BULLETIN OF MARINE SCIENCE. 89(3):699–716. 2013 http://dx.doi.org/10.5343/bms.2012.1063
Variability in the growth patterns of Loxechinus albus along a bathymetric gradient associated with a fishing ground
Carlos Molinet, Cecilia A Balboa, Carlos A Moreno, Manuel Diaz, Paulina Gebauer, Edwin J Niklitschek, and Nancy Barahona
Abstract Here we assess the growth pattern variability of the urchin Lochechinus albus (Molina, 1782) in a fishing ground where it was distributed between 0 and 100 m depth. The bathymetric gradient was divided into four strata, and urchin samples were collected for growth estimations. Images were used to characterize the urchin population along this bathymetric gradient, and historical records for the urchin fishery at this fishing ground were examined. Four algorithms were used to model growth, and the Akaike’s Information Criteria was used to determine the best model fit. Among the four bathymetric strata, both size and age composition, and the growth patterns of L. albus differed significantly.I n the shallowest stratum, urchins were smaller and younger than in the deeper strata. Urchins from 5 to 15 m depth displayed greater initial growth rates compared with urchins from 25 to 100 m depth; however, growth decelerated faster at 5–15 m depth than at deeper habitats. Based on results, we hypothesize that the growth pattern of L. albus observed in the shallowest stratum represents a case of age (size) truncation due to fishing, which requires further study.
Since the end of the 1990s, the sea urchin Loxechinus albus (Molina, 1782) has supported the greatest edible sea urchin fishery in the world (Vásquez et al. 1984, Guisado and Castilla 1987, Moreno and Vega 1988, Andrew and O’Neill 2000, Vásquez 2001, Moreno et al. 2006, 2011, Kino and Agatsuma 2007, Pérez et al. 2010). This fishery began in Chile in the 1940s, but steady growth did not occur until the mid-1970s (Moreno et al. 2006). The fishing pressure led to a crisis in 2001, which led to the establishment of a management plan that took effect in 2005 M( oreno et al. 2006). Loxechinus albus is one of the key herbivores of the coastal ecosystems off Chile (Vásquez et al. 1984, Guisado and Castilla 1987, Moreno and Vega 1988, Vásquez 2001, Kino and Agatsuma 2007, Pérez et al. 2010). Its geographic distribution spans the southern cone of South America, from Isla Lobos in Peru (06°55.5´S, 80°42.5´W) in the Pacific to the central zone of Argentina (37°35´S) and the Malvinas/Falkland Islands (51°40´S) in the Atlantic (Schuhbauer et al. 2010). In the fjords and channels of southern Chile, around 90% of the L. albus population inhabits between 0 and 20 m depth (Inostroza et al. 1983, Moreno et al. 2011), even though its bathymetric distribution has been described as from the intertidal down to 340 m depth (Larraín 1975). This extended distribution range reported for L. albus is linked to the spe- cies’ capacity to feed on drifting macroalgae (Castilla and Moreno 1982, Contreras and Castilla 1987), which reaches deep habitats via physical transport mechanisms (Vetter and Dayton 1998, 1999).
Bulletin of Marine Science 699 © 2013 Rosenstiel School of Marine & Atmospheric Science of the University of Miami 700 BULLETIN OF MARINE SCIENCE. VOL 89, NO 3. 2013
It is reasonable to expect differences in individual growth rates for urchins in deeper strata associated with energetic limitations related to lower food availability (Ebert et al. 1999, Wing et al. 2003, Schuhbauer et al. 2010). In addition, but to a lesser extent, changes in temperature, salinity, and dissolved oxygen over a bathy- metric gradient also may have metabolic effects on sea urchins (Siikavuopio et al. 2007, 2008, Schuhbauer et al. 2010). Along with the trophic, metabolic, and reproductive factors that determine bathy- metric differences in L. albus individual growth patterns, it is reasonable to expect fishing effects as suggested byS chuhbauer et al. (2010). These effects can lead to density-dependent and trophic consequences observed in benthic species (e.g., Orensanz 1986), and compensatory growth as observed in fishes (e.g., Ali et al. 2003). Evolutionary responses induced by the selective pressure of the fishery have been ob- served in other species, such as Atlantic cod, Gadus morhua, Linnaeus, 1758 (Swain et al. 2007), and white seabream, Diplodus sargus (Valenciennes, 1830) (Pérez-Ruzafa et al. 2006). The fit of growth models in areas where larger individuals have been selectively re- moved by fishing results in truncated size and age structures, and can cause errone- ous estimates and interpretations of growth curves, with management consequences for the fishery (Götz et al. 2008, Hsieh et al. 2010). In addition to the range of factors that may affect the growth patterns of urchins, comparison of these patterns based on available literature is complicated by the diversity of models used to analyze observed growth in different populations and depth strata of interest (Grosjean 2001). Among the three published studies of L. albus growth, Gebauer and Moreno (1995) applied the von Bertalanffy model, Schuhbauer et al. (2010) selected the von Bertalanffy model, andF lores et al. (2010) selected the Tanaka model among three other models. Studies by Gebauer and Moreno (1995; 39°26´01˝S, 73°12´57˝W, inter- tidal) and Schuhbauer et al. (2010; 52°03´06˝S, 59°44´15˝W, 0–15 m depth) were car- ried out on urchins from unfished areas, while Flores et al. (2010; between 44°00´S and 44°30´S, <19 m depth) studied urchins from populations affected by intense fish- ing since the 1980s (e.g., Moreno et al. 2006, 2011). Here, we look at the variability in growth patterns of the only known population of L. albus distributed between 0 and 100 m depth (Moreno et al. 2011, Molinet et al. 2012), an area which is also actively fished. The discovery of this bed allowed us to study and compare growth patterns along a bathymetric gradient, where the population is fished down to 60 m depth, regardless of the physiological and legal restrictions that limit shellfish divers to 20 m depth. Thus, we compared the size and age structure of L. albus among four bathymetric strata, and fit four alternative growth models to data from each stratum, using an information criterion to select the best model. Finally, we compared the apparent growth patterns within the differ- ent strata, formulating and assessing, in an exploratory manner, various hypotheses that explain the differences observed.
Materials and Methods
Study Area.—The study area is characterized by a circular bathymetric depression down to 110 m depth located to the south of Chiloé Island (43°10´S, 73°39´W), near Quellón in northwestern Patagonia (Chile; Fig. 1A), the port with the highest landings of the L. albus molinet et al.: Growth of Loxechinus albus along a bathymetric gradient 701
Figure 1. Study area in the southern Chile. (A) The study area is circled; the arrow indicates the location of Quellón, the principal port for sea urchin landings in the study area. (B) The enlarged study area, with black squares indicating the position of transects carried out with the Seabotix LBV 200 remote operated vehicle. Gray contour lines indicate the depth (m) isobaths and negative numbers indicate the depth of the isobaths. The area surrounded by broken lines represents the estimated area occupied by the patch of Loxechinus albus, as reported by Molinet et al. (2009) and C Moreno (Universidad Austral de Chile, unpubl data). fishery (Molinet et al. 2011). Here, a bed of L. albus was found with heterogeneous densities down to 100 m depth (Molinet et al. 2009, 2012, Moreno et al. 2011), occupying an area of about 6 km2 (Fig. 1B). Additionally, landing records from the Instituto de Fomento Pesquero show a fishing ground that extends from the western limit of the bed toward the center of the hollow. Sample Collection.—Samples were collected from four strata along a bathymetric gra- dient: 5–15, 25–45, 50–70, and 75–100 m. The shallowest stratum was selected based on the greater frequency of catch up to 15 m deep in the records of the fishery. The other three 702 BULLETIN OF MARINE SCIENCE. VOL 89, NO 3. 2013 strata were arbitrarily selected. In the three deepest strata, samples were collected by towing a “chain sweep” trawl, modified from Campagna et al. (2005), a distance of 100 to 200 m. The trawl was 1 cm mesh size, 1-m2 mouth, and 2 m in length. In the shallowest stratum, samples were collected by divers due to the uneven nature of the sea floor that rendered trawling im- possible. Divers searched for and collected all individuals from the sampling area to complete the number of urchins per size class required for reading growth rings. From each depth stratum, 20–30 urchins per size class were collected, with intervals of 5 mm per size class where possible. In the case of the deepest samples, given that this is the only reported bed of L. albus below 50 m depth, only 1000 individuals were taken per stratum so as to not significantly affect abundance, which has been estimated around 54,000 individuals (Molinet et al. 2009). To obtain a representation of the bed size distribution, images were taken with a SeaBotix Little Benthic Vehicle LBV200 over 29 transects, each 100–150 m long and 0.3–0.8 m wide, to measure and count a sample of urchins within the study bed. The LBV200 was equipped with a Micron Nav USLB tracking system, with a transductor fixed to a mast and a GPS an- tenna configured to record the geographic position every 1 s. The geographic coordinates of the track were obtained from these data. The LBV200 also had a scaling system with 2 lasers set parallel to the optical axis of the main camera separated by 50 mm. This reference was used to obtain the test diameter (TD) of individuals, the size of the quadrant analyzed, and the area swept by each transect. The LBV200 navigation system and the type of substrate (90% gravel and sand) allowed the laser to remain perpendicular to the bottom. The device can detect urchins ≥10 mm TD, but 15 mm TD is the smallest reliable size due the cryptic state that some individuals may be on certain substrates. In stratum 5–15 m, two transects were completed; however, one of the videos malfunctioned and therefore density was determined based on only one transect. Sample Processing.—In the laboratory, each individual was measured with a vernier cali- per of 1 mm precision and wet weighed with a digital balance of 0.5 g precision. The upper part of each test was separated, labeled, and stored in a bag to obtain the five genital plates used for growth ring analysis. The reading of growth rings in echinoderms relies on the continual incorporation of calci- um carbonate in the structures that form the skeleton, such as spines, Aristotle’s lantern, and test plates (periproctals, ambulacral plates, interambulacral plates, ocular plates, and genital plates). In these structures, areas of rapid and slow growth can be observed as opaque and translucent rings, influenced by food availability, spawning season, and environmental fac- tors (Pearse and Pearse 1975). Growth was estimated for L. albus by reading the growth rings in the genital plates of ur- chin tests as described by Gebauer and Moreno (1995), Flores et al. (2010), and Schuhbauer et al. (2011). The genital plates were dried in an oven at 60 °C for 24 hrs, labeled, and stored. To facilitate the reading of growth rings, plates were immersed in sodium hypochlorite at 4% for 24 hrs to extract the remaining organic matter and bleach the plates. Plates were then sanded down on each side with fine water-sandpaperN o. 400 and 600, immersed in xylol (around 2 min) for observation under a stereoscopic microscope with reflected light, and then photo- graphed. The number of rings, as well as the distance of these to the center of the plate was recorded using the Image-Pro Plus software (B4.5, MediaCybernetics). To assess the uncertainty of age estimates, 30 urchins were selected randomly per stratum and three growth ring readings by two co-authors of this study were compared (two readings by CB, and one reading by PG). Errors in age determination (number of rings) were assessed using the average percentage error (APE) and the coefficient of variationC ( V; Campana 2001), estimated by:
1 R XXIJ- J APE J = 100% # RX| I = 1 J molinet et al.: Growth of Loxechinus albus along a bathymetric gradient 703
where Xij is the ith age determination of the jth urchin, Xj is the mean age estimated for the jth urchin, and R is the number of times that each urchin was read. APEj averaged over several urchins was used as an index of average percentage error. Coefficient of variation was the precision of the age estimate for the jth urchin and can also be averaged for several urchins to produce an average CV:
2 R XXij- j | i = 1 Q X j V CV j = 100% # . X j The test diameters (TD) of individuals recorded with the LBV200 were measured based on im- age frames of width ≤70 cm in the laboratory by projecting the images obtained onto 100 × 100 cm screen and using the 50 mm laser scale to measure each urchin. Urchins were measured in the middle axis of the picture. The error in the determination of TD using this methodology was approximately ±8 mm.
Density per transect was calculated as dt = Ut/At , where d is the density in the transect, U the number of urchins counted in the video of the transect, and A the area swept by the transect. Urchins were counted along each transect by examining each individual frame. Fishery Data.—Fishing data for the study area were obtained from port surveys conduct- ed during the fishery season (January–October) since 1986 by technician personnel of the Instituto de Fomento Pesquero (IFOP), at the port of Quellón (Fig. 1). Daily, random interviews were conducted with fishers arriving in port while landing their catch. Fishers were asked to provide information per fishing trip (defined as the departure/arrival of a boat to a fishing ground, resulting in the sampled catch) regarding the weight (kg) and origin of their catch, diving depth, fishing effort applied (number of divers and total amount of diving hours), trip duration, and distance sailed, among other variables. During the interview, a sample of their urchin landings was measured and weighed. Here, we selected information from our study area (encoded by 9255 in the IFOP database), which extends from the west end of the bed toward the center of the hollow (confirmed by in situ observations C Molinet). Landing data were obtained for years 1986, 1988–1990, 1996, 2001–2008, 2010, and 2011; size distribution data were obtained for 1988, 1989, 1990, 2001, 2004, 2005, 2007, 2010, and 2011. With this information catch per unit effort CPUE( ) was estimated per depth stratum (5–15, 25–45, and 50–70 m), and we developed size distribution graphics per depth stratum, which were compared with data collected during the study. Data Analysis.—The TD distribution of sampled urchins with the LBV200 and the age of the collected urchins were assessed relative to depth with the use of regression models for ordinal data (McCullagh 1980) using the ordinal package in R 2.14.0 (Christensen 2012). For this, TD distribution was separated into 12 intervals of 10 mm each, while ages were sepa- rated into 1-yr intervals. The growth of L. albus was modeled for each depth stratum using the Gompertz (1825), von Bertalanffy (1938), Schnute (1981), and Tanaka (Ebert et al. 1999) models (see Online Appendix 1). To assess differences in the growth function between depth strata, the Schnute (1981) mod- el was selected, which turned out to be the most informative model (lowest AIC) for all data observed throughout the four strata considered. A set of hypotheses corresponding to the different values of the parameters of the Schnute function were assessed using the likelihood ratio test, implemented in the lmtest library available in R.2.14.0 (Zeileis and Hothorn 2002): H1 (Null): Urchins among all bathymetric strata exhibit a similar growth pattern. H2 (Full): Urchin growth differs by bathymetric strata. H3: There are two urchin growth patterns: one for the 5–15 m depth stratum and another for the 25–100 m depth stratum. 704 BULLETIN OF MARINE SCIENCE. VOL 89, NO 3. 2013
H4: There are two urchin growth patterns: one common for both shallow strata (5–45 m) and another for the two deeper strata (50–100 m) H5: There are two urchin growth patterns: one for the three uppermost depth strata (5–70 m) and another for the deepest stratum (75–100 m) H6: Growth in L. albus is homogeneous until 4 yrs of age throughout all bathymetric strata (parameter c is equal in all strata). H7: The relative growth constant for L. albus (parameter b) is equal in all strata.
Results
Test Diameter and Urchin Bed Density.—Although determined from only one transcect, the density of L. albus was greatest in the surface stratum [5.1 indi- viduals (ind) m−2; Table 1]. From a previous study, density at 7–15 m depth based on three 40-m transects sampled in October 2011 was found to lie between 11–26 ind m−2 (C Molinet, Universidad Austral de Chile, unpubl data), which is consistent with a pattern of higher densities in the shallowest strata. Greatest variability in density was observed between 25 and 45, and 75 and 100 m, where standard deviation of density values were higher than the mean values suggesting an aggregated spatial distribution pattern (Table 2). The TD estimated using the Seabotix LBV200, and the TD and age from the col- lected urchins revealed increasing TD and average age gradients from the surface down to the deepest stratum (Figs. 2, 3, Table 2). These observed differences inTD among strata were also reflected in the size composition of the sample and by the regression model for ordinal data, which showed differences in TD distribution in urchins from all strata [AIC = 4458, likelihood ratio test P(χ2) = 0.014; Table 3]. TD of the collected urchins vs those imaged in the study bed (Table 3, Figs. 2, 3A–D) are not comparable due to differing sampling methodology (each with a dif- ferent set of objectives) and error in measuring TD from images. Mean age increased along a gradient between the surface and the deepest strata. Thus, individuals >9 yrs were not observed in 5–15 m, where around 90% of the individuals were between 4 and 7 yrs (Fig. 3E). In 25–45 m, a broader range of size classes was observed, with individuals between 2 and 12 yrs old (Fig. 3F), while strata 50–70 and 75–100 m contained mostly size classes between 5 and 14 yrs, with very few individuals <5 yrs (Fig. 3G,H). The regression model for ordinal data with the best fit was that which accounted for differences in the age distribution of collected urchins in all the strata [AIC = 5120.1, likelihood-ratio test P(χ2) = 5.7E-05; Table 3]. The APE of the three growth ring readings was 5.77 and the CV was 5.41, both considered acceptable following Campana (2001).
Table 1. Population density of Loxechinus albus in the four studied depth strata in the Quellón depression.
−2 Depth Area by Number of Sampled Urchin density (Urchin m ) stratum (m) stratum (km2) transects area (m2) Mean (SD) Minimum Maximum 5–15 0.55 1 104 5.1 25–45 2.13 9 1,027 1.4 (1.9) 0.2 4.0 50–70 2.16 11 1,113 2.2 (3.3) 0.1 8.0 75–100 1.16 7 949 1.3 (1.6) 0.2 4.2 molinet et al.: Growth of Loxechinus albus along a bathymetric gradient 705
Table 2. Mean (SD) of (1) the test diameter (TD) of Loxechinus albus sampled using the Seabotix LBV 200, (2) TD of collected urchins for age determination, and (3) the age of collected urchins per depth stratum (5–15, 25–45, 50–70, and 75–100 m).
Depth strata (m) TD bed (mm) TD collected (mm) Age collected (yr) 5–15 46.6 (11.90) 43.5 (14.4) 5.7 (1.4) 25–45 72.0 (23.30) 53.2 (24.9) 6.0 (2.7) 50–70 74.5 (17.50) 68.4 (21.3) 8.9 (2.9) 75–100 78.5 (18.13) 77.6 (17.7) 10.0 (2.5)
Bathymetric Variability in Growth Patterns.—Given that the Schnute model was the most informative for three of the four strata studied (see Online Appendix 1), this model was selected for a posteriori analyses. For this analysis we considered only individuals between 4 and 9 yrs old, because these were most con- sistently represented over all four depth strata. The comparison of growth models for the different depth strata based on Schnute’s model indicated significant differences among the strata (Table 4). The full model (H2) had a significantly higher likelihood AIC[ = 4907, likelihood-ratio test P(χ2) <
Figure 2. Size frequency distribution for urchins sampled with the LBV 200 Seabotix. (A) to (D) correspond to strata E1 to E4, respectively. The dotted gray line shows the minimum legal size for harvest in Chile. 706 BULLETIN OF MARINE SCIENCE. VOL 89, NO 3. 2013 collected the for growth study from strata to E4, respectively. E1 from each depth stratum to E4, (E1 respectively). Figure 3. (A) to (D) show the diameter) size show frequency (test to (D) distribution albus Figure (A) Loxechinus of 3. the frequency age show (E) distribution to (G) L. for albus molinet et al.: Growth of Loxechinus albus along a bathymetric gradient 707
Table 3. Evaluation of the test diameter distribution of urchins sampled using the LBV 200 and the age of collected urchins using regression models for ordinal data (McCulagh 1980). Best model fit was determined by the lowest Akaike’s Information Criterion (AIC) and its statistical significance was assessed by a likelihood-ratio test (LR, probability χ2). E1 = 5–15, E2 = 25–45, E3 = 50–70, and E4 = 75–100 m.
Number of Model parameters AIC Log likelihood LR statistic P(χ2) Test diameter E1=E2=E3=E4 11 5,047.1 −2,512.5 E1≠E2=E3=E4 12 4,461.4 −2,218.7 587.68 2.00E-16 E1≠E2≠E3=E4 13 4,462.0 −2,218.0 1.45 0.22901 E1≠E2≠E3≠E4 14 4,458.0 −2,215.0 5.99 0.01441 Age E1=E2=E3=E4 15 5,564.2 −2,767.1 E1≠E2=E3=E4 16 5,422.2 −2,695.1 143.96 2.20E-16 E1≠E2≠E3=E4 17 5,134.3 −2,550.1 289.93 2.20E-16 E1≠E2≠E3≠E4 18 5,120.1 −2,542.1 16.18 5.76E-05
0.05] than the null model (H1), and all growth models based on grouping neighbor- ing strata (hypotheses H3–H5, Table 4). H2 also won had a higher likelihood than the hypotheses of initial growth common to all strata, defined in H6 and H7. According to the estimated parameters for the Schnute (1981) function, L. albus in the shallowest stratum displayed a greater initial growth rate (relative growth con- stant a) compared to urchins from the 25–100 m depth strata (Fig. 4). However, the differences found in parameter b (incremental rate of relative growth constant) in- dicated that growth should decelerate faster at 5–15 m depth (b < 0) than at deeper habitats (1 < b), leading to significant differences in estimated size for age group 9 (y2 parameter; Fig. 4). However, urchins between 25 and 100 m depth had an initial pe- riod of decelerated growth (parameter a), which continued with an indefinite period of accelerated growth. Such a pattern might occur when individuals experience food limitation until they attain a critical size (Schnute 1981).
Table 4. Likelihood-ratio test to assess the statistical differences between the different hypotheses for the Schnute function parameters. Best model fit was determined by the lowest Akaike’s Information Criterion (AIC) and its statistical significance was assessed by a likelihood-ratio test (probability χ2).
Hypothesis AIC Degree of freedom Log Likelihood χ2 Pr(χ2) H2 4,907 17 −2,436.7 H1 5,039 5 −2,514.6 155.90 2.20E-16 H2 4,907 No convergence H3 H2 4,907 17 −2,436.7 H4 4,942 9 −2,462.1 50.75 2.93E-08 H2 4,907 17 −2,436.7 H5 5,033 13 −2,503.6 133.88 2.20E-16 H2 4,907 17 −2,436.7 H6 4,914 14 −2,443.4 13.51 0.00364 H2 4,907 17 −2,436.7 H7 4,940 13 −2,457.2 41.03 2.64E-08 708 BULLETIN OF MARINE SCIENCE. VOL 89, NO 3. 2013
Figure 4. Age distribution vs test diameter of individual urchins collected from each depth strata. The line shows the fit to Schnute’s growth function.
Fishery Data.—The fishery data contained rather scarce landing records between 1986 and 2000, and incomplete information regarding depth of catch. The number of fishing trips sampled from 5 to 15 m peaked at 58 in 2004, and dropped down to five trips during 2011, with intermediate peaks in between (e.g., 28 trips in 2008). Sampling from 25 to 45 m peaked in 2001, 2006, and 2007, at around 30 trips yr−1. The fishing trips exploiting 50–70 m were less frequent, up to 20 trips in 2001 and 9 trips in 2010 (Fig. 5). TheCPUE ranged between 30 and 1200 kg diver−1 d–1, for the shallowest stratum, and up to 600 kg diver−1 d–1 in the two deeper strata. Higher CPUE values were re- corded at the beginning of the sample period (Fig. 6). Finally, size distributions were recorded irregularly for different depth strata and years: between 1988 and 1989 no depth data were recorded with the catch. Only in 1990 and 2004 were records ob- tained for 5–15 m, with the size distribution concentrated between 80 and 125 mm TD in 1990, while in 2004 a significant fraction of the catch was below the minimum legal landing size (60 mm TD, Fig. 7). Stratum 25–45 m was the best represented in fishery’s landing data (available for 2001, 2004, 2005, 2007, and 2011) and indicates molinet et al.: Growth of Loxechinus albus along a bathymetric gradient 709
Figure 5. Number of sampled trips to the fishing ground associated with the urchin study site, obtained from benthic monitoring conducted by the Instituto de Fomento Pesquero (IFOP) since 1986. No information was available for 1987, 1991–1995, 1997–2000, and 2009.
Figure 6. Catch per unit effort (CPUE) determined for the fishing ground associated with the urchin study site. No information was available for 1987, 1991–1995, 1997–2000, and 2009. 710 BULLETIN OF MARINE SCIENCE. VOL 89, NO 3. 2013 that landed urchins were generally above the minimum legal size, with individuals up to 140 mm. TD data from stratum 50–70 m were only recorded in 2010, showing a size distribution pattern similar to that observed in landings from stratum 25–45 m (Fig. 7).
Discussion
Our comparison of L. albus growth patterns across a depth gradient revealed sig- nificant differences between depth strata. Individuals from 5 to 15 m depth grew faster before reaching 50 mm TD than urchins a greater depths, but growth then decelerated faster at 5–15 m depth than in deeper habitats. Thus urchins in the shal- lowest stratum reached 60 mm (the minimum legal size for harvest) after 6 yrs, at least 1 yr later than stratum 25–45 m. From our results, the simplest conceptual model is one that does not include juve- niles between 50 and 100 m depth and indicates shallow recruitment and progressive displacement to deeper habitats, consistent with the natural life cycle of L. albus, whose recruitment has been described in the shallow benthos (Bustos and Olave 2001, Vásquez 2001). Post-settlement habitat selection would fit a population expan- sion model, as proposed by MacCall (1990), as well as a situation where recruitment occurs along the entire depth gradient. Such a model is applicable to the Quellón depression, which has relatively homogenous habitat down to 100 m (Cáceres et al. 2008, Molinet et al. 2012), with food available in deeper strata (Añazco 2012, Molinet et al. 2012). Although it was not possible to accurately assess the relative importance of envi- ronmental, population, and fishing effects on urchin size and age structures, and ap- parent growth rates in the shallowest depth stratum, it is reasonable to assume that the 5–15 m depth stratum was the most affected by fishing pressure. Even though shellfish divers reported fishing activity at depths >20 m (consistent with that report- ed by Barahona et al. 2003, Zuleta et al. 2008), this should in theory be constrained by the physiological limits of divers and the technological limitations of hookah div- ing equipment. The unusual growth pattern observed for urchins at 5–15 m depth could be due to (1) migration of adult urchins toward deeper habitats, or (2) a truncated size dis- tribution due to the selective removal of adults by fishing activities. Migration of urchins has been observed in Paracentrotus lividus (Lamarck, 1816), but over a small spatial scale and apparently in response to food limitation (Fernandez et al. 2001). Unfortunately, we have no information on the migration patterns of L. albus. A truncated size distribution in the shallowest stratum is evident in the absence of in- dividuals >9 yrs of age and >75 mm TD, which contrasts with the size distribution re- corded in the 1990s. Furthermore, the variation in CPUE from 1986 to 2011, and the reduced number of fishing trips made to this fishing ground despite its close proxim- ity to Quellón (5 nmi), suggests strong fishing pressure, which is consistent with that reported by Moreno et al. (2011). The most intensive fishing ofL. albus to the south of Chiloé (i.e., surrounding the study area) began in the mid-1970s (Moreno et al. 2006). Since 2005, this area has produced 3000 and 4500 t of landings, and smaller average urchin sizes compared with other areas (Molinet et al. 2009, Moreno et al. 2011). It is therefore reasonable to assume that this area of easy access has experienced selec- tive removal of urchins. This selection may have affected the size-age relationship, molinet et al.: Growth of Loxechinus albus along a bathymetric gradient 711
Figure 7. Size composition determined for the fishing ground associated with the urchin study site, obtained from benthic monitoring conducted by the Instituto de Fomento Pesquero (IFOP). White bars, no depth recorded; light gray, <16 m depth; medium gray, 16–45 m depth; dark gray, 45–70 m depth. 712 BULLETIN OF MARINE SCIENCE. VOL 89, NO 3. 2013 because divers effectively select the faster growing individuals within a given cohort as these attain commercial harvest size more rapidly. This same phenomenon ex- plains the smaller sizes of adults at shallower depths compared with adults of the same age at greater depths. Flores et al. (2010) studied fishing areas 60–100 nmi south of our study area, where access is more problematic, and reported larger individuals than we observed at 5–15 m depth. Growth of those urchins was best modeled by Tanaka model, and the au- thors concluded that from a practical viewpoint, a linear model would be perfectly adequate in fisheries applications. However, this conclusion should be considered with caution because: (1) in the case of urchins at 5–15 m depth, the initial lag in growth could have been the prod- uct of density-dependent regulation mechanisms, and (2) the subsequent decline in growth rate could have been an artifact of selective removal of larger individu- als by the fishery. For example, Lau et al. (2011) observed that urchins Anthocidaris crassispina (Agassiz, 1863) occurring in high densities (>15 ind m−2) within a ma- rine reserve grew much slower than those outside the reserve, suggesting a density- dependent effect. While linear growth is not probable from a biological perspective (von Bertalanffy 1938,P auly 1981, Schnute 1981), it could be useful for adjusting highly truncated size structure data to the size of maximum individual growth, but explicitly assuming that there is age-size truncation. In light of the available information, a plausible hypothesis is that the model fit to the urchin growth data from 5 to 15 m depth is an extreme case of age (size) trunca- tion due to fishing, which has been reported for other fisheriesH ( sieh et al. 2010). The urchin growth patterns reported by Flores et al. (2010) represent moderate age truncation, given that their study area is less accessible to fishers (10–20 hrs transit from Quellón). Finally, the growth pattern of urchins at 25–45 m depth is consistent with observations by Gebauer and Moreno (1995) and Schuhbauer et al. (2010) in areas without fishing, and corresponds to populations without age (size) truncation (i.e., a “natural” growth pattern for L. albus in this study area). Selective removal of larger individuals by fishing has been reported by others, who also describe genetic effects on populations S( wain et al. 2007, Hutchings and Fraser 2008). These effects can result in the persistence of smaller individuals per age class despite good conditions for growth, and can lead to subsequent projections of catch decline (Swain et al. 2007). The evolutionary consequences of such effects can be dif- ficult to reverse H( sieh et al. 2010). Our results show that the fit of growth models based on information from dis- turbed populations, either due to fishing or other natural events, have the potential to lead to the erroneous interpretation of results, particularly in spatially structured and commercially exploited populations, where precautionary measure must be tak- en to avoid detrimental decision making (e.g., Orensanz et al. 2004). An effective measure for mitigating the effects of disturbances, such as fishing, is the implemen- tation of marine reserve areas (e.g., Clark 1996, Behrens and Lafferty 2004, Parnell et al. 2005, Lau et al. 2011). The halting of the selective effects of fishing enables the study of the life history of exploited species under more “natural” conditions. Such populations also allow the testing of the hypotheses regarding age truncation in ex- ploited populations (e.g., Swain et al. 2007, Hsieh et al. 2010). molinet et al.: Growth of Loxechinus albus along a bathymetric gradient 713
Acknowledgments
This project was funded by the FONDECYT 1100931 grant. We thank the crew of L/M Jairo and its captain J Cuevas for their support during the sampling campaigns. We thank R Roa-Ureta and A Parma who kindly proposed a number of changes to the manuscript. Finally we thank four anonymous reviewers who helped to improve this work.
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Date Submitted: 20 August, 2012. Date Accepted: 27 May, 2013. Available Online: 16 July, 2013. 716 BULLETIN OF MARINE SCIENCE. VOL 89, NO 3. 2013
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