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EVOLUTION & CONSERVATION OF CUTTHROAT TROUT (ONCORHYNCHUS CLARKII SSP.) IN THE SOUTHERN ROCKY MOUNTAINS

SIERRA MAGENTA LOVE STOWELL

B.A., University of Colorado 2007

A thesis submitted

Faculty of the Graduate School of the

University of Colorado in partial fulfillment

of the requirement for the degree of

Master of Arts

Department of Ecology and Evolutionary Biology

2011 This thesis entitled Evolution & Conservation of Cutthroat Trout (Oncorhynchus clarkii spp.) in the Southern Rocky Mountains written by Sierra Magenta Love Stowell has been approved for the Department of Ecology & Evolutionary Biology

Andrew Martin

Sharon Collinge

Christy McCain

Date

The final copy of this thesis has been examined by the signatories, and we find that both the content and the form meet acceptable presentation standards of scholarly work in the above mentioned discipline. Love Stowell, Sierra Magenta (M.A., Ecology & Evolutionary Biology)

Evolution & Conservation of Cutthroat Trout (Oncorhynchus clarkii ssp.) in the Southern Rocky Mountains

Thesis directed by Dr. Andrew P. Martin

ABSTRACT

Cutthroat trout are endemic to the cold waters of the American West. The subspecies probably evolved in isolated drainages during the Quaternary. I developed nuclear DNA markers to distinguish between closely related subspecies and estimate divergence times between populations in the southern Rocky Mountains. The subspecies native to Colorado are much older than previous estimates: using a molecular clock, I estimated that greenback and Colorado

River cutthroat trout split 0.79 MYA. Human movement of fish has obscured the evolutionary legacy of cutthroats. I used assessments of purity and stocking records for Rocky Mountain

National Park, combined with Geographic Information Systems, to assess the utility of geographic measures to serve as proxies for propagule pressure. I found no significant relationships among genetic purity, geographic configuration of roads and trails, and stocking history. Understanding the evolutionary history and the role of human actions in altering evolutionary history has important implications for conservation.

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ACKNOWLEDGMENTS

This research was made possible with funding from the Department of Ecology & Evolutionary Biology of the University of Colorado, the Colorado Division of Wildlife, the United States Fish & Wildlife Service, and the National Park Service. Jessica Metcalf provided invaluable advice and a wealth of knowledge on the conservation genetics of cutthroat trout. Chris Kennedy provided information on stocking records of fish in Colorado. Joshua Lamm steadfastly supported teaching, research, and writing efforts.

CONTENTS

CHAPTER 1: INTRODUCTION ……………………………………………………………... 1

CHAPTER 2: HISTORICAL BIOGEOGRAPHY OF CUTTHROAT TROUT IN THE SOUTHERN ROCKY MOUNTAINS ………………………………………………... 5

Introduction …………………………………………………………………………….. 5 Materials and Methods ……………………………………………………………….. 11 DNA Sampling & Amplification………………………………………………… 12 Phylogenetic Analysis ………………………………………………………….. 17 Results …………………………………………………………………………………. 19 Nuclear DNA …………………………………………………………………… 19 Combined Phylogenetic Analysis ………………………………………………. 21 Estimates of Divergence ……………………………………………………….. 24 Discussion ……………………………………………………………………………... 26 Young or Old? ...... …………………………………………... 26 Over and Over or Over and Around?………………………………………...... 27 Conclusions ……………………………………………………………………………. 29

CHAPTER 3: PREDICTING THE GENETIC PURITY OF CUTTHROAT TROUT IN A HEAVILY STOCKED NATIONAL PARK ………………………………………... 30

Introduction …………………………………………………………………………… 30 Materials and Methods ……………………………………………………………….. 37 Genetic Sampling & DNA Analysis ……………………………………………. 37 Morphological Assessments of Purity & Stocking Data Base………………….. 38 Geographic Information System ……………………………………………….. 39 Statistical Analyses …………………………………………………………….. 40 Results …………………………………………………………………………………. 41 Molecular Genetic Purity ……………………………………………………… 41 Purity and Anthropogenic Features …………………………………………… 43 Anthropogenic Features and Stocking Records ……………………………….. 47 Purity and Stocking Records …………………………………………………... 49 Discussion ……………………………………………………………………………... 51 Conclusions …………………………………………………………………………… 54

CHAPTER 4: CONCLUSIONS ……………………………………………………………... 55

SOURCES ………..……………………………………………………………………………. 58

APPENDIX: POPULATIONS & SPATIAL DATA ………………………………………... 67

LIST OF TABLES

Table 2.1- Final primer details for nuclear loci ………………………………………………... 13

Table 2.2- Details of populations used in phylogenetic analysis ……………………………… 15

Table 2.3- Descriptive statistics for nuclear and mitochondrial sequence data ……………….. 20

Table 3.1- Populations used in molecular genetic analysis of purity …………………………. 41

Table 3.2- Generalized linear model regression results for all data …………………………… 44

Table 3.3- Linear regression models for propagule pressure and spatial descriptions ………... 47

Table 3.4- Generalized linear models for purity and stocking ………………………………… 50

LIST OF FIGURES

Figure 2.1- Historical ranges of the extant subspecies of cutthroat trout ………………………. 6

Figure 2.2- Behnke’s hypothesis of diversification …………………………………………….. 8

Figure 2.3- Metcalf’s hypothesis of diversification …………………………………………….. 9

Figure 2.4- Map of cutthroat trout populations targeted for analyses …………………………. 16

Figure 2.5- Bayesian phylogenetic tree of nuclear loci ……………………………………….. 21

Figure 2.6- Bayesian phylogenetic tree of ND2 ………………………………………………. 22

Figure 2.7- Bayesian phylogenetic tree of nuclear and mitochondrial loci …………………… 23

Figure 2.8- Phylogenetic estimation of divergence …………………………………………… 25

Figure 3.1- Stocking intensity in Rocky Mountain National Park ……………………………. 33

Figure 3.2- Genetic purity of cutthroat trout in RMNP ……………………………………….. 34

Figure 3.3- Roads and trails in RMNP ………………………………………………………… 35

Figure 3.4- Purity and distance to features by grade ………………………………………….. 45

Figure 3.5- Purity and density of features by haplotype ………………………………………. 45

Figure 3.6- Generalized linear models for purity and features by haplotype …………………. 46

Figure 3.7- Linear models for propagule pressure and features ……………………………… 48

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CHAPTER 1

INTRODUCTION: CONSERVATION GENETICS OF CUTTHROAT TROUT

The greenback cutthroat trout (Oncorhynchus clarkii stomias) is among the most colorful of the cutthroat trout subspecies. The gular folds are characteristically light to deep red, reflecting the species’ common name, while the brownish-olivaceous back is dotted with large black spots and the paler belly ranges from light yellow to melon orange (Young 2009). This beautiful trout was described and named by E. D. Cope in 1872 (Behnke 1992). Native to the

South Platte and Arkansas drainages on the eastern slope of the Continental Divide in Colorado, the greenback occupied cold water streams, rivers and lakes above 3,000 m down to the plains, feeding omnivorously on invertebrate matter (Coleman 2007). With the arrival of European-

Americans in Colorado in the mid-19th century, the greenback spiraled toward extinction under the combined pressures of over-exploitation for recreation and subsistence; habitat degradation from mining, agriculture, and hydrological alterations; and displacement by and hybridization with introduced species. By 1919, the greenback was absent from the South Platte and was rarely caught by anglers in the upper reaches of the Arkansas (Wiltzius 1985). By 1937 the greenback was declared extinct (Green 1937).

In 1953, a graduate student on a camping trip in the mountains above Boulder, Colorado caught a trout he did not recognize. Experts soon verified that the greenback had been rediscovered and a search was enacted to find more remnant populations of greenbacks across their former range. Multivariate morphological analysis of meristic markers, coupled with professional intuition, provided evidence for at least nine populations of supposedly pure greenbacks, including the stream in which the original trout was caught. The rediscovered greenback cutthroat trout was afforded federal protection under the Endangered Species Act

(1973). A massive restoration effort was launched in 1978. Introduced and hybridized populations were eradicated from native habitat using electroshock fishing or chemical treatment with rotenone or antimycin and pure greenbacks from the remnant populations were stocked into the cleared habitat (USFWS 1998; Young 2009). Brood stock from remnant populations were cultivated in hatcheries and released into more than 30 restored waters, many of those in Rocky

Mountain National Park (USFWS 1998).

The recovery objective of the restoration plan was delisting of the greenback by the year

2000. Recovery was defined as the documentation of 20 stable populations representing a minimum of 50 hectares of lakes and ponds and 50 kilometers of stream habitat within the native range (USFWS 1983; see Young & Harig 2001). By 1998, managers considered the recovery effort a success and the de-listing of the greenback was considered imminent: over 639,000 individual fish had been distributed between 1985 and 1996 as part of the recovery effort (Young et al. 2002) and twenty stable populations existed, mostly in the South Platte drainage (USFWS

1998). Even though funding had been diminished in 1986, all that remained to be accomplished according to the 1998 Recovery Plan was to establish two additional stable populations in the

Arkansas River drainage and to prepare a long-term management plan to cover the greenback following delisting (USFWS 1998). As the greenback was moved towards delisting, various genetic studies called into question the definition of “Type A” (100% pure) greenbacks used under the 1998 plan (USFWS 2009; Kanda & Leary unpublished reports 1999-2000; Martin

2005 & 2009, Martin et al. 2005, Mitton et al. 2006, Metcalf et al. 2007, unpublished reports).

In 2007, Metcalf et al. broke the story based on molecular genetic evidence that many putatively pure populations of greenback cutthroat trout, including some that had been used as source populations during the restoration, were actually hybridized with a closely related subspecies, the Colorado River cutthroat trout (O. c. pleuriticus). The two subspecies are virtually indistinguishable by morphological assessments; this lack of distinction was presumed to reflect a short divergence time and shared evolutionary history (Behnke 1992), rather than hybridization and a shared anthropogenic history. Confusion and controversy over the genetic identity of pure greenback cutthroat trout brought recovery and delisting efforts to a halt. Today the taxonomic and conservation status of the greenback cutthroat trout remains in limbo. Further analyses of subspecies and population identity based on multiple lines of evidence are necessary to move forward.

The story of the greenback cutthroat trout is illustrative of the plight of the cutthroat species across its range for several reasons. First, the greenback is thought to have diversified in isolated drainage basins during Quaternary climate oscillations, a pattern of geographic structuring that is evident in other subspecies of cutthroat trout (Campbell et al. 2011; Smith et al. 2002; Loudenslager & Gall 1980). Understanding the evolutionary history of the subspecies is important not only for clarifying the taxonomy of the subspecies but also for making inferences about the geologic history of the American West and promoting effective conservation efforts.

Second, the subspecies face similar threats from overexploitation, habitat degradation, and introduced species across the species range. Humans have been moving trout across the

West and around the world, obscuring and erasing evolutionary histories. Hybridization between the subspecies and with introduced rainbow trout is rampant and of obvious conservation

concern. The legal status of hybrids is unclear: hybrids are not protected under the Endangered

Species Act, but given the volume of human traffic of fish, the purity of many, if not most, populations is questionable (Allendorf et al. 2005; Allendorf et al. 2004).

Finally, the lack of resolution in the taxonomic relationships between greenbacks and the other subspecies native to Colorado is reflective of the taxonomic confusion across the subspecies clade. The classification schemes of cutthroat trout run the gamut from the currently accepted 14 distinct subspecies to the 32 full species described by Jordan (Behnke 1992). While much of the difficulty in distinguishing meaningful clades probably results from shared recent evolutionary history and incomplete lineage sorting, part of it results from the confusion created by stocking and the lack of diagnostic markers for determining identity. Effective conservation efforts require accurate taxonomic assessments based on robust evidence. In the following chapters I explore the evolutionary history cutthroat trout subspecies in the southern Rocky

Mountains using molecular genetic markers and Bayesian methods for inferring species trees from gene trees, and I assess the relationships among genetic purity of cutthroat trout populations, geography of roads and trails, and propagule pressure of introduced trout. My goal is to expand our understanding of the evolutionary and anthropogenic history of cutthroat trout to inform conservation efforts of the unique subspecies.

CHAPTER 2

HISTORICAL BIOGEOGRAPHY OF CUTTHROAT TROUT IN THE SOUTHERN ROCKY MOUNTAINS

Introduction The cutthroat trout is an endemic species symbolic of the wild and pristine American

West: they were first described by the Coronado expedition in the 1540s and subsequently noted by the Lewis and Clark expedition in the 1800s. The first formal descriptions and collections were made by George Suckley, an amateur naturalist and surgeon in the U.S. Army, in 1861, and later by David Starr Jordan, the renowned ichthyologist and educator, who made museum accessions and classification schemes from the 1880s until 1930 (Behnke 1992). Jordan’s last recorded publication included 32 full species of rainbow and cutthroat trout (in Behnke 1992); modern taxonomy lists a single species of cutthroat trout, Oncorhynchus clarkii, and a single species of rainbow trout, O. mykiss, as its sister group. Fourteen subspecies of cutthroat trout are currently recognized based on morphology, genetics and geography, of which two are extinct and of the extant subspecies, three subspecies have been federally listed as Threatened or

Endangered and most have state protection (Young & Harig 2001).

The cutthroat subspecies are thought to have diversified through geographic isolation in major drainage basins during the period of Pleistocene glaciations (Metcalf et al. in prep; Behnke

1992). Cutthroat trout are the only freshwater fish species whose subspecies are found on both sides of the southern portion of the Continental Divide in North America, a major barrier to fish movement reaching elevations over 4,200 m in Colorado (Figure 2.1). Other genera of fish (such as Archoplites perches or the Ameiurus catfishes) have distributions in which distinct species are found on both sides of the divide, but divergence between species in these genera is relatively old (Pliocene and Miocene) (Mayden 1992). In contrast, the cutthroat trout is a single species with diverse subspecies whose discrete ranges are localized in isolated drainage basins. Thus, the timing of the diversification of the subspecies could be correlated with the timing of major geological events such as glacial maxima and minima.

Figure 2.1- Historic ranges of the twelve extant subspecies of cutthroat trout, Oncorhynchus clarkii. The photographs depict the six subspecies targeted in this study. The black hatched lines cover the historic range of rainbow trout, O. mykiss, the sister taxa to the subspecies complex. The red hatched lines cover the range of bull trout (Salvelinus confluentus). Map from www.westerntrout.org.

The first step in understanding the evolutionary history of the subspecies complex and making biogeographic inferences is to construct a robust phylogeny. A phylogeny is a hypothesis of the evolutionary relationships between the subspecies. From a well-supported

phylogeny we can look for congruence between divergence events in the phylogeny and geologic or paleoclimatic events. Alternatively, divergence times based on molecular sequences “provide unique data on the nature and timing of barriers and aquatic connections among basins” (Smith et al. 2002, p175; Craw et al. 2008) (as long as the datasets are independent (Upchurch 2008)).

If subspecies phylogeny aligns with geology, and we can estimate the timing of divergence, we can infer the history of the development of isolated drainage basins in the West. These inferences can then be applied to understand the biogeography of other endemic species inhabiting the same region. Furthermore, a robust phylogeny is essential to conservation efforts, which require an accurate taxonomy for maximum effectiveness in interpreting conservation legislation and implementing conservation strategies (Crandall et al. 2000; Cracraft 2002).

Currently, the prevailing hypothesis of the evolution of cutthroat trout, put forth by

Behnke, suggests that all subspecies originated within the last 1 million years, and that most diversification happened in the late Pleistocene and early Holocene, from 100,000 to 10,000 years ago (Behnke 2002). Furthermore, the existence of subspecies east of the Continental

Divide, including the Yellowstone (O. c. bouvieri), Rio Grande (O. c. virginalis) and greenback

(O. c. stomias) cutthroat trout, has been explained by multiple and recent (post-Pleistocene) high elevation stream capture events as the Pleistocene glaciers receded (Behnke 2002). Stream capture is a geomorphological phenomenon in which a stream or river is diverted from its own bed to flow into the bed of a neighboring watershed due to tectonic activity, natural damming, or erosion, all forces likely to be active during glacial recession. Stream capture is an important biogeographic force shaping the distribution of freshwater fauna and facilitating dispersal across mountain ranges (Zemlak et al. 2008; Craw et al. 2008). Behnke’s hypothesis of recent divergence implies that subspecies should exhibit limited, if any, detectable sequence divergence

and that greenback cutthroats in the South Platte and Arkansas should each be more closely related to the Colorado River cutthroat than to the Rio Grande, the other subspecies native to east of the Continental Divide (Figure 2.2).

Figure 2.2- Graphical depiction of Behnke’s hypothesis of cutthroat trout subspecies diversification in the southern Rocky Mountains: all subspecies are relatively young and arrived at their current distribution east of the Continental Divide by multiple independent stream capture events. According to this hypothesis, subspecies should exhibit limited sequence divergence and the greenback should be more closely related to the Colorado River cutthroat.

Recently, phylogenetic analysis of mitochondrial DNA sequences suggested that the subspecies probably originated in the early to middle Pleistocene (2.6 million to 500,000 years ago; Wilson & Turner 2009; Metcalf et al. 2007). Metcalf (2007) posits that the existence of cutthroat trout east of the Continental Divide may reflect a combination of high-elevation stream capture and low elevation movement of fish during glacial maxima. The close proximity of drainages and the lower divides between them on the eastern slope of the Continental Divide suggests that lower elevation movement between the Rio Grande, Arkansas and South Platte may be more plausible than multiple high elevation stream capture events from the western slope

drainages. In addition, ecological niche modeling of cutthroat trout distribution at the last glacial maximum implies that suitable cutthroat habitat existed much farther east than their current distribution, encompassing some of the confluences between major drainages (Metcalf 2007)

Metcalf’s hypothesis of more ancient diversification then implies that sequence divergence should be measurable and possibly reflect cyclic paleoclimate (Figure 2.3). The discrepancies between Metcalf and Behnke’s estimates of divergence times suggest two alternative hypotheses that I tested using sequence information from both mitochondrial and nuclear DNA.

Figure 2.3- Graphical depiction of Metcalf’s hypothesis of cutthroat trout subspecies diversification in the southern Rocky Mountains: all subspecies are more than 500,000 years old and arrived at their current distribution through a combination of headwater transfer during glacial minima and low elevation movement during glacial maxima. According to this hypothesis, subspecies should exhibit measureable sequence divergence and divergence dates should reflect cyclic paleoclimate.

Both alternative hypotheses suffer from limited data. Behnke’s (2002) hypotheses was based mainly on morphological descriptions and geography coupled with a large dose of expert intuition and a general mistrust and misunderstanding of genetic data (NY Times, 10/14/2007).

In discussing conflicting treatment of different subspecies of cutthroat trout under the 9

Endangered Species Act, Allendorf et al. (2005) note that Behnke’s morphological characters fail to distinguish between westslope and Yellowstone cutthroat and between pure west slope and rainbow trout. Morphological characters in general often fail to capture cryptic species and introgression.

On the other hand, Metcalf’s hypothesis is based on a small region of the mitochondrial genome and a handful of nuclear microsatellite loci. She used the mitochondrial gene NADH- dehydrogenase 2 (ND2) to estimate divergence time. In addition to the issue presented by the use of a single locus, the generally high mutation rate of mitochondrial DNA and rate heterogeneity among nucleotide positions, genes, and clades of organisms means that more ancient events may be missed (Avise 2004). She also used ten nuclear microsatellite loci to support the mitochondrial estimates. Microsatellites have an even more rapid mutation rate (10-

3-10-4 per generation, though this rate will vary across loci and among alleles of the same locus

(Macaubas et al. 1997)) and so will also fail to capture more ancient events. In addition, the advantage of that rapid evolution rate also tends to obscure phylogenetic relationships, since the rapidity of mutations means that structurally identical alleles may not be identical by descent (i.e. higher rates of homoplasy; Estoup et al. 2002). Ideally, robust phylogenetic analysis is based on multiple, independent genes (Vawter & Brown 1986) with a range of mutation rates to capture divergence across temporal scales.

Inferring phylogenetic history from gene sequences poses several challenges. First, accurate resolution of phylogenetic relationships requires accounting for the uncertainty generated by variation in lineage sorting of ancestral lineages (coalescence variance). Put more simply, gene trees can differ significantly in topology from species trees. Novel analytical approaches have recently emerged that use Bayesian methods for estimating the probability of

species trees given a set of independent gene trees (Edwards 2009). Second, robust estimation of phylogeny requires multiple trees for mitochondrial and nuclear genes. While mitochondrial

DNA sequences are easily obtained, and these data are available for cutthroat trout, constructing genealogies from nuclear genes requires more sequence data because nuclear genes are typically

10 to 20 times less variable than mitochondrial genes (Vawter & Brown 1986). In addition, isolation of single copy genes can be difficult because most nuclear genes are members of multigene families, a problem exacerbated by the fact that salmonids such as the cutthroat trout are ancient tetraploids as a consequence of a whole genome duplication event (Johnson et al.

1987). Since trout are tetraploid, primers developed for single genes can yield products from two or more paralogous genes. Recent techniques for intron-primed exon crossing, in which primers are designed within the unique introns flanking a particular paralog, have overcome some of these challenges (Ryynänen & Primmer 2006).

I inferred the evolutionary history of cutthroat populations in the southern Rocky

Mountains using Bayesian methods to infer the subspecies tree and estimate the divergence dates from ten nuclear loci and mitochondrial ND2 sequences. I developed a panel of nuclear single nucleotide polymorphisms using primers designed from rainbow trout sequences (NCBI), with a combination of intron- and exon-primed paralog capture, to distinguish between clades. I then estimated the divergence dates between subspecies by assuming a relaxed molecular clock

(Drummond et al. 2006) calibrated by fossil-estimated divergence dates.

Materials and Methods To test alternative hypotheses of subspecies diversification, my approach was to isolate and characterize nuclear single nucleotide polymorphisms (SNPs) from multiple individuals of the southern Rocky Mountain lineages of cutthroat trout. SNPs, single base substitutions or

deletions with a frequency greater than 1% in a population, are the most common type of sequence variation in many model organisms and are estimated to account for 90% of the variation in the human genome (Collins et al. 1999; Brookes 1999). They are inherited in

Mendelian fashion, are codominant and usually biallelic (because multiple mutations at the same site are unlikely given a low mutation rate, 10-8 -10-9 per generation), and are less susceptible to homoplasy than microsatellites (Brumfield et al. 2003; Sprowles et al. 2006). Primers were designed using the available genome sequence from the rainbow trout (NCBI), the sister taxon of cutthroat trout, and the online software Primer3 (frodo.wi.mit.edu). Initial primer sets were designed so that one primer is was located within an exon and the other within an intron. The exon sequence permits identification of the gene and alignment with other species of

Oncorhynchus and the intron sequence typically yields variable nucleotide positions. Primers were synthesized by Sigma Aldrich or Invitrogen.

DNA sampling & Amplification High molecular weight DNA was obtained from three or more individuals from sixteen populations of cutthroat trout and one rainbow trout. DNA was provided by the University of

Colorado, Brigham Young University, and Pisces Molecular in partnership with the Colorado

Division of Wildlife. SNPs were identified by screening a panel of four individuals from populations whose nuclear and mitochondrial purity were previously established, representing the Colorado River, greenback, Bear Creek and rainbow trout lineages (Martin et al. 2008), for

36 loci using 44 primer sets using polymerase chain reaction (PCR) and DNA sequencing. All loci were amplified using AmpliTaq Gold (Applied Biosystems), PuReTaq Ready-To-Go PCR

Beads (GE) or GoTaq Green (Promega), with a 1-6 minute hot start at 94- 95oC, and 30-35 cycles of 94oC for 15-30 seconds, 50-52oC for 20-30 seconds, and 68-72oC for 30-60 seconds,

with a final extension of 68-72o for 5-10 minutes. Amplification success and quality were checked using gel electrophoresis with 1-3% agarose gels in 0.5-1X TBE and ethidium bromide or SYBR Safe DNA gel stains. Sequencing was completed by Functional Biosciences, Inc.

(Madison, WI). All sequences were edited in Sequencher 4.0 (Gene Codes Corporation). SNPs were visually identified using the alignment of both forward and reverse sequences from the four individuals in the panel. Initially, 18 loci were identified as polymorphic and phylogenetically informative among the panel of greenback, Bear Creek, Colorado River, and rainbow trout.

From this set I chose 10 loci, based on sequence quality and coverage, to target in a larger sampling effort (final primer details in Table 2.1).

Table 2.1- Final primer details for nuclear loci. Primers were designed from O. mykiss sequences available on GenBank and then amplified for three or more individuals from each of 16 populations of cutthroat trout across the species range in the southern Rocky Mountains. Locus Primer Sequence Left Primer Sequence Right Ikaros GAGTGCAACCTCTG TCTTTGCCACCGAGG Pbx2Int3L&R CAACTCAAGCAGAGCACCTG TTCTTTGGCCTCTTCACTGG Cathepsin D GACAACAACAGGGTGGG GACAAGAGGTCCATTGC Id1 GTGGAGAACGGATGCTC AAACACCAGAAGTACATTG IL2b CTCCTAGCTGGAATCGATTATCT TGCTGATGTGGTGTTGCATA Pbx2Int5 AGCGAGGACTCTCCACACAC CTTGTGCTCCCATGTAGACAG Dax5UT GGACACCTACAGCACCGAAT GTTCAGTATCGTGGGCTGGT DBS AGACTGCCAGTGCCAGAAAT AGTAAGCTGCAAGCCCAAAA Sypg1Int1 CTCCCATGAGTCCTTGCTGT CTTGGTTCCTGAAGCCGTAG

After identifying variable loci among four populations, I expanded my taxonomic sampling to include three or more individuals from each of 16 populations of cutthroat trout from Colorado,

New Mexico, Utah, and Wyoming, representing two populations of Bonneville cutthroat trout

(O. c. utah), two Yellowstone, four Colorado River, four greenback, three Rio Grande, and the

Bear Creek population (Table 2.2, Figure 2.4). The relationship of the Bear Creek population with the greenback lineage is unclear. Based on molecular analysis of ND2, it is divergent from other populations of greenbacks (Metcalf 2007). The current hypothesis is that the Bear Creek population is a remnant population of the fish originally native to the South Platte drainage that has been transplanted to an isolated Arkansas drainage stream (Metcalf in prep.). Populations were chosen on the basis of the availability of mitochondrial, microsatellite, and AFLP data

(Metcalf unpubl., Rogers unpubl.) and to achieve a broad geographic sampling of the species range in the southern Rocky Mountains.

I amplified the ten variable loci for three or more individuals for each of these populations following the above procedure and edited the sequences returned from Functional

Biosciences in Sequencher to build overlapping sequence segments in both directions (contigs) with maximum coverage of individuals and highest sequence quality. Intrapopulation variation was limited so I aligned all individuals from each population to create a consensus sequence for the population. Alignment was checked using ClustalW in MEGA 5.0 (Tamura et al. 2011, submitted) and NEXUS files were exported for each locus for phylogenetic analysis. Nucleotide sequence variation among populations at each locus was characterized using DnaSP (Librado &

Rozas 2009) and the ability of the sequences to serve as molecular clocks was tested using

Tajima’s Relative Rate Test (Tajima 1993) in MEGA.

Table 2.2- Details of populations used in phylogenetic analysis. Current lineage was determined based on geography, morphology, and genetics. BEAR is the Break Creek population in the Arkansas River drainage; BONN is the Bonneville cutthroat, O. c. utah; CR is the Colorado River, O. c. pleuriticus; GB is the greenback, O. c. stomias; RG is the Rio Grande, O. c. virginalis. DNA samples were obtained from the University of Colorado, Brigham Young University, and Pisces Molecular. Major Sub- Current DNA Population Lat Long Drainage Drainage Lineage Source Bear 38.8 -104.95 Arkansas Arkansas BEAR CU Birch 43.05 -110.99 Snake Snake BONN CU Bear Lake 42.03 -111.20 Bear Bear BONN Pisces Big Beaver 40.05 -107.62 Colorado White CR Pisces Parachute 39.63 -107.99 Colorado Colorado CR BYU Navajo 37.19 -106.67 Colorado San Juan CR Pisces Nanita 40.48 -104.77 Colorado Colorado CR CU SF Hayden 38.31 -105.82 Arkansas Arkansas GB BYU Bobtail 39.74 -105.91 Colorado Williams Fork GB Pisces Antelope 38.66 -107.04 Colorado Gunnison GB Pisces Como 40.03 -105.53 South Platte Boulder GB CU Cross 38.22 -106.35 Rio Grande Saguache RG CU El Rito 36.54 -106.27 Rio Grande Rio Grande RG CU Rio Mora 35.92 -105.51 Rio Grande Pecos RG CU LeHardy1 44.70 -110.50 Yellowstone Yellowstone YS Pisces LeHardy2 44.70 -110.50 Yellowstone Yellowstone YS Pisces

Figure 2.4- Map of 16 cutthroat trout populations targeted for analyses representing 7 major drainage basins on both sides of the Continental Divide. Purple stars indicate Bonneville cutthroat, yellow indicates Yellowstone, blue Colorado River, green greenback, orange Rio Grande, and red Bear Creek. 16

Phylogenetic Analysis To test my alternative hypotheses of divergence times and phylogenetic relationships among the cutthroat trout subspecies in the southern Rocky Mountains, I used Bayesian analyses to infer the species tree of the cutthroat subspecies. First, I used the program BEAST to infer the phylogeny of the populations based on the nuclear loci alone. BEAST uses a Bayesian Markov

Chain Monte method for inferring phylogenies that is able to co-estimate phylogeny and divergence times using relaxed molecular clock models (Drummond & Rambaut 2007;

Drummond et al. 2006). Each locus was considered as an independent partition in the data with unlinked substitution models and unlinked molecular clocks assuming a relaxed clock with an uncorrelated exponential prior. The relaxed molecular clock allows for substation rate variation between lineages but does not require the inferred tree to be unrooted (Drummond et al.2006). I used jModelTest (Posada 2008) to find the most appropriate model of evolution for each locus.

The tree prior used the coalescent assuming constant size. I used 20,000,000 MCMC generations, sampling every 1,000 trees, with a burn-in of 2,000,000 generations. Stationarity of the posterior probability distribution and the ESS values for the priors was examined in Tracer to determine the appropriate burn-in (http://tree.bio.ed.ac.uk/software/tracer/). I then constructed a consensus tree for all nuclear loci from the posterior distribution of 18,000 trees with >0.95 posterior probability mean node height using TreeAnnotator (Drummond & Rambaut 2007).

I then used BEAST to infer the phylogeny of populations based on ND2 sequences from

GenBank and Metcalf (unpubl.). Because I was using a single locus, the data was not partitioned. I again determined the appropriate model using jModelTest and 20,000,000 MCMC generations, sampling every 1,000 trees with a burn-in of 2,000,000 generations as determined from the log file viewed in Tracer. A consensus tree from the posterior distribution of 18,000

trees was built in TreeAnnotator with a posterior probability cutoff of >0.95 reporting mean heights.

Nuclear and mitochondrial sequences may give conflicting species trees (Moore 1995).

Mitochondrial genomes have a more rapid rate of evolution, are inherited as a single linkage group, and a smaller effective population size (because they are maternally inherited and effectively haploid). Thus they are useful markers for resolving recent events and short internode distances. On the other hand, nuclear loci can be selected from distinct regions and can thus provide independence estimates of species trees that resolve deeper nodes because of a slower mutation rate. To evaluate the concordance between estimates of evolutionary history based on nuclear versus mitochondrial I used maximum likelihood to test all nuclear loci against the mitochondrial consensus tree and the mitochondrial locus against the nuclear consensus tree, implemented in MEGA. I then combined the nuclear and mitochondrial datasets to estimate a consensus tree from the posterior distribution of 11 loci using the same parameters as above in

BEAST.

Finally, to calculate rates and date lineages, BEAST requires a parameter of calibration of nodes within the phylogeny; the Time of Most Recent Common Ancestor (tMRCA) is usually based on fossil evidence but can also be determined using a sequence data and assuming a molecular clock (Wilson & Turner 2009). To account for inherent variation of estimated fossil and molecular dates, different prior distributions can be used when sampling the tMRCA parameter. According to Wilson & Turner (2009), these distributions can have hard bounds

(minimum or maximum dates known with confidence; lognormal and exponential distributions respectively) or soft bounds (little confidence on minimum or maximum dates; normal distribution). I added rainbow trout as an outgroup to infer a molecular-calibrated and fossil-

validated tree and estimate divergence times from the posterior distribution of node heights.

From the 11 loci, I further partitioned the data into subsets of rainbow trout versus all cutthroats.

Independent estimates of the split between rainbows and cutthroats range from 6 MYA to 8

MYA based on molecular sequence divergence (Smith et al. 2002, Reheis et al. 2002).

Cutthroat-like trout are found in the fossil record dating back 3.5 – 6 MYA (Smith et al. 2002;

Stearley & Smith 1993; Smith 1992). I used a normal distribution for the tMRCA prior with a mean of 7 MYA and a standard deviation of 0.96 to capture the range of 6-8 MYA. Again, I used 20,000,000 MCMC generations with a burn-in of 2,000,000 generations.

Results Nuclear DNA I analyzed 4,529 base pairs of nuclear DNA, including 73 variable sites, and 889 bp of mitochondrial DNA with 50 variable sites for multiple individuals from each of 16 populations of cutthroat trout. Nuclear sequences were much less variable than the mitochondrial locus (7.3

SNPs/locus on average compared to 50 SNPs in ND2 alone, or 1 SNP/100 base pairs of nuclear

DNA versus 5 SNPs/100 bp for mitochondrial DNA). Nucleotide diversity, π, ranged from

0.00046 (DBS) to 0.013 (CathD) (0.004 average) among nuclear loci. All nuclear sequences were best described by relatively simple models of evolution (F81, HKY or JC). Tajima’s D, a test statistic for mutation-drift equilibrium, was not significant at p = 0.1 for any of the nuclear or mitochondrial loci, indicating that the null hypothesis of neutrality cannot be rejected.

Descriptive statistics for the 10 nuclear and 1 mitochondrial loci are listed in Table 2.2. The χ2 test statistic for Tajima’s relative rate test was 0 for all loci (p = 1.0), meaning that I cannot reject the null hypothesis of equal rates between lineages and indicating that the sequences can be used as molecular clocks.

Table 2.3- Descriptive statistics for the nuclear (n = 10) and mitochondrial (n = 1, ND2) sequence data from 16 populations of cutthroat trout. k is the average number of nucleotide differences among sequences; π is the nucleotide diversity per site; Θ is a population genetics parameter (4Nµ); Tajima’s D is a statistical test for neutrality (calculated in DnaSP). The substitution model for each locus was validated using maximum likelihood in MEGA. missing Locus Length # SNPs # gaps G+C k π Θ/site Θ/seq Tajima's D Subs Model data Ik 706 4 5 0 0.32 0.94 0.001 0.002 1.24 -0.644 F81 Pbx2Int3R 288 4 16 8 0.42 0.50 0.002 0.004 0.99 -1.248 HKY CathD 397 15 4 0 0.39 4.95 0.013 0.009 3.73 1.092 F81 Id1 422 6 1 24 0.42 0.09 0.002 0.004 1.49 -1.076 F81 Il2b 530 6 0 1 0.42 1.34 0.003 0.003 1.49 -0.293 F81 Pbx2Int3L 281 4 0 21 0.45 0.59 0.002 0.004 0.99 -1.029 JC Pbx2Int5 460 6 0 1 0.44 0.80 0.002 0.003 1.49 -1.304 HKY Dax5UT 536 2 0 8 0.37 0.70 0.001 0.001 0.51 0.751 F81 DBS 454 2 0 26 0.43 0.21 0.000 0.001 0.51 -1.241 F81 Sypg1Int1 455 24 0 1 0.42 5.24 0.012 0.016 7.50 -1.129 F81 + G ND2 889 50 0 0 0.46 13.39 0.015 0.018 15.67 -0.617 TN93 + G

The posterior distribution of trees for nuclear loci alone shows several interesting patterns

(Figure 2.5). First, Bear Creek, a cutthroat population in the Arkansas River drainage east of the

Continental Divide, is highly divergent from the Rio Grande – Colorado River – greenback clade

(posterior probability > 0.91, branch length = 0.003 mutations/site). Second, the Bonneville cutthroats (Birch and Bear Lake) are not monophyletic: the Bear Lake population from northern

Utah groups with the LeHardy Yellowstone cutthroats. Third, while three populations of greenbacks (Antelope, Bobtail, and Como) and Colorado Rivers (Parachute, Navajo, and Nanita) respectively group together, Big Beaver (Colorado River) and SF Hayden (greenback) group together with low posterior probability (0.37). Support for the split of Big Beaver – SF Hayden from the other greenback populations is also low (0.47) and the split between the Colorado River and greenback clades is only slightly higher (0.66).

Figure 2.5- Consensus Bayesian tree of cutthroat populations using 10 nuclear loci (4,529 base pairs) and 20,000,000 MCMC generations constructed in BEAST assuming unlinked substitution and clock models . Color of text indicates putative current lineage (green = greenback, blue = Colorado River, orange = Rio Grande, maroon = Bear, yellow = Yellowstone, purple = Bonneville). Posterior probabilities are displayed at nodes. Scale bar indicates percent sequence divergence.

Combined Phylogenetic Analysis Metcalf’s estimation of subspecies divergence is reliant on mitochondrial sequence from

ND2 and COI. I constructed a Bayesian estimation of the species tree for the 16 cutthroat populations based on ND2 alone, compared it to the nuclear tree, and then built a tree from the combined data set. The ND2 dataset recovers a different tree topology than the nuclear dataset

(Figure 2.6). All Colorado River populations group together with high support (1.0 posterior probability) but are more closely related to a Yellowstone – Bonneville clade than to the

greenback – Bear – Rio Grande clade with lower support (0.54 posterior probability). SF

Hayden and Big Beaver group within their respective greenback and Colorado River clades, rather than together. Bear Lake is again recovered as more closely related to the Yellowstone populations but the other Bonneville population, Birch, does not group with the Bonneville –

Yellowstone clade. Bear Creek, a putative greenback, is more closely related to the Rio Grandes than to the other greenbacks with low support (0.39 posterior probability).

Figure 2.6- Consensus Bayesian tree of cutthroat populations using ND2 and 20,000,000 MCMC generations constructed in BEAST assuming unlinked substitution and clock models. Color of text indicates putative current lineage as in Figure 2.5. Posterior probabilities are displayed at nodes. Scale bar indicates percent sequence divergence.

I used all nuclear and mitochondrial locus to build a consensus tree based on 5,418 total base pairs (Figure 2.7). The combined consensus tree resolved the polyphyly of Big Beaver and

Parachute which grouped with the other Colorado River and greenbacks respectively. Unlike in the ND2 tree, the combined tree placed the Colorado Rivers and greenbacks as sister clades with low posterior probability (0.48). The Bear Creek lineage was recovered as the outgroup to the

Rio Grande – Colorado River – greenback clade with high posterior probability (1.0). The

Bonneville cutthroats remained polyphyletic but Birch returned to the Bonneville-Yellowstone group.

Figure 2.7- Consensus Bayesian tree of cutthroat populations using 10 nuclear and 1 mitochondrial loci and 20,000,000 MCMC generations constructed in BEAST assuming unlinked substitution and clock models. Color of text indicates putative current lineage as in Figure 2.5. Posterior probabilities are displayed at nodes. Scale bar indicates percent sequence divergence. 23

I evaluated the concordance between the trees using maximum likelihood analysis of the ability of the mitochondrial tree to explain the variation in the nuclear data and vice versa in

MEGA. Negative log likelihood values ranged from -86.32 (Sypg1Int1) to -935.39 (IkRedo) for the mitochondrial tree tested with the nuclear data. The negative log likelihood for the nuclear tree tested with the ND2 sequence data was -373.951. High negative log likelihood values for different loci can be interpreted as the relative influence those loci have in determining tree topology. Under this reasoning, Sypg1Int1 was the main locus driving discordance between the trees; all other loci had negative log likelihoods <-200 for recovering the topology of the other tree.

Estimates of Divergence Based on the combined nuclear and mitochondrial sequence data from 16 cutthroat populations and rainbow trout as an outgroup, I estimate that the Bonneville – Yellowstone cutthroat clade split from the cutthroats further south 2.13 million years ago during the Pliocene

(95% confidence interval: 3.153-1.2 MYA; 1.0 posterior probability) (Figure 2.8). Bear Creek split from the Rio Grande – Colorado River – greenback cutthroats 1.21 million years ago (95%

CI: 1.85-0.69 MYA; 1.0 posterior probability) during the Pleistocene. The Rio Grandes diverged from the lineage leading to the Colorado River – greenback clade 0.99 MYA (95% CI: 1.50-0.55

MYA; 0.99 posterior probability) and the Colorado Rivers and the greenbacks diverged 0.79

MYA (0.94 posterior probability). Based on this evidence, I reject Behnke’s hypothesis of young ages for the subspecies in the southern Rocky Mountains.

Figure 2.8- Consensus Bayesian tree of cutthroat populations using 10 nuclear and 1 mitochondrial loci and 20,000,000 MCMC generations constructed in BEAST assuming unlinked substitution and clock models. Populations are collapsed to subspecies; color of text indicates putative current lineage as in Figure 2.5. Posterior probabilities are displayed at nodes. Scale bar indicates time in millions of years. Node bars indicate the 95% posterior distribution of node heights.

Discussion The range of the charismatic cutthroat trout spans both sides of the length of the

Continental Divide in the United States. Biogeographic barriers between drainages are thought to have driven the proliferation of the subspecies as populations dispersed and were isolated in novel drainages (Behnke 1992; Rahel 2007). The prevailing hypothesis of cutthroat diversification suggests that the subspecies in the southern Rocky Mountains arose in the last

100,000 years in Holocene and the subspecies east of the Continental Divide arrived by multiple independent headwater transfers as the glaciers retreated (Behnke 1992). An alternative hypothesis proposes that the subspecies are much older, middle to late Pleistocene, and that they arrived east of the Divide through a combination of headwater transfer during glacial minima and low elevation movement during glacial maxima (Metcalf 2007). Understanding the evolutionary history of the subspecies underlies effective conservation efforts and can help clarify taxonomic uncertainty. I tested these alternative hypotheses using nuclear and mitochondrial DNA sequence data in a Bayesian phylogenetic framework assuming a relaxed molecular clock. The phylogenies presented in figures 2.7 and 2.8 represent our best understanding of cutthroat evolutionary history to date based on the most independent loci and robust analysis.

Young or Old? Based on the combined nuclear and mitochondrial data, I found that the subspecies were much older than proposed by Behnke. Using multiple independent loci helps account for the uncertainty in estimating species trees from gene trees (Edwards 2009) and using a relaxed molecular clock allows rates of evolution to vary among loci (Drummond et al. 2006). My estimates of the subspecies ages are consistent with recent estimates based on mitochondrial phylogenies (Wilson & Turner 2009; Metcalf et al. 2007; McKay et al. 1996), and with the distribution of fossil cutthroat west of the Continental Divide dating back 3-6 MYA (Smith et al.

2002; Stearley & Smith 1993; Smith 1992), and east of the Continental Divide dating back

12,500 – 900,000 YA (Bachhuber 1989; Rogers et al. 1985). The older ages of the subspecies suggests that the diversification of cutthroat trout may have been driven by climatic shifts from warmer and wetter to colder and drier during the Pliocene and Pleistocene (Dwyer & Chandler

2009) rather than warming and glacial recession during the Holocene. Given the uncertainty in the estimates of the divergence dates as well as in the estimates of timing of paleoclimatic trends,

I cannot determine exactly what paleoclimatic events drove what diversification events.

Over and Over or Over and Around? The ancestral cutthroat trout is thought to have spread east and southward from the

Pacific, eventually crossing the Continental Divide to arrive on the eastern slope of the Rocky

Mountains. The topology of the phylogenetic trees I estimated (Figures 2.7 and 2.8) group the greenbacks from east of the Continental Divide more closely with the Colorado River cutthroats from west of the Divide than with the Rio Grandes, also native to east of the Divide.

Interpretation of this result depends on the initial assumptions about the native range of the greenback cutthroat trout. If the greenback was originally native east of the Divide, then the phylogeny supports multiple independent crossing events by the Rio Grandes and greenbacks.

On the other hand, if the greenback was originally native west of the Divide, then the phylogeny supports a single trans-Divide crossing by the Rio Grandes and a shorter distance and lower elevation crossing by a greenback ancestor out of the Colorado River drainage. From methods used here, I cannot infer whether the subspecies crossed the Continental Divide or a local drainage divide. I cannot reject the hypothesis that the subspecies diversified through multiple independent headwater transfers, nor can I infer the directionality of those transfers with any confidence. Additional evidence, such as the distribution of other freshwater taxa who might

have followed similar pathways over the Divide, might shed light on the directionality and number of crossing events.

Several lines of evidence suggest that the greenback might actually be native to the western slope of the Divide. The placement of Bear Creek, a population from the Arkansas drainage that was probably transplanted from the South Platte, in the phylogeny as a divergent lineage more closely related to the Rio Grande cutthroat than to the other greenback populations indicates that it may have been the original cutthroat lineage native to the South Platte drainage, rather than the greenback. Historical records indicate that this population is the product of a single stocking event into an isolated stream using fish stock from the South Platte (Kennedy unpubl.). In addition, on-going work using historical trout samples to reconstruct the genetic identity and historical range of cutthroat trout in Colorado may reveal that what is currently considered the greenback lineage is native to the western slope, that the modern Bear Creek population represents an undescribed South Platte lineage that is extinct in its native range, and that the cutthroat trout native to the Arkansas drainage is not the greenback but rather an extinct lineage (Metcalf in prep.). Because of human movement of fish and uncertainty in estimates of divergence and paleoclimatic timing, definitively determining the evolutionary origins of subspecies in the southern Rockies is a challenge requiring significantly more molecular and historical data with accurate spatial descriptions.

The greatest contribution of this research to cutthroat conservation is not the estimation of divergence times. While understanding the evolutionary history of the subspecies is important for conservation efforts (e.g. Crandall et al. 2000, Campbell et al. 2011), this analysis is quite limited in scope and power to resolve subspecies relationships. I used a limited number of loci with only a few variable sites per locus. Current work using next generation sequencing

techniques and microchip arrays has captured hundreds of SNPs across hundreds of loci in

Oncorhynchus, the genus of Pacific salmon and trout including cutthroats (Abadia-Cardoso et al.

2011a, b; Pritchard & Garza in prep.). The greatest and most immediate utility of this research will be as further evidence supporting the unique identity of the clades. The ability of the nuclear markers to recover the mitochondrial tree topology supports the use of mitochondrial markers to infer the identity of historic samples of cutthroat trout for which nuclear DNA sequence is not (currently) available. If we can obtain mitochondrial data for museum samples of trout that were collected prior to massive movements of fish by humans, and we know that the mitochondrial identity is predictive of the nuclear identity, we can infer the identity of museum with greater confidence. Clarifying the identity of current and historical populations of cutthroat trout is a crucial and timely contribution to cutthroat conservation in the southern Rocky

Mountains.

Conclusions Based on Bayesian analysis of multiple independent loci from 16 populations, I estimated the evolutionary history of cutthroat trout lineages in the southern Rocky Mountains. Based on molecular estimates of divergence between the lineages (> 0.79 MYA), I rejected the prevailing hypothesis of younger ages for the subspecies (<0.5 MYA). However, based on the topology of the tree reflecting the relationships between subspecies, I cannot reject the hypothesis of multiple independent headwater transfer events of Colorado River cutthroats into new drainages east of the Continental Divide. Making biogeographic inferences from this tree topology depends on our assumptions about the historic range and taxonomic identity of the subspecies.

Understanding the evolutionary history and clarifying subspecies identity are important for effective conservation efforts of cutthroat trout in the southern Rocky Mountains.

CHAPTER 3

PREDICTING THE GENETIC PURITY OF CUTTHROAT TROUT IN A HEAVILY STOCKED NATIONAL PARK

Introduction After habitat degradation and destruction, the introduction of non-native species is one of the most significant threats to native freshwater flora and fauna (Dudgeon et al. 2006). In freshwater systems, introduced species affect reproduction, survival, and movement patterns in native species (Peterson & Fausch 2003), disrupt ecological interactions (Blanchet et al. 2007), and alter disease dynamics (Poulin et al. 2011) and ecosystem functioning (Simon & Townsend

2003). In addition to these threats, hybridization between native and introduced species can have strong evolutionary impacts on native species, including reduced fitness and wasted reproductive effort (Muhlfeld et al. 2009; Kanda et al. 2002), loss of co-adapted gene complexes (Gilk et al.

2004), and extinction (Rhymer and Simberloff 1996). Hybridization can also have ecological consequences, for example by altering predation rates if hybrids have increased or decreased fitness (Ryan et al. 2009; Didham et al. 2005; Fitzpatrick & Shaffer 2007).

Besides evolutionary and ecological impacts, introduced species have a high monetary impact: the approximately 50,000 introduced species in the United States are estimated to cause losses and damages approaching $120 billion dollars per year (Pimentel et al. 2005). An estimated one third of all endangered and threatened species in the United States are listed, at least in part, due to the action of introduced species (Bright et al. 1995). The eradication and control of invasive species is therefore a major priority for conservation agencies. Of special concern in the American West are cutthroat trout (Oncorhynchus clarkii ssp.),

native to freshwater drainages from the Pacific coast to the eastern slope of the Continental

Divide, from British Columbia to Mexico. All currently recognized and extant subspecies are

protected or proposed for protection by some form of state or federal conservation management

(Young and Harig 2001). Cutthroats are not only top predators in stream ecosystems and

substantial prey items for larger terrestrial predators, but also important recreational, subsistence,

and aesthetic targets for human consumption. The subspecies share an evolutionary history

dating back at least six million years with each subspecies representing a unique evolutionary

history of diversification within isolated drainage systems (Behnke 1992; Wilson & Turner 2009;

Metcalf 2007). Three subspecies are native to Colorado: the Colorado River cutthroat (O. c.

pleuriticus), the Rio Grande (O. c. virginalis), and the greenback (O. c. stomias). Cutthroat

subspecies hybridize readily with each other and with the rainbow trout, Oncorhynchus mykiss, the sister taxon to the subspecies complex (Allendorf & Leary 1988). Human movement of fish such as cutthroat breaches the biogeographic barriers that fostered diversification, altering and erasing the evolutionary trajectory of taxa, and promoting biotic homogenization (Rahel 2007;

Metcalf et al. 2007).

Introductions are often intentional, especially in aquatic systems into which fish are introduced for recreation, subsistence, and conservation purposes. European-Americans have been moving fish around the West since their arrival: one of the earliest records of fish stocking in Colorado dates back to 1862, fourteen years prior to statehood, when sunfish were imported into the state by oxcart (Wiltzius 1985). By the 1880s, trout culture was an established industry in Colorado, propagating native cutthroats as well as exotic brook and rainbow trout (Wiltzius

1985). What are currently considered distinct subspecies in Colorado were treated as a single

taxon, the black-spotted trout, and lineages from distinct drainages were mixed and shipped for stocking (Behnke 1992). Yellowstone (O. c. bouvieri) and westslope (O. c. clarkii) cutthroats were also mixed, shipped, and stocked as black-spotted trout across the West (Behnke 1992).

Trout were introduced by private individuals, angling clubs, and government agencies, initially for recreation and commercial purposes and later to support conservation efforts. Early stocking was regarded as necessary and desirable to improve existing stocks and to spread fish into fishless waters (Cambray 2003); later stocking efforts were seen as necessary measures to counteract population declines (Marie et al. 2010), probably resulting from the combination of habitat loss, angling pressure, and stocking into marginal habitat, among other causes.

Introductions through stocking happened on a massive scale. In Rocky Mountain

National Park alone, nearly 20 million eggs, fry, or fingerlings were stocked into 1500 km of

stream or lakes between 1898 and 1964. For the 36 populations depicted in Figure 3.1, more

than 8 million fish were stocked into 172 km of stream in the same period. Genetic surveys of

populations in the Park established that populations of the trout do not necessarily contain pure

populations of greenback cutthroats east of the Continental Divide, nor pure Colorado River

cutthroats in their respective native ranges (Figure 3.2) (Martin et al. 2008). The revelation that

many of the putatively pure cutthroat populations in the state, including those that had been used

in reintroduction efforts, were actually hybridized with non-native cutthroats (Metcalf et al.

2007) has effectively halted restoration efforts, which were moving towards the delisting of one

of the subspecies.

Figure 3.1- Stocking intensity of trout in Rocky Mountain National Park. For the 36 populations highlighted in this map, more than 8 million fish were stocked into 172 km of stream between 1886 and 33

1968.

Figure 3.2- Left: Purity (highest percentage of any cutthroat lineage) of cutthroat populations in 36 populations in Rocky Mountain National Park based on combined mitochondrial haplotype and morphological assessment data. Darker colors indicate higher purity, lighter colors indicated higher introgression. Right: Estimated distribution of greenback, Colorado River, Rio Grande, and Yellowstone cutthroat in the Park based on mitochondrial haplotype surveys of 365 individuals from 13 of the 36 populations.

In order to effectively conserve the genetic legacy and potential of cutthroat trout, it is

important to understand the spatial distribution of genetic purity of populations that have

potentially been compromised by stocking. Much of the literature on invasion biology focuses

on the species traits and abiotic and biotic characteristics that contribute to the successful

establishment and spread of non-native species (Simberloff 2009). Various studies have considered the spatial spread of hybridization between native and introduced trout (Thibault et

al. 2010; DeHann et al. 2010; Heath et al. 2010; Muhlfeld et al. 2009) and the impacts of

stocking on the genetic integrity of native populations (Marie et al. 2010). Increasingly, 34

propagule pressure—the number and spatial and temporal patterns of invading organisms—is

shown to be an important determinant of establishment and spread (Simberloff 2009). More than

980 km of roads and trails run through Rocky Mountain National Park (Figure 3.3)

Figure 3.3- The distribution of roads (light orange; 283.43 km) and trails (dark orange; 704.64 km) in Rocky Mountain National Park shown against a 10 m Digital Elevation Model. 35

Given the role of humans as “invasion vectors” transmitting trout across the landscape

(Drake & Mandrak 2010), the spatial patterns of human-mediated hybridization are likely

predicted by the spatial configuration of human transportation networks. Stocking records that

would give a more direct estimate of propagule pressure are often difficult to compile and

incomplete, especially considering the likelihood of undocumented stockings. Results of previous studies suggest that distance to anthropogenic features such as roads or trails (Bennett et al. 2010; Mehner et al. 2009; Foxcroft 2004) or density of those features (Scott 2006) can be used a proxy for propagule pressure to predict the presence of hybridized populations. Here I combine molecular genetic and morphological assessments of purity of cutthroat trout populations in Rocky Mountain National Park with a geographic information system (GIS) to ask whether purity is predicted by distance or density of anthropogenic features and test those predictions against stocking records for the Park.

The question of the relationship between genetic purity and the spatial arrangement of anthropogenic features in the Park suggests several alternative hypotheses. First, that the genetic purity of populations is positively correlated with distance to or density of roads or trails, as found by Mehner et al. (2009) or Muhlfeld et al. (2009). In other words, populations that are more geographically isolated from human features are more likely to be genetically pure and not hybridized. Alternatively, genetic purity may be negatively correlated with distance to or density of roads or trails. Trout were frequently introduced into historically fishless waters (above barriers and/or in marginal habitat), either out of the desire to increase angling opportunities and improve the bounty of nature, or out of necessity to protect putatively pure populations from invaders and hybrids (Wiltzius 1985; Young & Harig 2001). In other words, those populations that are farther from roads or trails are more likely to be hybridized and not genetically pure.

Finally, genetic purity may not be correlated with the geographic arrangement of anthropogenic

features at all. I tested these alternative hypotheses using regression and an information theoretic approach (Akaike 1974).

Materials & Methods Genetic Sampling & DNA Analysis To explore the relationship between purity and the spatial arrangement of roads and trails,

this research takes advantage of molecular genetic data collected in the Park prior to 2009.

Adipose fins were sampled from 365 individual cutthroat trout in 13 populations in Rocky

Mountain Park by Martin et al. from (2005 to 2007). DNA was extracted using QIAGEN DNA

extraction kits and the accompanying protocol. Campbell et al. (2011), Wilson & Turner (2009),

Metcalf et al. (2007), and others have used mitochondrial DNA sequences from the NADH

Dehyhdrogenase 2 (ND2) gene to assess the phylogenetic relationships between Oncorhynchus lineages. Martin et al. amplified the complete ND2 gene for 353 individuals according following

Metcalf et al.’s (2007) protocol and determined the complete sequence for 168 individuals. The remaining individuals were assessed using restriction fragment polymorphism (RFLP) analysis with two diagnostic enzymes, Btg I and Hpy II, for grouping with one of several different subspecies. These enzymes can be used to identify Colorado River and greenback cutthroat trout and to determine if Rio Grande or Yellowstone is present, although the RFLP method cannot distinguish between the latter two subspecies. Martin et al. also determined sequences from individuals of reference populations of the cutthroat trout subspecies as used in Metcalf et al.

(2007). Reference populations were chosen based on a combination of geography and genetic analysis. For each population, they calculated the proportion of each subspecies mtDNA types using Bayesian methods assuming no prior information in the program structure (Pritchard

2000).

In addition to mtDNA, Martin et al. (2008) also analyzed nuclear microsatellites from

362 individuals for 10 loci (H12, H18, H126, H204, H220, J132, K216, K22 described in

Pritchard et al. (2007); Och10, Och16 described in Peacock et al. (2004)). Amplification and

electrophoreses were conducted by Nevada Core Genomics Facility. Genotypes were assigned

using GeneMapper (Applied Biosystems) at the University of Colorado. Genotypes were also

assigned for 369 individuals from the subspecies reference populations for the same loci. The

sizes and number of alleles, the observed and expected heterozygosity, and deviations from

Hardy-Weinberg expectations were summarized for all populations and each locus separately

using the program Arlequin (Excoffier et al. 2005).

Morphological Assessments of Purity & Stocking Data Base In addition to molecular genetic data on the purity of cutthroat populations in the Park, I also used data collected by the National Park Service (in conjunction with the Colorado Division of Wildlife and the United States Fish and Wildlife Service) describing the subspecies identity of

Park populations. Based on professional assessment of morphological and meristic characteristics such as the description of spotting and the number of vertebrae, pyloric ceaca, or gill rakers, populations were assigned a letter grade, “A” being pure cutthroat, “C” being obvious hybrids (USFWS 1998). I translated these letter grades to percentages to facilitate numerical analyses (A=100, A- = 90, B+ = 89, B=85, B- =80, etc.). These assessments were included as part of a database of stocking records compiled by the NPS and USFWS, which included 1017 recorded stocking events (C. Kennedy unpubl.). Between 1886 and 1968, nearly 20 million eggs, fry, or fingerlings were stocked into more than 1500 km of streams and lake shores. I limited the data set by the including only those streams for which I had phenotypic or genotypic data, stocking records, and named locations definable in NPS hydrological GIS layers (see

below). This left a total of 43 populations, 11 for which I have molecular genetic data and 32 for which I have letter grade data. These sets of populations were considered both separately and in combination. See Appendix for details of final data set.

Geographic Information System Geographic information systems (GIS) are an important tool for describing and predicting species distributions (Knouft et al. 2006), modeling species movement across landscapes (Spear et al. 2005), and predicting species responses to climate change (Waltari &

Guralnick 2009). GIS has also been used to examine the spatial spread of hybridization and the geographic distribution of propagule pressure in rainbow trout (Bennett et al. 2010) and the environmental variables associated with the spread of invasive freshwater fish (Marchetti et al.

2004). Streams, lakes, trails, roads, and park boundary layers were downloaded from the

National Park Service Data Store (science.nature.nps.gov/nrdata/). A digital elevation model

(DEM) layer at 10 m resolution was obtained from the United States Geological Survey (USGS)

Seamless Data Server (http://seamless.usgs.gov/). Spatial data were edited, manipulated, and analyzed using ArcGIS (version 9.3, ESRI, Redlands, California, USA). A cell size of 10 m was used throughout the analysis. Prior to creating the GIS model, all data were processed to improve computational efficiency and assure spatial accuracy between data layers. The streams, lakes, trails, and roads layers were reduced in spatial extent to include only the area within

RMNP by converting the park boundary layer to a polygon and using Clip. All data were reprojected to North American Datum (1983) if they were not already in that projection.

The hydrology layers were incorporated with the phenotypic and genotypic data by joining the layers with a table of the identity data by stream name. The roads and trails layers were treated independently and together by merging to create a single layer of anthropogenic

features. In order to evaluate the relationship between streams and roads/trails, I built a distance

grid (Euclidean Distance tool) from the roads, trails, and merged roads and trails layer, and ran

zonal statistics comparing the distance grid and the steam layer. I joined this statistics table to

the attribute table of the stream layer that already included the identity data. I also evaluated the

impact of density of anthropogenic features on genetic purity by building line density grids. I

ran zonal statistics relating the density grids with streams, and joined those statics table with the

stream attribute table. See Appendix for spatial data by population.

Statistical Analyses To quantify the relationship between purity and anthropogenic features and to assess the

ability of the spatial arrangement of those features to serve as a proxy for propagule pressure, I

used linear and logistic regression. I considered the relationship between purity as a binary variable (pure or not, regardless of subspecies identity), distance to and density of roads and/or trails, stocking history (maximum and total individuals stocked and number of stocking events), and an index of propagule pressure (rate of stocking: total stocked divided by number of stocking events) for the combined dataset and the subsets of assessments by genotype and phenotype.

Treating purity (for the haplotype and grade data) as a binary rather than a continuous variable was a concession to the lack of intermediate levels of hybridization in the data set; most

populations were either 100% pure cutthroat (by grade or haplotype) or 50% hybridized. For

each model in each data set I calculated Akaike’s Information Criterion (AIC) (Akaike 1974) and

Akaike weights (wi) to assess the relative plausibility of each model (regression). The lowest

relative AIC was considered the best fitting model. wi can be interpreted as the weight of

evidence that a given model i is the best model given the data and the set of candidate models

assessed. Analyses implemented were in the computing environment R (R Development Core

Team 2011) using generalized linear models with a logit (binomial) link, simple linear models,

and correlation using Pearson’s method (Bolker 2008).

Results Molecular Genetic Purity Martin et al. (2005) identified 42 unique mitochondrial haplotypes from six different subspecies. Within the 365 individuals surveyed in Rocky Mountain National Park, they detected four of the six subspecies; all haplotypes sampled in the Park grouped within the O. c. pleuriticus (Colorado River, CR), O. c. bouvieri (Yellowstone, YS), O. c. stomias (greenback,

GB), and O. c. virginalis (Rio Grande, RG) subspecies clades. Of all individuals, 56% had a haplotype characteristic of lineage GB (206 individuals), 35% (130) had a haplotype characteristic of lineage CR, and 8% (8) grouped with YS/RG. Only Forest Canyon had definitive RG individuals (based on sequences and RFLP analysis). Table 3.1 lists the populations surveyed along with assessments of purity and lineage identity.

Table 3.1- Populations used in molecular genetic analysis of population purity. Columns reflect the number of individuals sampled for mitochondrial DNA and microsatellites; the majority proportion and lineage identity based on mitochondrial haplotype; the majority admixture coefficient, q, and lineage identity based on structure analyses of nuclear DNA; the average estimated genetic diversity, Ho, based on microsatellites; and whether stocking records and spatial data were available.

Population mtDNA micros Proportion ID q ID Ho Rcd & GIS Bear Lake 27 28 1.0 GB 0.80 GB 0.34 Y NF Big Thompson 30 30 0.97 YS 0.95 GB 0.6 Y Fern Lake 30 30 1.0 GB 0.96 GB 0.37 Y Forest Canyon 30 30 0.77 CR 0.77 CR 0.66 N Hague Creek 31 29 1.0 GB 0.98 GB 0.26 Y Hazeline Creek 31 28 1.0 CR 0.99 CR 0.31 N Lawn Lake 29 30 1.0 GB 0.58 GB 0.44 Y Odessa Lake 30 30 1.0 GB 0.65 GB 0.43 Y Upper Ouzel Creek 31 31 1.0 CR 0.63 CR 0.84 Y Timber Lake 30 32 1.0 GB 0.92 GB 0.48 Y West Creek 30 30 1.0 GB 0.51 CR 0.6 Y Willow Creek 5 5 0.6 GB 0.52 YS 0.64 Y Ypsilon Lake 31 29 1.0 CR 0.99 CR 0.42 Y 41

Across the 10 microsatellite loci surveyed in 362 individuals, the number of alleles per locus ranged from 6 (H204, K222, Och10) to 23 (Och16) (results not shown). Average observed heterozygosity, Ho, across all populations ranged from 0.30 (Och10) to 0.74 (K216) (Table 3.1).

Within populations Ho ranged from 0.26 (Hague Creek) to 0.84 (Ouzel Creek). Independent tests (130) of genotype conformation with Hardy-Weinburg expectations were not significant for any population at (0.05/130 = 0.000385). For individual populations, only 3 loci across all 13 populations departed significantly from expectations at (0.05/10 = 0.005).

Using the microsatellite allele frequencies and the reference populations for each of the four subspecies in structure assuming four distinct groups, the probability of belonging to a particular group was calculated for each individual. The admixture coefficient, q, of the majority lineage for each population is show in Table 3.1. In three populations, all individuals assigned with high probability (>0.98) to either lineage GB (Hague) or CR (Hazeline and Ypsilon). In all other populations, individuals were a mixture of GB and CR alleles, with average q values for

GB ranging from 0.17 to 0.96. Two populations (Ouzel Creek and Willow Creek) showed evidence of alleles from YS in addition to GB and CR alleles. One population (Forest Canyon) had alleles from RG. Martin et al. (2008) did not detect any evidence of rainbow trout.

Overall, for eight of the populations with more than 50% GB nuclear DNA, seven contained only GB mitochondrial DNA. For the five populations that were mostly CR nucDNA, two contained only CR mtDNA and another two contained less than 3% GB mtDNA. Martin et al. (2008) considered mtDNA type a good predictor of whether a population was more GB- or

CR-like. Quantitatively, the binary purity by haplotype was a poor predictor of binary purity by

nucDNA (R2 = 0.022, p = 0.662, r = 0.149). Continuous nuclear purity was weakly correlated

with average observed heterozygosity (R2 = .304, p = 0.077, r = -0.553).

Purity and Anthropogenic Features No relationships between genetic purity of cutthroat populations and the spatial patterns

of roads or trails in Rocky Mountain National Park were statistically significant. None the less,

there were some discernable relationships among predictors and responses based on p-values for

intercepts, AIC, and Pearson’s correlation coefficient (Table 3.2). Purity in the nuclear

(structure) and combined (both grade and haplotype) datasets were best explained by a negative

association with distance to trails: as distance to trails increased, purity decreased (wi = 0.264,

0.394, Pearson’s r = -0.535, -0.213). Purity in the grade dataset was best explained by a negative association with distance to either roads or trails (wi = 0.212, r = -0.167). For these data sets, distance to roads or trails also had higher relative probabilities (wi = 0.394, 0.250) and

comparable ΔAIC and r values (ΔAIC = 0.325, 0.106; r = -0.515, -0.207). Purity in the haplotype dataset was best explained by a positive association with density of roads and trails (wi

= 0.232, r = 0.283). Average heterozygosity (as a measure of nuclear genetic diversity within a

population) was positively correlated with distance to trails: as distance between streams and

trails increased, heterozygosity increased (wi = 0.179, r = 0.329). However, ΔAIC values for this

data set were all <1.0 and relative probabilities were comparable. Figures 3.4 – 3.6 depict some

of the data as described using regression.

Table 3.2- Generalized linear model (logistic) regression results with Akaike’s Information Criterion

(AIC), ΔAIC (the change in AIC from the lowest value among models), and wi (Akaike’s weight, the relative probability of a particular model) as well as Pearson’s correlation coefficient, r. Relationships for which the intercept values were statistically significant at p < 0.1 and wi was highest are highlighted in green. Dist is the average distance to the feature (m); Dens is the average density of features (cells/10m2).

Data Set Predictor Response Intercept Coefficient AIC ΔAIC wi r Both Trail Dist Purity 0.736 -4.03E-04 59.778 0 0.264 -0.213 Road Dist 0.075 -8.01E-05 61.25 1.472 0.126 -0.104

Both Dist 0.718 -3.93E-04 59.884 0.106 0.250 -0.207

Trail Dens 0.166 415.563 61.127 1.349 0.135 0.115

Road Dens 0.406 147.879 61.691 1.913 0.101 0.022

Both Dens 0.225 267.318 61.304 1.526 0.123 0.096

Haplotype Trail Dist Purity 2.656 -0.001 12.485 0.384 0.191 -0.451 Road Dist 2.970 -0.0013 13.706 1.605 0.104 -0.256

Both Dist 2.479 -0.001 12.937 0.836 0.153 -0.395

Trail Dens -0.031 3875.530 12.115 0.014 0.230 0.314

Road Dens 1.386 0.009 14.011 1.910 0.089 0.149

Both Dens -0.038 3922.557 12.101 0 0.232 0.283

Nuclear Trail Dist Purity 83.868 -0.012 96.562 0 0.394 -0.535 (structure) Road Dist 78.536 -0.0005 100.213 3.651 0.063 -0.074

Both Dist 83.685 -0.012 96.888 0.325 0.335 -0.515

Trail Dens 73.298 3816.764 99.989 3.427 0.071 0.162

Road Dens 76.600 5680.503 100.217 3.655 0.063 0.072

Both Dens 73.291 2691.778 100.042 3.480 0.069 0.072

Nuclear Trail Dist Diversity 0.454 8.84E-05 -4.182 0 0.179 0.329 (Avg Ho) Road Dist 0.413 1.84E-05 -3.759 0.423 0.145 0.270

Both Dist 0.455 6.95E-05 -4.070 0.112 0.169 0.315

Trail Dens 0.546 -6.77E+01 -4.064 0.118 0.169 -0.314

Road Dens 0.514 -2.25E+02 -4.035 0.147 0.166 -0.310

Both Dens 0.541 -5.49E+01 -4.115 0.067 0.173 -0.320

Grade Trail Dist Purity 0.351 0.000 47.371 0.048 0.207 -0.163 Road Dist 0.046 0.000 47.770 0.447 0.170 -0.121

Both Dist 0.350 0.000 47.323 0 0.212 -0.167

Trail Dens 0.060 104.962 48.210 0.887 0.136 0.028

Road Dens 0.093 217.750 48.194 0.871 0.137 0.036

Both Dens 0.045 104.619 48.193 0.870 0.137 0.037

Figure 3.4- Relationship between average distance to road or trail and purity by grade assessment. p- value for estimate of coefficient and Pearson’s correlation coefficient are displayed. Inset: GIS map of distance to road or trail by waters as zones in Rocky Mountain National Park.

Figure 3.5- Relationship between average density of roads and trails and purity by haplotype assessment. p-value for estimate of coefficient and Pearson’s correlation coefficient are displayed. Inset: GIS map of density of roads and trail by waters as zones in Rocky Mountain National Park. 45

Mountain National National Mountain

ximum percentage of individuals pertaining to any to any pertaining individuals of percentage ximum played. No relationships were statistically significant. statistically were relationships No played. , dis are Logistic regression relationships between haplotype purity (estimated by ma by (estimated purity haplotype between relationships regression Logistic

- value Pearson’s ad correlation coefficient, r -

Park. p Figure 3.6 Rocky in trails) and (roads features anthropogenic of density) and to (distance patterns spatial and lineage) cutthroat given tures (roads and and (roads tures , are displayed. relationships No were valuethe slope for Pearson’s and correlation coefficient, r

r models (logistic regression) for the relationships between haplotype purity (estimated by maximum percentage percentage maximum by (estimated purity haplotype between relationships Generalized the for regression) linea models (logistic r

- ils) in Rocky Mountain National Park. p - Park. National Mountain Rocky ils) in of individuals pertaining to any given cutthroat lineage) and spatial patterns (distance to and density) of anthropogenic fea anthropogenic of density) to and (distance patterns spatial and lineage) cutthroat given any to pertaining individuals of tra Figure 3.6 statistically significant. statistically 46

Anthropogenic Features and Stocking Records The use of spatial patterns of anthropogenic features to predict purity is predicated on the assumption that those spatial patterns are a valid proxy for propagule pressure. I used linear regression to test whether an index of propagule pressure (total stocked/number of stocking events) was associated with any spatial patterns of anthropogenic features of roads and trails. Of the six predictors, the density of roads was the best predictor (wi = 0.992) of propagule pressure and was a moderate correlation (r = 0.535). Density of roads explained a low proportion of the variation in the data (R2=0.287) with a slope significantly different from zero (p = 0.0002). All other predictors had low relative probabilities and high ΔAIC values (Table 3.3). Figure 3.7 depicts a selection of the relationships between anthropogenic features and stocking patterns.

Table 3.3- Linear regression models for relationship between propagule pressure index and spatial descriptions of anthropogenic features using all populations in the combined dataset (n = 43). Road density is highlighted in orange as a statistically significant predictor of propagule pressure.

R- p- Predictor Intercept Coefficient AIC ΔAIC w r square value i Trail Dist 14470 0.389 0.002 0.787 920.645 14.448 0.001 0.042 Road Dist 16617 -0.466 0.015 0.433 920.071 13.874 0.001 -0.123 Both Dist 14590 0.244 0.001 0.866 920.693 14.496 0.001 0.026 Trail Dens 13711 1640694 0.010 0.529 920.303 14.106 0.001 0.099 Road Dens 12523 17263795 0.287 0.0002 906.197 0.000 0.992 0.535 Both Dens 11849 3798913 0.085 0.057 916.890 10.693 0.005 0.292

) is the only only the is ) 2 king events) of cutthroat trout in Rocky Mountain National Park. Park. National Mountain Rocky in trout cutthroat of events) king , are displayed. Average density of roads (cell/10m roads density of Average displayed. , are

r Relationship between spatial patterns (distance to and density) of anthropogenic features (roads and trails) and an index index an and trails) and (roads features anthropogenic of density) to and (distance patterns spatial between Relationship

- value,Pearson’s and correlation coefficient, - , p 2 Figure 3.7 stoc of stocked/number individuals (total pressure propagule of The R pressure. propagule of predictor significant statistically

Purity and Stocking Records Ultimately, the use of spatial patterns of anthropogenic features as a proxy for propagule pressure in order to explain the spatial distribution of genetic purity rests on the assumption that stocking has a discernible effect on the purity of cutthroat trout populations. I tested whether any of the stocking variables (year of earliest recorded stocking event, greatest number of fish stocked in any given event, the total number of fish stocked between 1898 and 1964, the number of recorded stocking events) and an index of propagule pressure accounting for number of individuals and rate of stocking (total stocked/times stocked) were significant predictors of purity by any dataset (Table 3.4). Again, none of the relationships were statistically significant, and most correlations were | r | < 0.5. The haplotype assessment of purity was negatively associated with the maximum number of fish stocked in a single event (wi = 0.260, r = -0.444): the more fished stocked in a single event, the less likely the population was to be pure. The nuclear assessment of purity (by structure) was positively associated with the maximum number of fish stocked in a single event (wi = 0.234, r = 0.245): the more fish stocked in a single event, the more likely the population was to be pure. The grade assessment of purity was negatively associated with the number of times stocked (wi 0.342, r = -0.256): the more times a stream was stocked, the less likely populations were to be pure. The combined (grade and haplotype) assessment of purity was negatively correlated with the total number of fish stocked between 1898 and 1964

(wi = 0.287, r = -0.237): the more fish stocked overall, the less likely the populations were to be pure. The nuclear assessment of genetic diversity was positively correlated with the year of the earliest recorded stocking (wi = 0.582, r = 0.545): the more recently a population was stocked, the more genetically diverse it was likely to be.

Table 3.4- Results of generalized linear models for the relationship between purity and stocking. Predictors for which the intercept value was statistically significant (p < 0.10) are highlighted in green. The purity dataset “both” includes grade and haplotype assessments of purity. Predictor variables were year of earliest recorded stocking event (Early), greatest number of fish stocked in any given event (Max), the total number of fish stocked (Tot), the number of stocking events recorded (Times), and an index of propagule pressure (Tot/Times = Prop Press). Also listed are Akaike’s Information Criterion; the change in AIC from the lowest value model, ΔAIC; Akaike’s weights, wi; and Pearson’s correlation coefficient, r.

Purity Data Set Predictor Intercept Coefficient AIC ΔAIC wi r Both Early 23.706 -0.012 61.383 2.083 0.101 -0.088 Max 0.091 -1.03E-05 59.772 0.472 0.227 -0.214

Tot 0.072 -1.22E-06 59.300 0 0.287 -0.237

Times 0.887 -3.80E-02 59.917 0.617 0.211 -0.205

Prop Press 0.096 -3.68E-05 60.308 1.008 0.174 -0.182

Haplotype Early 99.548 -0.051 13.543 0.859 0.169 -0.282 Max 2.753 -2.30E-05 12.684 0 0.260 -0.444

Tot 2.618 -5.40E-06 12.747 0.063 0.252 -0.430

Times 1.251 2.26E-02 14.380 1.696 0.111 0.068

Prop Press 3.162 -0.0001 13.129 0.445 0.208 -0.375

Nuclear Early 388.395 -0.163 100.058 0.467 0.186 -0.140 (structure) Max 71.070 1.09E-04 99.591 0 0.234 0.245 Tot 74.490 9.11E-06 100.189 0.598 0.174 0.088

Times 71.576 3.80E-01 99.951 0.360 0.196 0.170

Prop Press 69.360 5.16E-04 99.804 0.213 0.211 0.204

Nuclear Early -10.713 5.84E-03 -6.807 0 0.582 0.545 (Avg Ho) Max 0.497 1.92E-08 -2.924 3.883 0.084 0.005 Tot 0.492 3.29E-08 -2.938 3.869 0.084 0.037

Times 0.580 -7.00E-03 -4.299 2.508 0.166 -0.343

Prop Press 0.499 -3.77E-08 -2.923 3.884 0.084 -0.001

Grade Early -5.985 0.003 48.220 2.190 0.114 0.023 Max 0.048 -7.74E-06 47.374 1.344 0.175 -0.163

Tot 0.037 -9.32E-07 46.880 0.850 0.224 -0.202

Times 0.682 -0.047 46.030 0 0.342 -0.256

Prop Press 0.479 -2.29E-05 47.749 1.719 0.145 -0.123

Discussion The spatial effects of stocking on the genetics of natural populations are variable. In

some cases, introgression with introduced taxa leads to loss of geographic population structure

and increased homogenization (Eldridge et al. 2009; Small et al. 2007; Ayllon et al. 2006;

Williamson & May 2005). In others, even when introgression is possible, populations appear to

be resistant to hybridization and to maintain unique genetic and geographic structure, often in the face of considerable propagule pressure (Rourke et al. 2010; Matala et al. 2008; Larsen et al.

2005; van Houdt 2005). In general, invasion facilitated by human alteration of the landscape and

increase of propagule pressure accounts for variation in the biogeography of fish invasions

globally (Leprieur et al. 2008).

The main goal of this research was to assess the relationship between the spatial patterns

of human transportation features and purity of cutthroat trout populations in Rocky Mountain

National Park and to evaluate the usefulness of those spatial patterns to serve as a proxy for

propagule pressure. The ability to predict the purity of populations based solely on geographic

factors would be of great use to management, given the ready availability and ease of

manipulation of GIS data and the difficulty of compiling stocking records which are presumed to

be incomplete in the first place. Previous research on the spatial spread of introgression between

native and invasive fishes indicate that higher levels of introgression are associated with

increased anthropogenic landscape changes (Heath et al. 2010; Marchetti et al. 2004) and

propagule pressure indices that account for distance from introduction point (Bennett et al.

2010). Human transportation networks have served as transportation routes and introduction points for non-native species (Drake & Mandrak 2010; Floerl et al. 2009). I did not find any statistically significant relationships between genetic purity as assessed by mitochondrial DNA

haplotypes, nuclear DNA microsatellites, or phenotype and spatial patterns of human transportation networks. The distance to and density of roads and trails in the vicinity of streams cannot be used to predict the purity of cutthroat populations in Rocky Mountain National Park with statistical confidence.

Many millions of trout were stocked into Rocky Mountain National Park between the arrival of European-Americans and the commencement of restoration activities. The construction of an exhaustive database of fish stocking in Colorado comprising nearly 86,000 recorded stocking events revealed that nearly 1.25 billion fish were stocked between 1872 and

1972 (C. Kennedy unpubl.). Historically, populations in different drainage basins were isolated by geography but that relationship has been overcome (Eldridge et al. 2009; Rahel 2007).

Introduced trout hybridized with native species, obscuring the evolutionary signal of diversification in isolated drainage basins. Stocking of trout continues today: the Leadville and

Hotchkiss National Fish Hatcheries in Colorado raise over 2.5 million trout per year for stocking in lakes and reservoirs with the goal of augmenting angling opportunities and increasing revenue from angling expenditure (http://www.fws.gov/ 2011); the Colorado Division of wildlife publishes biweekly reports during the spring, summer, and fall of stocking efforts across the state

(http://wildlife.state.co.us/ 2011). While I did not find any significant associations with purity and propagule pressure, given the sheer volume of trout introductions, I would argue that propagule pressure of introduced trout is still a relevant measure of the establishment success and spread of non-native lineages and hybridization.

The lack of significant associations among measures of purity, propagule pressure, and spatial patterns of human features may be attributable to limitations of the datasets. Phenotypic assessments of purity are immediately suspect: though Campbell et al. (2011) argues that

professional phenotypic assessment of introgression in Yellowstone cutthroat trout was highly accurate, Metcalf et al. (2007) demonstrated using molecular methods that putatively pure populations of cutthroats were actually hybridized. In addition, the morphological and meristic characters used to distinguish cutthroat subspecies (as in Behnke 1992) are not diagnostic. The molecular genetic assessments used in this study suffer from a small sample size that may not reflect the distribution of purity in the Park. A larger data set might be more sensitive to levels of hybridization rather than the binary pattern I found (most populations were either 100% or

50% pure).

On the other hand, the variables I considered might not be relevant to my question. For example, Ruesink (2005) found that date of first introduction was not related to establishment success of introduced freshwater fish. Also, Halbisen & Wilson (2009) found that variable introgression from stocking in populations of lake trout was not predicted by recorded stocking history alone and suggested that other genetic, ecological, or anthropogenic factors may facilitate reproduction between native and stocked fish.

Finally, the scale of the analyses might not be appropriate for the question. Rocky

Mountain National Park is a small footprint (1,075.53 km2) considering the size of the historical ranges of cutthroat trout in the Southern Rocky Mountains and the volume of fish stocked.

Spatial patterns of genetic purity may be visible on a larger state-wide or regional scale.

Hatchery stocks were often delivered by milk cans on trains to individuals requesting eggs from the United States government (Behnke 1992). The spatial distribution of cutthroat purity may be associated with the spatial patterns of railroads. In addition, a measure of the spatial patterns of human features that incorporates the effective distance between streams and features in addition to Euclidean distance, as I calculated, may be appropriate.

Conclusions I found that mean distance and density of roads and trails did not predict the genetic

purity of cutthroat populations in Rocky Mountain National Park (RMNP). Some spatial

patterning is evident: for example, all pure populations of greenbacks were located east of the

Continental Divide within their native range. The spatial patterning of greenback purity in

RMNP merits further exploration of other potential explanatory variables through techniques

such as spatial regression. However, given the magnitude of historical and restoration stocking

within the Park, further analyses are unlikely to uncover any meaningful spatial patterns in the

distribution of genetic purity. Stocking was initiated in the streams within the Park even before

the Park was formally established and was carried out by private individuals, state agencies and

federal departments with little to no coordinated and prior planning. Even under the restoration

program, reintroduction planning was largely ad hoc and conservation goals and strategies varied

widely over the decades of the restoration. Factors describing the extent of stocking such as the

total number of fish stocked and the number of stocking events did not predict the current genetic

purity of cutthroat trout populations. The fact of the matter may be that we cannot predict the

genetic purity of cutthroat populations due to the off-the-cuff nature of fish stocking. Ultimately, everything may be everywhere, at least when it comes to cutthroat trout in Rocky Mountain

National Park.

CHAPTER 4

CONCLUSIONS: PROSPECTS FOR CUTTHROAT CONSERVATION

Cutthroat trout are an important component of biodiversity in the American West and a significant target for conservation efforts across the range of the twelve extant subspecies

(Wilson & Turner 2009). The distribution of cutthroat trout among the basins and mountain ranges of the west reflects a complex evolutionary history shaped by dramatic geologic and paleoclimatic events (Campbell et al. 2011; Smith et al. 2002). In the previous chapters, I presented evidence from molecular phylogenetic analyses that the subspecies are actually much older than the prevailing hypothesis put forth on the basis of morphology and professional intuition suggests (Behnke 1992). The older Pleistocene origination times for subspecies in the

Southern Rocky Mountains argues that the subspecies may actually warrant individual designation as full species. As species and subspecies are treated differently under the letter of the Endangered Species Act (Allendorf et al. 2005), the taxonomic designation of cutthroat trout will have important implications for cutthroat conservation at the local and regional levels.

Historically, the subspecies were isolated by substantial biogeographic barriers.

Waterfalls, catchment divides, and mountain ranges prevented the exchange of individuals between drainages, allowing the subspecies to diverge in isolation. Human-facilitated movement of non-native fish, deliberately or inadvertently, has breached those biogeographic barriers, allowing previously differentiated lineages to hybridize thus obscuring and erasing the unique evolutionary history that developed over hundreds of thousands to millions of years (Rahel

2007). Of course, dispersal and events are important evolutionary events leading to the diversification of regional biotas; however, the pace, volume, and spatial extent of human movement of species is unprecedented in evolutionary history (McKinney & Lockwood 1999). I presented evidence that at a small scale, the volume of fish stocking overwhelms the spatial signal of genetic purity: pure populations may persist but the distribution of purity cannot be predicted by geography and stocking patterns.

Effective conservation efforts assume that we can accurately identify the target taxon and that we know the extent of its historic range with some certainty. The story of the greenback cutthroat trout illustrates the possible outcome of conservation efforts enacted under faulty assumptions and information. The recovery of the greenback from the beyond the grave was a major conservation success story. The revelation that putatively pure populations were hybridized due to human movement of fish prior to and during the restoration brought the recovery effort to a halt and embroiled the project in controversy. Understanding the current distribution of cutthroat trout in southern Rocky Mountains and accurately inferring the historical distribution will require not only robust molecular and morphological assessments but also a thorough review of the historical records of fish movements. Establishing the identity and distribution of cutthroat lineages in the past as well as determining how the identity and distribution has been shuffled by stocking. The taxonomy of cutthroat trout is under revision in light of forthcoming molecular sequence data from museum specimens to better reflect the evolutionary history of the lineages, prior to massive stocking efforts.

The future of the hybridized populations is uncertain given that few, if any, pure populations of cutthroat trout native to the eastern slope of the Continental Divide remain. The first restoration effort took great care to remove all supposedly non-native trout before introducing putatively restoration stock (Young 2009). To undertake such an effort anew on the

basis on the latest evidence (e.g. Metcalf et al. 2007), would be to lose the evolutionary legacy of the native cutthroats that is currently preserved in the hybrid populations. Research efforts should focus on shoring up the evidence for unique lineage identities by turning to museum samples and historical records. Conservation efforts should focus on protecting the remaining evolutionary legacy and potential of cutthroat trout while assessing the possibility of propagation of the Bear Creek lineage in its native drainage and continuing the search for remnant pure populations.

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Appendix: Populations of cutthroat trout used to analyze relationships between spatial human transportation networks and various measures of genetic purity. Lineages (Line) included CR = Colorado River, GB = Greenback, YS = Yellowstone, CUT = cutthroat unidentifiable to subspecies, RG = Rio Grande. Data was collected from NPS phenotypic assessments of purity (G = grade) and molecular genetic assessments of purity collected by Martin et al. (2005, 2008) (H = Haplotype). Distance (Dist) to trails, roads, and roads or trails is recorded in m. Density (Dens) of trails, roads, and trails and roads is recorded in cells/10m2. (Early) refers to the earliest recorded stocking data; (Max) is the maximum number of eggs, fry, or fingerlings released during any given stocking event for a particular water; (Total) is the total number of eggs, fry, or fingerlings released over all stocking events; (Times) is the number of stocking records available for a particular water. Stocking records were compiled by Chris Kennedy (NPS, USFWS); GIS analyses for distance and density were conducted by Sierra Love Stowell, Jessica King, Gabriel Nilsen, and Arthur Smith (University of Colorado at Boulder). See Chapter 3.

Tr + Rd Trail Road Tr + Rd NAME Pure? Line Data Trail Dist Road Dist Early Max Total Times Dist Dens Dens Dens FIFTH LAKE 0 CR G 1013.60 9542.37 1014 0.0000 0.0000 0.0000 1964 1500 1500 1 FAY LAKES 1 GB G 1246.69 4053.20 1247 0.0000 0.0000 0.0000 1942 13759 13759 6 TIMBER CREEK 1 CR G 197.24 2090.26 181 0.0007 0.0002 0.0009 1943 5000 9500 3 HAYNACH LAKES 0 YS G 174.28 7022.89 174 0.0003 0.0000 0.0003 1939 4000 16000 5 PETTINGELL LAKE 0 CUT G 1322.81 4857.58 1323 0.0000 0.0000 0.0000 1934 10000 12000 2 DREAM LAKE 1 GB G 24.35 1160.60 24 0.0025 0.0000 0.0025 1921 26680 134976 20 PEAR LAKE 1 GB G 107.63 0.00 108 0.0005 0.0000 0.0005 1937 10200 14200 2 SANDBEACH LAKE 1 GB G 204.29 3153.24 204 0.0003 0.0000 0.0003 1932 26680 63680 8 HUNTERS CREEK 1 GB G 690.26 3281.06 685 0.0005 0.0001 0.0005 1939 8000 8000 1 SPRUCE LAKE 1 GB G 46.18 4870.32 46 0.0009 0.0000 0.0009 1898 20400 48400 6 LAKE HAIYAHA 0 YS G 162.73 1623.11 163 0.0014 0.0000 0.0014 1922 70000 176060 18 ARROWHEAD 0 GB G 3293.40 3466.38 3213 0.0000 0.0000 0.0000 1913 30000 98500 10 LAKE OUZEL LAKE 1 GB G 107.59 5601.27 108 0.0008 0.0000 0.0008 1932 26680 59680 6 LAKE NANITA 1 CR G 118.31 6753.11 118 0.0004 0.0000 0.0004 1918 20000 30000 3 GLASS LAKE 0 GB G 58.65 3484.32 59 0.0006 0.0000 0.0006 1922 40000 106000 10 THE LOCH 0 GB G 77.46 2069.92 77 0.0013 0.0000 0.0013 1914 50000 206220 18 THUNDER LAKE 0 CUT G 100.80 6952.35 101 0.0008 0.0000 0.0008 1924 50000 221896 19 SF CACHE LA 1 GB G 862.36 9738.26 862 0.0003 0.0000 0.0003 1892 54264 207894 15 POUDRE

ONAHU CREEK 0 CR G 549.06 2353.34 534 0.0007 0.0003 0.0010 1918 35000 143436 10

Appendix Cont’d: Tr + Rd Trail Road Tr + Rd Pure? Line Data Trail Dist Road Dist Early Max Total Times Dist Dens Dens Dens NAME CRYSTAL LAKE 1 GB G 292.78 6263.33 293 0.0006 0.0000 0.0006 1913 50000 178596 12 ROARING RIVER 1 GB G 104.30 3074.87 95 0.0010 0.0002 0.0012 1932 45240 261902 17 ADAMS LAKE 1 CR G 5060.69 0.00 5061 0.0000 0.0000 0.0000 1925 25000 31864 2 COLUMBINE 0 CR G 3444.12 0.00 3444 0.0001 0.0000 0.0001 1930 25000 35000 2 CREEK LOOMIS LAKE 1 GB G 124.69 5188.52 125 0.0003 0.0000 0.0003 1944 20000 20000 1 CONY CREEK 1 GB G 480.14 2397.61 480 0.0007 0.0000 0.0007 1940 40000 48160 2 EAST INLET 0 CR G 225.90 5652.32 226 0.0005 0.0000 0.0005 1916 50000 365968 14 PARADISE CREEK 0 CR G 3020.99 7952.87 3021 0.0001 0.0000 0.0001 1930 58640 186640 7 HIDDEN VALLEY 1 GB G 594.42 160.01 144 0.0004 0.0015 0.0019 1922 100000 517254 18 CREEK NORTH INLET 0 CR G 743.42 4627.66 743 0.0005 0.0000 0.0005 1917 122000 488368 16 GLACIER CREEK 0 CUT G 119.62 1019.04 69 0.0019 0.0009 0.0027 1915 100000 1719260 52 BIG THOMPSON 0 GB G 741.14 1041.13 606 0.0010 0.0008 0.0018 1915 165000 1857480 46 RIVER COLORADO RIVER 1 CR G 201.88 969.90 178 0.0010 0.0007 0.0017 1904 185000 1471815 33 WILLOW CREEK 0 GB H 1987.36 2296.90 1670 0.0000 0.0000 0.0000 1939 5000 5000 1 OUZEL CREEK 1 CR H 245.45 5021.20 245 0.0009 0.0000 0.0009 1943 8160 8160 1 BEAR LAKE 1 GB H 10.05 266.03 10 0.0028 0.0008 0.0035 1900 40000 185994 21 TIMBER LAKE 1 GB H 258.87 3908.67 259 0.0003 0.0000 0.0003 1926 24000 54492 5 ODESSA LAKE 1 GB H 116.81 3939.44 117 0.0009 0.0000 0.0009 1904 40000 248520 22 WEST CREEK 1 GB H 2346.92 6136.89 2347 0.0000 0.0000 0.0000 1924 37700 133700 11 LAWN LAKE 1 GB H 145.88 6081.54 146 0.0007 0.0000 0.0007 1904 30000 124944 10 FERN LAKE 1 GB H 96.17 3797.68 96 0.0011 0.0000 0.0011 1901 60000 245640 15 HAGUE CREEK 1 GB H 980.10 6759.96 980 0.0003 0.0000 0.0003 1936 42240 52240 3 FOREST CANYON 0 YS H 793.05 9503.14 793 0.0004 0.0000 0.0004 1917 160000 635132 20 n = 43 Average 759.26 3980.75 733.60 0.00 0.00 0.00 1923.63 46166.12 246061.16 11.93

68 SD 1111.132 2684.35 1104.704 0.0006 0.0003 0.0008 15.038 43127.13 425486.68 11.325