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Transactions of the American Fisheries Society 136:82–94, 2007 Ó Copyright by the American Fisheries Society 2007 DOI: 10.1577/T06-001.1

[Article]

Genetic and Ecological Divergence Defines Population Structure of Sockeye Salmon Populations Returning to Bristol Bay, Alaska, and Provides a Tool for Admixture Analysis CHRISTOPHER HABICHT,* LISA W. SEEB,

AND

JAMES E. SEEB

Alaska Department of Fish and Game, Gene Conservation Laboratory, 333 Raspberry Road, Anchorage, Alaska 99518-1599, USA Abstract.—We examined the population genetic diversity and structure of sockeye salmon Oncorhynchus nerka spawning in tributaries of Bristol Bay, Alaska, a region that supports the largest commercial fisheries for sockeye salmon in the world. Genetic variation among the sockeye salmon populations, as revealed by microsatellite data, was shallower than that found in other areas of comparable size around the Pacific Rim. This finding was driven by similarity among populations rearing in the four largest lake systems located on the southeastern side of the bay (upper and lower Ugashik, Becharof, Naknek–Grosvenor–Coville, and Iliamna lakes). Sockeye salmon in lakes located above known obstacles to migration on the southeastern side and in tributaries on the northwestern side showed variation and structure that were more typical of the species. Management of these important fisheries assumes knowledge of the composition of stock mixtures captured in each fishery. We investigated the potential of microsatellite data to provide stock composition estimates. We examined 58 collections and identified eight genetically discrete reporting groups. These reporting groups give fishery managers the opportunity to quantify stock components within fishing districts and thereby improve management precision.

lake was suggested by Burger et al. (1997) for outlet and inlet spawners and by Hendry et al. (2000) for tributary and beach spawners. Natural selection and genetic drift may produce differing gene frequencies between populations below and above obstacles to migration (Allendorf and Seeb 2000; Habicht et al. 2004; Ramstad et al. 2004), and bimodal runs composed of early and late spawners are often genetically differentiated (Wilmot and Burger 1985; Seeb et al. 2000; Fillatre et al. 2003; Ramstad et al. 2003). Precise homing and, thus, restricted gene flow (e.g., Stewart et al. 2003) led us to hypothesize that genetic differences would distinguish among spatial, ecological, and temporal isolates within the Bristol Bay drainages (Wood et al. 1989; Fillatre et al. 2003; Nelson et al. 2003). The Bristol Bay fishery is divided into districts that are designed to target populations destined for the major drainages (Figure 1). Management is escapement based and follows the maximum sustained yield (MSY) principle (Minard and Meacham 1985; Peterman et al. 2003); each district is opened and closed repeatedly during the season to harvest surplus production while meeting escapement goals set for the local drainage (Fair et al. 2004). One assumption used in both determining the MSY and assigning catch to drainages is that all the catch in each district is destined for the local drainage. However, this assumption may be violated. Tagging and scale pattern analysis suggests that district harvests are not all destined for the local drainages (Mathisen 1969; Straty 1975). Age compo-

Bristol Bay, Alaska, supports the largest commercial fishery for sockeye salmon Oncorhynchus nerka in the world; harvests range from 5 to 40 million fish annually (Fried and Yuen 1985; Hilborn et al. 2003). Key to the success of this sustainable fishery has been the conservation of sockeye salmon biodiversity (Hilborn et al. 2003). This biodiversity is derived from a wide variety of life history types and multiple distinct, locally adapted populations. Hilborn et al. (2003) concluded that the stability and sustainability of Bristol Bay sockeye salmon and the fisheries that rely on them have been greatly influenced by different populations performing well at different times over the last century. Sockeye salmon are anadromous, semelparous fish that typically spawn near (or sometimes in) nursery lakes, where progeny rear for 1–3 years. Juveniles then migrate to sea, where they undergo vast oceanic migrations to mature; adults ultimately return to their natal tributaries to spawn and die. Variation in allele frequencies observed among spawning aggregates from different nursery lakes is thought to reflect restricted gene flow (Varnavskaya et al. 1994; Wood 1995; Seeb et al. 2000). Variation within drainages has also been observed. The hypothesis that ecological isolation can lead to genetic subdivision of populations within a nursery * Corresponding author: [email protected] Received January 3, 2006; accepted September 16, 2006 Published online January 15, 2007

82

BRISTOL BAY SOCKEYE SALMON POPULATION STRUCTURE

83

FIGURE 1.—Spawning locations (sites) of adult sockeye salmon that were sampled to examine genetic relationships among populations spawning in drainages of Bristol Bay, Alaska. Major nursery lakes and drainages are labeled. Numbers correspond to site numbers listed in Table 1. The key denotes population assignments for each collection site based on hierarchical loglikelihood analyses.

sition analyses have been historically used to subdivide the catch into individual drainage components, but this method can lead to large biases (Bernard 1983; Bue et al. 1986; Flynn and Hilborn 2004). If large portions of the catch in each district are not destined to spawn in the local drainage, then the MSY model inputs will be flawed, resulting in unsustainable harvest of fish from some drainages or forgone harvest of surplus fish. In this paper, we (1) identify the life history traits that lead to genetic subdivision among sockeye salmon spawning aggregates and (2) use microsatellite data from spawners throughout Bristol Bay to examine the relative proportion of genetic variability accounted for within and among spawning aggregates. We assessed the structure among fish at collection sites to identify discrete spawning populations, which were combined based on life history and genetic similarity and geographic proximity to produce genetically distinct reporting groups (stocks) for mixed-stock analysis (MSA; Utter and Ryman 1993). The MSA was tested to determine the applicability of this method in estimating stock compositions of district catches. Previous genetic analyses of sockeye salmon in Bristol Bay have only been conducted over limited geographic areas (Habicht et al. 2004; Olsen et al. 2004; Ramstad et al. 2004). Here, we examine sockeye salmon systems

ranging from the Meshik River on the northern Alaska Peninsula to the Togiak River in northwestern Bristol Bay (Figure 1). Methods Adult sockeye salmon were sampled on spawning grounds in all major spawning areas in Bristol Bay from the Meshik River to the Togiak River (Table 1; Figure 1). Heart tissue, fin tissue, or both were collected and placed on wet ice or in ethanol. Within 12 h, samples on wet ice were frozen and stored at 808C. Samples in ethanol were decanted, resuspended in ethanol, and stored at room temperature. Genomic DNA was extracted using either the Puregene DNA isolation kit (Gentra Systems, Minneapolis, Minnesota) or a DNeasy 96 tissue kit (Qiagen, Valencia, California). To ensure complete ethanol evaporation, step 15 of the DNeasy extraction protocol was modified by incubating the 96-well plates at 568C overnight instead of at 708C for 15 min. Eight microsatellite loci were screened for variation (Table 2). We amplified DNA on a PTC225 Peltier thermocycler (MJ Research, Watertown, Massachusetts) using polymerase chain reaction (PCR) conditions modified from Olsen et al. (2000). Three multiplex panels were created. We amplified One102,

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TABLE 1.—Collection data for sockeye salmon sampled from spawning sites to examine genetic relations among populations spawning in Bristol Bay, Alaska, drainages. Obstacles to spawning migrations were (yes) or were not (no) present. Reporting group Drainage and collection site Meshik Landlock Creek Ugashik Ugashik outlet Ugashik narrows Ugashik Creek Egegik Becharof Creek Cabin Creek Naknek Headwaters Creek Up-a-tree Creek Margot Creek Idavain Creek Hardscrabble Creek Grosvenor, tributary American Creek Alagnak Kulilk River Moraine Creek Battle River Kvichak Iliamna River late run Flat Island beach Woody Island beach Triangle Island beach Finger Beach Knutson Bay beach Lower Talarik Creek Dennis Creek Gibraltar River Southeast Creek Dream Creek Nick N. Creek Copper River Tommy Creek Chinkelyes Creek Newhalen River Tazimina River Chulitna Bay beaches Kijik Lake beach Kijik River Little Kijik River Upper Tlikakila River Nushagak Mulchatna River Stuyakuk River Koktuli River King Salmon River Upper Nushagak River Nuyakuk River Allen River beach Tikchik River Nuyakuk Lake beaches Wood Bear Creek Agulowok River Lynx Lake tributary Agulukpak River Lake Kulik tributary

Collection date

Sample size

Obstacles

Site collection number

Nursery lakea

Populationb

Fine scalec

Broad scaled

Allele richnesse

1 Aug 2001

72

No

1

1

1

1

1

12.43

26 Aug 2000 24 Aug 2000 20 Jul 2001

100 100 96

No No No

2 3 4

2 2 2

2 2 2

2 2 2

1 1 1

11.29 11.20 11.09

11 Aug 2000 14 Aug 2000

100 100

No No

5 6

3 3

3 3

3 3

1 1

12.51 11.20

21 15 22 15 23 15 15 22 18

Jul 2001 Aug 2001 Aug 2000 Aug 2001 Aug 2000 Aug 2003 Aug 2003 Aug 2000 Aug 2001

36 100 100 100 100 96 96 100 99

Yes

7 8 9 10 11 12 13

4 4 4 5 5 5 5 5 5

4 4 4 5 5 5 5 5 5

2 2 2 1 1 1 1 1 1

8.49

Yes No No No No No No

4 4 4 5 5 6 6 6 6

5 Sep 2001 4 Sep 2001 4 Sep 2001

100 100 100

Yes Yes Yes

14 15 16

7 8 8

6 7 7

6 7 7

3 4 4

10.42 8.28 8.63

17 18 19 20 21 22 23

8 9 9 9 9 9 10 10 10 10 10 10 10 10 10 10 11 11 12 12 12 12 12

8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 9 9 10 10 10 10 10

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 5 5 5 5 5

9.57 11.29 11.34 10.77 11.06 11.03 10.36

9.33 10.99 11.41 11.09 11.80 10.91

17 23 19 26 24 25 26 23 23 25 26 22 25 28 24 22 3 29 5 4 19 19 24

Oct 1999 Aug 2000 Aug 2001 Aug 2000 Aug 2000 Aug 2000 Aug 2000 Aug 2001 Aug 2000 Aug 2000 Aug 2000 Aug 2001 Aug 2000 Aug 2000 Aug 2000 Aug 2000 Sep 2002 Aug 2001 Oct 1999 Oct 2000 Sep 2001 Sep 2001 Sep 2001

100 99 100 100 85 100 100 70 100 100 100 100 100 100 100 98 96 100 100 101 100 100 100

No No No No No No No No No No No No No No No No Yes Yes Yes Yes Yes Yes Yes

24 25 26 27 28 29 30 31 32 33 34 35 36 37 38

9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 10 10 11 11 11 11 11

27 14 13 18 19 19 17 21 16

Aug Aug Aug Aug Aug Aug Aug Aug Aug

2001 2000 2000 2001 2001 2000 2000 2001 2000

100 96 100 96 96 96 96 96 100

No No No No No No Yes Yes Yes

39 40 41 42 43 44 45 46 47

12 12 12 12 12 12 13 13 13

13 13 13 13 13 13 14 15 15

11 11 11 11 11 11 12 12 12

6 6 6 6 6 6 6 6 6

9.56 9.82 9.94 10.24 10.33 9.17 10.83 9.68 10.23

2 22 22 21 1

Aug Aug Aug Aug Aug

2001 2001 2001 2001 2001

100 96 96 96 100

No No No No No

48 49 50 51 52

14 14 14 14 14

16 16 16 16 16

13 13 13 13 13

7 7 7 7 7

10.09 9.39 10.56 10.28 9.54

10.13 10.95 11.07 10.46 10.46 10.46 10.65 10.26 10.83 10.40 8.27 8.40 8.74 8.29 9.30

85


TABLE 1.—Continued. Reporting group Drainage and collection site Igushik Francis Creek Middle Creek Togiak Gechiak Lake

Collection date

Sample size

Obstacles

Site collection number

Nursery lakea

Populationb

Fine scalec

Broad scaled

Allele richnesse

17 Aug 2003 17 Aug 2003

96 96

No No

53 54

15 15

17 17

14 14

7 7

9.32 8.89

21 Aug 2000

96

No

55

16

18

15

8

11.28

a

Nursery lakes: 1 ¼ Meshik River; 2 ¼ Ugashik lakes; 3 ¼ Becharof Lake; 4 ¼ Brooks Lake; 5 ¼ Naknek Lake; 6 ¼ Grosvenor–Coville lakes; 7 ¼ Nonvianuk Lake; 8 ¼ Kukaklek Lake; 9 ¼ Iliamna Lake; 10 ¼ Six-Mile Lake; 11 ¼ Lake Clark; 12 ¼ Nushagak River; 13 ¼ Tikchik lakes; 14 ¼ Wood River; 15 ¼ Igushik River; 16 ¼ Togiak River. b Populations: 1 ¼ Meshik River; 2 ¼ Ugashik lakes; 3 ¼ Becharof Lake; 4 ¼ Brooks Lake; 5 ¼ other Naknek lakes; 6 ¼ Nonvianuk Lake; 7 ¼ Kukaklek Lake; 8 ¼ Iliamna Lake (late); 9 ¼ Iliamna Lake beach; 10 ¼ Iliamna Lake tributary; 11 ¼ Six-Mile Lake; 12 ¼ Lake Clark; 13 ¼ Nushagak River; 14 ¼ Chauekuktuli Lake; 15 ¼ Tikchik-Nuyakuk lakes; 16 ¼ Wood River; 17 ¼ Igushik River; 18 ¼ Togiak River. c Fine-scale reporting groups for mixed-stock analysis (MSA): 1 ¼ Meshik River; 2 ¼ Ugashik lakes; 3 ¼ Becharof Lake; 4 ¼ Brooks Lake; 5 ¼ other Naknek lakes; 6 ¼ Nonvianuk Lake; 7 ¼ Kukaklek Lake; 8 ¼ Iliamna Lake; 9 ¼ Six-Mile Lake; 10 ¼ Lake Clark; 11 ¼ Nushagak River; 12 ¼ Tikchik lakes; 13 ¼ Wood River; 14 ¼ Igushik River; 15 ¼ Togiak River. d Broad-scale reporting groups for MSA: 1 ¼ southeastern Bristol Bay below obstacles to migration; 2 ¼ Brooks Lake; 3 ¼ Nonvianuk Lake; 4 ¼ Kukaklek Lake; 5 ¼ Lake Clark; 6 ¼ Nushagak–Tikchik River; 7 ¼ Wood–Igushik lakes; 8 ¼ Togiak River. e Average allele richness across all loci (standard sample size ¼ 55 fish).

One108, One109, and uSat60 using touchdown PCR, starting with a 618C annealing temperature for a cycle, decreasing by 18C for each subsequent cycle down to 568C; we then ran 25 cycles using a 568C annealing temperature. We amplified Omy77 and Ots107 with a similar touchdown protocol and used a range of 52– 478C and a final annealing temperature of 478C for 25 cycles. We similarly amplified One111 and Ots3 using a range of 56–518C and a final annealing temperature of 518C for 25 cycles. Microsatellites were size fractionated on acrylamide gel using a Prism 377 DNA sequencer (Applied Biosystems, Foster City, California); alleles for each locus were scored, and data were tabulated for importing into statistical software according to the methods of Olsen et al. (2004). Allele frequencies are available from the Alaska Department of Fish and Game (ADFG 2006). Loci were tested for deviations from Hardy–Weinberg (HW) expectations using GENEPOP (version 3.3,

updated version of Raymond and Rousset 1995). Critical values were adjusted for multiple tests (Rice 1989). For collections made at the same site at different times, we performed pairwise exact tests for genic differentiation (Goudet 1995), calculated in GENEPOP with Markov chain parameters (5,000 as the dememorization number, 1,000 batches, and 1,000 iterations/ batch). If the exact tests indicated homogeneity between a pair of collections, then we grouped collections in further analyses (collections grouped within sites or single collections taken at different sites will be referred to as ‘‘site collections’’). Cavalli-Sforza and Edwards (1967) genetic distances between all site collections were computed, and an unrooted neighbor-joining (N-J) tree was produced in PHYLIP (version 3.5c; updated version of Felsenstein 1989). Allele richness was calculated for all site collections at each locus using FSTAT (Goudet

TABLE 2.—Microsatellite loci data for sockeye salmon spawning in the drainages of Bristol Bay, Alaska, along with original references for the microsatellite loci. Genetic differentiation indices (FST) are shown. Observed heterozygosity

Locus

FST

Mean

Omy77 One102 One108 One109 One111 Ots107 Ots3 uSat60 Overall

0.050 0.017 0.016 0.021 0.025 0.023 0.058 0.028 0.026

0.58 0.84 0.88 0.86 0.87 0.28 0.29 0.52 0.64

Number of alleles

Range within collections

Total observed

Range within collections

After pooling

Allele size range (base pairs)

Reference

0.41–0.71 0.74–0.90 0.81–0.92 0.76–0.90 0.72–0.94 0.15–0.53 0.04–0.63 0.29–0.62

15 21 29 19 43 12 15 17

4–10 11–19 10–20 10–15 16–32 3–8 3–9 3–12

6 5 12 6 17 3 8 8

82–115 185–275 110–269 113–185 184–273 82–132 71–103 102–140

Morris et al. 1996 Olsen et al. 2000 Olsen et al. 2000 Olsen et al. 2000 Olsen et al. 2000 Nelson and Beacham 1999 Banks et al. 1999 Estoup et al. 1993

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1995), and an average was calculated for each site collection across loci. Site collections were combined into nursery lake collection aggregates based on their proximity to and suspected use of nursery lakes (i.e., site collections from tributaries to a lake, beach spawners within a lake, and spawners in the lake outlets). Lakes separated by peninsulas or by short, low-gradient streams or rivers were grouped together and treated as a single nursery lake (Table 1). Site collections within the Nushagak River drainage (below the Tikchik lakes), which contains no lakes (Figure 1), were combined into one collection aggregate. An analysis of molecular variance (AMOVA; Excoffier et al. 1992) was conducted using ARLEQUIN version 2.0 (Schneider et al. 2000) to examine the amount of variance accounted for by nursery lake and drainage for all site collections and for a subset of nursery lakes on the southeastern side of Bristol Bay that appeared clustered in the N-J tree. For each locus, we also used FSTAT to calculate the genetic differentiation index FST (Weir and Cockerham 1984) for site collections. Homogeneity of allelic frequencies among site collections was tested using log-likelihood ratios (modified from Weir 1990). The log-likelihood ratio is distributed approximately as a chi-square with (n 1)(m 1) degrees of freedom, where n is the number of alleles and m is the number of site collections in the test. The likelihood values can be summed over all loci to obtain a total value at each level of analysis. The total allele frequency dispersion at each locus was subdivided into within-drainage and among-drainage components in a hierarchical fashion. Hierarchical levels were organized to test for homogeneity (1) among sites within lakes, (2) among lakes within drainages, and (3) among drainages. Rejection of the null hypothesis of homogeneity indicates the presence of discrete spawning populations. These tests of allele frequency homogeneity are analogous to tests of the null hypothesis that the FST between collections is equal to zero (Chakraoborty and Leimar 1987). When significant within-lake variation was detected via the hierarchical likelihood test, we used loglikelihood tests to examine, when possible, the variation between site collections of early- and laterun fish and between site collections of beach and tributary spawners within lakes (i.e., ecotypes). These tests were performed for each variable while maintaining the other variable constant. For example, for the Iliamna Lake drainage, we tested for differences between early and late spawners within collections of tributary spawners (the only late collection available contained tributary spawners). This method allowed for

the results to be interpreted without confounding the variables. The Statistical Program to Analyze Mixtures (SPAM; version 3.7b; updated version of Debevec et al. 2000) and the Pella and Masuda (2001) pseudoBayesian method were used for data in which alleles were unpooled and pooled (OPTIBIN; Bromaghin and Crane 2005) to examine how the genetic relationships identified in the N-J tree translated to identifiable components in mixture samples. We used 100% simulated mixtures from the finest-scale geographic groupings visualized in the N-J tree. Based on the performance of these initial simulations, we pooled the finest-scale groupings that exhibited high levels of misallocation among them to produce reporting groups that had a minimum of 88% correct allocation to the region under study. Pooled allele frequencies are available from ADFG (2006). Results Throughout Bristol Bay drainages during 1999– 2003, 58 collections averaging 96 individuals were made at 55 locations (Table 1; Figure 1). Number of alleles observed at each of the eight loci ranged from 12 to 43, but within any collection we observed between 3 and 32 alleles (Table 2). Average allele richness for a standard sample size of 55 individuals varied from 8.27 to 12.51 (Table 1). Observed heterozygosity at each locus ranged from 0.28 to 0.88 and varied widely within loci among collections (e.g., at Ots3, observed heterozygosity ranged from 0.04 to 0.63; Table 2). No deviations from HW expectations were observed. No significant differences in allele frequencies were detected at the three sites where collections were made on different dates (Headwaters Creek, American River, and Lower Talarik Creek). These paired collections were pooled for the remaining analyses, resulting in 55 site collections having a minimum sample size of 72 fish and an average sample size of 102 fish. The FST values ranged from 0.016 to 0.058 among loci; overall FST was 0.026 for site collections. The N-J tree in Figure 2 shows a strong southeast– northwest segregation and high divergence of site collections from some drainages. In southeastern Bristol Bay, site collections from each drainage generally clustered together, but the relationships were weak (low bootstrap values). With the exception of Knudsen Bay Beach spawners, there was a general but weak segregation between beach spawners and tributary spawners. These Iliamna Lake site collections clustered most closely with site collections from the Naknek River (excluding Brooks Lake). The next most similar site collections were from the Egegik, Ugashik,


87

FIGURE 2.—Unrooted neighbor-joining tree (Cavalli-Sforza and Edwards 1967) based on eight microsatellite loci assayed from sockeye salmon captured at 55 locations (sites) within tributaries draining into Bristol Bay, Alaska. Population symbols match those in Figure 1. The smaller numbers correspond to site numbers listed in Table 1 and depicted in Figure 1, whereas the larger numbers denote the broad-scale reporting groups listed in Table 1.

and Meshik rivers. All of these site collections were made on the southeastern side of Bristol Bay and all were situated below known obstacles to migration. This general pattern was correlated with geographic proximity. Site collections from Six-Mile Lake were the next most similar. Cumulatively, these collections will be referred to hereafter as ‘‘southeast below obstacles’’ (SBO). The remaining site collections from southeastern Bristol Bay were more divergent, but they all clustered by lake (Nonvianuk, Kukaklek, and Brooks lakes and Lake Clark). Northwestern Bristol Bay site collections were also generally clustered by lake or river. Site collections from the Nushagak River downstream of the rapids in

the Nuyakuk River formed a single branch. Site collections from the Wood River were also on a single branch that was most similar to the branch with the Igushik River site collections. Site collections made in the Tikchik lakes (upstream of the Nuyakuk River rapids) were the most heterogeneous; Nuyakuk Lake beaches and Allen River beach were on one branch, while the Tikchik River formed a separate branch. Gene diversity analysis indicated that 97.2% of the variation was accounted for within site collections (Table 3). Of the remaining variation, 86% was attributed to nursery lake. We also conducted the analysis with only the SBO site collections. The results indicated that 99.4% of the variation was accounted for

88

HABICHT ET AL.

TABLE 3.—Analysis of molecular variance used to examine genetic relations among sockeye salmon spawning in Bristol Bay, Alaska, as applied to site collections partitioned by nursery lake (as designated in Figure 1 and Table 1) (*P , 0.001). Source of variation Among nursery lakes Among site collections within nursery lakes Within site collections Total

Sum of squares

df

Variance components

Percentage of variation

15

657.19

0.0615

2.40*

38 10,902 10,955

169.38 27,136.61 27,963.19

0.0097 2.4891 2.5604

0.38* 97.22

within site collections (Table 4). Of the remainder, only 50% was accounted for by nursery lake. In both analyses, the amount of variation among site collections and within nursery lakes was similar (0.28– 0.38%). Hierarchical log-likelihood analysis collapsed the 55 site collections into 18 populations distinguished by drainage and nursery lake and sometimes by ecotype and run timing (Table 1). Mixture Analyses We conducted simulation studies using populations or groups of populations that clustered in the N-J tree and that were geographically proximate (reporting groups; Table 1) to evaluate the management use of the observed genetic structure. Consistent with Bromaghin and Crane (2005), our simulations using pooled alleles resulted in a higher rate of correct allocation than those with unpooled alleles. Therefore, we present results based on pooled alleles. When we evaluated the reporting groups defined by the major branches (large-scale reporting groups; Table 1), the simulations yielded high rates of correct allocation (88–96%; Table 5). As expected, simulations conducted on fine-scale reporting groups (Table 1) showed higher levels of misallocation (Table 6). In particular, the misallocation rate among populations within the SBO group was high relative to that of other drainages separated by marine habitat (Table 6). Among the fine-scale reporting groups, misallocations were mostly to reporting groups within the broad-scale reporting groups, which explains the better performance of the MSA simulations for the broad-scale groups.

Discussion In this paper, we have presented a comprehensive analysis of the population structure of sockeye salmon inhabiting Bristol Bay, the source of the largest commercial fishery for sockeye salmon in the world. We examined populations ranging from the Meshik River system on the northern Alaska Peninsula to the Togiak River in northwestern Bristol Bay and found significant variation over all lakes and within all drainages except for the Ugashik, Egegik, Wood, and Igushik rivers. We found that genetic structure among sockeye salmon stocks from the large, southeastern Bristol Bay lakes was shallow, whereas structure among stocks above obstacles to migration and among stocks from northwestern Bristol Bay was more typical of the species. Variation within and among Site Collections The level of separation defined by the term ‘‘population’’ is not homogeneous throughout the literature (Waples and Gaggiotti 2006). Here, we used log-likelihood tests to define populations. This analysis is a conservative test because the degrees of freedom reflect the entire pattern of diversity around Bristol Bay. As a result, our site collections are analogous to ‘‘populations’’ as defined in most other studies. The observed proportion of variation attributed to differences among site collections (2.8%; Table 3) is lower than that previously described for sockeye salmon over similar geographic areas (Table 7). For example, in Cook Inlet, Alaska, Seeb et al. (2000) attributed 11.2% of the variation to differences among

TABLE 4.—Analysis of molecular variance used to examine genetic relations among sockeye salmon spawning in Bristol Bay, Alaska, as applied to site collections made below obstacles to migration on the southeastern side of the bay. Collections are partitioned by nursery lake (*P , 0.001). Source of variation Among nursery lakes Among site collections within nursery lakes Within site collections Total

Sum of squares

df

Variance components

Percentage of variation

5

38.49

0.0063

0.28*

20 5,370 5,395

69.63 11,871.27 11,979.39

0.0061 2.2107 2.2230

0.28* 99.44

89


TABLE 5.—Reporting group (broad-scale) assignment percentages for simulations using mixed-stock analyses for adult sockeye salmon from Bristol Bay, Alaska. Analyses were estimated with SPAM 3.7b (updated version of Debevec et al. 2000) using simulated mixtures composed of 100% mixtures of 400 fish from reporting groups. Reporting groups were produced by combining fine-scale groups, based on misallocations in Table 6, until a minimum of 88% correct allocation was obtained. Some reporting groups represent individual nursery lakes; others combine multiple drainages. Numbers in the column headings correspond with group numbers in the first column. Standard deviations are in parentheses. Site collections making up each reporting group are described in Table 1. Source reporting group Allocated reporting group 1. 2. 3. 4. 5. 6. 7. 8.

1

East below obstacles Brooks Lake Nonvianuk Lake Kukaklek Lake Lake Clark Nushagak–Tikchik River Wood–Igushik River Togiak River

95 0 0 1 1 2 1 0

2

(2) (0) (1) (1) (1) (1) (1) (0)

4 94 0 0 0 1 1 0

3

(2) (2) (0) (0) (0) (1) (1) (0)

9 0 89 0 0 1 0 0

4

(3) (0) (3) (0) (1) (1) (0) (0)

site collections. In northern British Columbia, Wood et al. (1994) attributed 8.0% of the variation within major drainages to differences among site collections. Within southern British Columbia, the estimated amount of variation accounted for by differences among site collections within years was 4.7% (Withler et al. 2000) to 6.3% (Nelson et al. 2003). Although the variation accounted for by differences among site collections was smaller in Bristol Bay than elsewhere, the amount of variation partitioned among lakes versus within lakes (86%) was similar to values previously reported (80–100%; Wood et al. 1994; Seeb et al. 2000; Withler et al. 2000; Nelson et al. 2003). The N-J tree also supported clustering of site collections by lake, and the hierarchical log-likelihood

7 0 0 92 1 1 0 0

5

(2) (0) (0) (2) (1) (1) (0) (0)

3 0 0 0 96 0 0 0

6

(1) (0) (0) (0) (2) (1) (0) (0)

5 0 0 0 0 88 6 0

7

(2) (0) (0) (0) (1) (4) (3) (0)

2 0 0 0 0 6 91 0

8

(1) (0) (0) (0) (1) (3) (3) (0)

2 0 0 0 1 1 2 92

(1) (1) (0) (0) (1) (1) (1) (2)

analysis indicated that the variation among drainages is significant. A dichotomy in the partitioning of genetic variation among lakes and drainages seems to be correlated with the nature of the spatial separation. Consistent with expectations, we found that variation among site collections between lakes was small when the separation between lakes consisted of short, low-gradient streams (i.e., Ugashik, Wood, and Igushik River drainages). However, we also found shallow genetic structure among site collections between lakes on the southeastern side of Bristol Bay, below obstacles to migration (SBO site collections). The SBO site collections clustered tightly in the N-J tree and followed geographic proximity (i.e., site collections forming the Naknek River and Iliamna Lake branches

TABLE 6.—Spawning group (fine-scale) assignment percentages for simulations using mixed-stock analyses for adult sockeye salmon from Bristol Bay, Alaska. Analyses used simulated mixtures composed of 100% mixtures of 400 fish from the fine-scale geographic groups visualized in the neighboring-joining tree (Figure 2). These groups represent individual populations. Numbers in the column headings correspond to numbers in the first column. Results from each simulation are presented in each column; SDs are in parentheses. Site collections making up each fine-scale group are described in Table 1. Source fine-scale group Allocated fine-scale group 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15.

Meshik River Ugashik lakes Becharof Lake Other Naknek lakes Brooks Lake Nonvianuk Lake Kukaklek Lake Iliamna Lake Six-Mile Lake Lake Clark Nushagak River Tikchik lakes Wood River Igushik River Togiak River

1 64 8 4 11 1 0 0 5 1 0 2 2 2 0 0

2

3

4

5

6

7

8

9

10

11

12

13

14

15

(6) 2 (2) 1 (1) 2 (2) 0 (1) 0 (0) 0 (0) 0 (1) 0 (0) 0 (0) 0 (0) 1 (1) 0 (1) 0 (0) 0 (4) 76 (5) 10 (5) 2 (2) 1 (1) 1 (1) 1 (1) 1 (1) 0 (1) 0 (0) 0 (1) 1 (1) 0 (0) 1 (1) 0 (3) 8 (4) 71 (6) 1 (1) 0 (0) 1 (2) 0 (1) 2 (2) 0 (1) 0 (0) 0 (0) 0 (1) 0 (0) 0 (0) 0 (5) 4 (3) 3 (2) 75 (5) 1 (1) 1 (1) 1 (1) 5 (3) 2 (1) 0 (0) 1 (1) 1 (1) 0 (1) 1 (1) 0 (1) 0 (0) 0 (0) 0 (1) 94 (2) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 0 (1) 1 (1) 0 (1) 0 (0) 89 (3) 0 (0) 0 (1) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 0 (1) 1 (1) 0 (1) 1 (1) 0 (0) 0 (0) 92 (2) 1 (1) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 0 (3) 6 (3) 12 (4) 15 (5) 1 (1) 5 (3) 4 (2) 86 (4) 8 (3) 2 (1) 3 (2) 2 (1) 1 (1) 1 (1) 1 (1) 0 (1) 0 (1) 1 (1) 0 (1) 0 (1) 0 (0) 1 (1) 86 (4) 0 (1) 0 (0) 0 (0) 0 (0) 0 (0) 0 (1) 1 (1) 0 (1) 1 (1) 0 (0) 0 (1) 1 (1) 1 (1) 2 (1) 96 (2) 0 (1) 0 (0) 0 (0) 1 (1) 1 (2) 1 (1) 0 (1) 1 (1) 1 (1) 0 (1) 0 (1) 1 (1) 0 (0) 0 (0) 87 (3) 6 (3) 3 (2) 4 (3) 1 (2) 1 (1) 1 (1) 1 (1) 0 (1) 0 (0) 0 (0) 0 (1) 0 (0) 0 (0) 2 (2) 83 (4) 2 (2) 2 (2) 1 (2) 0 (1) 0 (1) 0 (1) 1 (1) 0 (0) 0 (0) 0 (0) 1 (1) 0 (0) 3 (2) 4 (2) 88 (4) 9 (4) 1 (1) 0 (0) 0 (0) 0 (1) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 2 (2) 2 (2) 4 (3) 80 (4) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 92

(0) (0) (1) (0) (1) (0) (0) (1) (0) (1) (1) (1) (1) (1) (2)

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TABLE 7.—Percentage of the total genetic variation in adult sockeye salmon accounted for by differences between site collections within and among nursery lakes in drainages of Bristol Bay, Alaska, as compared with Cook Inlet, Alaska, and British Columbia. Nursery lake variation

Region Bristol Bay (all lakes) Bristol Bay (SBOa only) Cook Inlet, Alaska Northern British Columbia Southern British Columbia Fraser River, British Columbia a

Within lakes

Among lakes

Reference

0.4 0.3 0.4 1.0 0.0

2.4 0.3 11.2 8.0 6.3

This study This study Seeb et al. 2000 Wood et al. 1994 Nelson et al. 2003

0.7

4.7

Withler et al. 2000

SBO ¼ southeastern sites below obstacles to spawning migration.

were most closely related, followed by more geographically distant site collections forming the Meshik, Ugashik, and Egegik rivers; Figure 2). In addition, the AMOVA indicated that lakes only accounted for 50% (0.28/0.56) of the variation among site collections in this group, compared with 86% (2.40/2.78) for the total (Table 4). In contrast, for lakes separated by highgradient waterways, the collections followed a more typical pattern of genetic structuring for sockeye salmon populations. This more typical pattern is characterized by relatively little variation among site collections within lakes and relatively high variation among site collections from different lakes. This high variation shows little correlation with the geographic proximity of the lakes. Evolution of Population Structure in Southeastern Sites below Obstacles Is the structure of sockeye salmon populations from the SBO areas of Bristol Bay uniquely shallow for the species? The SBO populations are located in multiple drainages separated by marine habitat. Sockeye salmon populations separated by marine habitat in other regions are highly differentiated (Nelson et al. 1998, 2003; Seeb et al. 2000; Table 7). For example, Seeb et al. (2000) found significant divergence among all major drainages of Cook Inlet, and Nelson et al. (2003) found the same along the central coast of British Columbia. The high fidelity of spawning sockeye salmon to their natal streams and the concomitant low straying rate are thought to be associated with adaptations to variables associated with nursery lakes and spawning habitats. Numerous studies have suggested that the nursery lake is the primary unit of genetic structuring (Wood et al. 1994; Seeb et al. 2000; Utter 2004) and that sockeye salmon tend to home with greater fidelity than other Pacific salmon (Quinn et al. 1999).

For the SBO site collections, the pattern of genetic structure evident in the N-J tree, AMOVA, and hierarchical log-likelihood analysis may be due to higher-than-typical straying rates among these populations or to shorter divergence time. All SBO sites except the Meshik River (where no nursery lake exists) have large nursery lakes, are approximately the same short distance from the ocean, and have similar elevations (4– 15 m above sea level). Therefore, these lakes probably have geophysically similar spawning and rearing habitats. As a result, selection pressures for precise homing may be relaxed, resulting in higher fitness for straying sockeye salmon among these drainages than is found elsewhere in the species’ natural range. The genetic relationships evident in the N-J tree appear to correlate with geographic proximity (Figure 2), suggesting gene flow among proximate drainages. Shorter divergence time (and therefore less genetic drift) among the SBO site collections also provided another plausible hypothesis for the lack of genetic diversity among them. All the SBO lake areas were covered by glaciers as recently as 14,000 years ago (Stilwell 1995), whereas the Nushagak River drainage was largely ice free more the 27,000 years ago (Coulter et al. 1965). This situation is similar to the glacial pattern in Cook Inlet between the Kenai Peninsula lakes (which were ice covered 10,000–14,000 years ago) and the Susitna Valley (which was ice free 27,000 years ago; Coulter et al. 1965; Reger and Pinney 1996). In Cook Inlet, deeper genetic structure in sockeye salmon was also observed in the Susitna Valley (Seeb et al. 2000). Diversity between Beach and Tributary Spawners Life history and morphological differences between beach and tributary spawners have been well documented within the Iliamna Lake drainage (Blair et al. 1993; Quinn et al. 2001; Gende et al. 2004). These include differences in spawn timing, adult size, age structure, hump size and shape, egg size, and substrate selection. Sexual selection, predation, and incubation conditions are thought to drive this divergence. Otolith microstructure analyses indicated that these differences are also maintained through precise homing fidelity to natal incubation sites (Quinn et al. 1999). Genetic differentiation between beach and tributary spawners has also been reported for sockeye salmon in Lake Washington (Washington State; Hendry et al. 2000), Meziadin Lake (British Columbia; Beacham et al. 2005a), and Kuril Lake (Russia; Beacham et al. 2006). From the hierarchical log-likelihood tests we found significant variation within two lake groupings: Iliamna Lake and the Tikchik lakes. Both tributary and beach spawners were sampled within these lakes, and both


early and late runs were sampled within Iliamna Lake. Genetic differentiation among temporally segregated spawning aggregates has been well established in sockeye salmon from other drainages (Wilmot and Burger 1985; Seeb et al. 2000; Fillatre et al. 2003; Ramstad et al. 2003), so timing must be accounted for when investigating differences between tributary and beach spawners. Within the Iliamna Lake drainage, we found significant variation between site collections of early and late runs when ecotype was held constant (i.e., tributary collections only). When run timing was held constant (i.e., early spawners only), we found significant divergence between beach and tributary spawners. The Tikchik lakes system analysis is also complicated by two variables: ecotype and the presence of two different lakes. In this system, one of the beach spawning sites and the one tributary site drain into the same lake complex: the Tikchik and Nuyakuk lakes are separated by a peninsula and are biologically a single lake. The other beach site (Allen River beach) drains into Chauekaktuli Lake, which is separated from the Tikchik–Nuyakuk lakes by a 2-km, low-gradient river and which can be considered a biologically separate lake. The test for differences between site collections from the two lakes (when holding ecotype constant) was significant. However, the test for differences between site collections of beach and tributary spawners within the Tikchik–Nuyakuk lakes were not significant. Therefore, the variation we detected in the hierarchical analysis was due to differences between lakes rather than ecotypes. Finally, comparison between beach and tributary spawners was also possible in the Lake Clark drainage. All collections from the Lake Clark drainage clustered on a single branch (Figure 2), and no significant variation was detected within Lake Clark in the hierarchical log-likelihood tests. The N-J tree also showed no apparent segregation by ecotype within the Lake Clark site collections (Figure 2). These results agreed with the more comprehensive study of Lake Clark site collections by Ramstad et al. (2004), who found no evidence of isolation by ecotype. Differences in age of colonization may provide an explanation for the differences observed between beach and tributary spawners in the Iliamna drainage but not in Lake Clark or the Tikchik–Nuyakuk lakes. Recent evidence suggests that Lake Clark populations were founded much more recently than Iliamna Lake populations (Habicht et al. 2004; Ramstad et al. 2004). These more recently founded populations have had less time for genetic drift to act on neutral loci. Little work has been done to document founding events in the Tikchik–Nuyakuk lakes region, but the relatively

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low average allele richness detected in Tikchik River collection (Table 1) suggests more recent colonization or bottleneck events (e.g., Garza and Williamson 2001; Spong and Hellborg 2002). Management Applications Tagging and scale pattern analyses suggest that sockeye salmon captured in district harvests are not all destined for the local drainages. For example, some sockeye salmon tagged upstream of the Egegik management district were subsequently harvested in the Kvichak and Ugashik districts (Mathisen 1969). In a more comprehensive tagging study, Straty (1975) found that often 10%, and as many as 20%, of fish captured and tagged in Bristol Bay districts were either recaptured in other districts or observed as escapements in drainages outside of the district in which the fish were tagged. Likewise, scale pattern analysis indicates that more than 30% of fish are destined to spawn in drainages other than the district of capture (Bue et al. 1986; Menard and Miller 1997). Use of the MSY principle in the management of fisheries requires an understanding of the relationship between escapement and subsequent recruitment for each drainage. Escapement is estimated via counts (from sonar or by observers in riverside towers) of fish passing into a major drainage (Minard and Meacham 1985). Recruitment is more complicated to estimate because it requires both estimates of subsequent escapement and harvest. Most sockeye salmon in Bristol Bay spawn at age 4, 5, or 6. Subsequent escapement is estimated by partitioning escapement into cohorts via age composition analysis from scale samples. Harvest is estimated by using age composition analysis to partition harvest into cohorts, assuming that all catch in each district is destined for the local drainage(s). Fair (2003) provided a detailed explanation of the use of escapement and recruitment for the setting of escapement goals. The resolution offered by genetic MSA for identifying stock component proportions in mixed-stock samples from Bristol Bay far exceeds that of any previous stock identification technique, including scale pattern or age composition data (Bue et al. 1986; Menard and Miller 1997). Satisfying management needs with MSA requires examining, in concert, the genetic structure among the populations, the ability of the markers to distinguish among groups within this structure, and management needs. We found structure evident at the drainage level, in some cases down to a lake level, and in one case down to the spawning substrate (beach or tributary spawner) level. This structure has the potential to identify components of a mixed-stock fishery; indeed, in 100% of simulations,

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the eight microsatellite markers presented here provided greater than 88% correct allocations to the larger reporting groups. Our results are based on variation observed at microsatellite loci. These loci are assumed to behave in a neutral manner. The addition of more neutral loci and incorporation of selected loci into the model may provide the power to resolve fine-scale groups in reporting regions. Variation between populations within lakes may be lower for neutral loci, such as microsatellites, than for selected loci (Ford et al. 1999; Ford 2000, 2001; Stewart et al. 2003). We are currently investigating loci based on single nucleotide polymorphisms (Elfstrom et al. 2006) that probably reflect both adaptive and neutral variation. In particular, we are focusing on loci with potential adaptive variation, including the major histocompatibility complex (Miller et al. 2001; Beacham et al. 2005b). We anticipate that future applications will include markers reflecting both neutral and adaptive variation. Based on data presented here and additional data currently under collection by ADFG, we anticipate that MSAs will provide very fine-scale information for managers. Pilot studies are currently underway to provide MSA on an in-season basis (24–48-h turnaround). We expect that genetic MSA will eventually become the preferred method of stock identification in Bristol Bay. Acknowledgments Key expertise on sockeye salmon spawning distributions was provided by Steve Morstad, Keith Weiland, and Dick Russell. We thank Lowell Fair, Jeff Regnart, and Brian Bue for providing a fishery management context for this study and for embracing this methodology as a management tool. This work would not have been possible without the support and effort of Tom Quinn, Ray Hilborn, Daniel Schindler, and many students from the University of Washington, who provided samples in the Iliamna Lake and Wood River drainages. We thank Carol Ann Woody and Kristina Ramstad for sharing collections from the Lake Clark drainage and the many members of the Gene Conservation Laboratory (ADFG) for collecting samples elsewhere in Bristol Bay. Substantial laboratory efforts of Jeff Olsen, Katia Pronzati, Sheri Wilson, and other members of this laboratory provided the more than 44,000 genotypes reported in this study. This manuscript was improved through reviews by Christian Smith and three other reviewers. This project was funded by grant NA96FW0196 from the National Marine Fisheries Service and grants R0205 and R0305 from the North Pacific Research Board.

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