© 2004 by ICES/CIEM International Council for the Exploration of the Sea/Conseil International pour l'Exploration de la Mer
Interactions between fish and fisher's spatial distribution and behaviour: an empirical study of the anchovy (Engraulis ringens) fishery of Peru
a Institut de Recherche pour le Développement (IRD), Centre de Recherche Halieutique Méditerranéenne et Tropicale, Avenue Jean Monnet BP 171, 34203 Sète Cedex, France
b Instituto del Mar del Peru (IMARPE) Esquina Gamarra y Gral. Valles S/N; Chucuito, Callao, Lima, Peru
*Correspondence to S. Bertrand: tel: +33(0)499573200; fax: +33(0)499573295. e-mail: sophie.bertrand{at}ird.fr.
Fishing data provide, with wide spatio-temporal coverage, inexpensive information about exploited species, but a precondition for their interpretation is a good comprehension of fish and fisher spatial dynamics and interactions. In Peru, anchovy (Engraulis ringens) is exploited by an industrial fleet of about 800 purse-seiners operating all along the coast. Using simultaneous acoustic survey and commercial fishing data for the 1998–2001 time period, we present a preliminary, exploratory, and empirical approach to identify the nature of potential interactions between Peruvian anchovy and fisher behaviour. We show that (i) Peruvian anchovy exhibited a composite spatial strategy for the study period, i.e. a change in biomass was associated with both change in geographical extension and density; (ii) fishing behaviour significantly varied within and among vessels in terms of travel duration, searching duration, and number of fishing sets; and (iii) interactions between fish and fisher behaviours differed according to the spatial scale. At a fish stock scale (the scale of fishing ground selection for fishers), fishing was more efficient with low biomass and high spatial concentration (low stock range and high biomass); at a local fish spatial scale (the scale of searching for a school inside the fishing ground), fishing performance was favoured by high mean local abundances and low spatial concentration (the way fish is distributed inside its stock range); finally, at the school scale (the scale of the fishing set), both high abundance and high spatial concentration were favourable to fishing success.
Keywords: fish spatial distribution, fisher behaviour, Peruvian anchovy, spatial concentration, spatial scale
Received 3 July 2003; accepted 22 June 2004.
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Introduction |
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An ecosystem approach to fisheries (FAO, 2003) requires synthetic indicators to characterize processes and changes in marine ecosystems. Among other characteristics, these indicators must be easy to compute and understand, intuitively interpretable, and applicable to data from observation or model. As such, commercial fishing data provide inexpensive sampling of exploited species with wide spatio-temporal coverage. But interpretation of these data is reliant on a good understanding of ins and outs of the variability in catchability (q) (e.g. Arreguín-Sánchez, 1996; Fréon and Misund, 1999; Harley et al., 2001). Catchability varies mainly with the behaviour of fish, fishers, and their interactions. The need to assess impacts of policy changes on fishing activity has led to various modelling approaches focusing on fisher behaviour and its determinants: fishers as a forager (e.g. Allen and McGlade, 1986; Hilborn and Walters, 1987; Healey and Morris, 1992; Gillis et al., 1993), fishers as an economic agent (e.g. Gordon, 1953; Opaluch and Bockstael, 1984; Sampson, 1991; Mackinson et al., 1997; Sanchirico and Wilen, 2000), fishers as a multivariate statistical individual (e.g. Gaertner et al., 1996, 1999; Vignaux, 1996), and virtual fishers in a virtual fishery (e.g. Dreyfus-León, 1999; Millischer, 2000). Apart from the modelling based on optimal foraging theory (MacCall, 1990), spatial strategies of fish have been studied mainly using analytical approaches relating abundance, stock range, and aggregation patterns (see Petitgas, 1998 for a review). Connections between spatial distributions of fish and fisheries were analysed indirectly (using commercial fishing data) in terms of ideal free distribution (IFD; Fretwell and Lucas, 1970) of vessels and its deviations (e.g. Abrahams and Healey, 1990; Gillis, 2003). Very few empirical field studies have focused directly on the behaviours of both fish and fishers using simultaneous and independent sources of information (Rose and Leggett, 1991; Hancock et al., 1995; Potier et al., 1997). We propose to apply such an approach to the Peruvian anchovy (Engraulis ringens) purse-seine fishery.
During the past decade, anchovy landings have ranged from 1 to nearly 10 million metric tonnes in Peru (Ñiquen et al., 2000). Since 1983, the IMARPE (Peruvian Institute of the Sea) has carried out one to four acoustic surveys per year to assess pelagic fish biomass and spatial distribution. Furthermore, since the 1970s, IMARPE has developed several programmes of observers at sea, onboard commercial fishing vessels, to monitor the anchovy population. From this rich database, our goal is to extract from spatially resolved fishing data, information that could improve our understanding of the functioning of exploited ecosystems. As a first exploratory step, we propose an empirical approach based on multivariate statistical analysis of simultaneous acoustic survey and commercial fishing data in order to identify the nature of potential interactions between Peruvian anchovy dynamics and fisher behaviour.
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Material and methods |
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Commercial fishery data
Anchovy is exploited by a specialized fleet of about 800 purse-seiners with hold capacity ranging between 30 and 800 m3 (80% between 100 and 400 m3). Fishing trips are relatively short, about one day. Owing to its economic relevance, this fishery is strictly regulated through fishing closures (about 4 months a year) and quotas. The latest programme of observers at sea onboard fishing vessels started in 1996 and provides, on a daily basis, valuable information about the fishing trips (boat reference, hold capacity, date leaving the port, port of leaving, travel duration, searching duration, total catch, number of fishing sets) and the fishing sets (exact spatial position, catch, species composition) of about 25 purse-seiners randomly selected and distributed all along the Peruvian coast. For this study, we selected data corresponding to the acoustic survey periods. In total, we analysed a set of 795 fishing trips operated by 110 different boats (Table 1).
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Fishing effort was basically described for each trip by travel (TD) and searching (SD) durations and number of fishing sets (FSN). From the position of the fishing sets we computed for each fishing trip: (i) the mean inter-fishing set sailing distance (IFSD) and; (ii) the inertia ("I"), i.e. the spatial dispersion between the fishing set positions. The range of hold capacity surveyed was not homogeneous for all survey periods, and fishing trip catch was highly significantly correlated (linear regression, r2 = 0.257, F[1,793] = 274.20, p < 0.01) with the hold capacity of the vessel. Thus we used the filling rate of the hold (FRH, ratio of trip catch by hold capacity, Table 2) as a catch index. From the ratio of FRH to TD, SD, FSN, IFSD, and "I" we obtained six performance indices annotated TDp, SDp, FSNp, IFSDp, and Ip (Table 2).
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Acoustic survey data
We selected six acoustic surveys performed during the fishing season and covering the fishing grounds: (i) 23 November to 23 December 1998 (Sv 98 11–12); (ii) 23 November to 15 December 1999 (Sv 99 11–12); (iii) 8 June to 6 July 2000 (Sv 00 06–07); (iv) 9 October to 10 November 2000 (Sv 00 10–11); (v) 2 July to 9 August 2001 (Sv 01 07–08); and (vi) 2 October to 13 November 2001 (Sv 01 10–11) (Table 1). Acoustic data were collected onboard IMARPE research vessels with a SIMRAD EK500 echosounder connected to 38–120 kHz, split-beam, hull-mounted SIMRAD transducers. The water column was sampled to a depth of 250 m. The on-axis and off-axis calibration was performed using 23-mm and 60-mm copper spheres and a standard procedure (Foote et al., 1987). Standard survey design consisted of parallel transects running from 9.3 km from the coast to about 185.2 km offshore with an inter-transect distance varying between 22.2 and 25.9 km according to the cruise. Acoustic back-scattered energy by the surface unit (sA) was recorded in each elementary sampling distance unit (ESDU) of 1852 m. Species composition was identified using fishing trawls, echo trace characteristics, and the abiotic conditions. We selected the data corresponding to anchovy, and we considered sA as an index of the anchovy local abundance.
Resource characteristics
Various quantitative and spatial descriptors of the resource can be obtained from the acoustic surveys. Firstly, IMARPE routinely produces "stock scale" indices for each survey: stock abundance (B in metric tonnes) and stock range (S in km2) estimates. A theoretical (i.e. with a uniform distribution hypothesis) stock density (D in metric tonnes per km2) is then calculated.
As fish are not evenly distributed inside their distributional area, the simple notion of stock range is insufficient to describe the way fish occupy space (Swain and Sinclair, 1994). A variety of dispersion indices of geographic distribution have been used in the literature (see Gauthiez (1997) and Petitgas (1998) for reviews). Distributional indices, generally used for survey data, rely on histograms of cumulative frequencies of density against the corresponding occupied surfaces: Lorenz curves (Myers and Cadigan, 1995), concentration profiles (Hilborn and Walters, 1992), or geostatistical aggregation curves (Petitgas, 1998). Based on these histograms, there are two quantitative indices describing the concentration or patchiness of the spatial distribution of fish inside its stock range: "Gini index" from Myers and Cadigan (1995) and "space concentration index" or "Ss" from Petitgas (1998).
We estimated the cumulative frequency by first ranking non-zero sA ESDU observations by decreasing sA values. We then plotted the cumulative empirical distribution of the fraction of ESDU (y) against the corresponding cumulative fraction of sA (x). We fitted an exponential function to the empirical plot, using non-linear least square regression (Equation 1).
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As the regression was strongly affected by the large number of ESDUs contributing little to the overall sA, we fitted the exponential model to the first 90% of cumulative sA. We measured the goodness-of-fit using a one-sample Kolmogorov–Smirnov test. From Equation (1) we could compute a space concentration index (Ss; Petitgas, 1998) by integrating the difference between the identity function (representing a theoretical homogeneous distribution) and each exponential function (Equation 2).
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| (2) |
We also computed the mean sA (sA+) and the standard deviation of sA (sdsA+) over ESDUs with anchovy. The sA+ is a local (i.e. ESDU scale) density index where fish are present independently of any interpolation method. The sdsA+ provides a measure of the heterogeneity of fish distribution inside its range of distribution.
Hilborn and Walters (1992) reviewed three spatial strategies for fish by which to manage changes in biomass: (i) a proportional density model (i.e. an increase of biomass leads to an increase of density); (ii) a constant density model (i.e. an increase of biomass leads to an extension of the range of distribution); and (iii) a composite model (i.e. an increase of biomass leads to an increase of both density and range of distribution). To investigate which model was appropriate for our case study, we performed linear regressions between stock biomass (B) and stock range (S), local density (sA+), and the product S by sA+.
Interactions between fish distribution and fishing behaviour
To test if the fishing behaviour varies according to the survey period and vessels we ran a series of analyses of variance on fishing effort indices. In order to visually illustrate the variability of fishing behaviour within vessels during different periods and among vessels for the same period, we overlaid positions of fishing sets on anchovy distribution maps. To map the anchovy distribution, we interpolated anchovy sA using the natural neighbour interpolation procedure from Surfer (Golden Software, CO, USA).
To explore, without a priori theory-based insights, potential interactions between fisher behaviour and resource patterns, we performed a principal component analysis (PCA; Lebart et al., 2000), with the fishing trip performance indices as continuous active variables. To facilitate identification of general patterns of interaction, continuous resource descriptors (B, S, D, sA+, sdsA+, and Ss) were categorized. Based on the distribution of data inside the distribution range of each variable, we defined three modalities: (i) low, (ii) medium, and (iii) high for all resource descriptors, except for Ss which was categorized in two modalities (low and high) (Table 1). A first run showed that IFSDp and Ip were strongly and significantly correlated. As performance related to inertia, Ip, was easier to interpret in terms of area explored per fishing trip, the PCA was performed with TDp, SDp, FSNp, and Ip as active variables (Table 2). In the reduced factorial space obtained, we performed hierarchical clustering analysis (HCA) in order to identify consistent groups of fishing behaviours. Illustrative variables (FRH, "effort" indices and resource descriptors, see Table 2) were projected on the factorial space. The potential correlations between them and the classes of fishing behaviour were analysed through the level of significance of Student's t-test associated with the HCA (Lebart et al., 2000).
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Results |
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TopIntroductionMaterial and methodsResultsDiscussionReferences |
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Fish patterns
Peruvian anchovy spatial patterns are known to depend strongly on seasonal (summer–winter) and interannual patterns, i.e. the alternation of El Niño/La Niña episodes (Csirke, 1989; Ganoza et al., 2000; Gutiérrez, 2001). Schematically, environmental conditions during summer or El Niño lead to a reduction of the anchovy habitat and consequently to a contraction of the stock in coastal waters (Csirke, 1989; Muck, 1989). In contrast, cold conditions (La Niña or winter) lead to an expansion of the range of anchovy habitat. Our study period presents a variety of environmental conditions with the end of the El Niño of 1997–1998 in the case of the survey 98 11–12 and La Niña for the other surveys. In terms of intra-annual conditions, four surveys were performed during austral spring and summers (Sv 98 11–12, Sv 99 11–12, Sv 00 10–11, and Sv 01 10–11) and two during austral winters (Sv 00 06–07 and Sv 01 07–08).
In this environmental context, acoustically estimated biomasses ranged between 2.7 (end of the El Niño event) and 8.0 million metric tonnes (Table 1); range of distribution (S) varied in a ratio of 1 to 5; stock density (D) in a ratio of 1 to 2, and local densities (sA+) in a ratio of 1 to 2.5. Consequently, the study period offered a wide variety of scenarios in terms of densities and distributions.
The goodness-of-fit of the exponential functions to the empirical concentration profiles was highly significant (Kolmogorov–Smirnov test, p < 0.01) for the six surveys. A higher Ss implies a higher deviation of the fish distribution from a theoretical homogeneous distribution and thus higher spatial concentration (Figure 1). The highest value for Ss (i.e. strongest spatial concentration) occurred during the survey period Sv 98 11–12 at the end of an El Niño event and during the austral summer (Table 1).
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As expected, anchovy biomass presented a highly significant linear relationship with the product S by sA+ (linear regression, r2 = 0.85, F[1,4] = 22.90, p < 0.01). On the other hand, we did not find any significant linear relation between biomass (B) and stock range (S; linear regression, r2 = 0.46, F[1,4] = 3.43, p > 0.05) or local densities (sA+; linear regression, r2 = 0.24, F[1,4] = 1.24, p > 0.05). The proportional density and constant density models are thus discarded and a composite model (Hilborn and Walters, 1992) with varying density and spatial range can be assumed appropriate as a model of spatial strategy of Peruvian anchovy.
Interactions between fish and fisher behaviours
ANOVAs showed that travel durations, searching durations, and number of fishing sets were significantly different among the survey periods and the vessels (Table 3). For instance, according to the survey, the mean travel duration ranged between 20 and 26 h, the mean searching duration between 5 and 7.5 h, and the mean number of fishing sets between 2.5 and 3.1 (Table 1). Depending on the vessel, the mean travel duration ranged between 12 and 53 h, the mean searching duration between 2 and 21 h, and the mean number of fishing sets between 1 and 7. Combined maps of fishing sets and anchovy distributions illustrated differences of fishing spatial behaviour according to fish distribution (see Figure 2a–c for the example of the same vessel during three different periods) and the vessels (see Figure 2d–f for the example of three vessels with the same fish distribution).
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For instance, boat "1876" fished farther from the coast during Sv 00 06–07, which increased its mean travel duration (21.6 h instead of 17.28 and 18.08 h for Sv 99 11–12 and Sv 00 10–11, respectively). Moreover, during Sv 00 10–11, boat "1876" seemed to change zone more frequently during the same trip, which led to an increased mean searching duration (6.07 h instead of 3.33 and 3.92 h for Sv 99 11–12 and Sv 00 06–07, respectively). During Sv 99 11–12, vessel "983" exhibited more dispersion of its fishing sets than vessels "353" and "1032". For this reason, vessel "983" presented travel and searching durations that were relatively higher than those of the other vessels (Vessel "983": mean TD = 29.77, mean SD = 6.69; Vessel "353": mean TD = 21.87, mean SD = 3.56; Vessel "1032": mean TD = 28.75, mean SD = 8.5).
Our main purpose being to understand interactions between fish and fishers' behaviours, we used multivariate statistics to distinguish the part of fishing behaviour variability due to variability of fish spatial patterns. The first two principal components of the PCA represented respectively 60.7% and 23.2% of the total variance of fishing trips (Figure 3). The first factor was interpreted as a general performance component. The second component discriminated mostly spatial performance. In this reduced factorial space, hierarchical cluster analysis focused on a4-class typology of fishing trips, with each class significantly associated with different modalities of resource descriptors (Figure 3). The resource descriptors we used de facto describe processes at different spatial scales. Indices B, S, and D give information on abundance and spatial distribution at the stock spatial scale; sA+, sdsA+, and Ss describe abundance and spatial distribution at the ESDU scale, i.e. at an intra-stock or local spatial scale (Table 4). Class 1 was the largest one (53% of fishing trips) and corresponded to low performance trips. It was related at a stock scale to low density (D), high range (S), high abundance (B), and, at a local scale, to low abundance (sA+) and high spatial concentration (Ss). The second larger group, class 2 (36.5%), corresponded to good performance trips. It was associated at a stock scale to high density (D), low abundance (B), and range (S), and, at a local scale, to high abundance (sA+). Class 3 (1%) included trips for which good performance was particularly related to low dispersion of sets. It was significantly related to high density (D) and medium range (S) at a stock scale and to high local abundances (sA+). Class 4 (9.5%) constituted the most successful trips and was mainly related to local low spatial concentration (Ss) and also to medium local abundance (sA+), stock abundance (B), stock density (D), and stock range (S).
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Discussion |
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TopIntroductionMaterial and methodsResultsDiscussionReferences |
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Fish spatial strategy
Our results suggest that a composite range-density model is appropriate for Peruvian anchovy, as MacCall (1990) had also found for northern anchovy (Engraulis mordax). Fréon and Misund (1999) assumed that environmental conditions determined the size of the available habitat of small pelagic fish and thus that fish adjust their density according to this "available habitat" and the current level of biomass. Pitcher (1995) underlined that the behavioural characteristics of pelagic fish intimately reflect the intrinsic volatility of the ecological niche that they have evolved to exploit. In this sense, a composite spatial strategy may allow anchovy to dampen the effects on its population of its highly volatile ecosystem where "variability is normality" (Marco Espino, Instituto Antártico Peruano (INANPE), Peru, pers. comm.). Actually, even if affected by recurrent strong collapses, Peruvian anchovy can tolerate fairly unfavourable environmental conditions by adapting its spatial distribution (Bertrand et al., in preparation).
Interactions between fish and fisher behaviours
One question that may arise looking at Figure 2 could be: why do all the fishing sets not occur in the core of the anchovy aggregations? A first "technical" explanation is that aggregations of anchovy are liable to be highly mobile over a single day (Miguel Ñiquen, IMARPE, pers. comm.). So, at the moment of each set, anchovy may have not been distributed in the exact position as mapped from the acoustic surveys. Second, the large biomasses of anchovy that occur off Peru, combined with their highly aggregative behaviour, may generate suitable fishing grounds even on the margin of the clusters. Some authors have shown that economic optimization constraints (Sampson, 1991) or expectation of competition with other vessels (Gillis and Peterman, 1998) may lead to this discrepancy between an observed vessel's distribution and optimal fishing strategies.
The nature of interactions between fish and fisher behaviours varied according to spatial scale (Table 4). Abundance and spatial concentration were defined respectively at a stock scale by B and the combination of S and D, and at an intra-stock scale by sA+ and Ss.
At a "stock scale", three typological classes of fishing trips (1, 2, and 3) showed a positive relation between fishing performance and spatial concentration. Classes 1 and 2 showed an inverse relation between fishing performance and abundance. These trends are consistent with the strong depensatory relations reported in the literature between catchability and biomass (Csirke, 1989) and catchability and stock range (Winters and Wheeler, 1985). Rose and Kulka (1999) evoked a "hyper aggregation" mechanism by which a biomass reduction can lead to decreasing stock range and increasing densities. But if this pattern is described through a case study, the causal mechanism remains to be identified for each study case. In the Peruvian context, biomass depletions for anchovy are usually related to warm events such as El Niño. During an El Niño, the range of habitat of anchovy is dramatically reduced and limited to coastal refuge areas (Muck, 1989). The presence of high fish densities close to the coast makes fishing ground identification (usually by plane and radio communication between boats) easier, and thus increases fish vulnerability. This mechanism generates the high fishing success classically reported during the beginnings of El Niño events (Csirke, 1989; Bouchon et al., 2000). Simultaneously, fish mortality caused by unfavourable environmental conditions and the limited carrying capacity of the refuge areas can lead to drastic biomass reductions. Moreover, Bertrand et al. (in preparation) showed that this concentration in coastal refuge areas could lead to underestimation of acoustic biomass and thus artificially accentuates these tendencies.
At an "intra-stock scale", classes 1, 2, and 3 showed a positive relation between abundance (sA+) and fishing performance. Classes 1 and 4 illustrated that spatial concentration (Ss) was inversely related to fishing success. Actually, at a fishing ground scale, anchovy schools are located using bird radar, omni-directional sonar, and echosounder. The range of detection of these tools does not allow instantaneous search of the entire fishing ground. Thus, if fish are highly aggregated (high Ss) inside the fishing grounds, the probability of missing commercially interesting aggregations increases. Finally, at a "school scale", in a purse-seine fishery, high abundance and spatial concentration of fish (i.e. dense well-delimitated schools) makes fish more available to the net and allows more efficient fishing sets than if fish are scattered.
In conclusion, our results suggest that Peruvian anchovy manage changes in biomass by adjusting its density and its range of distribution. This strategy could be an adaptation of fish to this highly variable environment. Second, we showed that fishers do adapt their behaviour in terms of travel duration, searching duration, and number of fishing sets according to the spatial configuration of anchovy stock. And third, we showed that, as for many ecological processes (Levin, 1992), the sense of interactions between descriptors of fish and fisher behaviours depends strongly on the spatial scale. This preliminary and parsimonious approach showed that the way fish occupy space at a stock scale as well as inside its stock range is a relevant factor in our understanding the variability of fishing success. Spatial concentration of fish has already been invoked as a source of bias in cpue when the perspective is biomass evaluation (Fréon and Misund, 1999). However, as the way small pelagic fish occupy space depends on the available habitat and the current biomass, spatial organization of fish could be itself a pertinent indicator of population and ecosystem condition. Finally, owing to the multi-scale gregarious behaviour of anchovy (school, cluster of schools, cluster of clusters, etc.) and the resulting one of fishers, one of the next steps will consist in searching for invariant scale descriptors of spatial organization and interactions.
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Acknowledgements |
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We are grateful to Mariano Gutierrez and Marilú Bouchon from IMARPE, who provided data and useful information for this work. We are also grateful to Arnaud Bertrand for his active participation and to Gordon Swartzman for his helpful comments. Finally, we thank François Gerlotto, who made possible the realization of this work. This work is a contribution of the IRD's Research Unit ACTIVE UR061.
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References |
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Abrahams M.V. and Healey M.C. (1990) Variation in the competitive abilities of fishermen and its influence on the spatial distribution of the British Columbia salmon troll fleet. Canadian Journal of Fisheries and Aquatic Sciences 47:1116–1121.
Allen P.M. and McGlade J.M. (1986) Dynamics of discovery and exploitation: the case of the Scotian shelf groundfish fisheries. Canadian Journal of Fisheries and Aquatic Sciences 43:1187–1200.
Arreguín-Sánchez F. (1996) Catchability: a key parameter for fish stock assessment. Reviews in Fish Biology and Fisheries 6:221–242.[Web of Science]
Bertrand A., Segura M., Gutiérrez M., Vásquez L. Pelagic fish and environmental variability: an integrative approach of spatio-temporal adaptive strategies during El Niño 1997–98 in Peru. Fish and Fisheries (in preparation).
Bouchon M., Cahuín S., Díaz E., Ñiquen M. (2000) Captura y esfuerzo pesquero de la pesquería de anchoveta peruana (Engraulis ringens). Boletín Instituto del Mar del Perú 19:109–115.
Csirke J. (1989) Changes in the catchability coefficient in the Peruvian anchoveta (Engraulis ringens) fishery. In Pauly D., Muck P., Mendo J., Tsukayama I. (Eds.). The Peruvian Upwelling Ecosystem: Dynamics and Interactions(ICLARM, IMARPE, Callao, Peru) vol. 18: pp. 207–219 438 pp.
Dreyfus-León M.J. (1999) Individual-based modelling of fishermen search behaviour with neural networks and reinforcement learning. Ecological Modelling 120:287–297.[CrossRef][Web of Science]
FAO. (2003) The ecosystem approach to marine capture fisheries. FAO Technical Guidelines for Responsible Fisheries, no. 4 (Suppl 2). 112 pp.
Foote K. G., Knudsen H. P., Vestnes D. N., MacLennan D. N., Simmonds E. J. (1987) Calibration of acoustic instruments for fish density estimation: a practical guide. ICES Cooperative Research Report, No. 144. 69 pp.
Fréon P. and Misund O.A. (1999) Dynamics of Pelagic Fish Distribution and Behaviour: Effects on Fisheries and Stock Assessment(Fishing News Books, Blackwell Science, London) 348 pp.
Fretwell S.D. and Lucas H.L. (1970) On territorial behaviour and other factors influencing habitat distribution in birds. Acta Biotheorica 19:16–36.
Gaertner D., Pagavino M., Marcano J. (1996) Utilisation de modèles linéaires généralisés pour évaluer les stratégies de pêche thonière à la senne en présence d'espèces associées dans l'Atlantique Ouest. Aquatic Living Resources 9:305–323.[CrossRef][Web of Science]
Gaertner D., Pagavino M., Marcano J. (1999) Influence of fisher's behaviour on the catchability of surface tuna schools in the Venezuelan purse-seiner fishery in the Caribbean Sea. Canadian Journal of Fisheries and Aquatic Sciences 56:394–406.
Ganoza F., Castillo P.R., Marin D. (2000) Variaciones estaciónales en la distribución y biomasa de anchoveta entre 1983 y 2000. Boletín Instituto del Mar del Perú 19:157–177.
Gauthiez F. (1997) Structuration spatiale des populations de poissons marins demersaux. Caracterisation, consequences biometriques et halieutiques. Doctoral thesis, Université Claude Bernard, Lyon, France. 251 pp.
Gillis D.M. (2003) Ideal free distributions in fleet dynamics: a behavioral perspective on vessel movement in fisheries analysis. Canadian Journal of Zoology 81:177–187.
Gillis D.M. and Peterman R.M. (1998) Implications of interference among fishing vessels and the ideal free distribution to the interpretation of CPUE. Canadian Journal of Fisheries and Aquatic Sciences 55:37–46.
Gillis D.M., Peterman R.M., Tyler A.V. (1993) Movement dynamics in a fishery: application of the ideal free distribution to spatial allocation of effort. Canadian Journal of Fisheries and Aquatic Sciences 50:323–333.
Gordon H.S. (1953) An economic approach to the optimum utilization of fishery resources. Journal of Fisheries Research Board of Canada 10:442–457.
Gutiérrez M. (2001) Efectos del evento El Niño 1997–98 sobre la distribución y abundancia de anchoveta (Engraulis ringens). In Tarazona J., Arnts W.E., Castillo de Maruenda E. (Eds.). El Niño en América Latina: impactos biológicos y sociales(Consejo Nacional de Ciencia y Tecnología, Lima, Peru) pp. 55–72 327 pp.
Hancock J., Hart P.J.B., Antezana T. (1995) Searching behaviour and catch of horse mackerel (Trachurus murphyi) by industrial purse-seiners off south-central Chile. ICES Journal of Marine Science 52:991–1004.
Harley S.J., Myers R.A., Dunn A. (2001) Is catch-per-unit-effort proportional to abundance? Canadian Journal of Fisheries and Aquatic Sciences 58:1760–1772.
Healey M.C. and Morris J.F.T. (1992) The relationship between the dispersion of salmon fishing vessels and their catch. Fisheries Research 15:135–145.[CrossRef][Web of Science]
Hilborn R. and Walters C.J. (1987) A general model for simulation of stock and fleet dynamics in spatially heterogeneous fisheries. Canadian Journal of Fisheries and Aquatic Sciences 44:1366–1369.
Hilborn R. and Walters C.J. (1992) Quantitative fisheries stock assessment. Choice, Dynamics and Uncertainty(Chapman and Hall, New York, London) 570 pp.
Lebart L., Morineau A., Prion M. (2000) Statistique Exploratoire Multidimensionnelle 3rd edn (Dunod, Paris) 439 pp.
Levin S.A. (1992) The problem of pattern and scale in ecology. Ecology 73:1943–1967.[CrossRef][Web of Science]
MacCall A.D. (1990) Dynamic Geography of Marine Fish Populations(University of Washington Press, Seattle) 153 pp.
Mackinson S., Sumaila U.R., Pitcher T.J. (1997) Bioeconomics and catchability: fish and fishers behaviour during stock collapse. Fisheries Research 31:11–17.[CrossRef][Web of Science]
Millischer L. (2000) Modélisation individu centrée des comportements de recherche des navires de pêche. Thèse de Doctorat, Ecole Nationale Supérieure Agronomique de Rennes. 246 pp.
Muck P. (1989) In Pauly D., Muck P., Mendo J., Tsukayama I. (Eds.). Relationships between anchoveta spawning strategies and the spatial variability of sea surface temperature off Peru. The Peruvian Upwelling Ecosystem: Dynamics and Interactions. Proceedings of the Workshop on Models for Yield Prediction in the Peruvian Ecosystem24–28 August 1987Callao, Peru ICLARM Conference Proceedings, 18: 207–219.
Myers R. and Cadigan N. (1995) Was an increase in natural mortality responsible for the collapse of northern cod? Canadian Journal of Fisheries and Aquatic Sciences 52:1274–1285.
Ñiquen C.M., Bouchon C.M., Cahuín V.S., Díaz A.E. (2000) Pesquería de anchoveta en el mar peruano. Boletín Instituto del Mar del Perú 19:109–115.
Opaluch J.J. and Bockstael N.E. (1984) Behavioral modelling and fisheries management. Marine Resource Economics 1:105–115.
Petitgas P. (1998) Biomass-dependent dynamics of fish spatial distributions characterized by geostatistical aggregation curves. ICES Journal of Marine Science 55:443–453.
Pitcher T.J. (1995) The impact of pelagic fish behaviour on fisheries. Scientia Marina 59:295–306.[Web of Science]
Potier M., Petitgas P., Petit D. (1997) Interactions between fish and fishing vessels in the Javanese purse seine fishery. Aquatic Living Resources 10:149–156.[CrossRef][Web of Science]
Rose G.A. and Kulka D.W. (1999) Hyperaggregation of fish and fisheries: how catch per unit effort increased as the northern cod (Gadus morhua) declined. Canadian Journal of Fisheries and Aquatic Sciences 56:118–127.
Rose G.A. and Leggett W.C. (1991) Effects of biomass-range interactions on catchability of migratory demersal fish by mobile fisheries: an example of Atlantic cod (Gadus morhua). Canadian Journal of Fisheries and Aquatic Sciences 48:843–848.
Sampson D.B. (1991) Fishing tactics and fish abundance, and their influence on catch rates. ICES Journal of Marine Science 48:291–301.
Sanchirico J.N. and Wilen J.E. (2000) Dynamics of spatial exploitation: a metapopulation approach. Natural Resource Modelling 14:391–418.
Swain D.P. and Sinclair A.F. (1994) Fish distribution and catchability: what is the appropriate measure of distribution? Canadian Journal of Fisheries and Aquatic Sciences 51:1046–1054.
Vignaux M. (1996) Analysis of vessel movements and strategies using commercial catch and effort data from the New Zealand hoki fishery. Canadian Journal of Fisheries and Aquatic Sciences 53:2126–2136.
Winters G.H. and Wheeler J.P. (1985) Interactions between stock area, stock abundance and catchability coefficient. Canadian Journal of Fisheries and Aquatic Sciences 42:989–998.
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