A GENETIC APPROACH TO DETERMINE RIVER OTTER ABUNDANCE IN MISSOURI
______
A Thesis
presented to
the Faculty of the Graduate School
at the University of Missouri-Columbia
______
In Partial Fulfillment
of the Requirements for the Degree
Master of Science
______
by
REBECCA A. MOWRY
Drs. Matthew E. Gompper and Lori S. Eggert, Thesis Supervisors
JULY 2010
The undersigned, appointed by the dean of the Graduate School, have examined the thesis entitled
A GENETIC APPROACH TO DETERMINE RIVER OTTER ABUNDANCE IN MISSOURI presented by Rebecca A. Mowry, a candidate for the degree of master of science, and hereby certify that, in their opinion, it is worthy of acceptance.
Matthew E. Gompper
Lori S. Eggert
Charles F. Rabeni
Jeff Beringer
ACKNOWLEDGEMENTS
I would like to thank everyone who helped me throughout this process, for design assistance, sample collection, laboratory guidance, and friendship. I would like to thank the Eggert and Gompper Labs - Stephanie Manka, Bill
Peterman, María José Ruiz-López, Elizabeth O'Hara, Dr. Marissa Ahlering, Dr. Ryan Monello, Morgan Wehtje, Dr. Abi Vanak, and Anirrudha Belsare - for constant guidance, patience, and encouragement. Dr. Walter Wehtje and Dr. Ray Semlitsch were irreplaceable for their guidance in the initial design of my project. I would also like to thank all the other students in the School of Natural Resources and the Division of Biological Sciences for friendship, support, and for keeping me sane, especially (in no particular order) Barb Keller, Chris Hansen, Mike Burfield, Chris Rota,
Gabrielle Coloumbe, Cathy Bodinof, David Jachowski, Lisa Sztukowski, Kate Hertweck, Judith Toms, Andrew Cox, Sarah Wolken, Jen Hamel, and Sloane Everett. I am especially grateful to Theresa Schneider for priceless help processing the seemingly endless amount of scat samples. Thanks to Columbia for all the music, food, parks, and bike trails, and for being so conveniently located near my family in Sedalia, Ohio, Texas, and Colorado (to whom
I also, of course, am always extremely grateful). I had an unexpectedly good time here.
Lastly, I wish to thank my committee members for pushing me to excel in this research project, and making this a much more positive and fulfilling experience than I ever could have dreamed was possible. I consider myself very fortunate to have been given the chance to work with you.
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TABLE OF CONTENTS
ACKNOWLEDGEMENTS ...... ii
LIST OF TABLES ...... v
LIST OF FIGURES ...... vii
ABSTRACT ...... ix
CHAPTER 1 - RIVER OTTER (LONTRA CANADENSIS) POPULATION SIZE ESTIMATION FOR EIGHT RIVERS IN MISSOURI
1. Abstract...... 1
2. Introduction...... 2
3. Methods
Sample collection ...... 5
Optimizing microsatellite loci and multiplex PCR ...... 6
Calculating genotyping errors ...... 8
DNA extraction of field samples ...... 8
Genotyping ...... 9
Sexing ...... 9
Population estimation ...... 10
Model development ...... 11
4. Results
Optimizing microsatellites and calculating errors ...... 13
Genotyping of field samples and population estimation ...... 14
Model selection ...... 15
5. Discussion ...... 16
6. Conclusions...... 23
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TABLE OF CONTENTS (continued)
7. Literature Cited ...... 23
CHAPTER 2 - POPULATION SUBSTRUCTURE AND LANDSCAPE USE BY RIVER OTTERS IN MISSOURI
1. Abstract...... 45
2. Introduction...... 46
3. Methods ...... 48
4. Results ...... 51
5. Discussion ...... 53
6. Literature Cited ...... 57
Appendices ...... 73
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LIST OF TABLES
Table Page
CHAPTER 1
1. Microsatellites from Beheler et al. (2004, 2005) used for genotyping river otter (Lontra canadensis) samples in Missouri. Loci ending in “R” or “R2” indicate primers that were redesigned for shorter product lengths, expressed in base pairs (bp). For error testing, PCRs were performed at the optimal annealing temperature (AT) for each locus, but all PCRs were performed at 59°C during multiplexing of field-collected scat samples...... 32
2. Results of error testing from matched river otter scat and tissue samples collected in Missouri. Amplification success rates are provided by locus for scat and tissue samples. Errors are given as a percentage of total successful amplifications (PCRs which could be assigned a genotype), and include allelic dropout and false alleles ...... 33
3. Description, biological justification, and predictions of each a priori hypothesis developed for predicting river otter population size in Missouri. Scat samples were categorized as either fresh (collected within 1 day of defecation) or old (collected 1-6 days after defecation) ...... 34
4. Genotyping success rates (percent of genotypes which were complete for at least seven loci) for each river, section, and season. NA indicates that the river was not sampled for that time period ...... 35
5. Genotyping success rates by time and type of scat for field samples. “Unknown” samples are those that were not labeled by type...... 36
6. Minimum, CAPWIRE, and model estimates for river otters, by river and season. The predicted densities from the model are rounded to the nearest whole number ...... 37
7. Minimum population estimates (unique genotypes) obtained per river, given as total per river and per section/sampling period ...... 38
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8. Sexes and number of recaptures for each river otter detected. Though otters in each river are designated with the same letter, no otters were found in multiple rivers...... 39
9. Ranked AICc results for the eight a priori hypotheses predicting population size of river otters ...... 40
10. Random combinations of river sections to further evaluate the accuracy of the top two predictive models. “All” combines all river sections (n=27) and contains more than the minimum number of unique genotypes (63) because of recaptures of individuals across sections and seasons...... 41
CHAPTER 2
1. Years and locations of river otter reintroductions across the state of Missouri, USA. Source: J. Beringer, MDC. NWR: National Wildlife Refuge. WA: Wildlife Area. SL: Slough ...... 63
2. Minimum otter population sizes, sex ratios, and densities for eight rivers in Missouri, USA, based on fecal genotyping (described in Chapter 1). For rivers which were sampled more than once (Big Piney, Roubidoux, and West Piney), total number of genotypes are given in bold above the counts per season (accounting for otters which appeared in both seasons) ...... 64
3. Number of alleles (A), number of alleles corrected for sample size (Arare), and observed (Ho) and expected (He) heterozygosity values for each microsatellite in each river otter population in Missouri, USA...... 65
4. FST values (top half of matrix) and geographic distances (km, bottom half of matrix) between all population pairs ...... 66
5. Results from the Evanno et al. (2005) test for STRUCTURE simulation of river otter subpopulations in eight Missouri rivers...... 67
6. Summary of daily movement patterns for river otters detected at more than one latrine site (n), calculated for winter and spring study periods in Missouri, USA...... 68
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LIST OF FIGURES
Figure Page
CHAPTER 1
1. Map of study area in central Missouri, USA. Dark circles delineate approximate latrine site locations. Green circles and star represent locations of major regional cities...... 42
2. Error rates and amplification success (with standard deviations) across time, averaged across all loci ...... 43
3. Relationships between the two most supported candidate variables and otter density. The top model predicting otter density, H8, incorporated both variables, whereas the next model H5 used only scats per latrine...... 44
CHAPTER 2
1. Isolation by distance analysis showing relationship between genetic distance (FST) and geographic distance in kilometers. Typically, this relationship is linear, as individuals in adjacent populations linked by movement and dispersal are more genetically similar. In the Missouri river otter populations, no such relationship exists (Mantel test, p = 0.202)...... 69
2. Distribution of the five cluster assignments suggested by STRUCTURE. Unlike Figure 3, which displays only the dominant cluster assignments for each individual, this analysis represents all cluster likelihoods averaged for all individuals per river. CO, CR, BP, RO, and OF showed strong cluster homogeneity (i.e. one dominant assignment likelihood), whereas WP, NI, and MA were less likely to be dominated by a single cluster...... 70
3. Geographic representation of STRUCTURE simulations, displaying only dominant cluster assignment(s) for each otter per population weighted with the strength (% likelihood) of that assignment. Otters which assigned equally to multiple clusters were divided; e.g. Niangua (8) included two otters, but one was equally likely to assign to clusters B and C, while the other otter more strongly assigned to Cluster D. Size of pie charts corresponds to sample size (number of otters in the population)...... 71 vii
4. Average daily movement rate of female vs. male river otters across both seasons (a) and for males in spring vs. winter (b). Bold lines indicate the median distance traveled per day, with minimum and maximum values indicated by the dashed lines. Note the two female outliers in 5(a), including the otter with the greatest recorded movement rate for this study (3.2 km/day)...... 72
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A GENETIC APPROACH TO ESTIMATE RIVER OTTER ABUNDANCE IN
MISSOURI
Rebecca Mowry
Drs. Matthew Gompper and Lori Eggert, Thesis Advisors
ABSTRACT
Extirpated from Missouri by the 1930s, river otters (Lontra canadensis) were reintroduced by the Missouri Department of Conservation (MDC) from 1982-1992. Since the reintroductions, concerns over the legitimacy of otter trapping and the predator’s effects on sport fish populations have sparked controversy. The MDC responded by increasing efforts to monitor river otter populations, using latrine site counts to measure relative abundance across several rivers in Missouri. However, the actual number of otters represented by these counts was unknown. To address this question, I extracted
DNA from scat samples collected along 8 rivers in the winter and spring of 2009, using
10 microsatellite markers plus sexing markers to estimate the number and sex of otters. I then developed a model to estimate population size from latrine site index variables, observing that the number of scats per latrine and the density of active latrines across the river best predicted population size. I then used the genotypes to calculate the genetic diversity of the otter populations, evaluate the distribution of genotype clusters across the landscape, and track otter movements between latrines. Unexpected genetic similarities indicated that otters translocated to different areas may have come from the same source populations. Overall, this project has demonstrated the utility of genetic methods for estimating otter abundance, provided insight into the genetic diversity of the populations, and presented a model for inexpensive monitoring of river otter populations in the future.
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CHAPTER 1
RIVER OTTER (LONTRA CANADENSIS) POPULATION SIZE ESTIMATION FOR EIGHT RIVERS IN MISSOURI
ABSTRACT
River otters (Lontra canadensis) were believed to have been extirpated from the state of
Missouri by the mid-1930s. Over a ten-year period beginning in 1982, the Missouri
Department of Conservation (MDC) reintroduced 845 river otters to 43 sites across the state. Since the reintroduction, MDC has used various estimators to assess otter abundance, including analyses of survival rates of the reintroduced otters and reproductive rates based on necropsies of otters harvested during the first Missouri trapping season in 1996. The resulting abundance estimates differed widely. Here I assess the value of latrine site monitoring as a mechanism for quantifying river otter abundance.
Analyses of fecal DNA to identify individual animals may result in improved population estimates and have been used for a variety of mammal species, but using these methods for otters remains problematic. I optimized laboratory protocols, redesigned existing microsatellite primers, and calculated genotyping error rates to enhance genotyping success for a large quantity of samples. I also developed a method for molecular sexing. I then extracted DNA from 1426 scat samples and anal sac secretions (anal jelly) found during latrine site counts along eight rivers in southern Missouri in 2009. Error rates were low for the redesigned microsatellites. I obtained genotypes at 7-10 microsatellite loci for
24% of samples, observing highest success for anal jelly samples (71%) and lowest for
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fresh samples (collected within one day of defecation). Sixty-three total otters (41 males,
22 females) were identified in eight rivers, ranging from two in the Niangua River to 14 in the Big Piney and Osage Fork of the Gasconade Rivers. Density estimates ranged from
0.069 to 0.511 otters per km. Lincoln-Petersen and CAPWIRE mark-recapture estimators were used to quantify abundance in rivers when there were sufficient data for the analyses, and both analyses resulted in population estimations similar to the minimum genotyping estimate. In addition, I used linear regression to contrast models predicting population size using latrine site indices easily collected in the field, and the most supported model combined scats per latrine and latrines per km to predict abundance.
INTRODUCTION
In Missouri, the Nearctic river otter (Lontra canadensis) is the apex predator of aquatic ecosystems and was believed to be extirpated from the state by the mid-1930s (Bennitt and Nagel 1937). In 1982, the Missouri Department of Conservation (MDC) initiated recovery efforts, translocating 845 otters (primarily from Louisiana, but also from
Arkansas and Ontario) over a 10-year period to 43 sites across the state (Hamilton 1998).
In 1996, in response to high population estimates obtained from mathematical models based on the survival rates of the translocated otters (Erickson and McCullough 1987), as well as reports that otters were adversely affecting wild and farm pond fish populations, the MDC initiated a state-wide trapping season that resulted in a harvest of 1054 otters.
The MDC then began utilizing samples obtained from harvested otters to estimate population sizes based on reproductive rates and age/sex structure. Subsequent population estimates have produced inconsistent results, the most recent of which projected the population in the year 2000 to be as high as 18,211 individuals (Gallagher
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1999). Because of the discrepancies and concerns that these numbers may be overestimates, early trapping seasons were controversial (Goedeke and Rikoon 2008).
The abundance of otters is a critical information gap that needs to be filled to better address these controversies.
Since 2001, MDC has been counting latrine sites along Ozark rivers (Roubidoux
Creek, Big Piney River, West Piney Creek, Niangua River, Osage Fork of the Gasconade
River, Current River, Courtois Creek and Maries River) to estimate relative otter densities. River otters use latrine sites for defecation and intraspecific communication
(Macdonald and Mason 1987, Melquist and Hornocker 1983), with social groups typically using sites together and returning to the same sites throughout the season
(Gallant et al. 2007). Counting latrine sites to estimate abundance may not accurately represent river otter populations, however, as latrine site use may vary both seasonally and yearly, and latrine site numbers eventually plateau due to the tendency of multiple otters to use latrines (Gallant et al. 2007). Thus there is a need to test the value of latrine site counts for estimating river otter populations.
Analyses of fecal DNA to identify individual animals may facilitate an improved population estimate and has been used to survey a variety of mammal species such as wolves (Canis lupus; Lucchini et al. 2002, Cariappa et al. 2008), snow leopards (Uncia uncia; Janecka et al. 2008), coyotes (C. latrans; Kays et al. 2008), Eurasian badgers
(Meles meles; Tuyttens et al. 2001), and forest elephants (Loxodonta cyclotis; Eggert et al. 2003, 2007). However, fecal DNA is typically degraded and exists in small quantities compared to DNA from blood or tissue samples, making extraction and polymerase chain reaction (PCR) amplification problematic (Schwartz et al. 2006). Novel laboratory
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techniques have been developed to alleviate these problems, and field work has emphasized the importance of obtaining the freshest of samples to reduce the extent of
DNA degradation. However, extracting and genotyping DNA from otter (Lontra and
Lutra spp.) scat remains notably problematic. Dallas et al. (2003) hypothesized that
Eurasian otter (Lutra lutra) DNA degrades at a much faster rate than DNA extracted from feces of other carnivores, and Prigioni et al. (2006) suggested the small size of otter scat may contribute to extraction problems. Additionally, river otters show seasonal prey switching in some habitats (Roberts 2008), which may affect the quality of the scat samples collected during spring. Genotyping success may also be affected by the humid environments typical of the streamside and riparian habitats used by otters (Farrell et al.
2000).
Nonetheless, scat samples of river otters are relatively easy to find due to the exposed nature of the communal latrines, the tendency for multiple otters to use a single latrine, and the general restriction of the animal to the immediate banks of the river transect. By genotyping scat samples at a panel of polymorphic microsatellite loci, individuals can be distinguished and counted to produce minimum size estimates (i.e. the total number of unique genotypes identified). In addition, data analysis programs such as
CAPWIRE (Miller et al. 2005) have been developed for noninvasive genetic surveys to estimate sizes of small populations (<100 individuals) by incorporating recaptures in a single session as well as capture heterogeneity. Furthermore, simple Lincoln-Petersen models can be used to estimate abundance when closed populations are sampled more than once.
I redesigned microsatellite loci to amplify shorter fragments (Kohn and Wayne
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1997), optimized multiplex PCRs, and developed a method for molecular sexing. I then extracted DNA from 1426 samples collected along stretches of eight rivers in southern
Missouri, USA in 2009. I genotyped these samples to estimate otter abundance and sex ratios in each river, and compared my estimates with CAPWIRE and Lincoln-Petersen estimation whenever possible. Finally, with this data, I developed and compared a priori hypotheses for predicting population size using latrine site indices.
METHODS
Sample collection
Field collection of scat samples occurred between 6 January and 23 April 2009 along stretches of eight rivers in south-central Missouri, USA (Fig. 1): Big Piney (23.5 km),
Courtois (22.4 km), Current (27.4 km), Maries (27.2 km), Niangua (29.0 km), Osage
Fork of the Gasconade (31.7 km), Roubidoux (34.4 km), and West Piney (24.8 km).
River otters have been shown to decrease movement through their home ranges during winter (Gallant et al. 2007), maximizing the likelihood of system closure during this period. In addition, river otters increase use of latrine sites for scent-marking during the breeding season (December - April; Hamilton and Eadie 1964, Stevens and Serfass
2008), potentially facilitating increased collection of anal jelly. Two rivers (Big Piney and Roubidoux) were sampled in different seasons, once in January and again in April.
Furthermore, on the Roubidoux, Courtois, and Current Rivers, sampling occurred twice during the winter to allow mark-recapture population calculations.
Two canoes with two technicians each scouted both banks of each river, marking latrine sites and clearing all scat and anal jelly. After six days, the technicians returned to each latrine site and collected all scats. Samples estimated to have been deposited within
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one day were marked “fresh”, and samples deposited between one and six days were marked “old”. These classifications were based on moisture content, appearance, and odor, and I acknowledge the possibility of overlap and miscategorization. Anal jelly samples were recorded separately and not categorized as fresh or old. Technicians collected each scat sample in a separate sealable plastic bag. Upon returning to the field station, all samples were stored at -20°C. I did not use a storage solution due to the scale of the project (>1400 samples) and the need for rapid scat collection in the field.
The MDC also provided matching scat and tissue samples from 34 river otters harvested in Missouri. Scat samples were removed from carcasses and extracted once per day for up to 7 days, to test for differences in DNA extraction success and genotyping error rates for different-aged scats. Scats were refrigerated between sampling days.
Optimizing microsatellite loci and multiplex PCR
I selected ten microsatellite loci identified by Beheler et al. (2004, 2005), choosing those with no obvious deviance from Hardy Weinberg equilibrium, high allelic diversity, and low to no frequency of null alleles. Kohn and Wayne (1997) noted the importance of targeting smaller fragments when amplifying DNA from feces, due to the lower quality of DNA available. Thus, I designed new primers for nine of the ten loci to amplify shorter fragments of target DNA, amplifying less of the flanking region surrounding the repeat region (Table 1).
DNA extractions from the 34 matched scat and tissue samples were performed by
MDC personnel using DNeasy Blood and Tissue Kits and QIAamp Mini Stool Kits
(QIAGEN) and the manufacturer’s protocols. Using DNA extracted from tissues, I tested each microsatellite locus individually along an annealing temperature gradient to
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determine the optimal annealing temperature. All PCRs were done in a hood that was decontaminated with UV light between uses, with aerosol barrier pipet tips to prevent contamination. I performed PCRs in 25 μl volumes containing 1X PCR Gold buffer
(Applied Biosystems), 2.0 μM deoxyribonucleotide triphosphates (dNTPs), 0.4 μM each unlabeled forward and reverse primers, 0.8X bovine serum albumin (BSA), 2.0 mM
MgCl2 solution, 0.5 u AmpliTaq Gold© DNA polymerase (Applied Biosystems), and 1.0
μl (15-50 ng) DNA extracted from the tissue of one harvested river otter. The PCR profile consisted of an initial cycle of 95°C for 10 minutes; followed by 35 cycles of denaturation at 95°C for 1 minute, a variable annealing temperature gradient (53-60°C) for 1 minute, and primer extension at 72°C for 1 minute; followed by a final extension cycle of 72°C for 10 minutes. Each reaction included a negative control to detect contamination. All loci were then tested for polymorphism on DNA extracted from tissues of seven harvested otters.
All loci amplified well at an annealing temperature of 59°C. I designed two multiplex reactions of five loci labeled with fluorescent dyes (Table 1) for amplifying and genotyping the field samples. PCRs were performed in 10 μl volumes containing 5.0 μl
Master multiplex mix (QIAGEN), 0.5 μM diluted primer mix, 0.8X BSA, and 1.2 μl fecal
DNA extract. The PCR profile consisted of an initial cycle of 95°C for 15 minutes; followed by 40 cycles of denaturation at 94°C for 0.5 minutes, primer annealing at 59°C for 1.5 minutes, and primer extension at 72°C for 1 minute; and a final extension cycle at
60°C for 30 minutes. Each reaction included a positive control to standardize allele scoring and a negative control to detect contamination.
Calculating genotyping errors
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I amplified DNA from the 34 matched scat and tissue samples individually for each locus, combining PCR products with different fluorescent labels for fragment analysis
(Table 1). Fragment analysis was performed at the University of Missouri DNA Core
Facility in a 3730 96-capillary DNA Analyzer with Liz 600 size standard (Applied
Biosystems). I analyzed results using GeneMarker™ AFLP/Genotyping Software
(Softgenetics LLC, State College, PA) and assigned genotypes manually.
I computed the rates of successful amplification, allelic dropout and false alleles across time (scat age 0-7 days) and among microsatellite loci for the matched scat and tissue samples. I calculated rates of allelic dropout and false alleles by dividing the number of amplifications with these errors by the total number of genotypes (Broquet and
Petit 2004). I tested for significant deviations from heterozygosity values expected under
Hardy-Weinberg equilibrium and for linkage disequilibrium in GENEPOP 4.0.9 (Raymond and Rousset 1995). I also calculated the probability of identity (PID, Paetkau and
Strobeck 1994) and PID for randomly sampled siblings (PIDsib, Waits 2001) to determine the power of the set of microsatellite loci to differentiate individuals.
DNA extraction of field samples
I extracted DNA from the field samples in a separate extraction room with and equipment and supplies dedicated to noninvasively-collected samples. I selected approximately 180 mg of scat using either razorblades or forceps, choosing pieces of scat from various areas of each sample, especially the ends (Fike et al. 2004). To increase DNA yields, I followed the extraction protocol recommended in the QIAamp Mini Stool Kit (QIAGEN) for isolation of DNA from stool for human DNA analysis, with the following modifications: (1) after addition of the Inhibitex© tablets, samples were centrifuged for 5
8
minutes instead of 3; and (2) the incubation period for the final elution was extended from 1 minute to 10 minutes. For every 49 samples (one QIAGEN kit), I included a negative extraction to control for reagent contamination. I stored extractions and the remainder of the scat immediately at -20°C.
Genotyping
I tested each extraction using two microsatellite loci (RIO07R and RIO16R) that exhibited high amplification success rates during previous error testing (Table 2). I used the PCR protocol for individual loci, increasing the number of cycles to 45. I then repeated PCRs at all 10 microsatellite loci for samples that amplified at one or both of the screening loci using the multiplex protocol.
To generate consensus genotypes, I used the comparative method (Frantz et al.
2003, Hansen et al. 2008), confirming heterozygotes after two matching genotypes, and homozygotes after three (see Appendices 2 and 3 for a more detailed description of the genotyping protocol). Occasionally, genotyping was repeated for up to five PCR runs to confirm a genotype. All genotypes were assigned by the same researcher to avoid bias.
Samples that failed to generate consensus genotypes across seven or more loci (based on
PIDsib calculations; see Results) were discarded from further analysis. I then compared genotypes manually for identification of unique individuals and recognition of recaptures.
Sexing
Primers developed by Dallas et al. (2000) to amplify the male-specific SRY gene in
Eurasian otters (Lutra lutra) were redesigned to amplify the Nearctic river otter SRY gene, resulting in a 111-bp fragment (SRY2F: 5'-GAGAATCCCCAAATGCAAAA-3' and SRY2R: 5'- CTGTATTCTCTGCGCCTCCT-3'). I used this primer set in conjunction
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with primers designed for Eurasian otters to amplify the zinc-finger protein gene
(ZFX/ZFY) in both males and females [using primers ZFXYRb (Mucci and Randi 2007) and P1-5EZ (Aasen and Medrano 1990)]. Combining these two methods resulted in amplification of the SRY gene in males, as well as amplification of the larger ZFX/ZFY gene in both sexes to confirm positive amplification and eliminate false female calls (e.g. electrophoresis of fragments would result in two bands for males, and one band for females).
PCRs were performed in 25 μl volumes, using the same protocol for primer redesign and optimization, except that the number of cycles was increased to 50, the annealing temperature was 57°C, and 3.0 μl of DNA extract was used. Each reaction included a negative control to detect contamination and two positive controls (DNA from tissue samples of a known male and female) to confirm successful PCR amplification.
Females were confirmed after at least three positive PCR runs showing amplification of the ZFX/ZFY fragment only, and males were confirmed after at least two positive PCR runs showing amplification of both fragments.
Population estimation
The minimum population size for each river was determined by counting the number of unique genotypes. Many methods exist for extrapolating noninvasive genetic data to account for unidentified individuals, such as rarefaction curves, maximum likelihood curves, and Bayesian methods; simulations by Petit and Valiere (2006) concluded that
Bayesian methods provided the most accurate estimates of true population size, but were still biased due to the models’ inability to incorporate capture heterogeneity. Thus, I used the computer program CAPWIRE (Miller et al. 2005) to estimate abundance in the entire
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river (i.e. not each individual section) based on a single sampling session. Likelihood ratio tests were conducted to determine the presence of capture heterogeneity. Where heterogeneity was confirmed, the Two Innate Rates Model (TIRM) was used to estimate population size. If capture heterogeneity was not confirmed, the Even Capture Probability
Model (ECM) was used. When rivers were sampled in both winter and spring, I calculated population size for each season.
For those rivers that were resampled after an additional 6-day period (Courtois and Current; not enough samples were successfully genotyped to allow this analysis for the Roubidoux), a modified Lincoln-Petersen model (Chapman 1951) was also used to estimate population size (N) using the equations
where M equals the number of individuals identified in the first session (6 days), C equals the number of individuals identified in the second session (12 days), and R equals the number of individuals identified in the first session which were “recaptured” in the second session. Population closure was assumed based on the short time span between sampling periods, and the tendency for river otters to restrict movements during winter months (Reid et al. 1994, Gallant et al. 2007).
Model Development
I used an information-theoretic approach to contrast the performance of a series of noninvasive relative abundance indices to predict actual river otter population size.
Because the models needed to reflect indices that could be easily collected during field
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sampling, the following variables were included in the models (Table 3): number of active latrines (latrines which contained scat 6 days after clearing), total scat samples, average scats per latrine, number of anal jellies, number of fresh (< 1 day old) samples, and number of old (1-6 days old) samples. I did not include interaction terms due to uncertain biological justification and the possibility of obtaining negative abundance estimates when applying the model equation to latrine site indices. To account for differences in river and section length, all indices (except scats per latrine) were calculated as densities (e.g. scats per km), using otter density (instead of raw population size) as the response variable.
Preliminary testing in R (Versions 2.5.0 and 2.10.0) suggested the presence of one highly influential data point (Big Piney, spring, Section 2). The Big Piney spring population was the only one in which an even sex ratio was observed (and was the only population female-biased in winter), and CAPWIRE results indicated that this river contained more individuals than were identified (see Results); thus, I removed this data point from the subsequent analyses. I also added one control data point representing the fact that a minimum of one fresh scat found would indicate the presence of least one otter. In addition, to account for variation in confidence due to different rates of genotyping success (Table 4), I weighted each data point by the genotyping success rate
(weighted as deviation from the 24% average, where the weight of data points with greater than average genotyping success was > 1.0 and the weight of data points with lower than average genotyping success was between 0 and 1.0). (See Appendix 4 for data set.)
Linear models for all hypotheses were generated using R, and Akaike’s
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Information Criterion corrected for small sample size (AICc; Burnham and Anderson
2004) was used to evaluate the relative support of each model. Following identification of the model with the highest Akaike weight (wi), I applied the resulting equation to the removed outlier to determine the ability of the model to predict population size of that river section. In addition, I applied the equations of the two top models to 20 random river combinations to evaluate the predictive power of the models at variable sample sizes.
RESULTS
Optimizing microsatellites and calculating errors
All ten loci (nine of which were redesigned) chosen from Beheler et al. (2004, 2005) were polymorphic in Missouri river otters (Table 2). Overall error rates ranged from
0.013 (RIO06R) to 0.110 (RIO15R), with a multi-locus average of 0.059. Rates of allelic dropout and false alleles were similar (0.028 and 0.031, respectively). GENEPOP analysis indicated that the observed genotypes did not deviate from those expected under Hardy-
Weinberg equilibrium, and there was no linkage disequilibrium among loci. The multilocus probability of identity (PID) was determined to be PID = 4.33x10-14, and
-5 PIDsib = 2.25x10 . Using PID and PIDsib values, I determined that a minimum of seven
-4 loci were needed to differentiate siblings (PIDsib = 5.00x10 ).
For scats collected from the 34 harvested river otters, the freshest scat samples (0 days) yielded the lowest amplification success rates and the highest error rates (0.491 and
0.126, respectively; Fig. 2). Amplification success increased steadily thereafter, peaking at day 5 (0.760). Error rates decreased beyond day 0 and showed a slight increase following day 3.
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Genotyping of field samples and population estimation
The overall genotyping success rate of field samples across all rivers, seasons, and scat types was 24% (based on the number of samples for which multi-locus genotypes were assigned at seven or more loci). Similar to the pattern observed with the harvested otter samples, I observed a decrease in amplification success between fresh and old samples
(Table 5). The genotyping success rate of anal jelly was significantly higher (71%) than that of scat (ANOVA, F1, 1411 = 176.45, p < 0.001), and there was a significant difference between the success rates of old (24%) and fresh (12%) scat (ANOVA, F1, 1310 = 27.16, p
< 0.001). I also observed a difference in genotyping success between winter and spring, with higher success rates observed in winter (January - March; 26-31%) and lower rates in spring (April; 18%); however, this effect was not statistically significant.
Sixty-three individuals (41 males, 22 females) were identified across the eight rivers, ranging from two otters in the Niangua River to 14 otters in the Big Piney River and Osage Fork of the Gasconade River (Tables 6 and 7). The average density across all rivers was 0.239 otters per km, with the highest winter density occurring in the Osage
Fork River (0.442 otters per km) and the lowest density occurring in the Niangua River
(0.069 otters per km). Over both seasons, the highest density was in the Big Piney River
(0.511 otters per km). In two of the rivers, I observed a seasonal increase in density; densities for the Big Piney and Roubidoux Rivers averaged 0.215 otters/km in winter and
0.401 otters/km in spring.
Genotyping success rates per river varied greatly, from 2.7% in the Niangua River to 100% in the Maries River (Table 4). Fifty-six of the otters had complete genotypes
(genotyped at all ten loci); four individuals lacked genotypes at one locus, two lacked
14
genotypes at two loci, and one lacked genotypes at three loci. All genotypes are presented in Appendix 1. Recapture rates ranged from 1 to 24 per otter (Table 8). Across all rivers, the average number of recaptures was 4.5 ± 3.7 (SD) per otter. Of 63 total genotypes identified, 13 (21%) were captured only once. CAPWIRE estimated the same population sizes as the minimum in seven of 11 analyses, and five of those had variances of 0 otters
(Table 6). Lincoln-Petersen results for the Courtois and Current Rivers were also the same as the minimum population size (no variance for Courtois, and a low variance for
Current; Table 6).
To further assess differences in genotyping success among different types of scat,
I evaluated the number of otters that would have been detected if only those scat types had been collected. Of 63 total otters identified in all eight rivers, 22 individuals were represented by fresh scat samples only (34%), 33 individuals were represented by anal jelly samples only (52%), and 58 individuals were represented by old scat samples only
(92%). Forty individuals (63%) were represented by a combination of fresh and anal jelly samples, and 60 individuals (95%) were represented by old scat samples and anal jelly samples. Thus the collection of fresh and anal jelly samples added relatively few additional individuals to the census results.
Model selection
Of eight a priori hypothesis (Tables 3), the top ranked model was H8 (Table 9), which used scats per latrine and active latrines per km to predict otter density (Fig. 3; r2 =
0.7619, p < 0.001). The regression for H8 generated the equation: otter density = 0.01574
+ 0.03103 (scats per latrine) + 0.18036 (latrines per km). This value multiplied by the river length (km) results in an estimate of abundance. Overall, the average deviation from
15
the genotyping estimates was 1.46 ± 1.37 (SD) otters. When applied to Big Piney, spring, section 2 (removed from the regression analysis), the model estimated a total of eight otters, three below the total identified by genotyping. The maximum underestimate was
3.6 in Roubidoux, winter, section 2 (six otters detected, 2.4 predicted; genotyping success
55%), and the maximum overestimate was 6.2 in Roubidoux, spring, section 1 (three otters detected, 9.2 predicted; genotyping success 15%).
The model was robust in predicting population size for both sections combined, accounting for the detection of individuals in both sections. The average deviation from the minimum genotyping estimate was 2.4 ± 2.1 (SD) otters, the maximum overestimate was 7.7 in Roubidoux, spring (11 detected, 18.7 predicted, genotyping success 14%), and the maximum underestimate was 2.6 in Big Piney, spring (12 detected, 9.4 predicted, genotyping success 24%).
I also tested the top two models on random combinations of river sections
(excluding Niangua; Table 10). Both models tended to overestimate total population size,
H5 slightly more than H8. The average deviation from the genotyping estimate was 5.7 otters for H8 and 6.7 otters for H5 (not including the population estimate of all rivers pooled, for which H8 overestimated N by 28 otters and H5 by 35 otters). There was no indication that different sample sizes tended to produce greater deviations.
DISCUSSION
The overall genotyping success of my samples is low (24%) but higher than many similar studies, especially when considering the number of microsatellite loci used. For river otters, Ben-David et al. (2004) reported a 33% success rate for scat described as “fresh”, genotyped for up to seven PCR runs on only one microsatellite locus (RIO05). Hansen et
16
al. (2008) showed a 56% success rate for genotyping at one locus, but that rate dropped to
8% when generating consensus genotypes across four loci; these authors also focused on collection of the freshest available samples. Guertin et al. (2010) achieved a 12% success rate for fresh and anal jelly samples genotyped across at least seven loci. For Eurasian otters (Lutra lutra), Hájková et al. (2009) collected only fresh samples and anal jellies, repeated PCRs up to 16 times for 10 microsatellite loci, and reported 60% genotyping success on at least nine loci. I discarded samples that did not provide a consensus genotype on at least seven loci after 4-5 PCR runs. This design was intended to prevent errors in otter identification, even though it inherently decreased the overall success rate.
I believe that the increase in genotyping success observed in this study, compared to previous Nearctic river otter studies, is likely a result of the redesigned primers, which amplified smaller fragments of DNA.
Genotyping success rates can be influenced by a variety of factors. Fike et al.
(2004) determined that storage method, individual microsatellite used, and type of scat influenced amplification success and frequency of genotyping errors. Other studies described the effects of ambient temperature at collection and storage time (Hájková et al.
2006); age of scat (Dallas et al. 2003); and DNA extraction method (Lampa et al. 2008).
Sieving feces to remove prey remains and homogenize unequal epithelial cell distribution
(Kohn et al. 1995, Hansen et al. 2008) or using storage buffers or silica desiccant at collection (Foran et al. 1997) might have improved my success rate but were not practical for 1426 samples. Due to time and funding limitations, I did not re-extract failed samples or perform additional PCR runs to attempt to recover them.
I observed highest amplification and genotyping success in anal jelly samples,
17
consistent with previous studies (Fike et al. 2004, Hájková et al. 2006, Hájková et al.
2009, Coxon et al. 1999, Lampa et al. 2008). However, contrary to suggestions from other studies using fecal DNA analysis of river otters (Hájková et al. 2009, Ben-David et al. 2004, Prigioni et al. 2006) as well as the general consensus for fecal-based molecular ecology studies (e.g. Wasser et al. 1999, Foran et al. 1997), my results suggest that collection of only very fresh samples from the field may not improve genotyping success rates. In both field and carcass-collected scat samples, fresh scats had lower genotyping success rates than older fecal samples (1-6 days after collection). Had I collected only anal jelly and fresh samples, only 63% of the total individuals would have been detected, while analysis of only old samples would have represented 92% of the total counted population. Addition of anal jelly samples to the old samples would only have increased the population size by two individuals. Therefore, despite the high amplification success of anal jelly samples, I found that the information they provided was largely redundant.
To determine why I observed lower amplification success for fresh samples, I tested several post hoc hypotheses, including testing for suboptimal DNA concentration
(too high or too low; Mangiapan 1996) of failed samples with a spectrophotometer and testing for PCR inhibition by substances in failed samples by “spiking” PCRs of these samples with DNA from a positive control. I also evaluated the possibility that the DNA of these samples rubbed off onto the plastic bag before freezing by re-extracting them using material rinsed off the interior surface of the bag. None of these hypotheses were supported. Farrell et al. (2000) observed a substantial decrease in amplification success of canid and felid scat in the wet season in Venezuela compared to the dry season, suggesting that collection of fresh samples in plastic bags may trap moisture, creating a
18
humid environment inside the bag which may encourage growth of mold or bacteria during the hours before the sample is frozen. The activity of these organisms may hasten the rate of DNA degradation, inhibit PCR amplification, or result in a non-uniform distribution of DNA on the surface of the scat. Using a storage buffer, silica desiccant, or paper bag for fresh samples, while storing drier (and presumably older) samples in plastic bags, may be a suitable compromise to enhance genotyping from as many samples as possible while maintaining the collecting pace necessary for large-scale projects.
Overall, the methods developed in this study were effective for processing the large number of samples collected at latrine sites, and led to river otter density estimates comparable to those reported elsewhere. Previous studies of river otters using traditional field methods (e.g. radio telemetry, snow tracking, and radioisotopes) generated L. canadensis density estimates of 0.17 - 0.37 otters per km (average 0.26) in western Idaho
(Melquist and Hornocker 1983), 0.26 - 0.45 otters per km of shoreline on an Alaskan coastline (Bowyer et al. 2003), and a predicted maximum density of 0.40 otters per km of river or shoreline in the interior west (Melquist and Hornocker 1983, Melquist et al.
2003). In two study areas in Missouri, Erickson et al. (1984) observed densities of 0.13 -
0.25 otters per km. Genotyping studies of Eurasian otters (Lutra lutra) found densities of
0.18 - 0.20 otters per km in southern Italy (Prigioni et al. 2006), 0.45 – 0.83 otters per km in Slovakia and the Czech Republic (Hájková et al. 2009), and 0.17 otters per km along the Drava River in Hungary (Lanszki et al. 2008). The only published report of density estimation for Nearctic river otters using genetic methods (Guertin 2009) found densities between 0.37 - 0.63 otters per km in a coastal population on Vancouver Island, British
Columbia, Canada. Generally, my estimates fell within these ranges.
19
For the Big Piney and Roubidoux Rivers, sampled in both winter and spring, the population size and density nearly doubled in the spring, despite the lower genotyping success rates that were typical of that time period and the tendency for otters to increase latrine visitation during the winter breeding season (Stevens and Serfass 2008).
Furthermore, the sex ratio data indicate that these rivers, as well as the West Piney River, also became more male-biased in the spring (7M:8F in winter, 16M:9F in spring). Male- biased sex ratios for river otters across the United States have been reported frequently
(see Melquist and Hornocker 1983 for summary). Hamilton and Eadie (1964) observed equal sex ratios in winter with a shift toward a male-bias in spring, and Blundell et al.
(2002) suggested that males may increase home range size to increase female encounters during mating season. In addition, Lauhachinda (1978) observed a male bias when examining river otter fetuses from harvested pregnant females (173M:100F).Thus, the increase in population size and male-biased sex ratios observed in spring in my study sites may reflect an increase in adult male abundance (possibly from movement of neighboring males into the study transects), birth of young, or both.
In most cases, the population estimates using CAPWIRE were not significantly different than the minimum size observed through genotyping (Table 6). However, two rivers deserve additional attention. For the Niangua River, the extremely low sample size
(two individuals with only five captures), combined with the low genotyping success rates for the river (13%), suggest the population was likely underestimated. The model based on latrine site density and scats per latrine reflected this uncertainty by predicting a higher number of otters (Table 6), but CAPWIRE did not. Results derived from
CAPWIRE estimations also predicted a much greater population size than was observed,
20
with wider confidence intervals, for the Big Piney River in both winter and spring. The genotyping success rates here were high in winter (40%) and average in spring (24%), despite the low number of anal jellies collected (which have higher genotyping success rates compared to scat). This disparity was probably due to the lower-than-average otter recapture rate (3.07) and higher proportion of individuals captured only once (29%). This discrepancy may also have reflected differences in latrine use by males and females. In winter, the Big Piney was the only site that showed a female-biased sex ratio, which shifted to an even sex ratio in spring (when all other rivers were male-biased). If female otters had recently given birth during the spring, they may have decreased latrine visitation altogether or restricted use to a few sites close to the den (Melquist and
Hornocker 1983), decreasing recapture rates.
Scat abundance can act as an index to population size (e.g. Houser et al. 2009,
Janecka et al. 2008). Assuming equal detection and constant defecation rates, and one or more “truthing” exercises using fecal genotyping, one can examine the relationship between raw scat counts, latrine use, and population size. The relationship between scats per latrine and latrines per kilometer reflected the number of otters using those sites in a
6-day period (by individual section as well as in combined sections), tending to produce slight overestimations for combinations of randomly selected river sections (Table 10).
The model implies that population size is predicted not so much by the number of scats occurring across the study area, but rather by the distribution of those scats at communal latrine sites (scats per latrine) and the total number of active latrines in the landscape
(latrines per km). It is important to recall that this model is based on surveys of active latrines, and that returning to latrines after an initial survey to clear scat from the sites is
21
crucial for the model to be effective (ensuring that all scat found was deposited within a certain time frame). This result reflects that scent-marking by river otters plays an important role in intra-specific communication (Kruuk 1992), particularly among males
(Rostain et al. 2004), and latrines may serve not only as simple waste defecation locales, but also as advertisements of reproductive status or territoriality markers.
The model developed here detected possible underestimations of population size due to low genotyping success rates (e.g. Roubidoux and Niangua Rivers), and did not show clear problems relating to sex ratio skew (Big Piney River). Although anal jellies were the most consistent in providing positive genotyping results, I observed a substantial difference in anal jelly deposition related to sex; of 69 anal jelly samples that could be assigned to a particular otter, 80% were deposited by males. This evidence that anal jellies are produced by only a portion of the total population was reflected in the model selection process, which indicated that anal jelly as a predictive variable was of little value at predicting total density.
The sex ratio was biased toward males for all rivers except the Big Piney. Three rivers (Courtois, Maries, and Niangua), which also had the lowest minimum population sizes, contained males only. The Courtois and Maries Rivers were not stocked with otters during the initial reintroductions (Chapter 2). Blundell et al. (2002) reported that male otters in Prince William Sound, Alaska disperse further than females; thus, the low population size and male-biased sex ratios observed in the Courtois and Maries Rivers may indicate recent colonization by dispersing males.
In contrast, the Niangua River was stocked with otters in 1988 and 1990 (Chapter
2), and the low genotyping success and likely population underestimation (Table 4)
22
suggest that more otters probably existed in Niangua than were detected by genotyping.
However, the abundance of anal jellies collected here suggests that Niangua probably also contained a male-biased population, and the abundance estimate derived from the model predicted a lower population size than the other stocked rivers (Big Piney, Current, and Osage Fork Rivers). Historically, the otter population in the Niangua River was believed to be much higher, and trapping in this river from 2003-2006 (MDC, unpublished data) may have resulted in a population decline. In this case, as in Courtois and Maries, current patterns may indicate recolonization by males.
CONCLUSIONS
This project demonstrated that noninvasive latrine surveys and fecal genotyping can provide insight into the population sizes and sex ratios of river otter populations in
Missouri. The model I developed will allow managers to continue monitoring the river otter population by counting active latrines (following an initial survey to locate and clear scat), to produce abundance estimates and recognize population trends within or between rivers. In conjunction with ongoing fish surveys (D. Knuth and J. Beringer, MDC, unpublished data), these estimates will guide management activities toward the long-term maintenance of both river otters and fish.
LITERATURE CITED
Aasen, E. and J. F. Medrano. 1990. Amplification of the ZFY and ZFX genes for sex
identification in humans, cattle, sheep and goats. Biotechnology 8:1279-1281.
Beheler, A. S., J. A. Fike, L. M. Murfitt, O. E. Rhodes, Jr., and T. L. Serfass. 2004.
Development of polymorphic microsatellite loci for North American river otters
23
(Lontra canadensis) and amplification in related mustelids. Molecular Ecology
Notes 4:56-58.
Beheler, A. S., J. A. Fike, D. Dharmarajan, O. E. Rhodes, Jr., and T. L. Serfass. 2005.
Ten new polymorphic microsatellite loci for North American river otters (Lontra
canadensis) and their utility in related mustelids. Molecular Ecology Notes 5:602-
604.
Ben-David, M., H. Golden, M. Goldstein, and I. Martin. 2004. River otters in Prince
William Sound and Kenai Fjords National Park: Distribution, relative abundance,
and minimum population size based on coastal latrine site surveys. Interagency
Collaborative Report Progress Report, Prince William Sound Science Center Oil
Spill Recovery Institute.
Bennitt, R. and W. O. Nagel. 1937. A survey of the resident game and furbearers of
Missouri. University of Missouri Studies 12:1-215.
Blundell, G. M., M. Ben-David, P. Groves, R. T. Bowyer, and E. Geffen. 2002.
Characteristics of sex-biased dispersal and gene flow in coastal river otters:
implications for natural recolonization of extirpated populations. Molecular
Ecology 11:289-303.
Bowyer, R. T., G. M. Blundell, M. Ben-David, S. C. Jewett, T. A. Dean, and L. K. Duffy.
2003. Effects of the Exxon Valdez oil spill on river otters: injury and recovery of a
sentinel species. Wildlife Monographs 153:1-50.
Broquet, T. and E. Petit. 2004. Quantifying genotyping errors in non-invasive population
genetics. Molecular Ecology 13:3601-2608.
24
Burnham, K. P. and D. R. Anderson. 2002. Model selection and multimodel inference: a
practical information-theoretic approach. Second edition. Springer, New York,
New York, USA.
Cariappa, C. A., S. Breck, and M. Neubaum. 2008. Estimating population size of
Mexican wolves noninvasively. Ecological Restoration 26:14-16.
Chapman, D. G. 1951. Some properties of the hypergeometric distribution of applications
to zoological sample censuses. University of California Publications in Statistics
1:131-160.
Coxon, K., P. Chanin, J. Dallas, and T. Sykes. 1999. The use of DNA fingerprinting to
study population dynamics of otters (Lutra lutra) in Southern Britain: a feasibility
study. R&D Technical Report W202, Environment Agency, Bristol, UK.
Dallas, J. F., D. N. Carss, F. Marshall, K. P. Koepfli, H. Kruuk, P. J. Bacon, and S. B.
Piertney. 2000. Sex identification of the Eurasian otter Lutra lutra by PCR typing
of spraints. Conservation Genetics 1:181-183.
Dallas, J. F., K. E. Coxon, T. Sykes, P. R. F. Chanin, F. Marshall, D. N. Carss, P. J.
Bacon, S. B. Piertney, and P. A. Racey. 2003. Similar estimates of population
genetic composition and sex ratio derived from carcasses and faeces of Eurasion
otter Lutra lutra. Molecular Ecology 12:275-282.
Eggert, L. S., J. A. Eggert, and D. S. Woodruff. 2003. Estimating population sizes for
elusive animals: the forest elephants of Kakum National Park, Ghana. Molecular
Ecology 12:1389-1402.
25
Eggert, L. S., G. Patterson, and J. E. Maldonado. 2007. The Knysna elephants: a
population study conducted using faecal DNA. African Journal of Zoology 46:19-
23.
Erickson, D. W., C. R. McCullough, and W. R. Porath. 1984. River otter investigations in
Missouri. Pittman-Robertson Project. W-13-R-38, Final Report, Missouri
Department of Conservation. 47 pp.
Erickson, D. W. and C. R. McCullough. 1987. Fates of translocated river otters in
Missouri. Wildlife Society Bulletin 15:511-517.
Farrell, L. E., J. Roman, and M. E. Sunquist. 2000. Dietary separation of sympatric
carnivores identified by molecular analysis of scats. Molecular Ecology 9:1583-
1590.
Fike, J. A., T. L. Serfass, A. S. Beheler, and O. E. Rhodes, Jr. 2004. Genotyping error
rates associated with alternative sources of DNA for the North American river
otter. IUCN Otter Specialist Group Bulletin 21A. 16 pp.
Foran, D. R., K. R. Crooks, and S. C. Minta. 1997. Species identification from scat: an
unambiguous genetic method. Wildlife Society Bulletin 25:835-839.
Frantz, A. C., L. C. Pope, P. J. Carpenter, T. J. Roper, G. J. Wilson, R. J. Delahay, and T.
Burke. 2003. Reliable microsatellite genotyping of the Eurasian badger (Meles
meles) using faecal DNA. Molecular Ecology 12:1649-1661.
Gallagher, E. 1999. River otter population monitoring. M.S. Thesis, University of
Missouri, Columbia, Missouri, USA.
26
Gallant, D., L. Vasseur, C. H. Berube. 2007. Unveiling the limitations of scat surveys to
monitor social species: A case study on river otters. The Journal of Wildlife
Management 71:258-265.
Goedeke, T. L. and S. Rikoon. 2008. Otters as actors: Scientific controversy, dynamism
of networks, and the implications of power in ecological restoration. Social
Studies of Science 38:111-132.
Guertin, D. 2009. Assessment of contaminant exposure, diet, and population metrics of
river otters (Lontra canadensis) along the coast of southern Vancouver Island.
M.S. Thesis, Simon Fraser University, British Columbia, Canada.
Guertin, D. A., A. S. Harestad, M. Ben-David, K. G. Drouillard, and J. E. Elliott. 2010.
Fecal genotyping and contaminant analyses reveal variation in individual river
otter exposure to localized persistent contaminants. Environmental Toxicology
and Chemistry 29:275-284.
Hájková, P., B. Zemanova, J. Bryja, B. Hájek, K. Roche, E. Tkadlec, and J. Zima. 2006.
Factors affecting success of PCR amplification of microsatellite loci from otter
faeces. Molecular Ecology Notes 6:559-562.
Hájková, P., B. Zemanova, K. Roche, and B. Hajek. 2009. An evaluation of field and
noninvasive genetic methods for estimating Eurasian otter population size.
Conservation Genetics 10:1667-1681.
Hamilton, W. J. Jr. and W. R. Eadie. 1964. Reproduction in the otter, Lutra canadensis.
Journal of Mammalogy 45:242-252.
27
Hamilton, D. 1998. Missouri river otter population assessment: Final report 1996-97 and
1997-98 trapping seasons and petition for multi-year export authority. Missouri
Department of Conservation, Jefferson City, MO.
Hansen, H., M. Ben-David, and D. B. McDonald. 2008. Effects of genotyping protocols
on success and errors in identifying individual river otters (Lontra canadensis)
from their faeces. Molecular Ecology Resources 8:282-289.
Houser, A. M., M. J. Somers, and L. K. Boast. 2009. Spoor density as a measure of true
density of a known population of free-ranging wild cheetah in Botswana. Journal
of Zoology 278:108-115.
Janecka, J. E., R. Jackson, Z. Yuquang, L. Diqiang, B. Munkhtsog, V. Buckley-Beason,
and W. J. Murphy. 2008. Population monitoring of snow leopards using
noninvasive collection of scat samples: a pilot study. Animal Conservation
11:401-411.
Kays, R. W., M. E. Gompper, and J. C. Ray. 2008. Landscape ecology of eastern coyotes
based on large-scale estimates of abundance. Ecological Applications 18:1014-
1027.
Kohn, M., F. Knauer, A. Stoffella, W. Schroder, and S. Paabo. 1995. Conservation
genetics of the European brown bear – a study using excremental PCR of nuclear
and mitochondrial sequences. Molecular Ecology 4: 95-103.
Kohn, M. H. and R. K. Wayne. 1997. Facts from feces revisited. Trends in Ecology and
Evolution 12:223-227.
Kruuk, H. 1992. Scent-marking by otters (Lutra lutra): signaling the use of resources.
Behavioral Ecology 3:133-140.
28
Lampa, S., B. Gruber, K. Henle, and M. Hoehn. 2008. An optimisation approach to
increase DNA amplification success of otter faeces. Conservation Genetics 9:201-
210.
Lanszki, J., A. Hidas, K. Szentes, T. Revay, I. Lehoczky, and S. Weiss. 2007. Relative
spraint density and genetic structure of otter (Lutra lutra) along the Drava River
in Hungary. Mammalian Biology 73:40-47.
Lauhachinda, V. 1978. Life history of the river otter in Alabama with emphasis on food
habits. PhD Dissertation, Auburn University, Auburn, Alabama, USA.
Lucchini, V., E. Fabbri, F. Marucco, S. Ricci, L. Boitani, and E. Randi. 2002.
Noninvasive molecular tracking of colonizing wolf (Canis lupus) packs in the
western Italian Alps. Molecular Ecology 11:857-868.
Macdonald, S. M. and C. F. Mason. 1987. Seasonal marking in an otter population. Acta
Theriologica 32:449-462.
Mangiapan, G., M. Vokurka, L. Schouls, J. Cadranel, D. Lecossier, J. van Embden, A. J.
Hance. 1996. Sequence capture-PCR improves detection of mycobacterial DNA
in clinical specimens. Journal of Clinical Microbiology 34:1209-1215.
Melquist, W. E. and M. G. Hornocker. 1983. Ecology of river otters in west central
Idaho. Wildlife Monographs 83.
Melquist, W. E., P. J. Polechla, Jr., and D. Toweill. 2003. River otter (Lontra
canadensis). Pages 708-734 in G.A. Feldhamer, B.C. Thompson, and J.A.
Chapman, editors. Wild mammals of North America: biology, management, and
conservation. Second edition. Johns Hopkins University Press, Baltimore,
Maryland, USA.
29
Miller, C. R., P. Joyce, and L. P. Waits. 2005. A new method for estimating the size of
small populations from genetic mark-recapture data. Molecular Ecology 14: 1991-
2005.
Mucci, N. and E. Randi. 2007. Sex identification of Eurasian otter (Lutra lutra) non-
invasive DNA samples using ZFX/ZFY sequences. Conservation Genetics
8:1479-1482.
Paetkau, D. and L. Strobeck. 1994. Microsatellite analysis of genetic variation in black
bear populations. Molecular Ecology 3:489-495.
Petit, E. and N. Valiere. 2006. Estimating population size with noninvasive capture-mark-
recapture data. Conservation Biology 20:1062-1073.
Prigioni, C., L. Remonti, A. Balestrieri, S. Sgrosso, G. Priore, N. Mucci, and E. Randi.
2006. Estimation of European otter (Lutra lutra) population size by fecal DNA
typing in southern Italy. Journal of Mammalogy 87:855-858.
Raymond, M. and F. Rousset. 1995. Genepop (version 1.2): population genetics software
for exact tests and ecumenicism. Journal of Heredity 86:248-249.
Reid, D. G., T. E. Code, A. C. H. Reid, and S. M. Herrero. 1994. Spacing, movements,
and habitat selection of the river otter in boreal Alberta. Canadian Journal of
Zoology 72:1314-1324.
Roberts, N. 2008. River otter food habits in the northern Missouri Ozark streams. M.S.
Thesis, University of Missouri, Columbia, Missouri, USA.
Rostain, R., M. Ben-David, P. Groves, and J. A. Randall. 2004. Why do river otters
scent-mark? An experimental test of several hypotheses. Animal Behaviour
68:703-711.
30
Schwartz, M. K., S. A. Cushman, K. S. McKelvey, J. Hayden, and C. Engkjer. 2006.
Detecting genotyping errors and describing American black bear movement in
northern Idaho. Ursus 17:138-148.
Stevens, S. S. and T. S. Serfass. 2008. Visitation patterns and behavior of Nearctic river
otters (Lontra canadensis) at latrines. Northeastern Naturalist 15:1-12.
Tuyttens, F.A.M., B. Long, T. Fawcett, A. Skinner, J.A. Brown, C.L. Cheeseman, A.W.
Roddam, and D.W. MacDonald. 2001. Estimating group size and population
density of Eurasian badgers Meles meles by quantifying latrine use. Journal of
Applied Ecology 38:1114-1121.
Waits, L. P., G. Luikart, and P. Taberlet. 2001. Estimating the probability of identity
among genotypes in natural populations: cautions and guidelines. Molecular
Ecology 10:249-256.
Wasser, S. K. C. S. Houston, G. M. Koehler, G. G. Cadd, and S. R. Fain. 1999.
Techniques for application of faecal DNA methods to field studies of Ursids.
Molecular Ecology 6:1091-1097
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Table 1. Microsatellites from Beheler et al. (2004, 2005) used for genotyping river otter (Lontra canadensis) samples in Missouri. Loci ending in “R” or “R2” indicate primers that were redesigned for shorter product lengths, expressed in base pairs (bp). For error testing, PCRs were performed at the optimal annealing temperature (AT) for each locus, but all PCRs were performed at 59°C during multiplexing of field- collected scat samples.
Multiplex PCR Optimal Locus Primer sequence (Forward F and Reverse R) Error Field product AT testing samples (bp) (˚C)
RIO01R2 F:Ned-TGAGGTATGGATAGAAGATTGATGA 1 2 146-154 59 R:GCTTGACCTTGAGCAACTTACTT
RIO02R F:Vic-TAGAGTGGGGCGCCTAAGTT 1 1 117-135 59 R:TTACTCGCCAATGGTTCAGC
RIO04R F:Pet-TCTGCCTTTTCAAATTCTCCA 1 1 98-116 59 R:CCCTTTTCTCCCTTTTCTCTC
RIO06R F:Ned-TCCTGTTTCACAAAATCAAACAA 3 2 126-138 59 R:AAAGACCAATAGTTCATCCAGTTC
RIO07R F:Fam-AAGCACTTCCAGATATCAGTTGC 2 2 87-101 56 R:CCGCCTCCCTGTTAGAAGTT
RIO08R F:Vic-TCCTGAGGCATAAGGAAGACA 2 1 104-114 59 R:ACTTGCCTGCTGACATTGAA
RIO11 F:Fam-TCTTCCACTTTTCAATTTAGGTA 1 1 150-160 56 R:GCCCAAAGGTTCACTATCAAG
RIO13R* F:Fam-GCACATGGGCTTTTATGAAGA 2 1 144-168 59 R:GCACACGTGGTAAGATGAGC
RIO15R F:Ned-CTGACCCAAAATGAATAACAGAA 3 2 137-141 59 R:TTCTGCTTGGTTCAGTGCAT
RIO16R* F:Vic-GCCCGTGGTCACTTTACCT 3 2 149-161 59 R:CACAGTAGAGGGACATTTGCAC
*Labels switched for field sample genotyping to condense loci into two multiplex reactions while preventing overlaps in allele ranges.
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Table 2. Results of error testing from matched river otter scat and tissue samples collected in Missouri.
Amplification success rates are provided by locus for scat and tissue samples. Errors are given as a percentage of total successful amplifications (PCRs which could be assigned a genotype), and include allelic dropout and false alleles.
Number of Success rate Success rate Total errors Allelic False Locus alleles (tissue) (scat) dropout alleles
RIO01R2 7 0.882 0.620 0.092 0.028 0.064
RIO02R 7 1.000 0.564 0.044 0.037 0.007
RIO04R 6 0.941 0.570 0.030 0.022 0.008
RIO06R 4 0.588 0.324 0.013 0.000 0.013
RIO07R 7 0.971 0.682 0.032 0.026 0.006
RIO08R 7 1.000 0.665 0.085 0.052 0.033
RIO11 7 1.000 0.553 0.023 0.023 0.000
RIO13R 5 0.794 0.570 0.031 0.023 0.008
RIO15R 3 0.882 0.698 0.110 0.045 0.065
RIO16R 4 0.882 0.721 0.094 0.037 0.057
Mean 5.7 0.894 0.596 0.059 0.031 0.028
33
Table 3. Description, biological justification, and predictions of each a priori hypothesis developed for predicting river otter
population size in Missouri. Scat samples were categorized as either fresh (collected within 1 day of defecation) or old
(collected 1-6 days after defecation).
Hypothesis Model* Description; prediction
H1 latperkm Number of active latrines per km; may increase with population size.
H2 scatperkm Total scats per km; may increase with population size.
H3 jellyperkm Anal jellies per km; may increase with population size.
H4 freshperkm Fresh scats per km; may increase with population size.
H5 scatperlat Number of scats per latrine site; may increase with population size.
34
H6 jellyperkm+freshperkm+ Proportion of each type of scat per km river; population size may be a oldperkm function of each otter depositing similar amounts of each type of scat.
H7 scatperlat+ Scats per latrine and abundance of anal jellies; population size may be jellyperkm predicted by a combination of scats per latrine and jelly per km.
H8 scatperlat+latperkm Scats per latrine and latrines per km; population size may be predicted by the average number of scats per latrine, and the number of latrines.
Hglobal latperkm+scatperkm+ All variables; each index contributes to estimation of population size. jellyperkm+freshperkm+ oldperkm+scatperlat
inter intercept only Random effects
Table 4. Genotyping success rates (percent of genotypes which were complete for at least seven loci) for each river, section, and season. NA indicates that the river was not sampled for that time period.
River Section Winter - Winter - Winter - Spring - 0 days 6 days 12 days 6 days
Big Piney 01 NA 0.308 NA 0.273 02 NA 0.500 NA 0.211
Courtois 01 NA 0.400 NA NA 02 NA 0.375 0.368 NA
Current 01 NA 0.342 0.304 NA 02 NA 0.250 0.290 NA
Maries 01 NA NA NA 1.000 02 NA NA NA 0.167
Niangua 01 NA 0.027 NA NA 02 NA 0.560 NA NA
Osage Fork 01 NA 0.219 NA NA 02 NA 0.356 NA NA
Roubidoux 01 NA 0.111 0.077 0.145 02 0.182 0.550 NA 0.144
West Piney 01 NA ---* NA 0.308 02 NA 0.333 NA 0.184
* = Section was searched, but no scat was found.
35
Table 5. Genotyping success rates by time and type of scat for field samples. “Unknown” samples are those that were not labeled by type.
Month Anal jelly (n) Fresh (n) Old (n) Unknown (n) Total (n) January 0.80 (5) 0.10 (20) 0.31 (45) 0.00 (5) 0.27 (75)
February 1.00 (9) 0.16 (19) 0.19 (53) 0.00 (3) 0.26 (84)
March 0.71 (48) 0.21 (130) 0.30 (409) None 0.31 (587)
April 0.64 (39) 0.06 (210) 0.19 (424) 0.14 (7) 0.18 (680)
Total 0.71 (101) 0.12 (379) 0.24 (931) 0.07 (15) 0.24 (1426)
36
Table 6. Minimum, CAPWIRE, and model estimates for river otters, by river and season. The predicted densities from the model are rounded to the nearest whole number.
River Genotyping estimate, Min. density CAPWIRE estimate CAPWIRE Model sex ratio (M:F) (otters/km) (95% CI) model used estimate
Big Piney winter 6 (2:4) 0.255 9 (6-16) TIRM 5 spring 12 (6:6) 0.511 17 (12-26) TIRM 9 total 14 (6:8)
Courtois* 3 (3:0) 0.134 3 (3-3) ECM 3
Current* 11 (8:3) 0.403 11 (11-11) TIRM 9
Maries 3 (3:0) 0.110 3 (3-3) TIRM 5
Niangua 2 (2:0) 0.069 2 (2-2) TIRM 6
Osage Fork 14 (9:5) 0.442 14 (14-15) TIRM 16
Roubidoux winter 6 (4:2) 0.174 6 (6-6) ECM 6 spring 10 (8:2) 0.291 11 (10-13) TIRM 19 total 11 (8:3)
West Piney winter 3 (1:2) 0.121 5 (3-10) TIRM 3 spring 3 (2:1) 0.121 3 (3-3) ECM 3 total 5 (2:3)
*Lincoln-Petersen estimation 3.0 ±0 otters in Courtois, 11.1±0.11 otters in Current.
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Table 7. Minimum population estimates (unique genotypes) obtained per river, given as total per river and per section/sampling period.
Total/ Section Total Genotypes (M:F) River and Total Sex ratio Lengths Winter Spring Length (km) (M:F) Section (km) 6 days 12 days 6 days
Big Piney (23.5) 14 01 14.0 1:2 NA 3:0 6:8 02 9.5 1:2 NA 5:6
Courtois (22.4) 3 01 11.3 1:0 NA NA 3:0 02 11.1 2:0 3:0 NA
Current (27.3) 11 01 11.7 4:1 3:1 NA 8:3 02 15.6 5:2 5:2 NA
Maries (27.2) 3 01 13.7 1:0 NA NA 3:0 02 13.5 3:0 NA NA
Niangua (29.0) 2 01 16.3 1:0 NA NA 2:0 02 12.7 1:0 NA NA
Osage Fork (31.7) 14 01 12.9 4:1 NA NA 9:5 02 18.8 6:4 NA NA
Roubidoux (34.4)* 11 01 14.6 2:0 1:0 3:0 8:3 02 19.8 4:2 NA 8:2
West Piney (24.8) 5 01 13.5 0:0 NA 0:1 2:3 02 11.3 1:2 NA 2:1
* = Section 2 of the Roubidoux was also sampled in the winter at 0 days, when all fresh samples found during latrine site searches were collected. However, of 11 scats collected, only 2 generated genotypes at seven loci, and both of these yielded ambiguous results.
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Table 8. Sexes and number of recaptures for each river otter detected. Although otters in each river are designated with the same letter, no otters were found in multiple rivers.
Otter BP CO CR MA NI OF RO WP
A 2(F) 5(M) 11(M) 1(M) 4(M) 5(M) 5(F) 7(M)
B 1(M) 5(M) 8(M) 2(M) 1(M) 2(F) 12(M) 6(F)
C 5(F) 3(M) 5(M) 5(M) --- 3(M) 8(M) 1(F)
D 3(M) --- 10(M) ------9(M) 4(M) 1(F)
E 4(M) --- 5(M) ------3(F) 5(M) 2(M)
F 6(F) --- 5(M) ------4(M) 1(F) ---
G 7(F) --- 3(F) ------9(F) 2(M) ---
H 6(F) --- 6(F) ------3(F) 9(F) ---
I 1(F) --- 5(F) ------5(M) 5(M) ---
J 2(M) --- 1(M) ------3(M) 1(M) ---
K 1(M) --- 1(M) ------9(M) 5(M) ---
L 2(M) ------24(M) ------
M 2(F) ------3(M) ------
N 1(F) ------1(F) ------
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Table 9. Ranked AICc results for the eight a priori hypotheses predicting population size of river otters.
2 Hypothesis Description K AICc ∆AIC wi r p-value
H8 scatperlat+latperkm 3 -123.68 0.00 0.67 0.76 < 0.001
H5 scatperlat 2 -120.99 2.17 0.23 0.73 < 0.001
H7 scatperlat+jellyperkm 3 -118.44 4.71 0.06 0.71 < 0.001
H1 latperkm 2 -116.85 6.31 0.03 0.68 < 0.001
Hglobal latperkm+scatperkm+ 7 -114.26 8.90 0.01 0.75 < 0.001 jellyperkm+freshperkm+ newperkm+scatperlat
H2 scatperkm 2 -109.15 14.01 0.00 0.58 < 0.001
H6 jellyperkm+freshperkm+ 4 -104.87 18.29 0.00 0.56 < 0.001 newperkm
H4 freshperkm 2 -101.74 21.42 0.00 0.44 < 0.001
H3 jellyperkm 2 -92.67 30.49 0.00 0.22 0.007
inter intercept only 1 -87.14 36.54 0.00 NA < 0.001
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Table 10. Random combinations of rivers to further evaluate the accuracy of the top two predictive models. “All” combines all rivers and seasons (except Niangua) and contains more than the minimum number of unique genotypes (63) because of recaptures of individuals in separate sampling periods. River sections are combined. Beside river abbreviations, S=spring, W=winter,
5=first sample collection date, 10=second collection date (5 days after initial sample collection).
Combination Unique Genotypes Predicted N, H8 Predicted N, H5 MA + WP-S 6 13 14 OF + CR-5 24 26 26 CR-10 + RO-S 20 29 30 BP-S + BP-W 18 15 14 CO-5 + OF 17 22 23 CR-5 + CR-10 + RO-S 30 39 41 BP-S + OF + CR-5 36 35 35 BP-W + CR-10 + RO-S 26 35 36 MA + WP-S + CO-5 9 15 16 RO-5 + CR-5 + OF 30 34 35 OF + CR-5 + CR-10 + BP-S 46 45 45 OF + RO-S + BP-S + CO-5 39 51 50 CO-5 + MA + WP-S + WP-W 12 19 21 BP-W + BP-S + MA + CR-10 31 30 31 CO-5 + CO-10 + MA + BP-W 15 16 16 CO-5 + CO-10 + BP-W + BP-S + MA 27 27 28 WP-W + WP-S + OF + CR-5 + CR-10 40 48 51 RO-W + RO-S + WP-W + WP-S + BP-S 34 50 52 MA + OF + CO-5 + WP-S + BP-W 29 40 42 CR-5 + WP-W + BP-S + CO-10 + OF 42 44 46 All (except NI) 85 113 120
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Figure 1. Map of study area in central Missouri, USA. Dark circles delineate approximate latrine site locations. Green circles and star represent locations of major regional cities.
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Figure 2. Error rates and amplification success (with standard deviations) across time, averaged across all loci, for the matched scat and tissue samples collected from 34 harvested otters.
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Figure 3. Relationships between the two most supported candidate variables and otter density. The top model predicting otter density, H8, incorporated both variables, whereas the next model H5 used only scats per latrine
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CHAPTER 2
POPULATION STRUCTURE AND LANDSCAPE USE BY RIVER OTTERS IN MISSOURI
ABSTRACT
Over a ten-year period beginning in 1982, river otters (Lontra canadensis) were reintroduced to Missouri, having been extirpated more than 50 years previously. Most of the 845 otters were translocated from Louisiana (others from Ontario and Arkansas) and were released at 43 sites across the state. The reintroduction is widely considered one of the most successful carnivore recovery programs in history, with an estimated 11,000 -
18,000 otters existing in the state in 1999-2000. Using a combination of GIS data and genetic data obtained from microsatellite genotyping of fecal samples from eight southern Missouri rivers, I evaluated the genetic diversity and connectivity between rivers, examined daily movements for otters captured multiple times, and made inferences about the genetic structure of the eight otter populations. Overall, the river otter population showed high genetic diversity, genetic structure analysis suggested the existence of five distinct subpopulation clusters distributed throughout the eight rivers, and no evidence of isolation by distance was observed. Daily movement patterns averaged 0.76 - 1.13 km/day, and evidence of male social groups was observed. Despite evidence of long-distance movements made by individual otters in a short time span, the
GIS and genetic data collectively suggest that 20-30 years after restoration efforts, the
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current Missouri river otter population still reflects the genetic structure of the source populations.
INTRODUCTION
After a nearly 50-year absence due to extirpation from overharvesting (Bennitt and Nagel
1937), the river otter (Lontra canadensis) was successfully restored to the state of
Missouri following a ten-year, statewide reintroduction effort spearheaded by the
Missouri Department of Conservation (MDC). Between 1982 and 1992, 845 river otters were released at 43 sites across 35 counties (approximately 20 otters of equal sex ratio per site; Hamilton 2007; Table 1). Most of the otters were obtained from a single individual from Louisiana, who purchased the animals from private trappers around the
Houma area (D. Erickson, pers. comm.); however, a few were translocated from
Arkansas, USA and Ontario, Canada (Raesly 2001). Also, a small remnant population
(35-70 otters) may have existed in the southeastern corner of the state prior to the reintroductions (Hamilton 2007), although the evidence for this is equivocal.
The Missouri reintroduction effort has been regarded as one of the most successful carnivore recovery programs in history (Breitenmoser et al. 2001) and regulated trapping was reinitiated in 1996. Population estimates since the reintroduction, based on life tables incorporating reproductive rates and sex ratios from otter carcasses harvested during trapping seasons, ranged from 3,000 in 1995 (Hamilton 1998) to over
18,000 in the year 2000 (Gallagher 1999). MDC estimated that the population decreased to their management goal size of 10,000 animals in 2007 (Hamilton 2007) following trapping seasons driven by high pelt prices ($40-$120). In 2009, after several years with
46
very low pelt prices, MDC lifted all bag-limit restrictions on otter trapping across the state, hoping to continue monitoring to ensure maintenance of viable populations of otters and fish (J. Beringer, pers. comm).
Few studies have been initiated on the recovered Missouri river otter population beyond state-directed population assessments, evaluations of monitoring techniques, and local-scale studies of diet and habitat use (e.g. Erickson and McCullough 1987, Roberts et al. 2008, Crimmins et al. 2009, Boege-Tobin 2005, Roberts 2003). In part, this data void is due to the logistical difficulties of conducting such studies. However, the use of noninvasively-collected DNA for evaluation of population genetics of recovering carnivore populations is becoming increasingly widespread. For example, Kendall et al.
(2009) used hair from hair traps and rub stations to evaluate the genetic structure of recovering grizzly bears in Montana, and several Eurasian otter (Lutra lutra) population genetic studies used DNA extracted from scat and anal jelly samples (Dallas et al. 2003,
Janssens et al. 2008, Jansman et al. 2001). However, the use of such methods for addressing landscape-scale questions about Nearctic river otters has not occurred. While river otter-specific microsatellite primers have been developed (Beheler et al. 2004,
2005), they have only been used for a handful of fecal studies examining local populations (Hansen et al. 2008, Guertin et al. 2010).
Using genotypes derived from fecal samples collected along eight rivers in southern and south-central Missouri, I evaluated basic measures of genetic diversity, within-river movement patterns, and population connectivity and substructure among rivers. The rivers in the study area were chosen due to their high recreational and sport
47
fishing value, estimated higher otter densities, and more liberal trapping regulations in previous years, making them particularly relevant for determining population size for management decisions, as well as for identifying the factors influencing the genetic patterns in this reintroduced population. Otters in rivers which are nearer to each other, linked by common waterways or ponds, would be expected to show more genetic similarities than more distant populations; however, since the restoration efforts began only 20-30 years ago, the genetic structure of the eight rivers may show similarities reflecting the source populations from which the founding otters were translocated.
METHODS
Surveys for otter scats were conducted on eight rivers in south and south-central Missouri between 6 January and 23 April, 2009: Big Piney, Courtois, Current, Maries, Niangua,
Osage Fork of the Gasconade, Roubidoux, and West Piney (Chapter 1), each divided into two sections of approximately equal length for sampling. Of these, only Big Piney,
Current, Niangua, and Osage Fork were directly stocked with otters during the 1982-1992 reintroductions (Table 1). Courtois, Maries, Roubidoux, and West Piney were naturally recolonized by dispersing otters. These rivers span multiple primary watersheds in the study area and contain river otter densities from 0.069 - 0.511 otters per km (Chapter 1,
Table 6). Upon locating a latrine site, field crews cleared all scat, returned after five full days had passed, and collected all newly defecated scat and anal jelly (anal sac secretion) samples. GPS coordinates for each latrine were recorded.
Methods for optimizing and redesigning microsatellite loci for river otter fecal samples, calculating genetic error rates, and extracting DNA from field samples are given
48
in Chapter 1. In brief, I redesigned microsatellite primers to amplify shorter DNA fragments, observing low rates of allelic dropout (0.028) and false alleles (0.031) for these primers tested on DNA extracted from matched scat and tissue samples from harvested otters. I then extracted DNA from 1426 field samples collected along the eight rivers, obtaining genotypes (repeated 4-5 times to reduce genotyping error rates) across at least seven loci for 343 (24%) of these samples.
Collectively, I identified 63 river otters, ranging from two in the Niangua River to
14 in the Big Piney River. After individual otters in each river were identified by their unique genotypes, tests for deviations from Hardy-Weinberg equilibrium and linkage disequilibrium were conducted in GENEPOP 4.0.9 (Raymond and Rousset 1995, Rousset
2008) and evaluated using Bonferroni corrections. Observed and expected heterozygosity values were calculated in ARLEQUIN 3.11 (Excoffier et al. 2005). Allelic diversity was corrected for unequal sample sizes using rarefaction in the program HP-RARE
(Kalinowski 2005), which calculated the allelic diversity for a sample size of two otters
(minimum population estimate, Niangua River; Table 2).
I evaluated the genetic structure of the eight populations using the program
STRUCTURE 2.2 (Pritchard et al. 2000), which estimates the likelihood of population clustering based on allele frequencies at each microsatellite locus. The program calculates the probability of a variable number of population clusters (K), identifying the most likely number of clusters as the K value with the highest likelihood. Results were based on an admixture model with a burn-in period of 10,000 iterations followed by runs of
100,000 iterations, with allele frequencies independent in each population (λ = 1). I
49
performed ten independent tests for each K value from 1 to 9, following Evanno et al.
(2005) to determine the most likely K value by calculating ∆K. The Big Piney,
Roubidoux, and West Piney Rivers were sampled more than once during the study period
(e.g. in winter and in spring), but because no otter was ever observed moving between rivers, genotypes collected in different seasons were pooled by river.
Genetic distances (FST) were calculated for each population pair using ARLEQUIN
3.11, and significance values were computed using permutation tests. I tested for isolation by distance using the Isolde option in GENEPOP, with the default parameters (minimum distance between samples 0.0001 and 1000 permutations for Mantel test). To calculate geographic distance for the Isolde analysis, I used the straight-line distance (km) separating the midpoints of each river, measured in ArcGIS 9.3. Because of the extensive overlap of individuals' locations along each river during a given 6- or 12-day period, isolation by distance was not evaluated between individuals.
Using ArcGIS, I calculated the total distance each otter traveled in the 6- or 12- day study period by measuring the distance between all the latrine sites in which it was identified. Because these movements may represent only a fraction of otters' home ranges, and because the time period over which otters were observed was not always uniform (e.g. when otters appeared in both river sections that were not sampled simultaneously), total distances were not compared by sex or season; instead, I calculated daily movement rates. I manually traced the course of each river between latrines at a resolution of approximately 1:1000 meters, using Hawth's Tools (Beyer 2004) to calculate total distances. I then divided this total by the time period over which the otter
50
was observed. For rivers that were sampled separately in both winter and spring, I calculated distances for each of these periods independently, assuming otters may shift or expand their home ranges between winter and spring (Gallant et al. 2007). Individuals identified at only one latrine site were not included in this analysis.
RESULTS
No population showed significant deviation from Hardy-Weinberg equilibrium (Table 3), and no significant linkage disequilibrium existed for any loci in any population. The number of alleles per locus for each population differed primarily due to population size variation (2 -14 otters per river); corrected for sample size, the allelic diversity did not differ significantly between rivers (ANOVA, F7,80 = 1.24, p = 0.295).
The mean FST between rivers was 0.071 (Table 4). The maximum observed FST value (0.160) occurred between the Courtois and Roubidoux Rivers, while 11 pairwise comparisons showed values below 0.005. The maximum straight-line geographic distance (160.9 km) occurred between the Courtois and Niangua Rivers, the minimum
(4.5 km) occurring between the Big Piney and West Piney (the West Piney transect flows into the Big Piney transect at its approximate midpoint). However, no significant relationship existed between genetic and geographic distances, indicating no evidence for isolation by distance (Mantel test, p = 0.202; Fig. 1).
STRUCTURE simulations indicated that the 63 river otter genotypes clustered into five groups (Table 5). These groups corresponded most prominently with the Roubidoux,
Osage Fork, Big Piney/West Piney, Courtois, and Current Rivers (Maries and Niangua showed less dominance by a single cluster; Figs. 2 and 3). Roubidoux and Courtois
51
individuals were each dominated by a single cluster, and Osage Fork showed similar cluster homogeneity except for one individual which assigned more strongly to the
Current River cluster. The Big Piney and West Piney Rivers showed strong cluster similarity, except for the occurrence of the Current cluster in Big Piney only.
Interestingly, several geographically distant rivers shared dominant clusters, such as the
Maries River (which contained the Osage Fork cluster) and the Niangua River (which was similar to the Current, Roubidoux, and Big Piney/West Piney clusters, but not the much nearer Osage Fork cluster; Fig. 3).
River otters moved extensively throughout the rivers during the study period, but no individuals were detected in more than one river (Table 6). Three male otters in the
Roubidoux traveled the maximum total distance observed, which was 32.1 km in eight days (2.29 km/day; approximately the entire length of the Roubidoux transect), and were detected at many of the same latrine sites. A female in the Roubidoux showed the greatest daily movement (3.18 km/day). Across both seasons, males were detected at more latrine sites [3.46 ± 1.82 (SD)] and showed a significantly greater average daily movement rate
[1.13 ± 0.71 (SD) km/day, median 0.94 km/day)] than females [average 2.88 ± 0.96 (SD) sites/otter, 0.66 ± 0.76 (SD) km/day, median 0.53 km/day; ANOVA, F1,51 = 4.66, p =
0.04; Fig. 4a)]. I did not calculate the significance of the seasonal difference in daily movements of females, since only two females could be evaluated in the winter period.
However, males did not show significantly different daily movements between winter and spring (ANOVA, F1,35 = 1.14, p = 0.29; Fig. 4b). In spring only, the difference between male and female movement was marginally significant (ANOVA, F1,43 = 2.98, p
52
= 0.09).
DISCUSSION
The movement patterns I observed using GIS tracking of genetically identified otters are similar to other reported daily movement rates of river otters. Previous studies have reported otter movements of 1.6 - 2.0 km/day (Mack et al. 1994) and 0.7 - 5.1 km/day
(with a maximum observed consecutive-day distance of 42 km; Melquist and Hornocker
1983) in Idaho, 1.4 - 3.9 km/day in southeast Texas (Foy 1984), and 1.0 - 3.4 km/day in
Minnesota (Route and Peterson 1988). In this study, average daily movements ranged from 0.53 - 1.41 km/day (maximum 0.71 - 3.18 km/day). Male home ranges have been found to be larger than those of females (Boege-Tobin 2005, Gorman et al. 2006,
Melquist and Hornocker 1983), which is likely to increase encounter rates with females
(Blundell et al. 2002b). In northeast Missouri, a previous study found an average home range of 17 km (range 9 - 43 km) for males and 9 km (range 7 - 12 km) for females
(Boege-Tobin 2005), while averages elsewhere in the species’ habitat range from 8 - 78 km (Melquist and Hornocker 1983) and 16 - 148 km (Mack et al. 1994) in Idaho, and 20 -
40 km of shoreline in Alaska (Bowyer et al. 1995). Because the total distances traveled by otters in this study were calculated over very short time periods, these distances likely underestimate total home range size. However, the maximum distance we observed between scats of an individual (32.1 km) falls within these averages.
The longest observed distance traveled per day was recorded for a female in the
Roubidoux River (3.18 km/day). However, this observation was atypical, as females typically showed less movement overall compared to males. Multiple males in my study
53
areas were frequently located at the same latrine sites and showed movement patterns suggestive of substantial home range overlap and joint space use. In addition, GIS calculations allowed me to identify a possible male social group, an aspect of river otter social structure commonly reported throughout the species’ range. Three male river otters traveled the same length of the Roubidoux river transect in eight days, covering a total observed distance of 32.1 km. Blundell et al. (2002a, 2004) reported that male social groups (not necessarily consisting of related individuals) in Prince William Sound,
Alaska may forage cooperatively, and Gorman et al. (2006) observed signs of cooperation between males in Minnesota, suggesting that males may not be territorial or may be jointly defending territories.
Differences in movement rates of males between winter and spring are frequently reported (Melquist and Hornocker 1983, Reid et al. 1994) but were not observed in this study, or in a previous study of Missouri river otters (Boege-Tobin 2005). In my study, however, this result may have been confounded by the detection of young males in the spring, at which time they may have been moving with their mothers, skewing the average daily movements. These measurements are probably also dependent on the local environment and seasonal weather variations; seasonal variation in home range size may be more pronounced in more severe climates, such as Idaho (Melquist and Hornocker
1983) and Alberta (Reid et al. 1994). River otters in Missouri may increase movements on unseasonably warm days in the winter, and movement patterns may differ between rivers according to the density of conspecifics or prey available.
While the life histories of river otters are primarily tied to the river ecosystem,
54
they have been observed traversing land between streams (about 3.0 km; Melquist and
Hornocker 1983) as well as occupying farm ponds [(Schwartz and Schwartz 1981,
Hamilton 1999, J. Beringer (pers. comm.)]. Thus overland movement and/or dispersal between disconnected rivers (using ponds as "stepping stones" between them) is possible, although presumably far less frequent than traditional foraging movements up or down a river system. Nonetheless, such movements are important when considering the population-level genetic patterns observed in the eight rivers. The Courtois, Maries,
Roubidoux, and West Piney Rivers did not receive translocated otters (the other four rivers were stocked with otters; Table 1). Due to their proximity, the similarities between the Big Piney and West Piney Rivers are not surprising; both rivers showed similar
STRUCTURE cluster patterns and observed heterozygosities. The Roubidoux and Courtois
Rivers both showed strong STRUCTURE cluster homogeneity, indicating a possible founder effect occurring in those rivers; the very high proportion of males in those rivers
(M:F = 8:3 in Roubidoux, 3:0 in Courtois) support this observation, as male river otters disperse further than females and may be primarily responsible for initial colonization of unpopulated habitat (Blundell et al. 2002b). The lower He in the Roubidoux River relative to the other rivers also supports a likely founder effect. Overall, the presence of otters in rivers not initially stocked with translocated individuals, containing primarily males and/or showing evidence of founder effects, suggests that the Missouri population is still expanding geographically, and otters are recolonizing suitable habitats from which they were previously extirpated.
The processes occurring in the Maries and Niangua Rivers, which also contained
55
very small population sizes and no females, are less clear. Niangua was stocked with otters in 1988 and 1990 (Table 1), and until recently was thought to contain a large population of otters (based on harvest reports and scat indices; MDC, unpublished data).
However, the estimated 2009 population size was much lower than those for the other rivers stocked with otters (Chapter 2). Heavy harvest rates reported in 2003-2006 (73 otters reported trapped in 2006, compared to 12 in Roubidoux and 21 in Osage Fork;
MDC, unpublished data) may have caused a decline in the otter population and facilitated a subsequent influx of male otters from other areas, which may explain the genetic differentiation of the two males identified there in 2009.
Two of the Maries River otters were similar to the Courtois River population, while one more closely assigned to the Osage Fork cluster, indicating that the otters here probably emigrated from different sources. However, the geographic distance between the Maries, Courtois, and Osage Fork Rivers (and between other genetically similar rivers), the overall patterns of genetic diversity evident in Table 1, and the pairwise FST and isolation by distance evaluations in Table 4 and Fig. 1, cannot be explained by typical river otter movement and dispersal. It is highly unlikely that the movements of individual otters between rivers as geographically distant as Courtois and West Piney,
Osage Fork and Maries, and Osage Fork and Current, explains the STRUCTURE analysis patterns. Instead, the population substructure evident in this analysis is likely an artifact of the reintroduction, with otters from the same or similar source populations translocated to different areas. Population structure analyses of Louisiana river otters showed substantial population differentiation between different river drainages (Latch et al.
56
2008). Thus, the five genetic clusters may actually derive from genetically distinct populations in Louisiana (Latch et al. 2008) and other source populations. Genetic comparisons to otters in those sources may serve to test this hypothesis, as well as provide a means for assessing evidence of a remnant Missouri population. The possibility of multiple source populations may also explain the high levels of genetic diversity observed throughout the eight river systems; about 20 otters were translocated during each reintroduction event, and several rivers were stocked more than once (Table 1).
Such a strategy would have facilitated maintenance of high levels of genetic diversity for an extended period of time. If this is the case, the development of population substructure as a function of local processes occurring in the Missouri streams will take much longer than the current time span (20-30 years since reintroduction), and important barriers to gene flow may not become visible in the near future.
LITERATURE CITED
Beheler, A.S., J.A. Fike, L.M. Murfitt, O.E. Rhodes, Jr., and T.L. Serfass. 2004.
Development of polymorphic microsatellite loci for North American river otters
(Lontra canadensis) and amplification in related mustelids. Molecular Ecology
Notes 4:56-58.
Beheler, A.S., J.A. Fike, D. Dharmarajan, O.E. Rhodes, Jr., and T.L. Serfass. 2005. Ten
new polymorphic microsatellite loci for North American river otters (Lontra
canadensis) and their utility in related mustelids. Molecular Ecology Notes 5:602-
604.
57
Bennitt, R. and W. O. Nagel. 1937. A survey of the resident game and furbearers of
Missouri. University of Missouri Studies 12:1-215.
Beyer, H.L. 2004. Hawth's Analysis Tools for ArcGIS. Available at
http://www.spatialecology.com/htools.
Blundell, G.M., M. Ben-David, and R.T. Bowyer. 2002a. Sociality in river otters:
cooperative foraging or reproductive strategies? Behavioral Ecology 13:134-141.
Blundell, G.M., M. Ben-David, P. Groves, R.T. Bowyer, and E. Geffen. 2002b.
Characteristics of sex-biased dispersal and gene flow in coastal river otters:
implications for natural recolonization of extirpated populations. Molecular
Ecology 11:289-303.
Blundell, G.M., M. Ben-David, P. Groves, R.T. Bowyer, and E. Geffen. 2004. Kinship
and sociality in coastal river otters: are they related? Behavioral Ecology 15:705-
714.
Boege-Tobin, D.D. 2005. Ranging patterns and habitat utilization of northern river otters
in Missouri. Ph.D. Dissertation, University of Missouri - St. Louis, St. Louis,
Missouri, USA.
Bowyer, R. T., J. W. Testa, and J. B. Faro. 1995. Habitat selection and home ranges of
river otters in a marine environment: Effects of the Exxon Valdez oil spill. Journal
of Mammalogy 76:1-11.
Breitenmoser, U., C. Breitenmoser-Wursten, L. N. Carbyn, and S. M. Funk. 2001.
Assessment of carnivore reintroductions. Pp. 241-281 in J. L. Gittleman, S. M.
58
Funk, D. W. Macdonald, and R. K. Wayne, editors. Carnivore Conservation.
Cambridge University Press, Cambridge, United Kingdom.
Crimmins, S.M., N.M. Roberts, D.A. Hamilton, and A.R. Mynsberge. 2009. Seasonal
detection rates of river otters (Lontra canadensis) using bridge-site and random-
site surveys. Canadian Journal of Zoology 87:993-999.
Dallas, J.F., K.E. Coxon, T. Sykes, P.R.F Chanin, F. Marshall, D.N. Carss, P.J. Bacon,
S.B. Piertney, and P.A. Racey. 2003. Similar estimates of population genetic
composition and sex ratio derived from carcasses and faeces of Eurasian otter
Lutra lutra. Molecular Ecology 12:275-282.
Erickson, D.W and C.R. McCullough. 1987. Fates of translocated river otters in
Missouri. Wildlife Society Bulletin 15:511-517.
Evanno, G., S. Regnaut, and J. Goudet. 2005. Detecting the number of clusters of
individuals using the software STRUCTURE: a simulation study. Molecular
Ecology 14:2611-2620.
Excoffier, L.G., G. Laval, and S. Schneider. 2005. ARLEQUIN ver. 3.0: an integrated
software package for population genetics data analysis. Evolutionary
Bioinformatics Online 1:47-50.
Foy, M. K. 1984. Seasonal movement, home range, and habitat use of river otter in
southeastern Texas. M.S. Thesis, Texas A&M University, College Station, Texas,
USA.
Gallagher, E. 1999. Monitoring trends in reintroduced river otter populations. M.S.
Thesis, University of Missouri, Columbia, Missouri, USA.
59
Gallant, D., L. Vasseur, C.H. Berube. 2007. Unveiling the limitations of scat surveys to
monitor social species: A case study on river otters. The Journal of Wildlife
Management 71:258-265.
Gorman, T.A., J.D. Erb, B.R. McMillan, and D.J. Martin. 2006. Space use and sociality
of river otters (Lontra canadensis) in Minnesota. Journal of Mammalogy 87:740-
747.
Guertin, D.A., A.S. Harestad, M. Ben-David, K.G. Drouillard, and J.E. Elliott. 2010.
Fecal genotyping and contaminant analyses reveal variation in individual river
otter exposure to localized persistent contaminants. Environmental Toxicology
and Chemistry 29:275-284.
Hamilton, D.A. 1998. Missouri otter population assessment. Missouri Department of
Conservation, Columbia.
Hamilton, D.A. 1999. Controversy in times of plenty. Missouri Conservationist 60:17-24.
Hamilton, D.A. 2007. Missouri’s river otter saga. Missouri Conservationist 68:26-31.
Hansen, H., M. Ben-David, and D.B. McDonald. 2008. Effects of genotyping protocols
on success and errors in identifying individual river otters (Lontra canadensis)
from their faeces. Molecular Ecology Resources 8:282-289.
Jansman, J., P.R.F. Chanin, and J.F. Dallas. 2001. Monitoring otter populations by DNA
typing of spraints. IUCN Otter Specialist Group Bulletin 18:12-19.
Janssens, X., M.C. Fontaine, J.R. Michaux, R. Libois, J. de Kermabon, P. Defourny, and
P.V. Baret. 2008. Genetic pattern of the recent recovery of European otters in
southern France. Ecography 31:176-186.
60
Kalinowski, S.T. 2005. HP-RARE: a computer program for performing rarefaction on
measures of allelic diversity. Molecular Ecology Notes 5:187-189.
Kendall, K. C., J. B. Stetz, J. Boulanger, A. C. Macloed, D. Paetkau, and G. C. White.
2009. Demography and genetic structure of a recovering grizzly bear population.
Journal of Wildlife Management 73:3-17.
Latch, E.K., D.G. Scognamillo, J.A. Fike, M.J. Chamberlain, and O.E. Rhodes, Jr. 2008.
Deciphering ecological barriers to North American river otter (Lontra canadensis)
gene flow in the Louisiana landscape. Journal of Heredity 99:265-274.
Mack, C., L. Kronemann, and C. Eneas. 1994. Lower Clearwater aquatic mammal survey
(Project Number 90-51). Bonneville Power Administration, Portland, Oregon.
Melquist, W.E. and M.G. Hornocker. 1983. Ecology of river otters in west-central Idaho.
Wildlife Monographs 83:1-60.
Pritchard, J. K., M. Stephens, and P. Donnelly. 2000. Inference of population structure
using multilocus genotype data. Genetics 155:945-959.
Raesly, E. J. 2001. Progress and status of river otter reintroduction projects in the United
States. Wildlife Society Bulletin 29:856-862.
Raymond, M. and F. Rousset. 1995. GENEPOP (version 1.2): population genetics software
for exact tests and ecumenicism. Journal of Heredity 86:248-249.
Reid, D. G., T. E. Code, A. C. H. Reid, and S. M. Herrero. 1994. Spacing, movements,
and habitat selection of the river otter in boreal Alberta. Canadian Journal of
Zoology 72:1314-1324.
61
Roberts, N.M. 2003. River otter food habits in the Missouri Ozarks. M.S. Thesis,
University of Missouri, Columbia, Missouri, USA.
Roberts, N.M., S.M. Crimmins, D.A. Hamilton, and E. Gallagher. 2008. An evaluation of
bridge-sign surveys to monitor river otter (Lontra canadensis) populations.
American Midland Naturalist 160:358-363.
Rousset, F. 2008. Genepop '007: a complete reimplementation of the Genepop software
for Windows and Linux. Molecular Ecology Resources 8:103-106.
Route, W. T. and R. O. Peterson. 1988. Distribution and abundance of river otter in
Voyageurs National Park, Minnesota. Research/Resources Management Report
MWR-10. U.S. Department of the Interior, National Park Service.
Schwartz, C.W. and E.R. Schwartz. 1981. The Wild Mammals of Missouri, 2nd edition.
University of Missouri Press, Columbia, Missouri, USA.
62
Table 1. Years and locations of river otter reintroductions across the state of Missouri, USA. Source: J.
Beringer, Missouri Department of Conservation. NWR: National Wildlife Refuge. WA: Wildlife Area. SL:
Slough.
Year Location County No. Year Location County No. Otters Otters
1982 Swan Lake NWR Chariton 12 1989 Middle Bourbeuse Riv Franklin 24 Middle Meramec Riv Crawford 21 1983 Fountain Grove WA Linn 10 Middle Salt Riv Macon 10 Lamine Riv Cooper 20 Upper Gasconade Wright 20
1984 Fountain Grove WA Linn 7 1990 Big Piney Riv Texas 20 Ted Shanks WA Pike 24 Big Piney Riv Pulaski 21 Four Rivers WA Vernon 18 Current Riv Dent 17 Rebel’s Cove WA Putnam 6 Dry Wood Crk Barton/Vernon 22 Little Chariton Riv Chariton 7 Gasconade/Hazelgreen Pulaski 20 Horse Crk Barton 22 1985 Big Creek Daviess 20 Jacks Fork Riv Texas 17 Blackwater-Perry WA Pettis 18 Niangua Riv (2) Laclede 17 Little Chariton Riv Chariton 12 Osage Fork Riv Webster 21 Rebel’s Cove WA Putnam 6 Pomm-Tin Town Polk 20 Schell-Osage WA Vernon 20 S. Grand/Big Crk Henry 18 Shoal Creek Caldwell 20 1991 Eleven Point Riv Oregon 21 1986 Cuivre River-Argent SL Lincoln 23 Bryant Crk Douglas 20 Cuivre River-West Frk Lincoln 22 Gasconade/Bell Chute Maries 20 Moreau River-Burris Frk Moniteau 21 Jacks Fork Riv Texas 2 Platte River-Castile Crk Platte 18 James Riv Christian 20 Rebel’s Cove WA Putnam 1 Loutre Riv Montgomery 4 South Fabius Riv Marion 21 North Fork, White Riv Douglas 20
1987 One Hundred Two Riv Andrew 22 1992 Perche Crk Boone 20
1988 Bourbeuse Riv Gasconade 20 Meramec Riv Dent 20 Middle Fabius Riv Knox 20 Middle Salt Riv Macon 10 Niangua Riv Dallas 20
63
Table 2. Minimum otter population sizes, sex ratios, and densities for eight rivers in Missouri, USA, based on fecal genotyping (Chapter 1). For rivers which were sampled more than once (Big Piney, Roubidoux, and West
Piney), total number of genotypes are given in bold above the counts per season (accounting for otters which appeared in both seasons).
Population estimate Length of River and sex ratio (M:F) transect (km) Density
Big Piney 14 (8:6) 23.5 winter 6 (2:4) 0.255 spring 12 (6:6) 0.511
Courtois 3 (3:0) 22.4 0.134
Current 11 (8:3) 27.3 0.403
Maries 3 (3:0) 27.2 0.110
Niangua 2 (2:0) 29.0 0.069
Osage Fork 14 (8:6) 31.7 0.442
Roubidoux 11 (8:3) 34.4 winter 6 (4:2) 0.174 spring 10 (8:2) 0.291
West Piney 5 (2:3) 24.8 winter 3 (1:2) 0.121 spring 3 (2:1) 0.121
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Table 3. Number of alleles (A), corrected number of alleles (Arare), and observed (He) and expected (He) heterozygosity values for each microsatellite locus in
each river otter population in Missouri, USA.
Big Piney (BP) Courtois (CO) Current (CR) Maries (MA) n 14 3 11 3 Locus A A (rare) Ho He A A (rare) Ho He A A (rare) Ho He A A (rare) Ho He RIO01R2 4 2.1 0.714 0.537 4 3.2 0.667 0.867 4 2.4 0.662 0.662 2 2.0 1.000 0.600 RIO02R 6 2.4 0.571 0.614 3 2.6 0.667 0.733 5 2.7 1.000 0.740 3 2.3 0.667 0.600 RIO04R 3 1.3 0.143 0.140 3 2.8 1.000 0.800 2 1.5 0.273 0.247 1 1.0 NA NA RIO06R 4 2.4 0.714 0.643 3 2.6 1.000 0.733 3 2.4 0.778 0.680 3 2.6 0.667 0.733 RIO07R 6 2.8 0.643 0.746 4 3.2 1.000 0.867 4 2.8 0.800 0.768 5 3.6 1.000 0.933 RIO08R 5 2.3 0.571 0.574 3 2.3 0.333 0.600 3 1.9 0.455 0.437 3 2.6 0.667 0.733 RIO11 4 2.5 0.714 0.691 3 2.3 1.000 0.733 4 2.4 0.818 0.645 3 2.6 1.000 0.733 RIO13R 6 2.9 0.714 0.794 4 3.0 0.667 0.800 7 3.1 0.818 0.840 4 3.2 0.667 0.867 RIO15R 3 1.9 0.357 0.421 2 1.9 0.667 0.533 2 1.9 0.364 0.520 2 1.9 0.667 0.533 RIO16R 6 2.6 0.769 0.677 2 1.9 0.667 0.533 5 2.8 0.900 0.774 3 2.6 1.000 0.733
65 All loci 4.7 2.3 0.591 0.584 3.1 2.6 0.767 0.720 3.9 2.4 0.684 0.631 2.9 2.5 0.815 0.719
Niangua (NI) Osage Fork (OF) Roubidoux (RO) West Piney (WP) n 2 14 11 5 Locus A A (rare) Ho He A A (rare) Ho He A A (rare) Ho He A A (rare) Ho He RIO01R2 3 3.0 0.500 0.833 4 2.7 0.769 0.726 5 2.7 0.900 0.737 5 2.8 0.800 0.756 RIO02R 3 3.0 1.000 0.833 9 3.2 0.857 0.849 5 2.4 0.909 0.645 5 3.1 0.800 0.822 RIO04R 1 1.0 NA NA 2 1.8 0.429 0.476 3 1.5 0.273 0.255 4 2.7 0.600 0.733 RIO06R 3 3.0 1.000 0.833 3 2.2 0.714 0.595 2 1.9 0.727 0.485 4 3.1 0.000 0.857 RIO07R 4 4.0 1.000 1.000 6 3.0 0.786 0.815 2 1.5 0.273 0.247 3 2.5 0.600 0.689 RIO08R 3 3.0 1.000 0.833 4 2.5 0.786 0.667 3 2.3 0.909 0.628 3 2.5 0.200 0.689 RIO11 4 4.0 1.000 1.000 4 2.6 0.692 0.717 3 2.3 1.000 0.636 3 2.5 1.000 0.711 RIO13R 3 3.0 0.500 0.833 7 3.0 0.857 0.794 4 2.2 0.636 0.567 4 2.8 0.600 0.778 RIO15R 1 1.0 NA NA 2 1.3 0.143 0.138 2 1.3 0.182 0.173 2 2.0 0.200 0.556 RIO16R 2 2.0 1.000 0.667 3 2.1 0.786 0.542 3 2.0 0.636 0.507 3 2.3 0.667 0.600 All loci 2.7 2.7 0.875 0.854 4.4 2.4 0.682 0.632 3.2 2.0 0.645 0.488 3.6 2.6 0.547 0.719
Table 4. FST values (top half of matrix) and geographic distances (km, bottom half of matrix) between all population pairs. For river abbreviations, see Table 3.
BP RO CO OF CR NI WP MA
BP --- 0.116*** 0.137** 0.133*** 0.082*** -0.035 0.077** 0.088*
RO 29.5 --- 0.160*** 0.147*** 0.157*** 0.018 0.096* 0.106*
CO 102.9 110.1 --- 0.040* 0.133*** 0.015 0.061 0.005
OF 54.0 28.0 134.8 --- 0.151*** 0.005 0.112*** 0.014
CR 46.4 68.5 67.9 97.4 --- 0.024 0.081** 0.095*
NI 87.3 61.0 160.9 32.8 129.0 --- 0.000 -0.079
WP 4.5 27.4 107.0 52.1 51.4 85.4 --- 0.044
MA 118.1 98.4 100.2 104.5 123.4 110.0 119.3 ---
*p < 0.05; **p < 0.01; *** p < 0.001
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Table 5. Results from the Evanno et al. (2005) test of STRUCTURE simulation results for river otter subpopulations in eight Missouri rivers. The number K with the highest ∆K value indicates the most likely number of genetic clusters (in this case, K = 5).
K mean L(K) ∆K 1 -1717.5 ---
2 -1750.9 7.0
3 -1640.3 15.9
4 -1658.4 1.1
5 -1577.1 50.0
6 -1651.8 2.0
7 -1733.6 2.4
8 -1772.6 2.5 9 -1783.8 0.8
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Table 6. Summary of daily movement patterns for river otters detected at more than one latrine site (n), calculated for winter and spring study periods in Missouri, USA.
Males Females River Avg. no. Max Avg. Avg. no. Max Avg. n locations distance/ distance/ n locations distance/ distance/ per otter day (km) day (km) per otter day (km) day (km) Winter Big Piney 0 NA NA NA 2 3.5 0.71 0.53 Niangua 1 2.00 0.10 0.10 0 NA NA NA Roubidoux 4 2.25 2.72 1.97 0 NA NA NA West Piney 1 2.00 0.49 0.49 0 NA NA NA Average 6 2.17 2.72 1.41 2 3.5 0.71 0.53
Spring Big Piney 2 2.50 1.58 0.98 4 2.50 0.69 0.38 Courtois 3 2.67 1.00 0.78 0 NA NA NA Current 6 4.50 2.38 0.95 3 2.67 0.67 0.44 Maries 2 3.00 0.94 0.79 0 NA NA NA Osage Fork 9 4.11 1.83 1.09 4 2.50 0.56 0.32 Roubidoux 5 4.00 2.29 1.67 2 3.00 3.18 1.90 West Piney 1 3.00 0.36 0.36 1 5.00 1.54 1.54 Average 29 3.72 2.38 1.07 14 2.79 3.18 0.67
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Figure 1. Isolation by distance analysis showing relationship between genetic distance (FST) and geographic distance in kilometers. Typically, this relationship is linear, as individuals in adjacent populations linked by movement and dispersal are more genetically similar. In the Missouri river otter populations, no such relationship exists (Mantel test, p = 0.202).
69
100% 90% 80% 70% 5 60% 4 50% 3 40% 2 30% 1 20% 10% 0% CO CR BP WP RO OF NI MA
Figure 2. Distribution of the five cluster assignments suggested by STRUCTURE.
Unlike Figure 3, which displays only the dominant cluster assignments for each individual, this analysis represents all cluster likelihoods averaged for all individuals per river. CO, CR, BP, RO, and OF showed strong cluster homogeneity
(i.e. one dominant assignment likelihood), whereas WP, NI, and MA were less likely to be dominated by a single cluster.
70
Figure 3. Geographic representation of STRUCTURE simulations, displaying only dominant cluster assignment(s) for each otter per population weighted with the strength
(% likelihood) of that assignment. Otters which assigned equally to multiple clusters were divided; e.g. Niangua (8) included two otters, but one was equally likely to assign to clusters B and C, while the other otter more strongly assigned to Cluster D. Size of pie charts corresponds to sample size (number of otters in the population).
71
a b
72
Figure 4. Average daily movement rate of female vs. male river otters across both seasons (a) and for males in spring vs.
winter (b). Bold lines indicate the median distance traveled per day, with minimum and maximum values indicated by the
dashed lines. Note the two female outliers in 5(a), including the otter with the greatest recorded movement rate for this study
(3.2 km/day).
Appendix 1. Genotypes for all individuals along ten microsatellites (01R2, 02R, etc.) identified in the eight rivers. BP=Big Piney, CO=Courtois, CR=Current, MA=Maries, NI=Niangua, OF=Osage Fork,
RO=Roubidoux, WP=West Piney.
01R2 02R 04R 06R 07R 08R 11 13R 15R 16R BP-A 154154 131131 110110 130130 101101 108108 156158 158158 139139 155155 BP-B 146154 131133 110110 134134 087095 104108 156156 144148 139141 155157 BP-C 154154 121131 110110 130134 101101 108110 154156 144152 137139 155157 BP-D 146154 131131 110110 134134 091101 108114 154156 144152 137139 155161 BP-E 150154 129131 110110 126130 101101 106108 152158 158164 139141 155161 BP-F 146154 123127 110110 130138 091101 104106 152152 164164 139141 155157 BP-G 154154 121123 110110 130134 101101 108108 154156 144152 137137 155157 BP-H 154158 123131 110110 130134 099101 108108 156156 144152 139139 153155 BP-I 146154 131131 110110 130134 087093 108110 152156 150158 139139 155155 BP-J 146154 121131 106110 134138 099101 110114 152156 152158 139139 153157 BP-K 146154 123133 110110 130134 093101 108108 154154 158158 139139 153155 BP-L 154154 131131 110110 134134 093095 108108 152156 144152 139139 BP-M 150154 131131 110110 130134 087091 108108 152156 144144 139139 149151 BP-N 146154 131131 110116 126130 087087 104108 152156 144164 139139 155155
CO-A 146150 123123 108110 126134 093095 108110 156158 144156 139141 155157 CO-B 154154 119127 100110 130134 087093 104104 154156 154158 139141 155155 CO-C 150162 123127 100108 126134 087091 104104 154156 158158 139139 155157
CR-A 154158 127131 110110 126130 091101 104108 152154 146162 139141 153155 CR-B 154158 127131 110110 130134 093101 104108 152152 158162 139141 155161 CR-C 146154 127131 110110 126130 091091 108108 154158 146164 139141 157161 CR-D 154154 123135 100110 130134 091093 108108 152154 146162 141141 161161 CR-E 158158 129131 110110 126134 093101 108108 152152 150164 139139 155157 CR-F 150158 131135 110110 126126 097097 104110 152154 146150 139139 155157 CR-G 154154 123131 110110 130134 093101 108108 154156 164164 141141 155157 CR-H 154154 131135 110110 130130 091093 104108 152154 146146 141141 155157 CR-I 154158 123131 110110 126130 091101 104108 152154 162164 139141 153155 CR-J 146154 123131 100110 108108 154156 144158 139139 CR-K 150158 131135 100110 091097 108108 152154 160162 141141 157159
MA-A 146154 123127 110110 126134 089091 104110 154156 164164 139141 155157 MA-B 146154 127127 110110 126130 097101 106110 154156 150158 139139 155157 MA-C 146154 127131 110110 134134 091099 104104 152156 156158 139141 147157
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NI-A 146158 129131 110110 126134 091095 104108 152156 158158 139139 155157 NI-B 154154 127131 110110 134138 099101 106108 154158 150164 139139 155157
OF-A 146150 119121 110110 126134 095097 110110 152154 150158 139139 155155 OF-B 154158 119133 110110 134134 087095 104110 152158 158158 139139 155157 OF-C 150158 121123 110114 126134 095097 104106 152152 146158 139139 155155 OF-D 146154 127129 110114 126130 093097 108110 154156 156158 139139 155157 OF-E 146146 127131 114114 134134 091093 104110 154154 146164 139139 155157 OF-F 146154 123123 110110 126134 091097 104108 154154 146146 139139 155161 OF-G 154158 127133 110114 126126 087087 110110 156156 144158 139139 155157 OF-H 154158 127131 110114 130134 087097 104106 154156 144156 137139 155157 OF-I 146154 125127 110110 130134 087097 104110 152154 144158 137139 155157 OF-J 150158 121135 114114 126134 093095 104110 152158 146158 139139 155157 OF-K 146154 121123 110114 126134 087097 106110 152154 158166 139139 155161 OF-L 146146 127131 110114 126134 093097 106110 154156 146164 139139 155157 OF-M 146146 127127 110110 126134 101101 110110 154156 158164 139139 155157 OF-N 123127 110110 134134 087087 104110 150158 139139 155155
RO-A 146154 129131 110116 134134 091099 108110 152154 144158 139139 155155 RO-B 150154 129131 110110 126134 091091 104108 152156 158158 139139 153155 RO-C 142150 123131 108110 126134 091091 104108 152156 158158 139141 153155 RO-D 146158 129131 110110 126134 091091 104108 152156 156158 139139 155157 RO-E 142154 129131 110110 126134 091091 108110 152156 158158 139141 155155 RO-F 125131 110110 126134 091099 108110 152156 144158 139139 155157 RO-G 146154 127131 110110 134134 091091 104108 154156 150158 139139 155157 RO-H 150154 123131 110110 126134 091091 108108 152156 150158 139139 153155 RO-I 146154 129131 110110 126134 091091 108110 154156 158158 139139 155155 RO-J 146154 129131 110110 126134 091091 108110 152156 144150 139139 155157 RO-K 154154 131131 110116 134134 091099 104108 152156 144158 139139 155155
WP-A 146154 129131 110110 126126 091091 108108 152154 150150 139139 155155 WP-B 150154 123131 100108 134134 091101 108110 152156 146158 139139 155157 WP-C 146154 123131 110110 099101 110110 152156 152158 141141 WP-D 154154 121131 106110 138138 099101 114114 152156 152158 141141 WP-E 148156 133133 100108 128128 091091 110110 154156 152152 139141 153155
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Appendix 2. Detailed description of genotyping methods, particularly assignment of confidence intervals and other techniques for recognizing potential genotyping errors
(e.g. false alleles and allelic dropout).
Genotypes were assigned a confidence level ranging from 1-5, with 1 representing very low confidence and 5 being very high confidence (Appendix 3). Samples that showed no or irregular allele peaks, usually at lower fluorescence values, were described as “bad” and were not assigned a confidence level. Confidence scores of 1-2 generally represented fragment analysis results with fluorescence values of 200 or less, or higher fluorescence values with highly questionable allele peaks. Scores of 3 were usually results with fluorescence values between 400-1000, or higher fluorescence values with questionable allele peaks. Scores of 4 were generally assigned to strong fluorescence values (greater than 1000) but which displayed smaller alleles that could potentially represent allelic dropout. Scores of 5 were assigned to samples with high fluorescence values and no questionable allele peaks. When stutter bands or multiple alleles were present, genotypes were called but marked with a description of the irregularity (most commonly on samples with confidence levels of 3 or 4).
Consensus genotypes were assigned for heterozygotes with two matching PCR runs, and homozygotes were confirmed after three matching PCR runs. All genotypes were called by the same researcher to avoid bias. When samples were repeated up to 5 times, those that were already confirmed were genotyped again. Occasionally, samples with fewer than the required number of matching PCR runs were assigned consensus genotypes if all calls were of high confidence, but all such cases were flagged. In
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addition, samples that met the required number of matches but were of generally low confidence were also flagged. This method was employed to maximize the number of samples that could be genotyped, while reducing errors.
Following the elimination of failed samples, I compared genotypes manually for identification of unique individuals and recognition of recaptures, taking into account all samples flagged for poor quality, rounding, or too few matching genotypes (e.g. two homozygous calls at locus). Some samples were labeled as “ambiguous”, meaning that the consensus genotypes generated were at less-informative loci which matched more than one previously identified otter, or the genotypes were of consistently lower confidence. These samples were not included in further analyses. Genotypes which appeared to match otters located in other rivers were checked carefully for contamination issues at every step in the sample processing.
One otter identified in the Courtois River had a matching genotype to an otter identified in West Piney Creek. However, after checking the DNA extraction daily data forms, I concluded that this was a misidentification caused by contamination of the
Courtois sample by one of the West Piney samples processed in the same batch. Also, one otter identified in the Niangua River had a very similar genotype to an otter found in the Roubidoux. No contamination was detected between these samples. Besides these two cases, no other close similarities between individuals in different rivers were detected.
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Appendix 3 (page 78). Examples of confidence level assignments for GeneMarkerTM genotypes on locus RIO16R. Genotypes were assigned manually (e.g. allele calls suggested by the program were not always used). Generally, smaller peaks to the left of the called allele are disregarded as "stutter" bands, but occasionally represent real alleles that have dropped out. Small peaks occurring after the called alleles were common and generally disregarded but flagged. Also, peaks appearing one base pair above the called allele are added by Taq during PCR, and disregarded.
bad: No clear allele peaks, and overall fluorescence values below 200.
1(a): Potential allele peaks, but fluorescence below 200. 1(b): Low fluorescence
and unclear peaks.
2(a): Unclear allele peaks, but higher fluorescence. 2(b): Clear allele peak but low
fluorescence.
3(a) and (b): Fluorescence values higher (>1000). 3(b): Possible second allele at
156.1.
4(a): Very high fluorescence, but allele peaks are similar in height; 152.9 may be
a real allele or stutter. 4(b): High fluorescence, but small peak at 152.5 may not be
a real allele.
5(a) and (b): Very high fluorescence and unquestionable allele peaks
(homozygote and heterozygote).
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Appendix 4. Data set used for AICc model selection. BP02-S was not included in the analysis, and a control data
point was added. Success (%) indicates the genotyping success relative to the 24% average.
Section Latperkm Scatperkm Scatperlat Freshperkm Newperkm Jellyperkm Density Success CO01-05 0.354 1.327 3.750 0.000 1.327 0.000 0.089 1.667 CO02-05 0.270 0.721 2.667 0.360 0.360 0.000 0.180 1.563
CO02-10 0.451 1.712 3.800 0.180 1.441 0.090 0.270 1.533
BP01-W 0.286 0.929 3.250 0.214 0.714 0.000 0.214 1.283
BP02-W 0.842 2.105 2.500 0.421 1.368 0.316 0.316 2.083
BP01-S 0.429 1.571 3.667 0.571 1.000 0.000 0.214 1.138
79 BP02-S 1.790 14.000 7.824 2.737 10.526 0.737 1.158 0.879
MA01 0.219 0.292 1.333 0.000 0.146 0.146 0.073 4.167
MA02 0.370 1.778 4.800 0.074 1.704 0.000 0.222 0.696
WP01-W 0.000 0.000 0.000 0.000 0.000 0.000 0.000 4.167
WP02-W 0.531 1.593 3.000 0.531 0.885 0.177 0.266 1.388 WP01-S 0.370 0.963 2.600 0.296 0.593 0.074 0.074 1.283
WP02-S 0.885 4.336 4.900 1.681 2.566 0.089 0.266 0.767
OF01 1.008 8.140 8.077 1.163 6.434 0.543 0.388 0.913
OF02 1.223 11.489 9.391 2.181 8.989 0.319 0.532 1.483
CR01-05 0.513 3.248 6.333 0.769 1.880 0.598 0.427 1.425
CR02-05 0.577 4.872 8.444 1.346 3.077 0.449 0.449 1.042
CR01-10 0.684 4.786 7.000 1.111 3.077 0.598 0.342 1.267
CR02-10 0.641 4.423 6.900 1.603 2.115 0.705 0.449 1.208
NI01 0.491 2.270 4.625 0.368 1.841 0.061 0.061 0.113
NI02 0.236 0.709 3.000 0.158 0.236 0.315 0.079 2.333
RO02-0 0.354 0.556 1.571 0.455 0.051 0.051 0.051 0.758
RO01-05 0.548 1.233 2.250 0.069 1.027 0.137 0.137 0.463
RO02-05 0.404 1.010 2.500 0.253 0.556 0.202 0.303 2.292
80 RO01-10 0.274 0.890 3.250 0.069 0.822 0.000 0.069 0.321
RO01-S 1.164 8.973 7.706 2.877 4.247 1.849 0.206 0.604
RO02-S 1.212 14.343 11.833 5.455 8.737 0.152 0.505 0.600
control 0.051 0.051 1.000 0.051 0.000 0.000 0.051 4.167