A multivariate approach for defining fishing tactics from commercial catch and effort data

Transcription

1 51 A multivariate approach for defiig fishig tactics from commercial catch ad effort data Domiique Pelletier ad Jocelye Ferraris Ferraris 65 Itroductio Abstract: Fishig tactics correspod to fishig practices at the scale of the fishig operatio. I the case of mixed fisheries, they are defied as a combiatio of target species, gear, ad fishig locatio, at a give time of the year. This paper proposes a approach to determie fishig tactics from the aalysis of commercial catch ad effort data. The approach is based o typologies of fishig operatios ivolvig multivariate descriptive methods like factorial aalyses ad classificatio techiques. The obtaied types of fishig operatios are cosidered as fishig tactics. The approach is applied to two cotrastig examples, a small-scale Seegalese fishery ad a Celtic Sea fishery that operates at a larger scale. Catch per species ad fishig effort are computed per tactic. Results emphasize the importace of fishig locatio ad seasoal effects for characterizig fishig tactics. Résumé : Les tactiques de pêche correspodet aux pratiques de pêche à l échelle de l opératio de pêche. Das le cas des pêcheries composites, elles se défiisset comme la combiaiso d ue ou plusieurs espèce(s)-cible(s), d u egi et d u lieu de pêche, à ue époque de l aée. Cet article propose ue approche pour détermier les tactiques de pêche à partir de l aalyse de doées commerciales de captures et d effort de pêche. L approche est basée sur des typologies d opératios de pêche mettat e jeu des méthodes descriptives multivariées comme des aalyses factorielles et des techiques de classificatio. Les types d opératios de pêche obteus sot cosidérés comme des tactiques de pêche. L approche est appliquée à deux exemples cotrastés, ue pêcherie artisaale séégalaise, et ue pêcherie hauturière de la mer Celtique. La capture par espèce et l effort de pêche sot calculés par tactique. Les résultats motret l importace du lieu de pêche et des effets saisoiers das la caractérisatio des tactiques de pêche. Pelletier ad Received February 3, Accepted July 9, J15004 D. Pelletier 1 ad J. Ferraris. Laboratoire MAERHA, IFREMER BP 1105, Nates Cedex 03, Frace. 1 Author to whom all correspodece should be addressed. domiique.pelletier@ifremer.fr Preset address: IRD (ex-orstom) BP A5, Nouméa, Nouvelle-Calédoie, Pacifique Sud. Studies o fisheries dyamics ad stock assessmet have traditioally focused o the resource, leavig aside the fisher compoet, which is aki to omittig the predator i a predator prey system (Hilbor ad Walters 199, p. 104). However, igorig fisher behaviour i models ca result i a wrog perceptio of the dyamics of the fishery, i erroeous stock assessmets, ad fially i iappropriate maagemet advice. This is particularly strikig i multispecies multifleet fisheries, also called mixed fisheries. I such fisheries, may species are caught at oce i a give area due to uderlyig species assemblages that reflect ecological commuities. Also, there are techological iteractios betwee the fleets (Murawski 1984), i.e. the fleets exploit the same stocks either simultaeously or sequetially, either i the same area or i segregated areas. A fleet is ofte idetified by the vessel ad (or) crew characteristics, the gear used, ad sometimes the mai species targeted. However, this is usufficiet to describe fisher behaviour i that vessels of a give fleet may exhibit differet fishig practices, depedig o the skipper ad may fluctuatig factors icludig market coditios (e.g., see Hilbor ad Ledbetter 1985). Target species may thus chage i the course of the year, ad for a give target species, the gear used ad fishig locatio may also chage i relatio to the spatial ad seasoal dyamics of correspodig populatios. As a cosequece, each fishig practice is likely to impact exploited stocks i a particular way. This heterogeeity of fishig practices withi a give fleet precludes ay attempt to accurately assess the relatioship betwee the total fishig effort of the fleet ad the resultig fishig mortality exerted o the exploited stocks. Such a relatioship must be evaluated for each fishig practice. A first step is the idetificatio of these practices for every fleet i the fishery. Like Lewy ad Vither (1994), we believe that there are two levels at which fishig practices must be characterized, amely the fishig uit (vessel ad crew) ad the fishig operatio. These pertai to differet time scales: a few hours or a day for the fishig operatio versus oe or several years for the fishig uit. I this paper, we are oly iterested i fishig practices at the scale of the fishig operatio. The decisios made before each fishig operatio should be uambiguously described by the combiatio of a fishig locatio, a gear to use, ad oe or several target species. Such combiatios have bee termed métiers (Biseau ad Godeaux 1988; Laurec et al. 1991; ad others), directed fisheries (Lewy ad Vither 1994), fishery maagemet uits (Murawski et al. 1983), fishig strategies (Rogers ad Pikitch 199; He et al. 1997), ad fishig tactics (Laloë ad Samba 1991). We prefer to use this last term to reflect the decisios made before each fishig operatio. For a give fleet or fishery, idetifyig fishig tactics co- Ca. J. Fish. Aquat. Sci. 57: (000)

2 5 Ca. J. Fish. Aquat. Sci. Vol. 57, 000 sists i determiig the mai types of fishig operatios, i.e., costructig groups of fishig operatios that correspod to similar choices i terms of target species, gear, ad fishig locatio. Further isight ito the tactics is give by additioal variables like moth or seaso. I defiig fishig tactics, the selectio of fishig locatio is cetral: fishers allocate fishig effort i a give area o the basis of prior iformatio about the profitability determied by costs ad expected beefits (Hilbor ad Ledbetter 1979; Sampso 1991). Existig studies aimig at defiig fishig tactics from the aalysis of commercial data geerally rely o applicatios of cluster aalysis to catch per species (species compositio), per fishig operatio or per fishig trip. Lewy ad Vither (1994) ad He et al. (1997) defied clusters of fishig trips based o the similarities betwee the species assemblages observed i catches. They examied i additio other characteristics of the clusters, such as fishig zoe, seaso, vessel size, ad fishig effort. Murawski et al. (1983) also aalyzed species compositios to determie clusters of combiatios of fishig area, depth, ad moth. Rogers ad Pikitch (199) used three methods to validate fishig tactics defied i a earlier study from fishers ad maager iterviews, with respect to types of catch profiles (relative species compositio) obtaied from observer data. Bertigac (199) determied types of species compositio by applyig factorial aalyses ad classificatio methods to catch per uit effort (CPUE) data from Atlatic Frech trawlers. Auxiliary variables such as home port ad vessel characteristics were used to describe chages i fishig activity over years. Hece, most existig papers focus o catch data, which might be appropriate for characterizig fishig areas (Murawski et al. 1983) or for validatig predefied fishig practices (Rogers ad Pikitch 199). However, fishig tactics are ot defied solely from catch compositio (see above). Furthermore, target species (which is by defiitio tied to fisher decisio) may ot be accurately reflected by catch. I several papers, this was mitigated by relatig the clusters with additioal variables like vessel characteristics or fishig grouds (Lewy ad Vither 1994; He et al. 1997). Ideally, fishig tactics should be explicitely characterized by target species, gear, fishig locatio, ad time of the year. Whereas the last three are directly determied from effort data, target species could be better discrimiated by aalyzig joitly catch ad effort data. I this paper, we propose a methodology to idetify fishig tactics from multivariate aalyses of both catch ad effort data with spatial ad temporal refereces. These data as well as additioal iformatio were used to characterize the clusters obtaied, with a associated statistical diagostic of sigificace. I order to illustrate the wide applicability of the proposed methodology, it was applied to two cotrastig examples, oe from a small-scale Africa fishery ad the other from a large-scale Frech fishery. A methodology to determie fishig tactics from commercial catch ad effort data I this sectio, we preset a methodology to costruct the groups of fishig operatios (i.e., the tactics) from the aalysis of commercial catch ad effort data. Catch ad effort data are collected idepedetly from each other. To be able to aalyze them joitly, the aalysis successively ivolves two steps: (i) idetifyig the differet types of species compositio from the catch data ad (ii) idetifyig fishig tactics from both the types of species compositio ad the effort data (Fig. 1). To obtai fishig tactics, it is ecessary to have iformatio at the scale of the fishig operatio. I geeral, commercial catch ad effort data per fishig operatio cosist of large data sets with may variables ad may idividuals (i.e., fishig operatios). Variables related to catch iformatio iclude umbers or toage caught per species, whereas variables related to effort iclude, amog others, those related to gear, vessel, fishig locatio, ad date. Some of these variables are quatitative, whereas others are categorical. I additio to obtaiig fishig tactics, it is also desirable to idetify the values of the variables that characterize them. Oe must the resort to methods that are capable of (1) providig a reduced descriptio of large data sets, () aalyzig relatioships betwee variables, (3) costructig clusters of idividuals, ad (4) characterizig these clusters by explaatory variables. The methods must be able to hadle either quatitative or categorical variables. Multivariate descriptive techiques are helpful for these purposes. I a first step, objectives 1 ad are attaied through factorial aalyses (a type of ordiatio techique, see below) based o a geometric represetatio of the data. I a secod step, a classificatio (sometimes called clusterig) is carried out o factorial coordiates to address objectives 3 ad 4. The factorial aalyses used i this paper are pricipal compoets aalysis (PCA), twoway correspodece aalysis (TWCA), ad multiple correspodece aalysis (MCA). PCA is a well-kow techique used with quatitative variables. TWCA ad MCA are equivalet techiques for categorical variables. Aalyzig this kid of data table icludes selectig the relevat variables ad idividuals, codig the variables, applyig factorial aalyses ad classificatio, ad fially iterpretig clusters of idividuals. The whole process is i essece iterative. It will be termed typology i the rest of the paper. We first preset the mai features of the factorial aalyses ad classificatio techiques used i the typologies, emphasizig the critical choices i usig these methods ad the differeces betwee the factorial aalyses used i the paper. I particular, we explai how similarities betwee idividuals are evaluated, depedig o both the type of aalysis ad the ature of the variables. This is crucial, sice for a give data set, dramatically differet clusters may be obtaied i the classificatio step, depedig o the aalysis performed (ad other choices such as data codig) i the first step. Because the goal of this study is to determie groups of idividuals, these methods are preseted from the stadpoit of iertia ad distaces betwee idividuals (see below). Because o such presetatio could be foud i the accessible literature, we give the basic algebra ecessary to thoroughly uderstad the essece of each method i relatio to the objectives of the typology. We the explai how we use factorial aalyses ad classificatio to come up with typologies of fishig operatios. Iertia ad factorial aalyses A typical data set of commercial data may be framed as a data matrix X of umerical values x ij with rows ad p colums, each row correspodig to a idividual (e.g., a fishig operatio) ad each colum correspodig to a variable (e.g., a variable of iterest for the fishig operatios). Each idividual may thus be represeted i the p-dimesioal space defied by the p colum vectors, so that the distace d(i,i ) betwee two idividuals i ad i may be defied. The iertia of the data set is defied as 1 (1) I= mm d (, ii ) = md (, ig) i= 1 i = 1 i i i= 1 i

3 Pelletier ad Ferraris 53 Fig. 1. Flowchart of the methodology used to obtai the fishig tactics i the two examples. I each example, two typologies are performed: (i) o catch data leadig to approachig the target species by a categorical variable, ad (ii) o both this categorical variable ad effort data to idetify fishig tactics. where m i ad m i are the weights of idividuals i ad i ad G is the ceter of gravity of the idividuals, i.e., the jth coordiate of G is g mσ m x, j = 1 i= 1 i ij m beig the sum of the idividual weights. The iertia of the data set may thus be see as a measure of the variability of the idividuals aroud G. Factorial aalyses yield the coordiates of the idividuals i the p-dimesioal space where axes are ordered accordig to decreasig cotributio to the iertia of the data set. These ordered axes are called factorial axes or sometimes ordiatio axes (e.g., see Rice 1985). The first axis is the oe alog which the idividuals are most scattered, i.e., they are best discrimiated. This amouts to maximizig the iertia of the projectios of the idividuals o this axis. The iertia ad thus the factorial axes deped o the way distaces betwee idividuals are computed, which differs accordig to the type of factorial aalysis (see Appedix). The spaig vectors of the factorial axes are the ormalized eigevectors of a square matrix A (see Appedix), called the iertia matrix because its trace (i.e., the sum of its diagoal terms) correspods to the iertia I of the data set defied i eq. 1. Diagoalizig A yields t () A = UΛU where U is the matrix of ormalized eigevectors ad Λ the diagoal matrix of eigevalues. Each p eigevector beig a liear combiatio of the iitial variables, it defies a ew variable, the variace of which is the correspodig eigevalue. The iertia of the data set is the the sum of the eigevalues. Oe may calculate the coordiates of the idividuals ad the p variables i the vector space spaed by the eigevectors (see Ap-

4 54 Ca. J. Fish. Aquat. Sci. Vol. 57, 000 pedix). More geerally, the coordiates of ay idividual for which the same p variables have bee measured may be calculated (see Appedix). The coordiates of ay variable that has bee measured o the same idividuals could also be derived. Idividuals ad variables that were ot used to obtai the factorial axes are called illustrative (the others are called active). They are i geeral useful i iterpretig the results. Factorial aalyses provide a geometric represetatio of idividuals ad variables, which is easier to iterpret tha the iitial data table. I additio, they allow reductio of the dimesios of the data table by retaiig oly the axes that explai up to a give part of the iertia. This feature may also be helpful i elimiatig margial effects that might blur the structure of iterest i the data set. The geeral shape of the histogram of eigevalues shows the gai i accouted for iertia offered by retaiig a give umber of axes (see the first applicatio). Classificatio Classificatio techiques aim at groupig the idividuals ito clusters that are both well separated ad as homogeeous as possible with respect to the observed variables. Cluster homogeeity may thus be related to iertia. Cosider a partitio P C of the idividuals ito C clusters. Let c deote the umber of idividuals i the cth cluster. Followig Huyges classical theorem, the iertia of the data set defied i eq. 1 may be decomposed ito withicluster ad betwee-clusters iertia: (3) I = I + I = betwee ( P ) withi ( P ) C C C C c c c c= 1 c= 1 i= 1 md( G, G) + md(, ig) i c where G c is the ceter of gravity of the cth cluster ad c mc = Σ i= 1 mi is the weight of G c. I this paper, we use a hierarchical ascedig classificatio (HAC) techique i which the clusters are built by successive pairwise agglomeratios of elemets based o the miimum variace criterio of Ward (1963). Hierarchical techiques are useful whe the desired umber of clusters is ot kow i advace because they provide ested partitios uder the form of a dedrogram. For very large data sets, hierarchical clusterig is istead applied to homogeeous groups obtaied i a first step from a ohierarchical partitioig (Lebart et al. 1984, pp ). The partitioig algorithm used i the first applicatio relies o clusterig aroud movig ceters, which belogs to K meas methods (McQuee 1967). The variatio of the aggregatio criterio is used to select the appropriate umber of clusters by cuttig the dedrogram. A break i this variatio idicates that the correspodig step has agglomerated rather differet etities, leadig to a substatial icrease i the heterogeeity of resultig clusters. Selectig the appropriate umber of clusters is ot oly tied to statistical or umerical cosideratios, but also depeds o the objective of the aalysis. A limited umber of clusters may, for istace, be desirable whe the results will serve to costruct a fishery model. I cotrast, oe might be iterested i obtaiig more clusters to gai a better isight ito the data set. Selectig the umber of clusters should also rely o the relevace of cluster iterpretatio. The latter is achieved by examiig the differeces i meas (resp. frequecies) betwee each cluster ad the total populatio of idividuals, for each umerical variable (resp. each category). Such differeces are statistically tested usig test values (Lebart et al. 1984). Whe the test value is sigificat, the mea (resp. the frequecy) of the variable (resp. the category) is sigificatly higher or lower i the cluster compared with the total populatio of idividuals, ad the variable (resp. the category) is said to be characteristic of the cluster. A combied approach to obtai typologies I this paper, each typology is obtaied from a factorial aalysis (PCA, TWCA, or MCA) followed by a classificatio o the factorial coordiates. There are several reasos for performig a classificatio after a factorial aalysis. First, the factorial axes ad the projectios of idividuals o these axes may be difficult to iterpret. For istace, the third axis describes proximities betwee idividuals that are residuals with respect to the first two axes. Secod, some idividuals may be very ifluetial i the costructio of axes ad drive the results i a excessive maer. Classificatio palliates each of these drawbacks because it summarizes the data set i a way that is much easier to iterpret tha idividual projectios. Besides, it is to some extet more robust to margial idividuals tha factorial aalysis because of the iterative algorithms used. Coversely, there are also several reasos for performig a factorial aalysis before a classificatio. Whatever the iitial variables, it provides a geometric descriptio of the idividuals, the variables, ad the relatioships betwee them that is helpful i explorig the structure of the data set. For istace, illustrative idividuals are more easily allocated to a cluster through their factorial coordiates. Also, factorial coordiates are eeded to project clusters o factorial axes, which facilitates their iterpretatio. Our results were obtaied from the software SPAD.N (CISIA 1996), which is specifically desiged for multivariate descriptive aalyses ad allows the likage of several kids of aalyses i a user-friedly maer. However, the factorial aalyses (PCA, TWCA, ad MCA) ad the classificatio techiques quoted i the paper are available i most statistical software, icludig Splus ad SAS. The artisaal fishery of Seegal Backgroud to the fishery The small-scale marie fishery of Seegal is amog the most importat artisaal fisheries of West Africa, with, for example, fishers ad 5661 caoes i 199. The fishery s catch ( t) cotributes to more tha 70% of the atio s aual ladigs. This tropical fishery takes place i a upwellig area where most demersal ad pelagic species migrate ad spaw durig the upwellig seaso due to icreased primary ad secodary productio. Fisher behaviour varies with the seasoality of fish catchability, so that fishers chage gears, target species, ad fishig locatios durig the year. The catch is characterized by a great umber of species domiated by clupeoids (Sardiella aurita, Sardiella maderesis); other species caught iclude sea bream (Pagellus bellottii, Sparus caeruleostictus, Detex sp.), catfish (Arius sp.), groupers (Epiephelus aeeus, Epiephelus goreesis), cephalopods (Octopus vulgaris, Sepia officialis), sharks, ad rays. The fleet cosists of caoes with legth ragig from 3 to 0 m. Most caoes are motorized with a egie power ragig from 6 to 9 kw. Crew size lies betwee oe ad 1. Fishers use 4 categories of gear belogig to five mai types: pursig ets, driftig gill ets, set ets, hooks ad lies, ad beach seies. Trip duratio is limited to 1 h except for caoes equiped with a ice box, which may fish further from home port. A give caoe may have several gears ad resort to differet fishig practices, depedig o target species.

5 Pelletier ad Ferraris 55 Fig.. Locatio of the small-scale Seegalese fishery. Fishig locatios are idicated by shaded areas or arrows (directios). Data The data collectio system icludes a cesus for fishig uits carried out twice a year ad catch ad effort samplig throughout the year. A cesus of caoes by fishig village ad by gear provides a exhaustive descriptio of fishig uits. The data used here were obtaied from the survey udertake i 199 by the Cetre de Recherches Océaographiques de Dakar-Thiaroye (CRODT) i the port of Kayar orth of Seegal (Fig. ). Samplig for catch ad effort data follows a stratified scheme, with oe stratum per regio, fortight, ad gear. Fishig effort is estimated by the umber of trips per gear ad workig day. Regardig catch, approximately 10% of fishig trips are sampled i each stratum, based o a twostage scheme with days as primary uits ad ladig caoes as secodary uits. For each sampled fishig trip, fishig locatio, trip duratio, depth, crew size, ad species compositio of catch are recorded. Every fishig locatio (Fig. ) has a ame that reflects either the geography or a characteristic of the locatio (e.g., a directio where to fish). I Kayar i 199, caoes were cesused i April (491 crafts) ad i November (301 crafts). Correspodig total effort was estimated to be fishig trips, of which 689 were sampled for catch. Amog these trips, 551 beloged to the had-lie category. Durig those trips, 8 fishig locatios were visited ad 11 species were caught. For these caoes, gear was coded i three categories: G1 for caoes with o egie, G for caoes with egie power 9 kw, ad G3 for caoes with egie power >9 kw. Idetificatio of catch profiles A data matrix was built with fishig trips usig had lie as idividuals ad catch per species as variables (Fig. 1). Oly fishig trips with ozero catch were cosidered, i.e., 547 trips. For each trip, absolute catch was trasformed ito a catch profile (i.e., a relative species compositio) by dividig each catch per species by the total catch. This removed the differeces i catch levels betwee trips, which are ofte liked to both the time of the year ad to crew size. Data were the log trasformed to symmetrize their distributio. A oormalized PCA was ru i order to accout for differeces i abudaces betwee rare species ad domiat species. All the factorial axes were retaied, ad a HAC of the factorial coordiates led to a partitio ito eight clusters that explaied 44.4% of the iertia of the data. For each cluster, oe species was foud to be highly characteristic, ad several other species showed sigificat test values. O the whole, 35 species out of 11 exhibited a highly sigificat test value. Ulike cluster Octopus, which was characterized by a sigle species (Octopus vulgaris), the other clusters were geerally determied by several species. The cluster was the amed after the domiat species or by a term describig the species assemblage. For istace, the cluster Grouper was characterized by several species of Serraidae associated with other demersal species. The largest ad best-discrimiated cluster was Sea bream (170 trips), which was characterized by the species Pagellus bellottii. Clusters Octopus (816 trips) ad Grouper (85 trips) were characterized by species livig o rocky substrate, whereas clusters Large-eye detex (331 trips) ad Deepwater group (331 trips) were foud to correspod to deepwater species. Clusters Sailfish (5 trips), Goatfish (33 trips), ad Warmwater group (868 trips) were tied to species maily caught durig the warm seaso. Each cluster correspods to oe type of catch profile, which was the cosidered as a categorical variable termed catch profile. Each trip with a ozero catch was thus assiged a category of catch profile. I the absece of fisher iterviews, a catch profile provides iformatio about target species. We will relate it to

6 56 Ca. J. Fish. Aquat. Sci. Vol. 57, 000 Fig. 3. Represetatio of active categories o the first two factorial axes of the secod typology i the Seegal example. This shows the relatioships betwee the 51 categories of the four variables used to describe the fishig operatios. Differet fots are used to report each variable (see box at the bottom of the plot). The iertia accouted for by each axis is show i the iset. The arrow idicates the 16th value, the cumulated iertia for the 16 first axes beig 59.74%. L1, L, etc., are fishig locatios (see Fig. ). fishig coditios (depth, locatio, etc.) ad (or) fishig gear i order to complete the idetificatio of target species ad to defie fishig tactics. Idetificatio of fishig tactics For each trip, fishig activity was described by a umber of variables (locatio, gear, moth, crew size, ad depth). These variables were all foud to be sigificatly correlated with each other ad with catch profile (probas of the χ test, <0.001). I the followig, we retaied oly those variables that appear explicitly i the defiitio of a fishig tactic, i.e., target species approximated through catch profile, fishig locatio, gear. ad moth. Our aim was to examie (i) which categories of these variables were associated i the defiitio of fishig tactics ad (ii) which similarities betwee fishig trips might be evideced with respect to these variables. For this purpose, a MCA was applied to the data matrix built with the 547 fishig trips as idividuals ad the four categorical variables: catch profile, fishig locatio, gear, ad moth (which resulted i 51 categories) (Fig. 1). Niety-four fishig trips with o catch profile (because of a ull catch) were treated as illustrative idividuals. I this case, the catch profile was coded as a additioal category correspodig to missig data. The histogram of eigevalues (Fig. 3, iset) shows that the first four axes predomiate (0.4% of the iertia), with a break i the decrease of the iertia accouted for by the axes. These four axes are cosidered as relevat to depict the relatioships betwee the categories. As a illustratio, the first two factorial axes (Fig. 3) showed a close lik betwee deep locatios (L3, L1, ad L8) o the oe had ad the catch profiles Deepwater group ad Large-eye detex o the other. Sea bream ad Grouper were related to the cold upwellig seaso (December May), while Sailfish, Octopus, Goatfish, ad Warmwater group appeared to be associated with the warm seaso (July October). Motorized caoes (G ad G3) were opposed to caoes with o egie (G1) o the secod factorial axis. I a secod step, a classificatio was applied to the factorial coordiates obtaied from the MCA so that clusters of fishig trips could be obtaied o the basis of the relatioships betwee the four categorical variables. To elimiate margial effects that might blur the structure of the data set,

7 Pelletier ad Ferraris 57 Fig. 4. Fishig effort i umber of trips as a fuctio of moth (a) per gear ad (b) per tactic i the Seegal example. Fig. 5. Effort per tactic ad average catch rate of the target species for the tactics (a) Sea bream ad (b) Grouper i the Seegal example. oly the first 16 factorial axes (60% of the iertia; Fig. 3, iset) were retaied for classificatio. A two-step classificatio led to seve clearly idetified clusters. The 94 illustrative idividuals were allocated to the earest cluster o the basis of their factorial coordiates. This aalysis assiged to the same cluster (i.e., to the same tactic) the trips that exhibited similar categories of the active variables (catch profile, fishig locatio, gear, ad moth). These fishig tactics were the iterpreted from both the active ad illustrative variables (Table 1). Clusters 6 ad 7 were arbitrarily pooled ito a sigle tactic (Deepwater group i Table 1) because they appeared similar for all the variables except seaso. Six tactics were fially retaied (Table 1). The most characteristic species of the illustrative variable catch per species is assumed to represet the target species of each tactic. Fishig effort was calculated per gear (Fig. 4a) ad per tactic (Fig. 4b). Total effort per fortight was extrapolated from the sample usig a raisig factor per gear that is the total umber of sampled fishig trips divided by the total estimated effort per gear. The decrease i fishig effort durig the warm seaso oly appears for the tactics Sea bream ad Octopus i Jue,ad Grouper i August. Catch rates per species may also be calculated for each tactic (Fig. 5). For the target species of the tactic, they are i geeral larger tha catch rates calculated per gear. Note that for motorized crafts, catch rates are quite similar irrespective of egie power (G ad G3). For the Sea bream tactic, the catch rate of P. bellottii follows the same tred as the fishig effort. The tactic strogly targets this species with a average catch rate of 39.9 kg per trip (±33. kg) for P. bellottii versus a overall catch of 53.7 kg per trip (±39.1 kg). For the Grouper tactic, catch rate ad effort show differet temporal patters. Effort icreases markedly from November to February ad from April to July i relatio to the immigratio of E. aeeus i the regio. The catch rate for this species thus remais relatively costat throughout the year. Ulike the Sea bream tactic, the Grouper tactic targets several species of commercial iterest, so that the catch rate of the target species is 9.4 kg per trip (±13.9 kg) versus a overall catch of 34.5 kg per trip (±36.9 kg). The Celtic Sea groudfish fishery Backgroud to the fishery ad data The Celtic Sea is a cotietal sea that is bouded o its easter ad orther sides by Frace, Uited Kigdom, ad Irelad (Fig. 6). The mai commercial species fished i this area iclude Atlatic cod (Gadus morhua), Norway lobster (Nephrops orvegicus), whitig (Merlagius merlagus), hake (Merluccius merluccius), mokfish (Lophius budegassa, Lophius piscatorius), megrim (Lepidorhombus whiffiagois), ad rays (i particular, cuckoo ray (Raja aevus)). Some

8 Table 1. Characterizatio of the tactics obtaied from the classificatio of fishig trips based o the four categorical variables. Catch profile (tactic ame i bold) Active variable Illustrative variable Cluster size Gear Locatio (Fig. ) Moth Catch per species (target species uderlied) Depth (m) Crew size 1. Sea bream 1490 (4) G, G3 L14, L6, L13, L1, L7 Feb., Ja., Mar., Dec., Pagellus bellottii , 3, 5 Nov., May Decapterus rhochus Brachydeuterus auritus Detex caariesis. Grouper 131 (15) G3 L, L4, L5, L8, L15, L17 Jue, July, Apr. Epiephelus aeeus 10 5 ad 3, Warmwater group Sparus caeruleostictus Pomadasys icisus Epiephelus goreesis Pomatomus saltator Plectorhychus mediterraeus Epiephelus gigas 3. Goatfish 339 () G1 L15, L40, L10, L11 July, Aug., Sept. Pseudupeeus prayesis , Diplodus vulgaris Pomadasys rogeri Sparus caeruleostictus Plectorhychus mediterraeus 4. Sailfish 449 (13) G L0, L13, L7 Aug., Sept., July, Oct. Istiophorus platypterus 5 75 Warmwater group Scyris alexadrius Coryphaea hippurus Brachydeuterus auritus 5. Octopus 111 (39) G1 L9, L11, L40, L10, L17 Mar., Oct., May, Octopus vulgaris <5 1 Warmwater group Nov., Apr. 6. Deepwater group 716 (1) G3 L16, L3, L1, L17 Mar., Dec., Apr., Oct., Detex spp. >75 4, 5 Sept., Feb., Ja. Brachiostegus semifasciatus Brotula barbata Scorpaea spp. Merluccius seegalesis Cetrophorus spp. Trachurus trachurus Note: Oly characteristic variables or categories are reported. All variables are raked by decreasig order of sigificace as give by their test value (ot reported). The tactic is amed after the most characteristic category of the variable catch profile (bold). The target species is assumed to be the most characteristic category of the variable catch per species (uderlied). I the secod colum, the umber of illustrative idividuals assiged to the cluster (i.e., the tactic) is reported i paretheses. I the gear colum, G1 correspods to o egie, G correspods to egie power <9 kw, ad G3 correspods to egie power >9 kw. Depth is coded i seve categories, but oly characteristic depth rages are reported for clarity. 58 Ca. J. Fish. Aquat. Sci. Vol. 57, 000

9 Pelletier ad Ferraris 59 Fig. 6. Locatio of Celtic Sea with traditioal ames of fishig locatios ad aual allocatio of fishig effort (umber of hours towed) for the Nephrops tactic. Shaded areas idicate the amout of fishig effort spet by rectagle. The grey level is proportioal to the umber of hours towed. other species are also caught i large quatities, like haddock (Melaogrammus aeglefius), Europea lig (Molva molva), poutig (Trisopterus luscus), squids (Loligo forbesii, S. officialis), red gurard (Aspitrigla cuculus), ad pollack (Pollachius pollachius). A large part of the catch is take by a Frech fleet of medium-sized (18 5 m log) bottom trawlers that operate all year roud, makig trips of about 1 days. Effort data cosist of logbooks, whose provisio is compulsory for stocks uder Europea Uio maagemet. Logbooks idicate fishig locatios at the scale of Iteratioal Coucil for the Exploratio of the Sea (ICES) statistical rectagles, i.e., zoes of 1 logitude per 0.5 latitude (about 30 square autical miles i this area), as well as the fishig effort allocated i each rectagle i hours towed. Catch data are provided by the automatic cesus of sales o the market hall. They iclude the weight ad value of the catch per species for each sale of each vessel. Because the vessels sell their catch upo each retur to the port, both the effort per rectagle ad the total catch (per species) are kow for each trip of a give vessel. Ufortuately, the catch per rectagle is oly kow if the vessel fished exclusively withi a sigle rectagle (sigle-rectagle trip). The data used i this aalysis pertai to 65 vessels exploitig the Celtic Sea i 1993, correspodig to 56 trips. Idetificatio of target species i relatio to fishig locatio ad seaso As i the Seegal example, target species are ot kow directly from the data, sice oly species caught are available i the data. If we assume that skippers select fishig locatio depedig o target species, the latter should be deduced from the species compositio that characterizes the selected fishig locatio at a give time of the year. At this stage, each statistical rectagle for a give moth was thus put ito correspodece to a species compositio that was likely to be obtaied whe fishig there at that time. This most likely species compositio was supposed to be the set

10 60 Ca. J. Fish. Aquat. Sci. Vol. 57, 000 Table. Characterizatio of the clusters obtaied from the aalysis of the mea CPUE per rectagle moth. Cluster ame Cluster size Typical catch per species Abudat species of average CPUE per species that could be computed over all the catch realized i this rectagle durig that moth. However, it was ecessary to restrict the aalysis to those trips durig which oly oe rectagle was visited (siglerectagle trips). Otherwise, catch ad rectagle could ot be uambiguously matched. I 1993, this correspoded to approximately 30% of the trips. The data relative to these trips were reformatted ito a table of 534 idividuals (the available combiatios of statistical rectagles ad moths) ad 63 variables (the species) where each cell value was the mea CPUE of a species calculated over all trips correspodig to a give rectagle moth (Fig. 1). Oly 53 species were cosidered as active variables. A ormalized PCA ad a HAC were successively carried out o this data table so as to obtai a typology of rectagle moths based o species compositio. This typology thus teded to group rectagle moths with similar species abudaces. Normalizatio implies that rare species were give the same weight as abudat species. The dedrogram (ot reported) showed seve well-separated clusters that explaied 7% of the iertia of the data set. This partitio was cosidered as satisfyig because it was aimed at idetifyig the mai species compositios ad ot at explaiig the whole variability of the data set. The 63 species were the used to characterize the clusters. A species was assumed to be typical of a give cluster if, i additio to a sigificat positive test value, it correspoded to oegligible CPUE i the fishery or if it was kow through prior kowledge to be a target species. Clusters were amed after their typical species compositio. For each cluster, several species were foud to be typical (Table ). Most of the typical species (see Table ) are kow as target species, whereas others are merely icidetal catch ofte associated with the target. Four clusters correspod to traditioal target species of the Celtic Sea: mokfish, Nephrops, rays, ad gadoids, while the remaider correspod to coastal stocks. Clusters were also characterized by zero or low abudaces for some species that exhibited sigificat egative test values. To a large extet, the clusters of rectagle moths reflected the seasoal evolutio of the Rare species Mokfish rays 103 Lophius sp., Raja aevus, Raja circularis Nephrops (o), gadoids Joh dory cuttlefish 3 Zeus faber, Mullus, Sepia, Loligo, Aspitrigla cuculus, Nephrops (o) Mustelus, Raja motagui Bass gurard 3 Dicetrarchus labrax, Eutrigla gurardus, Sepia, Mullus Nephrops 73 Nephrops, Lepidorhombus, Glyptocephalus cyoglossus, Squalus acathias, Raja batis Raja aevus, Lophius, Loligo, Sepia, Aspitrigla cuculus, Raja circularis, Zeus faber Rays 6 Raja clavata, Raja motagui, Psetta maxima, Rajidae, Coger coger, Solea vulgaris Gadoids 6 Gadus, Merlagius, Melaogrammus, Microstomus, Merluccius, Pollachius vires, Pollachius pollachius, Molva, Pleuroectes Poutig gurard 11 Trisopterus luscus, Aspitrigla cuculus, Trigla lucera, Scyliorhius caicula, Scyliorhius stellaris, Microstomus, Loligo, Coger coger, Rajidae, Scophtalmus Lophius, Nephrops, Raja aevus, Lepidorhombus, Raja circularis Lepidorhombus, Lophius, Raja circularis Note: Species are raked from the more to the less typical. The cluster is amed after the most typical species compositio. Abudat meas that the species is typically caught i the class, rare meas the reverse, ad o i paretheses meas that the species is totally abset from these rectagle moths. The species ame is omitted whe there is o ambiguity. spatial distributio of the exploited stocks. For istace, the cluster Rays was maily foud i witer i the orther Celtic Sea. The cluster Gadoids was marked i September i the orther Celtic Sea, i relatio to high abudaces of whitig. I cotrast, the cluster Mokfish was tied to the cotiuous presece of mokfish i the souther Celtic Sea throughout the year. Idetificatio of fishig tactics I a secod step, data per trip were cosidered to determie fishig tactics. Note that the gear was the same for every trip; therefore, fishig tactics were to be defied from the combiatio of target species, fishig locatio, ad time of the year. For each trip, the date ad the fishig effort per rectagle were kow, so that the previous typology could be used to recode fishig effort per rectagle at a give date as fishig effort per target species. Of course, this was possible oly for the trips that visited rectagles at dates for which the correspodig rectagle moth beloged to a cluster of the previous typology, which amouted to 493 trips out of 56 iitial records. A data matrix of 493 idividuals ad seve categories of the active variable, oe for each target species, was thus costructed; each elemet of the matrix was the fishig effort spet o a give target species durig a give trip (Fig. 1). A TWCA was foud to be appropriate for aalyzig associatios betwee the categories of the target species variable. I additio, ulike a PCA, TWCA removes the differeces i total fishig effort betwee trips because trips are aalyzed usig the proportio of the trip s effort per target species (ad ot the total effort per target species) (see z ij i the Appedix). A HAC o the resultig factorial coordiates yielded a partitio of fishig trips ito ie clusters, which was cosidered to be satisfactory, give the available data (Table 3). This typology teded to assig to the same cluster (i.e., the same tactic) the fishig trips that showed similar allocatios of fishig effort betwee the categories of the active variable i the TWCA (i.e., the target species). This variable was used both as active variable i

11 Pelletier ad Ferraris 61 Table 3. Characterizatio of the clusters of trips obtaied from the aalysis of fishig effort per target species. Cluster (tactic) Cluster size Target species Effort (%) Typical fishig locatio(s) Nephrops 98 North ad cetral CS, South Porcupie, Grade Vasière Typical time of the year ad effort allocatio i % 80% from March to August Rays 46 Norther CS, Irish Sea (VIIa) From November to Jauary Nephrops 38 Gadoids Gadoids 49 Northeaster CS, Bristol Caal 64% from February to May, 5% i September Nephrops Gadoids 96 Northeaster CS, Bristol Caal 59% from February to Jue, % i August ad September 5 76 Rays 97 Northeaster CS, Irish Sea From September to February 6 0 Joh dory cuttlefish 93 Easter CS, wester chael (VIIe), souther CS, Grade Vasière 50% from November to December, 39% from Jauary to Jue 7 59 Mokfish 97 Souther CS, Petite Sole, Chapelle Seasoality ot marked Poutig gurard 98 Norther Bishop, easter chael Seasoality ot marked Bass gurard 97 Souther Bishop 95% from December to March Note: Each tactic is amed after the most characteristic target species (bold). Fishig locatios are give i Fig. 6. CS deotes Celtic Sea stricto sesu, i.e., ICES divisios VIIg ad VIIh. the TWCA ad to characterize the clusters. The target species of a tactic was assumed to be the most characteristic target species of the correspodig cluster. To characterize the clusters of trips, the statistical rectagles visited durig the trip were coded as biary variables (presece absece) ad the moths were cosidered as a categorical variable. Except for clusters ad 3, each fishig tactic was strogly characterized by a sigle target species (Table 3). Fishig tactics with the same target species might differ by fishig locatios ad (or) time of the year, like tactics ad 5 or tactics 3 ad 4. Some tactics exhibited a strog seasoal effect, like tactics 1, 6, ad 9, whereas others were more evely practiced all year log, like tactics 7 ad 8. I terms of fishig effort, the mai tactics were Nephrops, Mokfish, ad Poutig gurard, each of which represeted 35, 4, ad 15% of total fishig effort, respectively. Oly the Nephrops ad Mokfish tactics were typical of the Celtic Sea per se, the Poutig gurard tactic focusig more o the chael area (Fig. 6). The Nephrops tactic appeared to be largely cofied to the orther Celtic Sea (Fig. 6), whereas the Mokfish tactic was typical of the souther Celtic Sea. The species compositios of catch for these three mai tactics illustrate the difficulty of defiig target species from realized catch (Fig. 7): eve for a give tactic, catch compositio was always a balaced mixture of several species. I 1993, the catch of the Nephrops tactic comprised oly 30% Nephrops, ad the catch of the Mokfish tactic oly 30% mokfish. The catch of the Poutig gurard tactic was made up of 1% poutig ad 14.5% gurards but also 4% cuttlefish ad 11% log-fied squid. However, cuttlefish was ot foud to be characteristic of the tactic because of a high variability i catch amog the trips i the tactic. Discussio Fishig tactics The two examples preseted cotrasted from several stadpoits. O the oe had, the Seegal fishery is artisaal, with fishers goig out fishig for 1 day at most. May gears are used. There is o compulsory declaratio system; hece, data collectio was desiged ad implemeted by scietists. Fishig locatios are idetified through their ames, but ot georefereced. O the other had, the Frech Celtic Sea fishery is idustrial, with fishers goig out fishig for approximately 1 days. Oly oe gear is used. The data collectio system relies o compulsory logbook declaratios of effort per trip ad automatic cesus of catch per sale o the market hall. Fishig locatios are idetified by ICES statistical rectagles. Despite these differeces, a similar methodology was applied i both examples to defie fishig tactics as a combiatio of target species, gear, fishig locatio, ad time of the year. I the literature reviewed, target species were determied oly from catch data. I this paper, target species were determied usig joitly species compositio, gear, fishig locatio, ad time of the year. Target species obtaied i this way should be closer to reality, sice fishig locatio, ulike catch, is almost totally depedet o fisher decisio. Hece, fishig locatio appeared to be cetral i defiig fishig tactics. I the Celtic Sea example, catch ad effort data could be liked through the typology of rectagle moths. Because catch is highly variable from oe trip to aother, data aalysis had to average out these fluctuatios. I the Seegal example, idividual catch profiles were summarized ito oly eight types. I the Celtic Sea example, the typology of rectagle moths was based o average CPUE, which smoothed out trip variability at that scale, ad was more likely to provide a accurate picture of the spatiotemporal distributio of species abudace. Although idividual trip variability was averaged out to determie target species, it was take ito accout whe obtaiig fishig tactics, sice these are groups of idividual fishig trips. Data collectio From the data collectio stadpoit, the determiatio of

12 6 Ca. J. Fish. Aquat. Sci. Vol. 57, 000 Fig. 7. Catch per species for the three mai tactics foud i the Celtic Sea fishery. fishig tactics could be improved if fishig locatios were more precisely defied, e.g., usig positioig systems ad appropriate databases. I the Seegal example, fishig locatios were loosely defied. I the Celtic Sea example, effort locatio was ot kow at scales smaller tha the rectagle, ad most importat, catch was ot eve kow per rectagle for 70% of the trips. I these examples, like i may fisheries, data collectio systems provide o iformatio at the scale of the fishig operatio, so that the obtaied fishig tactics are types of fishig trip rather tha types of fishig operatio. I the Seegal example, fishig trips were so short that it is likely that oly oe fishig tactic was used durig a trip, which may ot be the case i the Celtic Sea example. I the latter example, two tactics appeared to be composite, with a fishig effort allocated betwee two or three target species, actually reflectig the fact that several tactics were used durig correspodig trips. The results might also be helpful i adaptig samplig desigs for commercial data i that more pertiet strata may be costructed, samplig effort may be reallocated betwee strata, or eve iformatio codig may be revisited. For istace, i the Seegal case, iitial gear codig was ot precise eough to reflect the diversity of the types of had lies used i the fishery. A commo methodology The approach proposed i this paper aimed at determiig a few fishig tactics from a iitial large umber of data, i.e., may idividuals (the trips) ad may variables (catch ad effort data). The proposed methodology relies o two successive typologies, where the cluster umber of the first oe serves as a categorical variable i the secod oe, allowig the joit aalysis of catch ad effort data. Each typology cosists i a factorial aalysis followed by a classificatio. O the oe had, factorial aalyses provide a geometric represetatio of the idividuals ad variables that helps i explorig the structure of the data set. Selectig a limited umber of ordiatio axes eables oe to retai oly the variability of iterest for the classificatio step. I additio, clusters ca be projected o these axes to facilitate their iterpretatio. O the other had, classificatio techiques summarize the iformatio cotaied i the retaied axes ad yield the clusters of idividuals. Both factorial aalyses ad classificatio techiques may utilize either quatitative or categorical variables. Furthermore, both types of variables may be aalyzed joitly i the classificatio step, owig to the use of factorial aalyses that provide a uified represetatio of idividuals through factorial coordiates. These techiques accept illustrative variables that do ot

13 Pelletier ad Ferraris 63 cotribute to the ordiatio axes but that might still be of iterest i describig the axes or characterizig the clusters. I the Seegal example, the tactics (i.e., the clusters) were amed after the catch profile that was a active variable, but the target species of each tactic was assumed to be the most characteristic catch per species of the cluster, i.e., a illustrative variable. For the trips of this tactic, the catch of the target species was thus higher ad less variable tha for the other trips. Similarly, the techiques used accept illustrative idividuals that might otherwise distort the aalysis as active idividuals, so that they ca still be projected o axes or allocated to clusters. Also, idividuals with missig iformatio may be allocated to clusters o the basis of other variables, e.g., the trips with ull catch i the Seegal example As much as possible, objective criteria were proposed at each stage of the aalysis. These are mostly based o iertia, helpig, for istace, i selectig the umber of axes to be retaied i the factorial aalyses or the umber of clusters i the classificatio. Test values allow rakig of the variables that describe the clusters accordig to how well they characterize them. Both egative ad positive values are iterestig for this purpose. However, there still remais a part of subjectiveess i the aalysis. Data codig is of primary importace; data might be trasformed ito logs, percetages, or otherwise. The distace might rely o ormalized variables or ot, o the Euclidia distace, or o the χ distace, leadig to differet weightigs of the variables ad idividuals. Such choices are guided at the same time by the objectives of the aalysis, by the structures of the data set, ad by the relevace of the results with respect to prior kowledge of the fishery. For istace, i the first typology of the Seegal example, a ormalized PCA led to a dedrogram (i the HAC) from which it was difficult to select a pertiet umber of clusters. A clear partitio could oly be see after performig a oormalized PCA. I cotrast, a ormalized PCA led to a satisfyig dedrogram i the first typology of the Celtic Sea example. Like ay modellig approach, these typologies are i essece a iterative process goig back ad forth betwee the aalysis ad the results. Recall that the techiques used do ot require ay a priori assumptios about the distributio of the variables. The results are oly descriptive for the data set; o iferece ca be draw from the extrapolatio of results to the whole populatio sampled. Last, ote that fishig tactics appear as a combiatio of several variables, possibly categorical variables. Such objects are termed symbolic objects i statistics. Techiques were recetly developed to aalyze this type of iformatio (Diday et al. 1996). They might prove useful i this kid of study. Perspectives Fishig tactics reflect simultaeously the decisio of the fisher (target species, gear, locatio, ad time of the year) ad the results of that decisio (the catch). Hece, a typology of fishig tactics provides a sythetic represetatio of the fishig operatios, which is ot a objective per se. It is rather a ecessary step i uderstadig the dyamics of mixed fisheries. A typology is useful for several purposes. First, it yields a basis to partitio the fleet ito compoets that bear similar impacts o the resources. Catch per species ad effort may be calculated per compoet. I this respect, it might help i evaluatig maagemet measures that target particular fishig activities. I practice, this kid of measure has bee traditioally assessed by resortig to fleet partitios based o measurable vessel characteristics like gear ad egie power or others. Our results show that vessel characteristics aloe were ot sufficiet to describe fishig tactics, as there exist several tactics for a give gear ad (or) a give set of vessel characteristics. Secod, accoutig for fishig tactics is useful for improvig the evaluatio of the impact of a fishery o the correspodig resources. Species-directed efforts ad correspodig abudace idices may be calculated per tactic ad utilized i curret assessmet models, e.g., for calibratig VPA. Fially, the tactics ca serve to build a model of the dyamics of a mixed fishery. Such a model may be used to quatitatively assess the cosequeces of maagemet measures o stock dyamics. Murawski ad Fi (1986) ad Marchal ad Horwood (1996) developed models that provide the optimal fishig effort allocatio betwee several fishig tactics. Laurec et al. (1991) used typologies of fishig trips i a simulatio model of the Celtic Sea Frech groudfish fishery that aimed at evaluatig the cosequeces of techological iteractios. Laloë ad Samba (1991) preseted a model of the Seegal small-scale fishery that mimics the selectio of fishig tactics o the basis of expected reveues. The typology of fishig tactics i the Celtic Sea fishery is curretly used to develop a model of the fisheries dyamics that will serve to evaluate the cosequeces of differet kids of maagemet measures. I mixed fisheries, the exploitatio simultaeously affects a umber of stocks. Sigle-species maagemet measures such as total allowable catch are ot adequate for cotrollig exploitatio levels (Murawski et al. 1983; Lewy ad Vither 1994) ad ca, for istace, lead to icreased discardig for the less profitable species (Paulik et al. 1967). The importace of fishig locatio ad seasoality i the defiitio of the tactics suggests that these should be explicitly take ito accout i populatio dyamics models ad stock assessmet. I this perspective, idetifyig fishig tactics is oly a startig poit; it would be ecessary to trace the activity of each fishig uit throughout the year. This falls beyod the scope of this paper ad requires further data collectio ad aalyses. Nevertheless, aalyzig ad modellig fishig activity with referece to fishig locatio ad seasoality remais idispesable for the evaluatio of alterative maagemet measures like limitig fishig effort for either certai tactics or certai zoes durig some periods of the year. Ackowledgemets The authors thak Verea Trekel, Stéphaie Mahevas, ad Beoit Mesil for readig earlier versios of the mauscript. Two aoymous referees are also gratefully ackowledged. Refereces Bertigac, M Les redemets par espèce de la pêche