© 2006 International Council for the Exploration of the Sea
The effect of coastal topography on the spatial structure of anchovy and sardine
a Hellenic Centre of Marine Research, PO Box 2214, 710 03 Iraklion, Crete, Greece
b University of Patras, Department of Biology, 26500 Patra, Greece
*Correspondence to M. Giannoulaki: tel: +30 810 337831; fax: +30 810 337820. e-mail: marianna{at}her.hcmr.gr.
Acoustic-survey data from 1995 to 2004 (six acoustic surveys in summer and two in winter) in the Aegean and Ionian Seas (eastern Mediterranean Sea) were analysed to investigate the spatial organization of European anchovy and European sardine populations. The potential effect of certain topographic characteristics (e.g. area, bottom depth, and the degree of land enclosure) on the spatial structure of the fish was studied in different geographic subareas (i.e. how topography affects the organization of fish into clusters of schools). Parameters calculated by geostatistical techniques were used as descriptors of the spatial organization. The results indicate the significant effect of area and land enclosure on the spatial structures of both species, suggesting that environmental spatial heterogeneity attributable to coastal topography affected the way fish schools were organized into aggregations. In summer, the spatial structure of sardine was more heterogeneous in subareas with increased land enclosure, whereas the spatial structure of anchovy was not significantly related to any of the area characteristics examined. In winter, the spatial structure of both species was more heterogeneous in subareas with increased enclosure and in small rather than larger subareas. The findings are discussed in terms of the species' response to their environment.
Keywords: anchovy, coastal topography, exhaustive variograms, geostatistics, land enclosure, sardine
Received 31 May 2005; accepted 11 October 2005.
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Introduction |
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 Top Introduction Material and methodsResultsDiscussionReferences |
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Small pelagic fish such as anchovy and sardine are important components of the world's fisheries, contributing approximately one-third to one-half of the global annual tonnage of wild-fish catches (Fréon and Misund, 1999; Bakun and Broad, 2003). They are also important to the ecosystem they inhabit from an ecological point of view (Bakun and Broad, 2003), because they constitute mid-trophic-level populations (Cury et al., 2003).
Fish abundance data are usually characterized by spatial structures owing to spatial autocorrelation. Knowledge of the spatial organization of small pelagic fish stocks such as anchovy or sardine is important to fisheries scientists, because it may affect both stock catchability and the results of assessment surveys. It may also contribute to an understanding of the ecological processes that affect the population (Fréon and Misund, 1999).
Most studies of small pelagic fish spatial organization use geostatistical techniques and focus mainly on the analysis of the spatial patterns in fish distribution in order to optimize the design of sampling schemes and to increase the precision of abundance estimates (e.g. Petitgas, 1993; Maravelias and Haralabous, 1995; Maravelias et al., 1996; Petitgas and Lévénez, 1996; Barange and Hampton, 1997; Bez and Rivoirard, 2001). A different approach was taken by Paramo and Roa (2003), who attempted to associate the spatial characteristics of small pelagic fish patches with large-scale habitat features, such as temperature and salinity.
The effect of area topography on small pelagic fish spatial structure is seldom tackled in fisheries ecology (Giannoulaki et al., 2003). Anchovy and sardine populations exist in different types of ecosystems (Fréon and Misund, 1999; Bakun and Broad, 2003), but most studies of their spatial organization have been conducted in areas with a minimum effect of land (e.g. Senegalese waters, Petitgas and Lévénez, 1996; the southern Benguela upwelling region, Barange and Hampton, 1997). However, a recent study in the eastern Mediterranean (Giannoulaki et al., 2003), which was conducted in an area with variable coastal topography, showed a significant effect of certain topographic characteristics on the spatial organization of the overall small-pelagic-fish assemblage. Although useful in terms of fisheries exploitation, fish catchability and management, that work did not clarify how the spatial structures of individual species dominating the area (i.e. anchovy and sardine) relate to the multispecies structures presented. For instance, does area topography influence the single-species spatial characteristics in the same way as the overall pelagic-fish assemblage?
In the present study, data from eight acoustic surveys (six in summer and two in winter) in the Aegean and Ionian Seas (eastern Mediterranean Sea) were used to examine the effect of certain topographic characteristics on the spatial structure of co-occurring populations of European anchovy Engraulis encrasicolus and European sardine Sardina pilchardus. The surveyed area was heterogeneous and consisted of variously sized open and semi-enclosed subareas (Figure 1). The data were analysed by geostatistical techniques (omnidirectional and exhaustive variograms; Isaaks and Srivastava, 1989; Rossi et al., 1992), and multiple regression analysis was applied to examine the effect of topographic parameters on geostatistical descriptors of the spatial distribution.
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Material and methods |
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TopIntroductionMaterial and methodsResultsDiscussionReferences |
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Sampling
Acoustic data were collected during eight research surveys carried out along predetermined transects (Figure 1, Table 1; Bazigos, 1976) in the northern Aegean Sea (Thracian Sea, Strymonikos Gulf, and Thermaikos Gulf: in June of 1995, 1996, 2003, and 2004), the central Aegean Sea (North and South Evoikos Gulf: July 1998, June and December 1999, December 2000; North Evoikos Gulf: June 2003, June 2004), and the Ionian Sea (Corinthiakos Gulf, Patraikos Gulf, and Ionian Sea; July 1998, June and December 1999, January 2001). The acoustic equipment used was a Biosonic dual-beam, 120 kHz, V-fin echosounder. The speed of the vessel was eight nautical miles per hour. Sampling took place mainly during daylight except on certain days when open-sea parts of the surveyed area beyond the continental shelf were sampled at night to save ship's time. These open-sea areas were dominated by zero values for fish abundance, because anchovy and sardine are generally distributed shallower, over the continental shelf. Acoustic echoes were registered continuously along transects and were integrated over one nautical mile (hereafter referred to as mile) that served as the Elementary Distance Sampling Unit (EDSU). A pelagic trawl with a vertical opening of 10 m and a 10 mm codend was deployed to identify echo traces. The catch composition of 153 hauls revealed that the main species in all areas were Engraulis encrasicolus and Sardina pilchardus; Sardinella aurita and Trachurus spp. were found in lesser quantities. Echo discrimination into species was based on echo-trace characteristics (MacLennan and Simmonds, 1992; Reid, 2000).
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Data analysis
The acoustic data analysis was performed separately for nine subareas (Figure 1, Table 1). The division into these subareas was based largely on their geographic separation or differences in hydrographic and bathymetric characteristics, or one of these factors alone (Somarakis et al., 2002a, b; Giannoulaki et al., 2003), but also taking into account the existing transect design.
In each subarea, the overall analysis followed two major steps: first, geostatistical analysis was applied separately for anchovy and sardine echo to analyse and visualize the "intrinsic" spatial structure of each species in each subarea. Second, the effect of topography (i.e. traits related to area topography) on the calculated geostatistical parameters was examined. As the acoustic data were highly skewed and some extremely high values were observed, a natural logarithm (ln) transformation was used to normalize them. To avoid possible trends, we estimated a multiple-regression model of ln(integrated echo) against longitude, latitude, and bottom depth (Giannoulaki et al., 2003).
Omnidirectional variograms were computed using the residuals derived from the multiple-regression models based on the semi-variogram estimator (Armstrong, 1984):
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(h)) is defined as the variance of the difference between values that are h distance units apart, u(x) the sum of acoustic cross-section/mile at location x, u(x + h) the sum of acoustic cross-section/mile at location h (miles) away from x, and var(u) is the variance. The spherical model was chosen as the most appropriate to fit the omnidirectional variogram (Cressie, 1991). The three parameters of the omnidirectional variogram (range, sill, and nugget effect) were estimated. In addition, multidirectional variogram plots (in order to examine anisotropy; Isaaks and Srivastava, 1989; Rossi et al., 1992) were computed for each subarea. The multidirectional variogram plots (i.e. exhaustive variograms in Rossi et al., 1992) were computed as a contour plot of the directional variograms in 16 directions, calculated with a 22.5° step and ±22° tolerance (Giannoulaki et al., 2003 and references therein). This two-dimensional graph of spatial continuity allows a quick and comprehensive appraisal of the directional spatial dependence of the data, and detects range anisotropy with respect to data variability in different directions, showing the spatial structure as a function of distance, with contours depicting the autocorrelation values (Rossi et al., 1992; Goovaerts, 1997; Ecker and Gelfand, 1999).
Changes in the spatial structure attributable to the morphology of the surveyed areas were subsequently examined by backward, stepwise, multiple regression analysis (Zar, 1985). An F-test was used to test the significance in regression models. Independent variables were accepted as statistically significant at the 0.05 level of significance.
For each subarea, we considered the degree of enclosure, the area, and the mean bottom depth, as well as the bottom depth variation as adequately describing area topography (Table 1). As independent variables we therefore used:
- the enclosure index (EI = Sc/Sn), where Sc is the length of the area's coastline and Sn the length of the line that defines the boundaries of the examined area (Figure 1), indicating the degree of enclosure of a basin (De Leiva Moreno et al., 2000). Measurements were taken at the same scale, and the degree of brokenness from a totally straight coastline was assumed to be constant among sites;
- the mean bottom depth (B);
- the coefficient of variation (CV) of the bottom depth (depth heterogeneity);
- the area of the subarea in nautical mile2 (A).
As dependent variables we used:
- the range (
s), i.e. the maximum size of fish patches (Reid, 2000);
- the nugget effect (cop) resulting from the omnidirectional variograms expressed on a relative scale (i.e. cop/model variance, in %). Higher percentages of nugget indicate that a lesser percentage of the overall data variance is explainable by spatial autocorrelation (Goovaerts, 1997). Nugget percentages close to 100% (i.e. "pure" nugget) imply that spatial autocorrelation is absent (Rossi et al., 1992; Reid, 2000).
With regard to the exhaustive variograms, we also used as dependent variables the following geometric descriptors measured by image analysis:
- (iii) the surface area enclosed by the 100% standardized data-variance contour (A100);
- (iv) the anisotropy ratio (
), defined as the ratio of the major vs. the minor axis of the best-fitted ellipse surrounding the 100% variance contour of the variogram (Isaaks and Srivastava, 1989; Ecker and Gelfand, 1999);
- (v) the anisotropy direction (AD), defined as the tangent of the anisotropy angle (
) of the major axis of the best-fitted ellipse surrounding the 100% variance contour of the variogram. Angles were measured counterclockwise from the east (Isaaks and Srivastava, 1989).
- (iv) the anisotropy ratio (
The anisotropy-related parameters (, AD) are mainly related to the way the autocorrelation range (i.e. the maximum size of fish spatial structure) changes with direction (Isaaks and Srivastava, 1989; Ecker and Gelfand, 1999).
- (vi) the ratio R = A60/A100, i.e. the surface (A60) enclosed by 60% of the variance contour vs. the surface area A100. The ratio R was calculated as a common index for all subareas that estimate the rate of echo autocorrelation change. The numerator of this ratio was selected as the surface enclosed by the minimum contour that incorporated the nugget effect (i.e. the non-spatial component of the variogram) in all exhaustive variograms (A60 in the present study). The ratio R provides a measure of the heterogeneity within the distribution of fish schools (Giannoulaki et al., 2003). A low value of R means that, within a small distance, the data variance has changed by 60% (i.e. a heterogeneous spatial structure of fish school distribution). A high value of R indicates a more progressive change in the integrated echo with distance (i.e. a homogeneous spatial structure of fish school distribution).
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Results |
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TopIntroductionMaterial and methodsResultsDiscussionReferences |
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The omnidirectional variogram of the residuals of ln(integrated echo) exhibited autocorrelation (maximum size of fish patches), which varied from 2 to 37 miles for sardine (Table 2, Figure 2) and from 4 to 22 miles for anchovy (Table 3, Figure 3). Anisotropy was apparent in almost all cases, i.e. the autocorrelation range changed with direction, but it was inconsistent in both direction and magnitude at any particular location (Tables 2 and 3, Figure 4). The ratio R of the exhaustive variograms (Table 2), which provides a measure of the heterogeneity in fish-school distribution, ranged from 0.001 (North Evoikos Gulf in summer 2003) to 0.235 (Strymonikos Gulf in summer 2004) for sardine (Table 3) and from 0.005 (Thracian Sea in summer 2003) to 0.235 (Strymonikos Gulf in summer 2004) for anchovy (Table 2). The range of these values within the different subareas supports a hypothesis that fish clustering, i.e. the organization of schools into patches, is related to the spatial topography. In addition, the variability of geostatistical parameter values within each subarea could be related to other factors, such as between-survey hydrographic variability, the presence of other species, population characteristics, and species biology.
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Further, in the case of sardine, the stepwise multiple regression analysis showed that, from the geostatistical parameters examined (i.e.
s, cop, A100, , AD, and R), only s and R were related to some of the independent variables (Table 4). In summer, the size of subarea (A) was significantly related to s whereas the enclosure index (EI) was significantly related to R (Table 4). Specifically, high values of s were estimated in large subareas and small values of R in basins with increased land enclosure (Table 4). In winter, R was the only parameter significantly related to EI and A. Small values of R were estimated in enclosed and, for a given degree of enclosure, in smaller sub-basins (Figure 5a).
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Unlike sardine, there was no significant relationship for summer anchovy surveys, whereas in winter, only R of the geostatistical parameters examined was related to certain topographic variables (Table 4) for this species. R was significantly related to EI, A, and bottom depth (B). Further, smaller values of R were estimated in shallow waters of more enclosed and smaller sub-basins (Figure 5b). In the winter models, the simple regression analysis (Table 4) showed the contribution of each parameter to the model. A explained a greater percentage of the overall variance than EI for both species (Table 4).
The general lack of significant relationships for s (except for one model) as opposed to R (three significant cases) indicated that the area geometry, rather than its size, affected the organization of the spatial structure of the anchovy and sardine aggregations.
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Discussion |
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TopIntroductionMaterial and methodsResultsDiscussionReferences |
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We studied the link between topographic features and certain characteristics of the spatial organization of anchovy and sardine populations using acoustic data from different areas with varying degree of land enclosure. In applying geostatistical techniques to yield information relating to the spatial continuity of data, both omnidirectional and exhaustive variograms showed the existence of a well-defined spatial pattern, which varied depending on subarea and season.
The maximum resolution of the study was driven by the minimum sample size of one nautical mile, so the estimated variograms resolved mesoscale structures larger than school size. Specifically, the maximum size of the patches, i.e. the autocorrelation range (as) of the omnidirectional variograms (Reid, 2000), of anchovy and sardine varied, depending on subarea and sampling period. The smallest anchovy and sardine patches were mainly observed in small gulfs (i.e. North Evoikos Gulf and Patraikos Gulf; Tables 3 and 4). The average size of sardine patches (
12 miles2) was similar to that calculated for S. pilchardus in the Catalan Sea (10 miles2; Fréon and Misund, 1999, and references therein). It was also close to the results for pelagic stocks (dominated by anchovy and sardine) in the Bay of Biscay (8 miles2; Fréon and Misund, 1999, and references therein). The maximum size of the patches of E. encrasicolus estimated in open-sea areas (i.e. the Thracian Sea) is generally comparable (20 miles2) with that observed for E. capensis (20 miles2) in the southern Benguela (Barange and Hampton, 1997).
Multiple regression analysis supports the suggestion that area geometry influences mainly the organization of the spatial structure (i.e. R) of anchovy and sardine aggregations, rather than their size or direction. These results from single-species analysis agree with those for the overall assemblages of small pelagic fish in the region (Giannoulaki et al., 2003). Further, the relationship between R and area topography was observed for both seasons in the case of sardine, although it was only significant for anchovy during winter.
For summer surveys, sardine aggregations were more heterogeneous in subareas with increased land enclosure, i.e. smaller values of R, indicating great differences in sardine concentration over small distances, whereas enclosure or other topographic characteristics did not seem to affect the spatial structure of anchovy aggregations. In general, the heterogeneity in fish aggregations is amplified when local topography increases the environmental patchiness (Fréon and Misund, 1999). The great heterogeneity of enclosed basins stemming from the coastline effect, the terrestrial influence, etc., and the subsequent heterogeneity in the availability of resources affect the spatial structure of sardine aggregations. However, these factors did not seem to apply to anchovy aggregations during summer. Summer is the reproductive period for anchovy in the Mediterranean Sea (Stergiou et al., 1997; Somarakis et al., 2002b, 2004). Consequently, its aggregations might be associated mainly with spawning, which itself is related to productivity and hydrography (Somarakis et al., 2002b; Giannoulaki et al., 2005) rather than area topography.
In winter, sardine aggregations were more heterogeneous in enclosed sub-basins and, for a given degree of enclosure, in small basins. Similarly, anchovy aggregations were more heterogeneous in the shallow waters of enclosed and small basins during winter. Hence, subarea size is also important to the organization of the spatial structure of both anchovy and sardine during the colder, low productivity months of winter. Within the range of values used in the present study and for a given degree of enclosure, both species aggregations were more heterogeneous in small than in large subareas.
During winter, both anchovy and sardine tend to concentrate in more protected areas, such as small gulfs or coastal, shallow waters. Fish generally tend to avoid the adverse weather (with its enhanced turbulence) in open waters (Fréon and Misund, 1999), and move towards more protected areas such as small gulfs. Further, during winter, sardine concentrate in coastal waters to spawn (Skrivanic and Zavodnic, 1973; Ettahiri et al., 2003; Somarakis et al., in press), and inshore, shallow waters are also the preferred habitat for 0-group fish that dominate anchovy stocks during winter (Giannoulaki et al., 2005).
Given the heterogeneity in food availability and the limited available space in small areas, the increase in concentration of both species in small gulfs during winter may result in heterogeneous spatial structures of fish aggregations. Within a cluster of schools, the distribution in space can be very heterogeneous, with several nuclei or cores (Fréon and Misund, 1999). Moreover, an increase in fish biomass within an area would mostly result in the formation of denser schools and the "strengthening" of the original clusters of schools, so enhancing spatial heterogeneity rather than increasing the number of schools (Petitgas et al., 2001). These results generally meet the "fish trap" theory (sensu Bakun and Cury, 1999), because fish entering a limited space are mainly trapped in existing clusters of schools instead of forming new clusters.
The maximum size of sardine patches (i.e. the autocorrelation range) was related to the size of subarea (A) during summer, whereas there was no significant relationship for anchovy or for either species in winter. Summer is the post-recruitment period for sardine around Greece (Voulgaridou and Stergiou, 2003), so enrichment of sardine aggregations with young fish could result in the formation of bigger patches with fish tending to occupy most of the available space.
The results presented here show that the spatial structure of single species is different from that estimated for the overall small-pelagic fish assemblage (Giannoulaki et al., 2003). Specifically, the single-species analysis indicated that, in winter within a small subarea, anchovy and sardine had a spatially heterogeneous distribution (smaller R; Figure 5). This implies bigger fish concentrations locally and hence deviation from a spatial homogeneous school distribution (bigger R), the finding for the overall small-pelagic-fish assemblage within the same area (Giannoulaki et al., 2003). Therefore, the homogeneous spatial structure of the overall fish assemblage seems to decompose into heterogeneous spatial structures of the co-existing species. This heterogeneity merely represents differences in spatial strategy of the two different species, i.e. the way fish adjust their density to achieve optimum exploitation of available food resources.
Besides topography, variability in oceanography, e.g. currents, fronts, or upwelling, could be responsible for the unexplained variance of the spatial characteristics of fish patches. The presence or absence of such features within an area could influence patch formation and therefore the characteristics of the aggregations (Fréon and Misund, 1999; Fréon et al., 2005) by their effect on food concentration and zooplankton retention.
In conclusion, the degree of land enclosure and size of the habitat influence the spatial structure of anchovy and sardine in the eastern Mediterranean. This effect varied with season and species. The effect of area was more pronounced during winter for both species studied here. The characteristics of anchovy aggregations were related to area topography only during winter. These findings, relating the characteristics of anchovy and sardine aggregations to topography, have implications for both the design of assessment surveys and the management of small-pelagic-fish resources. They also imply an additional agent in the functioning of pelagic ecosystems related to habitat topography. The patchiness of the environment which influences production processes and, consequently, the spatial clustering of pelagic fish (Fréon and Misund, 1999) is related to the oceanography (Fréon and Misund, 1999; Fréon et al., 2005). However, as suggested by the present study, topographic characteristics might also play a role, and this fact should be borne in mind in planning for surveys, e.g. in determining appropriate inter-transect distances and kriging estimates.
Further, as small-pelagic-fish aggregations are located acoustically these days, the detection of fish patches in small, enclosed areas, where most schools are located relatively close to each other (heterogeneous aggregations), greatly increases the potential for catching a lot of fish in a short time. This should be taken into account when designing management plans in areas with such topographic characteristics. Finally, certain geostatistical parameters, such as trends in autocorrelation range, might serve as an index for monitoring stock status. For example, little or no spatial structure has been reported to have coincided with the collapse of the northern cod stock (Warren, 1997; Reid, 2000).
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Acknowledgements |
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The study was partially financed by the Commission of the European Union (Contract Numbers 97/048 and 98/039). We thank the captain and the crew of RV "Philia" for their assistance during the surveys.
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References |
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