northeast wildlife dna laboratory

Northeast Wildlife DNA Laboratory Applied DNA Sciences, East Stroudsburg University, 562 Independence road, suite 114,

East Stroudsburg, PA 18301 570-422-7892

GENETIC PROFILE, DIETARY ANALYSIS, AND PARASITIC INFECTIONS OF RIVER OTTERS (LONTRA CANADENSIS) IN PENNSLVANIA

Photo Credit: James Kauffman

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Prepared By: Northeast Wildlife DNA Laboratory East Stroudsburg University East Stroudsburg University, PA 18301

Larry L. Laubach, MS – Wildlife Technician Nikolai Kolba, B.S.- Research Technician Leilani Palmer, MS - Research Technician James Kauffman, MS - Research Technician Thomas Rounsville, MS- Laboratory Manager, Wildlife DNA Specialist Jane E. Huffman, MS, PhD, MPH – Laboratory Director

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CONTENTS

INTRODUCTION 1

River Otter Genetics 2

River Otter Diet 2

Parasitic Infections of River Otters 3

Ectoparasites of River Otters 3

Study Rational 3

METHODS 4

Sample Collection 4

Genotyping 4

Dietary Analysis 5

Parasite Analysis 6

RESULTS 7

Genotyping 7

Diet Analysis 15

Parasite Analysis 17

DISCUSSION 18

Genetic Profiles 18

Genetic Structure 19

Dietary Preferences of River Otters 19

Parasites of River Otters 22

Summary 23

LITERATURE CITED 24

APPENDIX I 29

APPENDIX II 33

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INTRODUCTION The North American, or Nearctic river otter, (Lontra canadensis, Schreber 1777) (Carnivora:

Mustelidae; Lutrinae), is a relatively large, semi-aquatic mustelid that occupies a variety of aquatic habitats throughout North America. In Pennsylvania, river otters were historically found throughout the majority of the Commonwealth (Rhoads 1903). However, otter populations declined statewide as a result of unregulated harvest, habitat degradation, development, tanning, coal, oil, timber, and other industrial activities (Eveland 1978). By the 1950’s, otters were limited mainly to the northeastern counties of the state, within the Pocono Mountains. Eveland (1978) estimated that by 1978 there were between 285-465 otters remaining in Pennsylvania. The majority (70%) of the remaining otters were found in Pike, Monroe, and Wayne counties. Otters were granted protection by the Pennsylvania Game Commission as non-game species in 1952 (Serfass et al. 1999).

The Pennsylvania River Otter Reintroduction program was initiated in 1982 in order to re-establish naturally-reproducing otter populations statewide. Individuals were reintroduced from multiple states, including Louisiana, Maryland, Michigan, New Hampshire, New Jersey, New York, and individuals translocated from the remnant Pennsylvania population (Serfass et al. 1993a; Raesly 2001). This reintroduction consisted of both wild, live-captured otters, and otters from private vendors. In Pennsylvania, otters were chosen due to availability (easily obtained), allozymes, subspecies variation, and otters from source states used successfully in other state reintroduction programs (Raesly 2001). Water quality improvements, wetland mitigation, legal protection, and a reintroduction program have allowed otters to expand considerably throughout the state. By 2004, 153 otters were successfully released within seven water systems (Figure 1) in central and western Pennsylvania (Hubbard and Serfass 2004).

Figure 1. Pennsylvania river otter reintroduction sites and number of otters released (in parentheses) during 1982-2004. (Hardisky 2013)

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River Otter Genetics Originally, otter reintroduction efforts in Pennsylvania used individuals relocated from New

York and the remnant population in Northeastern Pennsylvania. However, due to low levels of trapping success, river otters were also obtained for reintroduction from other states. Historically, Pennsylvania was thought to be a transition area between two otter subspecies, L.c. lataxina and L.c. canadensis (Serfass et al. 1998). Serfass et al. (1998) evaluated allozyme variability of river otter populations across the United States in order to establish a genetic basis for selecting river otters for reintroduction to Pennsylvania waterways. Results suggested that otters from Louisiana showed enough genetic similarity to Pennsylvania otters to be considered for reintroduction. Reintroduction efforts aimed to establish otter populations in waterways in close proximity, in order to take advantage of dispersal and gene flow between reintroduced populations (Serfass et al. 1993b). In 2004, reintroduction efforts ended, and the project focus shifted to post-translocation monitoring and evaluation. Currently, river otters in Pennsylvania are believed to contain historic, native alleles, in addition to new alleles from otters reintroduced from other states (Serfass et al. 1998). However it is not known whether the introduction of river otters from diverse geographic locations, to specific sites within the state, has created a population structure that would allow for assigning individuals to their respective populations based on their genotypic profile.

In Prince William Sound, otter populations are considered sympatric due to the lack of human-induced fragmentation between populations (Blundell et al. 2002a,b). Genetic and allelic distribution varied between populations, and these populations were considered to be genetically distinct. However, relatedness of these populations was positively correlated with proximity. Hardy-Weinberg Equilibrium (HWE) testing of the top population concluded that all microsatellite loci tested were in HWE, but a multi-locus test at individual testing locations indicated that all sites showed heterozygote deficiency. Heterozygous deficiency between populations was believed to be due to non-random mating and low genetic variance (Blundell et al. 2002a, b).

River Otter Diet Various methods have been used to determine the dietary habits of mammalian carnivore

species, including stomach content and fecal sample analysis (Greer 1955; Goszczynski 1976; Colares and Waldemarin 2000; Prigioni et al. 2006; Dalerum et al. 2009; Zunna et al. 2009; Rysava-Novakova and Koubek 2009). Stable isotope analysis has also been used (Ben-David et al. 1998) to determine otter diet. Otter diet is most often investigated through fecal sample analysis (Kruuk 2006). Otter spraints can be easily collected from communal latrine sites used by otters for communication with conspecifics. However, this method does not allow for the allocation of prey remains to individual otters, as spraints of multiple individuals are often found in association.

Dietary preferences of Nearctic river otters have been investigated throughout their North American range (Greer 1955; Sheldon and Toll 1964; Toweill 1974; Melquist and Hornocker 1983; Serfass 1984; Serfass et al. 1990; Reid et al. 1994; Taylor et al. 2003; Baltrunaite 2006; Cote et al. 2008) using both stomach content analysis and fecal sample analysis. Otter diet has typically been quantified utilizing fecal analysis due to the availability of otter feces (spraints) found in communal latrine areas. However, stomach content analysis often provides a more accurate representation of otter diet due to the increased availability of undigested material and certainty of individual sample allocation to individual otters.

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Parasitic Infections of River Otters Multiple helminth parasites have been described from the intestinal tract, heart, and kidneys

of L. canadensis (Kimber and Kollias 2000). Investigations have been conducted to determine parasitic assemblages of otters in multiple states and Canadian provinces (Shoop and Corkhum 1982; Hoberg et al. 1997; Kollars et al. 1997; Kimber and Kollias 2000). Otter parasites identified in those reports included cestodes, acanthocephalans, nematodes, trematodes, and pentastomes. Hartup et al. (1999) also identified intraalveolar nematode parasites (Crenosoma spp.) in river otters. The clinical significance of these infections in river otters is poorly understood (Kimber and Kollias 2000). The incidence and occurrence of otter parasites could potentially have implications pertaining to translocation and reintroduction programs.

Babesia spp. are tick-borne, microscopic protozoans that parasitize the red blood cells of mammals. Over 100 species of Babesia have been identified to occur within wild mammals. A genetically unique Babesia species, similar to Babesia microti, has been identified in otters (Birkenheuer et al. 2007). Otters are known hosts for multiple species of ticks within the genera of Ixodes, Amblyomma, and Dermacenter (Birkenheuer et al. 2007). However, prior to Birken- heuer et al. (2007), tick-transmitted pathogens had not been reported, and tick-borne infections were thought to be non-pathenogenic (Kimber and Kollias 2000). The pathogenic potential of Babesia spp. in otters currently remains unknown (Birkenheuer et al. 2007). In the index case (Birkenheuer et al. 2007), the individual otter did not display clinical signs of infection, and did not exhibit reduced fitness, hemotological, or serum biochemical abnormalities (Tocidlowski et al. 2000).

Ectoparasites of River Otters The occurrence and prevalence of ectoparasites on otters is rare, which may be attributed to

their aquatic lifestyle and intensive grooming habits. However, transfer of ectoparasites may occur when otter prey upon other mammal species, or share den-sites with other mammals (Kimber and Kollias 2000).

Ticks that have been identified from otters include the lone star tick (Amblyomma america- num), the American dog tick (Dermacenter variabilis), the castor bean tick (Ixodes cookei), and the beaver tick (Ixodes banksi) (Serfass et al. 1992; Kimber and Kollias 2000). The potential for tick-borne illness exists when otters are parasitized by these ectoparasites. Ticks can serve as vectors for Rocky Mountain spotted fever, tularemia, Lyme disease, and various other pathogens. Implications of disease transmission arise in otter populations with heavy ectoparasite loads.

In Pennsylvania, the groundhog or castor-bean tick (I. cookei) has been identified on river otters. One species of flea (Oropsylla arctomys) was also collected from a Pennsylvania otter (Serfass et al. 1992).

Study Rational The goal of this study was to investigate aspects of river otter ecology in Pennsylvania. The

objectives were to: 1. Provide genetic profile of river otters in PA. 2. Provide an analysis of river otter diet in PA. 3. Identify the ecto and endo parasites of otters in PA.

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METHODS

Sample Collection Samples were collected from river otter carcasses obtained from the Pennsylvania Game

Commission. Carcasses consisted of incidental captures or vehicle strikes turned into the Penn- sylvania Game Commission between 2009 and 2014. An approximately 1 cm x 1 cm piece of tongue tissue was removed from each otter for use in genetic analysis. An approximately 1 cm x 1 cm piece of splenic tissue was also removed from each otter for use in parasite analysis. Stomachs and intestinal tracts were removed from otter carcasses, labeled, and frozen for later investigation into diet and parasitic infection. During necropsy, if a female was found to have pups, the pups were included in Babesia analysis and genotyping analysis.

Genotyping DNA was extracted from tongue tissue using a MoBio UltraClean DNA extraction kit (Carls-

bad, CA) using the manufacturer’s suggested protocol. Extracted DNA was stored at -20ºC. DNA samples were quantified using an Invitrogen Qubit fluorometer (Carlsbad, CA). A 2 to 200 dilution was used according to instrument protocol.

From the 20 microsatellite loci currently available for the North American River Otter (Lontra canadensis), 8 polymorphic loci were chosen for this analysis (Beheler et al. 2004, 2005; Palmer 2011) for samples collected from 2009 to 2012. Two more microsatellite loci (RIO04 and RIO10) were added for samples collected from 2013 to 2014 (Beheler et al.2005). To reduce pipetting errors and preparation times, an adapted multiplex from Palmer (2011) was used. Within this multiplex, were the 10 primers: RIO 01, 02, 04, 06, 07, 10, 11, 13, 18 and 19. PCR amplifications were performed in 11 µL reactions consisting of: 100 ng DNA, 5 µL master mix (2X), and 2 µL primer mix (10X). Following the loading of each set in 1.5 µL microcentrifuge tubes, one thermocycler program was used: an initial denature at 95°C for 5 minutes, then a touch down PCR for 13 cycles of: 94°C for 30 seconds, 61°C for 90 seconds, 72°C for 60 seconds. The annealing temperature of each subsequent cycle of touchdown PCR was reduced by 1.0°C, by utilizing the AutoX feature of the Applied Biosystems 2720 thermocycler. Following the touchdown PCR was 24 cycles of: 94°C for 30 seconds, 90 seconds of 55°C and 72°C for 60 seconds, followed by a final extension at 72°C for 30 minutes.

One microliter of PCR product from each sample was analyzed using a capillary electro- phoresis on an Applied Biosystems 3130 Genetic Analyzer (Forest City, CA). Before placing into the genetic analyzer, the PCR products were prepped using the following: 9.5 µL high dye formamide, 0.25 µL GeneScan 500 LIZ standard, and 1.0 µL of the product and then ran on the instrument. The results of the instrument were analyzed by GeneMapper 3.7 software (Applied Biosystems, Forest City, CA). The resultants were shown as multiple peaks and the highest peaks of the alleles were called at each locus when the intensity of each allele was greater than 200 relative fluorescence units (RFU’s) and within reasonable distance of the expected fragment size. Negative controls were run throughout all steps of the analysis to ensure that alleles were a direct result of microsatellite amplification and not background contamination (Rounsville 2012). Samples runs were considered successful if there was amplification at 7 out of 10 microsatellite loci.

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Cervus v3.0 (Kalinowski et al. 2007) was used to assign allelic frequency values to differentalleles generated during analysis of the microsatellite genotypes. Only the eight loci that were consistently analyzed for all successfully amplified samples were included in the Cervus analysis. The average number of alleles per locus, number of unique alleles, allele frequencies, probability of identity, observed heterozygosity and expected heterozygosity were calculated. Observed heterozygosity is a measure of the genetic diversity within a population where as expected heterozygosity is the measure of the probable genetic diversity within a population. The probability of identity is the probability that two unrelated individuals do not differ in genotype at a single locus.

To evaluate gene flow and calculate K, which is the best estimate value for the number of populations within a sample set, the program STRUCTURE v2.3.4 (Pritchard et al. 2000; Falush et al. 2003) was used. STRUCTURE estimates the likelihood of an individual belonging to a population using Bayesian clustering to calculate the differences in allelic frequencies. The program outputs graphs based on the likelihood of individuals belonging to populations (K). Populations are indicated by the value K= 1-10. Each run consisted of 1,000,000 replicates of the MCMC after a burn-in of 100,000 replicates. STRUCTURE models were run using population information, admixture and correlated models. The population information referenced the geographic region from which samples were obtained: 1) Northeast Pennsylvania; 2) North central Pennsylvania; 3) South central Pennsylvania; 4) Southeast Pennsylvania; and 5) Unknown Penn- sylvania locations. The admixture model was used which assumes populations are currently or recently experiencing gene flow at sufficient rates which individuals may have recent ancestors from more than one population (Martien et al. 2007). The correlated model calculates the differences in allele frequencies when assuming the allele frequencies of one population are related to allele frequencies in another population. This model using population information, admixture and correlated allele frequencies is thought to provide the best resolution in case of potentially subtle population structure (Falush et al. 2003; Latch et al. 2008). STRUCTURE Harvester was used to calculate the most likely ΔK (Dent and vonHoldt 2012). ΔK was only evaluated for these samples using the correlated allelic frequency model.

Dietary Analysis Stomachs and intestinal tracts were removed from otter carcasses, labeled, and frozen for

later investigation. For examination, stomachs were thawed and contents removed. Prey remains found in intestinal tracts during parasite investigations were included within the analyses. Stomach contents and prey remains were washed within a 1 mm sieve (Dalerum et al. 2009) to clean prey items for identification. Macroscopic items such as mammal bones, whole fish, exoskeletons, and amphibians were identified visually using a dissecting microscope. Fish scales and other microscopic remains were also investigated under a dissecting microscope to aid in identification. Microscopic slides were prepared using the method from Zunna et al. (2009) with representative fish scales from each stomach sample to identify prey species to family level using Daniels (1996). Scales were identified using gross morphology, circuli spacing, and annuli pattern formation (Britton et al. 2005). Regenerated, partially digested, and unidentifiable fish scales were excluded from analysis.

The primary method utilized to quantify prey items is Frequency of Occurrence (Jacobsen and Hansen 1996; Britton et al. 2005; Cote et al. 2008; Perini et al. 2009). This method quantifies the number of prey item occurrences as a proportion relative to total occurrences

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within all samples (total number of stomachs containing prey item/total number of stomachs x 100). Prey items were divided into eight categories: mammal, bird, reptile, amphibian, fish, crayfish, insects, and mollusks. Significant differences in prey selection between sexes were investigated using Chi-square (χ2) analysis (Serfass et al. 1990; Sales-Luis et al. 2007).

Parasite Analysis Intestinal tracts were opened longitudinally and contents removed by scraping internal

mucosa from the intestinal tract wall. Interior tract walls, internal mucosa, and all internal contents were investigated under a dissecting microscope for the presence of internal parasites. All discovered parasites were removed and fixed in alcohol. Parasite numbers were recorded and identified to species-level when possible. Data on prevalence and intensity were recorded.

Pennsylvania otters were tested for the presence of Babesia spp. using splenic tissue samples DNA from the tissue samples was extracted using a MoBio UltraClean DNA extraction kit (Carlsbad, CA), following the manufacturer’s protocol. A primary PCR was performed to amplify a portion of the 18S rRNA gene (using primers 3.1 and 5.1) which was followed by a nested PCR using primers RLB-F and RLB-R. The primers used for the nested PCR are found in Table 1. Each positive PCR product was purified to remove dNTPs and unincorperated primers using a PCR product clean up kit by ExoSAP-IT (Affymetrix, Cleveland, OH). Purified PCR products were then sequenced using a BigDye®Terminator v3.1 Cycle Sequencing Kit (Applied Biosystems, Forest City, CA). The remaining dye was removed using a DyeEx 2.0 Spin Kit gel filtration system (Qiagen, Germantown, MD). The positive, purified PCR product was then sequenced using an Applied Biosystems 3130 Genetic Analyzer (Forest City, CA) and Sequencing Analysis version 5.2 (Applied Biosystems, Forest City, CA). To confirm the analysis, the positive results for Babesia were sent to Cornell University The Institute for Biotechnology. The results were analyzed in the Basic Local Alignment Search Tool (BLAST) from the National Center for Biotechnology Information (NCBI). This search tool compares available sequences in the GenBank database from the National Institute of Health (NIH) to the sequence analyzed. The GenBank is a publicly available database with millions of different sequences available for public and research uses (http://www.ncbi.nlm.nih.gov/genbank/). Sequencing results allowed us to determine the type of Babesia species in river otters in Pennsylvania.

Table 1: Oligonucleotide primers used in Babesia spp. nested PCRs (Shaw 2011). The first two rows are for the primary nested PCR and the second nested PCR primers are found in rows 3 and 4.

Target Organism Target Gene Primer Name Primer Sequence (5’-3’) Reference Babesia/Theileria 18S rRNA 3.1 CTCCTTCCTTTAAGTGATA-

AG Yabsley et al. (2005)

Babesia/Theileria 18S rRNA 5.1 CCTGGTTGATCCTGCCAG- TAGT

Yabsley et al. (2005)

Babesia/Theileria Hepatazoon

18S rRNA RLB-F GAGGTAGTGA- CAAGAAATAACAATA

Schouls et al. (1999)

Babesia/Theileria Hepatazoon

18S rRNA RLB-R TCTTCGATCCCCTAACTTTC Schouls et al. (1999)

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RESULTS Genotyping Sixty-seven samples were analyzed from Pennsylvania with a success rate of 82% (55/67)

for obtaining a complete profile at 7 loci. Using the eight loci that were consistent throughout all sample analysis, Pennsylvania otters range from 6 (RIO06) to 14 (RIO18) alleles with an average of 9.4 alleles per locus.

The observed heterozygosity (HO), expected heterozygosity (HE), polymorphic information content (PIC), probability of identity (PI), and null frequency of allelic dropout were calculated for 8 loci (RIO01, 02, 06, 07, 11, 13, 18 and 19) in PA river otter populations. These results for samples from 2009-2012, for 2013-2014 and the combined totals are found in Tables 2, 3, and 4. Two river otters were pregnant. One otter had two pups the other had three. The offspring genotypic profiles are included in Table 5 where they can be compared with the mother’s genotypic profile.

Table 2. The number of alleles from each locus (K), the number of samples amplified at each locus (n), observed (HO) and expected (HE) heterozygosity, the polymorphic information content (PIC), and the Null Frequency of allelic dropout for each locus analyzed. The probability of identity (PI) using all loci is given for 2009-2012.

PA_2009-2012

Loci K n Ho HE PIC Null

Frequency RIO01 9 38 0.842 0.819 0.785 -0.0326 RIO02 7 37 0.730 0.763 0.723 0.0152 RIO06 6 36 0.694 0.648 0.591 -0.0254 RIO07 8 36 0.417 0.648 0.597 0.2238 RIO11 7 38 0.658 0.755 0.704 0.0634 RIO13 13 33 0.879 0.892 0.867 0.0016 RIO18 13 38 0.789 0.898 0.876 0.0549 RIO19 9 37 0.784 0.852 0.8210 0.0356 Mean 9 36.625 0.724125 0.784375 PI value = 4.9*10-10

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Table 3. The number of alleles from each locus (K), the number of samples amplified at each locus (n), observed (HO) and expected (HE) heterozygosity, the polymorphic information content (PIC), and the Null Frequency of allelic dropout for each locus analyzed. The probability of identity (PI) using all loci is given for 2013-2014. PA _2013/2014

Loci K n Ho HE PIC

Null Frequency

RIO01 6 17 0.647 0.752 0.690 0.0735 RIO02 5 17 0.824 0.791 0.731 -0.0444 RIO04 4 17 0.235 0.319 0.293 0.1183 RIO06 4 13 0.231 0.649 0.551 0.4696 RIO07 3 17 0.529 0.570 0.452 0.0248 RIO10 5 17 0.882 0.775 0.716 -0.0892 RIO11 5 17 0.471 0.752 0.685 0.2303 RIO13 7 13 0.385 0.834 0.775 0.3563 RIO18 10 17 1.000 0.891 0.850 -0.0743 RIO19 7 17 0.824 0.806 0.757 -0.0296 Mean 5.6 67 0.603 0.714 PI value = 5.95*10-10

Table 4. The number of alleles from each locus (K), the number of samples amplified at each locus (n), observed (HO) and expected (HE) heterozygosity, the polymorphic information content (PIC), and the Null Frequency of allelic dropout for each locus analyzed. The probability of identity (PI) using all loci is given for combined data 2009-2014.

Loci

RIO1 RIO2 RIO6 RIO7 RIO1 RIO3 RIO8 RIO9 Mean Probability of Identity: 4.49 x 10-

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Null Frequency 0.0028

0.0016 0.0717 0.1735 0.1113 0.0677 0.0248 0.0354

K n HO HE PIC

10 55 0.782 0.799 0.767 7 54 0.759 0.773 0.736 6 49 0.571 0.649 0.588 8 53 0.453 0.643 0.584 7 55 0.600 0.749 0.700

13 46 0.761 0.877 0.855 14 55 0.855 0.908 0.891 10 54 0.796 0.865 0.840 9.4 52.6 0.697 0.782

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Table 5. Genotypic profile of a female with two developing fetuses at 8 microsatellite loci. Pink cells indicate an allele inherited from the mother, blue cells indicate an allele inherited from the father, and yellow cells indicate an unknown inherited source.

Sample RIO01 RIO02 RIO06 RIO07 RIO11 RIO13

Mother 265 270 196 196 250 254 173 173 156 162 264 264

Pup1 270 270 196 198 250 254 173 173 156 162 264 264

Pup2 265 270 196 196 250 254 173 173 156 156 264 264

Sample RIO18 RIO19

Mother 148 148 277 281

Pup1 148 148 277 281

Pup2 148 148 277 277

Table 6. Genotypic profile of a female with three developing fetuses at 10 microsatellite loci. Pink cells indicate an allele inherited from the mother, blue cells indicate an allele inherited from the father, and yellow cells indicate an unknown inherited source

Sample RIO01 RIO02 RIO04 RIO06 RIO07 RIO10 Mother 270 270 192 198 264 264 251 263 175 175 254 258 Pup 1 270 270 184 192 264 264 251 251 173 175 250 254 Pup 2 270 270 190 192 264 264 263 263 173 173 248 254 Pup 3 270 270 184 198 264 264 173 175 248 254

Sample RIO11 RIO13 RIO18 RIO19 Mother 149 161 262 274 146 160 276 284 Pup 1 161 167 264 264 146 152 278 284 Pup 2 149 167 262 264 138 146 276 278 Pup 3 149 161 262 264 138 146 278 284

All of the samples that had amplification at least 7 of the 10 loci were included in the analysis of population structure (n=55). The program STRUCTURE was run using admixture and correlated population models without population information included (Figure 2) and admixture, and correlated models with population information included (Figure 3-6). Each model was subjected to running K=1-10 populations. Selected output graphs are included below (Figure 2-6). STRUCTURE Harvester output is included in Table 7 and Figure 7. The number of populations which correspond to the greatest value for ΔK is 3. Three of the four geographic regions sampled is defined by a dominant cluster group (Figure 8). The remaining population sampled, the southeast, is a mixture of those three structure groups.

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Figure 2. Output display of data from STRUCTURE v.2.3.4, displaying probability values for belonging to each putative population for all genotyped samples from PA. For this example, K=3, and as such there are three output population colors: green, red and blue; using correlated and admixture models. The height of each individual bar on the Y-axis indicates the probability that any sample would fit into the population of corresponding color. The values on the X-axis indicate the sampling populations are grouped by state which each group of samples sample were collected with PA=55. The numbers on the X axis are the known locations of trapped river otters throughout Pennsylvania, 1 = Northeast PA; 2 = North Central PA; 3= South Central PA; 4= Southeast PA and 5 = unknown location.

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Figure 3. Output display of data from STRUCTURE v.2.3.4, displaying probability values for belonging to each putative population for all genotyped samples from PA. For this example, K=2, and as such there are two output population colors: green and red; using the population information, correlated and admixture model. The height of each individual bar on the Y-axis indicates the probability that any sample would fit into the population of corresponding color. The values on the X-axis indicate the sampling populations are grouped by state which each group of samples sample were collected with PA=55. The numbers on the X axis are the known locations of trapped river otters throughout Pennsylvania, 1 = Northeast PA; 2 = North Central PA; 3= South Central PA; 4= Southeast PA and 5 = unknown location.

Figure 4. Output display of data from STRUCTURE v.2.3.4, displaying probability values for belonging to each putative population for all genotyped samples from PA. For this example, K=3, and as such there are three output population colors: green, red, and blue; using the population information, correlated and admixture model. The height of each individual bar on the Y-axis indicates the probability that any sample would fit into the population of corresponding color. The values on the X-axis indicate the sampling populations are grouped by state which each group of samples sample were collected with PA=55. The numbers on the X axis are the known locations of trapped river otters throughout Pennsylvania, 1 = Northeast PA; 2 = North Central PA; 3= South Central PA; 4= Southeast PA and 5 = unknown location.

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Figure 5. Output display of data from STRUCTURE v.2.3.4, displaying probability values for belonging to each putative population for all genotyped samples from PA. For this example, K=4, and as such there are four output population colors: green, red, blue, and green; using the population information, correlated and admixture model. The height of each individual bar on the Y-axis indicates the probability that any sample would fit into the population of corresponding color. The values on the X-axis indicate the sampling populations are grouped by state which each group of samples sample were collected with PA=55. The numbers on the X axis are the known locations of trapped river otters throughout Pennsylvania, 1 = Northeast PA; 2 = North Central PA; 3= South Central PA; 4= Southeast PA and 5 = unknown location.

Figure 6. Output display of data from STRUCTURE v.2.3.4, displaying probability values for belonging to each putative population for all genotyped samples from PA. For this example, K=10, and as such there are nine output population colors green, red, blue, green, purple, cyan, carrot, dark purple, peach, and navy blue; using the population information, correlated and admixture model. The height of each individual bar on the Y-axis indicates the probability that any sample would fit into the population of corresponding color. The values on the X-axis indicate the sampling populations are grouped by state which each group of samples sample were collected with PA=55. The numbers on the X axis are the known locations of trapped river otters throughout Pennsylvania, 1 = Northeast PA; 2 = North Central PA; 3= South Central PA; 4= Southeast PA and 5 = unknown location.

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Table 7. Calculated values and comparison of ΔK for the population information/correlated/ admixture allelic frequency model of STRUCTURE when analyzed by the Evanno method of STRUCTURE Harvester for PA data from the analysis. The greatest ΔK value for each model indicated the most likely hypothesis for the value of K. The column “Reps” indicate the number of iterations at each K value that was conducted using STRUCTURE.

K Reps ΔK Correlated 2 5 ---- 3 5 2.929664 4 5 0.387605 5 5 0.728293 6 5 1.14853 7 5 0.873796 8 5 1.037749 9 5 0.142572

10 5 ----

Figure 7. Output display of data from STRUCTURE Harvester graphically representing the calculated ΔK values for K=2, K=3, K=4, K=5, K=6, K=7, K=8, K=9 and K=10 of STRUCTURE’s the population information/correlated/admixture allelic frequency model for all the PA samples using the Evanno method.

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Figure 8. Map of Pennsylvania showing the proportion of the 3 STRUCTURE groups represented in each of the geographic regions.

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Diet Analysis Forty-eight stomachs from Pennsylvania otters were examined for the presence of prey

remains. Carcasses were obtained from 17 of 67 counties in Pennsylvania. The majority of otter carcasses were obtained from the eastern-half of the state (Figure 9). Eight stomachs out of 48 were empty (17%) and were excluded from diet composition analyses. Prey remains from 5 categories were identified, including fish, mollusks, crayfish (Cambarus spp.), insects, and amphibians (Table 8). Amphibian remains consisted of two salamanders and two frogs (Lithobates sp.) from two otter stomachs. Insects identified included crane fly (Diptera) larvae, hellgrammites (Neuroptera; Corydalidae), water beetles (Coleoptera) and stonefly larvae (Ple- coptera). No mammal or bird remains were identified. Figure 10 shows the frequency of each prey item occurence relative to all prey item occurrences.

Six fish families were identified as prey items in Pennsylvania otter stomachs (Table 8). This included species within the Centrarchidae (bass and sunfish), Salmonidae (trout), Cyprinidae (minnow), Esocidae (pike), Catostomidae (sucker), and Umbridae (mudminnow) families.

Figure 9. Pennsylvania counties from which river otter samples were collected for dietary analysis from 2009-2014.

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Table 8. Distribution of prey items in PA otters.

Prey Taxon % Occurrence

n=40 Fish 37 (93%) Mollusk 2 (5%) Crayfish 19 (48%) Insect 10 (25%) Amphibian 3 (8%) Mammal 0 (0%)

Figure 10. Frequency (%) of fish prey item occurrence compared to all prey items.

Table 9. Distribution of fish prey in PA otters.

Fish Family

% Occurrence n=37

Centrarchidae 18 (49%) Salmonidae 5 (14%) Cyprinidae 12 (32%) Esocidae 7 (19%) Catostomidae 8 (22%) Umbridae 2 (5%)

Figure 11. Frequency (%) of fish families in the otter diet.

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Centrarchids were most prevalent, followed by cyprinids and catostomids. Figure 11 shows the frequency of each fish family occurrence relative to all the fish family occurrences. No differences occurred between males and females with respect to prey item (Table 10) or fish family occurrences (Table 10).

Table 10. Chi-square (χ2) comparison of Pennsylvania otter diet by sex.

PA Males PA Females n=26 N=14 χ2 df p-value

Prey Item NO PO NO PO

Fish 24 92% 13 93% 0.066 1 0.7947 Mollusk 2 8% 0 0% 0.093 1 0.761 Crayfish 9 35% 5 36% 0.0003 1 0.9541 Insect 4 15% 6 43% 2.344 1 0.1257 Amphibian 3 12% 0 0% 0.479 1 0.4888 Mammal 0 0% 0 0% * * * Bird 0 0% 0 0% * * *

NO=Number of Occurrences, PO=Percent Occurrence.

Table 11. Chi-square (χ2) comparison of Pennsylvania otter diet by sex (fish prey).

PA Males PA Females n=24 n=13 χ2 Df p-value

Prey Item NO PO NO PO

Centrarchidae 11 46% 7 54% 0.015 1 0.9037 Salmonidae 2 8% 3 23% 0.717 1 0.3972 Cyprinidae 4 17% 6 46% 2.373 1 0.1235 Esocidae 4 17% 3 23% 0.0001 1 0.9716 Catostomidae 5 21% 4 31% 0.074 1 0.7863 Umbridae 1 4% 1 8% 0.264 1 0.6077

NO=Number of occurrences, PO=Percent occurrence

Parasite Analysis Intestinal tracts were investigated for the presence of internal parasites from Pennsylvania

otters (n=41). Carcasses were obtained from 17 of 67 counties in Pennsylvania. The majority of otter carcasses were obtained from the eastern-half of the state.

Intestinal parasites were found in 24/41 (59%) of river otters in Pennsylvania. Three species of helminth parasites were identified in river otters from Pennsylvania (Table 12).

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Table 9. Number of river otters infected with helminth parasites.

Infected/ Total Hosts

Prevalencea Intensityb

Baschkirovitrema incrassatum 22/41 53.7 1-39

Strongyloides lutrae 2/41 4.8 1-5

Acanthocephalans (Pomphorynchus sp.) 5/41 12.2 1-4

aPercent Occurrence bRange

Of the 17 river otters provided by the Pennsylvania Game Commission, 9 specimens were

successfully analyzed for the presence of Babesia spp. using the two nested PCR reactions. All 9 sequences obtained were aligned and a consensus sequence was obtained (Appendix II). The 465 bp sequence was a 99% base pair match according to National Center for Biotechnology Information’s Basic Alignment Search Tool (NCBI: BLAST) to the order Piroplasmida AJB-2006 18S ribosomal RNA gene, partial sequence (Accession No. EF057099).

No ectoparasites were observed on any of the carcasses collected from the Pennsylvania Game Commission.

DISCUSSION Genetic Profiles The genetic variation of the Pennsylvania river otters was evaluated with 8 polymorphic

microsatellite loci. Genotypic profiles were constructed for each individual for a total of 39 individuals from Northeast and 16 from 3 other regions of Pennsylvania (North Central, South Central, and Southeast). Genotypic profiles for all of Pennsylvania were run together in the program Cervus v3.0 (Kalinowski et al. 2007). North American river otters of Pennsylvania appear to be diverse with an allelic range of 6-14 alleles per locus and an average number of 9.4 alleles per locus. This allelic richness is important because bottlenecks and transitory reductions in the effective population would show rare alleles being more readily affected by drift than the more frequent ones (Comps et al. 2001). The higher allelic richness in the Pennsylvania river otters have a similar diversity shown by Brandt et al. (2014), Mowry (2010) and Latch et al. (2008). Brandt et al. (2014) reported genetic diversity between North Dakota and Manitoba river otters. The authors analyzed 121 samples using 9 microsatellite loci. The river otters in that study (Brandt et al. 2014) were recolonized from the Minnesota’s or South Dakota’s populations. Mowry (2010) found similar results using 10 redesigned microsatellite loci to amplify scat samples from 63 otters in Missouri. Latch et al. (2008) determined that Louisianan river otters do not use their dynamic waterways routes to travel between various watersheds by using 12 microsatellite loci in 111 samples from 3 different locations in Louisiana.

The measure of heterozygosity is useful to estimate the fitness of a population (Annavi et al.

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2014, Hansson et al. 2002). An average observed heterozygosity value greater than 0.600 represents a diverse population whereas values less than 0.600 indicate the possibility of inbreeding and the loss of genetic variation (Paetkau et al. 1998; Onorato et al. 2007). The average observed heterozygosity for Pennsylvanian river otters was 0.697 (Table 4).

In 2004, the Pennsylvania River Otter Reintroduction Program (PRORP) was completed with a total of 153 reintroduced river otters to eight water systems in the central and western parts of the state. The Cervus v3.0 results indicate genetic variation in the PA otter population however, most of the 2013-14 samples were from the Northeast region of Pennsylvania, as well as the 2009-12 samples, and not from the other 5 regions of Pennsylvania. The sample size may have not been large enough to gain a complete representation of the genetic diversity and population structure of river otters in other areas of PA. More samples from the introduction areas may be necessary to accurately evaluate the genetic structure of the river otter population statewide. Future work could utilize non-invasive sampling such as hair snares or fecal sample collection to obtain genetic information from other regions of Pennsylvania such as western Pennsylvania. McElwee (2008) and Mowry (2010) successfully extracted DNA from otter fecal samples. McElwee (2008) achieved a genotyping success rate of 24% for individuals at 7 out of 10 loci. Mowry (2010) suggests adding a species identification step when using fecal material for genetic analysis of otters because ten primers (RIO11-20) from Beheler et al. (2004, 2005) had amplified raccoon (Procyon lotor) DNA, as well as river otter in western New York. Amplification on the locus RIO11-20 has also occurred in other mustelids, including fisher (Martes pennanti) and American mink (Neovison vison) (Beheler et al. 2005).

Genetic Structure To investigate gene flow and the relationship of PA river otters, the program STRUCTURE

was used. Output graphs of structure are illustrated in Figures 2-6. Various models were run through STRUCTURE to determine the most probable number of populations (K) in the northeast, north central, south central and southeastern parts of PA using geographic information (population information) and without population information assuming admixture and the allele frequencies to be correlated among populations. All other parameters were set to default values (Pritchard and Wen 2003). The highest average likelihood was used as a post estimate for K for populations in the study areas of PA; however, when run without population information, STRUCTURE was unable to distinguish any populations or the populations were very subtle (see Figure 4). Using STRUCTURE, STRUCTURE Harvester (Evanno et al. 2005), the most probable ΔK value was ΔK= 3, the study region consists of three genetically distinct populations. This population level does not correlate to the number of watersheds covered from sampling, but this ΔK value would be expected considering the PRORP only concluded ten years ago. This time frame only allows for approximately five generations. This length of time is not sufficient to stabilize and distinguish populations. In the future the structure of populations in the study areas of Pennsylvania may start forming less subtle populations and more defined populations which can be depicted using STRUCTURE and STRUCTURE Harvester.

Dietary Preferences of River Otters in Pennsylvania Dietary analyses are useful for determining how species respond to ecological changes in

prey populations and habitat availability (Anoop and Hussain 2005). Investigating dietary preferences and prey availability allow us to better understand prey populations and community dynamics (DeBlase and Martin 1980; Galindo-Leal and Krebs 1998). As dietary specialists, it

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has been suggested that otters may impact aquatic prey populations. As a result, otter diet has been widely studied throughout North America (Kruuk 2006).

Dietary analyses have shown that Nearctic river otters feed mainly on fish and crayfish, in addition to insects, amphibians, reptiles, mollusks, and occasionally birds and mammals. The high occurrence of fish and crayfish prey found in this study is very similar to percentages described by others (Sheldon and Toll 1964; Toweill 1974; Serfass et al. 1990; Skyer 2006; Cote et al. 2008). Potential management implications arise as a result of increasing or expanding otter populations. Dietary preferences may change over time as a function of habitat alteration or succession, human influence, and shifts in prey abundance. By frequently quantifying dietary preferences and prey selection at both the individual and population levels, potential impacts on prey stocks can be assessed. Population level selection is likely a function of prey abundance distribution, and density. Selection at the individual level is likely a function of learning, special- ization, and social affiliation (Heggberget 1994).

Frequent dietary analysis of piscivorous fauna such as otters is often necessary to provide information regarding public concern for potential depredation of game fish populations. This argument may have some merit in artificial scenarios where otters have an advantage over prey species that are vulnerable due to human-influenced habitat characteristics. This can occur when otters enter small artificial or man-made impoundments where fish are most vulnerable to otter predation. This may be attributed to relatively shallow depths, lack of substantial aquatic structure, and above-average concentrations of large fish. However, others have attempted to quantify fish depredation by otters, and have found no evidence of otters severely reducing prey populations in natural settings (Melquist and Hornocker 1983). This may be a function of otters occupying large home ranges, moving between foraging areas on a seasonal basis. Otters within small lakes, ponds, or fish hatcheries, are likely not permanent residents but are temporarily utilizing a component of a larger home range.

Factors that likely affect the availability of fish to otters include available fish habitat, fish spawning ecology, winter ice coverage, seasonal water stratification, temporal hunting behavior by otters, and otter foraging behavior (Sheldon and Toll 1964). In PA, the most common fishes preyed upon by otters included species within the families Centrarchidae (bass and sunfish). This is likely due to the relative abundance of this species in PA. It may also be a function of slower swimming speeds attributed to species within this family. Centrarchids are generally considered to be slower-swimming fishes than other families such as the esocids (pikes) and salmonids (trout).

Other fish families have been represented in river otter diets in proportions similar to those in this study. Previous studies outlining the importance of the remaining fish families/groupings present in this study are included in Table 13.

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Table 13. Studies that have documented the proportion of otter diets composed of the specified fish grouping.

Group Studies

Castomids Serfass (1990); Noordhuis (2002); Cooke et al. (2005)

Cyprinids Toweill (1974); Serfass et al. (1990); Noordhuis (2002); Skyer (2006)

Salmonids Maynard et al. (1995); Jacobsen (2005); Reinbold et al. (2009); Perini et al. (2009)

Catfish* Sheldon and Toll (1964); Melquist and Hornocker (1983); Serfass et al. (1990); Noordhuis (2002)

*Although no catfish species were recorded in this study catfish have been shown to compose a portion of river otter diet and is considered an important component of their diet.

Crayfish have been described as a regionally or seasonally important prey item in river otter diet (Sheldon and Toll 1964; Serfass et al. 1990; Noordhuis 2002; Skyer 2006), occurring in 44.0% to 79.8% of otters investigated. In some habitat types, crayfish may be the most abundant component of otter diet throughout most of the year (Kauffman, personal observation). Crayfish require pristine aquatic habitats for most aspects of their life history. As a result, high water quality standards must be maintained to insure this staple is available to otters.

Insect remains have been identified as a part of otter diet throughout most of their range (Foster and Turner 1991). In spraint and stomach analyses, items eaten by predatory prey are difficult to discern from primary prey (Heggberget and Moseid 1994). It is often the assumption that these insects are frequently ingested incidentally through the consumption of fish by otters (Perini et al. 2009). Insects may be consumed by otters feeding on fish that retain these invertebrates within their stomachs. Insects may also be incidentally ingested during foraging for other bottom-dwelling invertebrates such as crayfish. Reid et al. (1994) also suggests that some invertebrates (such as snails) may be ingested as incidentals occurring within the digestive tracts of fish prey.

Multiple salamanders and two frogs were identified in two river otter stomachs from. The majority of samples were collected during winter trapping seasons when most amphibians are hibernating or extremely inactive. This may mean that otters are preying upon amphibian species during hibernation, possibly by overturning rocks or foraging within the bottom substrate.

No reptile remains were identified within stomach samples. However, field observations havereported that otters will prey upon reptile species during warmer months of the year. A river otter has been observed feeding on a northern water snake (Nerodia sipedon) (Kaufman, personal observation). Otters have also been known to prey on pond turtles (Noordhuis 2002).

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Mammals are occasionally eaten by otters, but occur as novel prey items in small percentages (1.0-4.0%) of overall diet composition (Sheldon and Toll 1964; Tumlison et al 1986; Serfass et al. 1990; Noordhuis 2002; Skyer 2006). Mammals identified as potential prey for otters include muskrat, beaver, squirrels, and other small mammals (Greer 1955; Tumlison et al. 1986; Reid et al. 1994; Williamson 2009). It has been suggested that otters may contribute to muskrat declines in areas where reintroductions have occurred, evidence to support this is lacking (Williamson 2009).

Vegetative remains were occasionally observed within otter stomach samples. However, vegetative matter may have been ingested while in the trap or during feeding or scent-marking activities (Ryder 1955). Plant remains consisted of small pieces of aquatic matter or eastern hemlock (Tsuga canadensis) needles. In some parts of their range, however, otters have been observed feeding on vegetative material such as blueberries (Sheldon and Toll 1964).

Parasites of River Otters in Pennsylvania The helminth assemblage identified in otters from PA is consistent with assemblages

identified throughout the otter’s range (Kimber and Kollias 2000). Baschkirovitrema incrassatum and Strongyloides lutrae are commonly identified in Nearctic river otters, in addition to multiple species of acanthocephalans and pentastomes. The pathogenic significance of these parasites is poorly understood, and infection is rarely clinically expressed. Clinical expression may occur with intense infections, or during stresses associated with live-capture or translocation projects. One individual otter stomach contained a large bolus of seventy-two S. lutrae individuals. An intense infection such as this may have impacted digestion or caused discomfort.

No ectoparasites were collected from the specimens obtained from the PA Game Commis- sion. The specinmens collected were mostly incidental captures and vehicle strikes. Incidental captures most frequently occur during lawful beaver trapping (Hubbard and Serfass 2004). Pennsylvania beaver trapping season occurs between the months of December and March (Pennsylvania Hunting and Trappping Digest). During this time ectoparasites, such as ticks, would be less common than in warmer months. Additionally it is uncertain how long ectoparasites will remain attached to a deceased otter. The length of time between vehicle strikes and pick up of the carcass may preclude collection of ectoparastites.

Of the 17 PA river otters tested 52% (9/17) were positive for Babesia spp.; 54.5% (6/11) of males were positive, and 100% (3/3) of the females were positive. Three fetuses were also tested and were negative for Babesia. However, the percentage rate for male and female river otters does not likely reflect the population level prevalence of infection due to the small sample size. These findings are similar to those of Birkenheuer et al. (2007), where 82% (32/39) of river otters from NC were positive for a unique Babesia sp. The sequences obtained from PA otters were a 99% match to those reported by Birkenheuer et al. (2007). No clinical signs or symptoms have been associated with Babesia infection in otters (Tocidlowski et al. 2000).

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Summary

Ten years have passed since the completion of the PA River Otter Reintroduction Program (PROPR). A combination of indices such as accidental captures and field surveys, show that the population of river otters at multiple locations throughout the state has increased significantly (Hardisky 2013). Assessing the overall health of river otter populations in the state requires knowledge of multiple facets of river otter ecology. This report aims to provide insight into the current state of river otter genetics, dietary preference, and parasitism.

As with any reintroduction program, the overall genetic diversity of the population is a concern in maintaining the long-term viability of that population. Currently the population genetic parameters of PA river otters indicate a diverse population. Observed heterozygosity is within an acceptable range of what would be expected. Three population structure groups have been identified in the state and appear to coincide with 3 geographic regions: northeast, northcentral, and southcentral. The southeast population is comprised of relatively equal proportions of each of those groups. Only ten years have passed from the completion of the PRORP, therefore, this study provides good baseline data for both management and future study. It will be important to continue to sample the river otter population to see how the population structure changes given more generations.

The diet of river otters in Pennsylvania consists mostly of fish and crayfish. This is consistent with what has been reported from other studies. The fish species most commonly preyed upon are bass and sunfish. These prey populations should be monitored in prime river otter habitats to ensure sustainable numbers.

The helminth parasites infecting river otters in Pennsylvania are Baschkirovitrema incrassatum, Strongyloides lutrae, Acanthocephalans (Pomphorynchus sp.), and pentastomes. These are common parasites found in river otters. The pathogenic significance of these parasites is poorly understood. Monitoring these infections and assessing the prevalence and intensity of them in wild populations is an important part of maintaining healthy populations.

Infection from Babesia sp. in river otters has been previously reported (Birkenheuer et al. 2007). No clinical pathology associated with this infection has been reported. In this study 52% of individuals tested were positive for Babesia infection. DNA sequencing showed that the species of Babesia infecting river otters in PA is either identical or closely related to the species reported by Birkenheuer et al. (2007). Although no ectoparasites were recovered from the sampled river otters, infection from Babesia sp. suggests that they are parasitized by Ixodes ticks, the purported vector of the parasite.

Considering the amount of resources utilized in the recovery of the river otter in Pennsylvania, it is imperative to continue monitoring and managing these populations. From a genetics perspective the population might still be considered in a state of recovery. As more generations pass the genetic structure of these populations should begin to form indicating barriers to gene flow, or homogenize indicating free movement of genes. The diet and parasite load in PA river otters is consistent with what is reported from numerous studies from throughout their range.

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APPENDIX I. Genotypes of the river otters from this study. X = no amplification at that locus; blank = locus not analyzed for that individual

Sample RIO01 RIO02 RIO04 RIO06 RIO07 RIO10 RIO11 RIO13 RIO18 RIO19

LC14M-‐282 266 184 265 259 173 249 157 X 136 280 269 195 266 259 175 253 157

144 280

LC14M-‐283 266 184 X 262 172 252 157 X 138 280 266 190

262 174 258 157

143 280

LC14M-‐284 270 184 255 259 174 255 160 256 142 280 274 190 265 259 174 257 160 270 149 280

LC14M-‐285 278 197 X 262 173 249 157 254 149 278 278 197

262 173 258 157 262 154 282

LC14M-‐286 266 184 264 259 175 247 149 254 136 284 270 184 264 267 178 253 155 266 144 284

LC14M-‐287 273 184 265 258 175 247 157 258 138 280 273 190 265 258 178 258 157 258 149 282

LC14M-‐288 266 184 265 259 163 253 155 262 138 280 278 197 265 259 173 253 155 266 144 282

LC14M-‐289 266 184 255 255 163 247 X 262 144 280 266 190 265 259 175 251

262 149 282

LC14M-‐290 262 195 255 X 173 247 155 267 142 273 266 195 265

173 257 157 267 151 278

LC14M-‐291 261 192 265 264 173 252 155 254 142 278 270 197 265 264 175 253 157 263 145 278

LC14M-‐292 270 192 265 263 175 253 149 263 147 276 270 197 265 263 175 257 161 263 160 284

LC14M-‐293 270 184 265 258 175 247 149 263 147 272 270 190 265 264 175 253 157 263 151 278

LC14M-‐294 262 189 265 X 173 249 157 265 147 272 274 197 265

175 253 157 265 149 284

LC14M-‐295 266 190 265 260 173 251 155 267 134 273 270 192 265 260 175 253 157 267 142 278

LC14M-‐296 270 184 265 263 173 249 161 265 147 278 270 192 265 263 175 253 167 265 151 284

LC14M-‐297 270 190 265 263 174 248 149 263 138 276 270 192 265 263 176 253 167 265 147 278

LC14M-‐299 266 184 265 251 172 248 149 263 138 278 270 197 265 251 174 253 161 263 147 284

LC10-‐1 270 184 X 250 175 X 156 254 138 281 274 198

250 175

156 256 152 285

LC10-‐2 270 184 X 250 175 X 156 254 142 275 273 198

250 175

156 254 150 281

LC10-‐3 261 184 X 250 175 X 150 262 144 279 270 196

250 175

158 272 148 279

30


LC10-‐4 271 184 X 250 173 X 156 258 148 275 274 192

250 175

158 264 152 283

LC10-‐5 263 184 X 250 173 X 150 264 144 279 274 190

250 173

154 270 144 283

LC10-‐6 258 184 X X 175 X 158 250 152 275 274 198

175

158 254 152 281

LC10-‐7 258 190 X 250 163 X 158 254 138 277 263 190

254 163

158 262 144 285

LC10-‐8 270 182 X 250 163 X 150 254 138 279 273 182

250 175

158 256 144 279

LC10-‐9 265 184 X X 163 X 150 262 146 281 270 190

173

158 268 146 281

LC10-‐10 270 190 X 250 175 X 158 262 152 281 273 198

250 175

168 272 152 285

LC10-‐13 271 184 X 250 163 X 150 266 146 277 271 190

258 175

158 270 160 287

LC10-‐399 270 192

262 175

150 266 148 275

274 198

266 175

158 272 160 287

LC10-‐414 271 194

254 173

150 256 144 273

274 198

262 173

156 266 160 281

LC10-‐419 271 184

254 173

158 264 146 273

271 196

258 173

158 274 146 279

LC10-‐433 265 198

250 173

158 270 144 275

270 198

250 173

158 272 146 281

LC10-‐1023 261 184

X 175

158 266 144 283

265 184

175

158 272 148 283

LC10-‐1031 270 190

250 175

156 262 142 279

272 196

258 175

156 270 152 279

LC10-‐1055 265 184

250 171

150 254 144 281

270 190

250 173

156 270 148 283

LC10-‐1056 265 184

250 171

150 264 138 283

270 188

250 173

150 270 142 285

LC10-‐1096 260 192

254 163

156 258 142 281

271 198

258 175

160 268 160 287

LC10-‐1097 265 182

250 X

X 256 142 X

265 194

254

256 142

LC10-‐1141 263 186

254 163

150 264 148 X

263 196

258 177

158 264 154

LC10-‐1142 261 196

250 163

158 262 140 283

270 196

250 175

158 266 148 283

LC10-‐1163 261 190

262 171

158 264 138 273

265 198

274 171

158 268 144 281

31


LC10-‐1164 261 192

250 171

156 254 138 273

272 192

258 173

156 262 160 279

LC10M-‐1172 270 184

250 175

158 256 146 275

273 184

250 175

158 262 146 275

LC10M-‐1183 261 184

250 163

150 254 144 279

270 196

258 173

158 262 148 283

LC10M-‐1202 270 184

262 X

158 254 138 273

272 184

274

168 264 142 273

LC10M-‐1203 261 184

250 171

158 264 144 279

265 184

258 171

158 270 148 283

LC10M-‐1206 265 192

250 171

156 262 144 273

265 198

250 171

158 270 148 279

LC10M-‐1212 261 192

250 173

156 256 142 277

273 198

258 173

160 266 158 283

LC10M-‐10340 X X

250 X

156 X 142 273

254

156

146 277

LC10M-‐pup1 270 196

250 173

156 264 148 277

270 198

254 173

162 264 148 281

LC10M-‐pup2 265 196

250 173

156 264 148 277

270 196

254 173

156 264 148 277

LC10M-‐LC1 270 184

250 173

150 254 138 279

273 184

258 173

158 256 144 283

LC10M-‐LC2 265 188

250 163

168 252 140 273

272 196

258 173

168 260 140 273

LC10M-‐LC3 265 184

250 163

156 264 142 279

270 190

250 163

158 272 152 279

LC10M-‐M 265 196

250 173

156 264 148 277

270 196

254 173

162 264 148 281 LC10M-‐NCRO10

261 190

250 163

150 254 150 285 270 198

258 169

150 262 160 285

LC10M-‐NCRO20

258 184

250 163

150 262 146 277 261 196

250 163

162 262 154 281

LC10M-‐NCRO30

270 184

250 173

156 262 148 279 270 190

258 173

158 270 160 283

LC10M-‐ROW1 265 184

250 171

254

275

270 184

258 173

262

277 LC10M-‐UKNRO1

263 190

173

158 258 148 281 275 198

173

158 266 158 281

LC10M-‐unk1 261 184

250 161

150 256 144 281

270 196

258 167

158 256 146 281

LC10M-‐unk2 261 184

250 171

150 256 150 277

270 196

258 173

158 264 154 279

32


LC10M-‐unk3 258 190

250 175

150 254 152 283

274 198

250 175

158 266 152 283

LC10M-‐unk4 270 184

250 163

150 254 144 279

273 198

258 173

158 256 156 283

LC10M-‐unk5 265 184

254 163

150 254 144 273

265 194

258 173

162 262 150 281

LC10M-‐unk6 270 184

250

150 254 144 279

273 184

254

156 256 144 283

LC10M-‐unk7 265 184

254 163

150

138 273

270 190

258 175

158

152 279

33

APPENDIX II. Babesia sp. ClustalW Sequence Alignment from eight positive samples.

northeast wildlife dna laboratory

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