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Fisheries Research 140 (2013) 105– 113

Contents lists available at SciVerse ScienceDirect

Fisheries Research

jou rn al hom epage: www.elsev ier .com/ locate / f i shres

stimators of discards using fishing effort as auxiliary information with anpplication to Iberian hake (Merluccius merluccius) exploited by the Portugueserawl fleets

rnesto Jardima,b,∗, Ana Cláudia Fernandesa

L-IPIMAR, Instituto Nacional de Recursos Biológicos, Av. Brasilia, 1449-006 Lisboa, PortugalEuropean Commission, Joint Research Centre, Institute for Protection and Security of the Citizen, Maritime Affairs Unit G.04, TP 051, 21027 Ispra (VA), Italy

r t i c l e i n f o

rticle history:eceived 12 March 2012eceived in revised form 5 December 2012ccepted 6 December 2012

eywords:iscardsiscards weightstimatorishing effortack-knifenfluential observationsakerawlortuguese

a b s t r a c t

Estimating discards is an important issue for fisheries management so that total mortality caused by fish-ing is considered. In 2003, a sampling programme addressing Iberian hake discards by the Portuguesefleets was implemented according to the requirements of the European Commission Data CollectionRegulation (Reg. EC No 1543/2000). A preliminary analysis of the data collected showed trip and haulduration to be potential sources of bias, because their relationship to discards per unit effort (DPUE) wasnot linear. This paper develops estimators for discards by weight using fishing effort as auxiliary infor-mation, describes a jack-knife procedure to identify influential observations, applies the methodologiesto Iberian hake as a case study and compares the results obtained by each estimator. The estimatorstested are based on Cochran’s estimator of the total (Cochran, 1977). Four estimators are compared, eachone consisting of a combination of (i) weighting or not weighting the observations by fishing effort and(ii) post-stratifying or not post-stratifying the trips by duration in days-at-sea. A jack-knife analysis ofobservations’ influence on the final estimate is performed by year and fleet. The results indicate that theestimator that uses both a weighted DPUE and post-stratification by the trip duration performs best andis the most robust to influential observations. The estimates of hake discards increase with time, while

a stable but oscillating pattern is observed in the demersal fleet. Total hake discards show a fluctuatingtrend until 2009 and a large drop in 2010. The minimum discard estimates after removing influentialobservations was 795 t in 2006 and the maximum was 1956 t in 2009, with coefficients of variation (CV)between 22% and 74%. Estimates using the full data set were higher but had lower CVs. The observedtrend in discards agrees with the recruitment estimates by the Portuguese International Bottom TrawlSurvey.

© 2012 Elsevier B.V. All rights reserved.

. Introduction

Returning living or dead organisms to the sea (“discarding”)s considered a waste of resources and is thus inconsistent withesponsible fisheries (Kelleher, 2005). Recently, the Europeannion has included discards on the revision of the Commonisheries Policy (COM/2011/0425) aiming to reduce discarding

ractices.

Discarded fish represent losses that are not documented inandings statistics. Most discards are killed during capture and

∗ Corresponding author at: European Commission, Joint Research Centre, Instituteor Protection and Security of the Citizen, Maritime Affairs Unit G.04, TP 051, 21027spra (VA), Italy. Tel.: +39 0332 785311/+351 213 027 000.

E-mail addresses: ernesto.jardim@jrc.ec.europa.eu (E. Jardim),cfernandes@ipimar.pt (A.C. Fernandes).

165-7836/$ – see front matter © 2012 Elsevier B.V. All rights reserved.ttp://dx.doi.org/10.1016/j.fishres.2012.12.006

sorting (Kaiser and Spencer, 1995), so they represent real removalsfrom the fish stocks (Chen and Gordon, 1997). Several authorsargue that including discards in stock assessment improves theestimation of stock dynamics and exploitation status (Chen andGordon, 1997; Punt et al., 2006; Chen et al., 2007; Dickey-Collaset al., 2007; Fernández et al., 2010; Jardim et al., 2010). Thus,accounting for the fishing mortality that is caused by discarding isan important subject.

Information about discarding practices are typically collected byobservers via at-sea sampling programmes. The sampling design isoften based on some form of systematic or random sampling toensure exchangeability between observed and unobserved units(e.g. trips). However, whether the data collected in such programs

are representative of the whole fishery depends on the proportionof trips with observers and on the assumption that the discard pat-terns of observed trips are the same as that of unobserved trips(Chen and Gordon, 1997).

106 E. Jardim, A.C. Fernandes / Fisheries Research 140 (2013) 105– 113

Table 1The sampling effort of the Portuguese on-board programme for sampling discards in trawl fleets that target either crustaceans or demersal fish, OTB CRU and OTB DEF,respectively between 2004 and 2010.

Year Fleet activity Sampling levels

Trips Trips % Trips Hauls

OTB CRU OTB DEF OTB CRU OTB DEF OTB CRU OTB DEF OTB CRU OTB DEF

2004 2924 8086 17 24 0.6 0.3 111 1252005 2874 7375 15 39 0.5 0.5 74 1592006 2595 6098 7 42 0.3 0.7 30 1942007 2331 6827 12 38 0.5 0.6 73 1622008 2408 6482 12 34 0.5 0.5 66 128

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can

2009 2820 5467 16 38

2010 2290 4589 16 31

In Europe, because there is no legal obligation to take observersn board, most sampling programmes are voluntary on the partf the fishing industry. In such cases, fully random sampling maye difficult to achieve, especially if the industry perceives thathe outcomes of the sampling program may be disadvantageousnd a large part of the fleet therefore drops from the samplingrame.

Benoit and Allard (2009) categorise the factors leading to non-andom deployment of observers to fishing trips into “accounted”nd “unaccounted” sources. Accounted sources, such as duration ofaul, are better understood and can be addressed during data anal-sis by e.g. post-stratification. Unaccounted sources are difficult touantify because of a lack of information, e.g. the unwillingness ofhe crew to collaborate with observers. Both types can affect theuality of the sampling and introduce bias to the estimations ofiscards.

The case of the hake (Merluccius merluccius) fishery in Iberianaters may illustrate such problems. The fishery is composed of

ortuguese and Spanish fleets using trawls, gill and trammel netsr longlines along the Iberian coast in ICES sub-Divisions VIIIc andXa. During the 1990s, a set of regulations that included a minimumanding size of 27 cm and changes in mesh size were introduced.he set of regulations changed the way the fishing industry oper-ted, and discarding is believed to have started in the secondalf of the decade (Fernández et al., 2010; Borges et al., 2005).etween 2004 and 2010, strong year classes entered the fishery.he capture of these individuals, together with the enforcement ofhe minimum landing size regulations, were expected to increasehe discard of small individuals. In 2003 a sampling programmeddressing Iberian hake discards by the Portuguese fleets wasmplemented following the requirements of the European Com-

ission Data Collection Regulation (Reg. EC No 1543/2000), and in010 estimates of the discards were included in stock assessmentsAnon., 2010). The implementation of this programme had the sameroblems as other voluntary programmes in finding vessels thatere willing to participate, but funding constraints also limited the

ampling effort and deployment of observers. Clearly, there wereotential sources of bias due to the non-random deployment ofbservers, which was a major concern because discard mortalitytrongly influences the results of stock assessment (Fernández et al.,010; Jardim et al., 2010) and because discard practices have a neg-tive effect on public opinion. Furthermore, there were concernshat the potential bias could be exacerbated by the low sample size.atio estimators rely on scaling the average values to the total ofn auxiliary variable in order to compute totals. Therefore, theyecome very sensitive to unusual observations when the sampleize is small.

The work presented here suggests methods to estimate the dis-ards by weight using effort as a scaling variable. Four methodsre considered, each consisting of a combination of (i) weighting orot weighting observations by fishing effort and (ii) post-stratifying

0.6 0.7 84 1350.7 0.7 103 116

or not post-stratifying fishing trips by duration in days-at-sea.Additionally, the estimator’s robustness to influential observationsis assessed using a jack-knife analysis.

2. Materials

To apply the methods developed and compare the resulting esti-mates, we used data collected by the Portuguese on-board samplingprogramme of Iberian hake discards in the Portuguese trawl fleetsbetween 2004 and 2010.

During this period, the sampling programme focused on thetrawl fleets that were operating off the continental shelf. There aretwo major fleets, one targeting crustaceans and operating in theSouthern area with cod-end mesh sizes of 55–59 mm or >70 mm,and another targeting fish and operating off the continental coastalarea with cod-end mesh sizes of 65–69 mm or >70 mm.

The data were collected by the on-board sampling programme ofthe Portuguese Institute for Biological Resources (INRB/L-IPIMAR).The programme uses a stratified random sampling design withfleet, quarter and region as strata, and fishing trip as the samplingunit. The sampling frame included cooperative commercial vesselswith an overall length between 12 and 40 m. Each vessel may besampled more than once per year. Two observers were deployedper trip to collect information about fishing activity according tothe following sampling protocol for each haul:

1. register general information, date, time, location, description ofthe sea floor, trawl speed and mesh size;

2. collect a sample of the unsorted catch on the deck, three boxesof approximately 12 kg taken from different places;

3. sort the sample according to the crew’s criteria about whichspecies are retained;

4. identify, measure and weigh all specimens (both retained anddiscarded);

5. collect information from the skipper about the weight of theretained species.

For each haul, the discard weight of each species is computedusing the ratio of the mass of all retained species to the mass ofretained species in the sample. At the trip level, discards by speciesare computed by summing over hauls. If some hauls were notsampled due to operational constraints, the observed discards arescaled to the trip level using the value of discards by weight perhour (DPUE) and the ratio between the trip’s effort and effort of theobserved hauls. For more information see Anon. (2011a). Samplinglevels for the period 2004–2010 are presented in Table 1 togetherwith the total effort deployed by each fleet.

Auxiliary information about fishing effort was collected fromlogbooks (haul duration in h) and auction information (number offishing trips per vessel), both of which were provided by the Por-tuguese Directorate-General for Fisheries and Aquaculture (DGPA).

E. Jardim, A.C. Fernandes / Fisheries Research 140 (2013) 105– 113 107

F r as a

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ig. 1. Iberian hake discards of the Portuguese trawl fleets by weight (kg) per houhe upper panel presents raw data while the bottom panel highlights trends and pr

he DGPA logbooks are recorded from the vessel’s originals. Thenformation from auctions is registered by the State-owned com-any that manages auctions markets, ‘DOCAPESCA’, which sendshe raw information to the Portuguese administration on a regu-ar basis. In Portugal it is mandatory to sell the retained catch inuctions, which exist all along the coast. Therefore the number ofshing trips recorded at the auction markets constitutes a censusf the fleet activity, although at a coarser scale than the logbook’secord of hours of fishing. During some years of the study periodot all logbooks were registered due to the lack of human resources.onsequently, to overcome the underestimation of discards by notcaling to the total fleet activity, the estimators include a final stephere estimates of discards are scaled to the total activity in num-

er of fishing trips.

. Methods

.1. Estimators

The estimators used in this work were designed as alternativeso the total estimator defined by Cochran (1977). When applied to

function of fishing time (h) by quarter (Q1–Q4) and fleet (C – crustacean, P – fish).s a smoother fit to the raw data by quarter and fleet.

discards with fishing effort as an auxiliary variable, the total estima-tor can be broken into two steps: the estimation of the mean DPUEby trip (e.g. kg/h), and the estimation of total discards in weight byscaling the DPUE to the total effort.

Our first alternative modified step one and estimated DPUE asa weighted mean with fishing effort (in hours fishing) as a weight-ing factor, so that trips with more fishing activity have a largercontribution to the estimate. The need to weight by effort becameapparent when the relationship between the total weight of thediscards and the fishing effort was not linear (Fig. 1). For example,the catchability of the gear might decrease over the course of longhauls, and produce a curved relationship between DPUE and fish-ing effort. If such a relationship is observed, a weighted averageof DPUE by fishing effort is an unbiased estimator of the averageDPUE. On the other hand, if the relationship is linear the weightedaverage will not affect the DPUE computation.

The second alternative modified step two and introduced a

post-stratification of trips according to the number of days-at-sea.Fig. 2 indicates that DPUE was not constant with the trip durationmeasured in days-at-sea. However, this variable was not possi-ble to control because the durations of the trips are not fixed.

108 E. Jardim, A.C. Fernandes / Fisheries Research 140 (2013) 105– 113

F our asc hligh

Far

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a

3

pmjaiaT

ig. 2. Iberian hake discards by the Portuguese trawl fleets by weight (kg) per hrustacean, P – fish). The upper panel presents raw data while the bottom panel hig

ollowing Thompson (1992), the estimator of the conditional vari-nce of the mean was used, which is the same as the stratifiedandom sampling estimator.

The third alternative is a combination of the previous ones.n summary, each method consists of a combination between (i)

eighting or not weighting the observations by fishing effort andii) post-stratifying or not post-stratifying the trips by duration inays-at-sea.

The mathematical descriptions and notation of the four methodsre presented in Tables 2 and 3.

.2. Analysis of influential observations with jack-knife

The ratio estimator used for totals associated with small sam-le sizes requires huge scaling factors, which make the sampleean less robust to extreme observations. We suggest using a

ack-knife (Quenouille, 1956) to identify influential observations

nd provide comparisons between estimates with and withoutnfluential observations. Such comparisons make it possible tossess which methods are more robust to influential observations.he rationale applied here is the same as that of Cook’s distance

a function of fishing days by trip, aggregated by quarter (Q1–Q4) and fleet (C –ts trends by presenting a smoother fit to the raw data by quarter and fleet.

(Cook and Weisberg, 1982) or Cleveland’s approach to robustfitting (Cleveland, 1993). If the estimate is based on a single sam-ple or a reduced number of samples, then the removal of suchobservation from the data set should be considered. It will be upto the analyst to decide if one observation should or should notbe removed from the data set. Note that removing an observa-tion is similar to weighting it by zero, an extreme down-weighting.From this perspective, the jack-knife procedure is similar to usinga model-based approach with a long tail distribution that down-weights the contribution of the less likely observations.

The jack-knife procedure was applied by year and fleet as fol-lows:

1. create a set of data sets with one trip removed from each andestimate the total discards by weight for each data set,

2. compute jack-knife residuals and standardise them to follow anormal distribution with mean 0 and variance 1,

3. remove all observations outside the 0.99 confidence interval ofa normal distribution with mean 0 and variance 1,

4. compute new estimates using the data sets without influentialobservations.

E. Jardim, A.C. Fernandes / Fisheries Research 140 (2013) 105– 113 109

Table 2Equations to estimate the amount of hake discards in the Portuguese trawl fleets. Four different approaches were used to calculate fleet-level estimates. The four methodswere based on (i) weighting or not weighting the observations by fishing effort to estimate DPUE and (ii) post-stratifying or not post-stratifying the trips by duration indays-at-sea. Lowercase letters represent sampled quantities while capitals represent population quantities. The “’’ sign is used to distinguish estimated quantities. L is theretained catch by weight (kg) of all species, C the catch by weight (kg) of all species, D the discards by weight (kg) of a single species, T the fishing effort (h), Y the discardsper unit effort (kg/h), also referred to as “DPUE”, G the trip duration (days), p the sample fraction of the discarded weight of one species in relation to the catch of all species,and q the sample fraction of the retained weight of one species in relation to the catch of all species. The index i = 1, . . ., N is used for fishing trips, j = 1, . . ., J for fleets, h = 1,. . ., H for hauls and s = 1, . . ., S for days. The variance at the haul level is derived from the variance of the ratio of two independent random variables.

Post stratification Estimation step DPUE weighted by fishing effort

No Yes

No Haul dijh = pijhq−1ijh

Lijh where pijh = dijhc−1ijh

and qijh = lijhc−1ijh

var(dijh) = L2ijh

[pijh(1 − pijh)q−2

ijh+ q−3

ijh(1 − qijh)p2

ijh

]

Trip yij = 1hij

∑hij

h=1

dijhtijh

dij =∑hij

h=1dijh

var(yij) = 1h2

ij

∑hij

h=11

t2ijh

var(dijh) var(dij) =∑hij

h=1var(dijh)

tij =∑hij

h=1tijh

Fleet yj = 1nj

∑nj

i=1yij yj =

∑nj

i=1dij∑nj

i=1tij

var(yj) = n−1j

[∑nj

i=1(yij−yj )

2

(nj−1) +∑nj

i=1var(yij)

]var(yj) =

(∑nj

i=1tij

)−2∑nj

i=1var(dij)

∑J Tj∗Gj

4

asisip

iT

TEwdrpa.

Overall

. Results

Our results show that the combination of post-stratificationnd weighting outperformed the other estimators and did nothow any major anomalies such as peaks in variance or sensitiv-ty to influential observations. Additionally, the results obtainedhow that the best method must include DPUE weighting, whichncreased estimates’ robustness to extreme observations, though

ost-stratification alone was not as relevant.

Observations of DPUE as a function of fishing time are presentedn the top panel of Fig. 1, with the quarter and fleet identified.he high variability in the observations makes it very difficult to

able 3quations to estimate the amount of hake discards in the Portuguese trawl fleets. Four dere based on (i) weighting or not weighting the observations by fishing effort to estimays-at-sea. Lowercase letters represent sampled quantities while capitals represent popetained catch by weight (kg) of all species, C the catch by weight (kg) of all species, D ther unit effort (kg/h), also referred to as “DPUE”, G the trip duration (days), p the sample fnd q the sample fraction of the retained weight of one species in relation to the catch of

. ., H for hauls and s = 1, . . ., S for days. The variance at the haul level is derived from the

Post stratification Estimation step DPUE weighted by

No

Yes Haul dijhs = pijhs

var(dijhs)

Trip yijs = 1hijs

∑hijs

h=1

dijhstijhs

var(yijs) = 1h2

ijs

∑hi

h=

Fleet yjs = 1njs

∑njs

i=1yijs

var(yjs) = n−1js

[∑

Overall

v

D =j=1 Nj

yj

var(D) =∑J

j=1

(Tj∗Gj

Nj

)2var(yj)

identify trends in the data. The bottom panel presents a smootherfit to the raw data highlighting trends per quarter and by fleet.It becomes clearer that DPUE decreases with longer fishing time.Similar results, but with DPUE as a function of trip duration indays-at-sea, are presented in Fig. 2. Trips of more than 3 dayswere not considered because they were very rare in the sampling(5–7% of total trips by both fleets). A clear pattern of decreas-ing DPUE with longer trips is observed in the first quarter for the

crustacean fleet and quarters 3 and 4 for the demersal fleet. Onthe other hand, DPUE increased with trip length for the demersalfleet in quarter 1. The other quarters do not show a clear pat-tern.

ifferent approaches were used to calculate fleet-level estimates. The four methodsate DPUE and (ii) post-stratifying or not post-stratifying the trips by duration inulation quantities. The “’’ sign is used to distinguish estimated quantities. L is thee discards by weight (kg) of a single species, T the fishing effort (h), Y the discardsraction of the discarded weight of one species in relation to the catch of all species,

all species. The index i = 1, . . ., N is used for fishing trips, j = 1, . . ., J for fleets, h = 1,variance of the ratio of two independent random variables.

fishing effort

Yes

q−1ijhs

Lijhs where pijhs = dijhsc−1ijhs

and qijhs = lijhsc−1ijhs

= L2ijhs

[pijhs(1 − pijhs)q−2

ijhs+ q−3

ijhs(1 − qijhs)p2

ijhs

]

dijs =∑hijs

h=1dijhs

js

11

t2ijhs

var(dijhs) var(dijs) =∑hijs

h=1var(dijhs)

tijs =∑hijs

h=1tijhs

yjs =∑njs

i=1dijs∑njs

i=1tijs

njs

i=1(yijs−yjs)2

(njs−1) +∑njs

i=1var(yijs)

]var(yjs) =

(∑njs

i=1tijs

)−2∑njs

i=1var(dijs)

d =∑J

j=1

∑S

s=1

Tjs∗PjsNjs

yjs

ar(d) =∑J

j=1

∑S

s=1

(Tjs∗Pjs

Njs

)2var(yjs)

110 E. Jardim, A.C. Fernandes / Fisheries Research 140 (2013) 105– 113

Fig. 3. Iberian hake discards by the Portuguese trawl fleets, scaled residuals of a jack-knife analysis of discard estimates. Estimators described as: ‘nw:NoPS’ – with neitherw cationw

Fmwtwnso

otw

TItTs–a

eighting nor post-stratification; ‘nw:PS’ – without weighting but with post-stratifieighting and post-stratification.

The standardised residuals from the jack-knife are shown inig. 3. All distributions have single modes and are close to sym-etrical, with some degree of skewness to the right. The estimatorith neither DPUE weighting nor post-stratification (nw:NoPS) has

he most skewed residuals, while the estimator with both DPUEeighting and post-stratification (w:PS) has the least. Using theormal distribution percentiles to identify influential observationseems appropriate and the 0.99 percentile guarantees that onlybservations far from the expected distribution are excluded.

Overall, 30 trips out of 342 were identified as influential

bservations. Of these, 11 were influential for all estimators simul-aneously. Table 4 presents the percentage of trips per year thatere identified in each method. The number of trips removed from

able 4berian hake discards by the Portuguese trawl fleets: the percentage of trips iden-ified to have a large affect on the estimate of discards, per year and per estimator.he estimators are described as: ‘nw:NoPS’ – with neither weighting nor post-tratification; ‘nw:PS’ – without weighting but with post-stratification; ‘w:NoPS’

with weighting but without post-stratification; and ‘w:PS’ – with both weightingnd post-stratification.

Year Estimator

nw:NoPS nw:PS w:NoPS w:PS

2004 0.05 0.05 0.05 0.072005 0.04 0.07 0.06 0.062006 0.04 0.04 0.04 0.042007 0.08 0.04 0.08 0.062008 0.07 0.07 0.04 0.072009 0.04 0.06 0.06 0.042010 0.04 0.07 0.04 0.07

; ‘w:NoPS’ – with weighting but without post-stratification; and ‘w:PS’ – with both

the data set was between 4% and 8% per year. In 68 occasions, thesetrips had a positive impact on the estimates, so that after removingthe relevant trip the estimate decreased, while in 5 cases, removingthe influential trips increased the estimated mean. The minimumchange observed was 5% while the maximum was 84%.

Fig. 4 shows the ratios between the estimates that use data setswithout influential observations and the estimates based on thefull data set for each estimator proposed. In all occasions, remov-ing the influential observations reduced the estimates of discards.In most cases, the variance also decreased after influential obser-vations were removed, although in 2005 and 2010 the estimateswith DPUE weighting and post-stratification had greater variance.

When comparing DPUE weighted by fishing effort withunweighted values (triangles versus circles), it can be seen that theweighted DPUE does not greatly decreases the estimates and vari-ances of the discards. While, the post-stratification option (blackversus white characters) does not show a clear pattern. Notably,the estimator with post-stratification (black circles) was the mostsensitive to influential observations, although in 2008 the estima-tor with neither weighting nor post-stratification (white circles)was the overall lowest estimate of discards and variance. The esti-mator that considers both weighting and post-stratification (blacktriangles) is the most robust, presenting ratio values that are closerto one. The estimator that considered weighting alone (white tri-angles) was the second most robust.

Comparing estimates by fleet (Fig. 5) showed that unweighted

estimates (dashed lines and circles) are higher and have lower vari-ance than weighted ones (solid lines and triangles), while there is noclear pattern for the effect of post-stratification (gray lines versusblack lines). The variance in the estimates of the discards is lower

E. Jardim, A.C. Fernandes / Fisheries Research 140 (2013) 105– 113 111

Fig. 4. Iberian hake discards by the Portuguese trawl fleets, ratios between the estimates that use the data set without influential observations and the estimates that uset ‘nw:Nb ion; a

ia(scflh

otdioo

itcrhiadt2d

5

lsaoa

he full data set, for discards and the variance of discards. Estimators described as:

ut with post-stratification; ‘w:NoPS’ – with weighting but without post-stratificat

n the crustacean fleet. Estimators that are weighted by DPUE (tri-ngles) have the lowest variance, but including post-stratificationblack triangles) has little effect. Unweighted estimates with post-tratification (gray circles) had a very high variance in 2007. Aomparison between the two fleets showed that the crustaceaneet has a trend of increasing discards and that the demersal fleetas a stable but oscillating pattern.

Table 5 and Fig. 6 present hake discards by the trawl fleetsperating off the Portuguese continental coast with the estima-or accounting for DPUE weighting and post-stratification by tripuration. Table 5 includes two estimates, one with and one without

nfluential observations, while Fig. 6 presents only estimates with-ut influential observations but also includes the survey estimatesf juvenile abundance.

Discard levels fluctuated until 2009 showing a steep declinen 2010. The lowest estimate, after removing influential observa-ions, was 795 t in 2006 and the highest was 1956 t in 2009. Theonfidence intervals are wide and the coefficient of variation (CV)anges between 22% and 74%. Estimates using the full data set wereigher, with a minimum of 910 t in 2004, a maximum of 3000 t

n 2009 and a lower CV between 17% and 68%. The CV increasedfter the removal of influential observations because of the overallecrease in the estimate of discards. The trend in discards trackshe recruitment estimates by the Portuguese IBTS survey (Anon.,011b), which is consistent with the idea that the major reason foriscarding is the minimum landing size of 29 cm (Anon., 2011b).

. Discussion

All sampling programmes based on on-board observers areikely to have problems with the random selection of trips and with

mall sample sizes, due to the lack of full cooperation from the fleetnd the high costs of observers’ deployment. Therefore, the meth-ds and results presented are not specific to the case study usednd can be generalised.

oPS’ – with neither weighting nor post-stratification; ‘nw:PS’ – without weightingnd ‘w:PS’ – with both weighting and post-stratification.

Both the computation of DPUE as a weighted mean of each trip’sDPUE and the post stratification of trips based on number of daysfishing increase the robustness of the estimator and reduce bias.Furthermore, the comparison among estimators showed how sen-sitive each estimator is to extreme observations.

The jack-knife analysis was able to identify observations thatchanged the estimates to a statistically unlikely extent. We decidedto remove these observations based on the assumption that if thesampling programme were properly randomised and if the samp-ling effort were large enough, the probability of selecting theseobservations would be much lower and their contribution to thefinal estimate would be smaller. This decision is open to dispute andsome authors may prefer other methods to deal with this type ofdata, such as model-based methods. The most important similarityis that both model-based and jack-knife methods use statisticallysound criteria to deal with such observations instead of subjectivecriteria based on expert judgment. The jack-knife method pre-sented here is clear, objective, replicable and flexible regardingthresholds. All of these are major advantages when dealing withthe removal of observations from a data set. Our approach was toreport the estimates and their precisions for both data sets, with andwithout influential observations, and to proved arguments aboutwhich was considered the best from our perspective.

We found a non-linear relationship between DPUE and haulduration. Rochet and Trenkel (2005) found a similar pattern in CPUEand Somerton et al. (2002) found that CPUE was negatively corre-lated with tow duration in a survey experiment. In Fulanda andOhtomi (2011), both CPUE and biomass estimations were signifi-cantly negatively correlated with the effective tow duration. Theseauthors discussed possible causes, and suggested that catchabil-ity might be influenced by the saturation of the trawl net and the

skipping of the ground rope over the sea bottom. However, themechanisms are not clear. Somerton et al. (2002) suggest a pos-sible ‘catch-by-surprise’ effect based on the herding behaviour offish or a mechanism of escape under the footrope, although they

112 E. Jardim, A.C. Fernandes / Fisheries Research 140 (2013) 105– 113

F d deviE ; ‘nw:w n.

dtaah

TIc

ig. 5. Iberian hake discards by the Portuguese trawl fleets, estimates and standarstimators described as: ‘nw:NoPS’ – with neither weighting nor post-stratificationithout post-stratification; and ‘w:PS’ – with both weighting and post-stratificatio

id not reach any clear conclusion. Battaglia et al. (2006) suggestedhat CPUE is higher in shorter hauls because (i) fish escape less often

t the beginning of the haul when individuals are surprised by therrival of the gear; (ii) fish are caught before and after the mainaul, either while the gear is settling or after the hauling starts; or

able 5berian hake discards (000 t) by the Portuguese trawl fleets: estimates using DPUE weigoefficient of variation and the 0.9 confidence interval.

Year Without extreme observations

Discards CV CI low CI up

2004 0.85 0.24 0.59 1.12

2005 1.50 0.22 1.08 1.93

2006 0.79 0.25 0.54 1.05

2007 1.83 0.39 0.91 2.75

2008 1.12 0.74 0.06 2.18

2009 1.96 0.24 1.35 2.56

2010 0.81 0.43 0.36 1.25

ations using data sets without influential observations by year for each estimator.PS’ – with neither weighting nor post-stratification; ‘w:NoPS’ – with weighting but

(iii) the gear loses catchability with the increasing saturation of thenet during longer hauls, allowing more escapes.

Regarding the non-linear relationship between DPUE and tripduration, Rochet and Trenkel (2005) propose that the overall triplength could influence discards and that vessels without freezing

hting and post-stratification with and without extreme observations by year, the

Full data set

Discards CV CI low CI up

0.91 0.26 0.61 1.221.80 0.17 1.41 2.200.93 0.24 0.65 1.212.32 0.68 0.30 4.351.81 0.48 0.68 2.933.00 0.20 2.23 3.770.93 0.32 0.55 1.31

E. Jardim, A.C. Fernandes / Fisheries R

Fig. 6. Iberian hake discards by the Portuguese trawl fleets, estimates (black lineatt

fmpcrdtc

ab1ihewtanapccseaiii

atbowd

ta

surveys lead to changes in CPUE and mean size? Fish. Res. 55, 63–70.

nd circle) by year with 0.90 confidence intervals (gray lines). Open triangles showhe Portuguese bottom trawl survey estimates in hundreds of individuals smallerhan 20 cm, which are considered juveniles.

acilities and those catching species primarily for a fresh marketay discard more on long trips. Catchpole et al. (2011) found

otential biases associated with the methods for calculating dis-ard proportions and daily rates. The authors suggested a discardate index based on hours fished to address the fact that the rate ofiscarding based on fishing days could be attributed to changes inhe amount of fishing in a day rather than to changes in sorting oratching practices.

The present data set did not allow the effect of factors like regionnd quarter to be analysed due to the reduced sample size, althoughoth can introduce bias to discard estimates (Stratoudakis et al.,999; Rochet and Trenkel, 2005). Our preliminary analysis also

ncluded the study of possible relationships with depth strata andake abundance from surveys, at a higher spatial resolution. How-ver, none of those factors seemed to affect DPUE. A GLM adjustedith Gamma errors and a log link function did not show a statis-

ically significant relationship between DPUE and depth or hakebundance. Given the high variability of discards and the limitedumber of trips sampled per strata, differences among quarters orreas might be so low that stratification is not able to improve therecision of the discard estimates. The potential disadvantage ofollapsing strata is that fine details about specific aspects of dis-arding may be lost. On the other hand, the higher variability intratified data would render them useless to investigate fine-scaleffects (Stratoudakis et al., 1999). Accordingly, we followed Rochetnd Trenkel (2005), who stated that stratified sampling may notmprove the precision of discard estimates if there is high variabil-ty and that stratification is only useful if the estimation of variances significantly reduced.

An important step forward on discard estimation can bechieved by analysing VMS data. Geographical tracking can iden-ify trips that do not follow the usual fishing pattern of the vesseleing sampled, which could result from changes in the behaviourf the skipper and crew when observers are on-board. Trackingill also allow analysis of any inconsistencies between the spatial

istribution of sampling and the fleet activity.

Having performed the comparison between the distinct estima-ors and taking into consideration the literature on the subject thepproach described in this paper mitigates possible bias introduced

esearch 140 (2013) 105– 113 113

by an unknown level of non-random deployment of observers andcontributes to provide better information for stock assessment.

Acknowledgements

The authors would like to acknowledge the team of on-boardobservers who collected the data and the skippers and crews thatcollaborated on the sampling programme, as well as the PortugueseDirectorate-General for Fisheries and Aquaculture for providing theinformation about fishing activity. The authors would also like tothank two anonymous referees for their thorough revision and sug-gestions. All of the data were collected under the framework ofthe European Commission’s Data Collection Regulation (Reg. EC No.1543/2000) and Data Collection Framework (Reg. EC 2008/199).

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