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Populations of marine species have been shown to differ in demographic, life history, and morphological traits across a variety of temporal and spatial scales (Caley et al. 1996, Agrawal 2001). Phenotypic traits of populations can vary in response to the selective pressures experienced in the environment. Numerous studies on fishes and invertebrates have shown that demographic and life history traits such as timing of maturation, growth, age structure, and recruitment can vary widely across the geographic range of a species, in response to environmental gradients, interspecific competition, prey availability, predation, and fishing pressure (Connell 1961, Hagen & Gilbertson 1972, 1973, Hamilton et al. 2007, Caselle et al. 2011).

Species harvested across large geographic scales often exhibit significant shifts in life history and demographic traits in response to size-selective harvesting pressure (Hutchings 2005, Hamilton et al. 2007, Law 2007). Past studies have shown that fishing can alter size and age structure (Hutchings 2005), growth rates (Enberg et al. 2012), sex ratios (Jivoff 2003), timing of maturation (Hutchings 2005, Law 2007), and sex change (Hamilton et al. 2007). Shifts in these key demographic traits may lead to changes in reproductive output, which could negatively impact fitness and resource sustainability in the future. Understanding the scales of variability among population traits is, therefore, an important aspect of fisheries that needs to be incorporated more robustly in the management of harvested species.

Prior studies have also shown that organismal body form or morphology can differ greatly among populations (Klingenberg et al. 2003, Costa et al. 2008). Potential ecological and environmental drivers of spatial differences in organismal body shape include but are not limited to predator avoidance (Hagen & Gilbertson 1973), competition for resources (Relyea & Auld 2005), environmental stress such as desiccation, or thermal exposure (Gleason & Burton 2013), and variability in habitat structure and composition (Sanford & Kelly 2011).

Individuals often attain larger sizes and older ages at higher latitudes, where waters are cooler and more productive. Past studies have provided support for this phenomenon, called Bergmann's rule (Bergmann 1847), in both terrestrial (Lindsey 1966) and marine systems (Caselle et al. 2011, Manyak-Davis et al. 2013). It is often unclear as to which environmental variables are responsible for driving the observed latitudinal patterns of life history variation because temperature, primary productivity, and other factors are strongly correlated with latitude. For photosynthetic organisms, the intensity of irradiance experienced with changing latitudes can strongly influence growth (Harvey 1942). Wave exposure may also influence longevity and patterns of growth in marine organisms (Dayton 1971). In addition, other environmental variables, such as habitat composition, can cause changes in life history and demographic traits over a variety of scales (Robertson et al. 2005). Variability in ecological conditions experienced by marine populations has also been shown to result in shifts in life history and demographic traits. For example, increased predation risk may alter the timing of sex change (DeMartini et al. 2005) and shift patterns of longevity, growth, and body condition in coral reef fishes (Ruttenberg et al. 2005, Walsh et al. 2012).

The Pacific geoduck Panopea generosa is an ideal study species to investigate geographic variation in life history and morphology because of its large geographic range. As an occupant of intertidal and subtidal soft substrates from Alaska to Baja California (Andersen 1971, Goodwin & Pease 1987), this clam is one of the largest burrowing bivalves in the world. Given its large size, commercial and recreational fisheries target P. generosa along the Pacific coast of North America, subjecting clam populations to the highest levels of exploitation (and selection pressures that may alter life history traits) at the northern and southern ends of the species range, in Washington and Baja California, Mexico, where commercial fisheries operate. Clams burrow as deep as 1 m below the surface into the sediment where they remain for their entire life, which can reach more than 150 y (Bureau et al. 2002). The extreme longevity of P. generosa, coupled with a permanent record of age and growth preserved in the shell, makes this species a suitable candidate for life history and dendrochronology studies of past environmental conditions (Bureau et al. 2002, Black et al. 2008).

The high demand and profit coupled with a decline of this species in the commercially fished waters of Baja California, Mexico, mandate further investigation of the population status of Panopea generosa throughout its range. To aid in the biological assessment efforts of different populations of P. generosa, data on life history and morphological parameters, including mean weight, mean valve length, age, longevity, and body condition, were collected and analyzed. Long-term data on sea surface temperatures (SST) and chlorophyll a (Chl a) at each sampling location were correlated with lifetime growth parameters to identify whether growth varied as a function of either environmental factors or latitude. Finally, geometric morphometric tools were used to investigate whether differences in environmental and habitat characteristics could affect morphology or shell shape. Overall, it was hypothesized that demographic, life history, and morphological traits of P. generosa would be site specific, driven by population responses to environmental gradients and exposure to harvesting.


Study Species

The Pacific geoduck Panopea generosa is a member of the bivalve family Hiatellidae, known to inhabit soft substrates such as sand, mud, gravel, and mixed loose substrates (Ricketts et al. 1968, Goodwin & Pease 1991). The range of P. generosa extends from exposed coastal areas, protected bays, and estuaries to inland seas. These long-lived clams (the oldest aged clam, to date, was 168 y old, Bureau et al. 2002) are broadcast spawners, which can mature as early as 2 y of age (Vadopalas et al. 2015) and continue to be reproductive at least until the age of 100 y (Sloan & Robinson 1984). Suitable depths range from the low intertidal zones to 110 m (Jamison et al. 1984). Geoducks burrow into the sediment up to a vertical depth of 1 m, remaining there for the rest of their lives, with very little movement thereafter. Each of the two valves comprises a hinge plate and consists of three layers. These layers, produced by accretionary growth, can be used to estimate the age of P. generosa and reconstruct their growth history (Shaul & Goodwin 1982, Sloan & Robinson 1984, Black et al. 2008). Average sizes of P. generosa range from 114 to 139 mm valve length and 512-1,510 g in weight, with the largest and heaviest clams found in British Columbia (Bureau et al. 2002, Rocha-Olivares et al. 2010, Hidalgo-De-La-Toba et al. 2015).

Study Sites and Collection

Geoducks for this study (Fig. 1) were collected between 2011 and 2014 at four sites (n = 30-50 clams per site) in California (two subtidal and two intertidal sites; CDFW permit #9042). Subtidal California clams (Catalina Island in 2011 and Santa Cruz Island in 2013) were collected haphazardly using SCUBA and a water pressure hose that liquefied the sediment surrounding a clam. Intertidal clams (Bodega Bay in 2013 and 2014 and Morro Bay in 2014) were collected using 1-m-long by 30-cm-diameter PVC pipes to mark burrows, and shovels to dig up clams. Geoduck samples from two additional locations (n = 6 sites in total), Dungeness West in Washington State and San Quintin, Mexico, were obtained from colleagues. Whole wet weight (g) was recorded using a precision electronic balance, and valve length (mm) was measured using calipers.

Age Estimation

Ages were estimated for individual clams from each population (no age estimates were available for the samples collected in Mexico) to obtain information on age structure, growth rates, and longevity, following techniques described by Vadopalas et al. (2011). Briefly, all valves were trimmed into squares using a small air-compression handsaw to keep the umbo (origin of growth) and hinge plate intact. The hinge plate was then sectioned, using an Isomet (Model #111280; Buehler) slow-speed saw, to allow for quantification of annual growth bands (Fig. 2A). Four thin sections were cut, two at the umbo, one midway, and another closest to the hinge plate edge, keeping thin sections at constant distances roughly 0.6 [micro]m apart. Then, the four sections per clam were glued onto a microscope slide using two to three drops of "Cytoseal 60" (Catalog # 23-244257; Thermo Scientific) slide preparation glue. The slides were left to dry for 24 h before polishing using 300-grit and 600-grit sandpaper with a rotating polishing machine (Isomet 3) at 200-300 RPMs.

To enhance ring clarity before aging, polished slides were etched in a 1% HCL bath for 2 min. Acetate peels (Fig. 2A) were taken using the methods developed by Shaul and Goodwin (1982). Peels were centered between two glass slides and the edges were taped to secure the peels. Prepared peels were then placed under a Leica DM4000 compound microscope (4x and 10x magnifications), and samples were aged using annual growth increment counts and the program ImagePro Plus v.7.0.

Lifetime Growth Curve Estimation

To reconstruct lifetime growth curves for each population, length and age data were fit to the von Bertalanffy growth model (VBGM) equation, as modified from the Fisheries R vignette (Ogle 2015). Von Bertalanffy growth model equations were adjusted according to the description by Perez-Valencia and Aragon-Noriega (2013) on Panopea globosa to visualize length-at-age clam data using

[mathematical expression not reproducible],

where L(t) represents the predicted valve length of the clamshell at age (t), [L.sub.inf] represents the predicted maximum asymptotic length parameter, K is the coefficient of growth or the curvature parameter (i.e., how fast [L.sub.inf] is reached), t represents age, and [t.sub.0] is the hypothetical age at which a clam had zero length (initial condition parameter). Following Robertson et al. (2005), [t.sub.0] was fixed to zero for each population because of the extreme difficulty in finding young individuals to anchor the growth curves.

To test whether growth curves were significantly different from each other among sampling locations, two approaches were used. First, 95% confidence intervals were calculated around the model fit for the growth curve for each sampling location. Second, 95% confidence intervals were calculated for the two most important growth equation parameters, [L.sub.inf] and K, in bivariate space. In both cases, lack of overlap of growth curves or curve parameters signified statistical significance at the [aslpha] = 0.05 level.

Life History Data Analysis

Length-weight relationships were fit using a nonlinear power function for each population, and length-weight parameters were compared among populations using an analysis of variance (ANOVA), followed by a Tukey post hoc analysis to identify how sites differed from one another. As a proxy for condition, Fulton's K condition factor was calculated (Ricker 1975) using the equation W/[L.sup.3] x 1,000 and compared among the populations using ANOVA.

Size-frequency and age-frequency distributions were compared among geographic locations using an ANOVA to test whether mean size or age differed geographically. Longevity was calculated as the top quartile of the age distribution for each location (i.e., [T.sub.max]; sensu Choat & Robertson 2002), to correct for bias in estimating the maximum age for small and unequal sample sizes.

To examine whether life history traits were correlated with long-term mean environmental conditions present at each sampling location, SST and Chl a data were obtained from satellite remote sensing data (AVHRR Pathfinder and MODIS) maintained by the Giovanni online data system through NASA Goddard Earth Sciences Data and Information Services Center (NASA GES DISC 2015). Sea surface temperatures and Chl a were averaged over a 4-km box offshore for each sampling region for a 10-y period between 2002 and 2012.

Geometric Morphometrics

The development of geometric morphometric techniques (Bookstein 1978, 1991, 1996, 1998) has enabled rapid advances in quantifying organismal form (i.e., Generalized Procrustes Superimposition, Rohlf & Marcus 1993) to isolate the effects of morphological changes, independent of potential confounding factors such as size, orientation, and translocation. Mahalanobis distance measurements were used to investigate differences in valve shape across the range of Panopea generosa. Valves were cleaned, dried, and numbered. The internal scars of each valve were traced with a graphite pencil to enhance visibility of internal scars in photographs. High-resolution images were taken with a Nikon D3100 macro lens placed on a tripod for stability. Images were used to visualize three anatomical features in geoduck valves: the umbo, the anteroventral adductor muscle scar, and the posteroventral pallial sinus scar (Fig. 2B). These internal features have been shown to best characterize morphological differences in geoduck clams (Leyva-Valencia et al. 2012).

Valve images were imported into Image J (Schneider et al. 2012) and 15 landmarks were emplaced to capture the anatomical features discussed earlier (Fig. 2B). Each landmark position was noted as x- and y-coordinates per landmark for a total of 30 coordinates and 15 landmarks per shell. Morphometric landmark data from Image J were then imported into the shape analysis program Morpho J v.l.06d (Klingenberg 2011). Morpho J was used to compare the landmark positions on each shell in geometric space via Generalized Procrustes Superimposition (Rohlf & Slice 1990, Goodall 1991).

Three steps were involved in superimposing clamshells in shape space (Bookstein 1991): First, location differences (translocation) of the clam valve in each picture were removed by generating a reference coordinate system (0,0) in Cartesian space. Second, shell valves were rescaled geometrically on their centroid size (the square root of the sum of square distances of each landmark to the centroid), and third, the orientation of clamshells was synchronized. Composite shell shape differences were then compared among sampling locations using a multivariate canonical variate analysis. To test whether shell morphology differed significantly among sampling locations, 90% confidence ellipses were calculated around each site, and significance was determined by the lack of overlap. Confidence intervals were calculated using resampling techniques by bootstrapping the site-specific distribution of shell shape variation 10,000 times with replacement.


Geographic Variation in Size Structure and Condition

Geoducks exhibited significant differences among groups in whole weight (ANOVA, [F.sub.4,179] = 61.98, P < 0.0001; Table 1, Fig. 3A) and valve length (ANOVA, [F.sub.5208] = 46.52, P < 0.0001; Table 1, Fig. 3B) among sampling locations. Whole weight data were not available for San Quintin, Mexico. Clams were largest at Dungeness West, followed by Santa Cruz Island, both intertidal sites, and last at Catalina Island (Fig. 3A). Dungeness West, Santa Cruz Island, and San Quintin were not significantly different from each other based on the average valve lengths of geoducks, but valve lengths were significantly larger than those obtained at Bodega Bay, Morro Bay, and Catalina Island (Fig. 3B). Valve length was a significant predictor of whole weight across all sampling locations and length-weight relationships were well described by a nonlinear power function (Y = a * [X.sup.b]). Interestingly, the power-scaling parameter b increased with increasing latitude up to Bodega Bay and decreased slightly at Dungeness West.

Clams showed significant spatial differentiation in Fulton's K body condition factor (ANOVA, [F.sub.4,179] = 14.69, P< 0.0001). Clams from Dungeness West and Morro Bay showed a significantly higher Fulton's K body condition index than clams from other locations. No significant differences in Fulton's K factor were detected among Bodega Bay, Santa Cruz Island, and Catalina Island. These results indicate that clams were heavier for a given length at those locations where the condition factor was higher.

Age, Growth, and Environmental Correlates

Age-frequency distributions among geoduck sampling locations differed significantly (ANOVA, [F.sub.4,172] = 32.96, P < 0.0001) (Fig. 4, Table 1). Clams from Dungeness West exhibited the largest age span of all five sites (5-104 y), whereas Bodega Bay (4-20 y) and Morro Bay (7-46 y) were characterized by the smallest range of ages of all sites, with frequent occurrences of individuals representing younger age classes. Santa Cruz Island (8-76 y) and Catalina Island (9-65 y) fell between Dungeness West and the two intertidal bay sites with a larger range of age classes represented, but less representation of the older age classes. Life expectancy, or longevity (mean of top quartile of clam ages), differed significantly among sampling locations (ANOVA, [F.sub.440] = 118.58, P < 0.0001; Fig. 5), with the highest life expectancy at Dungeness West (subtidal), followed by both subtidal island sites in California. The lowest life expectancy occurred at the two intertidal bay locations (Morro Bay and Bodega Bay). Comparisons of longevity with the average SST ([degrees]C) and Chi a (mg [m.sup.-3]) from 2002 to 2012 revealed no significant correlations across the sampling locations (SST: r = 2.0x [10.sup.-6], P = 0.9974; Chi a: r = 0.37, P = 0.5438).

Geoducks exhibited significant geographic variability in lifetime growth curves among the sampling locations (Fig. 6). Dungeness West, Bodega Bay, and Santa Cruz Island had the highest asymptotic length parameter value ([L.sub.inf], Fig. 6) after fitting the VBGM to the length-at-age data, whereas Morro Bay and Catalina Island had the lowest values of [L.sub.inf] (Fig. 6). Bodega Bay exhibited the highest growth rate coefficient (K) at 0.25, followed by Catalina Island (K = 0.24), Santa Cruz Island (K = 0.22), Dungeness West (K = 0.20), and Morro Bay (K = 0.15). Significant differences in growth curves can be visualized by examining the bivariate plot of 95% confidence intervals around the growth parameters of [L.sub.inf] and K (Fig. 7). Clams from Dungeness West, Bodega Bay, and Santa Cruz Island have similar growth patterns, whereas lifetime growth curves are significantly different at Morro Bay and Catalina Island, primarily driven by a lower [L.sub.inf].

Von Bertalanffy growth model parameters for the five sites were compared with the 10-y (2002 to 2012) mean of SST and Chl a levels (Fig. 8). Neither [L.sub.inf] nor K showed significant associations with either environmental variable; however, we did observe negative trends between [L.sub.inf] and SST ([r.sup.2] = 0.54, P = 0.16; Fig. 8A) and positive trends between [L.sub.inf] and Chl a ([r.sup.2] = 0.41, P = 0.25; Fig. 8B), suggesting that clams may reach larger sizes at locations that are cooler and more productive. Santa Cruz Island clams were outliers in both relationships between [L.sub.inf] and the environmental variables [SST ([degrees]C) and Chi a (mg * [m.sup.-3])]. Despite low sample sizes (n = 5 populations) and thus low statistical power, 54% of the variation in asymptotic size among locations could be explained by average SST and 41% of the variation could be explained by Chl a. The growth or curvature parameter K exhibited an opposite pattern to [L.sub.inf], but with much weaker trends (Fig. 8C, D).

Spatial Variability in Geoduck Morphology

Geoducks exhibited highly significant spatial variability in shell morphology among the six sampling locations (Fig. 9). The canonical variate analysis results indicated that 73% of the variation in shell morphology was explained by the first two canonical variates (CV) (Fig. 9). Canonical variate 1 explained 52.3% of the variation and was characterized by changes along the dorsoventral axis (from the umbo to the valve opening). For example, Catalina Island shells were more dorsoventrally compressed (positive CV1 value), whereas Dungeness West clams were more dorsoventrally expanded (negative CV1 value) in comparison with shells from all other locations. Canonical variate 2 explained 20.9% of the variation in shell morphology as a function of geographic location and was characterized by changes along the anteroposterior axis (from the area where a shell digs into the sand to the area where the siphon is located). Whereas Bodega Bay, Morro Bay, Santa Cruz Island, and San Quintin shells were similar in shape along the dorsoventral axis (CV1), they showed much more shape variation along the anteroposterior axis (CV2). The two intertidal sites, Bodega Bay and Morro Bay, exhibited a widening of the anterior end of the shells, whereas the subtidal sites, Santa Cruz Island and San Quintin, were more compressed at the anterior end of the shells.

Mahalanobis distances, the test statistic used to determine significant differences in multivariate measures of shape among sampling locations, indicated highly significant (P < 0.0001) differences among all groups. Significant differences in shell shape among locations were reflected in the site-by-site Procrustes distances, measuring the absolute amount of shape difference. Bodega Bay and Morro Bay, the two intertidal sites, exhibited the least amount of difference in distance to the grand average Procrustes distances. This result indicates that the intertidal clams at these two sites show a widening in the anterior region close to the ventral region (or valve opening) as compared with the subtidal sites. Bodega and Morro bays were more similar to each other in shell shape than any of the other subtidal sites, with 90% of the shape values on the positive CV2 axis. The subtidal sites were significantly different from the intertidal sites based on shell shape, with differences in shape primarily occurring on the dorsoventral axis of the shell (CV1 axis).


Geographic Variation in Size Structure and Condition Geoducks followed a latitudinal gradient in length, weight, and condition, with heavier and larger clams occurring in the northern latitudes. One possible explanation for this positive latitude versus size relationship is Bergmann's rule (Bergmann 1847), which relates this type of size increase in endotherms to a heat conservation mechanism: the larger the animal, the better it can conserve heat in the colder northern latitudes. There is evidence that Bergmann's rule applies to not only endotherms but also ectotherms. Ray (1960) found that 13 of 17 poikilotherms followed Bergmann's rule. Similar to Panopea generosa. Caselle et al. (2011) reported that the California sheephead Semicossyphus pulcher attained larger sizes at higher latitudes that were characterized by colder temperatures, and also fewer competitors. Heilmayer et al. (2004) showed a significant correlation between the growth efficiency in scallops and temperature, with elevated temperatures constraining growth. Frank (1975) found a strong latitudinal pattern of the black turban snail Tegula funebralis, with snails in the northern latitudes living longer and growing more slowly, but ultimately attaining larger sizes than snails in the southern latitudes. By contrast, Ruttenberg et al. (2005) showed that Stegastes beebei reached larger sizes and older ages in cooler locations at the Galapagos Islands, irrespective of the latitude.

Controversy continues to surround the applicability of Bergmann's rule to ectotherms. For example, studies have shown that the pattern of increasing size as a function of latitude is a phenotypic response to countergradient variation in environmental conditions, such as temperature and length of the growing season. Conover and Schultz (1995) used reciprocal transplant experiments with the Atlantic silverside (Menidia menidia) to test whether countergradient variation in growth and size (faster growth and larger individuals in cooler northern regions) was due to phenotypic plasticity in response to variable environmental conditions. Their results indicated a strong genetic basis for the observed differences in body form, growth, and condition across a broad latitudinal range off the east coast of North America. Transplanting of juvenile geoducks is a common practice in aquaculture facilities in the Pacific Northwest. Thus, reciprocal transplant experiments are a feasible option to address the potential genetic basis of the observed latitudinal size differences in geoducks in the future.

Geoducks are filter feeders that occur in intertidal habitats with highly variable temperature regimes to subtidal habitats that are less variable in temperature (at least on daily time scales). Both SST and Chl a may influence geographic patterns in body condition and weight-length relationships in these clams. Chlorophyll a, a proxy for oceanographic productivity, exhibited a strong negative correlation with temperature. Because of this autocorrelation, it is difficult to determine whether temperature, productivity, or both may be driving the observed size variation in Panopea generosa; however, previous studies have shown that size is positively correlated with Chl a in mussels (Page & Hubbard 1987) and scallops (MacDonald & Thompson 1985), which lends support for this hypothesis in geoducks.

Age, Growth, and Environmental Correlates

There were striking differences in age-frequency distributions among subtidal and intertidal locations. Geoduck populations in the intertidal sites exhibited a dramatic truncation in older age classes relative to the subtidal sites. Thus, clams were younger at the intertidal sites and older at the subtidal sites. This pattern suggests a potential depth refuge for the clams at the subtidal sites, where they experience higher survivorship than in the shallower intertidal areas. Possible explanations for differential survivorship include decreased abiotic stress, lower predation, disease, parasitism, and reduced fishing pressure in subtidal compared with intertidal locations.

Predation is likely to be greater on the subtidal clams than the intertidal clams and, thus, predation pressure is unlikely to explain the observed age structure variation in Panopea generosa. Predators of juvenile and adult geoducks include the sea stars Pisaster brevispinus and Pycnopodia helianthoides (Mauzey et al. 1968, Sloan & Robinson 1983), which are more common in subtidal locations. Jensen (1995) reported that crabs prey on geoducks in Puget Sound, Washington. Three of the crab species Jensen observed are also found at the intertidal sites in this study (Bodega Bay and Morro Bay): the red rock crab (Cancer productus), the graceful crab (Cancer gracilis), and the Dungeness crab (Cancer magister). Juvenile crabs of all three species were found at Bodega Bay when sampling for this study (personal observation). Because geoducks typically live below mean lower low water, predators such as fishes may also feed on geoducks. Andersen (1971) observed that siphon damage caused by predatory fishes, such as the cabezon (Scorpaenichthys marmoratus) and the spiny dogfish (Squalus acanthias) could impact growth. Both fishes are known to frequent Bodega Bay and Morro Bay waters and occur commonly at the subtidal locations sampled. One additional voracious predator, the sea otter (Enhydra lutris), is capable of feeding on geoducks, primarily in subtidal locations. There are reports from subtidal sites in southeast Alaska and Monterey Harbor, California, confirming that otters prey on deep-burrowing clams and that they are able to excavate them as deep as 0.5 m in the sediment (Kvitek et al. 1993). This maximum observed sediment depth is half the siphon length of the clam, which could provide a depth refuge from otters. Morro Bay has a resident sea otter population, which could explain the lack of old clams in that location if predation occurs during high tides; however, otters do not occur in Bodega Bay, where clams are also much younger than average.

Recreational fishing may provide an explanation for increased longevity in subtidal sites and truncation in older age classes in intertidal sites. In California, clams are primarily harvested recreationally from intertidal locations by digging for them at low tide. There is presently no harvest allowed for subtidal clams in California. Recreational clamming for geoducks is high in Bodega Bay (personal observation) when tides are low enough to dig up clams. Morro Bay, the other intertidal site in this study, was declared a State Marine Recreational Management Area in 2007, which restricts the take of living marine resources (Patyten & Serpa 2016). Before the closure, clamming activities in this area were primarily targeting Pismo clams. The decline of Pismo clams could have shifted fishing pressure to geoducks. Geoducks are long-lived, reaching ages over 100 y (Bureau et al. 2002). The Morro Bay State Marine Recreational Management Area closure is recent relative to the generation time of Panopea generosa, and thus the effects of past fishing pressure will likely be visible in the age structure for many decades to follow.

How body size changes as a function of age in Panopea generosa may be used to inform a stock assessment for this species. Traditionally, studies have used VBGM to understand size-at-age distributions in P. generosa (Goodwin & Shaul 1984, Calderon-Aguilera et al. 2010a). The growth coefficient, K, in VBGM is indicative of how fast a maximum length or [L.sub.inf] is reached. Growth curve coefficients as part of this study ranged from K = 0.15 to 0.25. This range falls within reported ranges for P. generosa populations in British Columbia, Washington, and Baja California (Calderon-Aguilera et al. 2010a), and showed weak correlations with environmental correlates. The largest geographic difference in VBGM was observed in the predicted maximum asymptotic length parameter [L.sub.inf]. Average temperature differences explained 54% of the variation in [L.sub.inf] among locations, whereas 41% of the variation could be explained by mean Chl a values. Asymptotic growth increased with decreasing temperature, greater latitude, and elevated Chl a levels (Santa Cruz Island was a significant outlier). Chlorophyll a serves as a proxy for phytoplankton availability, the primary food source of geoduck clams, and Chl a has been shown to positively influence bivalve growth in other systems (Pernet et al. 2007).

Spatial Variability in Geoduck Morphology

Population genetic analyses indicate that Panopea generosa appears to be generally panmictic throughout its range, with chaotic genetic patchiness (Vadopalas et al. 2004, Suarez-Moo et al. 2013, Vadopalas et al. 2012). Life history, demographic, and morphometric traits may still exhibit geographic structure, depending on the scale of spatial variability in environmental selection pressures. Even in the face of high gene flow, marine species can exhibit plasticity in life history and morphological traits (Koehn et al. 1976, Sanford & Kelly 2011).

Studies on morphology (shell shape) of other bivalves have reported evidence for shell plasticity in response to environmental variation such as substrate type, water quality, and ambient temperature (Innes & Bates 1999, Costa et al. 2008). In this study, shell shape was described using 15 internal shell landmarks via geometric morphometrics, finding significant geographic variation in morphology. More than half of the spatial variation in shell shape was explained by a dorsoventral compression of the clamshells (Fig. 9). Shell morphology did not change as a function of latitude. Instead, shape variation is likely explained by habitat composition or other environmental variables. Stanley (1970) showed that life modes or habits of clams could indeed influence their morphology or shape. He examined 95 western Atlantic bivalve molluscs representing 29 families and demonstrated that morphological features of the shell reflect bivalve habitat affinity. For example, a more streamlined or dorsoventrally compressed shell shape, similar to the shells of Panopea generosa from Catalina Island, characterizes rapid burrowers. In addition, shell thickness can indicate what type of substrate or disturbance regime a clam resides in and shell thickness (shell strength) is also positively correlated with disturbance frequency (Stanley 1970). Geoducks collected at the two intertidal sites and Santa Cruz Island featured very thick shells; all three habitats are high disturbance environments with larger wave exposure or strong tidal fluctuations.

Examination of the biomechanics and burrowing rates in geoducks by Tapia-Morales et al. (2015) indicated that juvenile clams burrowed 10 times faster in sand than in mud sediment. Both Morro and Bodega bays featured mud sediment, whereas Catalina and Santa Cruz islands had fine sand. Shells from Catalina Island were thinner (hence more brittle) and smaller for their age than those at all other sites. Depending on grain size and burrowing abilities, subsequent growth could be affected such that shell morphology adjusts to the microhabitat characteristics present in the local environment. Future studies should take sediment cores up to the depth that a clamshell resides in their natural habitat before digging them up. Shape was consistent within a site, but differed significantly among sites. Thus, shape is likely determined more by the habitat and other local conditions present at a site. Hinch et al. (1986) conducted reciprocal transplant experiments with freshwater clams (Lampsilis radiata) originating from sand and mud substrates, and found that clams had a phenotypic response to substrate by changing their shell shape.

Phenotypic Plasticity and the Environment

With an average larval duration of 47 days (Andersen 1971), there is a potential for high gene flow and extensive population connectivity across the species range of Panopea generosa, from Alaska to Baja California. Vadopalas et al. (2004) reported genetic patchiness of P. generosa populations using microsatellite markers, but their study did not include the entire range of P. generosa. By contrast, Miller et al. (2006) found a significant isolation-by-distance pattern in British Columbia Panopea populations. In addition, genetic studies on Panopea sp. in Washington State (Vadopalas et al. 2012) and Baja California, Mexico (Suarez-Moo et al. 2013), revealed microgeographic variation among populations, possibly due to environmental conditions such as hydrology, which supports findings of this study. Strong evidence for geographic variation in life history and morphometric traits in P. generosa on a larger spatial scale was found in this study. Whether these differences are due to local genetic adaptation or phenotypic plasticity in response to variable environmental conditions is unclear, as the loci under selection may not be linked to presumed neutral loci such as microsatellites. Whether there is connectivity between California clam populations and sites in Mexico and Washington requires further analyses, and makes for much needed research in this area.

Ecological Significance and Recommendations for Fishery Management

This study initiated research on longevity, age, and morphometric analyses of subtidal and intertidal California geoduck populations, providing comparisons with samples obtained from previously studied populations in the Pacific Northwest and Mexico, which support significant fisheries. Considering the profitability of geoduck fisheries in Mexico, Washington, British Columbia, and Alaska, this study provides a baseline to inform future geoduck fisheries management in California waters.

Further analysis of growth trajectories within a particular location could lead to an improved understanding of the interannual environmental variability and its effect on clam growth. Managers could use this information to inform fisheries models and stock assessments for Panopea generosa, which seek to forecast population biomass and target catch levels under different harvesting regimes. Estimating ages of samples elucidates the strength of different year-classes and the consistency of recruitment into the population. The two intertidal populations examined showed a paucity of older age classes compared with that in subtidal sites. Restricting harvesting or augmenting populations by using aquaculture seeding techniques could help to ameliorate this pattern. Life history and demographic information from this study will provide the critical parameters needed for those population modeling efforts. Natural resources managers may use information on significant spatial variability in geoduck life history traits to develop regional-specific fisheries regulations and management plans, tuned to the demographic variability present along the coast. Investigation of morphological plasticity and the afore-mentioned environmental traits affecting geoduck populations could aid managers with decisions on transplanting of juvenile clams across substrate types, should there be an interest in developing a geoduck aquaculture fishery in California.


This study was the first to attempt to quantify life history, demographic, and morphological variation in geoduck clams across most of the species range, finding strong effects of environmental clines and fishing pressure on size and age structure, growth rates, condition, and shell shape. This was also the first study to sample geoduck populations in California, and to compare the characteristics of California populations with the more frequently studied populations in the Pacific Northwest and Mexico, which are the locations of extensive fisheries. An improved understanding of the spatial scale of biological variation among populations can help to uncover the environmental drivers of observed ecological patterns while simultaneously aiding resource management and conservation efforts.


The authors would like to thank the following funding sources that supported this project: The Council on Ocean Affairs, Science, and Technology (COAST) grant, the Dr. Earl H. and Ethel M. Myers Oceanographic and Marine Biology Trust, the David and Lucile Packard Foundation, the MLML Wave Scholarship, and the National Shellfisheries Association--Pacific Coast section. The authors would like to thank Kai Lampson for bringing to our attention the need for researching the California geoduck populations. In addition, the following individuals played a significant part of the field and laboratory support team: G. Adams, J. Cochran, M. Cruickshank, E. Donham, C. Drake, J. Douglas, R. Fields, S. Flora, S. Jeffries, J. Jones, K. Lampson, S. Loiacono, C. Machado, M. Marraffini, C. Mireles, J. Ruvalcaba, B. San Miguel, D. Seals, D. Stein, A. Szesciorka, A. Valdez, M. Wheelock, A. Wood, T. Wood, and S. Worden. And last, the authors would like to extend their appreciation for the use of the facilities and dive support of Moss Landing Marine Laboratories; Derek Stein and Kai Lampson of the California Department of Fish and Wildlife at Santa Barbara, CA; and Bob Sizemore of the Washington Department of Fish and Wildlife at Olympia, WA.


Agrawal, A. A. 2001. Phenotypic plasticity in the interactions and evolution of species. Science 294:321-326.

Andersen, A. M., Jr. 1971. Spawning, growth and spatial distribution of the geoduck clam, Panopea abrupta (Gould) in Hood Canal, Washington. PhD diss., University of Washington, Seattle, WA.

Bergmann, C. 1847. Uber das Verhaltniss der Warmeokonomie der Thiere zu ihrer Gro[beta]e. Gottingen, Germany: Gottinger Studien. Book 3. 124 pp.

Black, B. A., D. C. Gillespie, S. E. MacLellan & C. M. Hand. 2008. Establishing highly accurate production-age data using the tree-ring technique of crossdating: a case study for Pacific geoduck (Panopea abrupta). Can. J. Fish. Aquat. Sci. 65:2572-2578.

Bookstein, F. L. 1978. The measurement of biological shape and shape change. In: Lecture notes in biomathematics, vol. 24. Berlin-Heidelberg-New York: Springer Verlag. 199 pp.

Bookstein, F. L. 1991. Morphometric tools for landmark data: geometry and biology. Cambridge, UK: Cambridge University Press. 459 pp.

Bookstein, F. L. 1996. Biometrics, biomathematics and the morphometric synthesis. Bull. Math. Biol. 58:313-365.

Bookstein, F. L. 1998. A hundred years of morphometrics. Acta Zool. Hung. 44:7-59.

Bureau, D., W. Rajas, N. W. Surry. C. M. Rand, G. Dovey & A. Campbell. 2002. Age, size structure and growth parameters of geoducks (Panopea abrupta, Conrad, 1849) from 34 locations in British Columbia sampled between 1993 and 2000. Can. Tech. Rep. Fish. Aquat. Sci. [??]:2413.

Calderon-Aguilera, L. E., E. A. Aragon-Noriega, C. M. Hand & V. M. Moreno-Rivera. 2010a. Morphometric relationships, age, growth, and mortality of the geoduck clam, Panopea generosa, along the Pacific coast of Baja California, Mexico. J. Shellfish Res. 29:319-326.

Caley, M. J., M. H. Carr, M. A. Hixon, T. P. Hughes, G. P. Jones & B. A. Menge. 1996. Recruitment and the local dynamics of open marine populations. Annu. Rev. Ecol. Syst. 27:477-500.

Caselle, J. E., S. L. Hamilton, D. M. Schroeder, M S. Love, J. D. Standish, J. A. Rosales-Casian & O. Sosa-Nishizaki. 2011. Geographic variation in density, demography, and life history traits of a harvested, sex-changing, temperate reef fish. Can. J. Fish. Aquat. Sci. 68:288-303.

Choat, J. H. & D. R. Robertson. 2002. Age-based studies on coral reef fishes. In: Sale, P. F., editor. Coral reef fishes: dynamics and diversity in a complex ecosystem, New York, NY: Academic Press, pp. 57-80.

Connell, J. H. 1961. The influence of interspecific competition and other factors on the distribution of the barnacle Chthamalus stellatus. Ecology 42:710-723.

Conover, D. O. & E. T. Schultz. 1995. Phenotypic similarity and the evolutionary significance of countergradient variation. Trends Ecol. Evol. 10:248-252.

Costa, C, J. Aguzzi, P. Menesatti, F. Anonucci, V. Rimatori & M. Mattoccia. 2008. Shape analysis of different populations of clams in relation to their geographical structure. J. Zool. (Lond.) 276:71-80.

Dayton, P. K. 1971. Competition, disturbance, and community organization: the provision and subsequent utilization of space in a rocky intertidal community. Ecol. Monogr. 41:351-389.

DeMartmi, E. E., A. M. Friedlander & S. R. Holzwarth. 2005. Size at sex change in protogynous labroids, prey body size distributions, and apex predator densities at NW Hawaiian atolls. Mar. Ecol. Prog. Ser, 297:259-271.

Enberg, K., C. Jorgensen, E. S. Dunlop, O. Varpe, D. S. Boukal, L. Baulier & M. Heino. 2012. Fishing-induced evolution of growth: concepts, mechanisms and the empirical evidence. Mar. Ecol. (Berl.) 33:1-25.

Frank, P. W. 1975. Latitudinal variation in the life history features of the black turban snail Tegula funebralis (Prosobranchia: Trochidae). Mar. Biol. 31:181-192.

Gleason, L. U. & R. S. Burton. 2013. Phenotypic evidence for local adaptation to heat stress in the marine snail Chlorostoma (formerly Tegula) funebralis. J. Exp. Mar. Biol. Ecol. 448:360-366.

NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) 2015. AVHRR Pathfinder and MODIS data. Greenbelt, MD: NASA/GSFC. Accessed July 1, 2015. Available at:

Goodall, C. 1991. Procrustes methods in the statistical analysis of shape. J. R. Stat. Soc. B 53:285-339.

Goodwin, C. L. & B. C. Pease. 1987. The distribution of geoduck (Panopea abrupta) size, density and quality in relation to habitat characteristics such as geographic area, water depth, sediment type and associated flora and fauna in Puget Sound, Washington. Dep. Fish. Tech. Rep. 102 pp.

Goodwin, C. L. & B. C. Pease. 1991. Geoduck, Panopea abrupta (Conrad, 1849) size, density, and quality as related to various environmental parameters in Puget Sound, Washington. J. Shellfish Res. 10:65-77.

Goodwin, C. L. & W. Shaul. 1984. Age, recruitment and growth of the geoduck clam (Panopea generosa, Gould) in Puget Sound, Washington. Wash. Dep. Fish. Prog. Rep. 215 pp.

Hagen, D. W. & L. G. Gilbertson. 1972. Geographic variation and environmental selection in Gasterosteus aculeatus in the Pacific Northwest, America. Evolution 26:32-51.

Hagen, D. W. & L. G. Gilbertson. 1973. Selective predation and the intensity of selection acting upon the lateral plates of threespine sticklebacks. Heredity 30:273-287.

Hamilton, S. L., J. E. Caselle, J. D. Standish, D. M. Schroeder, M. S. Love, J. A. Rosales-Casian & O. Sosa-Nishizaki. 2007. Size-selective harvesting alters life histories of a temperate sex-changing fish. Ecol. Appl. 17:2268-2280.

Harvey, H. W. 1942. Production of life in the sea. Biol. Rev. Camb. Philos. Soc. 17:221-246.

Heilmayer, O., T. Brey & H. O. Portner. 2004. Growth efficiency and temperature in scallops: a comparative analysis of species adapted to different temperatures. Funct. Ecol. 18:641-647.

Hidalgo-De-La-Toba, J. A., S. S. Gonzalez-Pelaez, E. Morales-Bojorquez, J. J. Bautista-Romero & D. B. Lluch-Cota. 2015. Geoduck Panopea generosa growth at its southern distribution limit in North America using a multimodel inference approach. J. Shellfish Res. 34:91-99.

Hinch, S. G., R. C. Bailey & R. H. Green. 1986. Growth of Lampsilis radiata (Bivalvia: Unionidae) in sand and mud: a reciprocal transplant experiment. Can. J. Fish. Aquat. Sci. 43:548-552.

Hutchings, J. A. 2005. Life history consequences of overexploitation to population recovery in Northwest Atlantic cod (Gadus morhua). Can. J. Fish. Aquat. Sci. 62:824-832.

Innes, D. J. & A. Bates. 1999. Morphological variation of Mytilus edulis and Mytilus trossulus in eastern Newfoundland. Mar. Biol. 133: 691-699.

Jamison, D., R. Heggen & J. Lukes. 1984. Underwater video in a regional benthos survey. In: Proceedings of the Pacific congress on marine technology. Honolulu, HI: Marine Technology Society.

Jensen, G. C. 1995. Pacific coast crabs and shrimps. Monterey, CA: Sea Challengers. 96 pp.

Jivoff, P. 2003. A review of male mating success in the blue crab, Callinectes sapidus, in reference to the potential for fisheries-induced sperm limitation. Bull. Mar. Sci. 72:273-286.

Klingenberg, C. P. 2011. MorphoJ: an integrated software package for geometric morphometrics. Mol. Ecol. Resour. 11:353-357.

Klingenberg, C. P., M. Barluenga & A. Meyer. 2003. Body shape variation in cichlid fishes of the Amphilophus citrinellus species complex. Biol. J. Linn. Soc. Lond. 80:397-408.

Koehn, R. K., R. Milkman & J. B. Mitton. 1976. Population genetics of marine Pelecypods. IV. Selection, migration and genetic differentiation in the blue mussel Mytilus edulis. Evolution 30:2-32.

Kvitek, R. G., C. E. Bowlby & M. Staedler. 1993. Diet and foraging behavior of sea otters in southeast Alaska. Mar. Mamm. Sci. 9:168-181.

Law, R. 2007. Fisheries-induced evolution: present status and future directions. Mar. Ecol. Prog. Ser. 335:271-277.

Leyva-Valencia, I., S. Ticul Alvarez-Castaneda, D. B. Lluch-Cota, S. Gonzalez-Pelaez, S. Perez-Valencia, B. Vadopalas, S. Ramirez-Perez & P. Cruz-Hernandez. 2012. Shell shape differences between two Panopea species and phenotypic variation among P. globosa at different sites using two geometric morphometrics approaches. Malacologia 55:1-13.

Lindsey, C. C. 1966. Body sizes of poikilotherm vertebrates at different latitudes. Evolution 20:456-465.

MacDonald, B. A. & R. J. Thompson. 1985. Influence of temperature and food availability on the ecological energetics of the giant scallop Placopecten magellanicus. I. Growth rates of shell and somatic tissue. Mar. Ecol. Prog. Ser. 25:279-294.

Manyak-Davis, A., T. M. Bell & E. E. Sotka. 2013. The relative importance of predation risk and water temperature in maintaining Bergmann's rule in a marine ectotherm. Am. Nat. 182:347-358.

Mauzey, K. P., C. Birkeland & P. K. Dayton. 1968. Feeding behavior of asteroids and escape responses of their prey in the Puget Sound region. Ecology 49:603-619.

Miller, K. M., K. J. Supernault, S. Li & R. Withler. 2006. Population structure in two marine invertebrate species (Panopea abrupta and Strongylocenlrotus franciscanus) targeted for aquaculture and enhancement in British Columbia. J. Shellfish Res. 25:33-42.

Ogle, D. H. 2015. FSA: fisheries stock analysis [R package version 0.6.16]. Available at:

Page, H. M. & D. M. Hubbard. 1987. Temporal and spatial patterns of growth in mussels Mytilus edulis on an offshore platform: relationships to water temperature and food availability. J. Exp. Mar. Biol. Ecol. 111:159-179.

Patyten, M. & P. Serpa. 2016. Guide to the central California marine protected areas. Accessed October 1. Available at:

Perez-Valencia, S. A. & E. A. Aragon-Noriega. 2013. Age and growth of the Cortes geoduck Panopea globosa in the upper Gulf of California. Indian J. Geo-Mar. Sci. 42:201-205.

Pernet, F., R. Tremblay, L. Comeau & H. Guderley. 2007. Temperature adaptation in two bivalve species from different thermal habitats: energetics and remodeling of membrane lipids. J. Exp. Biol. 210:2999-3014.

Ray, C. 1960. The application of Bergmann's and Allen's rules in poikilotherms. J. Morphol. 106:85-108.

Relyea, R. A. & J. R. Auld. 2005. Predator- and competitor-induced plasticity: how changes in foraging morphology affect phenotypic trade-offs. Ecology 86:1723-1729.

Ricker, W. E. 1975. Computation and interpretation of biological statistics of fish populations. Bull. Fish. Res. Board Can. 191: 1-382.

Ricketts, E. F., J. Calvin & J. W. Hedgpeth. 1968. Between Pacific tides, 4th edition. Stanford, CA: Stanford University Press. 652 pp.

Robertson, D. R., J. L. Ackerman, J. H. Choat, J. M. Posada & J. Pitt. 2005. Ocean surgeonfish Acanthurus bahianus. I. The geography of demography. Mar. Ecol. Prog. Ser. 295:229-244.

Rocha-Olivares, A., L. E. Calderon-Aguilera, E. A. Aragon-Noriega, N. C. Saavedra-Sotelo & V. M. Moreno-Rivera. 2010. Genetic and morphological variation of northeast Pacific Panopea clams: evolutionary implications. J. Shellfish Res. 29:327-335.

Rohlf, F. J. & L. F. Marcus. 1993. A revolution in morphometrics. Trends Ecol. Evol. 8:129-132.

Rohlf, F. J. & D. Slice. 1990. Extension of the procrustes method for the optimal superimposition of landmarks. Syst. Biol. 39:40-59.

Ruttenberg, B. I., A. J. Haupt, A. I, Chiriboga & R. R. Warner. 2005. Patterns, causes and consequences of regional variation in the ecology and life history of a reef fish. Oecologia 195:394-403.

Sanford, E. & M. W. Kelly. 2011. Local adaptation in marine invertebrates. Annu. Rev. Mar. Sci. 3:509-535.

Schneider, C. A., W. S. Rasband & K. W. Eliceiri. 2012. NIH Image to ImageJ: 25 years of image analysis. Nat. Methods 9:671-675.

Shaul, W. & C. L. Goodwin. 1982. Geoduck (Panope generosa: Bivalvia) age as determined by internal growth lines in the shell. Can. J. Fish. Aquat. Sci. 39:632-636.

Sloan, N. A. & S. M. C. Robinson. 1983. Winter feeding by asteroids on a subtidal sandbed in British Columbia. Ophelia 22:125-140.

Sloan, N. A.&S. M.C. Robinson. 1984. Age and gonad development in the geoduck clam Panopea abrupta (Conrad) from southern British Columbia, Canada. J. Shellfish Res. 4:131-137.

Stanley, S. 1970. Relation of shell form to life habits of the Bivalvia (Mollusca). Boulder, CO: The Geological Society of America, Inc. 296 pp.

Suarez-Moo, P. J., L. E. Calderon-Aguilera, H. Reyes-Bonilla, G. Diaz-Erales, V. Castaneda-Fernandez de Lara, E. A. Aragon-Noriega & A. Rocha-Olivares. 2013. Integrating genetic, phenotypic and ecological analyses to assess the variation and clarify the distribution of the Cortes geoduck (Panopea globosa). J. Mar. Biol. Assoc. U.K. 93:809-816.

Tapia-Morales, S., Z. Garcia-Esquivel, B. Vadopalas & J. P. Davis. 2015. Growth and burrowing rates of juvenile geoducks Panopea generosa and Panopea globosa under laboratory conditions. J. Shellfish Res. 34:63-70.

Vadopalas, B., J. P. Davis & C. S. Friedman. 2015. Maturation, spawning, and fecundity of the farmed Pacific geoduck Panopea generosa in Puget Sound, Washington. J. Shellfish Res. 34: 31-37.

Vadopalas, B., L. L. Leclair & P. Bentzen. 2004. Microsatellite and allozyme analyses reveal few genetic differences among spatially distinct aggregations of geoduck clams (Panopea abrupta, Conrad, 1849). J. Shellfish Res. 23:693-706.

Vadopalas, B., L. L. Leclair & P. Bentzen. 2012. Temporal similarity among year-classes of the Pacific geoduck clam (Panopea generosa Gould, 1850): a species exhibiting spatial genetic patchiness. J. Shellfish Res. 31:697-709.

Vadopalas, B., C. Weidman & E. K. Cronin. 2011. Validation of age estimation in geoduck clams using the bomb radiocarbon signal. J. Shellfish Res. 30:303-307.

Walsh, S. M., S. L. Hamilton, B. I, Ruttenberg, M. K. Donovan & S. A. Sandin. 2012. Fishing top predators indirectly affects condition and reproduction in a reef-fish community. J. Fish Biol. 80:519-537.


(1) Moss Landing Marine Laboratories, 8272 Moss Landing Road, Moss Landing, CA 95039; (2) Ventura College, 4667 Telegraph Road, Ventura, CA 93003; (3) School of Aquatic and Fishery Sciences, University of Washington, 1122 NE Boat Street, Seattle, WA 98195; (4)Washington Department of Fish and Wildlife, Natural Resources Building, 1111 Washington Street SE, Olympia, WA 98501; (5)CONACyT-Instituto Politecnico Nacional, Centro Interdisciplinario de Ciencias Marinas, Av. Instituto Politecnico Nacional s/n Col Play a Palo de Santa Rita, La Paz, B. C. S., C. P. 23096, Mexico

(*) Corresponding author. E-mail:

DOI: 10.2983/035.037.0502
TABLE 1. Life history parameters for five sites.

Site i = intertidal,          Mean                Mean valve
s = subtidal              n   weight (g)          length (mm)

Dungeness West, WA (s)    36  1,014 [+ or -] 122  137 [+ or -]7
Bodega Bay, CA (i)        30    532 [+ or -] 83   120 [+ or -]6
Morro Bay, CA (i)         50    473 [+ or -] 38   110[+ or -]3
Santa Cruz Island, CA (s) 37    814 [+ or -]50    137 [+ or -]3
Catalina Island, CA (s)   32    307 [+ or -]31    101 [+ or -]5
San Quintin, Mexico (s)   30  ad                  137 [+ or -]3

Site i = intertidal,       Mean longevity  Age range
s = subtidal               (years)         (min, max)

Dungeness West, WA (s)     85 [+ or -] 10  5, 105
Bodega Bay, CA (i)         15 [+ or -] 1   4, 20
Morro Bay, CA (i)          22 [+ or -] 1   7,46
Santa Cruz Island, CA (s)  57 [+ or -]4    8, 76
Catalina Island, CA (s)    57 [+ or -]6    9, 65
San Quintin, Mexico (s)    nd              nd

Site i = intertidal,
s = subtidal               [L.sub.inf]  K value

Dungeness West, WA (s)     141.9        0.20
Bodega Bay, CA (i)         137.3        0.25
Morro Bay, CA (i)          122.0        0.15
Santa Cruz Island, CA (s)  138.7        0.22
Catalina Island, CA (s)    103.6        0.24
San Quintin, Mexico (s)    nd           nd

[L.sub.inf] and K refer to the VBGM parameters fitted to the
age-at-size data at each site. Values represent the mean [+ or -]95%
CI. Sites are denoted with an "(s)" for subtidal and an "(i)" for
intertidal, nd, no data.
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Author:Wood, Gabriela; Hamilton, Scott L.; Vadopalas, Brent; Stevick, Bethany; Leyva-Valencia, Ignacio
Publication:Journal of Shellfish Research
Article Type:Report
Geographic Code:1MEX
Date:Dec 1, 2018
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