Use of otolith chemistry to discriminate juvenile Chinook salmon (Oncorhynchus tshawytscha) from different wild populations and hatcheries in Lake Huron

Journal of Great Lakes Research 37 (2011) 698–706

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Use of otolith chemistry to discriminate juvenile Chinook salmon (Oncorhynchus tshawytscha) from different wild populations and hatcheries in Lake Huron Stephen A.C. Marklevitz a,⁎, Brian J. Fryer b, David Gonder c, Zhaoping Yang b, James Johnson d, Ashley Moerke e, Yolanda E. Morbey a a

Department of Biology, University of Western Ontario, 1151 Richmond St., London, Ontario, Canada N6A 5B7 Great Lakes Institute for Environmental Research, University of Windsor, 401 Sunset Ave., Windsor, Ontario, Canada N9B 3P4 Upper Great Lakes Management Unit, Ontario Ministry of Natural Resources, 1450 Seventh Ave. East, Owen Sound, Ontario, Canada N4K 2Z1 d Alpena Fisheries Research Station, Michigan Department of Natural Resources, 160 East Fletcher St., Alpena MI 49707-2344, USA e Department of Biological Sciences, Lake Superior State University, 650 West Easterday Ave. Sault Ste. Marie, MI 49783, USA b c

a r t i c l e

i n f o

Article history: Received 19 April 2011 Accepted 2 August 2011 Available online 25 September 2011 Communicated by Michael E Sierszen Keywords: Elemental concentration Natal origin Tagging Fisheries management Population structure Freshwater lake

a b s t r a c t Chinook salmon (Oncorhynchus tshawytscha) in Lake Huron consist of wild and hatchery-reared fish distributed among several populations. This study tested whether otolith chemistry can be used to identify the natal origin of Chinook salmon in this system. Concentrations of nine elements (Mg, K, Mn, Fe, Zn, Rb, Sr, Ba, and Pb) in the otoliths of Chinook salmon juveniles from 24 collection sites (17 streams and 7 hatcheries) around Lake Huron were analyzed using laser-ablation inductively-coupled mass spectrometry. Differences in otolith chemistry were found between rearing environments (wild and hatchery), among geological regions (Precambrian, Ordovician, Silurian, Devonian, and Carboniferous), and among collection sites. Discriminant function analysis showed high classification accuracies of juveniles to their rearing environment (wild versus hatchery, 82%), geological region (84%), and collection site (87%) of origin. With these values, there is excellent potential for otolith chemistry to be used to predict the natal origin of adults, and thus inform research and management of Chinook salmon in Lake Huron. © 2011 International Association for Great Lakes Research. Published by Elsevier B.V. All rights reserved.

Introduction The fish community in Lake Huron has experienced substantial changes over the past 200 years, including the invasion of sea lampreys (Petromyzon marinus), alewives (Alosa pseudoharengus) and rainbow smelt (Osmerus mordax), and declining populations of lake trout (Salvelinus namaycush) (Dobiesz et al. 2005). One of the most significant alterations to the ecosystem was the intentional introduction of non-native Pacific salmonids, in particular the Chinook salmon (Oncorhynchus tshawytscha), beginning in 1968 (Kocik and Jones, 1999). The initial objectives of this stocking program were to establish a recreational salmonid fishery and to suppress uncontrolled alewife and rainbow smelt populations (Kocik and Jones, 1999). By 2009 over 109 million Chinook salmon had been stocked into Lake Huron (GLFC 2011). The stocking program began with donor gametes from the Green River hatchery in Washington State (Weeder et al. 2005), but now relies entirely on gametes collected from returning adults in

⁎ Corresponding author. Tel.: +1 519 661 2111x80116. E-mail addresses: [email protected] (S.A.C. Marklevitz), [email protected] (B.J. Fryer), [email protected] (D. Gonder), [email protected] (Z. Yang), [email protected] (J. Johnson), [email protected] (A. Moerke), [email protected] (Y.E. Morbey).

Lake Huron. Beginning in the early 1980s, it was apparent that Chinook salmon had begun to colonize and establish self-sustaining populations (Johnson et al. 2005). Wild-born individuals now comprise over 80% of the lake-wide population (Johnson et al. 2010). Little is known about the relative contributions of different wild populations to the lake-wide Chinook salmon fishery, yet this is necessary information for the successful management of this non-native species. The lack of information about which streams produce the majority of the lake-wide populations is due to a shortage of monitoring programs for Pacific salmon and the constraints of labor intensive and expensive mark-recapture studies of wild fish. Thus, our objective was to assess whether otolith chemistry (i.e. concentrations of various elements) can be used to discriminate the natal origins of Lake Huron Chinook salmon. As otoliths grow, they incorporate elements at concentrations indicative of the environment (Campana and Thorrold, 2001). Because of the archival properties of otoliths, unique elemental compositions persist through the life of a fish that can allow identification of natal origin and movement patterns (Brazner et al. 2004a, 2004b; Hand et al. 2008; Barnett-Johnson et al. 2008; Gibson-Reinemer et al. 2009). This method should work for Chinook salmon in Lake Huron. Young-of-the-year juveniles, or fry, reside in Lake Huron streams and hatcheries (Fig. 1) for 3–7 months (Kocik and Jones, 1999),

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699

Fig. 1. Map of geological regions in the Lake Huron watershed. Collection sites for wild fish (circles) and hatchery-reared fish (triangles) are shown. See Table 1 for site abbreviations. Note the overlap between the SYD and SSA collection sites.

providing a sufficient exposure to stream or hatchery water chemistry to allow deposition of otolith material characteristic of those environments. Ultimately, the section of otolith formed during the juvenile stage can be isolated from adults to predict their natal origin. Furthermore the streams and hatcheries of Lake Huron are situated across several distinct geological regions (Fig. 1). Since geological features are a major source of elements available for incorporation into the otolith, juveniles from different geological regions should have otoliths with distinct elemental composition (Ingram and Weber, 1999; Kennedy et al. 2000). Before the application of this method to adult otoliths, it is important to validate it using otoliths extracted from juveniles with different natal origins. Our objective was to test whether otolith chemistry was sufficiently differentiated among Chinook salmon juvenile populations to be a useful marker of natal origin. Specifically, we tested whether the chemistry of otoliths from Chinook salmon juveniles were distinct between rearing environments (wild or hatchery), geological regions with different bedrock geology (Precambrian, Ordovician, Silurian, Devonian, and Carboniferous), and collection sites (Fig. 1). In an attempt to limit the need for extensive sampling of Chinook salmon juveniles in future research, we also tested whether water chemistry and/or food chemistry (in hatcheries only) could predict otolith chemistry. Methods Sampling sites Young-of-the-year juvenile Chinook salmon, (n = 14–16 per collection site) were collected from seventeen Lake Huron streams and seven hatcheries during April–June (2006–08) by Ontario Ministry of Natural Resources, Michigan Department of Natural Resources and Environment, and Lake Superior State University personnel (Table 1, Fig. 1). The selected tributaries included all those known to support Chinook salmon in the Lake Huron watershed, based on observations by local biologists. The hatcheries represented all those

producing Chinook salmon for release into Lake Huron and included state-run hatcheries in Michigan and community-run hatcheries in Ontario. In the Sydenham River, the local hatchery releases Chinook salmon juveniles in June. Hatchery fish can be distinguished from wild fish by the presence of an adipose-fin clip and their larger sizeat-age, 5.3 ± 0.4 (SE) g compared to 2.2 ± 0.2 (SE) g respectively, in this system. Three collection sites (Sydenham River, Nottawasaga River, and Thompson State Fish Hatchery) were sampled multiple times. Fish were collected using standard electrofishing techniques or dip nets and euthanized in a 2 g·L −1 solution of MS222 (tricaine methane sulphonate). Each fish was measured (fork length and weight) and then preserved in an individually-labeled, acid-washed, high density polyethylene bottle filled with 95% laboratory grade ethyl alcohol. At the time of juvenile collections, water samples (n = 2 per collection site) were collected in acid washed bottles and water temperature and pH were measured from raceways in the case of hatchery fish or the stream in the case of wild fish. Hatchery food samples were collected from each hatchery. Collection sites were mapped onto a bedrock geology map using ArcGIS® (Fig. 1). A unified Great Lakes watershed bedrock geology layer was developed by merging North American bedrock geology data from Natural Resources Canada (NRCAN 2008) and United States Geological Survey (USGS 2008). These data were re-classified according to common geological time period of formation (oldest to youngest: Precambrian, Ordovician, Silurian, Devonian, and Carboniferous) and cropped using the Great Lakes watersheds outline layer, obtained from the Great Lake GIS project (GLGIS 2008). Otolith preparation The sagittae otoliths of the Chinook salmon juveniles were removed in the laboratory using a method modified from Secor et al. (1992). Otoliths were handled with DuPont tm No.5 titanium forceps and adhering tissue matter removed using a fine tip paint brush. The deposition of vaterite in the otolith alters the chemistry so otoliths containing noticeable deposits of vaterite were noted (Gauldie

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Table 1 Collection sites of Chinook salmon juveniles from Lake Huron tributaries and hatcheries, showing the date of collection and number of fish (n) collected. Samples were divided into training and test data for discriminant function analysis. In site abbreviations, an H denotes a hatchery site and a subscript number denotes a replicate sample. Geological regions are Precambrian (P), Ordovician (O), Silurian (S), Devonian (D), and Carboniferous (C). Collection site

Site abbreviation

Geological region

Date

n

Training data Root River Garden River Lauzon Creek Spanish River Gore Bay Fish and Game Club Kagawong Creek Mindemoya River Manitou River Nottawasaga River Beaver River Bighead River Sydenham River Sydenham Sportsmen's Association Sauble River Lake Huron Fishing Club Saugeen River Maitland River Bluewater Anglers Wolf Lake State Fish Hatchery Platte River State Fish Hatchery Thompson State Fish Hatchery Nunn's Creek Carp River St. Marys River

RT GD LAU SP GB(H) KAG MIN MAN NT BV BH SYD SSA(H) SB LHFC(H) SG MAT BWA(H) WL(H) PR(H) TS(H) NUN CR StM

P P P P O O S S O O O S S S S S D D C C S S S S

7 Jun 2007 7 Jun 2007 5 Jun 2008 5 Jun 2007 2 Jun 2008 4 Jun 2008 4 Jun 2008 4 Jun 2007 24 May 2007 22 May 2007 22 May 2007 30 May 2007 11 Jun 2007 23 May 2007 7 May 2007 18 May 2007 10 May 2007 8 Apr 2008 3 May 2007 15 May 2007 7 May 2007 25 May 2007 14 May 2007 26 Jun 2007

15 15 15 15 15 15 15 15 15 14 15 15 15 15 15 13 14 15 15 16 15 15 15 15

Test data Nottawasaga River Beaver River Bighead River Sydenham River Sydenham River Sydenham River Sydenham River Sydenham River Sydenham Sportsmen's Association Thompson State Fish Hatcherya Thompson State Fish Hatcherya

NT1 BV1 BH1 SYD1 SYD2 SYD3 SYD4 SYD5 SSA(H)1 TS(H)1 TS(H)2

O O O S S S S S S S S

8 May 2007 29 May 2006 30 May 2006 31 May 2006 2 May 2007 17 May 2007 12 Jun 2007 29 May 2008 26 May 2006 17 Apr 2007 17 Apr 2007

17 25 19 24 14 15 15 15 18 15 15

a

Collections from Thompson State Fish hatchery raceways using different commercial feed (TS(H)1 = BioDry and TS(H)2 = Silvercup).

1996; Oxman et al. 2007; Melançon et al. 2005, 2008). One otolith from each fish was selected for further analysis, excluding any that were broken, cracked or contained significant amounts of vaterite. Otoliths were aligned sulcus down and mounted with ethyl cyanoacrylate epoxy (Krazy Glue®) on a small square (1 cm 2) piece of acetate film (overhead stock). Each otolith was individually hand polished using 3 M™ lapping film before excess acetate film was removed and the otolith mounted to a petrographic slide. Otolith analysis and data integration The chemistry of each otolith was analyzed with laser-ablation inductively-coupled plasma mass spectrometry (LA-ICP-MS) at the Great Lakes Institute for Environmental Research (University of Windsor, Ontario, Canada). A laser beam of 785 nm wavelength was produced using a Quantronix Integra-C femto second laser source. For consistency, the 355 μm transect, as outlined in Smith et al. (2006), positioned in the posterior-dorsal quadrant, was laser ablated and analyzed. If the posterior-dorsal quadrant was cracked, absent, or contained vaterite, the posterior ventral quadrant was analyzed instead. Otoliths were moved from primordia (core) to the edge under a laser with an ablation spot size 20 μm (±2 μm) in diameter. To remove the surface layer and minimize the probability of surface contamination, otolith transects were first ablated (i.e., vaporized) with the laser traveling at 100 μm s −1 (20 × the analytical speed). The transect was then re-ablated at the analytical speed of

5 μm s −1. The laser-ablated otolith material was transported via argon gas to the ICP-MS analyzer (Thermo Electron X7-II). Data were gathered and processed for 20 isotopes: lithium ( 7Li), magnesium ( 25Mg), sulphur ( 33S), potassium ( 39K), calcium ( 43Ca and 44 Ca), manganese ( 55Mn), iron ( 57Fe), copper ( 63Cu and 65Cu), zinc 66 ( Zn and 67Zn), rubidium ( 85Rb), strontium ( 86Sr and 88Sr), tin ( 118Sn and 120Sn), barium ( 138Ba), cesium ( 140Ce) and lead ( 208Pb), using Thermo Scientific Plasmalab software. NIST 610 glass standards were ablated in duplicate at the beginning and end of every fifteenth otolith. These NIST standards were used to check for instrument precision (b10% coefficient of variation) and, using Ca 44 as a measure of the internal standard Ca, correct for instrumental drift and ablation yield (Ludsin et al. 2006a; Hand et al. 2008). Prior to each laser ablation transect a 60 s argon gas blank was performed for background subtraction (Ludsin et al. 2006a). Otolith chemistry data was isolated and integrated from the edge section of the otolith because it was most representative of the recent environmental chemistry experienced by the Chinook salmon juvenile and excluded maternal influences (Veinott and Porter 2005). In our study, standardized distance from the edge of the otolith (e.g., Bradbury et al. 2008; Veinott and Porter 2005; Brazner et al. 2004b) or core (e.g., Barnett-Johnson et al. 2008) would not necessarily be representative of the environment in which the fish had resided, because hatchery rearing can alter the growth rate and structure of the otolith (Smith et al. 2006). The edge section was delineated by analyzing the chemical profile of the otolith and identifying the first

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sharp increase of the Zn and/or Sr signals when moving from the primordia to the edge. These signals indicate the start of the fresh water or river growth phase in juvenile salmon (Arai et al. 2007). The edge section was defined as the region between the apex of the first sharp increase in the Zn and/or Sr signals to the point where the laser ablated off the otolith and onto the epoxy, indicated by a tin (Sn) signal above the reference gas blank. If the laser ablated through the otolith material to the epoxy (i.e., Sn above reference gas blank) these areas within the otolith chemical profile were excluded from further analyses. Analysis of water and hatchery food To reduce the need for extensive sampling of young-of- the-year Chinook salmon in the future, water and food samples were collected and analyzed to determine their relationship to otolith chemistry. Water samples were analyzed using a Thermo-Electron X-7-II ICPMS. Prior to ICP-MS analysis, water samples were filtered using Fisherbrand® filter paper (diameter: 12.5 cm; porosity: coarse; flow rate: fast), acidified to 1% HNO3 and refrigerated until analysis. An internal standard with beryllium (Be), indium (In) and thallium (Tl) in nitric acid was mixed with 10 g of sample water (Melançon et al. 2009) for analysis. To analyze elemental concentrations of the fish food, 0.45 g (±0.08 g) samples were digested with 10 mL of concentrated (68– 70%) trace grade nitric acid (HNO3) in a XP1500 microwave digestion vessel. After 5 min of pre-digestion at room conditions, samples were placed into a CEM Corporation MARS 5 1200 watts microwave digestion system at 100% power for 50 min at 210 °C and 500 psi. After a 20 min cool down sequence, samples were removed and 3 mL of 30% hydrogen peroxide (ACS grade) was added. The food samples were left at room temperature for 5 min before a second digestion in the microwave digestion system at 100% power for 20 min at 210 °C and 500 psi, followed by a 5 min cool down sequence. Samples were then transferred from the microwave digestion vessels into dry pre-weighed 125 mL LDPE Nalgene tm bottles rinsing five times with MilliQ water. Ten grams of the diluted internal standard solution containing Be, In and Tl with the concentrations of 10 ppb, 1 ppb and 2 ppb, respectively, was added to 0.05 g of the microwave digested food samples (Melançon et al. 2009). Samples were stored in sealed 15 ml tubes prior to ICP-MS analysis using the Thermo Electron X7II ICP-MS. All water and food samples, calibration standards, procedural and reagent blanks contained the internal standards (Be, In, and Tl) to correct for matrix and drift effects for each sample. Matrix and drift effects were determined on an individual-sample basis using multielement calibration standards and the 1% HNO3 internal standard solution measured before, during and after the sample measurements. Standard reference materials were included in the analysis runs.

701

complications arising from potential seasonal variability and pseudoreplication, only one collection per site was used in these analyses (i.e., the training data in Table 1). To maintain statistical power to test for effects of rearing environment and geological region, we combined the data for 2007 and 2008 (year-specific MANOVAs did not change the significance of the effect of rearing environment in M1, below; results not shown). Thus we assumed that variability in otolith chemistry would be greater among collection sites than within collection sites. Two factor MANOVA was used to test for differences in otolith chemistry (a vector of 9 elements) between rearing environments (E: wild versus hatchery), and collection site (Xc) as a factor nested within rearing environment (M1). To account for the potential confounding variable of geological region while testing for differences between rearing environments, three additional two-factor MANOVAs were performed for each geological region containing both wild and hatchery collection sites (i.e., Ordovician, Silurian, and Devonian). To test for differences in otolith chemistry among geological regions, two-factor MANOVAs were done separately for each rearing environment (M2a: wild; M2b: hatchery) with geological region (G), and collection site nested within geological region [Xc(G)] as factors. Significant MANOVAs were followed by ANOVAs of individual elements with Ryan-Einot-Gabriel-Welsch (REGWQ) post-hoc comparison tests to identify how elements differed among geological regions. Using the training data (Table 1), linear discriminant function analysis (DFA) was used to derive a function to maximally discriminate: 1) rearing environments (wild versus hatchery), 2) wild fish from different geological regions, 3) hatchery fish from different geological regions, and 4) collection sites. For each, the best DFA model (i.e., the set of elements producing the lowest error rate) was found using a backwards stepwise approach. The classification accuracy of the model was based on the resubstitution technique with priors proportional to the number of fish in each group (Tabachnick and Fidell, 2007). We took advantage of the additional collections and used them as test data (Table 1) in the DFA models, with the caveat that these did not represent a random subset of the training data. Multiple linear regression was used to test for the effects of sitespecific factors on the concentration of each element. Backwards stepwise selection was used to eliminate unimportant variables. The variables included the elemental concentration of the water, the elemental concentration of the food (for hatchery sites only), water temperature, water pH, fish fork length, and fish weight. To avoid pseudoreplication, the mean values of dependent and independent variables for collection sites were used in analyses. For hatchery sites, water temperature, pH and fish weight were excluded from analyses because of insufficient data. For stream sites, no information on Chinook salmon diet or elemental concentration of these diet items was available. Results

Statistical analyses Otolith chemistry: differences among collection sites Elements with greater than 75% of the data below detection limits (Li, Cu, and Ce) were excluded from statistical analyses, leaving nine elements remaining: Mg, K, Mn, Fe, Zn, Rb, Sr, Ba, and Pb. Six of the elements (Mg, Mn, Zn, Sr, Ba, and Pb) were log transformed to improve data normality for statistical analysis, as done in similar studies (Brazner et al. 2004a; Bradbury et al. 2008). SAS® (version 9.1.3) statistical software package was used for these and all subsequent statistical analyses. Several multivariate analysis of variance (MANOVA) models were used to test for differences in otolith chemistry between different rearing environments (E: wild versus hatchery), geological regions (G), and collection sites (Xc). Different models were run because the sampling design was not balanced: for example, wild and hatchery sites were not equally represented in all geological regions. To avoid

When pooling the data for all geological regions, otolith chemistry differed between wild and hatchery fish (M1: F9, 325 = 324.9, p b 0.0001) and among collection sites (M1: F198,2738.3 = 37.56, p b 0.0001). As shown by subsequent ANOVAs, each of the nine elements differed between rearing environments (Table 2). Two elements (Rb and Ba) had higher values in wild fish whereas seven elements (Mg, K, Mn, Fe, Zn, Sr, and Pb) had higher concentrations in hatchery fish. However, these differences between wild and hatchery fish were not observed consistently within geological regions for which there was comparable data (Ordovician: F9,61 = 165.1, p b 0.0001; Silurian: F9,144 = 306.9, p b 0.0001; Devonian: F9,19 = 131.9, p b 0.0001). Three elements, K, Fe, and Pb were consistently equal or higher in hatchery fish than wild fish within the geological regions (Fig. 2).

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Table 2 Summary of ANOVA results of the elemental concentrations of Chinook salmon juvenile otoliths between rearing environments (E: wild versus hatchery) and among collection sites nested within rearing environments [Xc(E)]. Post hoc comparisons between rearing environments are reported as inequalities between mean otolith elemental concentrations in wild and hatchery fish. Untransformed otolith elemental concentrations are presented. Element

Mg K Mn Fe Zn Rb Sr Ba Pb

E

Xc(E)

Concentration (ppm)

F1,22

F22,333

Wild

16.82⁎⁎ 28.36⁎⁎ 10.68⁎ 160.8⁎⁎ 10.54⁎ 9.78⁎ 1248⁎⁎ 417.5⁎⁎ 47.13⁎⁎

26.68⁎⁎ 20.36⁎⁎ 12.04⁎⁎ 212.3⁎⁎ 62.53⁎⁎ 10.84⁎⁎ 195.6⁎⁎ 81.70⁎⁎ 4.18⁎⁎

49 1759 9.42 455 285 0.536 504 10.4 0.016

Hatchery b b N b b N b N b

122 2008 4.87 516 290 0.365 1447 4.5 0.094

⁎ p b 0.05. ⁎⁎ p b 0.0001.

Further analyses showed that otolith chemistry differed among geological regions both in wild fish (M2a: F27,660.68 =110.2, pb 0.0001) and in hatchery fish (M2b: F27,266.41 =45.88, p b 0.0001), after accounting for differences among collection sites (M2a: F117,1702.6 = 24.71, pb 0.0001; M2b: F27,266.41 =151.7, pb 0.0001). According to subsequent

ANOVAs, all elements showed differences among geological regions in wild fish and all elements but K showed differences among geological regions in hatchery fish (Fig. 2). Otolith chemistry: DFA models The DFA model to discriminate rearing environment had an overall classification accuracy of 82%, correctly classifying 99% of the wild fish and 65% of the hatchery fish (Table 3, Fig. 3). This model excluded Zn to achieve the lowest error rate. Standardized DFA loadings indicated that Sr, Ba and Pb were important elements for the discrimination of rearing environment. When the model was applied to the test data, the accuracy of the DFA model was 76% for wild fish and 65% for hatchery fish (Table 3). The DFA model to discriminate geological region for wild fish had an overall classification accuracy of 75%, with excellent ability to distinguish wild fish from the Precambrian (92% classification accuracy) and Devonian (100% classification accuracy) regions (Table 3, Fig. 4). The model was less able to distinguish Silurian (75% classification accuracy) and Ordovician (54% classification accuracy) regions and classification errors often occurred between these two regions. This model included all elements to achieve the lowest error rate. The first two discriminant functions (DF1 and DF2) explained 95% of the observed variability (Fig. 4) and the standardized DFA loadings showed that Ba, Sr, Mn and Mg were the most important elements

Fig. 2. Comparison of the mean otolith elemental concentrations (ppm ± SE) of Chinook salmon juvenile otoliths among geological regions (P = Precambrian, O = Ordovician, S = Silurian, D = Devonian, and C = Carboniferous) and between rearing environments (wild = black bars; hatchery = open bars). Post hoc comparisons among geological regions are shown for wild populations (uppercase letters) and hatcheries (lowercase letters), with significant differences represented by different letters. Comparisons between rearing environments within geological regions are represented by the presence (no difference) or absence (significant difference) of a horizontal bar above each set of bars. Untransformed otolith elemental concentrations are presented.

S.A.C. Marklevitz et al. / Journal of Great Lakes Research 37 (2011) 698–706 Table 3 Classification matrices for the DFA models of rearing environment (E: wild versus hatchery) and geological region (G) within wild and hatchery collection sites. The proportions of individuals predicted by the DFA models to originate from the various origins are compared to their actual origin in the training and test data sets. Geological regions are Precambrian (P), Ordovician (O), Silurian(S), Devonian (D), and Carboniferous (C). Em dashes indicate geological regions with missing data. DFA model

Predicted origin E

Actual origin Training data Wild P O S D Hatchery O S D C Test data Wild O S Hatchery S

Wild

Hatchery

0.99

0.01

0.35

0.76

0.35

n G P

O

S

D

C

0.92 0 0.03 0

0.02 0.54 0.14 0

0.07 0.46 0.75 0

0 0 0.09 1.0

— — — —

— — — —

1.0 0.02 0.07 0

0 0.91 0 0

0 0.04 0.87 0.06

0 0.02 0.07 0.94

0 0

0.31 0.30

0.69 0.70

0 0

— —

—

0.02

0.60

0.33

0.04

0.65

0.24

0.65

251 60 59 118 14 106 15 45 15 31

144 61 83 48 48

for discrimination. When the model was applied to the test data, 53% were correctly classified to their geological region of origin (Silurian or Ordovician), with the majority of classification errors occurring to the alternate region. The DFA model to discriminate geological region for hatchery fish had an overall classification accuracy of 92%, with classification accuracies exceeding 87% for individual geological regions (Table 3, Fig. 4). This model excluded K and Pb to achieve the lowest error rate. The first two discriminant functions (DF1 and DF2) explained 98% of the variability among geological regions. Standardized DFA loadings indicated that Rb, Ba, Mg, and Mn were the most important elements in providing the power to discriminate. Of the 48 fish comprising the test data, 29 (60%) were correctly classified to the correct geological region (Silurian), with the majority of classification errors occurring to the Devonian (Table 3).

Fig. 3. Frequency histogram of the discriminant function scores (DF1) for the DFA model of rearing environment, showing the separation between wild fish (black bars) and hatchery fish (open bars). Increasing DF1 values are associated with increased Sr, Ba and Pb concentrations.

703

The DFA model to discriminate collection site had a classification accuracy of 87% and retained all elements (Table 4). The majority (90%) of the variability in otolith chemistry was explained by the first three DFs, with DF1 representing a gradient of wild to hatchery collection sites, DF2 representing latitude, and DF3 representing geological age. The standardized DFA loading for these three discriminant functions identified Fe, Zn, Sr and Ba as key elements enabling the discrimination of collection sites. Fifteen collection sites had greater than 90% classification accuracy (≤1 misclassified fish). The nine remaining populations had classification accuracies that ranged from 60 to 90% (2–6 misclassified fish). The majority of misclassified fish occurred to geologically similar or geographically proximal (b150 km separation) collections sites. When the DFA model was applied to the test data, the classification accuracy decreased to only 23% (Table 4). There was high assignment accuracy for the Thompson State Fish Hatchery (100%) collections and the 17 May 2007 Sydenham River collection. The majority of incorrect assignments occurred to collection sites within the same geological region (54%) or in geographical proximity (41%). Otolith chemistry: correlates Some site-specific factors were related to otolith chemistry, but these relationships were complex and differed between rearing environments (Table 5). In wild fish, the concentrations of Fe, Rb, Sr, and Ba in the water were related to otolith chemistry, and three of these relationships were in the expected positive direction. In hatchery fish, the concentrations of Fe, Zn, and Sr in the water and Mg, Mn, and Zn concentrations in the food were related to otolith chemistry, but only two of these six relationships were in the expected positive direction. In wild fish, concentrations of some elements were correlated with stream temperature (Fe), stream pH (Mn, Sr, and Ba), fork length (Mn and Fe), and body mass (Fe and Pb). Discussion Otolith chemical analysis is a promising tool for predicting the natal origin of Chinook salmon in Lake Huron. The chemistry of the Chinook salmon otoliths differed and could be used to predict the origin of fish at all spatial scales considered (rearing environments, geological regions, and collection sites). MANOVAs and ANOVAs showed significant differences in most of the elements between rearing environments (wild versus hatchery) (Table 2) and among geological regions (Fig. 3). According to DFAs, however, a few key elements were most important for discrimination of rearing environments (Sr, Ba, and Pb), geological regions (Ba, Sr, Rb, Mn, and Mg), and collection sites (Fe, Zn, Sr and Ba). The DFA models accurately classified fish in the training data set to their rearing environment (82%), geological region of origin (75% for wild fish and 92% for hatchery fish), and collection site (87%). When the DFA models were applied to test data, the accuracies were still reasonable for predicting rearing environment (73%) but declined when predicting geological region (53% for wild fish and 60% for hatchery fish). The DFA model had reduced performance in predicting the collection site of samples in the test data set (23%). The reduction of the predictive power of the DFA models at finer spatial scales suggests that sites within regions have similar stream chemistry. The non-random nature of misclassifications indicates that regional and geological processes played a major role in influencing otolith chemistry. Of the 71 misclassified fish in the DFA models predicting geological region of origin, 86% were classified to neighboring geological regions. Of the 46 misclassified fish in the DFA model predicting collection site, 76% were classified to collections sites in geographical proximity (b150 km) and 54% classified to a site within the same geological region. The misclassifications in our test data were further evidence of the regional and geological influences on otolith chemistry.

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Fig. 4. Bivariate plots of the discriminant function scores (DF1 and DF2) from the wild fish and hatchery fish DFA models of geological region of collection. Each point represents an individual fish with geological region of collection being represented by Precambrian (green circles), Ordovician (purple circles), Silurian (brown squares), Devonian (orange diamonds), and Carboniferous (yellow triangles). For wild fish, increasing DF1 scores are associated with increasing Ba and Sr and decreasing Mg concentrations, while increasing DF2 scores are associated with increasing Mn and decreasing Sr concentrations. For hatchery fish, increasing DF1 scores are associated with increasing Rb, Ba and Mg concentrations, while increasing DF2 are associated with increasing Rb and decreasing Mn concentrations.

The majority of misclassifications in the test data, while using the DFA model predicting collection site, were fish classified to collection sites in geographical proximity (b150 km) (93%) or within the same Table 4 Classification matrix for the DFA model of collection site. The proportion of individuals correctly and incorrectly predicted to their actual collection site in the training and test data sets is reported. Incorrect predictions are divided into those within the same geological region, within a neighboring region but within geographical proximity (b 150 km separation), or within a different geological region and not in geographical proximity. Actual origin

Predicted origin Within region

Neighboring region

Different region

n

Training data RT 0.60 GD 0.73 LAU 1.00 SP 0.73 GB(H) 1.00 KAG 0.93 MIN 0.60 MAN 0.80 NT 1.00 BV 0.71 BH 0.87 SYD 1.00 SSA(H) 0.93 SB 0.93 LHFC(H) 1.00 SG 0.92 MAT 1.00 BWA(H) 0.60 WL(H) 1.00 PR(H) 1.00 TS(H) 1.00 NUN 1.00 CR 0.60 StM 0.93

Correct

0.40 0.13 0 0.27 0 0 0.13 0.13 0 0.21 0.07 0 0 0 0 0 0 0 0 0 0 0 0.28 0.07

0 0.13 0 0 0 0.07 0.20 0.07 0 0.07 0.07 0 0.07 0.07 0 0.08 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0.07 0 0 0 0 0 0 0 0 0 0 0.40 0 0 0 0 0.13 0

15 15 15 15 15 15 15 15 15 14 15 15 15 15 15 13 14 15 15 16 15 15 15 15

Test data NT1 BV1 BH1 SYD1 SYD2 SYD3 SYD4 SYD5 SSA(H)1 TS(H)1 TS(H)2

0.88 0.84 0.63 0.04 0.07 0 0.80 0.73 0.33 0 0

0.12 0.08 0.32 0.87 0.93 0.07 0 0.07 0.67 0 0

0 0.04 0.05 0.08 0 0 0.20 0.21 0 0 0

17 25 19 24 14 15 15 15 18 15 15

0 0.04 0 0 0 0.93 0 0 0 1.00 1.00

geological region (40%). In general, factors affecting otolith chemistry should be similar within geographical and geological regions because of similar bedrock geology, surficial geological features, climate, atmospheric deposition, and anthropogenic inputs (Campana, 1999). The reduced ability of the DFA model to predict the collection site of fish in the test data may have been due to the limited spatial distribution of these additional samples combined with seasonal variation in factors affecting otolith chemistry. The majority of the samples in the test data came from rivers in southern Georgian Bay. These rivers were situated in the same geological regions and hence flowed over similar geological features. There were no errors in classifying fish from the Thompson State Hatchery, a collection site distant from all others. Seasonal cycling of elements, such as Sr, may have also contributed to the decline in DFA model accuracy (Bacon et al. 2004). With major regional and geological influences on the otolith chemistry our results suggest that seasonal cycling of elemental concentrations may only be an issue if finer-scale resolution of natal origins was desired. Fine-scale resolution of natal origins would require the quantification of the temporal variability in the otolith elemental concentrations or the analysis of isotope ratios such as 87Sr: 86Sr. Strontium isotope ratios are good geochemical markers of geological features such as bedrock geology and show minimal seasonal variation (Kennedy et al. 2000). Some of the coarse-scale variation in otolith chemistry was consistent with other studies or had a mechanistic explanation. For example, hatchery fish were characterized by otoliths with higher concentrations of seven elements (Mg, K, Mn, Fe, Zn, Sr, and Pb) and lower concentrations of two elements (Rb and Ba) than in wild fish. The artificial rearing infrastructure of a hatchery probably contributed to these differences in otolith chemistry (e.g. Zhang et al. 1995). For example, the presence of plumbing fixtures containing materials such as solder, brass parts, and copper, iron and/or PVC pipes has been shown to alter the concentrations of Mn, Fe, and Pb in effluent water (Hall and Murphy, 1993; Cerrato et al. 2006). The higher concentrations of Mn, Fe, and Pb in the otoliths of hatchery fish than in wild fish may have been caused by these plumbing fixtures. It should be noted that some of the differences between rearing environments could have been due to the confounding effect of bedrock geology. However, at least for Fe and Pb, concentrations were equal or higher in hatchery fish than in wild fish in the Ordovician, Silurian, and Devonian regions. At the scale of geological region, Sr, Mn (wild populations only), Mg, Rb (hatcheries only) and Ba were important elements for the discrimination of Chinook salmon. These elements, or a subset of these elements, have been shown to differentiate populations in other taxa and systems

S.A.C. Marklevitz et al. / Journal of Great Lakes Research 37 (2011) 698–706

705

Table 5 Results of backwards stepwise regression analyses showing how otolith elemental concentrations were affected by water chemistry, food chemistry, temperature, pH, fish length (fork length), and fish weight in wild and hatchery juveniles. The final models excluded non-significant factors (-) and unmeasured factors (X). Element

Intercept

Regression model Water

Food

Temp

pH

Length

Weight

r2

p

n

Wild log Mg K log Mn Fe log Zn Rb log Sr log Ba log Pb

1.01 2623 0.07 5.17 4.14 − 0.01

− 618 408 0.65 5.18 -

X X X X X X X X X

37.4 -

− 0.27 − 0.34 − 0.42 -

0.041 − 78.0 -

918 0.014

0.67 0.70 0.35 0.78 0.86 0.37

0.0001 0.0012 0.0035 b 0.0001 b 0.0001 0.0059

22 22 19 19 19 22 19 19 19

Hatchery log Mg K log Mn Fe log Zn Rb log Sr log Ba log Pb

4.3 1.0 757 2.58 2.75 -

− 275 20.4 0.175 -

− 0.001 − 0.007 X − 0.002 -

X X X X X X X X X

X X X X X X X X X

-

X X X X X X X X X

0.63 0.61 0.77 0.96 0.70 -

0.0106 0.0134 0.0092 0.0020 0.0195 -

9 9 9 7 5 9 7 9 9

(Brazner et al. 2004a, 2004b; Coghlan et al. 2007; Bradbury et al. 2008; Gibson-Reinemer et al. 2009). Differences in the concentrations of these elements in otoliths have been linked to water temperature, salinity, and geology (Fowler et al. 1995; Limburg 1995; Friedland et al. 1998; Bacon et al. 2004; Friedrich and Halden 2008), properties that differ at a coarse scale. In addition, Gibson-Reinemer et al. (2009) show positive relationships between the concentrations of Sr and Ba in water and fish otoliths. One notable geological pattern was the observation of higher concentrations of Mn, Rb and Ba in the otoliths of fish originating from the Precambrian region. This pattern is consistent with a similar study of sea lampreys using statoliths (Ludsin et al. 2006b). The relationship between water and otolith chemistry was complex, likely because otolith development is a complex physiological process affected by environmental factors (Campana 1999). Consequently, the elemental composition of water was not a good predictor for otolith chemistry in our study. Only a few elements (Rb, Sr, and Ba in wild populations; Zn and Sr in hatcheries) showed significant positive relationships between water chemistry and otolith chemistry. GibsonReinemer et al. (2009) and Zeigler and Whitledge (2010) also found positive relationships between water chemistry and otolith chemistry for Sr and Ba. It is possible that point-in-time (snapshot) water samples, as used on our study, do not adequately represent the aqueous environment in which the fish reside during the entire phase of stream residency. Water chemistry is likely to be highly temporally and spatially variable as it is influenced by precipitation, atmospheric deposition of airborne particles, and anthropogenic influences (Campana, 1999). Fortunately, the integrated chemical signature of Chinook salmon juvenile otoliths was sufficiently strong to allow discrimination of natal origins at a scale relevant to management. Previously in Lake Huron, marking of Chinook salmon juveniles with adipose fin clips, coded wire tags (CWT), and oxytetracycline (OTC) dye has been done to estimate the proportion of hatchery fish in the lake wide population and their movement patterns (Adlerstein et al. 2007; Johnson et al. 2010). However these approaches cannot identify the natal origin of wild fish. In our study, we attempted to collect Chinook salmon from all possible source populations (except for shoal spawning populations in the North Channel). From this effort we now know that we can use otolith chemistry to predict the natal origin of wild-born adults, at least to geographic region (b150 km). In particular, our baseline data would provide the most accurate estimates of natal origin for

adults that were young-of-the-year (juveniles) in 2007 and 2008. To maintain the high accuracy of this technique beyond the year classes used in our study, future research is needed to address the interannual variability in otolith chemical signatures for wild-origin fish. Our results suggest that there is excellent potential for using otolith chemical analyses to address key questions about the ecology and management of Chinook salmon in the Great Lakes. For example, it should now be possible to estimate the relative recruitment of different regions to the Chinook salmon fishery across Lake Huron. Because these salmon move among basins and across international borders, our results should assist Canadian and U.S. management agencies in coordinating their efforts to manage Chinook salmon more effectively. Acknowledgments We thank Robert Keetch and Luke Hillyer from the Ontario Ministry of Natural Resources (OMNR), Roger Greil and LSSU Aquatic Research Laboratory student assistants for their work in finding collection sites and conducting electrofisher sampling, and Brock Taylor for his help in preparing the otolith samples for analysis. We also acknowledge the in kind and logistical support from the OMNR, Michigan DNRE, University of Western Ontario, University of Windsor, and Lake Superior State University. Financial support was provided by the Great Lakes Fishery Commission's Fishery Research Program to YEM, BJF, DG, and JJ. and the University of Western Ontario. Appendix A. Supplementary data Supplementary data to this article can be found online at doi:10. 1016/j.jglr.2011.08.004. References Adlerstein, S.A., Rutherford, E.S., Clapp, D., Clevenger, J.A., Johnson, J.E., 2007. Estimating seasonal movements of Chinook salmon in Lake Huron from efficiency analysis of coded wire tag recoveries in recreational fisheries. N. Am. J. Fish. Manage. 27, 792–803. Arai, T., Ohji, M., Hirata, T., 2007. Trace metal deposition in teleost fish otolith as an environmental indicator. Water Air Soil Pollut. 179, 255–263. Bacon, C.R., Weber, P.K., Larsen, K.A., Reisenbichler, R., Fitzpatrick, J.A., Wooden, J.L., 2004. Migration and rearing histories of Chinook salmon (Oncorhynchus

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