characterizing genetic integrity of rear-edge trout...
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Conservation Genetics (2018) 19:1487–1503 https://doi.org/10.1007/s10592-018-1116-1
RESEARCH ARTICLE
Characterizing genetic integrity of rear-edge trout populations in the southern Appalachians
Kasey C. Pregler1,2 · Yoichiro Kanno1,2 · Daniel Rankin3 · Jason A. Coombs4,5 · Andrew R. Whiteley6
Received: 22 September 2017 / Accepted: 12 October 2018 / Published online: 30 October 2018 © Springer Nature B.V. 2018
AbstractVertebrate populations at the periphery of their range can show pronounced genetic drift and isolation, and therefore offer unique challenges for conservation and management. These populations are often candidates for management actions such as translocations that are designed to improve demographic and genetic integrity. This is particularly true of coldwater spe-cies like brook trout (Salvelinus fontinalis), whose numbers have declined greatly across its historic range. At the southern margin, remnant wild populations persist in isolated headwater streams, and many have a history of receiving translocated individuals through either stocking of hatchery reared fish, relocation of wild fish, or both during restoration attempts. To determine current genetic integrity and resolve the genetic effects of past management actions for brook trout populations in SC, USA, we genetically assessed all 18 documented remaining brook trout populations along with individuals acquired from six hatcheries with recorded stocking events in SC. Our results indicated that six of the 18 streams showed signs of hatchery admixture (range 57–97%) and restored patches retained genetic signatures from multiple source populations. Populations had among the lowest genetic diversity (min average HE = 0.147) and effective number of breeders (mean Nb = 31.2) esti-mates observed throughout the native brook trout range. Populations were highly differentiated (mean pair-wise FST = 0.396), and substantial genetic divergence was evident across major river drainages (max pair-wise FST = 0.773). The lowest local genetic diversity and highest genetic differentiation ever reported for this species make its conservation a challenging task, particularly when combined with other threats such as climate change and non-native species. We offer recommendations on managing peripheral populations with depleted genetic characteristics and provide a reference for determining which existing populations will best serve as sources for future translocation efforts aimed at enhancing or restoring wild brook trout genetic integrity.
Keywords Appalachian mountains · Effective population size · Genetic drift · Admixture · Microsatellite · Translocation
Introduction
Widespread evidence indicates that the modern rates of extinction in plants and animals exceed background rates in the fossil record (Burkhead 2012). In the north-ern hemisphere, southern populations of vertebrates have
Electronic supplementary material The online version of this article (https ://doi.org/10.1007/s1059 2-018-1116-1) contains supplementary material, which is available to authorized users.
* Kasey C. Pregler [email protected]
1 Department of Forestry and Environmental Conservation, Clemson University, Clemson, SC 29631, USA
2 Department of Fish, Wildlife, and Conservation Biology, Colorado State University, Fort Collins, CO 80523-1474, USA
3 South Carolina Department of Natural Resources, Clemson, SC 29631, USA
4 Department of Environmental Conservation, University of Massachusetts Amherst, Amherst, MA 01003, USA
5 USDA Forest Service, Northern Research Station, University of Massachusetts, Amherst, MA 01003, USA
6 Wildlife Biology Program, Department of Ecosystem and Conservation Sciences, University of Montana, Missoula, MT 59812, USA
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suffered disproportionately (Hoffmann et al. 2010; Evans et al. 2016) and this trend is expected to accelerate due to future environmental changes (Currie 2001; Davis and Shaw 2001). Declining populations at the southern edge of their range are often relegated to small, isolated habi-tat, which can have important demographic and genetic consequences. Cessation of gene flow following isolation can lead to a loss of genetic variation and pronounced genetic divergence through genetic drift (Allendorf 1986; Lesica and Allendorf 1995; Hampe and Petit 2005). Thus, rear-edge populations have often received intensive man-agement actions (Evans et al. 2016). For example, trans-locations of individuals are used to mitigate the impacts of decline (Griffith et al. 1989; Fischer and Lindenmayer 2000), where individuals are moved to establish repre-sentative, replicate, and resilient populations throughout the species’ historical range (IUCN 1987; Armstrong and Seddon 2007).
While translocations can be particularly important for species conservation, they can have variable success (Fis-cher and Lindenmayer 2000; Weeks et al. 2011; Redford et al. 2011). Translocations typically move individuals from one or more source populations into existing populations or vacant habitat (Weeks et al. 2011). It is important to bal-ance the potential benefits of such translocations against trade-offs and risks that could counteract conservation goals (Weeks et al. 2011). Despite extensive research on transloca-tions, the majority of these studies have focused on birds and mammals (Brichieri-Colombi and Moehrenschlager 2016; Hampe and Petit 2005; Fischer and Lindenmayer 2000). Translocations are common in fishes, but freshwater habitats have been far less studied in this regard (however see Huff et al. 2010, 2011; Robinson et al. 2017).
Freshwater habitats occupy < 1% of the Earth’s surface, yet are hotspots that support ~ 10% of all known species and 1/3 of vertebrate species (Dudgeon et al. 2006). Further-more, in addition to translocations for conservation, fishes have been extensively stocked with hatchery-raised conspe-cifics for recreational angling (Laikre et al. 2010; Gozlan et al. 2010). For instance, stocking is common for cold-water species like members of the Salmonidae family (Haak et al. 2010). Salmonids represent an important natural resource globally, and are one of the most angled fish species in the United States (Maillett and Aiken 2015). In particular, rain-bow trout (Oncorhyncus mykiss), brown trout (Salmo trutta) and brook trout (Salvelinus fontinalis) have expanded their distribution due to stocking (Gozlan et al. 2010). Histori-cally, these hatchery fish were stocked with little regard for the genetic make-up of or potential interactions with wild populations. Hybridization with hatchery fish may result in outbreeding depression and loss of local adaptation in wild populations (Rhymer and Simberloff 1996; Currens et al.
1997; Allendorf et al. 2001; Araki et al. 2007), increasing the likelihood of decreased fitness.
Brook trout are an iconic native salmonid in eastern North America occupying habitat from Canada to the southern Appalachians. Populations have declined throughout its native range, particularly at the southern range (Hudy et al. 2008) due to competition with invasive species (Larson and Moore 1985; Dewald and Wilzbach 1992), habitat loss, and climate change (Meisner 1990; Curry and MacNeill 2004; Wenger et al. 2011). Brook trout are typically restricted to small headwater streams at the southern range, which makes their isolated populations challenging to conserve (Hudy et al. 2008). In order to compensate for the decline in wild fish populations, hatchery reared brook trout have been stocked since the late 1800s (Krueger and Menzel 1979). Hatchery produced brook trout that are derived from northeastern US stocks are used in the majority of stock-ing efforts because southern brook trout were challenging to raise in hatcheries (Lennon 1967). Evidence of genetic swamping by hatchery fish in wild populations has been documented throughout the brook trout range (Krueger and Menzel 1979; McCracken et al. 1993; Hayes et al. 1996; Marie et al. 2010; Lamaze et al. 2012), and conserving and restoring populations with native genotypes is a manage-ment priority in southern populations (Habera and Moore 2005). These anthropogenic alterations can have deleteri-ous effects on a species’ genetic integrity. Here integrity is defined as conditions that have little to no anthropogenic influence, such that natural evolutionary and biogeographic processes are allowed to occur (Angermeier and Karr 1994). While factors that influence genetic integrity have been studied in parts of the northern range (Castric et al. 2001; Marie et al. 2010; Kanno et al. 2011; Annett et al. 2012; Fraser et al. 2014; Kelson et al. 2015), few studies exist for the southern-most range of brook trout (Stoneking et al. 1981; Hayes et al. 1996; Kazyak et al. 2018). This is of concern because the southern portion of the range is the most anthropogenically influenced as evidenced by a 50% reduction in current distribution compared to historic occur-rence (Hudy et al. 2008; Whiteley et al. 2014b). In addition to hatchery stocking, translocations of wild individuals have occurred as well. These wild translocations have included efforts to improve genetic integrity of extant populations (Robinson et al. 2017), and to reestablish populations in his-torical habitats (Kanno et al. 2016).
Here we present a case study of rear-edge brook trout populations at the southern-most limit in SC, USA, that have been subject to both historical stocking events for recrea-tional fishery and contemporary translocation actions for conservation. Whether translocations or stocking, records of fish movement have not always been kept and their demo-graphic and genetic consequences are rarely monitored (however see Robinson et al. 2017), presenting challenges
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for conserving small, isolated populations. This has created a need for an updated assessment of genetic diversity and integrity for the documented remaining wild populations in SC. We addressed the following questions: (1) what were the genetic effects of past management practices such as translocations and hatchery stocking events? (2) are southern genotype signatures still present in these populations, and how is that variation partitioned within and among remain-ing populations? Answers to these questions were then used to guide future conservation efforts at the southern edge of the range for brook trout.
Methods
Study area
This study was conducted in mountain streams of SC, USA, located at the southern distribution of brook trout, the only salmonid native to this region (Fig. 1). In North America, introduced rainbow trout have displaced many populations of native brook trout (Habera and Moore 2005; Kanno et al. 2016). Introduced rainbow trout typically occupy habitat
downstream, whereas brook trout occur upstream, often only above physical barriers that block upstream migration for rainbow trout (Larson and Moore 1985). Natural popu-lations of brook trout in the state are currently restricted to the 18 documented streams used in this study. Through-out the paper, we refer to study streams as patches. A patch represents a spatially continuous headwater network, within which movement of individuals was assumed in the absence of physical barriers, and follows the definition of the Eastern Brook Trout Joint Venture (Whiteley et al. 2014b; EBTJV 2016). Historic brook trout stocking in SC occurred from the 1800s to approximately the 1970s. In the 1990s, stocking resumed in response to a demand by anglers and has con-tinued in areas where stocked fish cannot access the extant brook trout streams due to physical barriers to movement.
In the 1990s, the SC Department of Natural Resources (SC-DNR) initiated an allozyme analysis to determine if hatchery admixture was present in remnant brook trout populations. The allozyme study assessed 11 out of the 18 patches (Bad, Crane, Emory, Falls, Headforemost, Indian Camp, Ira, Jacks, Matthews, Pig Pen, and Slicking) included in the present study. They identified evidence of two patches replaced by hatchery genotypes, and two southern genotype
Fig. 1 Map of eastern United States with current brook trout range shaded in gray and locations of hatchery samples in stars (a). Previ-ous translocation efforts in SC brook trout patches (b). Blue lines rep-resent translocations of southern fish, dashed blue lines represent fish
that were moved after the source patch was restored. Red lines rep-resent translocations of fish with identified hatchery admixture based on the 1990s allozyme results. (Color figure online)
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patches. The remaining patches were an admixture of hatch-ery and southern genotypes (Table 1) (Guffey 1993). Man-agement actions followed to conserve and restore southern brook trout populations, including removal of hybridized fish and subsequent translocation events. Most translocation efforts aimed at moving individuals from southern popula-tions to restore and enhance brook trout populations at other locations (i.e., conservation focus), but some moved hybrid-ized fish to other sites primarily for providing recreational angling opportunities (e.g., translocations of known hybrid-ized fish to Tammassee and Carrick Creeks; Fig. 1). While the allozyme study informed management actions, the des-ignation of southern (native) versus hatchery samples relied exclusively on a single diagnostic southern allele at the CK-A2* locus (Guffey 1993). This single-locus analysis lacked statistical power and, more importantly, used a locus that might not have been diagnostic across the southern range (Hayes et al. 1996). Accordingly, the present multi-locus microsatellite study was intended to more accurately assess the current genetic characteristics of brook trout populations at the southern distributional margin.
Field sampling
Genetic samples were collected between July 2014 and July 2016. Using backpack electrofishing, a total of 1111 young-of-the-year (YOY) brook trout were captured from 18 patches in upstate SC in the Santee and Savannah River drainages (Fig. 1). Our goals were to determine spatial population structure, degree of hatchery admixture, and to estimate single-cohort effective number of breeders (Nb) per patch. We sampled YOY (defined as less than 100 mm in total length) to conduct all of these analyses following recommendations in Whiteley et al. 2013. During summer, YOY can be identified based on length frequency histo-grams. Since sampling strategy (the number of individuals and locations where they are collected) can influence esti-mates of Nb through family representation effects (Whiteley et al. 2012), each patch was divided into a lower, middle, and upper section with a goal of 25 YOY sampled from each section (i.e., 75 YOY per patch). Smaller patches only contained two sections whereas one large patch (Matthews Creek) contained five sections.
Hatchery samples (ranging from 30 to 46 individuals per hatchery) were obtained from six hatcheries (Berlin Hatch-ery, New Hampshire; Burton State Fish Hatchery, Georgia;
Table 1 Allozyme results from the unpublished SC-DNR hatchery admixture study (Guffey 1993) and the subsequent management actions and translocation events that took place from 2005 to 2010 during the restoration of Crane, King, Laurel Fork, and Moody Creeks
NA values for allozyme results indicates that a given patch was not included in the study conducted in the 1990s
Allozyme results Hatchery admixed fish and invasives removed Received translocations from Year of restora-tion
Savannah drainage Bad Creek Hybrid Carrick Creek NA Received hatchery admixed fish from Crane Restored King and Crane 2006 Crane Hybrid Hatchery admixed fish removed Jacks, Slicking, and 3 creeks in Georgia 2006 Indian Camp Hybrid Ira Branch Hybrid Jacks Branch Native King NA Brown trout removed Jacks, and 1 creek in Georgia and North Caro-
lina2005
Laurel Fork NA Restored King 2010 Moody NA Restored King 2008 Pig Pen Hybrid Tammassee NA Received hatchery admixed fish from Crane
Santee drainage Emory Creek Hybrid Restored Crane and King 2012 Falls Creek Hybrid Headforemost Hybrid Laurel Creek NA Matthews Hatchery in origin Slicking Native South Saluda NA
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Dry Mill Hatchery, Maine; Governor Hill Hatchery, Maine; Walhalla Hatchery, SC; Wytheville Hatchery, Virginia) along the East Coast. These hatcheries were chosen based on historic stocking records from D.C. Booth Historic National Fish Hatchery and Archives (Spearfish, South Dakota) and anecdotal records in SC (D. Rankin, SC-DNR, unpublished data). Stocking has not occurred in these stream patches since the 1970s. Our inference is based on the assumption that our hatchery samples accurately represent genetic com-position of the hatchery populations at the time of stocking.
Individuals were measured for total length and weight, and anal or caudal fin tissue was collected non-lethally as a DNA source. Samples were genotyped using 12 microsatel-lite markers: SfoC113, SfoC88, SfoD100, SfoD75, SfoC24, SfoC115, SfoC129, SfoB52, SfoC86, SfoD91a, SfoC38 (King et al. 2003) and SsaD237 (King et al. 2005), following pro-tocols for DNA extraction and amplification detailed in King et al. (2005). Loci were electrophoresed on an ABI Prism 3130xl genetic analyzer (Applied Biosystems Inc., Foster City, CA), and alleles were hand-scored using Geneious ver-sion 7.0.6 (Kearse et al. 2012).
Statistical analysis
Single cohort samples with large numbers of siblings from the same family can cause deviations from Hardy–Weinberg (HW) expectations, elevated linkage disequilibrium (LD), and bias in genetic structure analyses (Allendorf and Phelps 1981; Anderson and Dunham 2008; Rodriguez-Ramilo and Wang 2012; Whiteley et al. 2013; Waples and Anderson 2017). We performed all analyses, with the exception of Nb (for which all individuals were always included), with the entire dataset and with a ‘sib-purged’ dataset. The sib-purged data set was a truncated dataset where we randomly sampled full-siblings within families (Waples and Anderson 2017). We followed the ‘yank-2’ procedure of Waples and Anderson (2017), where we included all individuals from estimated full-sib families of size one and two, and randomly selected two individuals from families of size three or larger. This approach is expected to provide a balance between sib over-representation and the effects of reduced sample size from more severe ‘sib-purging’ (Waples and Ander-son 2017). Full-sibling family structure was determined for each patch using COLONY V2 (Wang 2004). Settings for COLONY analyses included the assumption of male and female monogamy, outbreeding model, very long run length with the full-likelihood model, sibship prior, and no allele frequency updates. Finally, we used a paired t-test to evalu-ate if summary statistic results were significantly different between the full and sib-purged datasets.
Since our stream patches were determined a-priori with-out any genetic information, and barriers to gene flow may be present in these patches, we assessed genetic structuring
within patches. If within-structure was found, patches were split. To investigate within patch structure, first we ran genetic clustering analyses using STRU CTU RE version 2.3.4 (Pritchard et al. 2000), and then calculated within patch genic differentiation tests. STRU CTU RE runs were conducted hier-archically, with a first run to identify hatchery admixture by including all 18 SC brook trout patches and six hatcheries. A second STRU CTU RE run was performed on only the 18 SC patches to identify genetic clustering among wild popula-tions. To verify spatial structuring of southern genotype fish, a third STRU CTU RE run was performed where we omitted any patches with evidence of hatchery admixture greater than 20 percent, as identified after the first and second runs. All STRU CTU RE runs were performed using 20,000 burn-in and 100,000 iterations, with five replicates for each value of K. We used the admixture model, with correlated allele frequencies, and no location prior. We tested K = 1–24 for the patch set that included hatcheries and we tested K = 1–18 for the patch set that excluded hatcheries. The number of clusters were determined by visually inspecting the likelihood plots. STRU CTU RE results were visualized by creating bar plots in the program STRU CTU RE PLOT (Ramasamy et al. 2014). We also used a discriminate analysis of principle compo-nents (DAPC) using the adegenet package (Jombart 2008) in program R (R Development Core Team 2008). DAPC was performed where clusters were determined using the find.cluster function for k ranging from 1 to 24 (SC patches and hatcheries dataset), and 1–18 (SC patches only). Clusters were evaluated using Bayesian Information Criterion (BIC) to determine the appropriate k value, where the k with the lowest BIC value is typically the optimal number of clus-ters. However, BIC values may keep decreasing after the true k value (Jombart et al. 2010), so we visually examined the rate of decrease in BIC to identify values of k, after which BIC values decreased only slightly (Jombart et al. 2010). A DAPC analysis was performed for each grouping using the dapc function for the best k identified as described above. We retained all axes of the principal component analysis to explain the variation within the data, and created an ordi-nation plot with the first and second axes to visualize the clusters. To further investigate within patch structure, we cal-culated pairwise FST values among sections within patches, and performed genic tests using Fisher’s exacts tests for all pairs of sections in GENEPOP. We applied the B-Y FDR (Benjamini and Yekutieli 2001) correction method to genic tests following Narum (2006).
After we redefined patches, genotype data were analyzed using GENEPOP (Raymond and Rousset 1995) to gener-ate summary statistics for genetic diversity, specifically the number of alleles and the proportion of observed (HO) and expected (HE) heterozygotes. We investigated non-random mating in our patches through assessing the conformity of loci to HW proportions, and linkage disequilibrium (LD)
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to assess non-random association of alleles among loci. Given the small and isolated study patches, we estimated FIS for each patch to assess the departure of observed from expected heterozygosity (Keller and Waller 2002). We estimated genetic differentiation with pairwise FST values among patches, and performed genic tests using Fisher’s exacts tests for all pairs of patches in GENEPOP and applied the B-Y FDR correction. Lastly, we performed a Mantel test (Mantel 1967) in GENALEX (Peakall and Smouse 2005) to investigate associations between FST and geographic distances across all patches. Statistical significance for the Mantel test was obtained by using 9999 permutations and a p-value of 0.05.
The effective number of breeders (Nb) was estimated for each stream patch using the linkage disequilibrium method in program NeEstimator 2.01 (Do et al. 2013) using a monogamous mating system and a minimum allele fre-quency of 0.02, following the method of Whiteley et al. (2013). We used the monogamy mating model because brook trout appear to conform more closely to monogamy than random mating (Coombs 2010). Nb is calculated as an equivalent to effective population size (Ne) when working with samples from a single cohort for species with genera-tional overlap and estimates the effective number of breeders that gave rise to that cohort (Waples 2005; Waples and Do 2010). All individuals were used to calculate Nb since family structure forms the basis for this estimation, and sib-purg-ing causes upward bias in estimates (Waples and Anderson 2017). Since studies have observed positive relationships among patch size, genetic diversity and effective population size (Whiteley et al. 2010; Peacock and Dochtermann 2012) we used a simple linear regression model to examine the effect of patch size on estimates of genetic variation within patches and Nb. We also analyzed the relationship between Nb and genetic variation within patches, and used a Pearson’s correlation to examine if our sample sizes were correlated with Nb.
Results
COLONY identified 485 families across the 18 SC patches, and 51% were single fish families (mean family size = 2.28). Letcher et al. (2011) demonstrated high accuracies of sibship reconstruction in COLONY based on the same 12 micros-atellite loci used in the present paper. Reconstructed full-sibling families composed of at least two individuals had a rate of correct family inference of 91.2% (0.7% SE) and for full-sibling families of at least five individuals the correct family inference was 97.7% (0.4% SE) (Letcher et al. 2011). Furthermore, it is common to have close to 50% singleton families for brook trout (Whiteley et al. 2014a), especially when sampling the population to minimize full-sibling
overrepresentation (Whiteley et al. 2012). Sibship removal reduced our mean patch sample size from ~ 62 for all indi-viduals to ~ 39 for the siblings purged dataset. We present genetic clustering results with the all individuals dataset due to the trade-offs between accounting for family effects and having large enough sample size for genetic structuring anal-yses (Patterson et al. 2006; Anderson and Dunham 2008). However results between the full and sibling-purged dataset were similar and we reached the same conclusions with both.
Our first STRU CTU RE run, which included genotype data from all 18 SC patches as well as the six hatcheries, revealed four genetic clusters. One of these clusters had six out of the 18 SC patches assigned with the hatchery sam-ples which presented the first evidence that these patches may have hatchery admixture (Fig. S1). The second STRU CTU RE run excluding hatchery samples showed that the 18 stream patches made up five genetic clusters, exhibiting further subdivision in comparison to the first STRU CTU RE run. We again had one cluster characterized with those six patches that may have hatchery admixture (Fig. 2), and the four remaining clusters which may consist of southern genotype fish. The third STRU CTU RE run, which removed those patches with evidence of hatchery admixture, corrobo-rated the genetic clustering assignment of the second run and showed four genetic clusters of southern fish.
STRU CTU RE and DAPC results were concordant, and the k-means clustering BIC and DAPC also found four clus-ters when 18 SC patches and six hatcheries were analyzed (Fig. S2). Once hatcheries were removed, BIC and DAPC results provided further evidence for five distinct clusters (Fig. 3) and DAPC cluster assignment (Table S1) was similar to the STRU CTU RE results. While hatchery admixture was present in six out of 18 patches, three of those six patches appeared to consist almost entirely of hatchery-descendant fish (Bad, Matthews, and Tammassee) (range 0.891–0.963), whereas the remaining three patches (Carrick, Emory, and Indian Camp) had varying proportions of admixture (range 0.255–0.542) (Table S2 and S3). Several patches (i.e. Car-rick, Emory, and Indian Camp) showed genetic assignment to multiple clusters and even to clusters from different river drainages (Fig. 4). The lower and middle sections of Car-rick could be entire replacement by hatchery descendants. Emory and Indian Camp exhibited hatchery admixture across all sections sampled within each of these patches. The DAPC plot (Fig. 3) showed evidence of a river drainage effect where axis 1 aligned wild brook trout clusters from an east–west gradient. Clusters belonging to Savannah (blue and green) and Santee (yellow and purple) river drainages separated from one another. Further structuring occurred within each of these drainages. Within the Santee there were clusters representing the middle and south Saluda basins, while the Savannah River drainage was structured into two clusters (blue and purple).
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We detected evidence for admixture among wild popula-tions that was likely associated with previous management actions (translocations) conducted in the early 2000s. In the STRU CTU RE bar plots (Figs. S1 and S2), Crane and Carrick both retained genetic signatures from previous translocations of the source populations such as the Santee drainage (yellow) fish that were moved into Crane, and the Savannah drainage (blue) fish that were moved into Carrick. Emory exhibited Savannah drainage (blue), hatchery (red), and Santee drainage (yellow) genetic signatures. The San-tee genetic signature present in Emory was likely whatever remnant population remained prior to receiving translocated fish from King and Crane.
There was little to no fine scale genetic structuring within the majority of SC brook trout patches, with the exception of Carrick and Laurel Creeks. FST values were greater than 0.2 in sections within Carrick Creek and
Laurel Creek (Fig. S3). Using Fisher’s method for com-bined p-values across all loci for genic differentiation tests (B-Y FDR correction, p < 0.0119), we found lower Carrick was significantly different (p < 0.0001) from the middle (FST = 0.11) and upper (FST = 0.33) sections, but the mid-dle and upper were not significantly different from each other (p = 0.0768, FST = 0.20). Individuals from upper Car-rick were assigned to patches with the apparent southern genotype signature in the STRU CTU RE plots, with several possible downstream migrants in middle Carrick (Figs. S1 and S2). STRU CTU RE q-score estimates revealed that eight individuals in middle Carrick were assigned to the blue Savannah drainage cluster where individual q-scores ranged from 0.611 to 0.943 (mean = 0.782). Documenta-tion of past management actions included the transloca-tion of individuals from restored King and Crane Creeks into upper Carrick. During sample collection we noted
Fig. 2 STRU CTU RE likelihood plot (a) and bar plot (b) for data including only the 18 South Carolina brook trout patches. Bar plot for K = 5. Solid black lines separate patches, dashed black lines represent approximate within patch sample divisions ordered from downstream to upstream
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the presence of a waterfall barrier between middle and upper Carrick sections. These two points taken together could explain the genetic differentiation observed with putative downstream migrants likely responsible for the non-significant results between upper and middle Carrick.
In Laurel Creek, the largest FST values were observed when comparing the lower section of Laurel Creek to the middle (FST = 0.265) and upper sections (FST = 0.245). Based on Fisher’s method for combined genic tests (B-Y FDR correction, p < 0.0117), we found that the lower sec-tion was significantly different from the middle and upper sections (p < 0.001), but the middle and upper sections were not significantly different from each other (p = 0.020; FST = 0.043). Laurel Creek has multiple waterfalls between the lower and middle sections, as well as a steep bedrock slide between the middle and upper sections that may be dif-ficult for fish to ascend. All Laurel Creek fish clustered with fish of southern genotype from the Saluda drainage (yellow) in the STRU CTU RE analysis. Based on these results, we assigned individuals in Carrick and Laurel Creeks to within patch origin prior to estimation of within-population genetic diversity summary statistics and Nb.
Within-population genetic diversity was low at many study patches. Expected heterozygosity (HE) ranged from 0.147 (Slicking Creek) to 0.667 (Emory Creek) (Table 2). Mean allelic richness ranged from 1 (Headforemost and
Jacks Branch) to 7 (lower and middle Carrick Creek) (Table 2). Across patches, mean FIS was 0.022 and ranged from − 0.141 to 0.325 (Table 2). High positive FIS values were present in Slicking (0.325) and Falls Creek (0.141). Positive FIS values can be due to family structure or a Wahl-und effect caused by population substructure (Wright 1951). However there doesn’t appear to be a clear biological cause of the variation in FIS values, particularly because sib-purged samples didn’t change FIS much which can suggest that fam-ily structure isn’t responsible. Following sequential Bonfer-roni correction, tests of deviation from HW proportions were significant in 3% of cases (6 of 240 tests, p < 0.0002) (Table 2). Prior to correction for multiple tests, deviations from HW proportions were significant (p < 0.05) in 24% (58 of 240) of cases (Table 2). Following sequential Bonferroni correction, tests for linkage disequilibrium were significant in 9% of cases (113 of 1320 tests, p < 0.00004) (Table 2). Prior to correction for multiple tests, 28% (367 of 1320) of tests for linkage disequilibrium were significant (p < 0.05). We found that by-locus summary statistics calculated with the all individuals dataset (Table S4) and the siblings purged dataset (Table S5) did not statistically differ (p > 0.05), with the exception of HE (p = 0.005) and LD (p = 0.007). Sib-purging decreased evidence of LD in these patches.
Genetic differentiation varied substantially among patches (Table 3). Pairwise FST values ranged from 0.020
Fig. 3 Bayesian information criterion (BIC) plot (a) and discriminant analysis of principal components (DAPC) plot (b) for K = 5 clusters. Cluster 1 (green) represents falls, and headforemost. Cluster 2 (blue) represents Crane, King, Laurel Fork, and Moody. Cluster 3 (red) rep-
resents the SC brook trout patches that have hatchery admixture (Bad, Carrick, Emory, Indian Camp, Matthews, and Tammassee). Cluster 4 (purple) represents Ira Branch, and Pig Pen. Cluster 5 (yellow) repre-sents Laurel Creek, Slicking, and South Saluda. (Color figure online)
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(Laurel Fork and Moody) to 0.773 (Ira Branch and Slick-ing), with a mean pairwise FST value of 0.396 across all comparisons. Large pairwise FST values, such as observed in Ira Branch and Slicking, could be due to spatial struc-ture by major river basins. We followed up this obser-vation with a hierarchical AMOVA that grouped identi-fied southern genotype patches (n = 12) into two groups representing each major basin (Savannah and Santee). AMOVA results showed evidence for spatial structure by basin in which among basin groups accounted for 20% of the variance, among patches within groups accounted for 30%, and within patches accounted for the remaining 50%. When comparing patches with evidence of hatchery admixture, FST values ranged from 0.083 (Matthews and lower and middle Carrick) to 0.309 (Bad and Tammassee) with a mean of 0.2202. Samples from hatcheries had rela-tively low pairwise FST estimates, and ranged from 0.051 (Berlin and Walhalla) to 0.303 (Wytheville and Dry Mill) with a mean of 0.193. Hatcheries have an anecdotal history of swapping strains among other hatcheries so it is not a surprise that the hatchery samples are more genetically similar. However, there are no known published records
of how much trading has occurred among hatcheries (but see Kazyak et al. 2018). Genic differentiation tests showed that all pair-wise patch comparisons were significant using Fisher’s method for combined p-values across all loci (B-Y FDR correction, p < 0.0057). Mantel test results revealed a correlation of r = 0.251 and p-value = 0.0004 across popu-lations illustrating a relationship between differentiation and geographic distance.
Point estimates of effective number of breeders (Nb) varied from 5.0 (upper Carrick Creek) to 116.1 (Matthews Creek) and patch size ranged from 84 ha (lower and mid-dle Carrick Creek) to 1413 ha (Matthews Creek) (Table 2). Confidence intervals for Nb were very wide for patches with low genetic diversity (i.e. Ira, Jack, Slicking, South Saluda) (Table S6). There was a significant (p < 0.001) positive relationship (effect size = 0.077) between patch size and Nb (Fig. 5). However, this was driven by Mat-thews Creek, and when it was omitted there was not a significant relationship (p = 0.054). There also was not a significant relationship between genetic diversity (HE) and patch size (p = 0.357) (Fig. S4), nor between HE and Nb (p = 0.885) (Fig. S5). Pearson’s correlation between patch sample size and Nb revealed a correlation coefficient of 0.42 (p = 0.071), which again appears to be driven by
Fig. 4 STRU CTU RE results that illustrate five clusters of brook trout patches in SC. Cluster 1 (green) represents Falls, and Headforemost. Cluster 2 (blue) represents Crane, King, Laurel Fork, and Moody. Cluster 3 (red) represents the SC Brook trout patches that have hatch-
ery admixture (Bad, Carrick, Emory, Indian Camp, Matthews, and Tammassee). Cluster 4 (purple) represents Ira Branch, and Pig Pen. Cluster 5 (yellow) represents Laurel Creek, Slicking, and South Saluda. (Color figure online)
1496 Conservation Genetics (2018) 19:1487–1503
1 3
Matthews Creek. After Matthews is omitted the correla-tion coefficient is 0.064 (p = 0.798).
Discussion
Genetic characteristics of southern populations
Our brook trout populations at the southern margin con-tained a mix of southern and northern-admixed genetic signatures. Six out of the 18 study patches exhibited signs of admixture from past stocking events. Three of those six patches showed high amounts of hatchery admixture and could be entirely hatchery descendants (Bad Creek, Matthews, and Tammassee; range 0.891–0.963), and 12 patches did not show signs of hatchery influences. We can-not definitively exclude the possibility of missing a hatch-ery source that was used for stocking. While over time genetic drift may have made these populations more dif-ferentiated from the individuals that were initially stocked, our genetic structuring results still showed evidence of admixture. Our review of historic records and anecdotal evidence was exhaustive and the six hatcheries used in this study should represent historical sources of hatchery
samples. Thus, these 12 populations are likely of southern descent and have persisted at the southern edge despite small habitat size and historical stocking efforts.
Genetic diversity in the southern patches was low rela-tive to other studies of brook trout. In fact, these patches have some of the lowest genetic diversity recorded (mean HE= 0.442, range 0.147–0.712) when compared to existing microsatellite-based studies from other parts of the brook trout range (across studies mean = 0.617; range 0.190–0.797, see Table S7 for study-specific estimates) (Castric et al. 2001; Kanno et al. 2011; Annett et al. 2012; Whiteley et al. 2013; Hoxmeier et al. 2015; Kelson et al. 2015). Low genetic diversity is a common trait of rear edge populations (Davis and Shaw 2001; Hampe and Petit 2005). We also observed very high pairwise FST values (mean = 0.396, range 0.020–0.773) among patches, suggesting a great deal of genetic drift has occurred in these isolated patches. Studies of similar spatial scale of < 100 km report mean FST = 0.124 (range 0.096–0.159) among brook trout sites (Whiteley et al. 2013). Rear edge populations can exhibit disproportionately high levels of genetic differentia-tion among populations, even between nearby ones (Hampe and Petit 2005).
Table 2 Summary statistics for SC brook trout patches averaged across all 12 loci
Table represents number of individuals (n) from each patch, and statistics include proportion of expected (HE) and observed (HO) heterozygotes, allelic richness (A), inbreeding coefficient (FIS), number of significant (p < 0.0002) deviations from Hardy–Weinberg (HW) following sequential Bonferroni correction, and significant (p < 0.00004) instances of linkage disequilibrium (LD) following sequential Bonferroni correction, and effective number of breeders (Nb). Results are presented for all individuals, and for the dataset with siblings removed (noted with a ′) with the exception of Nb which was only calculated for all individuals
Patch HE HE′ HO HO′ A A′ FIS FIS′ HW HW′ LD LD′ Nb
Bad Creek (n = 55, n′ = 36) 0.529 0.533 0.534 0.495 3 3 0.043 0.111 0 0 4 0 22.3Carrick Creek (n = 43, n′ = 22) (lower and middle) 0.639 0.686 0.636 0.621 7 7 0.075 0.108 0 0 13 4 6.5Carrick Creek (n = 23, n′ = 4) (upper) 0.634 0.617 0.702 0.687 4 3 − 0.110 − 0.152 0 0 2 0 5Crane Creek (n = 94, n′ = 56) 0.587 0.580 0.588 0.588 5 5 0.009 0.014 1 0 22 8 22Emory Creek (n = 84, n′ = 49) 0.667 0.679 0.672 0.683 6 6 − 0.005 − 0.005 0 0 32 9 16.8Falls Creek(n = 75, n′ = 47) 0.332 0.368 0.299 0.340 3 3 0.121 0.101 0 0 3 3 19.1Headforemost (n = 82, n′ = 64) 0.205 0.203 0.209 0.206 1 1 − 0.024 − 0.016 0 0 0 0 19Indian Camp (n = 46, n′ = 28) 0.613 0.623 0.596 0.613 5 5 0.029 0.025 0 0 7 0 34.5Ira Branch (n = 46, n′ = 28) 0.252 0.255 0.297 0.294 2 2 − 0.148 − 0.137 0 0 0 0 38.1Jacks Branch (n = 30, n′ = 19) 0.208 0.220 0.247 0.258 1 1 − 0.141 − 0.145 0 0 0 0 18.7King (n = 85, n′ = 54) 0.553 0.563 0.514 0.526 5 5 0.073 0.070 0 0 14 2 23.8Laurel Creek (n = 30, n′ = 18, 20) (lower) 0.255 0.270 0.238 0.259 1 2 0.038 0.022 0 0 0 0 InfiniteLaurel Creek (n = 37, n′ = 23) (middle and upper) 0.272 0.292 0.270 0.278 2 2 0.006 0.037 0 0 0 0 11.7Laurel Fork (n = 37, n′ = 20) 0.498 0.506 0.527 0.520 4 4 − 0.058 − 0.030 1 0 1 0 12.1Matthew (n = 104, n′ = 91) 0.614 0.623 0.627 0.636 6 7 − 0.010 − 0.018 0 0 3 1 116.1Moody (n = 35, n′ = 22) 0.411 0.458 0.422 0.465 3 3 − 0.033 − 0.031 0 0 2 0 14.1Pig Pen (n = 63, n′ = 38) 0.500 0.518 0.519 0.532 4 4 − 0.047 − 0.029 1 0 7 1 53.6Slicking (n = 46, n′ = 33) 0.147 0.148 0.121 0.123 1 1 0.325 0.312 1 1 0 0 56.9South Saluda (n = 39, n′ = 30) 0.282 0.287 0.252 0.263 2 2 0.068 0.045 0 0 0 0 50.6Tammassee (n = 57, n′ = 36) 0.505 0.503 0.539 0.525 3 3 − 0.069 − 0.042 0 0 2 1 25.9
1497Conservation Genetics (2018) 19:1487–1503
1 3
Tabl
e 3
Pai
rwis
e FS
T (b
elow
dia
gona
l) va
lues
, and
num
ber o
f sig
nific
ant e
xact
tests
for g
enic
diff
eren
tiatio
n (a
bove
dia
gona
l) fo
llow
ing
B-Y
FD
R c
orre
ctio
n fo
r mul
tiple
tests
(p =
0.04
7) fo
r 18
broo
k tro
ut p
atch
es in
SC
, USA
, and
six
hatc
herie
s
Patc
hB
adC
arric
k (lo
wer
and
m
iddl
e)
Car
rick
(upp
er)
Cra
neEm
ory
Falls
Hea
dfor
emos
tIn
dian
Ira
Jack
sK
ing
Laur
el
Cre
ek
(low
er)
Laur
el C
reek
(m
iddl
e an
d up
per)
Bad
1212
1212
1212
1212
1212
1212
Car
rick
(low
er a
nd
mid
dle)
0.24
312
1212
1212
1212
1212
1212
Car
rick
(upp
er)
0.33
80.
211
810
1211
1011
1110
1112
Cra
ne0.
358
0.25
10.
057
1112
1112
1111
1111
11Em
ory
0.26
60.
141
0.10
10.
143
1211
1111
1111
1112
Falls
0.49
50.
412
0.43
60.
445
0.34
712
1212
1212
1112
Hea
dfor
emos
t0.
591
0.50
80.
498
0.48
00.
406
0.47
312
1010
1111
10In
dian
0.31
40.
179
0.17
70.
207
0.13
70.
347
0.44
912
1212
1211
Ira
0.53
50.
466
0.46
90.
397
0.39
10.
657
0.70
10.
417
1111
1111
Jack
s0.
555
0.42
30.
425
0.38
30.
341
0.59
30.
642
0.33
20.
702
1111
11K
ing
0.36
90.
243
0.13
60.
145
0.16
70.
417
0.42
00.
188
0.44
10.
173
1111
Laur
el C
reek
(lo
wer
)0.
500
0.45
90.
451
0.45
30.
321
0.58
80.
700
0.41
80.
720
0.69
40.
472
9
Laur
el C
reek
(m
iddl
e an
d up
per)
0.52
40.
437
0.44
00.
447
0.28
40.
547
0.64
60.
410
0.71
00.
660
0.44
10.
244
Laur
el F
ork
0.41
60.
277
0.13
10.
118
0.18
70.
492
0.54
20.
217
0.51
60.
287
0.04
50.
548
0.53
0M
atth
ews
0.23
30.
083
0.26
20.
281
0.16
80.
436
0.49
10.
217
0.44
50.
438
0.29
10.
465
0.44
7M
oody
0.43
50.
302
0.17
30.
151
0.21
10.
516
0.57
60.
242
0.55
80.
308
0.05
20.
581
0.55
9Pi
g Pe
n0.
407
0.25
60.
273
0.25
80.
222
0.46
10.
542
0.21
40.
410
0.40
80.
258
0.53
70.
517
Slic
king
0.56
90.
487
0.49
10.
494
0.30
30.
573
0.67
00.
479
0.77
30.
735
0.49
00.
508
0.45
2So
uth
Salu
da0.
513
0.41
00.
409
0.44
30.
281
0.48
70.
594
0.37
50.
706
0.63
60.
422
0.46
30.
388
Tam
mas
see
0.30
90.
198
0.34
80.
363
0.28
60.
444
0.57
10.
250
0.53
40.
512
0.35
60.
523
0.53
4B
erlin
H.
0.22
50.
168
0.36
00.
376
0.26
00.
532
0.64
00.
298
0.56
80.
593
0.39
00.
588
0.58
4B
urto
n H
.0.
275
0.14
10.
216
0.24
60.
143
0.42
90.
499
0.19
70.
432
0.43
20.
257
0.46
40.
439
Dry
Mill
H.
0.29
40.
242
0.28
30.
306
0.21
80.
482
0.58
80.
267
0.51
80.
523
0.34
10.
469
0.48
0G
over
nor H
ill
H.
0.28
70.
203
0.38
10.
397
0.29
90.
519
0.62
30.
322
0.57
90.
589
0.41
10.
579
0.57
8
Wal
halla
H.
0.25
70.
145
0.35
00.
365
0.26
10.
512
0.61
80.
288
0.51
90.
576
0.38
30.
566
0.56
7W
ythe
ville
H.
0.29
20.
210
0.41
00.
425
0.31
40.
558
0.64
30.
353
0.60
00.
638
0.44
10.
618
0.60
9
1498 Conservation Genetics (2018) 19:1487–1503
1 3
Tabl
e 3
(con
tinue
d)
Patc
hLa
urel
For
kM
atth
ews
Moo
dyPi
g Pe
nSl
icki
ngSo
uth
Salu
daTa
mm
asse
eB
erlin
H.
Bur
ton
H.
Dry
Mill
H.
Gov
erno
r H
ill H
.W
alha
lla H
.W
ythe
ville
H.
Bad
1212
1212
1212
1212
1212
1210
11
Car
rick
(low
er a
nd
mid
dle)
1212
1212
1212
1211
1212
1212
12
Car
rick
(upp
er)
1112
1211
1111
1212
1212
1212
12
Cra
ne11
1211
1211
1212
1212
1212
1212
Emor
y10
1211
1211
1212
1211
1212
1212
Falls
1212
1212
1111
1112
1212
1212
12H
eadf
orem
ost
1012
1111
911
1212
1212
1212
12In
dian
1112
1112
1212
1212
1212
1212
12Ir
a10
1211
1111
1112
1212
1212
1212
Jack
s10
1210
1111
1012
1212
1212
1212
Kin
g9
128
1211
1212
1212
1212
1212
Laur
el C
reek
(lo
wer
)11
1210
126
712
1212
1212
1212
Laur
el C
reek
(m
iddl
e an
d up
per)
1112
1112
98
1212
1212
1212
12
Laur
el F
ork
125
1011
1112
1211
1212
1212
Mat
thew
s0.
315
1112
1212
1210
1212
1112
12M
oody
0.02
00.
347
1011
1112
1211
1211
1212
Pig
Pen
0.28
20.
290
0.31
212
1112
1212
1212
1212
Slic
king
0.59
60.
480
0.63
10.
589
712
1212
1212
1212
Sout
h Sa
luda
0.51
60.
427
0.55
00.
509
0.40
412
1212
1212
1212
Tam
mas
see
0.40
20.
280
0.42
20.
344
0.58
30.
520
1212
1112
1212
Ber
lin H
.0.
425
0.09
90.
463
0.39
80.
647
0.56
80.
300
1212
910
12B
urto
n H
.0.
285
0.13
00.
322
0.28
00.
502
0.43
00.
288
0.18
312
1212
12D
ry M
ill H
.0.
349
0.22
40.
381
0.37
00.
545
0.47
80.
313
0.23
10.
188
1210
11G
over
nor H
ill
H.
0.44
40.
175
0.47
80.
437
0.62
80.
551
0.31
30.
078
0.22
00.
257
1110
Wal
halla
H.
0.41
40.
114
0.44
80.
388
0.62
40.
551
0.30
30.
051
0.20
10.
258
0.08
09
Wyt
hevi
lle H
.0.
490
0.17
10.
527
0.45
00.
661
0.58
50.
372
0.11
70.
251
0.30
30.
155
0.14
3
1499Conservation Genetics (2018) 19:1487–1503
1 3
Estimates of Nb in study patches were low, indicative of small adult population size and/or limited spawning and rearing habitat (Whiteley et al. 2012; Fraser et al. 2014). Estimates of Nb were < 100 in all patches except Matthews Creek (Nb = 116), which was the largest patch in the study area. Variation in Nb estimates across sites has been linked to differences in habitat variability and quality (Belmar-Lucero et al. 2012; Whiteley et al. 2013; Ruzzante et al. 2016). In patches with low Nb estimates relative to their patch size (e.g., Laurel Fork; Nb = 12.1, patch size = 482 ha), conser-vation actions could be pursued to increase Nb by habitat improvement and/or removal of downstream interspecific competitors like non-native rainbow trout to increase habitat size. Low Nb might also suggest that these populations have reduced adaptive potential (Lande and Barrowclough 1987; Weeks et al. 2011; Fraser et al. 2014).
Despite extensive stocking and presumably an effect of genetic drift, there was evidence of a river drainage effect with distinct clusters in the Savannah and Santee river basins. Major river drainages in SC have had long-term tem-poral isolation due to lack of glacial modification (Rohde et al. 2009). As a result, populations can be highly differen-tiated between major drainages. In contrast, northern brook trout have relatively lower genetic differentiation among populations (Davis and Shaw 2001) given those geographic regions were recolonized by brook trout following glacial occupation (Danzmann et al. 1998). Similar north–south pat-terns of genetic diversity have been documented in other fish species (Bernatchez and Wilson 1998). Given the amount of genetic divergence we observed among patches, there is the
potential that the management actions of translocating fish across major drainage boundaries could negatively impact the recipient population. Our results (high divergence and genetic isolation) provide an opportunity to evaluate the risks and trade-offs with past conservation actions (Weeks et al. 2011).
Conservation and management
Common conservation practices may not apply to rear edge populations in part due to the combination of being severely impoverished in genetic diversity and lack of gene flow among populations in comparison to core-range populations (Hampe and Petit 2005). As such, conservation strategies need to be designed that consider unique aspects such as lim-ited habitat and low productivity, especially since conserv-ing the genetic integrity of rear edge populations requires strategies that maintain the greatest possible number of local populations, and connectivity among them. However, this can be of little use at rear edges particularly when inva-sive species are present. For instance, improving trout patch size and connectivity is typically important to increase Nb (Whiteley et al. 2013) and population size. Given that many brook trout patches in SC are located above a barrier with invasive rainbow trout below, it is very difficult to expand and connect previous habitat due to tradeoffs between iso-lation and competition with invasives (Fausch et al. 2009). Despite the risk of local extirpation if left isolated, isolation may be a necessity to preserve these brook trout populations. For situations where habitat expansion through increased
Fig. 5 Relationship between effective number of breeders (Nb) and patch area (hectares) (p < 0.001) for brook trout patches in SC. Gray shading represents 95% confidence interval
1500 Conservation Genetics (2018) 19:1487–1503
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connectivity are not feasible, more aggressive management actions such as translocations become important.
Translocations are a potential management tool to facili-tate genetic rescue or restoration for southern brook trout. Genetic rescue has recently gained traction in conservation of imperiled species like the Florida panther (Hedrick 1995) due to its ability to restore genetic diversity and reduce extinction risk by increasing a population’s absolute fit-ness through an increase in population size or growth rate (Whiteley et al. 2015). A recent study provides evidence for a positive effect of genetic rescue on genetic diversity, body size, and population growth rate through the first gen-eration in Virginia brook trout populations (Robinson et al. 2017). However, given that the streams we examined are very unproductive systems due to underlying geology and high acid deposition rates (Cada et al. 1987; Kulp and Moore 2005), population carrying capacity is inherently limited and potential for increased population size and growth rate may not be an expected outcome. However, even if an increase in absolute fitness is unlikely for the populations we examined, an increase in relative fitness and genetic diversity (genetic restoration; Hedrick 2005) by translocating just a few indi-viduals per generation into these low diversity patches could greatly benefit them by increasing evolutionary potential and ability to adapt to future environmental changes (Whiteley et al. 2015; Nathan et al. 2017).
Historical conservation approaches involved translocating individuals from nearby sources due to the risk that more distant populations may be locally adapted to their envi-ronment and translocations can potentially reduce fitness through outbreeding depression (Tallmon et al. 2004). In this situation, we recommend the following approach for SC brook trout patches. In our DAPC analysis, because Savan-nah drainage (blue) and Santee drainage (yellow) clusters are distinct from hatchery strains (red), they may be good candidates for restoration, whereas green and purple are closer spatially on the DAPC plot to the red cluster. Yel-low patches may be better sources when restoring patches in Santee River drainage, and blue patches are better suited for restoring patches in the Savannah River drainage. This should be taken with the caveat that drift could have resulted in the green and purple clusters being closer to the hatchery cluster by chance, and could still be their own distinct groups given that sub-basin groups in the southern range may be long isolated. However, it is important to balance and con-sider the risks of outbreeding depression against the risks that low genetic diversity and inbreeding depression pose to the long-term persistence of a population (Weeks et al. 2011). Little research has directly tested how geographically or genetically distant source populations can be from receiv-ing populations without negatively impacting fitness.
Depending on the number of fish removed, translocations also have potential to negatively impact source populations
(Armstrong and Seddon 2007), which are typically small in size in the case of southern brook trout. Reciprocal trans-locations have been demonstrated as an option to improve isolated and bottlenecked populations (Heber et al. 2013; Pavlova et al. 2017). An alternative approach could be the use of multiple source populations to minimize the negative demographic and genetic impacts on source populations, particularly when translocating to restored habitats with extirpated populations. Benefits of using multiple source populations have been documented in species of mammals and plants (Bodkin et al. 1999; Kirchner et al. 2006) such as increased population growth and genetic diversity. Addi-tionally, we may need to identify some source populations outside of SC. Patches like King and Crane Creeks, which received translocations from multiple source populations including streams from outside of SC, seem to have ben-efitted from this type of restoration. We lack a baseline for comparison of initial genetic diversity prior to restoration, but these two patches have maintained the highest genetic diversity relative to the other wild brook trout patches in SC. However we have not measured the effects of these translo-cations on fitness, so while these populations exhibited high genetic diversity, outbreeding depression may still have been a factor (Frankham et al. 2011). Given the current status of brook trout populations in SC in terms of number, census size, and genetic diversity levels, a more regional effort may be needed to restore these populations at the southern-most aspect of the brook trout range.
Future climate change scenarios predict increased popula-tion fragmentation and isolation, and further reductions in patch size (Hudy et al. 2008; Wenger et al. 2011). Under-standing mechanisms of population persistence for small populations, such as headwater brook trout, in an uncertain and changing climate is vital and necessitates a greater com-prehension of how translocations can be most effectively used to maintain genetic diversity. Maintenance of genetic diversity in these extant populations is critical to their future potential for adaptive response to environmental changes because records of extirpations and extinctions suggest that limits to adaptation are greatest during periods of rapid cli-mate change (Davis and Shaw 2001).
Overall this study provides insight into the genetic integrity of brook trout populations at the periphery of their range. Persistence of small populations with south-ern genetic signature is encouraging, but their low genetic diversity and lack of gene flow call for active management actions such as translocations. The use of translocations to mitigate anthropogenic impacts on biodiversity is increasing (Brichieri-Colombi and Moehrenschlager 2016), but there is little effort devoted to genetic monitoring post-release (Laikre et al. 2010) (however see Robinson et al. 2017). Translocation success should be measured based on ben-efits on receiving populations and negative effects on source
1501Conservation Genetics (2018) 19:1487–1503
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populations. Ideally, knowledge generated from monitoring can further feed into conservation actions to maintain local genetic diversity in small, isolated populations for their long-term persistence.
Acknowledgements This study was financially supported by the South-east Aquatic Resources Partnership, Trout Unlimited, Duke Energy, and the South Carolina Department of Natural Resources (SC-DNR). We thank a number of SC-DNR fisheries biologists and volunteers who conducted field sampling, as well as the Greenville Water Company for access to field sites. Two anonymous reviewers provided constructive comments that improved an earlier version of this manuscript.
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