Elemental fingerprints used to identify essential habitats: Nantucket bay scallop.
Article Type: Report
Subject: Scallops (Distribution)
Habitat (Ecology) (Research)
Authors: Broadaway, Bryanna J.
Hannigan, Robyn E.
Pub Date: 08/01/2012
Publication: Name: Journal of Shellfish Research Publisher: National Shellfisheries Association, Inc. Audience: Academic Format: Magazine/Journal Subject: Biological sciences; Zoology and wildlife conservation Copyright: COPYRIGHT 2012 National Shellfisheries Association, Inc. ISSN: 0730-8000
Issue: Date: August, 2012 Source Volume: 31 Source Issue: 3
Topic: Event Code: 690 Goods & services distribution; 310 Science & research Advertising Code: 59 Channels of Distribution Computer Subject: Company distribution practices
Product: Product Code: 0913070 Scallops NAICS Code: 114112 Shellfish Fishing SIC Code: 0913 Shellfish
Geographic: Geographic Scope: United States Geographic Code: 1USA United States
Accession Number: 303011389
Full Text: ABSTRACT Elemental fingerprinting of adult and juvenile Nantucket bay scallop shells, Argopecten irradians (Lamarck 1819) revealed distinct element/Ca ratios that can be used to distinguish provenance of bay scallops in Nantucket Harbor. Within this small harbor (~10 [km.sup.2]), we identified 3 distract habitat zones defined by the abundance of scallops at a location: zone 1 (barren), zone 2 (low abundance), and zone 3 (high abundance). Element/Ca ratios were associated with proximity to the harbor mouth, with elemental differences attributed to variation in salinity and pH. Using binary logistic regression, we identified the source zones based on shell elemental ratios (i.e., various elemental concentrations divided by [Ca]). We also identified boundary salinity and pH conditions that support large abundance of A. irradians within Nantucket Harbor. This information is crucial to seeding projects and the management of the Nantucket Harbor A. irradians population.

KEY WORDS: bay scallop, elemental fingerprinting, essential habitat, statistical modeling, Argopecten irradians

INTRODUCTION

Marine planktotrophic larvae, such as the bay scallop (Argopecten irradians), have the ability to travel great distances from their natal habitat. However, in some instances, larvae may be retained in a defined, local environment (Arnold et al. 1998). Elemental fingerprinting of shells has been used to reconstruct environmental life histories (variability in environmental exposures that lead to distinct elemental signatures from different locations) of organisms (Leng & Pearce 1999, Thorrold et al. 2002, Becker et al. 2005) and to identify essential habitats (e.g., Dorval et al. 2005). Essential habitat in this study is defined as a combination of physical structure, availability of food, substrates, hydrodynamics, and hydrology that makes the area ideal habitat for A. irradians (Minello 1999). If there is sufficient variation in elemental signatures in shells between sites, and sufficient stability in water chemistry within sites, then it is possible to determine retrospectively the location in which the organism lived prior to capture. Because these elemental signatures are unique to the environment and linked to individuals, they can serve as a natural tag for tracking movement patterns, eliminating the need for artificial tagging (Thorrold 2002 et al., Becker et al. 2005). Retrospective identification of habitats in populations that are geographically restricted is complicated given the potentially low variation in water chemistry between sites, which may preclude identification of unique fingerprints. In this study, essential habitat is determined by population estimates, or abundance, of A. irradians along a 50-m transect.

Often, studies that focus on habitat identification using natural tags, such as shell chemistry, are conducted in areas where sample sites are separated by 20 km or more to ensure chemical and physical heterogeneity between sites (e.g., Becker et al. 2005). However, there is a growing need for methods that determine the smallest spatial scale at which essential habitats can be distinguished accurately using shell chemistry. Identification of essential habitats is useful for ecological studies that assess impacts of environmental change on recruitment and survivorship, for management of critical habitats, and for determining geographical provenance of seafood (Hastein et al. 2001, Petraitis & Dudgeon 2004, Brown 2006). Here, we use elemental fingerprinting of shells to identify optimal habitat for A. irradians.

In bivalve shells, mechanisms for incorporation of trace elements into biogenic carbonates during shell formation differ across species and life stage as a result of physiological factors and chemical and physical characteristics of the environment (Dodd 1967, de Paula & Silveira 2009). Temperature, pressure, salinity, dissolved oxygen (DO), pH, and other environmental parameters can affect trace element speciation and bioavailability, and thus influence the trace element composition of the shell. Despite the effects environmental parameters have on element incorporation, elemental concentrations have been used for many purposes, including quantification of shell growth rates (Carre et al. 2006) as well as identification of natal habitat, water chemistry variation over time, recruitment success, stock structure, and movement patterns (Becker et al. 2005). A recent study by Becker et al. (2005) assessed spatial variability in the elemental composition of larval mussel shells (Mytilus californianus and Mytilus galloprovincialis), and showed that spatial resolution of geographical source was possible at a scale of ~20 km. Although mussels disperse as pelagic larvae, mussels are sessile as adults. To date, no one has examined whether it is possible to use elemental fingerprinting to resolve the location of essential habitats for bay scallops particularly at small (<10 km) spatial scales.

The goal of this research was to evaluate whether elemental chemistry of A. irradians shells, a rapidly growing nektonic species, could be used to identify zones defined by scallop abundance. Scallops are the only bivalves that have the ability to swim during the adult stage, making reconstruction of their life histories more complicated than that for sessile species. We developed an approach for identifying environmental correlates of essential A. irradians habitat characteristics at a meaningful spatial resolution. The environmental characteristics identified underused habitat that could be leveraged into the current management strategy of this commercially important species.

NANTUCKET HARBOR

We selected Nantucket Harbor (Fig. 1) primarily because of its significant commercially fished bay scallop (A. irradians) population. Commercial populations of A. irradians in Massachusetts occur in the waters of Cape Cod and the Islands. Landings of A. irradians in Massachusetts have declined precipitously during the past 2 decades from a mean of 399 mt ($1.4 million) in the 1980s to a mean of 47 mt ($1.1 million) from 2003 to 2010 (NMFS data; no data for 1997 to 2002). As landings have declined, the market value of Massachusetts A. irradians has increased. The market price and import pressure (especially from China) have led to a significant increase in coastal aquaculture research centered on A. irradians. Despite significant investments in aquaculture and seeding programs to increase recruitment and age-class yields, we know comparatively little about the movement patterns of A. irradians within Nantucket Harbor.

[FIGURE 1 OMITTED]

Nantucket Harbor, Massachusetts (Fig. 1) is approximately 10 [km.sup.2], much smaller than other locations where shell-based trace element records have been used to reconstruct life histories of bivalves (Becker et al. 2005). The harbor is considered to be well mixed as a result of the tidal influences on the shallow basin, and is protected by strict environmental regulations controlling waste management (Wilkin 2006). Characterization of habitats relied on elemental analysis of A. irradians shells from individuals collected at multiple locations within Nantucket Harbor. Using these data, we constructed a binary logistic regression model that enabled identification of source habitats and revealed a range of salinity and pH conditions that support high abundance of scallops.

MATERIALS AND METHODS

Sampling and Sampling Sites

Site locations and number of scallops retrieved followed the sampling protocols of the Nantucket Shellfish Management Plan (http://www.nantucketharborplan.com/). In this experiment, water sampling occurred between 9 AM and 11 AM to reduce the number of factors influencing pH, which is known to change over a daily cycle. We evaluated 32 sites (Fig. 1) from mid July to mid August in 2009 for the presence/absence of bay scallops (A. irradians). Twenty-two sites had adult scallops present (zones 2 and 3, Fig. 1). Four of these sites also had juveniles (sites 8, 13, 19, and 30) and 10 were barren (zone 1, Fig. I). In situ measurements of bottom water quality (e.g., pH (NBS standard scale) and salinity: Table 1) were also made at the time of collection. All sites were well oxygenated (>6.0 mg/L DO) with a temperature range from 22.4-26.5[degrees]C. Thirty adults were collected by divers from each of the zone 2 & 3 sites. When juveniles (spat; average shell height, 10.5 mm) were visible, we collected a small number (maximum of 10 from a site, 19 juveniles in total). Divers also recorded total scallop abundance on the 50-m physically taped transect line at each site. The critical threshold for zone identification was defined by dividing zones of reported abundance (n = 71), where this value divided 3 zones: zone 1 (0 scallops), zone 2 (1-70 scallops), and zone 3 (>70 scallops; Fig. 1). The median value of individuals was 71 and the median differentiated routinely between low- and high-abundance sites as determined by the shellfish managers at Nantucket. Using this median value we define zones with scallops as low abundance (zone 2, <71 scallops) and high abundance (zone 3, 71 scallops).

Analytical Procedure

Soft tissues were separated from the shells using a Teflon-coated spatula. The shells were then cleaned by initially scrubbing off loosely attached particles in 18.2-M Ohm water with a nonmetal brush (adapted from Barats et al. (2007)). Shells were then rinsed in glacial acetic acid for 60 sec, followed by 2 consecutive 1-min soaks in 18.2-M Ohm water. Samples were allowed to dry overnight under laminar airflow at room temperature.

The right valve was used for laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS). A section of the shell, approximately 2 mm in thickness, was prepared, from the umbo to the growing edge using an IsoMet diamond saw blade (Buehler Diamond Wafering Blade Series 15 LC blade) on an IsoMet low-speed saw. The section was secured to a glass petrographic slide using Crystal Bond adhesive (Electron Microscopy Sciences), and was hand polished before analysis. A total of 5 thin sections of adult shells per site were prepared.

The LA-ICP-MS instrumentation consisted of a laser ablation system (213-nm Nd:YAG; Cetac Technologies, Omaha, NE) connected to a Perkin Elmer ELAN DRC II ICP-MS (Perkin Elmer, Norwalk, CT). Thirteen isotopes were monitored: [sup.43]Ca, [sup.46]Ca, [sup.55]Mn, [sup.88]Sr, [sup.138]Ba, [sup.139]La, [sup.208]Pb, [sup.24]Mg, [sup.57]Fe, [sup.63]Cu, and [sup.66]Zn. Analyte signal was collected using a line scan along the edge of most recent deposition only with a spot size of 200 [micro]m at 25 [micro]m/sec. Signal processing was performed using GeoPro2010 Software (Cetac Technologies). The mean signal along the edge (~500 [micro]m; newest area of shell deposition) recorded at each isotopic mass for each scan was quantified as micromoles per mole of Ca using USGS carbonate standard reference materials, MACS-1 and MACS-3. The calibration standards were analyzed 3 times each at the beginning of an analysis, and again every 10 samples. [sup.43]Ca was monitored as an internal standard, and [sup.46]Ca was used for quantification. Elemental ratios (element/Ca) were used for statistical analysis to account for heterogeneity in shell matrix. We also measured total C and N in duplicate on a Costech 4010 Elemental Analyzer from powdered shell material collected along the edge (~1 mg per sample).

Statistical Analysis

All elements were normalized to Ca concentration except C and N. Multivariate statistics were used to determine whether the elemental ratios could be used to distinguish individual sites or regions within the harbor. For the binary logistic model used to predict from which zone, low or high abundance, an organism originated, a total of 118 individuals were used, with 54 from zone 2 and 64 from zone 3. Data reduction using factor analysis (principal component extraction with varimax rotation) identified elemental signatures used to allocate individuals to the zone of collection. Data from adult and juvenile scallops (edge only) were used to evaluate the ability of the binary logistic regression to allocate individuals correctly to their zone across life stages.

All statistical analyses were performed using SPSS 18. Binary logistic regression was used to preserve as much of the zone (low/high abundance) discriminatory information as possible, and the factor analysis was to reduce the variables to the lowest potential number of uncorrelated variables.

RESULTS

Of the 32 sites, 0-144 A. irradians individuals were surveyed along each of the 50-m transects. Based on relative abundances of scallops, we defined zones within the harbor as zone 1, barren; zone 2, low abundance; and zone 3, high abundance. None of the elemental ratios were found to be statistically different in adult shell edges between sites (P = 0.050). La/Ca, Pb/Ca, and Zn/Ca are shown as representations of adult shell edge elemental ratios that are not statistically different (P = 0.050; Fig. 2). Zone 2 samples typically show lower extremes in La/Ca and higher variance in Zn/Ca when compared with zone 3.

Next, ANOVA was used to test for differences in depth and bottom water quality (pH, temperature, salinity, DO) between scallop abundance zones. ANOVA indicated that there was a statistical relationship between depth and scallop abundance ([F.sub.{2,29}] = 3.135, P = 0.059), and no statistical relations between scallop abundance and bottom water quality (Fig. 3). Box plots show zone 1 often has higher variation in depth and bottom water quality than zones 2 and 3 (Fig. 3).

Principal component analysis coupled with factor analysis was used to see how the variation in elemental ratios related to physical water parameters. Factor analysis was used to describe the variance in shell chemistry and water quality parameters from zones 2 and 3 (e.g., pH and salinity) in terms of the lowest number of uncorrelated variables (factors). Using principal component extraction (zones 2 and 3), 5 factors were identified that explain 74.4% of the variance in the data set (Table 2). Factor 1 strongly influences La/Ca, Fe/Ca, Cu/Ca, Pb/Ca, and pH, and accounts for the variance in trace metal concentration and water column pH. Factor 2 accounts for variance in Sr/Ca, which is influenced by both temperature and salinity (Dorval et al. 2007). The first 2 factors explain a cumulative variance of 40.6%. Because ambient temperatures across the harbor are comparable within a given day, pH from factor 1 and salinity from factor 2 were identified as physical water quality parameters strongly influencing the shell elemental composition. Loading of samples on these factors, show that zone 2 and zone 3 cluster around an average salinity of 26 [+ or -] 2 (Fig. 4). Some of the highest salinity values and lowest pH values are associated with the barren sites not included in the factor analysis.

[FIGURE 2 OMITTED]

[FIGURE 3 OMITTED]

Factor 3 influences total C and Mg/Ca, and also accounts for some of the variance in Mn/Ca, Ba/Ca, and DO. Because A. irradians shell is composed primarily as a low-Mg calcite, we attribute factor 3 to variance associated with shell matrix composition. Factor 4 influences Zn/Ca and accounts for variance in protein composition and/or anthropogenic contamination. Factor 5 influences both nitrogen and depth, and is associated with diet as N in the shell is likely provided by the algae that are most abundant at mid-depth sites such as in zone 3. Based on principal component analysis, 5 elemental ratios (Mn/Ca, Sr/Ca, Fe/Ca, Cu/Ca, and Pb/Ca) were identified that account for variance across abundance zones. The model revealed the influence of pH and salinity on shell elemental ratios and also the differences in influence between abundance zones.

A binary logistic regression was used (adults, n = 100; juveniles, n = 18) to allocate specimens to the zone of collection based on shell Mn/Ca, Sr/Ca, Fe/Ca, Cu/Ca, and Pb/Ca. The binary logistic regression predicted membership (n = 118) in either zone 2 or zone 3 with 72.9% accuracy. Predicted zone membership probabilities (Fig. 5) show overlap between zones. Predicted probabilities for zone 2 sites were generally more variable than those from zone 3. The model presented here applies primarily to adults because juveniles were not collected in both zones. Binary logistic regression showed that individuals from zone 3 were correctly classified 90.6% of the time. The ability to classify individuals from zone 2 was barely better than chance (51.9%).

[FIGURE 4 OMITTED]

DISCUSSION

Allocation of individuals to the zone of collection relied on the variance of elements that accumulate in shell (Lin & Liao 1999). Elemental ratios were not different individually between zones (e.g., Pb/Ca zone 2 vs. Pb/Ca zone 3). Given that free metal ion activity is strongly controlled by pH, which varies little across zones, the lack of significant differences in shell Fe/Ca, Cu/Ca, and La/Ca is not surprising. Although not statistically significant, Pb/Ca in shells tended to be higher in zone 2 adults than in zone 3 adults. In mussel shells, individuals collected from habitats located close to areas of elevated dissolved Pb (e.g., anthropogenic input) have increased concentration in their shells (Richardson et al. 2001). Pb levels in whole shells of the New Zealand cockle, Chione (Austrovenus) stutchburyi, were similar to the soft tissues (Purchase & Ferguson 1986), whereas the lowest Pb levels are enriched in the oldest regions of a shell. Therefore, it could be that zone 2 habitats are affected more deeply by anthropogenic activity along the coast of the harbor. The purpose of this study was to build a robust model that characterizes elemental/Ca signatures and physical characteristics of sites that currently support large numbers of A. irradians. To characterize these habitats, sites were grouped based on overall abundance of A. irradians along 50-m transects at 32 sites. In this study, the abundance of A. irradians is assumed to be reflective of optimal conditions for both juvenile and adult A. irradians. Because our data were collected over a short period of time, they do not capture large-scale annual or seasonal variations in water quality.

[FIGURE 5 OMITTED]

Using elemental ratios in shells of adult and juvenile A. irradians, we were able to allocate abundance zones over a small spatial scale (<10 km). The lack of scallops at the head of the harbor is attributed to lower tidal flushing and local variations in depth resulting from sediment accumulation (Wilkin 2006). Head-of-the-harbor depths are greater than in other regions of the harbor (~7.3 m) that are closer to the shore and near the harbor mouth, where scallops were abundant (average depth, 1.5 m). Also, in the head of the harbor, the substrate needed to support eelgrass beds, which are critical substrates for juvenile settlement, is often absent.

Based on factor analysis, shell chemistry was associated with scallop abundance, indicating strong relations between the physical bottom water chemistry. Shell Mn/Ca, Fe/Ca, Cu/Ca, Pb/Ca, and Sr/Ca were used to allocate adults and juveniles to abundance zone. Previous research on other biogenic carbonate structures, such as fish otoliths (ear stones), concluded that salinity changes ([DELTA] = 4.84) strongly influenced carbonate Ba/Ca and La/Ca (Dorval et al. 2007). This is a smaller change than we observed in Nantucket Harbor ([DELTA] = 7.00) and so we can attribute some of the variance in shell Ba/Ca and La/Ca to differences in salinity between zones. The variability in shell Mg/Ca can be attributed to metabolically regulated partitioning of Mg within the low-Mg calcite shell of A. irradians. In the spotted seatrout, Mg/Ca incorporated into the otolith is expected to be regulated in blood plasma (Dorval et al. 2007), which may mean that, in other biogenic calcites, such as the low-Mg calcite of A. irradians shells, the partition coefficient may be influenced strongly by biochemical processes (Campana 1999) or preferential release of Mg from the lattice to the surrounding water, as in corals (Amiel et al. 1973). Variations in shell Mn/Ca, Fe/Ca, Sr/Ca, Cu/Ca, and Pb/Ca are most likely the result of changes in the surrounding water chemistry. We found individuals from zones 2 and 3 are discriminated using these shell elemental chemistries. By using the chemistry of the A. irradians shells collected at various ages, our model has provided evidence that there are areas, currently barren, within the harbor that could support scallops. Binary logistic regression modeling revealed differences in pH and salinity that are associated with a high abundance of scallops. Based on average values for zones 1, 2, and 3, the optimal pH was approximately 7.80 [+ or -] 0.13, with a salinity of 26 [+ or -] 2. Barren sites had greater variability. For example within zone 1, site 19 has a high salinity (32.4) with a relatively normal pH (7.71). However, site 31, also in zone 1, has reduced salinity (25.9) and a low pH (6.97), reflective of the distance from the harbor mouth and water freshening that occurs from runoff. The lack of scallops in zone 1 suggests that the pH or salinity extremes prevent scallops from residing at a given site. There were small differences in the water chemistry between zones 2 and 3 associated with their position in the harbor, which is reflected by small variations in elemental chemistry that is incorporated into the shell, providing further evidence that the harbor is well mixed.

In conclusion, based on binary factorial analysis, shell chemistry (Mn/Ca, Fe/Ca, Cu/Ca, Sr/Ca, and Pb/Ca) allocated individuals, with 72.9% overall accuracy, to either zone 2 or zone 3. Variations in pH and salinity were associated directly with zonal differences in the trace elements incorporated into the shell. The ability of the shells to incorporate and maintain the relative concentrations of metals in the surrounding water suggests that shell chemistry is useful in identifying essential habitats in Nantucket Harbor. Moreover, because the elemental chemistry is associated with salinity and pH, trace element chemistry can be used to evaluate changes in salinity and pH away from optimal conditions. Using shell chemistry to identify habitat could also provide an estimate of the overall health of a population given that shell chemistry differences are associated, here, with pH and salinity. For example, if the harbor becomes more acidic, free metal Pb concentration is likely to increase and Pb/Ca ratios would be expected to increase. Thus, the incorporation of Pb in bay scallop shells may be dependent on ocean acidity.

The ability of binary logistic regression to distinguish between habitat zones 2 and 3 also provides a management tool to assess habitat quality for conservation efforts. Based on the results presented here, combining water quality and population abundance, sites 2, 11, and 23 in zone 1 (currently barren) have the water quality characteristics necessary to support A. irradians, which could translate to an increase of 35,000-1,400,000 individuals. Thus, the information provided by our model could allow managers to make decisions on seeding habitats as well as identifying sites for conservation. In addition to providing crucial data for population management, this approach also provides insights into how A. irradians may respond to changes in pH associated with water freshening and ocean acidification.

ACKNOWLEDGMENTS

This research was supported financially by the EEOS Research Fellow Award program at the University of Massachusetts Boston. The manuscript was improved by the comments of several reviewers. Thanks to Jeff Mercer, Andrew Collin, Paul Sokoloff, Dr. Alan Christian, and Melanie Garate for diving. Thanks to Josi Herron, Eric Wilcox-Freeburg, Jeremy Williams, Nicole Henderson, Alex Barham, Alex Eisen-Cuadra, and Jenny Geldart Flashman for assistance in the field and laboratory. A special thanks to the town of Nantucket for their support in collecting specimens, and to Sarah Oktay at the University of Massachusetts Boston field station for resources and housing.

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BRYANNA J. BROADAWAY * AND ROBYN E. HANNIGAN

University of Massachusetts Boston, 100 Morrissey Boulevard, Boston, MA 02125

* Corresponding author. E-mail: bryanna.broadway001@umb.edu

DOI: 10.2983/035.031.0310
TABLE 1.
Physical bottom water quality parameters of Argopecten irradians
collection sites.

Site No.   Zone     Date      Depth (m)   Latitude (N)

1           3     8/11/2009      1.1      41.314722
2           1     8/5/2009       1.3      41.301667
3           3     8/11/2009      1.6      41.297778
4           3     7/28/2009      1.9      41.296667
5           3     8/12/2009      1.1      41.297222
6           3     7/29/2009      0.9      41.280000
7           3     8/12/2009      0.9      41.284722
8           3     7/21/2009      2.1      41.290000
9           2     7/29/2009      0.9      41.293889
10          3     8/4/2009       1.4      41.285833
11          1     7/28/2009      2.1      41.324167
12          3     8/11/2009      1.4      41.318056
13          3     7/22/2009      1.4      41.314722
14          3     8/4/2009       1.2      41.311111
15          2     8/5/2009       1.2      41.308056
16          2     7/22/2009      1.5      41.305
17          2     8/12/2009      1.2      41.300833
18          2     7/21/2009      1.4      41.298056
19          1     7/21/2009      1.2      41.292222
20          3     8/12/2009      0.9      41.294167
21          2     8/5/2009       1.9      41.294444
22          2     7/29/2009      2.4      41.291944
23          1     8/4/2009       1.2      41.332222
24          1     8/11/2009      2.3      41.344167
25          1     7/22/2009      2.1      41.325833
26          2     8/5/2009       3.9      41.298333
27          2     7/29/2009      2.5      41.303611
28          3     7/28/2009      2.1      41.310556
29          1     7/21/2009      7.3      41.306389
30          1     7/22/2009      2.7      41.320000
31          1     7/28/2009      5.6      41.334167
32          1     8/4/2009       1.5      41.318056

                                           Temp
Site No.   Longitude (W)   DO (mg/L)   ([degrees]C)    pH    Salinity

1            70.033056       7.84          26.5       7.54     25.8
2            70.032222       9.14          24.7       7.92     26.0
3            70.041944       9.90          24.7       7.89     25.7
4            70.054444       6.65          23.9       8.08     25.7
5            70.062500       8.87          23.4       7.98     25.8
6            70.085000       8.81          23.7       7.78     25.7
7            70.078611       9.43          23.6       7.93     25.7
8            70.074167       7.88          22.9       7.77     32.4
9            70.068333       7.89          23.9       7.91     25.8
10           70.083056       7.80          24.5       7.63     25.9
11           70.037778       8.52          24.4       7.82     26.0
12           70.045278       6.89          25.1       7.59     26.0
13           70.047778       9.08          23.3       7.63     28.5
14           70.053611       7.29          26.0       7.76     26.1
15           70.058889       6.32          24.7       7.86     26.0
16           70.063611       9.09          23.3       7.86     28.4
17           70.071944       7.66          23.6       7.84     26.0
18           70.078611       6.67          22.4       7.74     32.7
19           70.086111       7.85          22.5       7.71     32.4
20           70.090278       8.05          23.0       7.79     25.8
21           70.100833       2.67          24.0       7.76     25.7
22           70.094167       8.95          24.0       7.94     25.8
23           70.031667       6.84          26.1       7.55     26.1
24           70.016111       6.36          25.3       7.50     26.0
25           70.003611       9.08          23.7       8.01     28.4
26           70.073333       9.43          24.8       7.87     25.9
27           70.057500       7.77          23.0       7.68     25.8
28           70.048611       6.99          24.4       7.63     25.9
29           70.040556       6.91          25.6       6.70     30.5
30           70.038889       8.44          23.2       7.85     28.6
31           70.014722       6.97          24.1       7.76     25.9
32           70.021944       7.93          25.1       7.47     26.0

Date of collection and abundance zone (1, 0 scallops; 2, 1-70
individuals; 3, >70 individuals). DO, dissolved oxygen.

TABLE 2.
Eigenvalues and variance accounted for by factors extracted
using principal component analysis.

          Eigenvalue   Proportion (%)   Cumulative (%)

PC1         2.785          25.317           25.317
PC2         1.678          15.255           40.572
PC3         1.394          12.675           53.247
PC4         1.303          11.844           65.091
PC5         1.019           9.262           74.353
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