GENETIC MANAGEMENT PLAN FOR CALIFORNIA GOLDEN TROUT

2013 GENETIC MANAGEMENT PLAN FOR CALIFORNIA GOLDEN TROUT Prepared by: Molly Stephens, Bjorn Erickson, Andrea Schreier, Kat Tomalty, Melinda Baerwald, Bernie May, and Mariah Meek Genomic Variation Lab, Dept. of Animal Science, University of California, Davis

Photos: 1. California golden trout at Templeton Barrier, South Fork Kern River 2. South Fork Kern River at Templeton Meadow

Table of Contents Section 1. Introduction... 4 Section 1.1 Document objectives/executive summary... 4 Section 1.2 California golden trout background... 4 Distribution... 4 Species status... 5 Taxonomic overview... 5 Life history... 6 Biotic and abiotic threats... 6 History of species management... 7 Section 2. CAGT population genetic structure and diversity... 8 Section 2.1 Population genetic differentiation... 8 Section 2.2 Extent of introgression within native range... 9 Section 2.3 Genetic diversity of extant (non-introgressed) populations... 9 Section 3. Defining units of conservation for CAGT management...10 Section 4. Potential strategies for conserving CAGT genetic diversity...12 Section 4.1 Status quo...12 Section 4.2 Conservation of genetic diversity through refuge populations...12 Section 4.3 Conserving genetic diversity through creation of conservation hatchery (broodstock) population(s)...13 Section 4.4 Direct translocation of individuals (without hatchery broodstock)...15 Section 5. Genetic monitoring needs...17 Section 5.1 Monitoring CAGT genetic diversity...17 Section 5.2 Monitoring under supplementation and non-supplementation scenarios...17 Section 6. Adaptive management...18 Section 7. Recommendations...18 Section 7.1 Identification and prioritization of populations for conservation...18 Conservation of existing populations in their native range...18 Conservation of existing populations outside of their native range...18 Section 7.2 Conservation of genetic diversity through the creation of conservation hatchery (broodstock) populations...19 Section 7.3 Conservation of genetic diversity through creation of refuge populations...20 Section 7.4 Genetic monitoring recommendations...20 Section 7.5 Adaptive management recommendations...20 Works Cited...22 Tables and Figures...27 Appendix 1...33

Section 1. Introduction Section 1.1 Document objectives/executive summary California golden trout have been the subject of genetic examination for over four decades. This genetic management plan was prompted by the need for a review and synthesis of the most recent genetic studies and an examination of implications for species status and management. The California golden trout Conservation Assessment and Strategy (CDFG and USFS 2004) calls for genetic information to aid in management and decision-making for the species. This document is intended to provide a framework for evaluating California golden trout conservation options from a genetic perspective. More thorough reviews of the biology, ecology, and history of California golden trout exist elsewhere, and are cited throughout. While this document focuses on genetic factors, we recognize that genetic considerations alone are insufficient to govern management actions and must be considered alongside other variables, and in an adaptive management framework, in order for species conservation goals to be met. We assume a general understanding of basic biological and genetic concepts; the reader is referred to other sources for a general overview of genetic concepts and techniques referenced in the text. Section 1 provides relevant background for golden trout and a brief genetic background. Section 2 reviews what is known about California golden trout genetics specifically, based on the most recent decade of genetic research. Section 3 reviews several potential strategies for conserving genetic diversity and integrity of the species. Section 4 discusses potential strategies for conserving California golden trout genetic diversity. Section 5 outlines genetic monitoring needs, while Section 6 describes adaptive management needs. Section 7 closes with recommendations for conservation based on the currently available genetic data. Section 1.2 California golden trout background Distribution California golden trout (Oncorhynchus mykiss aguabonita, hereafter CAGT) are endemic to the drainages and tributaries of the South Fork Kern River (SFK) and Golden Trout Creek (GTC) in the Upper Kern River Basin of the southern Sierra Nevada of California (Figure 1). The majority of the species native range is located on U.S. Forest Service (USFS) land, primarily in designated Golden Trout Wilderness Area, with a small portion located on land in Sequoia National Park. The CAGT has been transplanted extensively since the late 1800 s, with individuals being moved within the CAGT native range (e.g., transplanted to fishless Mulkey Creek on the SFK), into other waters of the Kern Basin (prevalent in tributaries to the mainstem Kern River), throughout high-elevation lakes in the Sierra Nevada region (e.g. Ash Meadow and Diaz Creeks, in the Owens River drainage), out of state (e.g. Wyoming and Utah), and internationally (Pister 2008). It is worth highlighting the controversy surrounding the origins of the fish transplanted into Mulkey Creek, from which the Cottonwood Lakes population was derived. Support exists for Mulkey Creek s origins in either Golden Trout Creek (Vore 1928, Ober 1935) or South Fork Kern River (Evermann 1906, Anonymous 1913, Curtis 1934). Although Behnke (Behnke 1992a) supported a Golden Trout Creek origin, genetic studies showed a contemporary Mulkey Creek sample has a greater affinity with South Fork Kern River populations (Cordes et al. 2003). The Cottonwood Lakes transplanted population is also noteworthy not only for its history of being established from 50 fish transferred from Upper Mulkey Creek, but also for its use as a broodstock source for rearing California golden trout at Page 4

Hot Creek Hatchery. Hatchery records document the early practice of returning some hatcheryreared fish, which had the potential to be introgressed with rainbow trout, back to Cottonwood Lakes (described in CDFG and USFS 2004, pp 14-15). All transplants that occurred after the 1930 s are generally thought to have been derived from Cottonwood Lakes stock that had previously become introgressed with non-native rainbow trout (Leary 1995, Pister 2010). Precise transplant history for many localities is poorly understood, where many transplants by private individuals and management agencies have gone undocumented. Ellis (1920) and CDFG and USFWS (2004) provide some of the most complete accounts of CAGT transplanting. Species status The CAGT is a California Department of Fish and Wildlife (CDFW) Species of Special Concern, a USFS Sensitive Species, is considered threatened by the most recent American Fisheries Society Endangered Species Committee s compilation of imperiled and extinct species (Jelks et al. 2008), and is considered with high certainty to be in danger of extinction within 50-100 years according to the species status index of Moyle et al. (2008). The species was petitioned for emergency listing as endangered under the federal Endangered Species Act by Trout Unlimited on October 23, 2000. A 90-day finding issued by the US Fish and Wildlife Service (USFWS) found substantial evidence that listing may be warranted, but declined an emergency listing at that time and initiated a status review (Federal Register 2002). The USFWS determined in 2011 that listing was not warranted (Federal Register 2011), mainly due to conservation measures being taken to protect the species since the petition. The CAGT has been the focus of a multi-agency cooperative conservation agreement, signed in draft form in 2004, in an effort to prevent further species loss and improve CAGT status within its native range. Document signatories include the USFWS, USFS, and CDFW; the parties review all agency, NGO, and academic efforts to aid in habitat and population improvement for the species and revise the draft Conservation Agreement annually. Taxonomic overview Given that reclassification of members of the species mykiss from the Atlantic genus Salmo to the Pacific Oncorhynchus occurred relatively recently (Stearley and Smith 1993), it is perhaps not surprising that division below the species level has been challenging. The complexity of the relatively recently evolved subspecies of O. mykiss in California is revealed by the many changes in taxonomy over the past century since their first description by David Starr Jordan in 1882. The general forms of native Kern Basin trout that we recognize today, however, were first identified by Evermann in 1904, with their taxonomic rank changing alongside our changing understanding of rainbow trout diversity. Evidence from comprehensive morphological analysis of inland rainbow trout (Behnke 1992) supports the existence of California (aka Volcano Creek ) golden trout as a distinct subspecies (O. m. aguabonita). Genetic studies using mitochondrial DNA (mtdna) and nuclear DNA (ndna) markers (Bagley and Gall 1998), and amplified fragment length polymorphisms (AFLPs; Stephens 2007, Chapter 1) also supported the distinctive nature of CAGT relative to both hatchery and wild rainbow trout, as well as other native rainbow trout forms including Little Kern golden trout (O. m. whitei) and Kern River rainbow trout (O. m. gilberti). Evermann (1906) also noted that the CAGT forms in SFK and GTC appeared to be distinct from one another, describing them as Salmo aguabonita and Salmo roosevelti, respectively. Other studies have confirmed the distinctiveness of the SFK and GTC forms morphologically (Behnke 1992b) and genetically (Bagley and Gall 1998, Stephens 2007). The history of hybridization between introduced rainbow trout forms and CAGT (discussed as a threat in Biotic and abiotic threats below) has raised questions about the cause of these morphological and genetic differences. They could be the result of natural evolutionary divergence following Page 5

the geological activity that separated the two rivers near Tunnel Meadow (Cordes et al. 2006). Alternatively, the distinctiveness could be the result of anthropogenically induced introgression and restoration efforts in the SFK (reviewed in Pister 2008). For management purposes, Behnke (1992b), the Conservation Strategy (CDFG and USFS 2004), and genetic analyses to date (summarized in Appendix 1, Table 5) recommend differentiating them as separate management units (MUs): O. mykiss aguabonita (SFK) and O. mykiss aguabonita (GTC) unless future research otherwise refutes this assumption. Lack of sufficient numbers of historical samples from the SFK in particular limits the use of historical samples in resolving this question of the original phylogenetic relationships between GTC and SFK. Life history The lifespan of CAGT averages six to seven years, but they have been known to live as long as nine years (Knapp and Dudley 1990). This relatively long life span is accompanied by generally slow growth, which is negatively correlated with fish density (Knapp and Dudley 1990). CAGT reach sexual maturity after three years, and begin spawning in the spring, when stream temperatures consistently exceed 15 to 18 C (Stefferud 1993, Knapp and Vredenburg 1996). Knapp and Vredenburg (1996) found an average of two egg pockets per redd, with an average of 22 eggs per pocket. Stream characteristics important for CAGT growth and survival include bank stability and the availability of aquatic and riparian vegetation (Knapp and Dudley 1990). This vegetation is thought to provide habitat for the invertebrate fauna on which CAGT feeds, to provide cover from possible predators, and to offer cool water refuge during warmer parts of the day (Moyle 2002). CAGT are active both day and night, and maintain relatively limited home ranges (18 69 m), with most individuals traveling less than 10 m during a 10-day tracking study (Matthews 1996). Biotic and abiotic threats There are a number of threats that have impacted CAGT within its native range, many of which have been the target of previous and ongoing management. A more complete review of these threats can be found in Federal Register (2011), but the three primary threats are habitat disturbance caused by grazing, predation by and competition with non-native salmonids (particularly brown trout), and hybridization with introduced hatchery rainbow trout. A long history of grazing by livestock has affected much of CAGT native habitat (Knapp et al. 1998, reviewed in Federal Register 2011). Grazing impacts habitat by changing channel morphology and altering the presence of riparian vegetation, both of which have consequences for CAGT (Knapp and Matthews 1996, Knapp et al. 1998). Although grazing levels have decreased considerably, habitat impacts can linger, and Knapp and Matthews (1996) showed that even decreased levels of grazing are negatively correlated with CAGT density and biomass per square meter relative to ungrazed sites. On the other hand, the wider streambeds that result from grazing have been shown to provide more available spawning habitat, and actually increase the density of age-0 fish in a population (Knapp et al. 1998). Due to the populationlevel effects that could result from such demographic phenomena, managers should certainly consider habitat impacts from grazing in working with CAGT. These effects do not, however, appear to be of particular importance to the genetic management of CAGT, except in cases in which population sizes are greatly reduced as a result of grazing (and therefore become subject to the genetic concerns of small populations), or if the resultant habitat effects of grazing should prove to impose different selection regimes on golden trout populations. The latter might also occur through indirect effects, for example, grazing impacts on macroinvertebrate prey (Herbst et al. 2012). Page 6

The introduction of non-native salmonids, particularly brown trout, has also contributed greatly to the decline of CAGT in its native range, specifically in the SFK (Pister 2010, Federal Register 2011). Intensive management actions aimed at removal of non-native trout (detailed in History of species management below) have reduced this threat in many areas within the range, but have not removed it completely. Because most genetic management efforts discussed herein are likely to focus on areas in which CAGT is no longer significantly impacted by predation, however, our concerns relating to competition and predation are relatively limited. Of most relevance to the genetic management of CAGT is hybridization with non-native rainbow trout. Hybridization and resulting introgression are known to have a number of negative consequences, including fitness reductions (Muhlfeld et al. 2009), the loss of distinctive phenotypes, and even genomic extinction, or complete loss of the native genotype (Rhymer and Simberloff 1996). In the case of CAGT, hybridization has occurred primarily with introduced rainbow trout of hatchery origin. There exists a long history of stocking rainbow trout in the upper Kern River Basin to improve fisheries that were already beginning to be depleted by the early 1900 s (Pister 2010). The peak of stocking was probably 1931-1941, when 85,000-100,000 rainbows were planted every year (Gold and Gold 1976), but stocking of hatchery rainbows in the mainstem Kern River and in the SFK at Kennedy Meadow continued to support a popular put-and-take sport fishery until 2009. Stocking of the South Fork Kern River utilized only triploid (presumably sterile) rainbow trout starting in 2004 (C. McGuire unpublished data, (Pister 2008) and stocking discontinued in 2009 (C. McGuire, unpublished data). All introduced rainbow trout prior to 2004 readily interbred with CAGT, with the resulting offspring backcrossing into the parental lineages, creating introgression. Introgression can be difficult to diagnose morphologically, but can have lasting effects on population dynamics, as well as decreasing the genetic integrity of the native stock. Because of the long history of stocking non-native trout, introgression has impacted CAGT in most of its native range (Cordes et al. 2003, Cordes et al. 2006). In addition, the CAGT broodstock created in Cottonwood Lakes, used for stocking other locations with CAGT, were hybridized with rainbow trout (Leary 1995, Pister 2010). Due to the prevalence and potential long-term impacts of introgression, avoiding and minimizing introgression as a threat to species integrity should be a priority for management of CAGT. History of species management Human involvement with the fish of the Kern River basin has a lengthy history. By the beginning of the 20 th century, it was becoming clear that humans were playing a role in the evolutionary trajectory of golden trout (Evermann 1906). Chief among human activities were the stocking of non-native trout and the heavy levels of angling that made such stocking necessary (Pister 2008). The resulting negative impacts to CAGT became apparent to those working in the area by the mid-20 th century, accompanied by the realization that human intervention was needed on behalf of CAGT to avert its extinction (Pister 2010). This intervention mainly involved the construction and maintenance of artificial barriers, the removal of non-native trout above those barriers, and the restoration of habitat. There are currently three artificial obstructions to fish passage in the SFK, known from downstream to upstream as the Shaeffer, Templeton, and Ramshaw barriers. A detailed history of their construction and use is available in Pister (2008), and they are also discussed thoroughly in Federal Register (2011). As designed, these barriers seem to be effective at preventing the upstream migration of fish, particularly brown trout, thereby limiting access to the upper SFK. In concert with the construction of barriers, an effort to begin eradicating non-native trout began in earnest in 1969 and involved several approaches, some of which continue to the present time (Pister 2010). This effort is cited as perhaps the most involved and lengthy effort ever conducted for the restoration of any fish species, freshwater or marine (Pister 2010). Chemical treatment history for the SFK is described in detail in Pister (2008), but involved the Page 7

removal of CAGT, the use of several different types of fish removal treatments, and then the restocking of CAGT. This process has been very effective at removing brown trout, which no longer appear above the barriers in SFK, or are present in very low numbers (Pister 2010). The barriers and fish removal have been less effective at dealing with the problem of introgression, however, likely due to the difficulties in identifying introgressed fish and the apparently high degree of introgression in the SFK. Section 2. CAGT population genetic structure and diversity The genetic characteristics of CAGT populations residing both within and outside their native range is discussed below, with reference to genetic differentiation, introgression with nonnative rainbow trout, genetic diversity of extant CAGT populations, and the existence of native CAGT gene pools located in out-of-basin transplanted populations. More recent genetic studies of CAGT have used both microsatellite and single nucleotide polymorphism ( SNP ) markers (Table 1). Appendix 1 contains the most recent genetic analysis of both in-basin and out-ofbasin CAGT. Section 2.1 Population genetic differentiation As discussed previously in Section 1, genetic and morphometric assessments have identified CAGT as distinct from other rainbow trout subspecies and as having at least two major MUs: GTC and SFK. The distinction between GTC and SFK has been supported by all genetic analysis (Cordes et al. 2003, Stephens and May 2011; Appendix 1), with SFK usually appearing intermediate between GTC and non-native rainbow trout. This difference could represent either natural evolutionary divergence between GTC and SFK, or more recent anthropogenic influence of introgression between SFK and introduced trout. A number of outof-basin transplanted populations selected from the early Ellis transplant records appear to be non-introgressed (Stephens and May 2011) and are genetically similar to GTC, not SFK (Appendix 1), indicating that GTC was the likely source for most of these transplants. A number of transplanted populations, such as Milestone Creek and Salmon Creek, do not identify closely with CAGT from either MU, and therefore have likely been influenced by other subspecies of rainbow trout as well (Appendix 1). In addition to the major division of two MUs within the CAGT range, an analysis of genetic divergence among pairs of populations of CAGT showed significant pairwise F ST values for most pairwise comparisons (Appendix 1, Table 3). F ST is a measure of population differentiation or reduced gene flow, with a significant value indicating that population structure exists. Thus, it appears there is at least some resistance to gene flow between most sampled populations. These measures of population differentiation have important implications for management. In particular, the mixing of CAGT populations with high levels of divergence may result in outbreeding depression. Outbreeding depression refers to a loss of fitness that can occur when divergent populations hybridize, and is usually attributed to the loss of local adaptation or the disruption of co-adapted gene complexes (Edmands 1999, Frankham et al. 2002, Edmands 2007, Hedrick 2009). Previous studies have shown significant detrimental effects as a result of outbreeding depression, but accurately predicting the likelihood or severity of such effects is difficult, as they may not occur until several generations after the outbreeding event (McClelland (McClelland and Naish 2007). Frankham et al. (Frankham et al. 2011), however, have suggested that outbreeding depression is relatively unlikely, and that concerns about outbreeding depression may often be unnecessary. In certain cases, particularly those involving populations of low genetic diversity, the influx of divergent genes can actually be beneficial, a phenomenon known as genetic rescue and attributable to increased fitness among outcrossed individuals (Tallmon et al. 2004). Page 8

Similar to outbreeding depression, however, predicting the likelihood of beneficial outcrossing is difficult. Given these considerations, management of CAGT should be mindful of genetic divergence between populations that are going to be placed into contact with one another in either wild or hatchery settings. At the very least, individuals from the two MUs should continue to be managed separately. Section 2.2 Extent of introgression within native range Studies of introgression using both SNP and microsatellite markers have found introgression with non-native rainbow trout to be present in much of CAGT s native range, often at high levels (Cordes et al. 2003; Cordes et al. 2006 (Cordes et al. 2003, Cordes et al. 2006, Stephens 2007, Stephens and May 2011; Appendix 1). The lowest levels of introgression are found within the GTC drainage, and include several populations that show less than 5% introgression, including those we have included as Core 1 populations (see Table 2; Appendix 1, Table 5). Within GTC, the highest levels of introgression have been in headwater lakes, which is consistent with the known stocking history of the region (Cordes et al. 2006). Select out-of-basin transplant populations show low levels of introgression, often less than 5% (Stephens and May 2011; Appendix 1). Introgression levels are, in general, higher within the SFK than in GTC, and unlike in GTC, the highest levels of introgression in SFK appear in the lower reaches of the river, with introgression decreasing upstream (Cordes et al. 2003, Stephens 2007). Even the headwaters, however, show evidence of introgression with non-native rainbow trout. Again, this is consistent with a history that shows stocking of the lower reaches and upstream migration of the nonnative or introgressed fish. Much of this introgression likely occurred prior to the creation of barriers. In addition, the restoration efforts that coincided with barrier construction focused primarily on brown trout eradication, and less on removal of potentially introgressed CAGT, especially since detection of such fish was difficult at that time. Therefore the barriers, though effective at excluding brown trout, seem to have done little to mitigate the threat of introgression. It is important to note that there is often variation among estimates of introgression between marker types. There are a number of reasons for possible discrepancies, including the inherent difficulty in measuring introgression, particularly after a high number of backcrosses (Boecklen and Howard 1997, Hohenlohe et al. 2011). When comparing different marker types, it is important to note that the diagnostic SNP markers used in these analyses were developed using the Volcano Creek GTC population, and therefore may not accurately represent SFK populations, a problem referred to as ascertainment bias (Morin et al. 2004); this might also lead to different introgression estimates between SNPs and microsatellites. Differences in introgression estimates are discussed in Appendix 1, since the analysis presented there resulted in much more variable estimates of introgression for SFK populations than had been measured previously. Unfortunately it is difficult to determine which factors might be responsible for these discrepancies, which lead us to view SFK introgression estimates cautiously. The ongoing development of new molecular markers for CAGT and other rainbow trout subspecies may be able to provide more accurate or consistent measures of introgression. Given the history of non-native fish in the SFK, the long period of time during which there were no barriers to upstream movement, and the ongoing restocking of non-native trout during restoration, it is not surprising that introgression is relatively common. Consequently, when there are differences between estimates of introgression, it is usually prudent to use more conservative (i.e. higher) estimates of introgression for management purposes to avoid mistakenly underestimating the primary threat to CAGT. Section 2.3 Genetic diversity of extant (non-introgressed) populations Page 9

Genetic diversity was measured using microsatellites, and expressed as either allelic richness (number of alleles per microsatellite locus), or heterozygosity (the fraction of individuals having two different alleles at a locus based on alleles observed in the population or expected based on theoretical calculations). In general, diversity is higher in SFK than in GTC, and is even lower in out-of-basin populations (Appendix 1; Cordes et al. 2003). Most GTC populations, though, which exhibit the lowest introgression, have moderate levels of diversity (Appendix 1, Table 2). The diversity results, particularly in SFK, may be confounded by the presence of introgression in many populations, which would lead to high genetic diversity. For example, the influx of non-native rainbow trout genes would necessarily increase the number of alleles present in a population. It follows that introgressed populations (e.g., most SFK populations) may have higher genetic diversity simply because of the presence of non-native alleles, and not because of other demographic factors that can effect genetic diversity. The out-of-basin transplant populations had slightly lower genetic diversity than either SFK or GTC (Cordes et al. 2003, Stephens and May 2011; Appendix 1). This result is not surprising, given that transplanted populations are often started with a reduced number of individuals relative to the source population, and therefore do not represent the full diversity present in that population, a phenomenon known as a founder effect. The utility of these out-of-basin populations for augmenting or supplementing within-basin populations may therefore be relatively limited, since they may not provide additional genetic diversity. They would, however, have value as refugia of non-introgressed CAGT (see Table 2) Measures of genetic diversity are an important consideration for management of CAGT populations. Lack of genetic diversity within a population has been shown to have negative effects on both short-term and long-term persistence. In the short-term, low diversity often coincides with inbreeding and inbreeding depression, which has been shown to reduce fitness and increase extinction risk (Frankham and Ralls 1998, Frankham et al. 2002). In the longterm, genetic diversity is essential for adaptation to changing environments (Frankham et al. 2002 (Hedrick 2000, Frankham et al. 2002). It is therefore important for CAGT management to be mindful of retaining genetic diversity, and work where possible to avoid its decline (see Recommendations Section 7 below). As mentioned above, in-basin CAGT populations (including those regarded as Core 1, Table 2) do not currently show levels of genetic diversity low enough to cause concern, nor do they show genetic signatures of population bottlenecks (Appendix 1), suggesting that intervention for the preservation of genetic diversity is not needed at this time. Section 3. Defining units of conservation for CAGT management Given the distribution of extant genetic diversity described above, along with the MUs constituted by the populations in GTC and SFK and their respective tributaries, we can progress to defining units of conservation for CAGT. Within these major drainages, decisions must be made regarding how to best prioritize populations for conservation and management action based on introgression, genetic diversity, and population status. Genetic management plans often classify populations to prioritize them for recovery planning based on abundance, geographic location, genetic distinctiveness, genetic purity, and capacity to respond to conservation action (Alves et al. 2004, NMFS 2009). One strategy that incorporates many of these elements was developed by the National Marine Fisheries Service (NMFS) for Central Valley (CV) winter-run and spring-run Chinook and steelhead. The CV Chinook and steelhead recovery plan sorts populations into three categories, Core 1-3, to prioritize them for conservation. Core 1 populations are independent, have high abundance (low extinction risk), and contain unique genetic or ecological characteristics. Core 1 populations are those that have the greatest potential to respond to conservation action and NMFS recommends that Page 10

these populations form the foundation of recovery planning (NMFS 2009). Populations designated as Core 2 populations have a high potential to support ecological and genetic diversity of the species but may be of lower abundance, although they should have only a moderate risk of extinction (NMFS 2009). Core 2 populations should be given secondary priority in recovery efforts. Finally, Core 3 populations are those that are transient or intermittent, dependent on nearby populations for existence. However, these populations may contain valuable ecological and genetic diversity and may serve important roles in maintaining dispersal connectivity among populations and for spreading the risk of extinction (NMFS 2009). While such a strategy would be informative, two problems exist with such a scheme: first, limited information exists for individual population abundance trends, interdependence, and ecological characteristics of CAGT localities; second, introgression with non-native genotypes is an important consideration that is absent from such a strategy. Another strategy, illustrated in a recovery plan for Rio Grande cutthroat trout, graded populations (A, B, C, or D) based on the level of introgression observed within (Alves et al. 2004). A population must meet an introgression threshold grade of A- or better to be classified as a conservation core population, a designation analogous to the Core 1 designation of NMFS (2009) and a conservation population (analogous to Core 2 designation) must have a B or better introgression grade (Alves et al. 2004). A similar scheme might be applied to refine CAGT classification as Core 1, 2, or 3 populations, basing the classification on a combination of genetic diversity, abundance, and introgression level. We chose a strategy similar to Alves et al., where populations are categorized mainly on their level of introgression. We categorize Core 1 populations as a native range population with less than or equal to 5% introgression, the highest conservation priority, essentially below the lower threshold of introgression detection using STRUCTRE admixture analyses without reference populations. Core 2" is a native range population with 10% introgression, a threshold suggested as a possible cutoff for conservation of introgressed species (Allendorf et al. 2001). "SFK Uncertain" refers to CAGT populations located in the SFK management unit that have uncertain genetic status. "Transplant 1" is for transplanted populations, which are a conservation priority (though secondary to Core 1 populations); risks associated with these populations include lower genetic diversity, possible influence of selection in a different habitat, possible unknown species interactions, but detectable introgression less than 5%. "Transplant 2" refers to transplanted populations of secondary priority, owing to the existence of other transplanted alternatives that are less introgressed, more diverse, or of more certain genetic provenance. "Transplant Uncertain" refers to populations for which genetic estimates are too uncertain to classify or additional information is needed. "Introgressed" refers to an introgressed transplant population with greater than 10% introgression or introgressed SFK populations consistently high in introgression measures, likely of no conservation value. See Table 2 for our recommended classification scheme for CAGT golden trout samples and localities examined in the past 10 years using molecular genetic techniques. One major drawback to this approach is the use of absolute introgression cutoffs, which seem to create clear categories, yet are based on imperfect, potentially changing genetic estimates of introgression from a particular marker type. Advances in molecular technologies and analytical methodologies may provide more accurate estimates of introgression levels in the future and the designation of CAGT populations may have to be revisited as new methodologies and data become available. Another drawback is that this scheme does not incorporate demographic data, which is important when making inferences about the relationship between genetic connectivity and demographic connectivity (Lowe and Allendorf 2010). Managers are urged to incorporate population census sizes and effective sizes (see Luikart et al. 2010) in determining to what extent populations are interdependent and integrate such information with estimates of effective population size, Ne, which may require larger sample sizes (60-100 Page 11

individuals), and benefit from additional loci for accurate detection of trends (Tallmon et al. 2010). Section 4. Potential strategies for conserving CAGT genetic diversity The definition of conservation units for CAGT management is a hierarchical process that will first involve the designation of MUs and then the ranking of populations within MUs Core 1, etc. MUs are genetically differentiated populations that are functionally independent of other conspecific populations (Moritz 1994). Management strategies for each MU can be devised after populations are ranked. As discussed previously, genetic studies support the designation of two MUs for CAGT: GTC and SFK drainages. Within these drainages, different locations inhabited by CAGT may be ranked for conservation priority based on characteristics such as abundance, genetic/ecological distinctiveness, genetic diversity, and potential to respond positively to conservation interventions (See Table 2 for suggested rankings based on genetic data only). Once MUs and the areas within them are defined and ranked, several different conservation strategies may be employed. The first is a status quo strategy, where populations are monitored and protected from further decline in situ but no additional action is taken to protect CAGT. A second option is the creation of genetic refuges to protect existing CAGT populations, while a third strategy is to develop a conservation hatchery program to support existing CAGT populations or create new CAGT populations within or outside of their native basin. Finally, translocations may be conducted to supplement existing populations or create additional CAGT populations within or outside of the CAGT native range. We discuss these four options in more detail in the following subsections. Section 4.1 Status quo The status quo strategy maintains current protections for CAGT, making no changes to fisheries or land management practices and no efforts to curtail the current distribution of known hybridized CAGT populations. CDFW regulations require use of artificial lures and barbless hooks and allow a five-fish bag limit in accordance with policy to protect wild trout. Angler harvest and land management practices are discussed in Federal Register (2011), the latter including grazing in selected allotments, packstock use, and recreational use within native CAGT range. The risks associated with the status quo strategy are that CAGT populations may experience expansion of introgression levels and/or geographic extent, increasing threats to non-introgressed populations, and possibly a resulting loss of native genetic diversity (and therefore adaptive potential). Decreases in census or effective population size may lead to an increase in inbreeding and inbreeding depression (Hedrick and Kalinowski 2000). There is also the potential that a catastrophic event could wipe out one or more Core populations, resulting in the permanent loss of the genetic and life history diversity associated with those extirpated populations. This is particularly concerning for the Core 1 (low- to no-introgression) populations. Section 4.2 Conservation of genetic diversity through refuge populations Refuge populations are populations into which all stocking is prohibited (Araguas et al. 2009). In the case of CAGT, this means that no stocking of either rainbow trout or other CAGT would occur into a refuge population. Through the prohibition of stocking, these refuge populations are protected from genetic homogenization across CAGT populations (Araguas et al. 2009) as well as introgressive hybridization with non-native or hatchery fish. Refuge populations should be as genetically pure CAGT as possible, with a threshold introgression Page 12

level used to determine whether a population should be a candidate for refuge population status (Araguas et al. 2009). Allendorf et al. (2004) recommend that no more than 10% introgression be allowed in the determination of refuge populations. Even in the absence of stocking, it may be difficult to protect refuge populations from invasion by non-native or hatchery individuals if there are no physical barriers preventing dispersal into the refuge population (Araguas et al. 2009). Refuge populations may be maintained within or created outside the native basin. Within the native basin, existing Core 1 populations may be designated as refuge populations or nonintrogressed individuals from a Core 1 population may be used to create a refuge population in a location within the native basin previously uninhabited by CAGT. Refuge populations may be created outside of the native basin (Crawford and Muir 2008) as long as they are in an area that is isolated from contact with any other salmonid species with which hybridization might occur and their creation poses no risk to other threatened or endangered species in the area. All three types of refuge populations act as a safeguard for unique genetic, ecological, or behavioral characteristics of genetically distinct populations against catastrophic events. If the refuge populations are established with a large number of individuals and a high effective population size ( N e ) can be maintained, they may be used for genetic rescue in the event of continued decline of natural populations (Hedrick and Fredrickson 2009). There are risks and benefits associated with in-basin versus out-of-basin refuges. The designation of an existing CAGT population in-basin as a refuge population is beneficial because the CAGT in the existing population are already adapted to biotic and abiotic conditions in their native environment. However, refuge populations in the native basin (Such as the SFK) are at risk of infiltration by introgressed CAGT, hatchery CAGT, and rainbow trout. Out-of-basin transplant populations of CAGT may experience a lower risk of introgression with rainbow trout, but instead may be at risk of hybridization with other native salmonids, such as Kern River rainbow trout (Erickson 2013). In addition, out-of-basin transplant populations often are created with a small number of individuals and therefore may be subject to low census and effective population sizes, inbreeding depression, and genetic diversity loss through genetic drift, which have been show to increase extinction probability (Lande 1988, Saccheri et al. 1998, Frankham 2005, but see Peacock et al. 2010). These characteristics would make out-of-basin transplant populations less ideal candidates for refuge population status. Additional risks and benefits of using a source population(s) to create an in-basin or out-of-basin refuge population are discussed in following sections. Refuge populations have been established successfully for other endangered trout species. The creation of genetic refuges for native brown trout in the French Alps resulted in significantly fewer pure non-native individuals in the population without changing trout densities (Caudron et al. 2012). Araguas et al. (2009) indicate that refuge designation (along with alternate management strategies, i.e., fished, unfished, and catch-and-release) has prevented detectable introgression from increasing throughout the brown trout refuges. Section 4.3 Conserving genetic diversity through creation of conservation hatchery (broodstock) population(s) If census sizes of key populations decline to an alarming level, another potential option for increasing non-introgressed CAGT population numbers and size is through the creation of a non-introgressed broodstock population (via a conservation hatchery) and then outplanting propagated individuals. Artificial propagation has been used historically to improve fishing opportunities and more recently to mitigate for dams, hydroelectric projects, and other anthropogenic factors that decrease fish populations (Good et al. 2005). Lately, propagation efforts are taking on greater conservation emphasis to prevent extinction and reintroduce Page 13

salmonids into areas from which they have been extirpated (Flagg et al. 1999, Bowlby and Gibson 2011). The assumption has been that hatcheries increase survival during the egg to smolt stage (Naish et al. 2008). However, studies show the potential benefits of hatchery propagation can be outweighed by serious negative consequences. Hatchery propagation can cause decreased survival rates (Caroffino et al. 2008), rapid and cumulative negative genetic effects and fitness declines (Araki et al. 2008, Christie et al. 2012), and alterations of life history traits (WDFW 2008, HSRG 2009). Domestication selection - positive selection on traits for hatchery growth that come at the expense of traits suitable for performance in the wild - likely causes the observed decrease in fitness (Araki et al. 2008, Christie et al. 2012). We recommend the reader go to Naish et al. (2008) and Araki and Schmid (2010) for excellent reviews of the pitfalls of hatchery propagation. The performance of wild populations decreases when hatchery fish are present in a system (Araki et al. 2008). Therefore, serious consideration should be given to the potential negative effect of using hatcheries for reintroductions in basins where wild non-introgressed CAGT are present and release of hatchery-reared fish should potentially be avoided in these areas. Hatchery-propagation may be desirable to prevent extinction in the very short term for some stocks, but the negative genetic effects of artificial propagation can quickly outweigh any benefits gained from increased abundance after just a few generations of propagation (Bowlby and Gibson 2011). A complete evaluation of the risk-benefit tradeoffs should be conducted prior to the beginning of any conservation hatchery program (Waples and Drake 2004, Laikre et al. 2010). Ensuring continuous and substantial input into the broodstock from local, wild populations and using artificial propagation or rearing over very short time frames will help a conservation hatchery program increase the chances of avoiding the negative impacts of artificial propagation and increase the likelihood of reintroduction success (Frankham 2008, Williams and Hoffman 2009, Bowlby and Gibson 2011). There are a few examples where hatchery programs have avoided some of the pitfalls associated with artificial propagation (Hayes et al. 2004, Berejikian et al. 2008, Van Doornik et al. 2010, Heggenes et al. 2011, Kostow 2012). Their successes are due to practices that reduce domestication selection, minimize the duration of artificial propagation, protect and conserve natural genetic diversity, and support life history characteristics present in the wild populations. If it is decided to initiate a captive breeding program, it will be important to determine the most appropriate source population to develop the hatchery broodstock. The determination of this source will be dependent on the target location into which the captively reared individuals will be released. Given the above described drawbacks to hatchery production, it is imperative that extant, wild populations are not imperiled by the release of hatchery reared individuals. Therefore, the best location for hatchery release may be areas where there are no extant CAGT populations. Criteria for selecting an appropriate source for captive breeding include census size, genetic diversity, and genetic similarity to populations near the target location. It also will be important to determine the appropriate number of founding individuals for the starting broodstock to capture the genetic diversity present in the wild population. The 50/500 rule, originally proposed by Franklin (1980) provides a working framework, where an effective population size (N e ) of 50 is defined as an appropriate target minimum number to capture genetic diversity in the source population and avoid inbreeding, and with N e = 500 being a more appropriate long term goal for maintaining healthy and genetically robust populations. Frankel and Soule (1981) and Miller and Kapuscinski (2003) agree that 50 is an acceptable minimum, while Allendorf and Ryman (1987) suggest 100 breeding pairs is best to start a population. However, several studies have shown that 20-25 unrelated individuals, with an even sex ratio, can be sufficient for capturing approximately 97 percent of wild genetic diversity (Ralls and Ballou 1986, Lacy 1994, Frankham et al. 2002). The decision of how large to make the starting broodstock should be based on the genetic diversity found in the source population with the target of capturing as much as possible. Changes in heterozygosity and allelic diversity can also Page 14

occur at very different rates than changes in N e (Allendorf and Luikart 2007). Therefore, N e estimates should be used in concert with other standard genetic diversity indices to assess any changes in genetic diversity of hatchery, source, or outplanted populations over time. Another factor that must be considered if hatchery supplementation is used is the Ryman- Laikre effect (Ryman and Laikre 1991). This is caused by increased family sizes in hatchery propagated segments of the population compared to the newly established wild population. This disparity between family sizes causes an overall reduction in N e by increasing family size variance in the total population (hatchery and wild populations together). In the first generation of production and outplanting, this is not a concern if there are no other wild populations present in the area being supplemented. However, if there are wild fish present, or after the first generation of outplanting, the Ryman-Laikre effect can hinder reintroduction success by raising the N e of the broodstock to the potential detriment of the N e in the newly established wild population. To avoid this, efforts should be made to equalize family sizes in the hatchery to maintain large N e /N ratios. The tradeoff is that an increase in overall population census size due to hatchery production can also cause a reduction in N e, resulting in a loss of genetic variation that could ultimately negatively affect the likelihood of long-term reintroduction success. However, if population sizes remain high after hatchery supplementation ceases, the risk of inbreeding negatively affecting the population can be marginal (Waples and Do 1994). This is yet another argument for minimizing the number of generations of any hatchery supplementation. Finally, the importance of maintaining target broodstocks in complete isolation from other potentially hybridizing stocks is of fundamental importance. Past hybridization of native trout with non-native stocks in both hatchery and refuge populations subsequently used in restoration efforts and stocking have occurred in the past in the Little Kern golden trout (Deadman Creek broodstock, Stephens and May 2011) and CAGT (Cottonwood Lakes broodstock, Leary 1995, Cordes et al. 2006) greatly confounding ongoing conservation efforts in these areas. Section 4.4 Direct translocation of individuals (without hatchery broodstock) Direct translocation is the movement of wild fish from their native habitat to another location and can be a useful tool for protecting genetic diversity and increasing population size. Caudron et al. (2012) found that direct translocation of native Mediterranean strain brown trout in the Borne River led to the establishment of high density native populations (increase of 55-fold in three years) and changed the genetic composition of native populations, although these changes were restricted only to the exact areas into which the translocation occurred. Translocation differs from hatchery programs because fish are moved directly from one natural habitat to another, with no reproductive intervention in an artificial environment. Also, translocation may be conducted at any life stage. Translocation may involve reintroducing CAGT into an uninhabited region in-basin or out-of-basin, or it can involve moving individuals from one population into another. Translocations may have fewer genetic risks associated with them than hatchery programs (see Section 4.3 above) as long as the appropriate precautions are taken. Enough individuals must be used to establish the translocated population to avoid demographic constraints and inbreeding, and the correct source population must be selected. A benefit of translocation is that it preserves natural mating behavior and the offspring of translocated individuals are born into a natural environment, avoiding any domestication selection. Translocation has some advantages over the status quo conservation strategy. Reintroduction of CAGT via direct translocation of non-introgressed individuals may help spread the risk of extinction across multiple populations. If a catastrophic event such as a major flood or other natural disaster extirpated the source population, the translocated Page 15

population would preserve the unique genetic diversity and life history characteristics possessed by the source. Peacock et al. (2010) found that some reintroduced populations of Lahontan cutthroat trout preserved some genetic diversity no longer found in their source population. Individuals from the translocated population could be used to re-seed the extirpated population in the native habitat. There are some risks associated with translocation, however, particularly with out-of-basin transfers. Translocated individuals specifically adapted to environmental conditions in one environment may fail to thrive when reintroduced into a novel environment. Many introductions have failed due to translocation into environments too different from the native environment (Stockwell and Leberg 2002). The introduction of individuals from one population into a genetically divergent population may result in outbreeding depression. It is vital to consider the natural levels of population differentiation when determining where to translocate individuals to ensure the population structure that was shaped over evolutionary time is maintained. Finally, the translocation of individuals from one population into another may introduce non-native pathogens, which can negatively affect the recipient population and/or other aquatic species in that environment. CAGT shouldn t be translocated into any areas where their presence would be detrimental to any other threatened or endangered species. Adopting good translocation practices can ameliorate the risks associated with translocations. First, the appropriate source must be selected for translocation, particularly when individuals will be introduced into a recipient population. Similar to hatchery broodstock selection, a source population for translocation should be as genetically similar to the recipient population as possible (Fraser 2008). This reduces the likelihood that outbreeding depression will occur and increases the likelihood that the translocated individuals will thrive in their new habitat. If the translocation is to be a reintroduction into a previously inhabited region, the likelihood of reintroduction success will be increased if a population genetically or ecologically similar to the previous inhabiting population is selected as a source, as a genetically similar population may be better adapted to environmental conditions in the habitat (Young 1999). A source population also should have high levels of genetic diversity and have low levels of introgression. High levels of genetic diversity ensure the translocated population has the capacity to adapt to its new environment over time and respond to future environmental changes (George et al. 2009). Zeisset and Beebee (2012) found that reintroduction of common toads (Bufo bufo) using a geographically distant source with large census size and high genetic diversity established a robust population while reintroduction attempts with a local source population with a smaller census size and lower genetic diversity were unsuccessful. Allele frequencies at adaptive genetic loci in the reintroduced population from the large, distant source changed within ten years to be similar to the local source, indicating adaptation to the local environment (Zeisset and Beebee 2012). Any CAGT source used for translocation should have low levels of introgression to ensure that CAGT genetic diversity and ecological characteristics are preserved. When translocation is conducted, either within or out-of-basin, the number of translocated individuals is important to consider. Translocating too few individuals could induce a founder effect, resulting in a new population with low genetic diversity and a high potential for inbreeding after several generations. Populations suffering from a founder effect have a lower capacity to adapt to their new environment or persist through time because they lack sufficient genetic diversity may eventually suffer from inbreeding depression. It is also important not to translocate so many individuals from a source population as to cause the source population to suffer negative demographic or genetic consequences (Young 1999). Conducting translocations in multiple years can reduce the negative effects to source populations by removing fewer individuals at one time, as well as increase genetic diversity in the reintroduced or recipient population through the repeated incorporation of new individuals/genotypes (Young 1999, Page 16

Drauch and Rhodes 2007). Alternatively, individuals of multiple year classes can be translocated in a single event to capture high amounts of genetic diversity (Caudron et al. 2012). Section 5. Genetic monitoring needs Section 5.1 Monitoring CAGT genetic diversity Adequate genetic monitoring is a crucial component for successful population management and should be a priority in any genetic management program. Here we define genetic monitoring as having a temporal quality, in contrast with assessment, which denotes a single time point measure of population characteristics. As stated by Schwartz et al. (2007), we define monitoring as quantifying temporal changes in population genetic metrics or other population data generated using molecular markers. The information obtained from genetic monitoring can help managers identify changes to a population s structure, inbreeding status, degree of introgression, and level of genetic diversity. Understanding these factors facilitates effective adaptive management to meet goals for a population (Laikre et al. 2008 and also see Section 6). Long-term genetic monitoring of focal species requires standardized data collected over time and often across research laboratories (Smith et al. 2005, Welsh and May 2006). Such coordination efforts can be expensive, though the recent embrace of SNP markers greatly reduces the effort required for coordinating data sets. Use of either microsatellite or SNP markers allows for estimation of metrics such as genetic variation, effective population size, population structure, and migration (Schwartz et al. 2007). Given the goals for CAGT population management, evaluation of genetic diversity and the early detection of introgression are priorities. For a species of special concern, the retention of genetic diversity is very important. Also, the early identification of introgression is likewise important for species such as CAGT that are vulnerable to hybridization and introgression with other species (Schwartz et al. 2007). Genetic data collected over time is essential for better characterizing the extent to which hybridization functions as a threat to species persistence: that is, whether introgression is likely to increase, decrease, or remain stable over time or under particular environmental conditions (Laikre et al. 2008). Genetic monitoring programs geared at identifying hybridization have been proposed for multiple species and one is currently underway to identify hybridization between westslope cutthroat and rainbow trout (Hitt et al. 2003, Laikre et al. 2010). Section 5.2 Monitoring under supplementation and non-supplementation scenarios The creation of a genetic baseline is a necessary first step in any monitoring program. It establishes the initial status of the population(s) in question and allows one to identify trends over time that may necessitate changing management practices. After a sufficient initial genetic baseline is established, genetic sampling should be conducted at regular intervals through time. The recommended frequency of sampling will depend on whether or not supplementation is occurring. For CAGT populations that currently show little or no evidence of past introgression with other species, supplementation of the population should only be considered as a last resort if significant declines in population size or genetic diversity are observed. In the absence of supplementation, monitoring should be conducted at five-year intervals, as this is approximately the generation time for CAGT. In the case that supplementation occurs, monitoring should be conducted annually while supplementation continues. Monitoring should also be conducted annually for any additional populations that are established in a new habitat via translocation or through a conservation hatchery. Page 17

Section 6. Adaptive management It is likely that some CAGT conservation strategies will be more successful than others. While empirical evidence from previous conservation efforts can help guide CAGT managers, outcomes cannot always be predetermined when dealing with complex, and sometimes stochastic, ecological and environmental interactions. Therefore, it is imperative that CAGT managers make informed decisions using an adaptive management approach. The adaptive management process relies on careful design, management, and active monitoring to guide future conservation strategies based on what is working well. A useful guide when applying an adaptive management approach to a conservation program is available from the Conservation Measures Partnerships Open Standards for the Practice of Conservation (available at www.conservationmeasures.org). This guide details a five step cycle: 1) Conceptualize; 2) Plan Actions and Monitoring; 3) Implement Actions and Monitoring; 4) Analyze, Use, Adapt; 5) Capture and Share Learning. The iterative process not only enables better long-term conservation outcomes for a particular project but also serves as an educational resource for future projects with documentation of successes and failures. Section 7. Recommendations In this section we provide specific recommendations for genetic management of CAGT. We base our recommendations on the most current genetic data available for the species and our consideration of the risks and benefits for different genetic management strategies. Recommendations below are organized by management objective. Section 7.1 Identification and prioritization of populations for conservation Conservation of existing populations in their native range GTC and SFK should be treated as separate MUs due to their genetic distinctiveness. Locations within each MU should be ranked for conservation priority based on the ranking system (Table 2) which accounts mainly for introgression levels and in concert with non-genetic data, such as population census sizes and other criteria described by NMFS (2009). Core 1 populations should exhibit very little to no introgression ( 5%). See Table 2 for ranking of sample locations across the CAGT range. Genetic monitoring should be conducted to ensure that introgression levels do not change over time (Jackson et al. 2012). Conservation of existing populations outside of their native range Although priority should be given to populations within the CAGT native range for reasons discussed in Section 4.4, transplanted populations outside of the native range that exhibit little to no introgression should be protected, where possible. We have classified transplanted populations as Transplant 1 and Transplant 2, with Transplant 1 having a higher priority for conservation due to lower levels of introgression (Table 2). However, we recommend that outof-basin transplanted populations should not be used as a source for translocation into the native CAGT range or broodstock establishment due to concerns about pathogen introduction, potential adaptation to divergent environments, and reduced diversity. Should the species status change within the native range (e.g., substantial reductions in diversity, increases in introgression, large-scale extirpations), the potential use of out-of-basin stocks may need to be revisited. Page 18

Section 7.2 Conservation of genetic diversity through the creation of conservation hatchery (broodstock) populations Artificial propagation may be considered under three scenarios: 1) to rescue a severely declining population within the basin, 2) to create genetic refuges, or 3) to produce individuals for outplanting for a put-and-take fishery. Scenario 1 should only be employed if monitoring data (see recommendations below) shows severe decreases in population levels, indicating populations may go extinct without assisted propagation, and no genetically similar adjacent populations exist that might recolonize through natural or human-facilitated means. Under this scenario, we recommend that a hatchery only be used to bring adults in from wild populations, make parental crosses, and rear offspring for outplanting. New adults from the wild should be brought in each time this method of assisted mating is employed. We do not recommend the creation of a captive broodstock for this purpose due to the negative aspects of hatchery propagation, as discussed in Section 4, and its potential to prevent the establishment of a selfsustaining population. The source population for assisted reproduction should be from the declining Core 1 population in question and their offspring should only be outplanted for the time it takes to bring the population back to a self-sustaining state. Scenario 2 could be triggered by the observation of severely declining populations (as described in Scenario 1) or severe intrusion of hybridization that threatens Core 1 or Core 2 populations, and where the opportunity exists to restore genetically similar stocks to a nearby area where a genetic refuge can be created. An example might be the production of fish needed for a reintroduction effort following removal of hybridized CAGT (where insufficient Core 1 adults of the correct genetic provenance exist for use in reintroduction). Production of individuals for such efforts should proceed with all the precautions described for Scenario 1 above and the establishment of new populations should be done in areas that will not pose any threat to other threatened, endangered, or sensitive species. Given the current diversity estimates found in GTC and no substantial evidence of bottlenecks, along with the current protected status of the GTC habitat, we do not recommend the creation of a conservation hatchery for Scenarios 1 or 2 at this time. If managing agencies decide to create a put-and-take fishery (Scenario 3), a captive broodstock could be created from a Core 1 population(s) to produce individuals for outplanting. New, wild individuals of the correct genetic provenance (not hybridized, from a genetically diverse source within the appropriate MU) should be incorporated into the broodstock each year to maintain high genetic diversity. Scenario 3 should be recognized as having lower priority for species conservation, relative to the abovementioned scenarios. Outplanted fish should only be stocked in areas where they will not come in contact with other extant CAGT populations or pose a risk to any threatened or endangered species. An example of this would be the stocking of native CAGT for a fishery in the lower SFK, below Shaeffer Barrier. If any of these options are employed, we recommend the creation of a dedicated hatchery for CAGT production to avoid any possible introgression from non-cagt. Best hatchery practices should be employed to mimic natural conditions as much as possible to decrease domestication selection. Under all scenarios, individuals should only be taken from source population(s) after a thorough evaluation of the current population is completed. It must be determined that a potential source population has very low introgression levels, and that the census size and N e are large enough to allow removing individuals from the population without causing harm (or as in Scenario 1, that decline is so severe that extinction is imminent without facilitated reproduction using the remaining individuals). The genetic diversity and introgression levels of the source, hatchery, and outplanted populations should all be routinely monitored to Page 19

ensure the maintenance of high genetic diversity with no genetic contamination from non-cagt genotypes. Section 7.3 Conservation of genetic diversity through creation of refuge populations Core 1 populations exhibiting little to very low levels of introgression may be designated as genetic refuge populations to protect them from future hybridization and introgression with hatchery or non-native trout. These areas should remain unsupplemented with either hatcheryreared CAGT or rainbow trout. Populations located above barriers have the greatest chance of remaining free from introgression after designation as a genetic refuge. If a population is to be designated as a genetic refuge but is not located above a barrier, the construction of a physical barrier might be considered to provide additional protection against the invasion of stocked fishes; however, this must be considered in context with the needs of particular populations that require hydrologic connectivity to persist. Genetic refuge status does not preclude other management or use activities from occurring in the population. Genetic monitoring of genetic refuges should be conducted every five years to ensure that introgression levels remain unchanged. Section 7.4 Genetic monitoring recommendations Good maintenance and management of a tissue archive for all samples taken from CAGT is essential. Recommendations for managing an archive are detailed in Jackson (2012). Some of the main factors which must be considered are ease of sampling, proper preservation and storage, good organization, and ready availability for sharing among laboratories. Genetic assessment of isolated populations in which there is no active supplementation should be conducted every five years. For populations that are actively supplemented by a conservation hatchery program or translocation, genetic assessment should be conducted annually. The following genetic diversity indices should be evaluated during routine genetic monitoring of both supplemented and unsupplemented populations: Population census size (N c ), expected (H e ) and observed (H o ) heterozygosity, allelic richness (A R ; microsatellites), Hardy-Weinberg Equilibrium (HWE), linkage disequilibrium (LD), allele frequency change (F temporal ; microsatellites), genetic differentiation (F ST for SNPs ; G ST for microsatellites), inbreeding estimation (F IS ), and correlation between genetic and spatial distance (isolation by distance using a Mantel test). Genetic monitoring will be an important information source for determining when supplementation should be considered for a population. If monitoring reveals downward trends in either census size or heterozygosity, this is an indication that the population may not be able to persist without intervention. Supplementation should only be considered if these population metrics are declining. A genetic baseline should be established for all populations to allow identification of such trends. Supplementation is a last resort to stop the loss of a population and should not be considered for populations that have stable numbers and levels of genetic diversity (heterozygosity). Section 7.5 Adaptive management recommendations CAGT managers should take an adaptive management approach when planning and implementing conservation actions. This framework will enable empirical knowledge to be applied to improve long-term persistence of CAGT and serve as an educational resource for future conservation efforts. It is critical to remain open to new information, particularly in the context of evaluating introgression, which has been complicated by an inherently complex species history and anthropogenic influence in the CAGT system. Managers can make initial Page 20

decisions based on existing introgression estimates, while allowing new data and methodologies to inform ongoing species management. Acknowledgments Funding for genetic work was provided under CDFW Agreement #P1181006 and UFWS Agreement #F09AC00468. Page 21

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Tables and Figures Table 1. Microsatellite and SNP (single nucleotide polymorphism) markers used in analyzing both introgression and genetic diversity of CAGT (Cordes et al. 2003, Cordes et al. 2006, Stephens 2007, Stephens and May 2011). Not all markers were used in every study. Microsatellite OMM1036 OMM1037 OMM1089 OtsG85 OtsG423 OMM1322 OMM1082 OMM1097 Omy1009 OMM1051 OtsG249b OMM1088 OMM1081 OtsG3 Omy1011UW SNP A1A8_94 B9_388 FGG_gap259-260 G6PD_103 HOXD_170 ID1c_77 RAG15_gap272 RTDloop_316 Page 27

Table 2. Conservation Categories, based on summary of introgression values across genetic studies since 2001 (See Appendix 1, Table 5). Values are not weighted by data type and values from K=2 in Appendix 1 were not included in the introgression range, but may be noted in Comments where relevant. Conservation Categories are as follows: "Core 1" = a native range population with 5% introgression, the highest conservation priority. "Core 2" = a native range population with 10% introgression. "SFK Uncertain" = Uncertain genetic status; located within SFK MU, shows a range of (>10%) introgression; requires further genetic evaluation. "Transplant 1" = for transplanted pops, first priority; risks include lower genetic diversity, influence of selection in a different habitat, possibly unknown species interactions, but detectable introgression is <5%. "Transplant 2" = for transplanted pops, second priority; serve as lower priority backup, due to the existence of other OBT alternatives that are less introgressed, more diverse, or have more certain genetic provenance. "Transplant Uncertain" = genetic estimates too uncertain to classify or additional information needed. "Introgressed" = an introgressed transplant population with >10% introgression or introgressed SFK population consistently high in introgression measures, likely of no conservation value. RT refers to Rainbow trout, KRRT refers to Kern River rainbow trout. Locality Sample Years Introgression Range Category Comments Min Max 1 Golden Trout Creek Headwaters GTC 1999 0.00 0.03 Core 1 GTC at Big Whitney Meadow 2008 0.00 0.03 Core 1 GTC at Big Whitney Meadow 2001 0.00 0.04 Core 1 Upper Stokes Stringer 1999 0.00 0.05 Core 1 Middle Stokes Stringer 1999 0.00 0.03 Core 1 Middle Stokes Stringer 2005 0.00 0.04 Core 1 GTC Below Stokes Stringer 1999 0.02 0.03 Core 1 GTC Above Barrigan Stringer 2004 0.00 0.03 Core 1 Mouth of Barrigan Stringer 1999, 2005 0.00 0.08 Core 2 GTC Below Barrigan Stringer 1999 0.04 0.06 Core 1 Groundhog Creek 2000, 2005 0.01 0.03 Core 1 Johnson Creek 2000 0.00 0.02 Core 1 Salt Lick Creek 2000, 2005 0.00 0.01 Core 1 2005 sample showed 0.02-0.04 introgression; may be considered Core 2 simply due to position in watershed, but consider further evaluation or avoiding use as broodstock or adult transfer source Lower Johnson 1999 (0.00-0.02 introgression), Middle Johnson 1999 (0.00-0.08), and Johnson 2000 (0.00-0.02) samples evaluated; consider further evaluation or avoiding use as broodstock or adult transfer source 2000: 0.00-0.01; 2005: 0.01 (0.02 by 2013 K=2 analysis) Page 28

Locality Sample Years Introgression Range Category Comments Min Max 1 GTC Below Little Whitney Meadow 2001, 2005 0.02 0.05 Core 1 2001: 0.02-0.05; 2005: 0.00-0.05 Volcano Creek, Left Stringer 2005 0.00 0.01 Core 1 Volcano Creek 2000 0.00 0.02 Core 1 Lower Volcano Creek 1996 0.00 0.00 Core 1 South Fork Kern River Upper South Fork Kern 2001 0.04 0.12 SFK Uncertain SFK above Ramshaw Barrier 1999 0.04 0.13 SFK Uncertain SFK above Ramshaw Barrier 2006 0.02 0.02 SFK Uncertain SFK below Ramshaw Barrier 2002 0.08 SFK Uncertain Kern Peak Left Stringer 2002 0.08 0.13 SFK Uncertain Below Movie Stringer 2001 0.05 0.13 SFK Uncertain SFK below Ramshaw Barrier 2006 0.02 0.02 SFK Uncertain SFK above Templeton Barrier 2002, 2006 0.01 0.01 SFK Uncertain Upper Mulkey Creek 2001, 2006 0.01 0.01 SFK Uncertain Mulkey Creek above lower barrier 2009 0.00 0.11 SFK Uncertain Freckles Meadow, Upper 2009 0.00 0.05 SFK Uncertain Freckles Meadow 2006 0.01 0.01 SFK Uncertain Four Canyons Creek 2002 0.00 0.13 SFK Uncertain Additional concerns based on high 2013 K=2 estimate Additional concerns based on high 2013 K=2 estimate 2002: -0.20; 2006: 0.01; additional concerns based on high 2013 K=2 estimate for 2006 2001: 0.00-0.13; 2006: 0.01; additional concerns based on high 2013 K=2 estimate Additional concerns based on high 2013 K=2 estimate Additional concerns based on high 2013 K=2 estimate Additional concerns based on high 2013 K=2 estimate Four Canyons Cr North Fork 2009 0.00 0.44 SFK Uncertain Additional concerns based on high 2013 K=2 estimate SFK below Templeton Barrier 2002 0.24 SFK Uncertain 2002: -0.24; 2006: 0.02 SFK above Schaeffer Barrier 2002, 04,06 0.02 0.35 SFK Uncertain 2002: 0.25-0.35; 2004: 0.03-0.41; 2006: 0.02 2004: 0.02-0.34; 2006a: 0.06; 2006b: 0.04; 2004, additional concerns based on high 2013 SFK below Schaeffer Barrier 06a,b 0.02 0.34 SFK Uncertain K=2 estimate Strawberry Creek 2004 0.01 0.33 SFK Uncertain Additional concerns based on high 2013 Brown Meadow Creek 2006 0.01 0.01 SFK Uncertain K=2 estimate SFK Below Snake Creek 2006 0.06 0.06 SFK Uncertain Additional concerns based on high 2013 Page 29

Locality Sample Years Introgression Range Category Comments Min Max 1 K=2 estimate Dutch John Barrier 2008 0.25 0.77 Introgressed Monache Meadows 2002 0.32 Introgressed Middle Fish Creek 2001 0.08 0.48 Introgressed Upper Trout Creek 2001 0.29 0.99 Introgressed Kennedy Meadows 2003 0.75 0.95 Introgressed Rockhouse Basin 2004 0.61 0.61 Introgressed Transplants "Golden Pond", Wind River, WY 2003 0.01 0.01 Transplant 2 Only evaluated with SNP markers; possible SFK influence inferred based on likelihood it may have shared similar founding source as Wind River population Likely SFK influence; genetic bottleneck; some concern based on 2013 K=2 estimate Wind River, Wind River, WY 2003 0.01 0.02 Transplant 2 Dorst Creek, North Fork Kaweah R. 2004 0.00 0.20 Transplant Uncertain Unknown if SFK influence Eagle Scout Creek, Middle Fork Kaweah R. 2005 0.00 0.02 Transplant 1 Eagle Scout Lake, Middle Fork Kaweah R. 2005 0.00 0.02 Transplant 1 Granite Creek Lake Two, Middle Fork Kaweah R. 2005 0.78 1.00 Introgressed Practically RT Ferguson Creek Long Meadow, SFK. 2006 0.00 0.02 Transplant 1 Grizzly Creek East Fork, SFK 2004 0.00 0.02 Transplant 1 Bottlenecked Kennedy Creek, Middle Fork Kings R. 2004 0.00 0.03 Transplant 1 Scenic Meadow Creek, SFK 2006 0.00 0.02 Transplant 1 Lost Creek, Kern R. 2004 0.00 0.02 Transplant 1 McDermand Lake 3, Kern R. 2004 0.00 0.03 Transplant Uncertain Slide Creek, Kern R. 2008 0.02 0.06 Transplant Uncertain Possible KRRT or other influence suggested by K=2 analysis from Stephens and May 2013 Possible KRRT or other influence suggested by K=2 analysis from Stephens and May 2013 Salmon Creek, Kern R. 2004 0.04 0.04 Introgressed Does not cluster with CAGT -- possible KRRT or other influence Milestone Creek, Kern R. 2004 0.36 0.83 Introgressed Does not cluster with CAGT -- possible KRRT or other influence Upper Cold Creek, Kern R. 2001 0.00 0.05 Transplant1 Transplant with limited RT influence Rock Creek, Kern R. 2001 0.02 0.05 Introgressed Introgression for Upper (0-0.05), Middle (0-0.13) and Lower (.02-.05) Rock Creeks varied. Consider Introgressed transplant Page 30

Locality Sample Years Introgression Range Category Comments Min Max 1 due to high value for Middle Rock; also genetic drift and possibly related to SFK based on Cordes (2003) clustering Funston Lake, Kern R. 2002 0.02 0.24 Introgressed Introgressed and does not cluster with SFK or GTC (Cordes 2003) Upper Crabtree Lake, Kern R. 2002 0.00 Transplant 2 Genetic drift, likely related to SFK (Cordes 2003); additional genetic data would be useful, given limited loci examined Lower Crabtree Lake, Kern R. 2002 0.00 0.03 Transplant 2 Genetic drift, likely related to SFK (Cordes 2003); additional genetic data would be useful, given limited loci examined Crabtree Creek, Kern R. 2001 0.00 0.03 Transplant 2 Additional genetic data would be useful, given limited loci examined Ash Meadow Creek, Owens R. 2002 0.00 0.00 Transplant 2 Bottlenecked (Cordes 2003); cluster with SFK populations Diaz Creek, Owens R. 2002 0.00 0.22 Transplant 2 Bottlenecked (Cordes 2003); cluster with SFK populations Broodstock populations (transplants) Horseshoe Creek, Owens R. 1999 0.00 0.02 Transplant Uncertain More genetic data needed Cottonwood Lakes 2 (lakes 1,2,3), Owens R. 2000 0.02 0.19 Introgressed Introgressed, does not cluster with either SFK or GTC populations (Cordes 2006) Cottonwood Lakes 4 (lakes 4,5), Owens R. 2000 0.01 0.25 Introgressed Introgressed, does not cluster with either SFK or GTC populations (Cordes 2006) Little Cottonwood Creek, Owens R. 2000 0.00 0.01 Transplant Uncertain LCC1 and LCC 2 sampled; possible SFK influence Chicken Springs Lake, Golden Trout Creek 2000 0.00 0.30 Introgressed Population within GTC; eliminated via gillnetting Johnson Lake, Golden Trout Creek 2000 0.00 0.25 Introgressed Population within GTC 1 Max value does not include K=2 values from 2013 Structure analysis; See Appendix 1, Table 5 Page 31

Figure 1. Map depicting native range of CAGT, barriers present on SFK, and relevant administrative boundaries, including National Park (NP), National Forest (NF), Golden Trout Wilderness Area, and the historical native range for CAGT. Page 32

Appendix 1 Attached. Stephens, M.R., and B.P. May. 2013. Microsatellite Analysis of California Golden Trout Populations. Report to USFWS and CDFG (Agreement #F09AC00468 and #P1181006, respectively). March 30, 2013. 40pp. Page 33

GENOMIC VARIATION LABORATORY, UNIVERSITY OF CALIFORNIA DAVIS Microsatellite Analysis of California Golden Trout Populations Funding: UFWS Agreement #F09AC00468, CDFW Agreement #P1181006 Molly R. Stephens and Bernie May 3/30/2013

INTRODUCTION Genetic analysis of native California golden trout (hereafter CAGT ) populations has previously demonstrated moderate to severe impacts of introduced rainbow trout throughout the native range of the species (Stephens 2007, chapter 2). This study uses microsatellite DNA markers to complement existing genetic evaluations of CAGT; additional populations (Table A2.1) from Golden Trout Creek ( GTC ) and South Fork Kern River ( SFK ) as well as selected out-of-basin transplanted (OBT) populations (examined in Stephens& May 2011) have been included to provide an overall picture of rainbow trout introgression, genetic diversity, and structure of extant CAGT populations both within and outside the native range. The number of microsatellite markers used in this study is increased from that of previous genetic studies (i.e., six loci in Cordes Cordes et al. 2003; Cordes et al. 2006), to provide a more accurate assessment of hybridization and diversity of CAGT populations. METHODS Sampling and DNA extraction Fin clip samples were collected by California Department of Fish and Wildlife (CDFW) personnel and volunteers. Populations were sampled throughout the CAGT native range and from selected out-of-basin transplanted populations (Table 1). Wild resident rainbow trout from North Fork American River and two hatchery strains were also included as reference rainbow trout samples. Microsatellite data collection Microsatellite DNA loci were amplified in multiplexed reactions as described in Stephens (2007 (2007, Chapter 3). Fourteen of the sixteen loci amplified well and were subsequently scored for Page 2 of 40

analysis: OMM1036, OMM1037, OMM1089, OtsG85, OtsG423, OMM1322, OMM1082, Omy1009, OMM1051, OtsG249b, OMM1088, OMM1081, OtsG3, and Omy1011UW; two loci (OMM1083 and OMM1097) were dropped due to difficulty in scoring. PCR products were diluted to a 4:1 water to product ratio and 1.5ul of diluted product was added to 8.8ul of highly deionized formamide (Gel Company) and 0.2ul of LIZ600 size standard (Life Technologies). Samples were then denatured for 3 min at 95 C before electrophoresis on an ABI 3730 Genetic Analyzer (Life Technologies). Fragments were scored using STRAND software (Toonen& Hughes 2001; available at http://www.vgl.ucdavis.edu/strand). Raw allele sizes were exported and the MsatAllele R package (Alberto 2009, version 1.02; R version 2.12.1) used to bin alleles. Allele size names were further standardized for compatibility with scoring from previous studies of California golden trout and Kern River rainbow trout. Samples missing data for three or more loci were dropped from the final data set. Data Analysis Allele frequencies for each locality were calculated in CONVERT. GDA (Lewis& Zaykin 2001) was used to perform exact tests (Guo& Thompson 1992) for Hardy Weinberg Equilibrium ( HWE ) and linkage disequilibrium ( LD ) with 10,000 permutations to determine significance. All significance values resulting from multiple comparisons were corrected for Type I error using sequential Bonferroni correction (Rice 1989). The following descriptive statistics were calculated in Genalex (version 6.5, Peakall& Smouse 2012) for all remaining loci in each population: observed and unbiased expected heterozygosities (Nei 1978), number of alleles, number of effective alleles (allows for comparison of populations where number and distribution of alleles differs), and Fixation Index (F IS ). To account for differences in sample size, we estimated the rarefied mean number of alleles per locus (A r ) using HP-rare (version June Page 3 of 40

6, 2006; Kalinowski 2005). Private allelic richness was also calculated in HP-rare (Kalinowski 2005) assuming 40 gene copies and one sample used to represent each hierarchy. Fstat (Goudet 1995) was used to test for differences between groups of samples using 10,000 permutations and groups consisting of 1) SFK samples, 2) GTC samples, 3) transplant samples, and 4) rainbow trout samples (where the GCL sample was included as a member of the rainbow trout group based on results of later STRUCTURE analysis, described below). We used BOTTLENECK software (v.1.2.02, Piry et al. 1999) to test for evidence of population genetic bottlenecks using the Wilcoxon test under the two-phase model (TPM), which is most appropriate model (see discussion in Peery et al. 2012). We assumed parameter values of 90% for frequency of singlestep mutations and 30% for variance. We also tested the stepwise mutation model (SMM; Ohta& Kimura 1973) for comparison. Both genetic trees (phenograms) and ordination of genetic data were used to examine genetic relationships among samples. Trees were created in Poptree2 (Takezaki et al. 2010) using D A genetic distance (Nei et al. 1983) and UPGMA phenograms (Sneath& Sokal 1973) with 1000 bootstrap replicates to evaluate node support. Factorial Correspondence Analysis (FCA) was performed in Genetix (version 4.05.2, Belkhir et al. 1996-2004). The Bayesian clustering program STRUCTURE, version 2.3.3 (Pritchard et al. 2000) was used to determine the number of detectable genetic clusters (K) and to calculate posterior distributions of the admixture coefficient (q), or the proportional contribution of the observed groups to each individual s genotype. STRUCTURE uses an algorithm that defines groups by maximizing Hardy-Weinberg equilibrium within and minimizing linkage disequilibrium between groups. Page 4 of 40

We employed the admixture model and assumed correlated allele frequencies, with a burn-inperiod of 500,000 and 1,000,000 MCMC iterations for ten runs of each K for K=1-10. For our analyses, no prior information on population of origin was employed; the program was allowed to determine admixture proportions independent of assumptions about which populations represented pure golden trout or pure rainbow trout. Using Structure Harvester (Earl& Vonholdt 2012), the most likely number of genetic clusters was determined by finding the K with the largest second-order rate of change in negative log-likelihood values (Evanno et al. 2005), and also confirmed by locating the asymptote of the negative log-likelihood values for all runs of K and examining the distribution of q-values in individuals as recommended by the authors (Pritchard et al. 2000). CLUMPP (Jakobsson& Rosenberg 2007) was used to calculate a mean for the permuted matrices across replicates (values across runs of the same K) for both individuals and for population averages for genetic cluster membership. Individual-based results were visualized graphically using Distruct (Rosenberg 2004). Population differentiation was assessed using pairwise comparisons of F ST for individual populations, with significance determined by 18,060 permutations in Fstat (Goudet 1995) and corrected alpha (initial alpha = 0.05) for multiple comparisons. RESULTS Allele frequencies for all microsatellite loci are reported in Table S1. Tests for conformity to HWE revealed significant departures in 18 out of 602 locus-population comparisons after sequential Bonferroni correction (initial α = 0.05/602). Fifteen of these departures were attributable to locus Ots423, which is suggestive of one or more nonamplified alleles (null Page 5 of 40

alleles) at relatively high frequencies for this locus (Pemberton et al. 1995). Locus Ots423 was therefore excluded from further analyses. After dropping locus Ots423, 31 out of 3354 remaining pairwise locus-population combinations exhibited LD after sequential Bonferroni correction, with departures distributed evenly among locus-pairs and populations. Diversity measures are reported in Table 2. The ESL locality had the lowest values for all measures of diversity. Mean number of alleles per locus per population was 7.8, ranging from 1.9 (ESL) to 13.2 (BSC). The mean effective number of alleles ranged from 1.7 (ESL) to 6.8 (BTB). Allelic richness ranged from a low of 1.9 (ESL) to a high of 10.9 (BSC). Rainbow trout reference and SFK samples had higher average allelic richness (8.2 and 8.7, respectively) than transplant samples (mean A r = 4.4) or GTC samples (mean A r = 6.8). Private allelic richness was lowest in ESL (0.7), highest in AR (4.9), and mirrored the trend for A r, with higher values for RT and SFK (3.4 and 3.0, respectively), and lower average values for transplant (1.8) and GTC (2.7) samples. Mean expected heterozygosity ranged from a low of 0.353 (ESL) to a high of 0.778 (AR). Transplant and GTC populations averaged slightly lower (0.55 and 0.63, respectively) than SFK and RT populations (0.71 and 0.75, respectively). Tests for differences between groups showed statistically significant differences in observed heterozygosity (p = 0.006), allelic richness (p = 0.004), and F ST (p = 0.0138) between the SFK/GTC/transplant/rainbow trout groups. Two populations showed evidence of genetic bottleneck (p < 0.05): GCE and WR, with the latter locality being significant for the TPM but only marginally significant for the SMM (Table 2). Page 6 of 40

FST values were significantly different for the majority of population pairs (Table 3). Values for significant pairwise comparisons ranged from a high of 0.43 (FCCN and ESC) to a low of 0.01 (BSB04 and BRM). The first FCA diagram (Figure 3) depicts a cluster of rainbow trout individuals including the two hatchery strains and the Granite Creek Lake sample; a second cluster of golden trout individuals includes most of the sampled golden trout populations. Intermediate between the golden trout and rainbow trout clusters are the North Fork American River rainbow trout sample and the Dutch John CAGT sample. Lastly, the Milestone Creek (MSC) sample lies in a cluster outside all of these three groups. When rainbow trout (hatchery, wild, and GCL) and the Milestone Creek Samples are excluded, a second FCA diagram (Figure 4) shows two distinct clusters of golden trout: the first contains mostly South Fork Kern populations and the Wyoming sample; the second contains Golden Trout Creek and all other transplant populations. The Dutch John Barrier and Salmon Creek samples fall outside both of these clusters. The UPGMA dendrogram (Figure 2) supports a South Fork Kern group that does not include the Dutch John Barrier or North Four Canyons Creek sample and does include the Wyoming sample. This South Fork Kern group is distinct from a Golden Trout Creek and Transplants group that includes most transplant populations. The transplant populations from Salmon Creek, Milestone Creek, and Granite Creek Lake fall outside these two clusters, with the latter being closely aligned with Hatchery rainbow trout samples. Page 7 of 40

Structure results for individual clustering are shown in Table 4. The strongest support was for K=2 genetic clusters (see supplemental figure S1), with rainbow trout, South Fork Kern populations and select transplant populations comprising the first cluster and Golden Trout Creek and all other transplanted populations comprising the second cluster. Examination of the negative log likelihood plot (Figure S1a) for K=3 and above showed likely additional structure in the data set; a K of three captured the additional structuring of South Fork Kern populations (as a third genetic cluster) seen in FCA and UPGMA analyses. Clustering patterns were somewhat inconsistent across replicate runs of K f=4 or greater. The K = 4 scenario shows the detection of MSC (Milestone Creek) as a distinct genetic cluster, and K = 5 detects DOR (Dorst Creek) as a distinct genetic cluster. Genetic cluster membership averages for all samples are shown in Table 4 for K = 1-5. Proportion of rainbow trout cluster membership (ancestry) differs most strikingly between K = 2 versus K = 3 results, owing mainly to South Fork Kern populations being lumped with rainbow trout reference samples in the K = 2 scenario. Introgression values (the proportion of membership attributable to the first genetic cluster) remain relatively unchanged from K = 3 up. The range of rainbow trout introgression values for different STRUCTURE clustering scenarios is shown in Table 4. Values ranged from 0.65-0.99 for rainbow trout reference populations, 0-0.18 for ESC, ESL, FGC, GCE, KC, SCM, LST, MDL, and SLD transplant populations and 0-0.17 for Golden Trout Creek populations. Introgression values ranged much more broadly for other populations: 0-0.89 for KRSC, 0-0.83 for MSC, 0-0.67 for WR samples and 0.01-.98 for SFK samples. Page 8 of 40

DISCUSSION Genetic structuring of CAGT populations The sampled CAGT populations, including GTC, SFK, and most transplanted populations examined, are clearly distinct from hatchery rainbow trout reference populations (Figures 2 and 3; STRUCTURE K = 3, Table 4), and less distinct from American River ( wild ) resident rainbow trout samples (Figure 3). The CAGT sample taken at Dutch John (DJB), a small sample of three individuals in the lowest reaches of the South Fork Kern river, exhibited an intermediate position between golden trout and hatchery rainbow trout in the FCA (Figure 3) and did not cluster with other SFK populations in UPGMA (Figure 2). These individuals are likely to be heavily hybridized given that this is one of the lowest downstream reaches sampled in the SFK, with no barriers separating it from areas formerly stocked with rainbow trout. In addition to their genetic distinction from hatchery and wild rainbow trout, CAGT from both SFK and GTC (and its transplants) form their own well-supported clusters in UPGMA, FCA (Figure 4) and STRUCTURE (K = 3, Table 4) analyses. This is consistent with the geography of the region, with major drainages having apparently been separated for sufficient time to allow for genetic substructure, and also consistent with the purported GTC origin of most transplanted populations. The Four Canyons Creek North sample and the Dutch John sample also did not cluster with other SFK samples in the UPGMA tree only (Figure 2). As noted above, the DJB is likely heavily introgressed with rainbow trout, and the FCCN sample may also be introgressed, though it does not appear to have more rainbow trout influence than some other SFK samples (see introgression discussion below). Page 9 of 40

While a K = 2 scenario was supported as being the most likely result for STRUCTURE analysis (based on the Evanno et al. 2005 method alone), the K = 2 level of clustering fails to capture the additional population structure seen in the FCA and UPGMA tree (Figure 2), both of which show SFK populations as being distinct from GTC populations. The distinctiveness of the major drainages from one another is also more consistent with the structure seen in previous genetic studies (Cordes et al. 2003, 2006). These findings, along with the detection of additional structure in examining the negative log likelihood values of the STRUCTURE results (Figure S1a) argues in support of the K = 3 scenario, which does capture this additional SFK genetic structure. Additional genetic clusters resulting from higher levels of K (Table 4) likely reflect either populations exhibiting a large amount of genetic drift (i.e., the Dorst Creek sample, K = 5, Table 4) or introgression with other taxa (i.e., the Milestone Creek sample, K = 4, Table 4). As in Cordes et al. (2003), Mulkey Creek samples aligned with other South Fork Kern River samples in both FCAs and the UPGMA tree. They also align with South Fork Kern samples in STRUCTURE analyses for K = 3 and greater. While there has been some controversy regarding the origins of the fish stocked into Mulkey Creek (Samuel Mulkey purportedly took trout from either GTC, per Ober 1935, Vore 1928 and Behnke 1992 or from SFK, per Evermann 1906, Anonymous 1913, and Curtis 1934, and planted them into a fishless upper Mulkey Creek sometime before 1876), these now appear to be related to SFK fish. The simplest conclusion is that these fish were originally from SFK, not GTC; however, we cannot rule out the possibility that the original transplant was from GTC but did not establish or was subsequently displaced by fish from a SFK source. Page 10 of 40

The MSC sample had an average of 0.36 (K = 2) or 0.83 (K = 3) rainbow cluster membership, though with variable assignment between different runs of the same K. This sample also did not cluster with either GT or SFK populations in UPGMA (Figure 2) or in FCA (Figure 3). Similarly, KRSC (0.89 rainbow cluster membership) did not cluster with GT populations in the UPGMA dendrogram, clustered with other golden trout in the first, but not the second FCA, and clustered with SFK populations in the STRUCTURE analysis (K=3, Table 4). These populations may contain some mixture of CAGT and Kern River rainbow or other unknown trout stocks, causing them to cluster separately from both CAGT and hatchery rainbow trout (see Erickson 2013). The WR Wyoming sample interestingly aligned with the SFK/rainbow trout groups assuming K = 2, and aligned with SFK samples assuming K = 3. It also clustered with SFK fish in the FCA and UPGMA analyses. The history of this population and time of original transfer for this transplant population is unknown, but it appears to be more aligned with SFK sources (Figures 1-3). Lastly, the GCL sample appears to be comprised of rainbow trout individuals, based on its clustering with hatchery rainbow trout in the FCA (Figure 3), its position in the UPGMA (Figure 2), and its rainbow trout cluster membership in STRUCTURE analyses (Table 4). This population was the only transplant population examined that was thought likely to be stocked with hatchery rainbow trout (C. McGuire, pers. comm.), and the genetic results bear out this influence. Introgression in native CAGT populations The choice of K = 2 versus K = 3 is central in the calculation of rainbow trout introgression values, as can be seen from Table 4. As discussed above, justifications exist for selecting K = 2 (per delta K, Evanno et al. 2005) or K = 3 (additional structure detected, figure S1a; greater Page 11 of 40

consistency with FCA and UPGMA analyses of the same data). The majority of microsatellite estimates of rainbow trout introgression in GTC populations were similar to previous SNP and microsatellite estimates for the same samples in previous genetic studies, regardless of which K was chosen (Table 5). Likewise, introgression estimates for most transplanted CAGT populations are relatively low (<4%) and consistent with previous estimates from the same localities. The GTC samples MBS05 and GC05 are exceptions in that their estimates of introgression varied 10-15%, depending on the choice of K (two versus three), and they also vary slightly from estimates for earlier sample years for the same location (Table 5). Sample year introgression estimate variance may represent simple sampling variance associated with relatively small sample sizes; but the variance in introgression associated with choice of K illustrates the difficulty presented by choosing the appropriate value of K to represent rainbow trout introgression. This problem is even more apparent in SFK samples. In contrast, to GTC and GTC-derived transplant samples, microsatellite estimates of introgression for SFK populations varied widely between different clustering scenarios in this study and in comparison to SNP and microsatellite estimates from previous studies (Table 5). The challenges associated with defining introgression categories for CAGT, particularly for SFK populations, cannot be overstated. Rainbow trout introgression estimates obtained for SFK populations under the STRUCTURE K = 2 scenario seem implausibly high, given that their large rainbow trout ancestry proportions contradict FCA and UPGMA relationships represented by the same data set and by previous estimation of introgression using SNP methods. However, estimates under the K = 3 scenario are relatively low; they are only similar to the values for SFK populations in Stephens and May (2011) because a similar approach was used in accounting for Page 12 of 40

additional structure in the data set by analyzing the four SFK populations in that study alongside rainbow trout references as a subset of the larger golden trout data set (a similar effect to using the K = 3 scenario in the current study). Introgression under the K = 3 scenario is substantially lower in comparison to the same localities analyzed in previous studies (Table 5). The broad geographic sampling of SFK populations represented in the current study demonstrates that this phenomenon is not unique to the particular localities studied in 2011, but rather, occurs throughout the SFK. Two interpretations of the K = 3 scenario may be taken: first, SFK populations contain less introgression than previously thought, or second, the microsatellite markers do not have sufficient power to detect differences between more closely related SFK and rainbow trout groups. Greater genetic relatedness between SFK and hatchery/wild rainbow trout groups may exist due to either high levels of introgression between the two groups, or natural genetic relatedness between the two groups, perhaps owing to a more longstanding hydrological connection between the two groups prior to diversification. However, it seems equally plausible, given the extensive chemical treatment/reintroduction of SFK populations (Pister 2008, 2010) and history of rainbow trout stocking in the region that this introgression is anthropogenic in origin. These microsatellite data and the complexity of this system do not allow us to discriminate between the two possibilities. The noted difference between SNP and microsatellite estimates of introgression may be attributable to ascertainment bias, defined as the selection of loci from an unrepresentative sample of individuals which yields loci that are not representative of the spectrum of allele frequencies in a population (Morin et al., 2004). The golden trout ancestry-informative SNPs were discovered using only the Volcano Creek population, given the lack of any available Page 13 of 40

genetically pure SFK reference populations (Sprowles et al., 2006). The ascertainment bias inherent in these SNPs may render them relevant to assessing only introgression for GTC populations, but perhaps not SFK populations. Genetic diversity of extant CAGT Given the extensive history of restoration activities and population founding, it is perhaps surprising the level of genetic diversity that remains in CAGT populations. In contrast, studies of Little Kern golden trout (Stephens 2009) revealed much more extensive genetic bottlenecks using the same microsatellite markers. Only two CAGT populations in this study exhibited signals of population bottlenecks, though even this result should be interpreted cautiously given the relatively small sample sizes of those two localities. It may also be that sample sizes were generally too small to retain sufficient power to detect bottlenecks. Allelic diversity and heterozygosity was, as expected, generally lower in transplanted populations, which were likely founded a century ago with small numbers of individuals and little to no gene flow from adjacent populations. Heterozygosity of GTC populations is slightly lower than that of rainbow reference or SFK populations. Given the ambiguity surrounding the hybridization status of the SFK populations, it is difficult to say whether the slightly higher heterozygosity of SFK populations is a natural phenomenon or whether it may be attributed to the diversifying influence of rainbow trout alleles through introgression. The moderate diversity seen in the VCLS sample is notable, given that previous examination (Cordes 2001) had shown a sample from this type-locality to be lacking in diversity, possibly owing to the collection method of that sample and the small number of microsatellites used. Extant genetic diversity for CAGT populations is similar to that Page 14 of 40

of Kern River rainbow trout populations examined for a nearly identical set of microsatellite loci (Erickson 2013). Future data needs The question of SFK introgression origins could be addressed using large-scale genotype data. First, such data could be used in a phylogenetic context to evaluate whether genetic admixture with rainbow trout is more likely natural (incomplete lineage sorting or natural secondary contact) or more recent (i.e., anthropogenic) in origin by evaluating the size distribution of phylogenetically discordant segments; the size of segments supporting discordant trees in the case of incomplete lineage sorting is expected to be small (e.g., Hobolth et al. 2011). Genomic data can also be evaluated in a mapping context, examining LD blocks to provide insight into the recency of introgression between groups, with the size of shared blocks depending on the type of admixture (Buerkle& Lexer 2008; Stolting et al. 2013; Winkler et al. 2010). Large-scale SNP analyses (thousands of SNPS) have the potential to uncover recent introgression (Goedbloed et al. 2013) and estimate individual-level (as opposed to population- or sample-wide average) introgression levels (Hohenlohe et al. 2013); this approach is currently tenable for a non-model species, such as golden trout. Whether additional genetic data are necessary depends upon whether the information is needed for management purposes. If SFK populations were being considered for removal entirely (or below a particular barrier), if they threatened any GTC populations, or if they were a determinant in whether the species as a whole were eligible for protected status, such information could lend valuable insight into the genetic relationships and the question of introgression levels in SFK populations. Page 15 of 40

Management implications The microsatellite data set, as with previous genetic analyses, clearly supports the treatment of GTC and SFK populations as separate management units, with GTC populations being the most genetically distinct from rainbow trout. In addition to the higher-level structure observed at the drainage (management unit) level, significant pairwise F ST values were observed for nearly all population pairs, with some exceptions, including populations with extremely small samples (e.g., DJB and GCL) and several pairwise comparisons within SFK samples. This result should discourage the intentional mixing of populations within GTC in particular, given the significant pairwise differences observed. The moderate to high levels of genetic diversity and lack of genetic bottlenecks in extant CAGT populations is encouraging, though interpretation of this result is ambiguous for SFK populations, which may be hybridized to an unknown extent. For management purposes, SFK populations should be considered to have unknown introgression levels. The selected transplant populations examined in this study, while genetically affiliated with GTC populations, should be considered to have secondary conservation value relative to GTC fish, given their lower levels of diversity and the possibility that their out-of-basin environments may have exerted selective forces different from those experienced by populations within the native range. The Wind River sample from Wyoming clusters genetically with South Fork Kern River fish, and so is only of value if SFK populations are determined to be a genetically distinct unit that is not heavily influenced by hatchery rainbow trout introgression. Additionally, it is important to note that most of the transplanted populations examined in this study were specifically chosen based on known stocking history, as having a high likelihood of containing Page 16 of 40

native CAGT; thus the observed lack of introgression in transplanted samples should not be construed to be generally true of all transplanted populations, which are often sourced from potentially hybridized stocks and likely to be introgressed with rainbow trout. Acknowledgments: Funding for genetic work was provided under UFWS Agreement #F09AC00468 and CDFW Agreement #P1181006. We would like to thank Jerry Fuji, Christy McGuire, the CDFW wild trout crew, Richard Landis, and several volunteers for providing samples. The California Golden Trout Coordination Group and Christy McGuire, Tracy Purpuro, Brian Beale, and Dave Lentz from CDFW provided valuable discussions. Antonia Wong and Alisha Goodbla collected microsatellite data. Bjorn Erickson shared microsatellite data for Salmon Creek, Milestone Creek, and Hot Creek hatchery rainbow trout samples., Page 17 of 40

Table 1. Sample collections for the current study including the locality and associated code, watershed, collection date, number of individuals, and collection coordinates for downstream (DS) and upstream (US) sampled reaches (degrees North and East). Watershed abbreviations are as follows: NFAR = North Fork American River, HAT = hatchery, NFKW = North Fork Kaweah River, MFKW = Middle Fork Kaweah River, SFKG = South Fork Kings River, MFKG = Middle Fork Kings River, KR = Kern River, WR = Wind River Wyoming, GTC = Golden Trout Creek, SFK = South Fork Kern River ID Code Locality Watershed Coll. Date 0 AR North Fork American River NFAR 2010 N DS LAT. ( N) DS LONG. ( E) US LAT. ( N) US LONG. ( E) 0 MW Mt. Whitney Strain Hatchery HAT 2011 ---- ---- ---- ---- 0 RTHC Hot Creek Strain Hatchery HAT 3/14/2011 50 ---- ---- ---- ---- 1 DOR Dorst Creek NFKW 6/30/2004 40 36.66119 118.77041 36.66385 118.76662 2 ESC Eagle Scout Creek MFKW 8/23/2005 40 36.54398 118.58365 36.54666 118.57963 3 ESL Eagle Scout Lake MFKW 8/23/2005 9 36.54606 118.57566 ---- ---- 4 GCL2 Granite Creek Lake 2 MFKW 8/26/2005 3 36.51551 118.57862 ---- ---- 5 FGC Ferguson Creek SFKG 7/18-19/2006 40 36.66794 118.61170 36.66249 118.60949 6 GCE Grizzly Creek, East Fork SFKG 8/17/2004 18 36.84590 118.70450 36.8498 118.7046 7 KC Kennedy Creek MFKG Aug. 2004 27 36.91545 118.65247 36.88218 118.65085 8 SCM Scenic Meadow Creek SFKG 7/19/2006 40 36.68260 118.59317 36.68329 118.59413 9 LST Lost Creek KR 7/25/2004 40 36.46245 118.48490 36.46332 118.48555 10 MDL McDermand Lake 3 KR 7/30/2004 10 36.68288 118.40900 11 SLD08 Slide Creek KR 7/26/2008 40 36.92088 118.67068 36.91551 118.67245 12 KRSC Salmon Creek KR 7/10/2004 36 35.89090 118.33770 ---- ---- 13 MSC Milestone Creek KR 7/30/2004 54 36.64150 118.43959 36.64219 118.44181 14 WR Wind River WR ---- ---- ---- ---- 15 VCLS Volcano Creek, Left GTC 8/19/2005 41 36.34926 118.32317 36.3413 118.32115 16 LWC GTC Below Little Whitney Meadow GTC 8/11/2005 40 36.36739 118.3489 36.37019 118.34676 17 SLC Salt Lick Creek GTC 8/10/2005 33 36.38941 118.33517 36.39208 118.33682 18 GC Ground Hog Creek GTC 10/27/2005 40 36.36766 118.30637 36.36651 118.31003 19 MBS Mouth of Barrigan Stringer GTC 8/18/2005 40 31.4114 118.27757 36.41502 118.27896 20 ABS04 GTC above Barrigan Stringer GTC 7/19/2004 40 36.41675 118.27506 36.42068 118.27313 21 MSS Middle Stokes Stringer GTC 7/4/2005 40 36.43349 118.26139 36.43531 118.25807 22 BWM GTC at Big Whitney Meadow GTC 9/17/2008 40 36.42484 118.27486 36.42528 118.27536 23 ARB South Fork Kern R. above Ramshaw SFK 10/8/2006 40 36.36075 118.28104 ---- ---- 24 BRB06 South Fork Kern below Ramshaw SFK 10/7/2006 40 ---- ---- 36.36075 118.28104 25 UMC06 Mulkey Creek Upper SFK 8/21/2006 40 36.40754 118.17209 ---- ---- 10/18-26 MC Mulkey Creek above lower SFK 40 36.36296 118.20278 36.36652 118.19675 19/2009 27 FMU Upper Freckles Mdw SFK 10/20/2009 7 36.36714 118.17357 36.36653 118.17301 28 FRM Freckles Meadow SFK 8/24/2006 37 ---- ---- 36.36653 118.17313 29 FCCN North Four Canyons Creek SFK 9/9/2010 14 36.34466 118.16027 36.35907 118.16357 30 FCCN North Four Canyons Creek SFK 10/1/2010 1 36.351 118.1625 ---- ---- 31 ATB SFK above Templeton SFK 9/19/2006 41 36.32933 118.19112 ---- ---- 32 BTB06 SFK below Templeton SFK 9/19/2006 39 ---- ---- 36.32933 118.19112 33 BRM SFK at Brown Meadow Creek SFK 8/17/2006 40 36.29601 118.16259 36.2855 118.16891 34 SC04 Strawberry Creek SFK 9/27/2004 40 36.2977 118.18023 36.29825 118.18075 35 ASB06 SFK above Schaeffer SFK 8/15/2006 21 36.24644 118.19565 36.24865 118.19882 36 ASB04 SFK above Schaeffer Barrier SFK 6/20/2004 40 36.24644 118.19565 36.24865 118.19882 37 BSB04 SFK below Schaeffer SFK 6/20/2004 40 ---- ---- 36.25989 118.20501 38 BSBa SFK below Schaeffer Barrier A SFK 8/15/2006 21 ---- ---- 36.24644 118.19565 39 BSBb SFK below Schaeffer Barrier B SFK 8/16/2006 39 36.2287 118.182 36.25989 118.20501 40 BSC SFK River below Snake Creek SFK 10/19/2006 40 36.18097 118.15293 36.18145 118.15375 41 DJB SFK River at Dutch John SFK 6/4/2008 2 ---- ---- ---- ---- Page 18 of 40

Table 2. Sample sizes (N), number of alleles (Na), number of effective alleles(ae), allelic richness (Ar), Private allelic richness (Ap), observed (Ho) and expected (He) heterozygosity, and Fixation Index (F) for 43 populations of O. mykiss spp based on 13 microsatellite loci. Significance of BOTTLENECK Wilcoxon tests for stepwise (SMM) and two-phase (TPM) models is also shown in bold font. Locality codes given in Table 1. Pop N Na Ae Ar* Ap** Ho He F SMM TPM AR 31 11.2 5.8 9.9 4.9 0.728 0.778 2 1.00 0.92 MW 38 8.2 4.0 7.2 2.5 0.710 0.724 0.016 1.00 1.00 RTHC 50 8.5 4.9 7.5 2.9 0.758 0.759-0.002 0.85 0.32 DOR 40 3.2 2.2 2.9 1.0 0.469 0.461-0.008 0.66 0.63 ESC 40 2.7 1.8 2.5 1.0 0.418 0.403-0.046 0.48 0.21 ESL 9 1.9 1.7 1.9 0.7 0.299 0.353 0.118 0.23 0.05 GCL 3 3.2 2.7 3.2 1.4 0.667 0.577-5 n/a n/a FGC 40 5.5 3.4 5.1 2.0 0.603 0.594-0.023 0.55 0.12 GCE 18 4.2 3.2 4.2 1.8 0.583 0.564-0.029 0.00 0.00 KC 40 6.9 4.2 6.3 2.7 0.595 0.618 0.031 0.77 0.29 SCM 40 4.5 2.8 4.2 1.8 0.537 0.538 0.002 0.53 0.19 LST 39 5.8 3.5 5.3 2.2 0.600 0.596 0.064 0.53 0.25 MDL 10 4.4 3.1 4.4 1.6 0.685 0.601-0.122 0.28 0.09 SLD 40 7.8 4.6 6.9 2.6 0.674 0.666-0.015 0.92 0.58 MSC 50 5.3 3.4 4.9 1.8 0.650 0.645-0.005 1.00 0.96 KRSC 21 5.5 3.1 5.4 1.7 0.582 0.591 0.007 0.63 0.10 WR 23 4.4 3.2 4.3 2.1 0.560 0.551-0.010 0.06 0.00 VCLS 41 6.6 3.7 5.6 2.4 0.587 0.590 0.015 0.95 0.52 LWM 40 8.7 4.4 7.2 2.9 0.648 0.638-0.029 1.00 0.90 SLC 33 6.1 3.3 5.4 2.0 0.578 0.573-0.022 0.90 0.69 GC 36 8.5 4.5 7.3 3.0 0.657 0.638-0.026 0.99 0.83 MBS 37 10.6 5.2 8.5 3.2 0.674 0.683 0.025 1.00 0.99 ABS 13 5.9 4.2 5.9 2.5 0.633 0.621-0.009 0.40 0.10 MSS 39 8.7 4.6 7.3 2.9 0.634 0.651 0.036 0.98 0.79 WM 40 8.7 4.4 7.1 3.0 0.654 0.647-0.008 1.00 0.95 ARB 40 11.6 5.9 9.6 3.3 0.737 0.745 0.004 1.00 0.97 BRB 40 11.9 6.3 9.7 3.6 0.758 0.747 0.001 1.00 0.93 UMC 40 6.9 4.0 6.0 2.2 0.640 0.629-0.030 0.77 0.37 MC 39 9.0 4.7 7.8 2.9 0.676 0.668-0.015 1.00 0.88 FMU 7 5.4 4.1 5.4 2.0 0.692 0.659-0.060 0.26 0.10 FRM 37 8.9 5.0 7.9 2.9 0.655 0.682 0.038 0.98 0.77 FCCN 14 3.8 2.4 3.8 1.2 0.600 0.532-0.139 0.83 0.61 ATB 41 12.2 6.5 10.0 3.7 0.731 0.753 0.029 1.00 0.89 BTB 39 11.7 6.8 9.8 3.6 0.780 0.755-0.038 0.97 0.50 BRM 25 10.7 6.2 10.0 3.5 0.751 0.728-0.032 0.95 0.74 SC 29 10.4 6.7 9.6 2.9 0.729 0.743 0.019 0.69 0.37 ASB06 18 10.5 6.2 10.5 3.9 0.756 0.751-0.012 0.98 0.94 ASB04 36 12.9 6.5 10.7 3.6 0.745 0.767 0.017 1.00 0.95 BSB04 28 12.2 6.5 10.8 3.6 0.726 0.748 0.025 1.00 0.98 Page 19 of 40

Pop N Na Ae Ar* Ap** Ho He F SMM TPM BSBa 21 10.8 5.9 10.7 3.4 0.729 0.752 0.031 1.00 0.95 BSBb 39 12.2 6.2 10.3 3.2 0.748 0.758 0.031 1.00 0.93 BSC 39 13.2 6.5 10.9 3.8 0.753 0.773 0.019 1.00 0.94 DJB 2 2.9 2.7 2.9 0.7 0.769 0.596-0.272 n/a n/a max 13.2 6.8 10.9 4.9 0.780 0.778 0.118 min 1.9 2.7 2.9 0.7 0.769 0.596-0.272 * localities with sample sizes less than ten should not be used for comparison of A R **A P calculated using a minimum sample size of 20 Page 20 of 40

Page 21 of 40 Table 3. Pairwise F ST for pairwise comparisons of populations. Non-significant values (5% nominal level) are indicated in boldface type. AR MW RTHC ABS04 GC05 LWM05 MBS05 MSS05 SLC05 UMC06 VCLS05 WM ARB ASB04 ASB06 ATB BRB06 BRM BSB04 BSBa BSBb BSC BTB06 DJB FCCN FRM MC SC04 DOR ESC ESL FGC FMU GCE GCL KC LST MDL SCM SLD WR KRSC MSC 0.17 0.17 0.18 0.14 0.21 0.17 0.21 0.17 0.11 0.10 0.11 0.10 0.12 0.11 0.11 0.10 0.10 0.10 0.11 0.09 0.22 0.10 0.29 0.33 0.29 0.20 0.11 0.20 0.18 0.18 0.21 0.22 0.21 0.23 0.21 AR 0.26 0.26 0.27 0.23 0.25 0.30 0.25 0.30 0.26 0.20 0.18 0.18 0.19 0.20 0.19 0.18 0.17 0.18 0.17 0.19 0.18 0.28 0.22 0.23 0.18 0.37 0.40 0.36 0.29 0.21 0.28 0.27 0.29 0.25 0.32 0.24 0.27 0.27 0.28 MW 0.24 0.24 0.25 0.22 0.23 0.28 0.23 0.26 0.24 0.17 0.17 0.17 0.18 0.17 0.14 0.26 0.20 0.21 0.17 0.32 0.37 0.34 0.27 0.18 0.27 0.19 0.25 0.27 0.23 0.30 0.22 0.26 0.25 0.26 RTHC 0.02 0.01 0.00 0.00 0.03 0.17 0.06 0.00 0.10 0.11 0.10 0.12 0.12 0.11 0.10 0.19 0.26 0.17 0.09 0.23 0.21 0.18 0.04 0.04 0.31 0.01 0.05 0.06 0.04 0.20 0.22 0.19 ABS04 0.02 0.01 0.01 0.05 0.02 0.06 0.10 0.10 0.06 0.09 0.11 0.12 0.11 0.10 0.21 0.25 0.13 0.09 0.22 0.18 0.05 0.14 0.05 0.32 0.03 0.05 0.06 0.09 0.04 0.18 0.19 0.18 GC05 0.02 0.02 0.02 0.18 0.06 0.02 0.12 0.13 0.08 0.11 0.14 0.13 0.12 0.08 0.21 0.27 0.17 0.11 0.20 0.18 0.03 0.06 0.31 0.03 0.03 0.08 0.09 0.05 0.21 0.21 0.18 LWM05 0.00 0.04 0.12 0.06 0.00 0.04 0.08 0.04 0.04 0.06 0.09 0.09 0.09 0.05 0.22 0.11 0.13 0.06 0.20 0.18 0.14 0.04 0.11 0.04 0.28 0.02 0.05 0.04 0.02 0.17 0.17 MBS05 0.04 0.14 0.00 0.05 0.09 0.10 0.06 0.06 0.09 0.11 0.12 0.11 0.10 0.20 0.25 0.13 0.09 0.20 0.17 0.04 0.13 0.03 0.30 0.02 0.05 0.04 0.06 0.02 0.19 0.17 MSS05 0.23 0.04 0.11 0.18 0.12 0.12 0.19 0.20 0.18 0.12 0.27 0.31 0.20 0.21 0.23 0.20 0.17 0.21 0.36 0.06 0.05 0.12 0.13 0.08 0.26 0.25 0.22 SLC05 0.21 0.08 0.08 0.09 0.09 0.08 0.21 0.23 0.06 0.06 0.30 0.33 0.30 0.20 0.10 0.19 0.28 0.21 0.12 0.24 0.10 0.09 0.23 0.23 UMC06 0.08 0.10 0.14 0.12 0.11 0.13 0.17 0.14 0.12 0.24 0.29 0.19 0.20 0.13 0.25 0.26 0.22 0.09 0.19 0.11 0.36 0.08 0.09 0.14 0.10 0.26 0.21 0.24 VCLS05 0.06 0.10 0.11 0.06 0.09 0.12 0.12 0.12 0.10 0.20 0.25 0.13 0.09 0.20 0.19 0.04 0.04 0.30 0.02 0.06 0.05 0.06 0.03 0.17 0.20 0.18 WM 0.03 0.02 0.01 0.00 0.02 0.04 0.05 0.04 0.03 0.01 0.08 0.04 0.06 0.02 0.18 0.22 0.20 0.10 0.04 0.10 0.22 0.08 0.10 0.13 0.05 0.11 ARB 0.00 0.02 0.02 0.01 0.00 0.01 0.01 0.00 0.02 0.05 0.13 0.04 0.05 0.01 0.23 0.27 0.23 0.13 0.03 0.14 0.19 0.11 0.09 0.08 0.13 0.18 ASB04 0.01 0.02 0.01 0.00 0.01 0.00 0.00 0.01 0.04 0.06 0.00 0.25 0.30 0.25 0.04 0.20 0.13 0.17 0.11 0.19 0.09 0.13 0.13 0.18 ASB06

Page 22 of 40 AR MW RTHC ABS04 GC05 LWM05 MBS05 MSS05 SLC05 UMC06 VCLS05 WM ARB ASB04 ASB06 ATB BRB06 BRM BSB04 BSBa BSBb BSC BTB06 DJB FCCN FRM MC SC04 DOR ESC ESL FGC FMU GCE GCL KC LST MDL SCM SLD WR KRSC MSC 0.00 0.01 0.02 0.03 0.03 0.02 0.00 0.04 0.05 0.01 0.20 0.24 0.20 0.11 0.03 0.11 0.21 0.09 0.12 0.08 0.14 0.06 0.12 0.17 ATB 0.01 0.03 0.04 0.03 0.02 0.00 0.09 0.03 0.05 0.01 0.19 0.23 0.20 0.10 0.04 0.10 0.22 0.08 0.10 0.08 0.14 0.05 0.11 BRB06 0.01 0.02 0.01 0.01 0.01 0.09 0.04 0.05 0.00 0.26 0.29 0.24 0.14 0.05 0.14 0.22 0.10 0.10 0.18 0.12 0.19 BRM 0.00 0.00 0.00 0.03 0.14 0.05 0.00 0.27 0.31 0.26 0.04 0.17 0.21 0.13 0.18 0.11 0.19 0.10 0.14 0.14 0.19 BSB04 0.00 0.01 0.03 0.14 0.06 0.01 0.30 0.33 0.27 0.18 0.05 0.17 0.19 0.14 0.19 0.12 0.21 0.10 0.14 0.20 BSBa 0.00 0.03 0.08 0.06 0.01 0.26 0.29 0.25 0.05 0.20 0.13 0.17 0.11 0.19 0.09 0.13 0.13 0.19 BSBb 0.02 0.06 0.14 0.04 0.06 0.01 0.24 0.27 0.23 0.14 0.04 0.19 0.12 0.10 0.17 0.08 0.12 0.13 0.17 BSC 0.08 0.04 0.05 0.01 0.21 0.25 0.21 0.11 0.04 0.11 0.21 0.09 0.12 0.08 0.06 0.11 0.17 BTB06 0.25 0.13 0.13 0.09 0.31 0.45 0.44 0.23 0.09 0.28 0.21 0.22 0.24 0.23 0.29 0.19 0.29 0.25 0.26 DJB 0.18 0.19 0.14 0.40 0.43 0.40 0.30 0.21 0.29 0.35 0.27 0.31 0.28 0.32 0.24 0.30 0.31 0.31 FCCN 0.01 0.04 0.26 0.30 0.28 0.18 0.00 0.18 0.24 0.18 0.13 0.21 0.11 0.12 0.21 0.21 FRM 0.05 0.28 0.33 0.31 0.20 0.01 0.21 0.25 0.17 0.20 0.24 0.13 0.22 0.24 MC 0.25 0.27 0.23 0.14 0.04 0.13 0.21 0.10 0.09 0.17 0.11 0.14 0.17 SC04 0.35 0.37 0.21 0.28 0.27 0.45 0.24 0.20 0.28 0.26 0.23 0.33 0.40 0.29 DOR 0.10 0.22 0.36 0.18 0.50 0.21 0.28 0.20 0.19 0.37 0.38 0.30 ESC 0.22 0.34 0.17 0.50 0.18 0.18 0.26 0.21 0.35 0.31 0.30 ESL 0.19 0.10 0.35 0.06 0.06 0.09 0.10 0.25 0.27 0.21 FGC

Page 23 of 40 AR MW RTHC ABS04 GC05 LWM05 MBS05 MSS05 SLC05 UMC06 VCLS05 WM ARB ASB04 ASB06 ATB BRB06 BRM BSB04 BSBa BSBb BSC BTB06 DJB FCCN FRM MC SC04 DOR ESC ESL FGC FMU GCE GCL KC LST MDL SCM SLD WR KRSC MSC 0.20 0.23 0.19 0.14 0.23 0.12 0.23 0.23 FMU 0.35 0.05 0.09 0.09 0.12 0.05 0.22 0.25 0.22 GCE 0.33 0.35 0.31 0.39 0.28 0.32 0.33 0.31 GCL 0.08 0.04 0.08 0.03 0.19 0.22 0.19 KC 0.13 0.13 0.08 0.24 0.24 0.21 LST 0.11 0.02 0.12 0.25 0.19 MDL 0.09 0.26 0.29 0.23 SCM 0.11 0.19 0.18 SLD 0.27 0.25 WR 0.25 KRSC MSC

Table 4. Number of genetic clusters detected using STRUCTURE and proportion of ancestry assigned to each cluster for each population (Codes and population names as given in Table 1). CLUMPP-summarized values are given for K=2-5, with K=2 being the most likely (see text for details). Range of values for the proportion of ancestry attributable to rainbow trout (cluster 1) for the K=1-5 scenarios is given as "RT range." Code Pop K=2 K=3 K=4 K=5 RT range 1 2 1 2 3 1 2 3 4 1 2 3 4 5 1 AR 0.95 0.05 0.65 0.03 0.31 0.84 0.04 0.10 0.02 0.84 0.04 0.10 0.02 0.01 0.65-0.95 2 MW 0.99 0.01 0.79 0.00 0.21 0.99 0.00 0.01 0.00 0.99 0.00 0.01 0.00 0.00 0.79-0.99 3 RTHC 0.99 0.01 0.79 0.00 0.21 0.98 0.00 0.01 0.01 0.98 0.00 0.01 0.00 0.00 0.79-0.99 4 GCL 0.99 0.01 0.78 0.00 0.22 0.98 0.00 0.01 0.01 0.98 0.00 0.01 0.00 0.00 0.78-0.99 5 DOR 0.02 0.98 0.20 0.79 0.01 0.00 0.99 0.01 0.01 0.00 0.01 0.00 0.00 0.98 0.00-0.20 6 ESC 0.01 0.99 0.00 0.99 0.00 0.00 0.79 0.00 0.20 0.00 0.99 0.00 0.00 0.01 0.00-0.01 7 ESL 0.01 0.99 0.00 0.99 0.00 0.00 0.80 0.00 0.20 0.00 0.98 0.00 0.00 0.01 0.00-0.01 8 FGC 0.02 0.98 0.01 0.99 0.01 0.00 0.98 0.01 0.01 0.00 0.97 0.01 0.00 0.02 0.00-0.02 9 GCE 0.02 0.98 0.00 0.99 0.01 0.00 0.97 0.01 0.02 0.00 0.98 0.00 0.00 0.01 0.00-0.02 10 KC 0.04 0.96 0.01 0.96 0.03 0.00 0.95 0.02 0.03 0.00 0.96 0.02 0.01 0.01 0.00-0.04 11 SCM 0.02 0.98 0.01 0.99 0.01 0.00 0.81 0.01 0.18 0.00 0.98 0.01 0.01 0.01 0.00-0.02 12 LST 0.02 0.98 0.01 0.99 0.01 0.00 0.98 0.00 0.01 0.00 0.94 0.01 0.00 0.05 0.00-0.02 13 MDL 0.84 0.01 0.82 0.17 0.01 0.79 0.13 0.01 0.84 0.14 0.01 0.01 0.01-14 SLD 0.18 0.82 0.02 0.77 0.21 0.01 0.73 0.18 0.01 0.76 0.20 0.01 0.01 0.01-0.18 15 KRSC 0.89 0.11 0.04 0.03 0.93 0.02 0.01 0.94 0.03 0.02 0.02 0.93 0.04 0.00 0.02-0.89 16 MSC 0.36 0.64 0.83 0.02 0.04 0.19 0.60 0.00 0.01 0.00 0.98 0.01 0.00-0.83 17 WR 0.67 0.33 0.02 0.83 0.00 0.09 0.70 0.20 0.00 0.87 0.01 0.05 0.00-0.67 18 VCLS05 0.04 0.96 0.01 0.97 0.02 0.01 0.97 0.02 0.01 0.01 0.95 0.02 0.00 0.02 0.01-0.04 19 LWM05 0.93 0.01 0.93 0.06 0.01 0.93 0.05 0.01 0.01 0.92 0.05 0.00 0.02 0.01-20 SLC05 0.02 0.98 0.01 0.98 0.01 0.00 0.98 0.01 0.01 0.00 0.96 0.01 0.00 0.03 0.00-0.02 21 GC05 0.11 0.89 0.01 0.90 0.09 0.01 0.90 0.02 0.01 0.90 0.06 0.01 0.02 0.01-0.11 22 MBS05 0.17 0.83 0.02 0.81 0.02 0.81 0.03 0.02 0.81 0.01 0.02 0.02-0.17 23 ABS04 0.05 0.95 0.01 0.95 0.04 0.01 0.94 0.03 0.03 0.01 0.94 0.02 0.02 0.02 0.01-0.05 24 MSS05 0.06 0.94 0.01 0.94 0.05 0.01 0.93 0.04 0.03 0.01 0.93 0.03 0.01 0.02 0.01-0.06 25 WM 0.06 0.94 0.01 0.95 0.04 0.01 0.94 0.03 0.02 0.01 0.94 0.03 0.01 0.02 0.01-0.06 26 ARB 0.57 0.43 0.02 0.28 0.70 0.01 0.24 0.70 0.05 0.01 0.19 0.74 0.01 0.06 0.01-0.57 27 BRB06 0.66 0.34 0.02 0.17 0.81 0.01 0.13 0.82 0.04 0.01 0.11 0.84 0.01 0.04 0.01-0.66 28 UMC06 0.85 0.01 0.03 0.95 0.00 0.02 0.78 0.20 0.00 0.02 0.96 0.01 0.01 0.00-0.85 29 MC 0.95 0.05 0.01 0.01 0.98 0.00 0.01 0.80 0.19 0.00 0.01 0.98 0.01 0.01 0.00-0.95 30 FMU 0.94 0.06 0.01 0.01 0.98 0.01 0.01 0.81 0.18 0.01 0.01 0.98 0.00 0.01 0.01-0.94 31 FRM 0.90 0.10 0.01 0.03 0.96 0.01 0.01 0.79 0.19 0.01 0.01 0.97 0.01 0.01 0.01-0.90 Page 24 of 40

Code Pop K=2 K=3 K=4 K=5 RT range 32 FCCN 0.98 0.02 0.01 0.01 0.98 0.01 0.00 0.98 0.01 0.01 0.00 0.98 0.00 0.00 0.01-0.98 33 ATB 0.69 0.31 0.01 0.83 0.01 0.12 0.84 0.04 0.01 0.11 0.86 0.01 0.03 0.01-0.69 34 BTB06 0.70 0.30 0.02 0.13 0.85 0.01 0.09 0.87 0.04 0.01 0.88 0.01 0.04 0.01-0.70 35 BRM 0.83 0.17 0.01 0.06 0.93 0.01 0.04 0.93 0.02 0.01 0.03 0.94 0.01 0.01 0.01-0.83 36 SC04 0.85 0.02 0.04 0.94 0.01 0.03 0.93 0.03 0.01 0.02 0.95 0.01 0.01 0.01-0.85 37 ASB06 0.89 0.11 0.02 0.04 0.93 0.02 0.03 0.93 0.02 0.02 0.03 0.94 0.01 0.01 0.02-0.89 38 ASB04 0.91 0.09 0.03 0.04 0.93 0.04 0.03 0.91 0.03 0.04 0.03 0.92 0.01 0.01 0.03-0.91 39 BSB04 0.95 0.05 0.05 0.01 0.94 0.04 0.01 0.94 0.02 0.04 0.01 0.94 0.01 0.01 0.04-0.95 40 BSBa 0.96 0.04 0.06 0.01 0.93 0.01 0.90 0.02 0.08 0.01 0.90 0.01 0.00 0.06-0.96 41 BSBb 0.95 0.05 0.04 0.01 0.95 0.03 0.01 0.94 0.02 0.03 0.01 0.95 0.01 0.01 0.03-0.95 42 BSC 0.93 0.06 0.02 0.93 0.05 0.01 0.92 0.02 0.05 0.01 0.93 0.01 0.01 0.05-0.93 43 DJB 0.98 0.02 0.34 0.01 0.65 0.46 0.01 0.53 0.00 0.49 0.01 0.45 0.00 0.05 0.34-0.98 Page 25 of 40

Table 5. Comparison of rainbow trout introgression estimates across three studies: Stephens 2007, Stephens and May 2011, and Stephens and May 2013. Sample sizes (N) for each data type in each study are given, with minisatellite ( minisat ), microsatellite ( msat ), nuclear SNP ( nsnp ) and mitochondrial SNP ( mtsnp ) or SNP (nsnp and mtsnp data combined) as the data types. Comparisons of 2007 STRUCTURE versus LEADMIX estimates are highlighted in red where values differ by more than 10%; orange highlighting indicates a difference of 5-10%. Comparisons between the 2013 and 2011 microsatellite estimates (and within 2013 estimates) are highlighted in yellow where a difference in introgression estimates is greater than 10%. Samples used as references ("Ref") in analyses use such information a priori are designated. Watersheds as in Table 1, with addition of Owens River ( OW ). Locality Code Waters hed 2006 1 2007 2 2011 4 2013 5 RT Allele Counts STRUCTURE msat N SNP msat. N (SNP,msat) minisat minisat LEADMIX (2003 3 ) m- sat N n SNP STRUCTURE mt SNP m- sat N (SNP,msat) STRUCTURE Transplanted samples "Golden Pond" 2003 GP WR 0.01 -- (11,11) -- -- Wind River 2003 WR WR 0.01 -- (29,29) -- -- 0.67 0.02 (23) Dorst Creek 2004 DOR NFKW 0.01 0.00 0.01 (40,38) 0.02 0.20 (40) Eagle Scout Creek 2005 ESC MFKW 0.02 0.00 0.00 (40,39) 0.01 0.00 (40) Eagle Scout Lake 2005 ESL MFKW 0.02 0.00 0.00 (9,9) 0.01 0.00 (9) Granite Creek Lake Two 2005 GCL MFKW 0.82 1.00 0.98 (3,3) 0.99 0.78 (3) Ferguson Creek Long Meadow 2006 FGC SFKG 0.02 0.00 0.01 (40,38) 0.02 0.01 (40) Grizzly Creek East Fork 2004 GCE SFKG 0.02 0.00 0.01 (18,18) 0.02 0.00 (18) Kennedy Creek 2004 KC MFKG 0.03 0.00 0.01 (40,40) 0.04 0.01 (40) Scenic Meadow Creek 2006 SCM SFKG 0.02 0.00 0.00 (40,40) 0.02 0.01 (40) Lost Creek 2004 LST KR 0.02 0.00 0.01 (40,39) 0.02 0.01 (39) McDermand Lake 3 2004 MDL KR 0.01 0.00 0.03 (10,10) 0.01 (10) Slide Creek 2008 SLD KR 0.04 0.03 0.06 (40,40) 0.18 0.02 (40) Salmon Creek 2004 KRSC KR 0.89 0.04 (21) Milestone Creek 2004 MSC KR 0.36 0.83 (52) Upper Cold Creek 2001 UCC KR 0.00 0.05 (30) Upper Rock Creek 2001 URC KR 0.00 0.09 (12) Middle Rock Creek 2001 MRC KR 0.00 0.13 (30) Lower Rock Creek 2001 LRC KR 0.05 0.02 (27) m- sat K=2 m- sat K=3 N Page 26 of 40

Locality Code Waters hed 2006 1 2007 2 2011 4 2013 5 RT Allele Counts STRUCTURE msat N SNP msat. N (SNP,msat) minisat minisat LEADMIX (2003 3 ) Funston Lake 2002 FL KR 0.02 0.24 (30) Upper Crabtree Lake 2002 CL KR 0.00 (18) Lower Crabtree Lake 2002 LCL KR 0.00 0.03 (14) Crabtree Creek 2001 CC KR 0.00 0.03 (27) Ash Meadow Creek 2002 AMC OW 0.00 0.00 (30) Diaz Creek 2002 DC OW 0.00 0.22 (30) Broodstock populations Horseshoe Creek 1999 HC OW 0.00 (27) 0.02 Cottonwood Lakes 2 (lakes 1,2,3) 2000 CL2 OW 0.11 0.12 (32) 0.05 0.02 (48,32) 0.11 0.19 (32) Cottonwood Lakes 4 (lakes 4,5) 2000 CL4 OW 0.11 0.17 (28,30) 0.05 0.01 (50,30) 0.11 0.25 (30) Little Cottonwood Creek 1 2000 LCC1 OW 0.00 (20) 0.01 (19,19) Little Cottonwood Creek 2 2000 LCC2 OW 0.00 (25) 0.01 (25,25) Chicken Springs Lake 2000 CSL GTC 0.00 0.12 (30) 0.04 0.01 (34,30) 0.00 0.30 (30) m- sat N n SNP STRUCTURE mt SNP m- sat N (SNP,msat) STRUCTURE Johnson Lake 2000 JL GTC 0.00 0.10 (31,26) 0.06 0.01 (39,26) 0.00 0.25 (26) Golden Trout Creek samples Headwaters GTC 1999 HW GTC 0.00 (27) 0.03 (29,29) GTC at Big Whitney Meadow 2008 WM GTC 0.03 0.00 0.03 (40,39) 0.06 0.01 (40) GTC at Big Whitney Meadow 2001 BWM GTC 0.00 (26) 0.02 (40,40) 0.04 0.00 (40,40) Upper Stokes Stringer 1999 USS GTC 0.00 0.02 (27,26) 0.02 0.01 (30,28) 0.00 0.05 (26) Middle Stokes Stringer 1999 MSS GTC 0.00 (24) 0.03 (29,29) Middle Stokes Stringer 2005 MSS05 GTC 0.02 (40,40) 0.04 0.00 0.02 (40,38) 0.06 0.01 (39) GTC Below Stokes Stringer BSS GTC 0.02 (27) 0.03 (30,30) 1999 GTC Above Barrigan Stringer ABS04 GTC 0.03 (28,28) 0.03 0.00 0.03 (28,27) 0.05 0.01 (13) 2004 m- sat K=2 m- sat K=3 N Page 27 of 40

Locality Code Waters hed 2006 1 2007 2 2011 4 2013 5 RT Allele Counts STRUCTURE msat N SNP msat. N (SNP,msat) minisat minisat LEADMIX (2003 3 ) m- sat N n SNP STRUCTURE mt SNP m- sat N (SNP,msat) STRUCTURE Mouth of Barrigan Stringer MBS GTC 0.00 0.08 (27,29) 0.04 0.02 (30,29) 0.00 0.06 (29) 0.06 0.00 (40,15) 1999 Mouth of Barrigan Stringer MBS05 GTC 0.04 (40,40) 0.17 0.02 (37) 2005 GTC Below Barrigan Stringer BBS GTC 0.06 (28) 0.04 (30,30) 1999 Groundhog Creek 2005 GC05 GTC 0.03 (39,39) 0.11 0.01 (37) Groundhog Creek 2000 GC00 GTC 0.01 Ref. (32,22) 0.03 0.01 (37,23) Ref. Ref. (22) Lower Johnson Creek 1999 LJC GTC 0.00 (23) 0.02 (32,32) Middle Johnson Creek 1999 MJC GTC 0.00 <0.01 (29,28) 0.02 0.02 (30,28) 0.00 0.08 (28) Johnson Creek 2000 JC GTC 0.00 0.02 (24,24) Salt Lick Creek 2000 SLC GTC 0.00 (40) 0.01 (40,40) Salt Lick Creek 2005 SLC05 GTC 0.01 (33,33) 0.02 0.01 (33) GTC Below Little Whitney LWM GTC 0.02 Ref. (22,19) 0.02 0.05 (38,19) Ref. Ref. (19) Meadow 2001 GTC Below Little Whitney LWM05 GTC 0.02 (40,40) 0.02 0.00 0.05 (40,40) 0.01 (40) 2005 Volcano Creek, Left Stringer VCLS05 GTC 0.01 (41,41) 0.01 0.00 0.01 (41,41) 0.04 0.01 (41) 2005 Volcano Creek 2000 VC GTC 0.00 Ref. (32,29) 0.01 0.02 (39,29) Ref. Ref. (29) 0.01 0.00 (39,39) Lower Volcano Creek 1996 GTLV GTC 0.00 (13) South Fork Kern samples Upper South Fork Kern 2001 USFK SFK 0.08 0.04 (42,30) 0.12 (30) SFK above Ramshaw Barrier 1999 ARB99 SFK Ref. (21,30) 0.12 (30,30) 0.04 0.13 (30) SFK above Ramshaw Barrier 2006 ARB06 SFK 0.57 0.02 (40) SFK below Ramshaw Barrier 2002 BRB02 SFK 0.13 (29,30) 0.09 0.08 (30) Kern Peak Left Stringer 2002 KPLS SFK 0.08 0.13 (30,30) 0.08 0.12 (30) Below Movie Stringer 2001 BMS SFK 0.13 0.05 (30,30) 0.09 0.08 (30) SFK below Ramshaw Barrier BRB06 SFK 0.66 0.02 (40) 2006 m- sat K=2 m- sat K=3 N Page 28 of 40

Locality Code Waters hed 2006 1 2007 2 2011 4 2013 5 RT Allele Counts STRUCTURE msat N SNP msat. N (SNP,msat) minisat minisat LEADMIX (2003 3 ) m- sat N n SNP STRUCTURE mt SNP m- sat N (SNP,msat) STRUCTURE SFK above Templeton Barrier ATB06 SFK 0.69 0.01 (41) 2006 SFK above Templeton Barrier ATB02 SFK 0.13 (30,30) 0.20 0.08 (30) 2002 Upper Mulkey Creek 2001 UMC SFK 0.02 0.01 0.00 0.13 (30) 0.02 0.00 (30,30) Upper Mulkey Creek 2006 UMC06 SFK (30,30) 0.85 0.01 (40) Mulkey Creek above lower barrier 2009 MC SFK 0.11 0.00 0.01 (40,40) 0.95 0.01 (39) Freckles Meadow, Upper 2009 FMU SFK 0.05 0.00 0.01 (7,7) 0.94 0.01 (7) Freckles Meadow 2006 FRM SFK 0.90 0.01 (37) Four Canyons Creek 2002 FCC SFK 0.09 (30,30) Ref. Ref. 0.13 0.00 (30,30) Four Canyons Cr North Fork 2009 FCCN SFK 0.44 0.00 0.01 (15,15) 0.98 0.01 (15) SFK below Templeton Barrier 2002 BTB02 SFK 0.17 (30,30) 0.24 0.08 (30) SFK below Templeton Barrier 2006 BTB06 SFK 0.70 0.02 (39) SFK above Schaeffer Barrier 2002 ASB02 SFK 0.35 -- (30,30) 0.33 0.25 (30) SFK above Schaeffer Barrier 2004 ASB04 SFK 0.33 0.34 (40,40) -- -- 0.41 0.03 (40,31) 0.91 0.03 (37) SFK above Schaeffer Barrier 2006 ASB06 SFK 0.89 0.02 (18) SFK below Schaeffer Barrier 2004 BSB04 SFK 0.29 -- (40,40) -- -- 0.34 0.18 0.02 (40,28) 0.95 0.05 (30) SFK below Schaeffer Barrier a 2006 BSBa SFK 0.96 0.06 (21) SFK below Schaeffer Barrier b 2006 BSBb SFK 0.95 0.04 (39) Strawberry Creek 2004 SC04 SFK 0.29 -- (40,40) -- -- 0.33 0.01 (40,39) 0.85 0.02 (30) Brown Meadow Creek 2006 BRM SFK 0.83 0.01 (25) Dutch John Barrier 2008 DJB SFK 0.77 0.50 0.25 (2,2) 0.98 0.34 (2) SFK Below Snake Creek 2006 BSC SFK 0.93 0.06 (39) m- sat K=2 m- sat K=3 N Page 29 of 40

Locality Code Waters hed 2006 1 2007 2 2011 4 2013 5 RT Allele Counts STRUCTURE msat N SNP msat. N (SNP,msat) minisat minisat LEADMIX (2003 3 ) Monache Meadows 2002 MM SFK 0.32 0.22 (30,30) 0.26 (30) Middle Fish Creek 2001 MFC SFK 0.22 0.08 (40,30) 0.48 0.25 (30) Upper Trout Creek 2001 UTC SFK 0.29 0.99 (30,30) 0.83 0.37 (30) Kennedy Meadows 2003 AKM SFK 0.94 0.95 (8,8) 0.88 0.75 (4) Rockhouse Basin 2004 RHB SFK 0.61 -- (10,10) -- -- Rainbow trout reference samples m- sat N n SNP STRUCTURE Hot Creek Strain 2002 HCS HAT 0.97 0.99 (30,29) Ref. Ref. (29) 0.99 1.00 (30,30) mt SNP m- sat N (SNP,msat) STRUCTURE Hot Creek Strain 2011 RTHC HAT 0.97 1.00 0.99 (50,50) 0.99 0.79 (50) Mt. Whitney Strain 2002 MWS HAT 0.99 0.98 (30,30) Ref. Ref. (30) Mt. Whitney Strain 2011 MW HAT 0.99 1.00 0.99 (30,38) 0.99 0.79 (39) Mt. Shasta Strain 2001 MSH HAT 0.98 0.99 (31,30) Ref. Ref. (30) 0.98 1.00 (30,30) NF American River 2010 AR NFAR 0.99 1.00 0.89 (20,30) 0.95 0.65 (31) NF American River 2000 NFAR NFAR 1.00 (8) 0.99 0.96 (20,24) Ref. Ref. (23) North Fork Navarro River 2000 NFNR NFNR 0.97 0.96 (31,29) Ref. Ref. (30) 1 Cordes et al. 2006 first identified alleles present in presumably hybridized lake samples that were absent from the presumably nonhybridized Golden Trout Creek and South Fork Kern River populations. This yielded a total of 14 presumed rainbow trout alleles spread over five of the six microsatellite loci; Twelve of these alleles occurred in Cottonwood Lakes Number 2, nine in Cottonwood Lakes Number 4, six in Johnson Lake, and three in Chicken Springs Lake. Nine of these alleles have been found to occur in wild steelhead (anadromous rainbow trout) and various strains of hatchery rainbow trout (authors, unpublished data). 2 Stephens 2007 reanalyzed the Cordes et al. 2003 and 2006 six-locus microsatellite data sets using STRUCTURE and compared to Cordes' 2003 LEADMIX estimates of introgression. New SNP data were also collected on additional population samples and analyzed for seven nuclear and one mitochondrial SNP and introgression estimates analyzed from the joint SNP data set. 3 Cordes et al. 2003 analyzed introgression using LEADMIX software and using parental population reference samples 4 Stephens and May 2011 analyzed transplanted populations and selected golden trout creek and South Fork Kern River populations using the seven SNPs used in Stephens 2007 and 11 microsatellite loci and nsnps. Estimates of introgression were made separately for nuclear and mitochondrial SNPs. 5 Stephens and May 2013 analyzed populations using 13 microsatellite loci, 11 of which were the same as Stephens and May 2011 m- sat K=2 m- sat K=3 N Page 30 of 40

Figure 1. Map depicting sampling localities used in this study. Locality codes as given in Table 1. Page 31 of 40

Figure 2. UPGMA dendrogram of Da genetic distances for all populations with bootstrap support (out of 1000 replicates) given at each node. Locality codes as given in table 1. Circles are used to categorize the sampled populations: Transplanted populations (open circle), rainbow trout reference populations (blue), South Fork Kern River (green), and Golden Trout Creek (orange). Page 32 of 40

Figure 3. FCA of microsatellite data for all individuals at all localities; codes as given in Table 1. The wild AR rainbow trout (in red) and lower SFK sample at DJB are intermediate between three rainbow trout samples (MW, RTHC, and the GCL transplant sample) and all other CAGT populations. Milestone Creek (MSC) is separate from the other groupings, including all transplants. Page 33 of 40