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STATISTICAL AND EMPIRICAL IDENTIFICATION OF MULTI-SPECIES HARVESTING ZONES TO IMPROVE MONITORING, ASSESSMENT, AND MANAGEMENT OF BENTHIC FISHERIES IN SOUTHERN CHILE Carlos Molinet, Nancy Barahona, Beatriz Yannicelli, Jorge González, Alejandra Arevalo, and Sergio Rosales ABSTRACT It has become widely accepted that the spatial complexity of the populations that sustain benthic fisheries necessitates monitoring, assessment, and management procedures. To date, the stocks and fisheries of benthic species in southern Chile have been evaluated and managed following single-species stock approaches, allocating total allowable quotas across internal political boundaries. In the present study, we analyzed fisheries data from 1996 to 2005 and applied two different methodologies to identify harvesting zones aimed at providing a better understanding of the spatial heterogeneity of the multi-species fisheries in southern Chile. As a first method, harvesting zones were defined by experts, followed by a posteriori multivariate analysis. As a second approach, zones were grouped utilizing hierarchical cluster analyses. The results from both classification methods revealed the existence of spatial heterogeneity in the fishery, which did not match current administrative boundaries. The harvesting zones proposed here by the expert panel appear more reliable than those emerging from hierarchical classification methods, and we suggest that these be used as a starting point to guide the spatially-explicit analysis, monitoring, and management of the dominant benthic fisheries. Regardless of the method of harvesting zone identification, our results indicate that the subzoning proposed herein for southern Chile benthic fisheries form a reasonable operative framework for a spatially-explicit adaptive management process. Both zone identification methods provided an insight into understanding the spatial complexity of the fishery and would potentially be useful in the analysis of other benthic coastal fisheries.

The successful management of marine resources relies on the establishment of appropriate institutions for governance, where the spatial scale of management relative to that of biological and fisheries processes is a key element (Hilborn et al. 2005, Parma et al. 2006, Lorenzen et al. 2010). Data collection, assessment, and fisheries management have been traditionally based upon the analysis of singlespecies stocks exploited by industrial fleets (Marchal 2008). In the past two decades, the inadequacy of this approach for monitoring, assessing, and managing mixed (sensu Pelletier and Ferraris 2000) or benthic fisheries has been recognized and documented (Caddy 1979, Stotz 2003, Botsford et al. 2004, Grabowski et al. 2005, Orensanz et al. 2005, Moreno et al. 2006, Marchal 2008). Benthic coastal fisheries are complex spatial structures derived from the heterogeneous spatial distribution of habitats and targeted species, and from the behavior of the often numerous small fishing boats involved (Orensanz et al. 2005). Such heterogeneity is not consistent with traditional stock assessment model assumptions, such as the existence of a homogeneous effort distribution (Hilborn and Walters 1992), and challenges Bulletin of Marine Science

© 2011 Rosenstiel School of Marine and Atmospheric Science of the University of Miami

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BULLETIN OF MARINE SCIENCE. VOL 87, NO 0. 2011

the adequacy of traditional management practices (Caddy 1975, 1989, Prince and Guzmán 1993, Orensanz and Jamieson 1998). A key step to improve the management of benthic coastal fisheries is to understand the fishing process, defined as the sequence of actions through which a shellfish resource is located, harvested, and eventually depleted by a fishing force composed of discrete fishing units [common denomination for any grouping of voyages (i.e., fishery or métier) or vessels (i.e., fleet), and for any miscellaneous grouping of vessels and/or voyages that does not have the status of a fleet, fishery, or métier (ICES 2003)]. In recent years, the concept of métier (ICES 2003) has been used to describe a group of fishing units and operations that share a combination of fishing gear, target species, fishing areas, and seasons of the year (Marchal and Horwood 1996, ICES 2003, Tzanatos et al. 2006, Marchal 2008), also called “fishing tactics” by Pelletier and Ferraris (2000). The nature of fishing units varies greatly in benthic fisheries. Understanding their spatial and temporal organization and relationships is considered the cornerstone to explicitly incorporate their technical interactions within the cycle of observing, assessing, forecasting, and management (Pelletier and Ferraris 2000, Verdoit et al. 2003, Mahévas et al. 2008, Marchal 2008). S�� patially-explicit analyses and management have been advocated as the most reasonable alternative for benthic fisheries. While population dynamics of proximate significance for stock assessment and management occur at macro, meso, and micro scales (sensu Orensanz and Jamieson 1998), suitable spatial units for management and monitoring must satisfy the distributional spatial scale of the exploited resources. However, the greatest source of error in stock assessment and management is to underestimate the extent of a stock unit (FAO 2005). Under this context, defining adequate spatial management units becomes one of the most critical unsolved issues in benthic coastal fisheries (Bruckmeier and Neuman 2005, Lorenzen et al. 2010). Benthic fisheries in southern Chile inland seas (41°S–46°S) account for > 60% of benthic fishery landings in the country. Fishing is based upon SCUBA diving from small boats on mono-specific fishing trips. Current management regulations are established by a management plan (Plan de Manejo de la Zona Contigua X y XI regiones, PMZC) established in 2005, which is the only fisheries management plan currently implemented in Chile. The first legal regulation that established quotas as part of the PMZC considered three wide spatial units defined on the basis of the administrative regions where the landings and/or actual catch of the fishing trip occurred (Moreno et al. 2006) X north, X south, and XI regions (Fig. 1). Although this spatial allocation criterion represented an improvement over the previous situation that lacked any spatial consideration, little has been learned about the spatial distribution of the effort and the status of the exploited stocks given the coarse scale of the indicators calculated over these large and heterogeneous areas. To propose coherent sub-areas that can serve as spatial units suitable for improving benthic fisheries stock assessment and management in southern Chile, we applied two approaches for grouping individual fishing grounds that share coherent features related to the fishing process. We designated harvest zones based on an expert judgement approach and a hierarchical classification scheme. We evaluated the patterns and coherence of the resulting zones and analyzed their environmental characteristics to determine the most optimal approach to managing these Chilean benthic fishery resources.

MOLINET ET AL.: SHELLFISH HARVESTING ZONES IN SOUTHERN CHILE

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Materials and Methods Study Area.—The study area covered the inland sea and the oceanic coast of southern Chile between Puerto Montt (41°20´S) and the Taitao Peninsula (46°56´S), which includes part of X and XI administrative regions of Chile (Fig. 1). This is an area of abrupt changes in coastal morphology and bathymetry. The east coast of the inland sea in both regions is characterized by a water column that is strongly stratified (Pickard 1971, Silva et al. 1995) due to high levels of fluvial discharge and runoff, which results in an east to west density gradient. Islands located east of Chiloé Island form an archipelago that obstructs north–south water flow (Silva et al. 1995). Oceanic water entering the inland sea comes mainly through the Boca del Guafo. This water flows to the south through the Moraleda Channel and to the north through the Corcovado Gulf, as far as the Chauque Islands (Silva et al. 1995, 1998). In the south of the study area, the Meninea Constriction marks the end of Moraleda Channel and separates the northern basin from the southern basin. The circulation pattern in the study zone is affected principally by tides, winds, and density gradients (Silva et al. 1995, 1998, Valle-Levinson et al. 2001, 2002, Cáceres et al. 2003a,b, Valle-Levinson and Blanco 2004). Data Description.—Benthic fisheries in the study area involve about 1000 small boats (7–15 m length) and ~5000 fishermen, who concentrate 80% of their fishing effort on the nine benthic species incorporated into the PMZC (Molinet et al. 2008): the clams, Venus antiqua (King and Broderip, 1832), Gari solida (Gray, 1828), and Mesodesma donacium (Lamarck, 1818); the mussels, Aulacomya atra (Molina, 1872) and Mytilus chilensis (Hupè, 1854); the sea urchin, Loxechinus albus (Molina, 1782); the seaweeds, Gigartina skottsbergii (Setchell & N.L.Gardner) and Sarcothalia crispate (Bory de Saint-Vincent); and crabs of the genus Cancer [mainly Cancer edwarsili (Bell, 1985)]1. These species are only caught by SCUBA diving, except for crabs, which are caught both by divers and by traps deployed up to 80 m depth. The primary source of fishing data for this study comes from port surveys conducted between 1996 and 2005 by the Instituto de Fomento Pesquero (IFOP) at the following ports: Calbuco, Maullín, Carelmapu, Pargua, Ancud, Pudeto, Dalcahue, Queilen, Quellón, Melinka, and Chacabuco (Fig. 1). Daily fishing boats arriving from fishing trips were randomly sampled. Fishermen were interviewed while landing their catch and were requested to provide information per trip and species regarding weight (kg) and origin of their catch, type of operation, fishing effort applied (number of divers and total number of diving hours), trip duration, and sailed distance, among other variables. During the interview, a sample of their catch was measured and weighed. The type of fishing operation was classified into either a: (1) port operation, defined as fishing trips where individual vessels returned with the entirety of their own catch, or (2) transhipping operation, where larger boats gather, transport, and land the catch that a group of small boats (7–15 m long) has obtained over a period of 1–2 d (Barahona et al. 2003, Orensanz et al. 2005). Transhipping operations were observed and recorded only for sea urchin, accounting for 88% of the total catch sampled for this species. The origin corresponds to sites of different sizes that the fishermen recognize by vernacular names (González et al. 2006). Data analysis involved two successive steps: (1) producing a standardized matrix of origins, species composition, and landing port by trip, and (2) identifying harvesting zones. We used two approaches to group origins into harvesting zones: (A) delimitation of sub-areas through expert judgment analysis of available data and field experience, followed by a posteriori mul1 Ng et al. (2008) reassigned C. edwardsii to the genus Metacarcinus [Metacarcinus edwardsii (Bell, 1835)]. Their criteria for this reassignment was based entirely on a paleogeographical work considering only carapace morphology. Molecular evidence (Veliz D, , unpubl data) does not support new nominations at this time and requires additional collections (Pardo et al. in press). Therefore, in the present study, we use the previous taxonomy and nomenclature of this group, e.g., Cancer spp.

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Figure 1. Study area in the inland sea in southern Chile. Numbered circles show the location of principal port of landing where the fishery landing was sampled: (1) Calbuco, (2) Maullín, (3) Carelmapu, (4) Pargua, (5) Ancud, (6) Pudeto, (7) Dalcahue, (8) Queilen, (9) Quellón, (10) Melinka, (11) Chacabuco. Black squares are the principal ports in both regions. The triangle indicates the location of Meninea Constriction. The line that crosses the Corcovado Gulf defines the regional administrative political boundary.


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tivariate analysis, and (B) identification of areas through a hierarchical cluster analysis methodology (Fig. 2). Despite the fact that data were collected on a daily basis, we used a monthly temporal resolution to simplify data analysis and interpretation. Catch origins where all species had catch frequencies < 10% of each species sub-totals were excluded for the same reasons. Final analysis matrix had 498 origins (rows) and 11 variables (columns). The first eight variables corresponded to the cumulated total catch (1996–2005) per origin for each of the nine species included in the management plan except sea urchins. Variables nine and 10 were cumulated sea urchin catches from transhipping (L. albus T) and port (L. albus P) operations, respectively. The 11th variable was the principal port of landing, defined as the port where the largest catch, by weight, from each origin was landed during the study period (1996–2005). It was not possible to estimate the total number of small boats that operate under the transhipping system. Whenever the dataset did not clearly identify the operation type (port or transhipping), boats landings > 2200 kg were assumed to be transhipping operations, while those landing < 2200 kg were assumed to be port operations. Identification of Harvesting Zones by Means of Expert Judgement.—Harvesting zones were defined as geographical shapes (polygons) identified and drawn on a map by an expert panel based on the four main criteria previously described: origin, species composition, landing port, and type of operation. An a posteriori general discriminant analysis was applied to �� determine �� the relative contribution of the different variables considered to the differentiation between the harvesting zones defined a priori. General �� discriminant analysis applies the methods of the general linear model (McCullagh and Nelder 1989) to the discriminant function analysis problem. The discriminant analysis was based on the assumption that all samples (or origins, in our case) belong to known categories (e.g., to a harvesting zone). Thus the optimization procedure was used to find the multivariate function that maximized the separation of the origins according to the proposed categories. While harvesting area (harvesting zone) corresponded to the categorical dependent variable, all remaining variables in the analysis matrix were considered continuous variables, except port of landing, which was handled as a categorical predictor. Wilks’ Lambda and tolerances for the effects in each sub model were estimated. Wilks’ Lambda is close to zero if any two groups are well separated (Legendre and Legendre 1998). Identification of Harvesting Zones by Hierarchical Classification Methods.— A hierarchical cluster analysis was applied to the dataset utilizing the Ward method (Ward 1963). This analysis was carried out using the nine benthic species (see Data Description), but with the sea urchin data from transhipping and port operation combined, as the inclusion of both operations separately in the cluster analysis did not improve the number of optimum clusters. Port of landing was not included in this classification because this method does not support categorical variables. For the validation of the clusters, we applied the internal measure or criteria referred to as Silhouette (Rousseeuw 1987, González 2005):

S^ i h =

^ b^ i h - a^ i hh , max " a^ i h, b^ i h,

where a(i) is the average dissimilarity of the i-object to all other objects in the same cluster; b(i) is the minimum of average dissimilarity of the i-object to all objects in other cluster (in the closest cluster). The Silhouette index is useful when compact and separate clusters are sought. This method assigns the quantitative measure s(i), known as the “width silhouette,” to each object of the cluster. This width silhouette varies as −1 ≤ s(i) ≤ 1, and indicates the correspondence of one object i with the cluster to which it assigned. A value of s(i) close to 1 is obtained when the dissimilarity of the object i from the rest of the objects of the cluster

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Figure 2. Flowchart of the methodology used to obtain the fishing units in nine benthic fisheries of Chilean inland seas. IFOP, Instituto de Fomento Pesquero.

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is much smaller than the smallest dissimilarity between the object i and the objects of the neighboring clusters, indicating that the object i is well classified. A s(i) value around 0 is an intermediate case, where the dissimilarities between object (i) and the assigned cluster and between the object and neighboring clusters are approximately equal; therefore, it is not clear whether the object has been well classified. When s(i) is close to −1, then the dissimilarity of object i within the assigned cluster is much greater than the dissimilarity between the same object (i) and the objects of the neighboring clusters, indicating that the object has been misclassified. An a posteriori discriminant analysis (Legendre and Legendre 1998) was then applied following a homologous procedure to the one used for the expert classification harvesting areas. Finally, we included port of landing and catch per species (including type of operation for L. albus) as categorical variables in a multiple correspondence analysis (Legendre and Legendre 1998) to evaluate the effect of the variable port of landing on the origins grouping, using statistical methods. Environmental Variables.—To identify whether the harvesting zones differed significantly in gross oceanographic characteristics, we conducted a principal component analysis (PCA) of Euclidean distance. For this analysis, geo-referenced oceanographic profiles of temperature (°C), salinity, and dissolved oxygen (ml L−1) were obtained from the public database of the World Ocean Circulation Experiment (WOCE, http://woce.nodc.noaa.gov/) and the National Oceanographic Data Center (CENDHOC, http://www.shoa.cl/). In total, 2340 temperature observations, 2263 salinity observations, and 1997 dissolved oxygen observations were obtained for the period 1948–1998. Because oceanographic data do not exist for each origin, environmental variables were considered in a separate analysis, where all available oceanographic profiles were assigned to pre-defined harvesting zones that represented all harvesting areas. Thus “mean” profiles of temperature, salinity, and dissolved oxygen were computed for each harvesting zone, as well as the standard deviation of each variable for each zone and depth. In the PCA, harvesting zones defined by expert judgement were used as a grouping variable, while temperature, salinity, and dissolved oxygen were used as the variables for analysis. Harvesting zones were characterized by mean and standard deviation surface values of temperature and salinity, since the greatest variations, both spatial (mean values) as well as seasonal (standard deviation), of these variables are expected to occur in surface waters of the study zone. Oxygen records at 50 m were used because this depth was well represented in terms of the amount of dissolved oxygen data available and because it was predominantly under the pycnocline, and thus suitable to inferring environmental conditions (e.g., water masses).

Results The species that contributed the most to the sampled landings were L. albus (35%), V. antiqua (33%), and G. skotsbergii (12%, Fig. 3A), which were also the species with the highest number of origins. Mesodesma donacium had the lowest values (1% of the total sampled landings). Quellón was the port where the highest cumulated landed biomass was sampled, followed by Chacabuco, Dalcahue, and Melinka (Fig. 3B). These four ports accounted for ~95% of total sampled landings (biomass). The origins most represented in sampled landings were all located around Chiloe Island and the Chonos Archipelago (Fig. 4). Sea urchins (L. albus) had the highest number of origins (368) and co-occurred in 90% of these origins with the red seaweeds S. crispate and G. skottsbergii (Table 1). Exploratory analysis of the data showed clear spatial patterns with regards the distribution of species (Fig. 4A–C), magnitude of total landings sampled (Fig. 4D), and principal port of landing by origin (Fig. 4E–F). Sea urchins (L. albus) and clams (V. antiqua) were exploited in almost the entire study area, while catch origins of

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Figure 3. Landings sampled by (A) benthic monitoring for each fishery species in the present study and (B) principal port of landing and their number of associated origins (number over each bar). Data collected by the Instituto de Fomento Pesquero between 1996 and 2005. See text for full species names.

Chilean surf clams (M. donacium) and crabs (Cancer spp.) exhibited a much more restricted distribution (Fig. 4A–C). Expert Classification of Harvesting Zone.—The expert panel identified 12 irregular harvesting zones with variable dimensions (Fig. 4G, Table 2). Harvesting zones 1–6 were located in the northern zone of the study area between Puerto Montt and the southern end of Chiloe Island; the remaining harvesting zones were located in the southern zone of the study area between the administrative border of Los Lagos and Aysén Regions and the Taitao Peninsula. Harvesting zones 2, 6, 7, and 12 accounted for 70.6% of the total landings sampled, while harvesting zones 1, 3, 9, and 11 contributed only 4.5% of the total landings sampled.

Figure 4. Spatial distribution of the (A–C) harvest origins associated with the catch species in the study area, (D) catch of the selected species associated with the origins in the study area, and (E–F) origins associated with the principal port of landing in the study area. (G) Expert classification of harvesting zones proposed as spatial units of analysis. See text for full species names.

MOLINET ET AL.: SHELLFISH HARVESTING ZONES IN SOUTHERN CHILE 9

V. antiqua A. atra M. chilensis G. solida L. albus Cancer spp. S. crispata G. skottsbergii M. donacium

V. antiqua 218 (100%)

A. atra 90 (78%) 115 (100%)

M. chilensis 63 (75%) 46 (55%) 84 (100%)

G. solida 135 (72%) 74 (39%) 41 (22%) 188 (100%)

L. albus 167 (45%) 92 (25%) 58 (16%) 157 (43%) 368 (100%)

Cancer spp. 33 (26%) 20 (16%) 12 (10%) 43 (34%) 77 (62%) 125 (100%)

S. crispata G. skottsbergii M. donacium 85 (80%) 105 (70%) 20 (67%) 49 (46%) 54 (36%) 9 (30%) 30 (28%) 33 (22%) 8 (27%) 78 (74%) 87 (58%) 14 (47%) 98 (92%) 135 (89%) 17 (57%) 19 (18%) 29 (19%) 3 (10%) 106 (100%) 73 (48%) 11 (37%) 151 (100%) 16 (53%) 30 (100%)

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Table 1. Number of origins (harvested locations) where the nine benthic fishery species in this study were recorded as co-occurring with another species. The percentage of co-ocurrence for each pair of species is indicated in parentheses. See text for full species names.

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The discriminant analysis carried out a posteriori indicated that 60.4% of the origins were well classified according to the variables catch per species, principal port of landing, and type of operation. The greatest concordance was observed in harvesting zones 2 and 10 (87% and 92.6%, respectively; Table 2). Harvesting zones 3 and 11 showed no matches relative to the assignment made from the classification function. These values were related to the number of origins assigned to each harvesting zone. While harvesting zone 10 included 94 origins, harvesting zones 3 and 11 included only 7 and 18 origins, respectively (Table 2). The first, second, and third standardized canonical discriminant function (canonical roots) accounted for 88% of the variability of the analysis (Fig. 5). The variable principal port of landing (11 ports) made the greatest contribution to classifying the origins in the first canonical root (50% of variability). Queilen, Quellón, and Chacabuco ports were positively correlated with sampled landings of L. albus T (transhipping), G. skottsbergii, and Cancer spp. All of these species are caught in remote areas by means of transhipping operations (only distinguished for L. albus in our database, Fig. 5A). The other ports were positively correlated to A. atra, M. chilensis, and M. donacium, which are landed from port operations. The second largest source of variation (22%, 72 % cumulative) showed a positive correlation between Calbuco, Dalcahue, Queilen, and Quellón ports and the species A. atra, S. crispate, and L. albus P. All the others ports were located in harvesting zone 2, positively correlated to Cancer spp., V. antiqua, and L. albus T (Fig. 5B). The third largest source of variation (16%, 88% cumulative) positively discriminated between origins associated to Quellón and Queilen ports and the species V. antiqua, G. solida, and L. albus P. It negatively discriminated the ports of Calbuco and Chacabuco and crabs Cancer spp. (associated with Chacabuco in the harvesting zone 10) and mussels (associated with Calbuco in the harvesting zone 1, Fig. 5C). Wilks’ Lambda and tolerance index revealed that between nine and 11 effects (out of the 11 used) resulted in a good separation of origins (Table 4). The species that contributed the most to the model were Cancer spp., M. chilensis, A. atra, M. donacium, and G. solida, while the ports that contributed the most to the model were Dalcahue, Calbuco, Pudeto, Ancud, and Maullín. These ports were located in the harvesting zones 4, 1, and 2, whose associated origins were spatially restricted. Among the environmental variables, salinity differed the most among harvesting zones. The mean and standard deviation values of salinity had the greatest, although opposite, weights on the first principal component (Fig. 6A,B), suggesting that zones with the greatest surface salinity exhibited less variability. The mean temperature, its standard deviation, and to a lesser degree, mean dissolved oxygen, had the greatest weights on the second principal component. Based on principal components analysis results, harvesting zones were classified into two large groups, mainly differentiated by salinity, following a longitudinal gradient (east–west). Thus harvesting zones located on the continental edge of the inland sea (harvesting zones 1, 5, 9, 10) were considered a distinct group from those located farther west (harvesting zones 2, 3, 4, 7, 8, and 12; Fig. 6B). In addition, temperature differences enabled a further separation of the largest group in two sub groups of harvesting zones, distributed across a latitudinal gradient (north–south). Thus harvesting zones 7, 8, and 12 comprised a subgroup associated with lower temperature waters, separated from harvesting zones located in the northern part of the study zone (2, 3, and 4; Fig. 6B). Harvesting zone 11 may be considered to belong to a transitional zone.

Number of origins 43 55 7 53 21 50 58 43 30 94 18 26 498

GDA classification (% correct) 83.72 87.03 0.00 66.03 4.76 70.00 55.17 13.95 40.00 92.55 0.00 34.61 60.36 Geographic area Principal port of landing Reloncaví Bay Calbuco Ancud Bay, Chacao Channel Ancud, Carelmapu Western Chiloé Island Ancud Chauques Islands to Desertores Islands Dalcahue, Queilen East coast of region X Dalcahue South of Chiloé Island Quellón Guaitecas Islands Melinka North of Los Chonos Archipelago Melinka, Quellón Northeast coast of region XI Quellón Southeast coast of region XI Chacabuco Center Los Chonos Archipelago Quellón, Melinka Western Los Chonos Archipelago Quellón, Melinka, Chacabuco

Primary fishery L. albus, V. antiqua V. antiqua, G. solida M. donacium V. antiqua, L. albus, M. chilensis A. atra, V. antiqua V. antiqua, L. albus, M. chilensis L. albus, V. antiqua, G. skottsbergii Sea urchin, G. skottsbergii V. antiqua, L. albus Cancer spp., L. albus, G. solida L. albus L. albus, G. skottsbergii

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Harvesting zone 1 2 3 4 5 6 7 8 9 10 11 12 Total

Table 2. Characterization of the 12 harvesting zones in southern Chile resulting from the expert classification, followed by the general discriminant analysis (GDA), and their characteristic geographic area, principal port of landing, and primary fishery species. See the text for full species names.

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Figure 5. Standardized discriminant function for the three first canonical axes for the expert classification of harvesting zones: (A) 1st root, (B) 2nd root, and (C) 3rd root . The number in each box indicates the relative contribution of the descriptors (all of which were significant) to the final accumulated discrimination explained upon adding each axis. See text for full species names.

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Table 3. Classification of five clusters resulting from hierarchical cluster classification of harvest origins, followed by a discriminant analysis (DA). In parentheses are the numbers of origins whose Silhouette widths were > 0, and therefore well classified. See the text for full species names. Number of cluster 1 2 3 4 5 Misclassified Total

Number of origins 417 (389) 25 (22) 5 (5) 28 ( 20) 23 (14) 48. 498.

DA classification (% correct) 97.95 100.00 100.00 84.21 92.30 94.17

Identification of Harvesting Zones by Hierarchical Clustering.—Cluster analysis indicated that the best classification was obtained by constructing five clusters (Fig. 7) with an average Silhouette width of 0.32. Cluster classification did not include the type of operation (i.e., urchins from transhipping vs port operations), because this distinction did not improve the Silhouette width, although we performed up to 30 clusters during the analysis. Cluster 1 grouped 417 origins with a Silhouette width of 0.36. Clusters 2, 3, and 4 also exhibited positive widths and were well classified, while cluster 5 exhibited a width near 0, reflecting a rather uncertain classification (Fig. 7). Silhouette widths < 0 were subtracted from each cluster and grouped together as misclassified. After subtracting the misclassified origins, cluster 1 grouped 389 origins from the entire study area and overlapped almost all remaining clusters (Fig. 8A,B). These remaining clusters grouped up to 28 origins per cluster (Table 3). Cluster 2 grouped origins dominated by the mytilids A. atra and M. chilensis, while cluster 3 only grouped five origins, all associated with surf clams distributed between southern Chiloe Island and the Chonos Archipelago (Fig. 8A). Cluster 4 grouped origins associated with the clam G. solida and crabs of the genus Cancer, while cluster 5 grouped a few origins, where all the species were observed in similar proportions. Cluster 3 was not assigned to any area because the three origins it grouped were not contiguous. The 48 misclassified origins included those with higher landings, particularly of the seaweeds S. crispata, G. skottsbergii, and the sea urchin, L. albus. Since most of these origins were contiguous in space, we grouped them together as a sixth harvesting zone labelled “misclassified” (Fig. 8B). The discriminant analysis carried out a posteriori resulted in 94.2% of the origins being well classified based on the variables related to catch per species. The largest concordance was observed in clusters 2 and 3 (100%, Table 3). The first, second, and third standardized CDF (canonical roots) accounted for 80% of the variability of the analysis. The source of variation with the greatest contribution to the classification of the origins (33%) was positively correlated with the catch of M. donacium, S. crispate, G. solida, and L. albus and negatively correlated with A. atra and M. chilensis (Fig. 9A). The second largest source of variation (25%) was positively correlated with the catch of M. chilensis and M. donacium, and negatively correlated with the catch of G. solida and L. albus (Fig. 9B). The third largest source of variation (22%) positively discriminated the landings of the species M. donacium, which was negatively correlated with almost all of the other species (Fig. 9C).

Sub set 1 2 3 4 5 6 7 8 9 10

Wilks’ No. of Cancer A. M. V. M. G. S. G. L. albus L. albus Lambda effects spp. atra chilensis antiqua donacium solida crispata skottsbergii P T 0.0039 11 0.92 0.88 0.92 0.76 0.91 0.90 0.80 0.80 0.85 0.85 0.0041 10 0.92 0.89 0.92 0.80 0.92 0.90 – 0.87 0.85 0.86 0.0041 10 0.97 0.89 0.92 0.76 0.91 – 0.80 0.80 0.86 0.86 0.0042 10 – 0.89 0.92 0.76 0.92 0.95 0.80 0.80 0.85 0.86 0.0042 10 0.92 0.91 0.92 – 0.96 0.90 0.85 0.80 0.88 0.86 0.0042 10 0.92 0.89 0.92 0.78 0.91 0.90 0.80 0.81 – 0.88 0.0042 10 0.92 0.89 0.92 0.79 – 0.90 0.80 0.80 0.85 0.86 0.0042 10 0.92 0.89 0.92 0.76 0.91 0.90 0.87 – 0.87 0.86 0.0042 9 0.97 0.89 0.92 0.80 0.92 – – 0.87 0.86 0.86 0.0043 10 0.92 0.89 – 0.76 0.91 0.90 0.80 0.80 0.85 0.85

Port Cco 0.27 0.27 0.27 0.27 0.27 0.27 0.27 0.27 0.27 0.27

Port Mau 0.22 0.22 0.22 0.22 0.22 0.22 0.23 0.22 0.22 0.23

Port Car 0.16 0.16 0.16 0.16 0.16 0.16 0.16 0.16 0.16 0.16

Port Par 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13

Port Anc 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25

Port Pud 0.26 0.26 0.26 0.26 0.26 0.26 0.26 0.26 0.26 0.26

Port Dal 0.33 0.33 0.33 0.33 0.34 0.33 0.33 0.33 0.33 0.33

Port Qln 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03

Port Qll 0.16 0.16 0.16 0.16 0.16 0.16 0.16 0.16 0.16 0.16

Port Cha 0.19 0.19 0.19 0.19 0.19 0.19 0.20 0.19 0.19 0.19

Table 4. Summary of the best subset of general discriminant models for Chilean harvesting zones proposed by expert classification using Wilks lambda and tolerances for the effects in each submodel. See text for full species names. Port key: Calbuco (Cco), Maullín (Mau), Carelmapu (Car), Pargua (Par), Ancud (Anc), Pudeto (Pud), Dalcahue (Dal), Queilen (Qln), Quellon (Que), Chacabuco (Cha).

MOLINET ET AL.: SHELLFISH HARVESTING ZONES IN SOUTHERN CHILE 15

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Figure 6. (A) Analysis of the principal components for the variables mean temperature (Temp mean) and its standard deviation (Temp StDev), mean salinity (Sal Mean) and its standard deviation (Sal StDev), and mean dissolved oxygen (DO Mean), and its standard deviation (DO STDev) based on Euclidean distances; and (B) their relationship with the resultant harvesting zones, grouped as a function of the variables temperature, salinity, and dissolved oxygen.

Figure 7. Classification of the harvest origins in five clusters applying a cluster hierarchical analysis and a validation procedure of the clusters through the width Silhouette criterion. Origins with a Silhouette width > 0 were defined as well classified and origins with a Silhouette width < 0 were defined as misclassified. J is the number of clusters, nj is the number of origins associated with the cluster J, and aveiЄCj is the average Silhouette width for each cluster.


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Figure 8. (A) Spatial distribution of the harvest origins in southern Chile from the hierarchical cluster classification, including misclassified origins as a sixth group. (B) Location and distribution of the resulting clusters schematized as harvesting zones.

The multiple correspondence analyses, which included port of landing, showed that the dimension 1 and 2 contained significant information to order origins, and the groups formed differed from those clumped by cluster analysis. This analysis distinguished at least six groups (Fig. 10), whose spatial segregation resulted in areas more coherent with those of the harvesting zones proposed by experts. Discussion The results of the harvest zone classification by either experts or through the use of hierarchical cluster techniques indicated that fisheries data could be used to identify spatial sub-units within the studied area. The number, location, and characteristics of the harvesting zones defined by experts and by hierarchical cluster analysis showed, however, clear disagreements. When harvest origins were statistically classified by hierarchical analysis considering only species landings as descriptive variables, five clusters were defined. The spatial distribution of the largest group of sites suggested the existence of one unit that encompassed almost the entire study area, ranging beyond the current boundaries of the administrative regions that operate in Chile. The spatial area occupied by the origins grouped within the largest clump would be consistent with the gross oceanographic characterization of the study area as an “inland sea system” (Silva et al. 2005). As the hierarchical classification results only depended on species landings composition, the large cluster 1 included origins

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Figure 9. Standardized discriminant function of the variables utilized for the first three canonical axes on hierarchical cluster analysis: (A) 1st root, (B) 2nd root, (C) 3rd root. The number in each box indicates the accumulated variance explained (all significant) upon adding each axis. See text for full species names.


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Figure 10. Multiple Correspondence Analysis for harvest origins, including the variables landings port, type of operation, and catch per species as categorical variables. Port of landings: Cal, Calbuco; Mau, Maullín; Car, Carelmapu; Par, Pargua; Anc, Ancud; 6, Pdt; Dal, Dalcahue; Qln, Queilen; Qll, Quellón; Mel, Melinka; Cha, Chacabuco. Species Gs, G. solida; Md, M. donacium; Va, V. antiqua; Aa, A. atra; Mch, M. chilensis; Gsk, G. skotsbergii; Sc, S. crispate; Csp, Cancer spp. Type of operation: LaP, L. albus port; Lat, L. albus transhipping. See text for full species names.

for dominant species of widespread distribution in the study area (e.g., urchins and clams). It may also reflect the wide interest fishermen have for these target species. The spatial distribution of areas defined by origins clumped in smaller groups reflect the more restricted distribution of some target species and/or fishing trips targeting them. Inclusion of port of landing in the multiple correspondence analysis resulted in a different classification of the origins. Mapping of origins by group generates identifiable spatial zones that represent areas of operation of rather discrete fishing units (considering equipment, target species, and area of operation), which can be addressed as metiérs (e.g., crabs caught with traps in the south, clams in the north, and seaweed, sea urchins, and transhipping in the southwest). The analysis of the 12 harvesting zones defined by experts indicated that principal port of landing, species composition, and type of operation (for sea urchins) were relevant to correctly assign origins to harvesting zones defined a priori. Only origins that fell in four of the 12 proposed harvesting zones (3, 5, 8, and 11) showed no or minimal percentage of correct classification. This seems to be explained by the minimal number of origins associated with these harvesting zones. A significant general discriminant function resulted from this analysis, where > 60% of the origins were correctly classified, providing confidence that most of the areas defined did capture

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important spatial characteristics of the fisheries. The misclassification of ~40% of the origins could have resulted from real situations and/or data limitations: (1) some targeted species have an ubiquitous distribution, (2) the trips associated with the port of landing Quellón also have ubiquitous destinations, and (3) the data set lacked the identification of transhipping operations for species other than sea urchins, and it lacked information on the origins related to transhipping operations. Also, (4) habitat and species patchiness within the origins may contribute to misclassification, because harvest origins likely correspond to sets of patches as proposed previously by Zuleta et al. (2008) for sea urchins. Harvesting zones located north of the Corcovado Gulf were mainly determined by their closeness to a principal port of landing. To the south of the Corcovado Gulf, historical and natural reasons (Martinic 2005) have led to a scarcity of ports. Therefore, in such areas, a large part of the fishing operations take the form of transhippings, especially toward the west. Thus, the larger landings per origin located the south of Chiloe Island and to the west of the Chonos Archipelago reflect the presence of sea urchin transhippings operations (Orensanz et al. 2005, Moreno et al. 2006). Although not included in the data description, information provided by fishermen suggest that harvesting zones 8, 9, 10, and 12 are also characterized by transhippings associated with G. skottsbergi (C Molinet, pers obs), which has not been clearly captured by the monitoring programs. For example, while a large percentage of origins falling into the SW harvesting zone 12 were correctly re-assigned by the general discriminant model, the hierarchical cluster analysis identified this area as an area of misclassified origins. Even if the sea urchin fishery spreads over the entire study area, data from harvesting zone 12 should be analyzed separately from other areas, because the dominant transhipping operation in the area likely changes the dynamics of the fishery. The large volumes of algae and sea urchin caught in this area may mask northern population trends if landing volumes and size structures are lumped together with those from areas where port operations dominate. The concentration of catches of several species in particular harvesting zones (e.g., 10, mainly crabs; 8 and 12, seaweeds and urchins; and 3, Chilean surf clam) and the clear association between a cluster of origins (based on landed species) and a particular port of landing indicate that the allocation of the fishing effort is not random, but heterogeneously distributed. These relationships could result from several factors. First, non-random harvesting likely reflects the natural distribution patterns of the species, as observed by Orensanz (1991) and Caddy (1975) for other fisheries. Although the fishermen’s knowledge of the distribution of the species may vary, it is reasonable to assume that the spatial distribution of the origins reflects the spatial distribution of the population, as suggested by Swain and Sinclair (1994). Furthermore, considering the existence of smaller scale spatial heterogeneity of the studied species (Molinet et al. 2010) than that of the origins studied here, and the fact that origins probably correspond to sets of patches for sea urchins (Zuleta et al. 2008), it is reasonable to assume that even within the harvesting zone, effort is not randomly distributed. Second, the higher profitability associated with L. albus, G. skottsbergii, and V. antiqua fisheries (Molinet et al. 2008) leads a greater profitable range for transhipping operations which affects effort. Profitability of fisheries has already been proposed as a conditioning factor for the spatial distribution of effort in small scale fisheries in Canada and Mexico (Lane 1988, Salas et al. 2004). Third, since the 1990s, toxic red tides have affected filter-feeding bivalves (V. antiqua, G. solida, M. dona-


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cium, A. atra, and M. chilensis; Guzman et al. 2002, Molinet et al. 2003), which has restricted the extraction and recording of bivalve mollusks in this zone. The environmental variables analyzed suggest the characterization and the division of the study area into four macro zones relative to temperature and salinity gradients. This division appears more consistent with the expert identification of harvesting zones than the classification resulting from cluster analysis, where one cluster included almost the entire study area. Expert classification of harvesting zones 2, 3, 4, 7, 8, and 12 were related more to the marine environment than to those harvesting zones affected by river runoff, and were the most heavily fished areas with the exception of harvesting zone 3, located in an extremely exposed zone for the operation of artisanal boats. In addition, harvesting zones 12, 7, and 8 (located in the southwestern part of the study area) showed the greatest co-occurrence of two species (L. albus and G. skottsbergii), and also formed a distinct group in terms of environmental characteristics. These harvesting zones were associated with a water column that was more homogeneous than the more continental harvesting zones and less influenced by river runoff (Pickard 1971), which would imply that these species are more abundant in more oceanic conditions and exposed coasts. Such spatial environmental differences likely directly and indirectly influence species natural productivity and survival, so the sub-divisions proposed by expert classification gain further support from the oceanographic analysis. Because the harvesting zones proposed by the expert panel seem more reliable than those emerging from hierarchical classification methods, we suggest that such an approach be used as a starting point to guide the spatially explicit analysis, monitoring, and management of benthic fisheries. Our results show a case where the expert classification followed by a validation process might render adequate definitions that are difficult to capture through classification analysis alone. In our case study, it is probable that the limitation of the classification analysis to differentiate harvesting zones resulted from the use of partial information as explanatory variables. Methodological improvements (leading to the relaxation of analysis assumptions for example) and new ideas to code expert knowledge into variables would probably contribute to obtaining more congruent results. The major differences between the harvesting zones designated using two different approaches seem to be related to the inclusion of landing port and type of operation in only the expert classification methodology. The inclusion of landing port in the analysis represents a first step to identify métiers in benthic fisheries of Chilean inland seas, because we can observe fishing trips using common fishing gear (diving), as well as those that target species in specific operational areas (e.g., crabs caught with traps in the south, clams in the north, seaweed in the southwest). However, monitoring must be improved in order to provide a better dataset (e.g., identification of operation type and origins related to the transhippings) for more conclusive analyses. Finally, the Chilean inland sea is a complex area and the spatial distribution of the studied species exceeds the size of the proposed harvesting zones (regardless of the method). Because the nine species studied posses dispersive larval stages (e.g., Guisado and Castilla 1987, Jaramillo et al. 2003, Marín et al. 2002), it is highly probable that the defined harvesting zones and the stock units are mismatched. Species-specific characteristics of the pelagic larval stages, together with local to regional oceanographic conditions, determine whether sedentary and spatially

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segregated benthic subpopulations are self-sustained or interconnected at meso- to macroscales. Such population connectivity is a key element in understanding the relationships between origins and harvesting zones, and should be incorporated into harvest zone designation if new data on connectivity become available. Acknowledgments We thank JM (Lobo) Orensanz, A Parma, C Moreno, A Zuleta, and E Niklitscheck for their invaluable support and advice. M Díaz Gómez substantially improved Figures 4 and 8. Comments by three anonymous reviewers significantly improved the manuscript. We also thank the editor for improved English syntax and spelling. This work was financed by the Fondo de Investigación Pesquera, (Project FIP 2005-51) Subsecretary of Fisheries, Chile.

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Addresses: (CM) 1: Instituto de Acuicultura, Universidad Austral de Chile, Los Pinos S/N balneario Pelluco, Puerto Montt, Chile. 2: Centro Trapananda, Universidad Austral de Chile, Portales 73, , Coyhaique, Chile. (NB) Instituto de Fomento Pesquero, Av. Blanco 839, Valparaiso, Chile. (BY) Centro de Estudios Avanzados en Zonas Aridas, Facultad de Ciencias del Mar, Universidad Católica del Norte, Larrondo 1281, Coquimbo, Chile, La Serena, Chile. (JG) Doctorado en Ciencias Aplicadas, mención Sistemas Marinos Costeros, Facultad de Recursos del Mar, Universidad de Antofagasta, Avenida Angamos 601, Antofagasta, Chile. (AA) Instituto de Ecologia y Evolución, Universidad Astral de Chile, Campus Isla teja, Valdivia, Chile. (SR) Profesor Juan Gómez Milla 3003 Dpto. 404, Ñuñoa, Chile. Corresponding Author: E-mail: . 1: Telephone: 56 65 277126. Fax: 56 65 255583. 2: Telephone: 56 67 234467.