTAT (Italian National Institute of Statistics) for the period 1951–1989 and in terms of gross tonnage (GT) from the Community Fleet Register for the period 1990–2014. Total fishing capacity was expressed as GT for the entire time-series and used as a proxy for fishing pressure in the analyses because no long-term records of fishing effort were available. It was also not possible to include in fishing pressure any estimate of the technological creep due to the lack of data (e.g., CPUE from scientific surveys; Engelhard, 2016).

The monthly discharge of the Po river (m3 /s), measured at Pontelagoscuro (Ferrara, Italy) for the period 1945–2014 was provided by the Regional Environmental Protection Agency of Emilia Romagna and used to calculate monthly mean annual discharge. Nutrient yearly discharge, in terms of annual input of nitrogen (NO3) and phosphorus (PO4) load (t/y) from the Po river, was obtained from Ludwig et al. (2009) for the period 1960–2000, and from EU FP7 PERSEUS project for the period 2001–2014.

The winter (December-March) station based index of the North Atlantic Oscillation index (Hurrell and National Center for Atmospheric Research Staff, 2016) was used to represent the large-scale climatic variability in the area in the period 1945–2014. The NAO is based on the difference of normalized sea level pressure between Lisbon (Portugal) and Stykkisholmur/Reykjavik (Iceland). Positive values of the NAO index are typically associated with stronger-than-average westerlies over the middle latitudes and wetter/milder weather over western Europe.

Finally, monthly data on sea surface temperature (SST) were downloaded from the International Comprehensive Ocean–Atmosphere Data Set (ICOADS). SST data at 13◦E– 45◦N, 15◦E–45◦N, 13◦E–43◦N, and 15◦E–43◦N for the period 1957–2014 were used to compute the annual mean values.

#### Ecological Indicators

Data were also used to compute ecological indicators, meant to provide a holistic description of the system. Disaggregated landings per species (landed quantities in kg per year) were integrated into metrics by applying a suite of ecological indicators (described in **Table 1**) based on TL, thermal preference, habitat and species' size.

Metrics used included: the mean Trophic Level of landings, an indicator of food webs structure (mTL; Pauly et al., 1998); the Mean Temperature of the Catch (MTC; Cheung et al., 2013), which is the average inferred temperature preference of the species weighted by their annual catch used for evaluating the effect of sea warming on fish communities; the ratio of small pelagic fish to demersal and benthic landings (P/D ratio; de Leiva Moreno et al., 2000); the Large Species Indicator (LSI), i.e., the biomass proportion in landings of large-sized fish species (Shephard et al., 2012); the ratio between elasmobranch and bony fish in the landings (E/B ratio; Piet and Pranovi, 2005); the Primary Production Required to sustain fishery catches (PPR; Pauly and Christensen, 1995) that is the amount of energy exported from the system by landings and can be seen as the ecological footprint of fishing activities (Swartz et al., 2010); the Q-90 statistic (Ainsworth and Pitcher, 2006) that is a variant on Kempton's Q index to evaluate biodiversity (Kempton and Taylor, 1976). Landings per Unit of Fishing Capacity (LPUC) were also computed considering the yearly total gross tonnage of the fleet.

# Analysis of Data

#### Multivariate Analysis

Landings data were aggregated into 12 functional groups, according to their taxonomical and ecological features, to reduce missing values and restrict the number of variables in order to better elucidate main changes. Invertebrates were subdivided into three groups, i.e., bivalves, cephalopods, and crustaceans (no reliable data for gastropods were available in the database and thus this group was omitted). Pelagic bony fish species were grouped into three size classes according to fish species Lmax (small: Lmax < 30 cm; medium: 30 cm ≤ Lmax < 90 cm; large: Lmax ≥ 90 cm). Analogously, three groups were used for aggregating demersal bony fish species by size. Flatfishes, sharks and "skates and rays" represented three further groups.

Functional groups' proportion in landings (%) was computed yearly in order to analyse landings composition and remove the effect of landings abundances. A fourth root transformation was applied to data to downweight the influence of predominant groups (Kaiser et al., 2000) and the similarity between every pair of years was computed using the Bray-Curtis similarity coefficient. Chronological clustering using the un-weighted pairgroup average (Legendre and Legendre, 1998) was applied to identify periods with similar landings composition. Functional groups' proportion in landings was compared among periods by means of the non-parametric Kruskal-Wallis H-test (α = 0.05). Ecological indicators were compared between consecutive periods identified through cluster analysis by means of the nonparametric Mann-Whitney U-test (α = 0.05). Analysis was done with PAST v. 3.14 (PAleontological STatistics; Hammer et al., 2001).

Ecological indicators and drivers relationship was analyzed for the period 1960–2014 (for which all drivers were available) through the statistical procedure BIO-ENV from PRIMER v. 6.1.5 (global BEST-test; Clarke et al., 2008). The method consists in the computation of the correlation coefficients between similarity matrices of ecological indicators and drivers, and identifies the combination of drivers that maximizes the correlation. Indicators and drivers were normalized prior to the construction of the Euclidean distance matrices, since they represented different units of measure. The skewness and the individual correlations between drivers were explored by constructing a draftsman plot and examining the resulting Spearman rank correlations to eventually reduce redundancy and dimensionality of the data. We included all drivers in the analysis after testing the absence of highly correlated drivers (ρ ≥ 0.95).

#### Time-Series Analysis

Trends in total landings (Y), ecological indicators and drivers time-series were analyzed using the Mann–Kendall test and the non-parametric Sen's method was used to quantify the magnitude (slope) of the trend, using the Microsoft Excel template MAKESENS (Mann-Kendall test for trend and Sen's slope estimates) developed by Salmi et al. (2002).

Drivers time-series were further explored using the sequential t-test analysis of regime shift (STARS v. 3.4) first developed by Rodionov (2004) in order to detect potential abrupt changes in the mean. The algorithm was applied to the filtered time series calculated by removing red noise through a "pre-whitening" procedure based on the IP4 method (Rodionov, 2006) to take into account the effect of serial correlation on shift detection. The cutoff length was set at 10 years, the significance level at 0.05 and Huber's weight parameter at 1.

# RESULTS

### Multivariate Analysis

Chronological clustering allowed distinguishing seven periods with different landings composition at a similarity threshold of 0.94: 1945, 1946–1954, 1955–1961, 1962–1985, 1986–1993, 1994– 2008, and 2009–2014 (**Figure 2**). Year 1945, which represented an autonomous cluster at this similarity threshold, was associated to the first period (then becoming 1945–1954).

The difference among periods of functional groups' proportion in landings was statistically significant for all functional groups. Landings were dominated by small pelagics in all periods, with a percentage contribution ranging between 37% (SD: 8%; 1945–1954) and 68% (SD: 3%; 2009–2014). Cephalopods was the second most relevant group in the landings in all periods, with a contribution ranging between 10% (SD: 2%; 1955–1961) and 20% (SD: 5%; 1986–1993). In the first two periods, the third group in terms of biomass was medium pelagics that represented more than 10% of the landings [13% (SD: 4%) and 10% (SD: 6%), respectively], but then medium pelagics declined in the following periods down to 1.1% (SD: 0.4%) in recent years (2009–2014). Medium demersals ranged between 12% (SD: 2%; 1945–1954) and 6% (SD: 1%; 1955–1961). The other groups contributed with <10% in all periods. It is worth noting that skates and rays showed a sharp decline, from 3% (SD: 1%) in 1945–1954 to 0.04% (SD: 0.01%) in 2009–2014 (**Figure 3**). Another interesting dynamic was showed by bivalves that from 2% (SD: 1%) of the landings biomass in the first period increased to 8% (SD: 6%) in the period 1986–1993, and then sharply declined down to 1.9% (SD: 0.4%) in the most recent period (**Figure 3**).

Mann-Whitney U-test results are reported in **Table 2**. Y, MTC, LPUC, PPR, and P/D ratio significantly increased between the first two periods, while mTL, LSI and E/B ratio significantly decreased (**Figures 4**, **5**). Between 1955–1961 and 1962–1985 only MTC significantly increased, while mTL, LSI, and P/D ratio significantly decreased (**Figure 5**). In the subsequent period, mTL and LSI significantly increased, while Y, LPUC, PPR, Q-90, and P/D ratio significantly decreased (**Figures 4**, **5**). Between 1986–1993 and 1994–2008 a significant increase of MTC, Q-90, and P/D ratio was observed, while E/B ratio further decreased (**Figure 5**). Finally, by comparing the last two periods (1994–2008 and 2009–2014) a significant decrease of MTC, PPR, LSI, Q-90, and E/B ratio occurred, while P/D ratio significantly increased (**Figure 5**).

The global BEST-test showed a moderate (ρ = 0.26–0.34) significant (p < 0.01) link between ecological indicators and drivers. The similarity matrices obtained with phosphorous discharge and fishing capacity correlated highest (ρ = 0.34) with the similarity matrix of ecological indicators.

FIGURE 2 | Chronological clustering (Bray-Curtis similarity, unweighted pair-group average) of the composition of landings for the years 1945–2014.

#### Time-Series Analysis

Landings (Y) significantly increased between 1945 and 2014 (Z = 3.36, p < 0.001, slope = 58.9 t year−1 , **Figure 4A**). Conversely, landings rescaled over fishing capacity (LPUC) significantly declined in the whole period of study (Z = −3.95, p < 0.001, slope = −8.13 t GT−1 year−1 , **Figure 4B**). The mTL significantly declined along the whole time series (Z = −3.38, p < 0.001, slope = −0.002 year−1 , **Figure 5A**). MTC significantly increased from 1945 to 2014 (Z = 7.27, p < 0.001, slope = 0.048◦C year−1 , **Figure 5B**). PPR showed no significant trend between 1945 and 2014 (**Figure 5C**). LSI and E/B ratio significantly declined from 1945 to 2014 (Z = −3.21, p < 0.01, slope = −0.002 year−1 ; Z = −8.67, p < 0.001, slope = −0.0003 year−1 , **Figures 5D,E**). The same temporal trend resulted for Q-90 (Z = −4.92, p < 0.001, slope = −0.021 year−1 , **Figure 5F**). The P/D ratio significantly increased from 1945 to 2014 (Z = 2.7, p < 0.01, slope = −0.02 year−1 , **Figure 5G**).

TABLE 2 | Significant differences (Mann-Whitney U-test, p < 0.05) of total landings (Y), landings per unit of capacity (LPUC), and ecological indicators between successive periods.

It is reported the median value and the interquartile range (in brackets) for the first period and significant positive (green) and negative (red) changes for successive periods.

As regards drivers, Po river mean annual discharge did not show a significant trend. However, the time-series presented a significant negative drop in mean in 2003 and a positive one in 2008 (**Figure 6A**). Nitrogen load significantly increased between 1945 and 2014 (Z = 4.17, p < 0.001, slope = 1.37 t year−1 ). The time-series presented a significant positive drop in mean in 1972 (**Figure 6B**). Conversely, phosphate load did not show any significant trend, but an increase in the mean value in 1972 and a significant decrease in 1988 (**Figure 6C**). Neither the NAO showed any significant trend, but two negative shifts in 1962 and 2009 and a positive shift between the two in 1972 (**Figure 6D**). SST significantly increased in the period of study (Z = 3.56, p < 0.001, slope = −0.01◦C year−1 ) and showed two positive shifts in mean in 1998 and 2011 (**Figure 6E**). Finally, also fishing capacity significantly increased (Z = 9.46, p < 0.001, slope = 89.88 GT year−1 ) without any significant shift in the mean (**Figure 6F**).

#### DISCUSSION

Six periods with significantly different community structure were identified through cluster analysis of the landings composition by functional group. The most abundant group was small pelagics (mainly European anchovy Engraulis encrasicolus and European pilchard Sardina pilchardus) in all periods, followed by cephalopods in almost all periods. Major changes in community composition between periods include skate and rays and medium pelagics decline, and bivalves dome-shaped trajectory.

The increase of bivalves' proportion in the landings from 2% in 1955–1961, to 5 and 8% in the following periods, is probably linked to the introduction in the mid-1950s of the "rapido" trawl, and successively in the 1970s of the hydraulic dredge, that substantially improved fishing efficiency. However, the proportion of this functional group in the landings decreased to 3% in 1994–2008, and further decreased to 2% in 2009– 2014. This sharp decrease is related to the collapse of the Mediterranean scallop (Pecten jacobaeus) in the late 1990s (data not shown) due to overfishing. In fact, between the 1960s and the 1990s, the species showed large fluctuations determined both by the intensity of fishing effort and by mass mortalities due to hypoxic conditions that occurred episodically over wide areas of the Northern Adriatic Sea (Hall-Spencer et al., 1999). A previous study (Maurizio and Castagnolo, 1986) showed that