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Selecting a subset of the commercial catch data for estimating catch per unit effort series for ling (Molva molva L.)
Fisheries Research 165 (2015) 115–120
Contents lists available at ScienceDirect
Fisheries Research journal homepage: www.elsevier.com/locate/fishres
Selecting a subset of the commercial catch data for estimating catch per unit effort series for ling (Molva molva L.) Kristin Helle a,∗ , Michael Pennington a , Nils-Roar Hareide b , Inge Fossen c a b c
Institute of Marine Research, PO Box 1870, Nordnes, 5817 Bergen, Norway Runde Environmental Centre, 6096 Runde, Norway Møreforsking Marin, PO Box 5075, 6021 Ålesund, Norway
a r t i c l e
i n f o
Article history: Received 10 December 2013 Received in revised form 5 December 2014 Accepted 7 December 2014 Handling Editor Prof. George A. Rose Keywords: Ling Mixed-species fishery Norwegian Red List Commercial data Estimating CPUE
a b s t r a c t Ling is an important species for the Norwegian longline fishery. Motivated by an apparent steep decline in a catch per unit of effort (CPUE) series for ling based on commercial longline data during the periods 1971–1993 and 2000–2003, ling was classified as Near Threatened and placed on the Norwegian Red List, which caused difficulties for the marketing of ling. To examine the validity of the conclusion that this CPUE series indicated that the ling stock was Near Threatened, we estimated CPUE series for the period 2000–2012 based on extensive logbook data available since 2000. The Norwegian longline fishery is a mixed-species fishery: therefore, we constructed three different CPUE series based on; (1) all catches including zeros, (2) selected catches that appeared to have target ling and (3) selected vessels for each year that seemed to have often targeted ling during that year. It was concluded that these CPUE series indicated that the abundance of ling in Norwegian waters has been fairly stable or increasing since at least 2000, and hence there was no compelling evidence that the abundance of ling was declining. Based on our analysis, ling was removed from the Norwegian Red List. Even though all three series indicated increasing abundance, their precision varied, and estimates of how much the ling stock has increased depended on the subset of the commercial data on which the CPUE series was based. © 2015 Elsevier B.V. All rights reserved.
1. Introduction Ling (Molva molva) has been fished by Norway for centuries, and the amount landed has been recorded since 1896 (Fig. 1). Approximately 65–70% of the commercial catch of ling is taken by vessels using demersal longlines, either as the target species or as bycatch (ICES, 2013). Although the fishery takes place from Rockall to the southern Barents Sea (Helle and Pennington, 2004), between 70 to 80 percent of the catch by Norwegian vessels is from the Norwegian Economic Zone (Fig. 2). Ling prefers hard seabeds, or sandy seabeds with large rocks. It inhabits depths that range from 60 to 1000 m, but is mainly found between 300 and 400 m (Pethon, 2005). It is believed that they occur alone or in small schools (Gordon et al., 1995). The maximum weight and length of a ling are about 40 kg and 2 m, respectively. Ling matures between five and seven years of age and may live for 30 years or more. The main spawning areas are between Scotland and Iceland, but ling also spawns along the Norwegian coast south
∗ Corresponding author. Tel.: +47 90959646. E-mail address: [email protected] (K. Helle). http://dx.doi.org/10.1016/j.fishres.2014.12.015 0165-7836/© 2015 Elsevier B.V. All rights reserved.
of Vesterålen (69◦ N) from April to June at depths between 100 and 300 m (Pethon, 2005). Ling feeds mainly on fish, but also on crustaceans, cephalopods, and echinoderms (Magnusson et al., 1997; Pethon, 2005). Fisheries independent scientific surveys do not cover the deep water habitats occupied by ling (Helle and Pennington, 2004). Consequently, to track the health of the stock it is necessary to develop indicators based on commercial data. For the Norwegian longline ling fishery, there are two sources of data for assessing the condition of the stock; the official landing statistics, and the logbook records collected by the Norwegian Directorate of Fisheries. The annual ling landings may reflect trends in the state of the stock, but this signal is confounded to a very large degree by changes in fleet size and pertinent fishery regulations, such as the quota for Northeast Arctic cod (Gadus morhua). Therefore, total landings may not be a good indicator of the condition of the ling stock. In particular, the longline fleet has experienced large changes in vessel and gear efficiency over the last 60 years (Bjordal and Løkkeborg, 1996). The early post-World War II fleet normally was comprised of wooden boats 15–21 m long, which had limited range and little storage capacity. During the 1960s, the fleet gradually shifted to larger steel boats which increased
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K. Helle et al. / Fisheries Research 165 (2015) 115–120
Fig. 1. Reported Norwegian landings of ling for the period 1896–2012.
the range of the fishery. These boats only fished during the day, and they all used hand-baited lines (Arnt Leinebø, longline skipper, personal communication). The vessel efficiency increased greatly in 1977 due to the introduction of autolines, which are longlines that are automatically baited, and by the end of the 1980s the fleet consisted of about 53 vessels larger than 21 m, and about 95% of the vessels were equipped with autolines (Magnusson et al.,
1997). From 1977, the year autolines were introduced, the number of vessels in the fleet increased continuously until 2000, and since 2000 all vessels used autolines. There have been no significant changes in fishing techniques or vessel characteristics since 2000 (ICES, 2013). Because of the large and increasing longline fleet, the fishery authorities, and even the fishers, were concerned that the fishing pressure would become too great, especially on cod and haddock, but also on ling and tusk. In addition, the fishers were concerned that a large and increasing longline fleet would reduce the overall profitability of the fleet. Therefore, regulations limiting the total number of quotas were introduced in 2000 that resulted in the reduction of the number of boats in the fleet from 72 in 2000 to 35 in 2006. Since 2006 the size of the fleet has been rather stable (Table 1). To assess the effect of the new regulations on abundance trends of ling and tusk, the Institute of Marine Research (IMR), in cooperation with the Norwegian Directorate of Fisheries, decided in 2003 to develop a more precise ling CPUE series based on fishers’ logbooks to monitor the stocks more closely. Further concerns were raised in 2006 when a CPUE series for ling was brought to the attention of the International Council for the Exploration of the Sea (ICES) that indicated that the abundance of ling had declined sharply during the period 1971 through 1993
Fig. 2. Norwegian Red List’s designated marine Ecoregions in the Norwegian coastal Exclusive Economic Zone (map is adapted from Lindgaard and Henriksen, 2011).
K. Helle et al. / Fisheries Research 165 (2015) 115–120
117
Table 1 Summary statistics for the data sets used to estimate three CPUE series for ling. The three data sets are: all the data including zero catches of ling; selected catches that contained more than 30% ling by weight; and selected vessels that caught ling on 100 days or more during the year, zeros not included. Northern stratum Year
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 Average
Total number of vessels
Number of vessels sampled
72 65 58 52 43 39 35 38 36 34 35 37 36 45
All data
Selected catches
Selected vessels
Total number of sets (n)
Number of sets with ling (m)
Number of vessels
Number of sets
Number of vessels
Number of sets
31 30 25 23 21 15 14 21 17 9 3 35 36 22
1916 2196 2073 1839 1389 1247 1252 2103 1855 926 417 4451 3360 1925
1064 1352 1345 925 643 775 928 1334 1039 655 303 2565 2016 1150
25 24 22 19 15 13 14 17 14 9 3 30 24 18
188 202 334 344 261 370 326 456 343 314 105 777 627 357
10 12 11 11 11 10 10 15 13 9 1 20 19 12
507 714 785 583 446 647 812 1151 1008 655 193 2015 1473 845
Total number of vessels
Number of vessels sampled
All data
72 65 58 52 43 39 35 38 36 34 35 37 36 45
22 20 19 15 10 8 12 15 12 8 1 22 20 14
Southern stratum Year
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 Average
Selected catches
Selected vessels
Total number of sets (n)
Number of sets with ling (m)
Number of vessels
Number of sets
Number of vessels
Number of sets
685 738 664 510 439 331 672 587 471 547 15 736 933 564
669 729 652 505 439 328 672 586 467 547 15 736 933 560
17 17 17 13 9 8 12 14 10 7 1 19 19 13
497 556 546 423 374 309 618 471 366 504 5 546 736 458
10 10 10 9 9 7 11 11 10 8 1 15 14 10
472 583 520 444 390 315 638 518 445 547 15 583 723 476
(ICES, 2006), which motivated ICES to recommend that the fishing pressure on ling be reduced (ICES, 2006). For the years 2000–2003, the IMR’s logbook-based CPUE series seemed to support the view that the ling stock was in poor shape (ICES, 2006). Consequently, in 2006 ling was categorized as Near Threatened and placed on the Norwegian Red List (Kålås et al., 2006). This had severe, continuing consequences for the marketing of ling, especially in the important Swedish market, since in Sweden, the large food chains have very strict ethical rules that forbid marketing products that are Red Listed or reported as threatened by the WWF’s seafood guide (http://wwf.panda.org). The Norwegian fishers protested the Red-Listing of ling by Norway. Their perception was that the ling stock was in good condition in Norwegian waters, and they argued that there were serious errors in the methods used to assess the condition of the ling stock. A species can only be put on the Norwegian Red List based on its condition in the Norwegian Economic Zone. Using the logbook records, the aim of this study was to examine the validity of the fishers’ belief that there was no strong evidence that the ling stock was threatened in Norwegian waters, and consequently this species should not be included on the Norwegian Red List. Since the longline fishery is a mixed-species fishery, and ling was not always the target species, three CPUE series for ling were constructed; one based on all the data, a CPUE series based only on selected catches that appeared to have targeted ling and lastly, a series based on selected vessels that seemed often to have targeted ling during a year.
2. Materials, methods and data selection 2.1. Data sources and stratification The Norwegian Directorate of Fisheries provided the logbook records for longliners in the fleet that were longer than 21 m and had a total landings of ling, tusk (Brosme brosme), and blue ling (Molva dipterygia) greater than 8 tons in a given year. These data included the total daily catch of all commercial species, where the vessel was fishing, and the number of hooks set each day. The number of logbooks collected varied considerably over the time series from around 20% to almost 100% of the longline vessels. From 2000 to 2010 only handwritten logbooks were available, while from 2011 all vessels delivered electronic logbooks. In 2010, a transition year, comparably few hand written logbooks were submitted because they had also been submitted electronically. However, because of data quality concerns, the electronically recorded logbook data were never released by the Norwegian Directorate of Fisheries. The Norwegian Red List is based on the International Union for Conservation of Nature criteria, and a species can only be put on the Norwegian Red List if it is spawning in Norwegian waters and its abundance is judged to be decreasing. To analyze abundance trends, the ling fishery in Norwegian waters was divided into two strata: a northern stratum covering the central Norwegian coast and southern stratum that covers the southern component of the fishery (Fig. 2). These two strata correspond with the Norwegian Sea Ecoregion and the North Sea Ecoregion along the coast of Norway
K. Helle et al. / Fisheries Research 165 (2015) 115–120
2.2. All catches, selected catches or selected vessels At a meeting with scientists at the IMR in 2007, representatives of the Norwegian longline fleet suggested that a CPUE series for ling in Norwegian waters, which was based on all catches, would include catches whether or not ling was targeted. Unfortunately, the logbooks do not record the target species. Therefore, the longline fishers recommended that the CPUE series for ling should be based only on those daily catches for which ling were more than 30% (in weight) of the total catch. In this paper, all the data and two subsets of the data were used to generate CPUE series for ling. The first series was based on all the data, which included a large proportion of catches that did not contain ling (Table 1). The second data set was the one suggested by the longline fishers; to include only sets that seem to have had targeted ling, that is those catches containing 30% or more by weight of ling. For the second data subset, rather than select individual catches that are deemed to have targeted ling, we have selected longline vessels that appear to have often targeted ling in a particular year (Maunder and Punt, 2004). The longline vessels for each year were selected based on the number of days a vessel caught ling, which was motivated by the following observation. In Fig. 3 are graphs of the average catch of ling per day versus the number of days a vessel caught ling during a year (all years included). For vessels that caught ling on less than 100 days during a year, the average catch per vessel was significantly correlated (Pr = 0.00) with the number of days a vessel caught ling (Fig. 3, upper pane), while there was no significant correlation (Pr = 0.47) for vessels that caught ling on a 100 or more days (Fig. 3, lower pane). Since if vessels were actually “surveying” the same segment of the ling population, then the average daily catch per vessel should not increase with “sample size” (i.e., days fished). Based on this analogy with an actual scientific survey, it was decided to estimate a CPUE series for ling based only on the subset of vessels that caught ling on 100 or more days during a year. Zero catches of ling by the selected vessels were not included in the third data set (Table 1). 2.3. Calculating CPUE series Three CPUE series for ling, each based on one of the three data sets, were estimated using a generalized linear model (GLM; see, for example, McCulloch and Searle, 2001; Bishop et al., 2004; Maunder and Punt, 2004; Venables and Dichmont, 2004; Yu et al., 2011). In particular, the model yi,j,k,l = c + i + ˛j + ˇk + ei,j,k,l
(1)
was found to be appropriate where: yi,j,k,l is the catch (kg) per hook in year i, month j for set l by vessel k; c is a constant; i , i = 2000–2012, is the year effect; ˛j , j = 1–12, is the month effect; ˇk is the vessel effect, k depends on the data set; and ei,j,k,l is the error term. Since data set one (all data, Table 1) contains a large proportion of zeros, the GLM model (1) was combined using the delta method (Pennington, 1983; Stefánsson, 1996; Maunder and Punt, 2004).
140 120
Catch per hook (x1000)
(Lindgaard and Henriksen, 2011). The official Norwegian landing statistics show that on average 72%, of the total catch in Norwegian waters was from the northern stratum, while on average 28%, was from the southern stratum. Less than 1% of the catch was taken in the Barents Sea Ecoregion (Fig. 2). There are no ling quotas for Norwegian longliners, only a licensing scheme that limits the total number of longline vessels set by the Norwegian Directorate of Fisheries.
100 80 60 40 20 0 0
20
40 60 80 Number of days fished
100
130
160 190 220 Number of days fished
100
140 120
Catch per hook (x1000)
118
100 80 60 40 20 0 250
Fig. 3. The average catch per hook of ling for each day by a vessel versus the number of days the vessel caught ling during a year: for vessels that caught ling on less than a hundred days during a year (upper pane), and for vessel that caught ling on 100 or more days during a year (lower pane). The plots are for all years combined.
That is the estimator of the year effect, i based on all the data is given by ˆi =
m ˆ , n i
(2)
where m is the number of catches of ling greater than zero, n is the total number of sets and ˆ i is the year effect based on model (1) using only the m positive catches. If the number of zeros is statistically independent of ˆ i and the distribution of zeros is assumed to be binomial, then the variance estimator of ˆ i is given by (Pennington, 1983, 1996)
var ˆi =
m (n − m) 2 m (m − 1) ˆ i . var ˆ i + 2 n (n − 1) n (n − 1)
(3)
The estimated year effects, based on either selected catches or selected vessels, were based on model (1) since the zero catches of ling were not included in either data set. Finally, the three estimated CPUE series for ling were: ˆi + ˆ i + cˆ , for the CPUE (m/n)ˆc , for the series based on all the data; and series based on the two subsets of data. Tukey’s HSD (honestly significant difference) test was used to evaluate if any two CPUEs in a series were significantly different. The confidence levels for Tukey’s HSD test takes into account that all possible pairs may be compared (see, for example, Box et al., 1978). Since the mean level of each series was a function of the data set employed, the relative standard error (rse) was used to compare the precision of each series.
K. Helle et al. / Fisheries Research 165 (2015) 115–120
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Fig. 4. Three estimated CPUE series for ling along with 95% confidence intervals for both the northern and southern strata based on: all data, including zeros; selected catches (catches containing more 30% of ling by weight); and selected vessels (vessels that caught ling on a 100 or more days during a year).
3. Results The graphs of the estimated CPUE series for both the northern and southern strata, based on all the data and the two subsets of the catch data, along with the estimated 95% confidence intervals are in Fig. 4. The average of the yearly relative standard error for
Table 2 The average CPUE for the first four years of the three series (2000–2003) and for the last four years (2009–2012). The estimated increases for all series in both strata were significant at the 95% confidence level. Average CPUE 2000–2003
Average CPUE 2009–2012
Change in %
Northern stratum All data Selected catches Selected vessels
12.1 45.9 24.9
27.8 78.9 41.1
129 72 65
Southern stratum All data Selected catches Selected vessels
26.0 46.3 36.4
62.3 86.2 73.1
139 86 100
the three methods for estimating a CPUE series were: 7.3%, for the estimates based on all the data; 4.8% for the selected catches data set; and 5.6% for the selected vessels data set. The averages did not include estimated relative error for 2010 since the CPUE estimates for 2010 were all based on a small amount of data (Table 1). The changes in the average CPUE indices from the first four years (2000–2003) until the last four years (2009–2012) are summarized and compared in Table 2. Based on Tukey’s HSD test, each pair of CPUE estimates one from each of the two time periods (for example, the 2001 CPUE and the 2011 CPUE from the series based on all the data in the northern stratum) were significantly higher at the 95% confidence level, except for some of the paired comparisons with the very imprecise 2010 CPUE estimates in the southern stratum. Based also on Tukey’s test, the average increase between the two time periods was significant higher at the 95% level for all three series in both strata. 4. Discussion and conclusions The three CPUE series for ling, one based on all the data and two others based on whether ling appeared to be targeted based
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K. Helle et al. / Fisheries Research 165 (2015) 115–120
on each set or whether a vessel seemed to target ling often during a year, resulted in CPUE series that generally showed similar trends (Fig. 4). In particular, all three series indicated that the abundance of ling in Norwegian waters may have been significantly higher in the period 2009–2012 than it was in 2000–2003 (Table 2). The major and important difference was that the CPUE series based on all the data indicated a much greater increase in abundance in both strata than either the catch selected or vessel selected series (Table 2). This apparent divergence supports the observation that different perceptions of the state of a stock can be formed from different CPUE series for the same stock, depending on whether the series is based on all the catches, or only on those in which the relevant species were targeted (Biseau, 1998; Girard et al., 2000). It also should be noted that the CPUE series based on selected catches was the most precise on average (avg. rse = 4.8%) even though it was based on many fewer catches (Table 1) than were the CPUE series based on selected vessels (avg. rse = 5.6%), or the series based on all the data (avg. rse = 7.3%). The Norwegian longline fleet has been rather homogenous over the last 15 years; however, there have been some changes, such as different hook types and baiting machine upgrades, none of which appeared to affect the CPUE estimates. For example, the greatest change was that the average number of hooks set per day has increased from 31 000 in 2000 to 37 000 in 2012, while the average catch versus the number of hooks set increased linearly, that is the average catch per hook did not depend on the number of hooks set (ICES, 2013). In 2010, ICES concluded that the ling CPUE series from the 1970s, which indicated a sharp decline in the abundance of ling, was flawed and, therefore, unreliable (ICES, 2010). Consequently, our results showing a stable or increasing CPUE along with the fact that there was not any downward trend in landings (Fig. 1), suggest that ling was not being over fished in Norwegian waters. This changed the perception of the status of the ling stock (ICES, 2010), and as a result, ling was removed from the Norwegian Red List in 2010 (Kålås et al., 2010). As always, it should be emphasized that commercial catch data are typically observational data; that is, there was no scientific control on how or from where the data were collected. Therefore, the level of uncertainty associated with any conclusions based on observational data is often unknowable (see, for example, Rosenbaum, 2002). Acknowledgements We thank Arnt Leinebø for providing information on the history and technical details of the Norwegian longline fishery. We also thank the boat owners who provided logbooks, Ellen Sophie Thobro for diligently and accurately entering the logbook data, Karen Gjertsen for the map of the fishing area, Trond Havelin for providing us
with the official data for the longline ling fishery and three anonymous reviewers for their valuable comments and suggestions. We also want to thank The Norwegian Seafood Research Fund—FHF for its generous financial support (Project Number 900669). References Biseau, A., 1998. Definition of fishing effort in a mixed-species trawl fishery, and its impact on stock assessment. Aquat. Living Resour. 11, 119–137. Bishop, J., Venables, W.N., Wang, Y., 2004. Analysing commercial catch and effort data from a Panaeid trawl fishery: a comparison of linear models, mixed models, and generalised estimating equations approaches. Fish. Res. 70, 179–193. Bjordal, Å., Løkkeborg, S., 1996. Longlining. Fishing News Books. Blackwell Science Ltd, Oxford UK, pp. 156 pp. Box, G.E.P., Hunter, W.G., Hunter, J.S., 1978. Statistics for Experimenters. John Wiley, New York, NY (653 pp.). Girard, M., Lorance, P., Biseau, A., 2000. Catch per unit effort of deep-sea species off the continental slope to the west of British Isles. Cybium 24, 97–104. Gordon, J.D.M., Merrett, N.R., Haedrich, R.L., 1995. Environmental and biological aspects of slope-dwelling fishes of the North Atlantic. In: Hopper, A.G. (Ed.), Deep-water Fisheries of the North Atlantic Oceanic Slope. Kluwer Academic Publishers, Amsterdam, pp. 1–26. Helle, K., Pennington, M., 2004. Survey design considerations for estimating the length composition of the commercial catch of some deep-water species in the Northeast Atlantic. Fish. Res. 70, 55–60. ICES, 2006. Report of the Working Group on the Biology and Assessment of Deep-sea Fisheries Resources. International Council for Exploration of the Sea Document CM 2006/ACFM: 28. ICES (504 pp.). ICES, 2010. Report of the Working Group on the Biology and Assessment of Deep-Sea Fisheries Resources (WGDEEP). ICES CM 2010/ACOM: 14 (504 pp.). ICES, 2013. Report of the Working Group on the Biology and Assessment of Deep-sea Fisheries Resources (WGDEEP). ICES CM 2013/ACOM: 17 (640 pp.). Kålås, J.A., Viken, Å., Bakken, T. (Eds.), 2006. The 2006 Norwegian Red List. Artsdatabanken, Norway (416 pp.). Kålås, J.A., Viken, Å., Henriksen, S., Skjelseth, S. (Eds.), 2010. The 2010 Norwegian Red List for Species. Norwegian Biodiversity Information Centre, Norway (480 pp.). Lindgaard, A., Henriksen, S. (Eds.), 2011. The 2011 Norwegian Red List for Ecosystems and Habitat Types. Norwegian Biodiversity Information Centre, Trondheim (119 pp.) http://www.biodiversity.no/Article.aspx?m=315&amid=12267. Magnusson, J.V., Bergstad, O.A., Hareide, H.-R., Magnusson, J., Reinert, J., 1997. Ling, blue ling and tusk of the Northeast Atlantic Nordic Council of Ministers. TemaNord 535 (64 pp.). Maunder, M.N., Punt, A.E., 2004. Standardizing catch and effort data: a review of recent approaches. Fish. Res. 70, 141–159. McCulloch, C.E., Searle, R., 2001. Generalized, Linear and Mixed Models. Wiley, New York. Pennington, M., 1983. Efficient estimators of abundance, for fish and plankton surveys. Biometrics 39, 281–286. Pennington, M., 1996. Estimating the mean and variance from highly skewed marine survey data. Fish. Bull. 94, 498–505. Pethon, P., 2005. Aschehougs Store Fiskebok (in Norwegian). H. Aschehoug & Co, Oslo (448 pp.). Rosenbaum, P.R., 2002. Observational Studies, second ed. Springer-Verlag, New York, NY (377 pp.). Stefánsson, G., 1996. Analysis of groundfish survey abundance data: combining the GLM and delta approaches. ICES J. Mar. Sci. 53, 577–588. Venables, W.N., Dichmont, C.M., 2004. GLMs, GAMs and GLMMs: an overview of theory for applications in fisheries research. Fish. Res. 70, 319–337. Yu, H., Jiao, Y., Winter, A., 2011. Catch-rate standardization for Yellow Perch in Lake Erie: a comparison of the spatial generalized linear model and the generalized additive model. Trans. Am. Fish. Soc. 140 (4), 905–918.
Contents lists available at ScienceDirect
Fisheries Research journal homepage: www.elsevier.com/locate/fishres
Selecting a subset of the commercial catch data for estimating catch per unit effort series for ling (Molva molva L.) Kristin Helle a,∗ , Michael Pennington a , Nils-Roar Hareide b , Inge Fossen c a b c
Institute of Marine Research, PO Box 1870, Nordnes, 5817 Bergen, Norway Runde Environmental Centre, 6096 Runde, Norway Møreforsking Marin, PO Box 5075, 6021 Ålesund, Norway
a r t i c l e
i n f o
Article history: Received 10 December 2013 Received in revised form 5 December 2014 Accepted 7 December 2014 Handling Editor Prof. George A. Rose Keywords: Ling Mixed-species fishery Norwegian Red List Commercial data Estimating CPUE
a b s t r a c t Ling is an important species for the Norwegian longline fishery. Motivated by an apparent steep decline in a catch per unit of effort (CPUE) series for ling based on commercial longline data during the periods 1971–1993 and 2000–2003, ling was classified as Near Threatened and placed on the Norwegian Red List, which caused difficulties for the marketing of ling. To examine the validity of the conclusion that this CPUE series indicated that the ling stock was Near Threatened, we estimated CPUE series for the period 2000–2012 based on extensive logbook data available since 2000. The Norwegian longline fishery is a mixed-species fishery: therefore, we constructed three different CPUE series based on; (1) all catches including zeros, (2) selected catches that appeared to have target ling and (3) selected vessels for each year that seemed to have often targeted ling during that year. It was concluded that these CPUE series indicated that the abundance of ling in Norwegian waters has been fairly stable or increasing since at least 2000, and hence there was no compelling evidence that the abundance of ling was declining. Based on our analysis, ling was removed from the Norwegian Red List. Even though all three series indicated increasing abundance, their precision varied, and estimates of how much the ling stock has increased depended on the subset of the commercial data on which the CPUE series was based. © 2015 Elsevier B.V. All rights reserved.
1. Introduction Ling (Molva molva) has been fished by Norway for centuries, and the amount landed has been recorded since 1896 (Fig. 1). Approximately 65–70% of the commercial catch of ling is taken by vessels using demersal longlines, either as the target species or as bycatch (ICES, 2013). Although the fishery takes place from Rockall to the southern Barents Sea (Helle and Pennington, 2004), between 70 to 80 percent of the catch by Norwegian vessels is from the Norwegian Economic Zone (Fig. 2). Ling prefers hard seabeds, or sandy seabeds with large rocks. It inhabits depths that range from 60 to 1000 m, but is mainly found between 300 and 400 m (Pethon, 2005). It is believed that they occur alone or in small schools (Gordon et al., 1995). The maximum weight and length of a ling are about 40 kg and 2 m, respectively. Ling matures between five and seven years of age and may live for 30 years or more. The main spawning areas are between Scotland and Iceland, but ling also spawns along the Norwegian coast south
∗ Corresponding author. Tel.: +47 90959646. E-mail address: [email protected] (K. Helle). http://dx.doi.org/10.1016/j.fishres.2014.12.015 0165-7836/© 2015 Elsevier B.V. All rights reserved.
of Vesterålen (69◦ N) from April to June at depths between 100 and 300 m (Pethon, 2005). Ling feeds mainly on fish, but also on crustaceans, cephalopods, and echinoderms (Magnusson et al., 1997; Pethon, 2005). Fisheries independent scientific surveys do not cover the deep water habitats occupied by ling (Helle and Pennington, 2004). Consequently, to track the health of the stock it is necessary to develop indicators based on commercial data. For the Norwegian longline ling fishery, there are two sources of data for assessing the condition of the stock; the official landing statistics, and the logbook records collected by the Norwegian Directorate of Fisheries. The annual ling landings may reflect trends in the state of the stock, but this signal is confounded to a very large degree by changes in fleet size and pertinent fishery regulations, such as the quota for Northeast Arctic cod (Gadus morhua). Therefore, total landings may not be a good indicator of the condition of the ling stock. In particular, the longline fleet has experienced large changes in vessel and gear efficiency over the last 60 years (Bjordal and Løkkeborg, 1996). The early post-World War II fleet normally was comprised of wooden boats 15–21 m long, which had limited range and little storage capacity. During the 1960s, the fleet gradually shifted to larger steel boats which increased
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K. Helle et al. / Fisheries Research 165 (2015) 115–120
Fig. 1. Reported Norwegian landings of ling for the period 1896–2012.
the range of the fishery. These boats only fished during the day, and they all used hand-baited lines (Arnt Leinebø, longline skipper, personal communication). The vessel efficiency increased greatly in 1977 due to the introduction of autolines, which are longlines that are automatically baited, and by the end of the 1980s the fleet consisted of about 53 vessels larger than 21 m, and about 95% of the vessels were equipped with autolines (Magnusson et al.,
1997). From 1977, the year autolines were introduced, the number of vessels in the fleet increased continuously until 2000, and since 2000 all vessels used autolines. There have been no significant changes in fishing techniques or vessel characteristics since 2000 (ICES, 2013). Because of the large and increasing longline fleet, the fishery authorities, and even the fishers, were concerned that the fishing pressure would become too great, especially on cod and haddock, but also on ling and tusk. In addition, the fishers were concerned that a large and increasing longline fleet would reduce the overall profitability of the fleet. Therefore, regulations limiting the total number of quotas were introduced in 2000 that resulted in the reduction of the number of boats in the fleet from 72 in 2000 to 35 in 2006. Since 2006 the size of the fleet has been rather stable (Table 1). To assess the effect of the new regulations on abundance trends of ling and tusk, the Institute of Marine Research (IMR), in cooperation with the Norwegian Directorate of Fisheries, decided in 2003 to develop a more precise ling CPUE series based on fishers’ logbooks to monitor the stocks more closely. Further concerns were raised in 2006 when a CPUE series for ling was brought to the attention of the International Council for the Exploration of the Sea (ICES) that indicated that the abundance of ling had declined sharply during the period 1971 through 1993
Fig. 2. Norwegian Red List’s designated marine Ecoregions in the Norwegian coastal Exclusive Economic Zone (map is adapted from Lindgaard and Henriksen, 2011).
K. Helle et al. / Fisheries Research 165 (2015) 115–120
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Table 1 Summary statistics for the data sets used to estimate three CPUE series for ling. The three data sets are: all the data including zero catches of ling; selected catches that contained more than 30% ling by weight; and selected vessels that caught ling on 100 days or more during the year, zeros not included. Northern stratum Year
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 Average
Total number of vessels
Number of vessels sampled
72 65 58 52 43 39 35 38 36 34 35 37 36 45
All data
Selected catches
Selected vessels
Total number of sets (n)
Number of sets with ling (m)
Number of vessels
Number of sets
Number of vessels
Number of sets
31 30 25 23 21 15 14 21 17 9 3 35 36 22
1916 2196 2073 1839 1389 1247 1252 2103 1855 926 417 4451 3360 1925
1064 1352 1345 925 643 775 928 1334 1039 655 303 2565 2016 1150
25 24 22 19 15 13 14 17 14 9 3 30 24 18
188 202 334 344 261 370 326 456 343 314 105 777 627 357
10 12 11 11 11 10 10 15 13 9 1 20 19 12
507 714 785 583 446 647 812 1151 1008 655 193 2015 1473 845
Total number of vessels
Number of vessels sampled
All data
72 65 58 52 43 39 35 38 36 34 35 37 36 45
22 20 19 15 10 8 12 15 12 8 1 22 20 14
Southern stratum Year
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 Average
Selected catches
Selected vessels
Total number of sets (n)
Number of sets with ling (m)
Number of vessels
Number of sets
Number of vessels
Number of sets
685 738 664 510 439 331 672 587 471 547 15 736 933 564
669 729 652 505 439 328 672 586 467 547 15 736 933 560
17 17 17 13 9 8 12 14 10 7 1 19 19 13
497 556 546 423 374 309 618 471 366 504 5 546 736 458
10 10 10 9 9 7 11 11 10 8 1 15 14 10
472 583 520 444 390 315 638 518 445 547 15 583 723 476
(ICES, 2006), which motivated ICES to recommend that the fishing pressure on ling be reduced (ICES, 2006). For the years 2000–2003, the IMR’s logbook-based CPUE series seemed to support the view that the ling stock was in poor shape (ICES, 2006). Consequently, in 2006 ling was categorized as Near Threatened and placed on the Norwegian Red List (Kålås et al., 2006). This had severe, continuing consequences for the marketing of ling, especially in the important Swedish market, since in Sweden, the large food chains have very strict ethical rules that forbid marketing products that are Red Listed or reported as threatened by the WWF’s seafood guide (http://wwf.panda.org). The Norwegian fishers protested the Red-Listing of ling by Norway. Their perception was that the ling stock was in good condition in Norwegian waters, and they argued that there were serious errors in the methods used to assess the condition of the ling stock. A species can only be put on the Norwegian Red List based on its condition in the Norwegian Economic Zone. Using the logbook records, the aim of this study was to examine the validity of the fishers’ belief that there was no strong evidence that the ling stock was threatened in Norwegian waters, and consequently this species should not be included on the Norwegian Red List. Since the longline fishery is a mixed-species fishery, and ling was not always the target species, three CPUE series for ling were constructed; one based on all the data, a CPUE series based only on selected catches that appeared to have targeted ling and lastly, a series based on selected vessels that seemed often to have targeted ling during a year.
2. Materials, methods and data selection 2.1. Data sources and stratification The Norwegian Directorate of Fisheries provided the logbook records for longliners in the fleet that were longer than 21 m and had a total landings of ling, tusk (Brosme brosme), and blue ling (Molva dipterygia) greater than 8 tons in a given year. These data included the total daily catch of all commercial species, where the vessel was fishing, and the number of hooks set each day. The number of logbooks collected varied considerably over the time series from around 20% to almost 100% of the longline vessels. From 2000 to 2010 only handwritten logbooks were available, while from 2011 all vessels delivered electronic logbooks. In 2010, a transition year, comparably few hand written logbooks were submitted because they had also been submitted electronically. However, because of data quality concerns, the electronically recorded logbook data were never released by the Norwegian Directorate of Fisheries. The Norwegian Red List is based on the International Union for Conservation of Nature criteria, and a species can only be put on the Norwegian Red List if it is spawning in Norwegian waters and its abundance is judged to be decreasing. To analyze abundance trends, the ling fishery in Norwegian waters was divided into two strata: a northern stratum covering the central Norwegian coast and southern stratum that covers the southern component of the fishery (Fig. 2). These two strata correspond with the Norwegian Sea Ecoregion and the North Sea Ecoregion along the coast of Norway
K. Helle et al. / Fisheries Research 165 (2015) 115–120
2.2. All catches, selected catches or selected vessels At a meeting with scientists at the IMR in 2007, representatives of the Norwegian longline fleet suggested that a CPUE series for ling in Norwegian waters, which was based on all catches, would include catches whether or not ling was targeted. Unfortunately, the logbooks do not record the target species. Therefore, the longline fishers recommended that the CPUE series for ling should be based only on those daily catches for which ling were more than 30% (in weight) of the total catch. In this paper, all the data and two subsets of the data were used to generate CPUE series for ling. The first series was based on all the data, which included a large proportion of catches that did not contain ling (Table 1). The second data set was the one suggested by the longline fishers; to include only sets that seem to have had targeted ling, that is those catches containing 30% or more by weight of ling. For the second data subset, rather than select individual catches that are deemed to have targeted ling, we have selected longline vessels that appear to have often targeted ling in a particular year (Maunder and Punt, 2004). The longline vessels for each year were selected based on the number of days a vessel caught ling, which was motivated by the following observation. In Fig. 3 are graphs of the average catch of ling per day versus the number of days a vessel caught ling during a year (all years included). For vessels that caught ling on less than 100 days during a year, the average catch per vessel was significantly correlated (Pr = 0.00) with the number of days a vessel caught ling (Fig. 3, upper pane), while there was no significant correlation (Pr = 0.47) for vessels that caught ling on a 100 or more days (Fig. 3, lower pane). Since if vessels were actually “surveying” the same segment of the ling population, then the average daily catch per vessel should not increase with “sample size” (i.e., days fished). Based on this analogy with an actual scientific survey, it was decided to estimate a CPUE series for ling based only on the subset of vessels that caught ling on 100 or more days during a year. Zero catches of ling by the selected vessels were not included in the third data set (Table 1). 2.3. Calculating CPUE series Three CPUE series for ling, each based on one of the three data sets, were estimated using a generalized linear model (GLM; see, for example, McCulloch and Searle, 2001; Bishop et al., 2004; Maunder and Punt, 2004; Venables and Dichmont, 2004; Yu et al., 2011). In particular, the model yi,j,k,l = c + i + ˛j + ˇk + ei,j,k,l
(1)
was found to be appropriate where: yi,j,k,l is the catch (kg) per hook in year i, month j for set l by vessel k; c is a constant; i , i = 2000–2012, is the year effect; ˛j , j = 1–12, is the month effect; ˇk is the vessel effect, k depends on the data set; and ei,j,k,l is the error term. Since data set one (all data, Table 1) contains a large proportion of zeros, the GLM model (1) was combined using the delta method (Pennington, 1983; Stefánsson, 1996; Maunder and Punt, 2004).
140 120
Catch per hook (x1000)
(Lindgaard and Henriksen, 2011). The official Norwegian landing statistics show that on average 72%, of the total catch in Norwegian waters was from the northern stratum, while on average 28%, was from the southern stratum. Less than 1% of the catch was taken in the Barents Sea Ecoregion (Fig. 2). There are no ling quotas for Norwegian longliners, only a licensing scheme that limits the total number of longline vessels set by the Norwegian Directorate of Fisheries.
100 80 60 40 20 0 0
20
40 60 80 Number of days fished
100
130
160 190 220 Number of days fished
100
140 120
Catch per hook (x1000)
118
100 80 60 40 20 0 250
Fig. 3. The average catch per hook of ling for each day by a vessel versus the number of days the vessel caught ling during a year: for vessels that caught ling on less than a hundred days during a year (upper pane), and for vessel that caught ling on 100 or more days during a year (lower pane). The plots are for all years combined.
That is the estimator of the year effect, i based on all the data is given by ˆi =
m ˆ , n i
(2)
where m is the number of catches of ling greater than zero, n is the total number of sets and ˆ i is the year effect based on model (1) using only the m positive catches. If the number of zeros is statistically independent of ˆ i and the distribution of zeros is assumed to be binomial, then the variance estimator of ˆ i is given by (Pennington, 1983, 1996)
var ˆi =
m (n − m) 2 m (m − 1) ˆ i . var ˆ i + 2 n (n − 1) n (n − 1)
(3)
The estimated year effects, based on either selected catches or selected vessels, were based on model (1) since the zero catches of ling were not included in either data set. Finally, the three estimated CPUE series for ling were: ˆi + ˆ i + cˆ , for the CPUE (m/n)ˆc , for the series based on all the data; and series based on the two subsets of data. Tukey’s HSD (honestly significant difference) test was used to evaluate if any two CPUEs in a series were significantly different. The confidence levels for Tukey’s HSD test takes into account that all possible pairs may be compared (see, for example, Box et al., 1978). Since the mean level of each series was a function of the data set employed, the relative standard error (rse) was used to compare the precision of each series.
K. Helle et al. / Fisheries Research 165 (2015) 115–120
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Fig. 4. Three estimated CPUE series for ling along with 95% confidence intervals for both the northern and southern strata based on: all data, including zeros; selected catches (catches containing more 30% of ling by weight); and selected vessels (vessels that caught ling on a 100 or more days during a year).
3. Results The graphs of the estimated CPUE series for both the northern and southern strata, based on all the data and the two subsets of the catch data, along with the estimated 95% confidence intervals are in Fig. 4. The average of the yearly relative standard error for
Table 2 The average CPUE for the first four years of the three series (2000–2003) and for the last four years (2009–2012). The estimated increases for all series in both strata were significant at the 95% confidence level. Average CPUE 2000–2003
Average CPUE 2009–2012
Change in %
Northern stratum All data Selected catches Selected vessels
12.1 45.9 24.9
27.8 78.9 41.1
129 72 65
Southern stratum All data Selected catches Selected vessels
26.0 46.3 36.4
62.3 86.2 73.1
139 86 100
the three methods for estimating a CPUE series were: 7.3%, for the estimates based on all the data; 4.8% for the selected catches data set; and 5.6% for the selected vessels data set. The averages did not include estimated relative error for 2010 since the CPUE estimates for 2010 were all based on a small amount of data (Table 1). The changes in the average CPUE indices from the first four years (2000–2003) until the last four years (2009–2012) are summarized and compared in Table 2. Based on Tukey’s HSD test, each pair of CPUE estimates one from each of the two time periods (for example, the 2001 CPUE and the 2011 CPUE from the series based on all the data in the northern stratum) were significantly higher at the 95% confidence level, except for some of the paired comparisons with the very imprecise 2010 CPUE estimates in the southern stratum. Based also on Tukey’s test, the average increase between the two time periods was significant higher at the 95% level for all three series in both strata. 4. Discussion and conclusions The three CPUE series for ling, one based on all the data and two others based on whether ling appeared to be targeted based
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on each set or whether a vessel seemed to target ling often during a year, resulted in CPUE series that generally showed similar trends (Fig. 4). In particular, all three series indicated that the abundance of ling in Norwegian waters may have been significantly higher in the period 2009–2012 than it was in 2000–2003 (Table 2). The major and important difference was that the CPUE series based on all the data indicated a much greater increase in abundance in both strata than either the catch selected or vessel selected series (Table 2). This apparent divergence supports the observation that different perceptions of the state of a stock can be formed from different CPUE series for the same stock, depending on whether the series is based on all the catches, or only on those in which the relevant species were targeted (Biseau, 1998; Girard et al., 2000). It also should be noted that the CPUE series based on selected catches was the most precise on average (avg. rse = 4.8%) even though it was based on many fewer catches (Table 1) than were the CPUE series based on selected vessels (avg. rse = 5.6%), or the series based on all the data (avg. rse = 7.3%). The Norwegian longline fleet has been rather homogenous over the last 15 years; however, there have been some changes, such as different hook types and baiting machine upgrades, none of which appeared to affect the CPUE estimates. For example, the greatest change was that the average number of hooks set per day has increased from 31 000 in 2000 to 37 000 in 2012, while the average catch versus the number of hooks set increased linearly, that is the average catch per hook did not depend on the number of hooks set (ICES, 2013). In 2010, ICES concluded that the ling CPUE series from the 1970s, which indicated a sharp decline in the abundance of ling, was flawed and, therefore, unreliable (ICES, 2010). Consequently, our results showing a stable or increasing CPUE along with the fact that there was not any downward trend in landings (Fig. 1), suggest that ling was not being over fished in Norwegian waters. This changed the perception of the status of the ling stock (ICES, 2010), and as a result, ling was removed from the Norwegian Red List in 2010 (Kålås et al., 2010). As always, it should be emphasized that commercial catch data are typically observational data; that is, there was no scientific control on how or from where the data were collected. Therefore, the level of uncertainty associated with any conclusions based on observational data is often unknowable (see, for example, Rosenbaum, 2002). Acknowledgements We thank Arnt Leinebø for providing information on the history and technical details of the Norwegian longline fishery. We also thank the boat owners who provided logbooks, Ellen Sophie Thobro for diligently and accurately entering the logbook data, Karen Gjertsen for the map of the fishing area, Trond Havelin for providing us
with the official data for the longline ling fishery and three anonymous reviewers for their valuable comments and suggestions. We also want to thank The Norwegian Seafood Research Fund—FHF for its generous financial support (Project Number 900669). References Biseau, A., 1998. Definition of fishing effort in a mixed-species trawl fishery, and its impact on stock assessment. Aquat. Living Resour. 11, 119–137. Bishop, J., Venables, W.N., Wang, Y., 2004. Analysing commercial catch and effort data from a Panaeid trawl fishery: a comparison of linear models, mixed models, and generalised estimating equations approaches. Fish. Res. 70, 179–193. Bjordal, Å., Løkkeborg, S., 1996. Longlining. Fishing News Books. Blackwell Science Ltd, Oxford UK, pp. 156 pp. Box, G.E.P., Hunter, W.G., Hunter, J.S., 1978. Statistics for Experimenters. John Wiley, New York, NY (653 pp.). Girard, M., Lorance, P., Biseau, A., 2000. Catch per unit effort of deep-sea species off the continental slope to the west of British Isles. Cybium 24, 97–104. Gordon, J.D.M., Merrett, N.R., Haedrich, R.L., 1995. Environmental and biological aspects of slope-dwelling fishes of the North Atlantic. In: Hopper, A.G. (Ed.), Deep-water Fisheries of the North Atlantic Oceanic Slope. Kluwer Academic Publishers, Amsterdam, pp. 1–26. Helle, K., Pennington, M., 2004. Survey design considerations for estimating the length composition of the commercial catch of some deep-water species in the Northeast Atlantic. Fish. Res. 70, 55–60. ICES, 2006. Report of the Working Group on the Biology and Assessment of Deep-sea Fisheries Resources. International Council for Exploration of the Sea Document CM 2006/ACFM: 28. ICES (504 pp.). ICES, 2010. Report of the Working Group on the Biology and Assessment of Deep-Sea Fisheries Resources (WGDEEP). ICES CM 2010/ACOM: 14 (504 pp.). ICES, 2013. Report of the Working Group on the Biology and Assessment of Deep-sea Fisheries Resources (WGDEEP). ICES CM 2013/ACOM: 17 (640 pp.). Kålås, J.A., Viken, Å., Bakken, T. (Eds.), 2006. The 2006 Norwegian Red List. Artsdatabanken, Norway (416 pp.). Kålås, J.A., Viken, Å., Henriksen, S., Skjelseth, S. (Eds.), 2010. The 2010 Norwegian Red List for Species. Norwegian Biodiversity Information Centre, Norway (480 pp.). Lindgaard, A., Henriksen, S. (Eds.), 2011. The 2011 Norwegian Red List for Ecosystems and Habitat Types. Norwegian Biodiversity Information Centre, Trondheim (119 pp.) http://www.biodiversity.no/Article.aspx?m=315&amid=12267. Magnusson, J.V., Bergstad, O.A., Hareide, H.-R., Magnusson, J., Reinert, J., 1997. Ling, blue ling and tusk of the Northeast Atlantic Nordic Council of Ministers. TemaNord 535 (64 pp.). Maunder, M.N., Punt, A.E., 2004. Standardizing catch and effort data: a review of recent approaches. Fish. Res. 70, 141–159. McCulloch, C.E., Searle, R., 2001. Generalized, Linear and Mixed Models. Wiley, New York. Pennington, M., 1983. Efficient estimators of abundance, for fish and plankton surveys. Biometrics 39, 281–286. Pennington, M., 1996. Estimating the mean and variance from highly skewed marine survey data. Fish. Bull. 94, 498–505. Pethon, P., 2005. Aschehougs Store Fiskebok (in Norwegian). H. Aschehoug & Co, Oslo (448 pp.). Rosenbaum, P.R., 2002. Observational Studies, second ed. Springer-Verlag, New York, NY (377 pp.). Stefánsson, G., 1996. Analysis of groundfish survey abundance data: combining the GLM and delta approaches. ICES J. Mar. Sci. 53, 577–588. Venables, W.N., Dichmont, C.M., 2004. GLMs, GAMs and GLMMs: an overview of theory for applications in fisheries research. Fish. Res. 70, 319–337. Yu, H., Jiao, Y., Winter, A., 2011. Catch-rate standardization for Yellow Perch in Lake Erie: a comparison of the spatial generalized linear model and the generalized additive model. Trans. Am. Fish. Soc. 140 (4), 905–918.