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Assessing broad life cycle impacts of daily onboard decision-making, annual strategic planning, and fisheries management in a northeast Atlantic trawl fishery

  • Friederike ZieglerEmail author
  • Evelyne A. Groen
  • Sara Hornborg
  • Eddie A. M. Bokkers
  • Kine M. Karlsen
  • Imke J. M. de Boer
ADVANCING SOCIAL AND ECONOMIC KNOWLEDGE IN LIFE CYCLE MANAGEMENT

Abstract

Purpose

Capture fisheries are the only industrial-scale harvesting of a wild resource for food. Temporal variability in environmental performance of fisheries has only recently begun to be explored, but only between years, not within a year. Our aim was to better understand the causes of temporal variability within and between years and to identify improvement options through management at a company level and in fisheries management.

Methods

We analyzed the variability in broad environmental impacts of a demersal freeze trawler targeting cod, haddock, saithe, and shrimp, mainly in the Norwegian Sea and in the Barents Sea. The analysis was based on daily data for fishing activities between 2011 and 2014 and the functional unit was a kilo of landing from one fishing trip. We used biological indicators in a novel hierarchic approach, depending on data availability, to quantify biotic impacts. Landings were categorized as target (having defined target reference points) or bycatch species (classified as threatened or as data-limited). Indicators for target and bycatch impacts were quantified for each fishing trip, as was the seafloor area swept.

Results and discussion

No significant difference in fuel use was found between years, but variability was considerable within a year, i.e., between fishing trips. Trips targeting shrimp were more fuel intensive than those targeting fish, due to a lower catch rate. Steaming to and from port was less important for fuel efficiency than steaming between fishing locations. A tradeoff was identified between biotic and abiotic impacts. Landings classified as main target species generally followed the maximum sustainable yield (MSY) framework, and proportions of threatened species were low, while proportions of data-limited bycatch were larger. This improved considerably when reference points were defined for saithe in 2014.

Conclusions

The variability between fishing trips shows that there is room for improvement through management. Fuel use per landing was strongly influenced by target species, fishing pattern, and fisheries management. Increased awareness about the importance of onboard decision-making can lead to improved performance. This approach could serve to document performance over time helping fishing companies to better understand the effect of their daily and more long-term decision-making on the environmental performance of their products.

Recommendations

Fishing companies should document their resource use and production on a detailed level. Fuel use should be monitored as part of the management system. Managing authorities should ensure that sufficient data is available to evaluate the sustainability of exploitation levels of all harvested species.

Keywords

Bycatch Cod Fuel Haddock LCA Fisheries management Shrimp Trawling 

1 Introduction

Life cycle assessment (LCA) methodology has been applied to seafood products since the early 2000s (Parker 2012; Vázquez-Rowe et al. 2012a; Avadí and Fréon 2013). These efforts have contributed to a more holistic understanding of the environmental impacts of fisheries and subsequent seafood supply chains. The main part of results in standard LCA impact categories (abiotic resource use and emission-based impacts such as climate change, eutrophication, and acidification) has been identified to be determined by the onboard use of fuel and refrigerants of fishing vessels (Vázquez-Rowe et al. 2012a). In addition, fuel efficiency in liters used per kilo (or value) of landing is an important indicator of the economic performance of many fisheries, especially for demersal trawlers (STECF 2013; Isaksen et al. 2015), because it often represents the main variable cost.

Fuel use per landing depends on the catch obtained per time spent fishing, especially in fisheries using active fishing methods, such as demersal trawling (Ziegler and Hornborg 2014). This catch rate varies with many aspects: fishing area, season, type of fishing vessel and fishing gear, technology used, individual skills, and weather conditions. Temporal variability in resource use of fisheries has been found to be substantial in some cases (Ramos et al. 2011) and less pronounced in others (Almeida et al. 2014). Both of these studies focused on variability between years, while temporal variability within a year has only recently been studied, with indications that it can be considerable (Almeida et al. 2014).

The relevant level of temporal resolution, however, depends on the application. A company choosing between different suppliers will find average environmental performance over, e.g., the time period of a contract more relevant than for a specific fishing trip. As a result, seafood LCA studies and product-specific carbon footprint standards (Ramos et al. 2011; BSI 2012; Standard Norway 2013), for example, advocate a 3- to 5-year average for important input data to account for inter-annual variability and make results valid for a longer time. The detailed level, on the other hand, can be more relevant for understanding what is important and how to internally improve within a company.

Fishing operations are managed on various levels. The individual fishing company plans on how to run the operation both on a day-to-day basis and also on a more long-term strategic level, e.g., based on annual distribution of fishing quotas. The aim is to optimize economic performance, given the constraints set by national and international regulations of the fishery. Fisheries management is the overarching regulatory framework that determines who can fish, where, when, what species, and using which gear types/technologies. It also regulates how the total quota is distributed to different actors (fleets, fleet segments, or individual vessels). These regulations exist at the international level (such as the EU Common Fisheries Policy) and at the national or even regional level. It has been shown that regulations (such as the size of the quota and technical gear regulations) can play an important role for the greenhouse gas emissions and other environmental impacts caused by fisheries and seafood products (Driscoll and Tyedmers 2010; Hornborg et al. 2012; Ziegler et al. 2013). However, current decision support in fisheries management excludes life cycle considerations such as fuel use, greenhouse gas emissions, and supply chain perspective and is entirely focused on the direct biological impacts of fishing (Hornborg 2014).

Understanding the reasons behind the variability in catch and fuel efficiency could potentially help to improve decision-making both at the company level and in the fisheries management system. In the case of producers (fishing companies), this type of information can be used to optimize the production, and in the case of companies further downstream the value chain, sourcing strategies could be adjusted, allowing demand to drive improvement. Increased understanding about this variability could also be used to improve the fisheries management system by, for example, designing both spatial and temporal limitations of fisheries and the distribution of fishing rights in the most resource-efficient way.

The LCA method was originally developed to assess impact categories for which robust cause-effect relationships exist, for example, climate change caused by greenhouse gas emissions. In LCA, all relevant types of impact of a production system are to be assessed and fisheries give rise to direct impacts on target and bycatch species (either discarded at sea or landed) and on seafloor habitats (when using dragged gear types like trawls). As fisheries management, certification and the public debate on sustainable fisheries focus on biological aspects as the central environmental impacts of fishing, accounting for them in seafood LCAs therefore represent an important improvement. Recently, indicators quantifying biological impacts of fishing in relation to seafood production have been developed (Nilsson and Ziegler 2007; Vázquez-Rowe et al. 2012b; Hornborg et al. 2013a, b; Emanuelsson et al. 2014), but to date, they have not been tested beyond the case studies in which they were developed and not in combination. Even so, further methodological development will be needed to fully represent the ecosystem impact of fishing. In the end, broad implementation of the newly developed fisheries-specific environmental impact categories to the standard set of LCA impact categories could visualize complementarities and tradeoffs between different types of environmental impact (Hornborg et al. 2012).

Therefore, our aim is to quantify broad environmental impacts of a fishery over time to identify the causes of variability. Better understanding of these causes can identify improvement options on different levels and improve decision-making. Assessment of both standard LCA as well as biotic impact categories can demonstrate synergies and tradeoffs between impact categories and make stakeholders more aware about the various aspects when they prioritize.

2 Methods

2.1 Goal and scope

The system explored included the fishing activities of a Norwegian demersal trawler between 2011 and 2014 targeting Atlantic cod (Gadus morhua), haddock (Melanogrammus aeglefinus), saithe (Pollachius virens), and northern shrimp (Pandalus borealis), mainly in the Norwegian and Barents Sea using single or double trawls. The vessel undertakes around 20 fishing trips per year, normally 4 to 14 days long, out of its home port Tromsø. In 2014, a few very long trips of up to two months were undertaken. Production of supply materials such as fuel, coolants, vessel, and fishing gear materials were included as these data could be provided without too much effort. However, data for resource use for vessel construction, maintenance, and end-of-life treatment of vessel and gear could not be provided within this study. We found it reasonable to assume though that these activities would most probably be less important to total impacts than the production of supply materials (Magerholm Fet et al. 2009). The functional unit was one kilo of landing, from one fishing trip. The trawler lands around 70 different products1 which normally have undergone primary processing onboard (heading, gutting, and freezing), except for northern shrimp, which are landed whole (frozen raw). An overview over the fishing trips undertaken in the study period in terms of trip start and end date, main target species, and fishing location is given in Tables S1 to S4, Electronic Supplementary Material 1.

In demersal trawl fisheries, several species are landed simultaneously. This represents a multiple output situation, and the environmental impact of the fishery has to be allocated to the various outputs in some way. In this study, we applied mass allocation, implying that the environmental impact of a process is allocated to the various outputs based on their relative mass. This choice was based on a preference for a stable way to allocate impacts to the various products landed simultaneously, reflecting the flows of biomass. Heads and guts are discarded at sea and did not account for any upstream resource use, i.e., all burdens of the fishery were placed on the landed part.

The 16 standard LCA impact categories recommended by the International Reference Life Cycle Data System (ILCD) Handbook (Wolf et al. 2012) were used. The following biological indicators were added: overfishing, bycatch impact on threatened species, bycatch impact on data-limited species, and seafloor area trawled in order to assess the biotic impacts of the fishery (Hornborg et al. 2013a; Emanuelsson et al. 2014; Ziegler et al. 2014).

Overfishing (OF) is calculated by comparing the current fishing mortality, F, with the target fishing mortality for maximum sustainable yield (MSY), F MSY, for each stock, as defined by the International Council for the Exploration of the Seas (ICES) for the years in question, following Eq. 1 (Emanuelsson et al. 2014). Of the species occurring in the landings of the trawler, Atlantic cod, haddock, hake (Merluccius merluccius), and shrimp had F MSY values defined by ICES in 2012 and values were defined for saithe in 2014. For background information on the MSY framework, which all EU fisheries shall follow by 2020 and which is the current goal of European fisheries policy, see ICES (2014a).
$$ OF=\frac{F}{F_{MSY}}-1 $$
(1)

Equation (1) leads to a quantitative stock-specific indicator of overfishing to be understood as “excess kilos fished per kilo landed” in that stock and year (Emanuelsson et al. 2014). This dimensionless indicator was then multiplied with the landings of each species/stock in each fishing trip to result in an aggregate value of overfishing per trip. It is important to note that just as for any other type of environmental impact, a low value for OF means that a fishery causes little impact in this impact category. When a stock was fished under target fishing mortality, i.e., F < F MSY, negative values result from the application of Eq. 1, but in these cases, the impact is set to zero (so that overfishing of one stock does not compensate for “underfishing” of another).

Bycatch impacts on threatened fish species were quantified by summarizing the amount (in kilos) of landings belonging to either of the categories Vulnerable, Endangered, or Critically endangered (VEC) fish on the Norwegian version of the Red List of Threatened Species (Kålås et al. 2010). This list follows the framework of the International Union for Conservation of Nature (IUCN) and was used also as the baseline of a method proposed for discard impact assessment by Hornborg et al. (2013a). Landings that did not have defined MSY reference points nor were assessed as threatened in the Norwegian Red List were classified as data-limited, indicating that limited information about their status is available. This is a new indicator proposed here, suggested to quantify the proportion of landings for which only limited data is available (Ziegler et al. 2014). The hierarchic approach to quantifying biological impacts of fishing used here, depending on what kind of data is available, is novel and has been developed and tested as part of this work.

Seafloor impacts of demersal trawling were quantified as seafloor area swept per kilogram landed. This was done by multiplying the width of the trawl in seafloor contact with the trawling speed and duration of the trawl haul, based on a model in Nilsson and Ziegler (2007). The area obtained was then divided by the landings during the same time. For further detail on all methods used, see Ziegler et al. (2014).

2.2 Data

Daily data was provided on fuel use (recorded every 12 h) and production (recorded continuously). Due to a time-lag between fishing activities and onboard processing, we decided to aggregate the data into fishing trips rather than looking at fishing days or even single trawl hauls. A small proportion of the fuel used was consumed in port, e.g., during maintenance, and was allocated equally to all landings that year. Full LCI data was obtained for 2011 and 2012, and fuel and production data for 2011 to 2014 (for an overview of the fishing trips, see Tables S1 to S4 in the Electronic Supplementary Material 1). To increase our understanding of the fishery, two semi-structured telephone interviews were carried out with the fishing vessel owner and one of the skippers in February and April 2014. They were interviewed regarding their onboard decision-making, the way they fish, and about their views on the way different fisheries are managed. The outcome is referred to in various sections, and both questions and answers can be found in full in the Electronic Supplementary Material 2.

All other data collected on gear use, fishing vessel materials, lubricants, use of chemicals such as detergents, anti-fouling, and other paints were provided on a suitable timescale, longer than a fishing trip. Background data for production of supply materials was taken from LCI database ecoinvent (v2.2). The lifetime of inputs such as vessel and gear was estimated by the skippers (Electronic Supplementary Material 2, questions 18 and 19). For all inputs with a lifetime over a year, impacts were equally distributed to all years.

Values for current (F) and target fishing mortality (F MSY) were taken from the annual scientific advice for exploited stocks for each year with the forecasted fishing mortality for next year (i.e., for example for 2011, the 2011 advice forecasting F for 2012 was used) available on the ICES website (ices.dk). The alternative would have been to use the 2012 advice stating what the F of 2011 was, and while that is a more reliable figure, it means the calculation for 2011 cannot be done until 2012. One of the overarching ideas of developing this approach was that it could be used for quick self-assessment of performance on high resolution. We treated all landings as coming from ICES areas I and II (Barents Sea and Norwegian Sea), although a small part of the landings actually came from the North Sea. The most recent Norwegian Red List of Threatened Species (Kålås et al. 2010) was used for assessment of impacts on threatened fish species. Data on trawl dimensions and speed for modeling seafloor area swept was provided by the skippers.

We tested the effect of year (2011, 2012, 2013) on fuel use per landing and the effect of type of landing (fish vs. shrimp) per year using a Kruskal-Wallis test (Sokal and Rohlf 1981). The most recent year, 2014, was left out since we did not have data for the full year (only for January to October).

3 Results

3.1 Inventory results

Table 1 shows an overview of collected data. Fuel use per landing showed considerable variation between fishing trips, so within years (Fig. 1). However, no significant difference between years was found, neither for all trips nor for trips targeting fish separately (P > 0.05). The harvesting pattern over the year exhibited a similar pattern between the years, with fishing trips targeting fish during the summer being most fuel efficient, except in 2014. That year was unusual in that the trawler undertook two very long trips of up to 2 months, one to the Russian Economic Zone to fish shrimp and one to Greenland to fish Greenland halibut (Reinhardtius hippoglossoides) (Table S4, Electronic Supplementary Material 1). The trip to the Russian Economic Zone (trip 7, 2014) was relatively efficient for targeting shrimp, while the trip to Greenland (trip 10, 2014) was a relatively inefficient one for targeting fish. In all years, fishing trips targeting shrimp used more fuel per kilo landed (P < 0.01).
Table 1

Inventory data (2011–2012). Use of materials and production per year

 

Value

Unit

Life time vessel

30

Years

Reinforcing steel

2030

Tonnes

Chromium steel

226

Tonnes

Life time rigg

1

Year

Rigg

42

Tonnes

Life time nets

1

Year

Trawl small (nylon)

2.1

Tonnes

Trawl large (nylon)

2.3

Tonnes

Anti-fouling use (Cu)

360

kg

Tap water use

1400

Tonnes

Cooling agent (NH3)

180

kg

Detergents

121

Tonnes

Fuel use in port1

37

Tonnes

Lubricating oil1

14

Tonnes

Fuel use fishing1

3100

Tonnes

Total landings1

6200

Tonnes

1Total average fuel use: 0.58 l/kg landed (fuel density 0.885 kg/l)

Fig. 1

Fuel use per trip in 2011 (a), 2012 (b), 2013 (c), and 2014 (d) with trips targeting shrimp indicated in light gray (trips targeting fish in dark gray). The value for trip 17 in 2012 is 8.0 l/kg landed and the value for trip 9 in 2014 is 7.1 l/kg landed

Table 2 shows average fuel use of all fishing trips as well as of those targeting fish and shrimp, separately. In an average between fishing trips, low-volume trips will influence the value more than in the overall average, i.e., when the total fuel use is divided by total landings. Since there are a few trips targeting shrimps that are low volume, high impact, the overall aggregated value (Tables 1 and 2) is lower than the average of all trips (Table 2).
Table 2

Fuel efficiency of the trawler presented in different ways (l/kg landed). The overall aggregated value is total fuel use per total landings per year. Other values represent averages of trips, either all or those targeting fish and shrimp separately

 

2011

2012

2013

2014

Fish trawling

0.56

0.54

0.56

0.87

Shrimp trawling

1.3

3.1

1.7

3.9

All trips

0.67

1.1

0.61

1.6

Overall aggregated value

0.58

0.58

0.46

0.71

3.2 Impact assessment results

3.2.1 Standard impact categories

Relative impact assessment results for 2012 are shown in Fig. 2. The production or combustion of fuel dominated all standard LCA impact categories, except for toxicity categories, which were dominated by production of materials for fishing vessel and gear. Many impact categories (climate change, acidification, marine eutrophication, particulate matter, photochemical ozone depletion, and terrestrial eutrophication) were driven by fuel combustion. Freshwater eco-toxicity was instead dominated by the production of materials for fishing vessel and gear due to the extraction of iron ore. Another group of impact categories (ozone depletion, freshwater eutrophication, ionizing radiation E, ionizing radiation (HH), land use, mineral, fossil and renewable resource depletion, and water resource depletion) were mainly driven by the production of the fuel. For greenhouse gas emissions, metal production for the vessel was responsible for 0.1–2.7 % of total emissions, production of trawl materials (metal and polymers) 0.01–0.7 % and the use of detergents for cleaning was responsible for 0.002–0.01 % of total emissions. These latter results represent fishing trips targeting fish in 2012.
Fig. 2

Relative contributions of different inputs to standard LCA impact categories in 2012. “Other” contains detergents (production and use), anti-fouling (production and use), water consumption, cooling agents (production and use), and lubricating oil (production)

3.2.2 Biological indicators

The assessment of biological impacts showed that the stocks targeted by the trawler (i.e., which had defined MSY reference points) almost exclusively followed the MSY framework. Hake from the northern stock was harvested at a rate exceeding MSY, in 2012. However, hake was only caught in small proportions in one or two fishing trips that year. The main target species (cod, haddock, and shrimp), on the other hand, were generally exploited at or under target levels, except in 2013, when the fishing mortality of haddock (0.56) exceeded F MSY (0.35). This led to 0.6 kg OF per kilo of haddock landed and an increase in average OF that year (Table 3). However, overall, the quantified values for overfishing were relatively low in all years, with no clear trend over the studied time period (Table 3). The only species considered being a target species that did not have defined reference points was saithe until 2013, in 2014 they were defined.
Table 3

Results of assessment of biological impacts of fishery of the freeze trawler

Year

Overfishing OF (kg/kg)

Bycatch VECa (kg/kg)

Bycatch D-Lb (kg/kg)

Seafloor impactc (m2/kg)

Seafloor impactd (m2/kg)

2011

0.0028

0.0042

0.27

433

284

2012

0.00074

0.0062

0.24

737

325

2013

0.0810

0.0058

0.20

e

e

2014

0.00072

0.0403

0.0802

e

e

aVulnerable, Endangered or Critically endangered

bData-limited

cIncluding the trips targeting shrimp, which have a larger seafloor area swept per kilo landed

dExcluding trips targeting shrimp

eFishing effort data for 2013 and 2014 was not available so seafloor area swept could not be calculated

The bycatch species landed, classified as VU, EN, or CR (Vulnerable, Endangered, or Critically endangered), were golden and beaked redfish (Sebastes marinus, EN, and Sebastes mentella, VU) and blue ling (Molva dypterygia, EN). Redfish is not a target species, but the skipper of the trawler knew quite well where and when he would get redfish (or other bycatch species) in the trawl, both from the depth where the trawl was set (redfish are deep-sea fish) and from making short test hauls before setting the trawl (Electronic Supplementary Material 2, question 16). The proportions are generally low (except in trip 13, 2012, which, interestingly, was a research trip when the vessel was contracted by the Fisheries Directorate). Also, in trips 9 and 11 in 2014, considerable proportions of the landings were constituted by redfish, which led to higher values for threatened species in 2014.

The composition of the landed bycatch (i.e., all species for which MSY reference points have not yet been defined) is shown in Fig. S1, Electronic Supplementary Material 3, for all trips during the 4 years. The composition varies between fishing trips, with the most common bycatch species (following this definition) being saithe, spotted and Atlantic wolffish (Anarinchas minor and A. lupus), golden and beaked redfish, and Greenland halibut. The proportion of data-limited landings decreased from around 20–25 % in 2011–2013 to 8 % in 2014, mainly due to the definition of reference points for saithe in 2014. The fishing trips in which almost no bycatch at all was landed were the trips targeting shrimp (Electronic Supplementary Material 3). In this fishery, species-selective sorting grids are used which let the fish escape, but retain shrimp in the trawl. This is done as a measure to protect vulnerable deep-sea fish species that are otherwise incidentally caught in deep-sea trawling such as shrimp trawling.

The seafloor area swept in the two years for which fishing effort data was available was 400–700 m2/kg landed including shrimp-targeting trips and approximately 300 m2/kg when excluded (Table 3). This difference is directly proportional to the difference in fuel efficiency, due to seafloor area swept being calculated from area swept per hour and catch per hour (catch rate).

We try to visualize the tradeoffs in Fig. 3 for all indicators, which shows that none of the fishing trips performs best for all of them. In 2012, both overfishing and the amount of data-limited landings were highest in trips 1–3 and 19, while landings of threatened species were highest in trip 13 and fuel and seafloor use in trip 17 (Fig. 3).
Fig. 3

Relative values for fishing trips in 2012 with the fishing trip having the highest value for each indicator set to 1 to demonstrate tradeoffs between different types of environmental impact

4 Discussion

4.1 Impact assessment results

We could confirm the dominance of fuel use in standard LCA impact categories which has been found before in LCA case studies of fisheries (Parker 2012 Vázquez-Rowe et al. 2012a, Avadí and Fréon 2013). Despite the unusually complete data inventory for other inputs, fuel use was responsible for close to 100 % of total greenhouse gas emissions, and many standard impact categories were correlated with this category. Fishing vessel construction and gear production are typically excluded from seafood LCAs, because their contribution to overall results is often negligible and because it is difficult to obtain representative data for them (Parker 2012). It has been suggested, however, that more attention should be paid to these inputs (Fréon et al. 2014). In the present study, results showed that the production of materials for the fishing vessel and gear was negligible in most impact categories. These inputs gave significant contributions only in the categories related to toxicity, due to iron ore extraction, and there represented around 50 % of the emissions, with fuel use responsible for the balance.

It is important to recall that in this fishery, the energy used for primary processing (i.e., heading, gutting, and freezing of the products) is included in the energy used at sea. It is not surprising therefore that fuel use dominates as much as it does, especially in the absence of use of synthetic refrigerants in onboard cooling systems. These have been shown to be responsible for up to 30 % of total greenhouse gas emissions in landings from similar fisheries (Ziegler et al. 2013). A drawback of onboard processing is the fact that the energy used for processing is based on combustion of fossil fuels, while electricity use in land-based processing in Norway is mainly generated from hydropower, which causes lower GHG emissions per unit of energy used.

4.2 Variability in fuel efficiency and causes

The average fuel use of the trawler (overall annual average 0.5–0.7 l/kg landed in the years studied, Table 2) compares well with that of other trawlers. Parker and Tyedmers (2014) showed that finfish trawling in Europe used on average 0.756 l/kg, with a wide range of 0.236–2.72 l/kg between the 55 data records included. Norwegian demersal trawlers have previously been reported to use on average 0.6 l/kg landed (Winther et al. 2009). Both these references include trawlers that do not process the catch onboard and would therefore be expected to use less fuel per kilo landed than the trawler in this study. Also, the landings of the trawler in this study are mainly in the form of headed and gutted fish, whereas most trawlers land gutted, head-on fish. If the fuel use of this trawler was calculated per gutted fish, it would be lower. Increased utilization of by-products, therefore, is an improvement option.

The main explanatory factor behind the variability in resource use between trips was the target species. Shrimp are trawled deeper with a considerably lower catch rate per hour trawled than when trawling for fish. The lower catch rate of shrimp versus fish trawling and how it influences fuel efficiency has been shown previously (Ziegler and Hornborg 2014). These factors together explain why trips targeting shrimp have a higher fuel use per kilo landed than those targeting fish.

Another factor that could be important for fuel efficiency is the steaming time. The skipper reported that the trawler used about equal amounts of fuel when trawling (at a lower speed with high resistance due to the trawl drag) as when steaming (at a higher speed with less resistance) (Electronic Supplementary Material 2, questions 8 and 9). However, the decision of whether to keep fishing or go steaming to a new fishing location is a balance between “investing” in the steaming and the expected catch at the new location compared to the known catch at the current location (Electronic Supplementary Material 2, question 6). Some fishing vessels cooperate and exchange information on the quality of catches (e.g., species, size, and volume) on their current fishing ground. The decisions of when to stop at one fishing location and going to the next one are based on this type of information and experience only, no calculations are done beforehand. The trawler is out at sea until the hold (cold storage room) is full, and remaining hold capacity is also an important factor for the decision of whether or not to move to a different fishing location (i.e., if little space is left, there is no point in going far). All in all, a fishing trip with a high catch rate can therefore be short and still be efficient, even if it is done far away from the home port.

The catch rate seems in fact to be a more important parameter than steaming distance. Even fishing trips that take place far away from the coast can be relatively fuel efficient when they have a high catch rate, e.g., when fishing in the area around Bear island around halfway between Spitsbergen and the Norwegian mainland or on Spitsbergen (e.g., fishing trips 6–8 and 10–11 in 2012, fishing trips 13–14 in 2013). The fishing trips targeting fish (i.e., not shrimp) further offshore are not consistently more fuel intensive than fishing trips along the coast. For example, trip 15 in 2012 is among the most efficient despite being done far north on Storbanken, compared to trip 19 in 2012, which had a slightly higher fuel use despite being done close to the coast on Fugløy Bank. What seems to be more important is whether the fish is caught in a geographically more limited area or in a larger one during a fishing trip, i.e., if the trawler is steaming long distances between fishing locations rather than to and from port in the beginning and at the end of a fishing trip. This may again be related to the catch rate as extensive steaming between hauls indicates that fishing is not very good. Trips 1–3 in 2012 took place close to the coast, but are rather spread out geographically, while trips 8, 10, and 11 took place around Bear Island, and the hauls are very close to each other; this indicates good fishing.

The fact that shrimp trawling is more energy intensive than fish trawling raises important questions on how to deal with mixed fishing when only annual data on fuel use is available for fisheries, as is most often the case when performing seafood LCAs. As evident in Table 2, it makes a big difference if trips can be separated according to target species. Estimates of average fuel consumption per landing are affected by whether they are weighted according to contribution to total landings or not. When they are not, low-volume trips like those targeting shrimp will influence the average more than if weighted. In addition, the trawler is contracted by the Fisheries Directorate several times per year which influences its fishing pattern and fuel efficiency. Having staff from the Fisheries Directorate onboard can both increase and decrease the fuel efficiency. If the trawler is asked to go to the fishing grounds where many fishing vessels are already fishing (indicating good fishing), fuel consumption per landing is lower than when the fishing vessel is asked to fish on random or fixed sampling locations resulting in lower catches (Electronic Supplementary Material 2, question 14). The trawler is compensated for such loss by extra quota, so-called research quota. Using the total annual average fuel use of the trawler (including shrimp trips) would not be representative to use in a seafood LCA of cod and haddock and vice versa. Therefore, specific data for fuel use in the actual fishery studied is preferable, even if it represents a shorter time period than one year.

4.3 Biological indicators

The biological indicators that were quantified showed that the species for which MSY reference points had been defined (and consequently were considered as target species) almost exclusively were harvested following the MSY framework. The obvious improvement option related to overfishing for the vessel is to avoid catching species harvested over their target fishing mortality as far as possible and for fisheries management to set quota and effort limitations in line with sustainable exploitation levels. Establishing reference points for all species caught in a fishery is also a prerequisite to enable evaluation of whether the exploitation level is sustainable or not. For the bycatch species classified as data-limited due to the absence of MSY reference points or IUCN Red List assessment, we cannot say anything about the potential impact, merely that little is known about the exploitation level and stock status. Some stocks may be in good condition, while others are not. Such uncertainty involves risk for overexploitation and calls for better coverage of stock assessment (Costello et al. 2012). All in all, if reference points are defined, these should be followed and the proportion of data-limited landings in commercial industrialized fisheries should be minimized. This indicator can be used to demonstrate improvement potentials for management. Definition of more reference points, as in the case of saithe in this study, enables improved assessment of the sustainability of fisheries.

The proportion of threatened fish species, i.e., the two redfish species and blue ling, was in general low. This does not mean that the aggregated impact of the whole fleet on these vulnerable species cannot be substantial. The two redfish species are classified to different levels of threat. Even so, a quota recommendation is given for beaked redfish by ICES and the trawler holds a quota for this species. Both for fishers and for consumers, it is confusing that a species assessed as threatened by the IUCN methodology sometimes can be assessed as sustainably fished by ICES and have a recommended quota. Gullstad et al. (2015) say that the stocks of redfish have been in a precarious state for a long time, although it has improved in recent years. The identification of these species is not straightforward and they are often reported simply as “redfish” (not by the trawler in this study though), which is an additional reason to avoid catching both species. It is also important to note that many species have not been assessed by the Red List framework and, therefore, are not included in this measure at present.

Discarding, a common practice in fisheries worldwide (Kelleher 2005), was excluded from this study due to a lack of data. Discard is the part of the catch that is thrown overboard for various reasons including that the fish is under the minimum landing size, has low economic value, has landing restrictions, or target species in landing size but of poor quality. Note that discards can consist both of target and bycatch species. In Norway, a discard ban has gradually been introduced since 1987, at first covering only cod and haddock. Since then the ban has been extended to cover more species (55 species in 2014), but there are exceptions, e.g. it only covers dead or dying fish (Gullstad et al. 2015). However, no discard monitoring program is operated by Norway (Gullstad et al. 2015). It is known that discard occurs even in countries where it is prohibited (Fiskeridirektoratet 2004; Condie et al. 2014), and it is occurring also in Norway (Gullstad et al. 2015). The studied vessel type category (Norwegian freeze trawlers) was in 2004 estimated to discard on average 5–10 % of the catch (Fiskeridirektoratet 2004) and we have not been able to find a more recent estimation. The skippers state that the vessel complies with Norwegian legislation and we only want to point out that this is a data gap; the discard problem does not disappear due to a partial ban while making it very difficult for a single fisherman to verify over-average performance in this respect.

The seafloor area swept gives a rough measure of the scale of areal dependence of this demersal trawl fishery, but it does not say anything about the actual environmental impact of trawling. The impact depends on what type of habitat is impacted, how often disturbance occurs, and whether sensitive habitats are protected from trawl impact or not (Kaiser et al. 2006). Mapping of benthic habitats for a more sophisticated seafloor impact assessment is progressing (e.g., mareano.no; benthis.eu). For this study, we were however unable to find benthic habitat maps for the fishing locations of the trawler.

There is an urgent need to integrate fisheries and environmental management and find meaningful indicators to monitor performance over time (Jennings and Le Quesne 2012). Assessment of these biological indicators conveyed important information on the environmental performance of the fishery, in addition to standard LCA impact categories, and can demonstrate tradeoffs or links between impact categories. This can be useful in order to discover concerns early on. If there are no concerns, it represents a way to document that a fishery actually is performing well, which could be a powerful message to those buying the products of the trawler, most often seafood processors.

4.4 Implications for management

The importance of how value chains are organized for their environmental performance has been identified by Baumann (2004), who observed that different organizations producing similar products with similar technologies can have a very different environmental performance. From the perspective of a single fishing enterprise, increased awareness about the importance of reducing fuel use and bycatch of threatened or data-limited species would be valuable. Increased awareness could lead to small changes in fishing behavior that would contribute to individual improvement both in terms of environmental performance and in the case of fuel use, also in economic performance. Examples comprise of aiming to fish in periods and fishing locations where target abundance is high while catches of overfished target species and bycatch are low. Gear modifications (improved sorting grid or other selectivity device, mesh size, or mesh shape) could contribute to landings being composed even more by sustainably caught target species, although increased selectivity has been shown to sometimes compromise fuel efficiency (Ziegler and Hornborg 2014). In addition, the fishery is planned for a year ahead, with the aim to distribute the various quotas held for different species in a way that they are used when both fish (or shrimp) abundance and market conditions are good. Prices tend to fall when fish abundance is highest (in January to March, Asche et al 2015). In theory, the trawler could land its entire cod quota in a couple of weeks, when the fish has the lowest economic value but at very low fuel costs; the company would then get a lower value out of the limited quota and not be able to fish during the rest of the year. The importance of onboard decision-making for the economic performance of the fishery was shown by Asche et al. (2015) who studied detailed data for the northernmost management region in Norway. Documenting the fuel use and catch rate frequently together with notes on special circumstances that may have affected the fuel efficiency (e.g., weather conditions or if the trawler was contracted by the Fisheries Directorate and for what purpose) would be very useful. This information would improve the possibilities to understand the difference in resulting fuel efficiencies and for the skipper to predict the costs and benefits of, e.g., moving to a new fishing location.

From a fisheries management viewpoint, fuel use should be monitored as part of the management framework due to the importance of fuel use both for environmental and economic performance of fisheries. The skippers reported that they sometimes go out “searching” for certain species at the end of the year, when some quotas have run out and the trawler only has quota left for a few species (Electronic Supplementary Material 2, question 10). This behavior is probably not fuel efficient and also risks contributing to larger amounts of bycatch, although this is difficult to track in the data reported here. In such instances, or in the case of an accidental overshoot of a quota, the skipper and owner of the vessel would prefer more flexibility in the management system, e.g., by allowing to transfer 10 % of a quota for a species between years (Electronic Supplementary Material 2, question 12). This is done in other countries with discard bans (Condie et al. 2014) and would enable more efficient use of the quotas overall. It is important to note that landing of a species that is classified as threatened (e.g., beaked redfish) or data-limited (e.g., Greenland halibut and wolffish) is certainly mandated by the management framework, with some species having recommended quotas by ICES, despite a lack of proper stock assessment. Unfortunately, this does not necessarily imply that it is sustainable, which is confusing both for fishers (who feel they are following scientific advice) and for consumers who think that what they find in the market should be alright. Therefore, there is no way around improving the data availability for all major commercially used stocks through stock assessment, defining biological reference points and then following them. Also, quota allocation favoring the most efficient actors and spatial/temporal limitations taking the resource use of the fishery into account would lead to improvement.

The improvement options found are in summary: For fishing companies, more detailed documentation of resource use would lead to increased insights of the importance of daily decision-making on the vessel on environmental and economic performance. Increased use of by-products would improve environmental performance of the products delivered by the trawler. The annual strategic planning could take into account experiences gained in previous years to optimize the fishery not only from an economic but also from an environmental point of view. Improved data availability would enable evaluation of sustainability of exploitation levels of fish stocks that currently are in the data-limited category. This must be the responsibility of managing authorities. These authorities should also aim for broader evaluation of fisheries, including life cycle considerations such as energy use and emissions, to avoid shifting burdens from one type of environmental impact to another. Allocating quotas and designing technical, spatial, and temporal regulations with these aspects in mind represent major improvement potentials for fisheries.

For seafood customers on any level (from processing company to end consumer), the first option is to ask for information about the environmental performance of products; the next one is to ask for improvement and to choose the lowest impact products, or to choose the supplier that has the most credible sourcing strategy.

5 Conclusions

Standard LCA results were highly dominated by fuel use. Resource use and impacts varied considerably within a year, but no significant differences were found between years. The resource efficiency, including fuel efficiency, mainly depended on the catch rate. Trips targeting fish species were more fuel efficient than those targeting shrimp, due to higher catch rate when targeting fish. Results indicated that steaming between fishing locations had a higher impact on fuel efficiency of the fishing trip than steaming to and from the port, which was also related to the catch rate. Tradeoffs between bycatch impacts and fuel use were demonstrated, using a hierarchic approach for assessment of biological impacts of fishing. The landings of the main target species of the trawler almost exclusively followed the MSY framework. The proportion of landings of threatened fish species was in general low, while that of data-limited species was higher. It is important that biological reference points are defined for all stocks fished commercially and to ensure that these reference points are followed. Assessing biological impacts in this way can be a way to demonstrate that a fishery causes low impacts as well as demonstrate improvement over time. Increased awareness about the importance of these choices both in daily decision-making onboard and in annual strategic planning can guide changes towards increased performance. Due to its importance both for economic and environmental sustainability, fuel use per landing is an indicator that should be monitored as part of the management framework, which would benefit from incorporating life cycle considerations.

Footnotes

  1. 1.

    Products are defined by species (e.g., cod, haddock), size (e.g., cod 1–2.5 kg; cod 2.5–4 kg), processing forms (e.g., J-cut), and certified by the Marine Stewardship Council (MSC) or not certified.

Notes

Acknowledgements

We are most grateful to Jan-Roger Lerbukt and Håvard Sigvaldsen for opening up the “environmental accounting system” of the trawler Hermes for us and for taking the time to explain their fishery to us. The work was funded by EU FP7 project WhiteFish (Grant agreement 286141).

Supplementary material

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ESM 1 (DOCX 24 kb)
11367_2015_898_MOESM2_ESM.docx (22 kb)
ESM 2 (DOCX 22 kb)
11367_2015_898_MOESM3_ESM.docx (257 kb)
ESM 3 (DOCX 257 kb)

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Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Friederike Ziegler
    • 1
    Email author
  • Evelyne A. Groen
    • 2
  • Sara Hornborg
    • 1
  • Eddie A. M. Bokkers
    • 2
  • Kine M. Karlsen
    • 3
  • Imke J. M. de Boer
    • 2
  1. 1.Sustainable Food Production, Food and BioscienceSP Technical Research Institute of SwedenGothenburgSweden
  2. 2.Animal Production Systems GroupWageningen UniversityWageningenThe Netherlands
  3. 3.Norwegian Institute of Fisheries and Aquaculture Research (Nofima)TromsøNorway

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