Onboard sorting process for angler sh species in Spanish North Atlantic (ICES
VIIIc, IXa) coastal  sheries is reviewed. The length e ect analysis across the years
sampled reveled an increasing trend in length of  rst retention (L50) since 2000,
the year when Minimum Landing Weight (MLW 500g) were implemented. Speci c
di erences in the length-based sorting process were found, being the less valuable
white angler discarded at larger lengths than the black species; further, the analysis
found that discard decision is taken at narrower length range for black angler.
This results indicate that  shers recognize angler species even at low length sizes,
conditioning the degree of adoption of MLW with regards to species relative market
value

Pérez-Contreras, M.N. (María Nélida)

Santos-Blanco, J. (Juan)

English

Repositorio Institucional Digital del IEO

To be presented in the WKFLAT, Benchmark Workshop on Flatfish Speciesand Anglerfish, 1-8 March 2012Modelling Anglefish Discard behaviour in Spanish NorthAtlantic Coast FisheriesJuan Santos, Ne´lida Pe´rezInstituto Espan˜ol de Oceanograf´ıa, C.O. Vigo, 36390, Pontevedra, SpainFebruary 29, 2012AbstractOnboard sorting process for anglerfish species in Spanish North Atlantic (ICESVIIIc, IXa) coastal fisheries is reviewed. The length effect analysis across the yearssampled reveled an increasing trend in length of first retention (L50) since 2000,the year when Minimum Landing Weight (MLW 500g) were implemented. Specificdifferences in the length-based sorting process were found, being the less valuablewhite angler discarded at larger lengths than the black species; further, the analy-sis found that discard decision is taken at narrower length range for black angler.This results indicate that fishers recognize angler species even at low length sizes,conditioning the degree of adoption of MLW with regards to species relative marketvalue.1 IntroductionThe onboard sorting criteria defining the retained and discarded fractions depends onfactors such as legal rules, market value, fishing strategy and targets or hold capac-ity. Among others, the interaction between market value and legal restriccions definingmarketable catch fractions has been recognized as one of the main discarding inducers.Regarding legal restriccions, TAC quota determines what species and how much of thesespecies can be marketed, while Minimum Landing Size/Weigth define the onboard sort-ing behavior within species; those fishes below the Minimum Landing Size/Weigth arenot allowed to be landed and must be thrown back to the sea.White (Lophius piscatorius) and black (L. budegassa) anglerfish are high valuablespecies for fishers operating in ICES Divisions VIIIc and IXa (North Atlantic Iberianwaters), being their stock under yearly assessment by the ICES Working Group of Hake,Monk and Megrim (WGHMM). Yearly TACs and Minimum Landing Weight (MLW,which specify a minimun of 500grs for landing individuals [CR 2406/96]) are the mostimportant assessment tools in force for both species. Previous analysis suggest thatanglers discards basically depends on market value rather than MLW (Dı´az, 2008). Themain aim of the present document is to re-analize sorting behaviour over the years1sampled by estimating yearly length of first retention (L50) using advanced regressionmodels, and compare the new results with those obtained by Diaz et al (2008).2 Material and Methods2.1 Data sourceBlack and white angler catch information from the Spanish North Atlantic (ICES VI-IIc, IXa North) coastal fisheries were used to model the onboard sorting behaviour onangler species. Data were obtained by the Spanish Discard Sampling Program -SDSP-,which follows the ICES standards (ICES, 2003; ICES, 2007) for onboard sampling. Thesampling protocol are based on a hierarchial design, where the target information, orUltimate Sampling Unit (USU=Number of individuals sampled) is nested within hauls(Secondary Sampling Unit [SSU]) within trips (Primary Sampling Unit [PSU]). Sampledtrips are randomly (or quasirandomly) drawn from predefined stratas (me´tiers). Onceonboard, the observer sistematically choose a number of hauls for sampling, and forevery sampled haul, biological information is obtained from both discarded and retainedcatch fraction. In case of discard data, the observer take a sample (d) from the totaldiscard volume (D), being the biological information in d subsequently raised to haullevel byrfd =Dd(1)2.2 Model settingsResponse variableDiscard behavior analysis is carried out by modeling the probability of a given fishto be retained during the onboard sorting process. For this purpose, anglers countswere splitted into n.r=Number of retained individuals, and n.d=Number of discardedindividuals, in order to calculate the proportion of individuals retained within sampledhaul within sampled trip;n.r(n.r + n.d)= θˆ (2)therefore θˆ is the empirical proportion of anglerfishes discarded by haul, which onlycan take values between 0 and 1. θˆ = 1 means that all individuals caugth duringthe haul j, j = 1, · · · J were retained onboard, while the opposite θˆ = 0 means thatall indiviuals were discarded. In other words, θ is the underlying probability definingretained/discarded fractions;n.r ∼ Binomial((n.r + n.d), θ) (3)Conditional means of θ and their variability are herein estimated under regressionmethods.22.3 Estimation methodGeneralized Linear Models (McCullagh and Nelder, 1989) relax the Linear Models (LM)assumptions allowing binary variables as model response, such as θ (representing theonboard sorting behavior). In this case, the model structure can be set using the logitlink function,θ =exp(η)1 + exp(η)(4)with η corresponding to the right-hand side of a classical LM,η = β0 + β1 ·X1, · · · , βp ·Xp (5)however, the hierarchial sampling design can implicity produce certain degree ofcorrelation between observations not considered in the GLM framework. This correlationcan be given because observations from hauls performed in the same trip could be moresimilar each others than hauls from different trips. Implicity, this hiphotesys suggestthat using a standard binomial GLM could violate the assumption of independencebetween observations. Generalized Linear Mixed Modelling (GLMM) framework relaxthe assumption of indepence between observations, allowing correlation structure withindata clusters, such as the PSU (trips). Using the GLMM framework makes it possiblethe joint modeling of:• Fixed effects: Variables of interest for assessing their effects on the dependentvariable.• Random effects: Clustered sampling units.2.4 Model Specifications• model type: Generalized Linear Mixed model (family=binomial,link=logit).• Likelihood function: REML.• Estimation Method: Laplace aproximation.• Package: lme4.• Function glmer().• Response n.r(n.r+n.d) = θˆ.• Fixed effects: length, year.• Offset= rfrrfd• Random effects: intercept=trip; slope= length.• Model selection: anova(...,test=’Chi’) criterion.33 Results3.1 White angler modelsModels fitted for white angler catch data are given below:rb1<-glmer(cbind(n.r,n.d)~factor(year)+ (1|trip)+offset(log(rf.r/rf.d)),family=binomial, data=rb)rb2<-glmer(cbind(n.r,n.d)~factor(year)+ (length|trip)+offset(log(rf.r/rf.d)),family=binomial, data=rb)rb3<-glmer(cbind(n.r,n.d)~factor(year)+length+(1|trip)+offset(log(rf.r/rf.d)),family=binomial, data=rb)rb4<-glmer(cbind(n.r,n.d)~factor(year)+length+(length|trip)+offset(log(rf.r/rf.d)),family=binomial, data=rb)rb5<-glmer(cbind(n.r,n.d)~factor(year)*length+(1|trip)+offset(log(rf.r/rf.d)),family=binomial, data=rb)rb6<-glmer(cbind(n.r,n.d)~factor(year)*length+(length|trip)+offset(log(rf.r/rf.d)),family=binomial, data=rb)The anova table (Table 1) indicates rb4 as the best model. rb4 propose that themain retention curve varies in terms of intercept (trip) and slope (length ) across theseries (random effects for longitudinal data, see Table 2) . The importance of each of thevariance components is given in Table 2 for model rb4. The retention ogive intercept(trip) is much more variable than the slope (length). Both variance components showvery high negative correlation value (-0.95), Table 2.Df AIC BIC logLik Chisq Chi Df Pr(>Chisq)rb1 13 2141.49 2236.18 -1057.74rb2 15 938.45 1047.71 -454.22 0.00 1 1.0000rb3 14 536.40 638.38 -254.20 1607.08 1 0.0001rb4 16 529.40 645.94 -248.70 411.05 1 0.0001rb5 25 550.94 733.04 -250.47 0.00 9 1.0000rb6 27 545.12 741.79 -245.56 9.82 2 0.0074Table 1: Model selection by test (‘Chi’) in Anova table. Best model highlighted in boldformat.4Groups Name Variance Std.Dev. Corr V6trip (Intercept) 63.32 7.95length 0.084 0.29 -0.95Table 2: Variance Components and correlations extracted from the best white anglermodel (rb4).Fixed effects from model rb4 and their relative significance are given below. Onlysome years > 2000 significantly differs from the reference 1994:Fixed effects:Estimate Std. Error z value Pr(>|z|)(Intercept) -13.77598 1.54841 -8.897 < 2e-16 ***factor(year)1997 0.03271 2.08737 0.016 0.987498factor(year)1999 -0.87755 50.83856 -0.017 0.986228factor(year)2000 -2.16909 1.73416 -1.251 0.211007factor(year)2003 -2.37912 1.16277 -2.046 0.040749 *factor(year)2004 -3.74280 1.25439 -2.984 0.002847 **factor(year)2005 -2.02365 0.86973 -2.327 0.019978 *factor(year)2006 -2.14947 1.47776 -1.455 0.145795factor(year)2007 -1.27108 1.02019 -1.246 0.212793factor(year)2008 -2.81579 1.12345 -2.506 0.012197 *factor(year)2009 -3.63275 1.08095 -3.361 0.000777 ***factor(year)2010 -1.58836 1.12148 -1.416 0.156685length 0.72002 0.05959 12.083 < 2e-16 ***Yearly L50 and Selention Range (SR) estimated from rb4 fixed parameters are givenin Table 3. Most of yearly L50 estimations since 2000 are significantly higher than thereference L199450 .L50 SR1994 19.13 3.051997 19.09 3.051999 20.35 3.052000 22.15 3.052003 22.44* 3.052004 24.33* 3.052005 21.94* 3.052006 22.12 3.052007 20.90 3.052008 23.04 * 3.052009 24.18 * 3.052010 21.34 3.05Table 3: Yearly L50 and SR estimated by the best model for white angler (rb4)53.1.1 Black angler modelsModels fitted for black angler catch data are given below:rn1<-glmer(cbind(n.r,n.d)~factor(year)+ (1|trip)+offset(log(rf.r/rf.d)),family=binomial, data=rn)rn2<-glmer(cbind(n.r,n.d)~factor(year)+ (length|trip)+offset(log(rf.r/rf.d)),family=binomial, data=rn)rn3<-glmer(cbind(n.r,n.d)~factor(year)+length+(1|trip)+offset(log(rf.r/rf.d)),family=binomial, data=rn)rn4<-glmer(cbind(n.r,n.d)~factor(year)+length+(length|trip)+offset(log(rf.r/rf.d)),family=binomial, data=rn)rn5<-glmer(cbind(n.r,n.d)~factor(year)*length+(1|trip)+offset(log(rf.r/rf.d)),family=binomial, data=rn)rn6<-glmer(cbind(n.r,n.d)~factor(year)*length+(length|trip)+offset(log(rf.r/rf.d)),family=binomial, data=rn)The anova table for black angler (Table 4) indicates that rn4 is the best fitted model.The estructure of this model does not differ from rb4. The importance of each of thevariance components is given Table 5 for model rn4 . As in the case of white angler, theretention ogive intercept (trip) is much more variable than the slope (length). Bothvariance components also show very high negative correlation value (-0.97).Df AIC BIC logLik Chisq Chi Df Pr(>Chisq)rn1 13 1351.52 1445.93 -662.76rn3 14 477.28 578.94 -224.64 876.25 1 0.0001rn2 15 604.18 713.11 -287.09 0.00 1 1.0000rn4 16 356.50 472.69 -162.25 249.69 1 0.0001rn5 25 399.47 581.01 -174.73 0.00 9 1.0000rn6 27 366.69 562.77 -156.35 36.77 2 0.0001Table 4: Model selection by Anova test (‘Chi’)Groups Name Variance Std.Dev. Corr V6trip (Intercept) 36.917693 6.07599length 0.063565 0.25212 -0.970Table 5: Variance Components and associated s.e. and correlation extracted from thebest black angler model (rn4).6Fixed effects and their relative significance for model rn4 are given below. Only someyears ≥ 2006 significantly differs from the reference 1994:Fixed effects:Estimate Std. Error z value Pr(>|z|)(Intercept) -9.8847 1.3144 -7.520 5.46e-14 ***factor(year)1997 0.8891 0.8222 1.081 0.279491factor(year)1999 -0.8643 1.6966 -0.509 0.610438factor(year)2000 -1.2489 0.8317 -1.502 0.133204factor(year)2003 0.7937 1.8377 0.432 0.665799factor(year)2004 -0.7338 2.1900 -0.335 0.737558factor(year)2005 -0.9934 0.9314 -1.067 0.286128factor(year)2006 -2.1262 0.9093 -2.338 0.019372 *factor(year)2007 -0.3856 1.1376 -0.339 0.734643factor(year)2008 -4.1676 1.1075 -3.763 0.000168 ***factor(year)2009 -1.7213 1.0848 -1.587 0.112572factor(year)2010 -1.5887 0.7736 -2.053 0.040028 *length 0.5799 0.0533 10.880 < 2e-16 ***Yearly L50 and Selention Range (SR) estimated from rn4 fixed parameters are givenin Table 6. Only three yearly L50 estimations since 2006 are significantly higher thanthe reference L199450 .L50 SR1994 17.05 3.791997 15.51 3.791999 18.54 3.792000 19.20 3.792003 15.68 3.792004 18.31 3.792005 18.76 3.792006 20.71* 3.792007 17.71 3.792008 24.23* 3.792009 20.01 3.792010 19.78* 3.79Table 6: Yearly L50 and SR estimated by the best model for black angler (rn4)74 DiscussionThe analysis carried out by Diaz et al. (2008) estimated yearly L50 separately for bothspecies; in other words, the authors fitted as many models as years for both species. TheGLMM’s (rb4 and rn4) herein fitted used all sampled years as data input. White anglermodel estimate yearly significant differences in the onboard sorting behaviour since 2000,while the black angler model estimate the first significant year in 2006. Both modelsuse 1994 as the reference year. This hypothesis testing is lack in the Diaz et al. (2008)approach. Altougth an increase in L50 is found for both species since year 2000, this pa-rameter have never reached the value of 31cm, the length associated to 500gr individuals(Diaz et al., 2008), suggesting that species market value is a major factor hindering thefull implementation of the MLW regulation. An inter-species L50 comparison supportsthis hyphotesis, given that estimations for the less valuable white angler indicates thatthis species is generally discarded at larger lengths than black species (see tables 3 and6). This result suggest that fishers may recognize angler species even at low length sizes,conditioning the degree of MLW adoption with regards to their relative market value.Further, the analysis found that discard decision is taken at narrower length range(SR) for black angler (tables 3 and 6) . The random effects results indicates that (trip)is the main variance component for both species sorting ogives, meaning that the ogiveintercepts (varying between trips) are more variable than their shape.5 ReferencesDı´az, P., Santos, J., Velasco, F., Serrano, A., and Pe´rez, N. 2008. Anglerfish discard es-timates and patterns in Spanish Northeast Atlantic trawl fisheries. – ICES Journalof Marine Science, 65: 1350–1361.ICES. 2003. Report of the Workshop on Discard Sampling Methodology and RaisingProcedures,2 – 4 September 2003, Charlottenlund, Denmark.. Final Report. TheICES Planning Group on Commercial Catch, Discards and Biological Sampling(PGCCDBS).ICES. 2007. Report of the Workshop on Discard Raising Procedures, 6–9 February2007, San Sebastian, Spain. ICES CM 2007. ACFM:06. 57 pp.McCullagh, P., Nelder, J., 1989. Generalized Linear Models. Chapman and Hall,London.

Modelling Anglerfish Discard behaviour in Spanish North Atlantic Coast Fisheries

Santos-Blanco, Juan

Pérez-Contreras, María Nélida

Digital.CSIC

http://www.repositorio.ieo.es/e-ieo/bitstream/10508/3733/1/Santos%2c%20J.%20et%20al.%202012.%20Modelling.pdf

Modelling Anglerfish Discard behaviour in Spanish North Atlantic Coast Fisheries

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