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An Evaluation of Population Restoration and Monitoring Techniques for Freshwater

Mussels in the Upper Clinch River, Virginia, and Refinement of Culture Methods for

Laboratory-Propagated Juveniles

Caitlin S. Carey

Thesis submitted to the faculty of the Virginia Polytechnic Institute and

State University in partial fulfillment of the requirements for the degree of

Master of Science

in

Fisheries and Wildlife

Jess W. Jones, Chair

Eric M. Hallerman

Marcella J. Kelly

September 27th

, 2013

Blacksburg, Virginia

Keywords: Freshwater Mussels, Epioblasma capsaeformis, Population Restoration and

Monitoring, Mark-Recapture, Culturing, Temperature

Chapter 3 © 2013 by Taylor & Francis

All other material © 2013 by Caitlin S. Carey

An Evaluation of Population Restoration and Monitoring Techniques for Freshwater

Mussels in the Upper Clinch River, Virginia, and Refinement of Culture Methods for

Laboratory-Propagated Juveniles

by

Caitlin S. Carey

ABSTRACT

From 2006–2011, four population reintroduction techniques were applied to three sites

within a reach of the upper Clinch River in Virginia designated suitable for population

restoration of the federally endangered oyster mussel (Epioblasma capsaeformis). These

techniques were: 1) translocation of adults (Site 1), 2) release of laboratory-propagated sub-

adults (Site 1), 3) release of 8-week old laboratory-propagated juveniles (Site 2), and 4) release

of stream-side infested host fishes (Site 3). Demographic data were collected in 2011 and 2012

by systematic quadrat and capture-mark-recapture sampling to assess reintroduction success,

evaluate reintroduction techniques, and compare survey approaches for monitoring freshwater

mussels. Estimates of abundance and density of translocated adults ranged from 450–577

individuals and 0.09–0.11/m2 in 2011, and 371–645 individuals and 0.07–0.13/m

2 in 2012.

Estimates of abundance and density of laboratory-propagated sub-adults ranged from 1,678–

1,943 individuals and 0.33–0.38/m2 in 2011, and 1,389–1,700 individuals and 0.27–0.33/m

2 in

2012. Additionally, three recruits were collected at Site 1. No E. capsaeformis were collected at

Sites 2 and 3. Capture-mark-recapture sampling produced similar mean point estimates as

systematic quadrat sampling, but with typically more precision. My results indicated that the

release of larger individuals (>10 mm) is the most effective technique for restoring populations

of E. capsaeformis, and that systematic quadrat and capture-mark-recapture sampling have

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useful applications in population monitoring that are dependent on project objectives. Systematic

quadrat sampling is recommended when the objective is to simply estimate and detect trends in

population size for species of moderate to larger densities (>0.2/m2). Capture-mark-recapture

sampling should be used when objectives include assessing a reintroduced population of

endangered species or at low density, obtaining precise estimates of population demographic

parameters, or estimating population size for established species of low to moderate density

(0.1–0.2/m2).

The ability to grow endangered juveniles to larger sizes in captivity requires improving

grow-out culture methods of laboratory-propagated individuals. A laboratory experiment was

conducted to investigate the effects of temperature (20–28°C) on growth and survival of

laboratory-propagated juveniles of the Cumberlandian combshell (Epioblasma brevidens), E.

capsaeformis, and the wavyrayed lampmussel (Lampsilis fasciola) in captivity. Results indicated

that 26°C is the optimum temperature to maximize growth of laboratory-propagated juveniles in

small water-recirculating aquaculture systems. Growing endangered juveniles to larger sizes will

improve survival in captivity and after release into the wild. As a result, hatcheries can reduce

the time that juveniles spend in captivity and thus increase their overall production and enhance

the likelihood of success of mussel population recovery efforts by federal and state agencies, and

other partners.

iv

ACKNOWLEDGEMENTS

I would like to thank my advisor, Dr. Jess Jones, for the opportunity and resources to lead

this research and for his continued support, patience, and guidance throughout this project. Your

passion for freshwater mussels and curiosity for knowledge has inspired me. I thank my

committee members, Drs. Eric Hallerman and Marcella Kelly, for their support and for providing

me with valuable advice during the planning stages and on my thesis manuscript. I am very

grateful to Dr. Hallerman for his financial support; through helping fund my tuition and the re-

opening of the aquaculture facility, you have given me more time to focus on my project as well

as a place to conduct my culture research. Thank you Dr. Kelly for all of the population

dynamics modeling and statistics expertise you have provided me over the years; your teaching

gave rise to my enjoyment of and pursuit of more knowledge in statistics and modeling

biological data. I would like to thank Bob Butler of the USFWS for all the support, time, and

guidance he has given to this project. Your editorial review of my thesis and publication, helping

me conduct field surveys, financial support of my temperature experiment, and advice the past

three years have been invaluable.

Field work was cooperatively conducted by personnel from USFWS, U.S. Geological

Survey, Virginia Department of Game and Inland Fisheries, The Nature Conservancy, Tennessee

Wildlife Resources Agency, and Virginia Polytechnic Institute and State University. Financial

support for this project was provided by the USFWS Gloucester,Virginia and Asheville, North

Carolina, Field Offices.

I’d like to thank Braven Beaty and The Nature Conservancy, Abingdon, Virginia for their

involvement in my field project and for allowing me easy access to Cleveland Islands, and to

Mike Pinder, Amanda Duncan, Megan Bradley, and Joe Ferraro from VDGIF for providing field

v

and laboratory assistance, release data, laboratory-propagated juveniles, and for their all around

support for this project. I would also like to thank everyone who contributed to this project—

without whom, this project could not have been completed. Thank you Andrew Phipps, Kasey

Ewing, Tim Lane, Jen Rogers, Amanda Graumann, Morgan Brizendine, Lee Stephens, Brian

Parks, Dan Hua, Matt Johnson, Shane Hanlon, Brett Ostby, Steve Ahlstedt, Gale Heffinger , Man

Tang, and the many other personnel from USFWS, U.S. Geological Survey, VDGIF, The Nature

Conservancy, Tennessee Wildlife Resources Agency, and Virginia Polytechnic Institute and

State University—for assisting me in the laboratory and conducting fieldwork in the Clinch

River. I would like to thank my fellow graduate students in the Department of Fish and Wildlife

Conservation, particularly Bonnie Myers, Laci Love, Shannon White, Jen Rogers, for their

support, friendship, and always good times.

Most importantly, I would like to thank my Mom, Dad, family and friends for their love

and encouragement. And finally, to my Papa—thank you for our countless early morning trips

into the estuary to crab, fish, and bond—you introduced me to my passion for working in aquatic

ecosystems.

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ATTRIBUTION

Several colleagues aided in the editorial review of one of my chapters presented as part of this

thesis, and subsequently published in the North American Journal of Aquaculture. A brief

description of their contributions is included here.

Chapter 3: Determining optimum temperature for growth and survival of laboratory-propagated

juveniles of two federally endangered species, Cumberlandian combshell (Epioblasma

brevidens) and oyster mussel (Epioblasma capsaeformis), and one non-listed species, wavyrayed

lampmussel (Lampsilis fasciola)

Chapter 3 has been published in the North American Journal of Aquaculture

Jess Jones, PhD, is currently a Restoration Biologist with the U.S. Fish and Wildlife Service,

stationed in the Department of Fisheries and Wildlife at Virginia Tech. Dr. Jones is a co-author

on this paper, and edited this article for publication.

Eric Hallerman, PhD, is currently a professor in the Department of Fisheries and Wildlife at

Virginia Tech. Dr. Hallerman is a co-author on this paper, and edited this article for publication.

Robert Butler, M.S., is currently a Biologist with the U.S. Fish and Wildlife Service in the

Asheville Field Office, North Carolina. Butler is a co-author on this paper, and edited this article

for publication.

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TABLE OF CONTENTS

Chapter 1. Restoring the Endangered Oyster Mussel (Epioblasma capsaeformis) to the

Upper Clinch River, Virginia: An Evaluation of Population Reintroduction Techniques…… 1

Abstract ………………………………………………………………………………….. 2

Introduction ………………………………………………………………………………….. 4

Methods ………………………………………………………………………………….. 7

Study Area……………………………………………………………………... 7

Predicted Estimates of Population Parameters (All Sites)……………………... 9

Habitat Measurements…………………………………………………………. 11

Quadrat Sampling……………………………………………………………… 12

Estimation of Population Parameters…………………………………………... 17

Results ………………………………………………………………………………….. 21

Site 1: Translocated Adults and Release of Laboratory-Propagated Sub-Adults 21

Site 2: Release of 8-week Old Laboratory-Propagated Juveniles 25

Site 3: Release of Stream-Side Infested Host Fish 26

Discussion ………………………………………………………………………………….. 28

Literature Cited ………………………………………………………………………………….. 41

Appendix A: Age-Class Categories and Matrices……………………………………………. 75

Appendix B: Sample Size Requirements (Statistical Analyses)……………………………... 92

Appendix C: Species List…………………………………………………………………….. 96

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Chapter 2. Evaluation of Systematic Quadrat and Capture-Mark-Recapture Survey

Techniques: Monitoring a Reintroduced Population of Oyster Mussels (Epioblasma

capsaeformis) in the Upper Clinch River, Virginia……………………………………………... 97

Abstract ………………………………………………………………………………….. 98

Introduction ………………………………………………………………………………….. 100

Methods ………………………………………………………………………………….. 103

Study Area……………………………………………………………………... 103

Epioblamsa capsaeformis Translocations and Releases……………………….. 104

Habitat Measurements…………………………………………………………. 105

Quadrat Sampling……………………………………………………………… 105

Capture-Mark-Recapture Sampling……………………………………………. 107

Comparisons of Sampling Methods and Population Size Estimates…………... 118

Growth………………………………………………………………………….. 118

Results ………………………………………………………………………………….. 119

Quadrat Sampling………………………………………………………………. 119

Capture-Mark-Recapture……………………………………………………….. 120

Comparisons of Sampling Methods and Population Size Estimates………… 126

Growth………………………………………………………………………….. 127

Discussion ………………………………………………………………………………….. 128


Appendix A: Cormack-Jolly-Seber Diagram and Program MARK Input Formatting……….. 156

Appendix B: Species List…………………………………………………………………….. 158

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Chapter 3. Determining Optimum Temperature for Growth and Survival of Laboratory-

Propagated Juveniles of Two Federally Endangered Species, Cumberlandian Combshell

(Epioblasma brevidens) and Oyster Mussel (Epioblasma capsaeformis), and One Non-Listed

Species, Wavyrayed Lampmussel (Lampsilis fasciola)………………………………………….. 159

Abstract ………………………………………………………………………………….. 160

Introduction ………………………………………………………………………………….. 162

Methods ………………………………………………………………………………….. 163

Gravid Mussel Collection……………………………………………………… 163

Host Fish Collection and Care…………………………………………………. 164

Infestation with Mussel Glochidia and Juvenile Mussel Collection…………… 164

Test Conditions………………………………………………………………… 166

Experimental Design and Statistical Analyses…………………………………. 167

Results ………………………………………………………………………………….. 168

Epioblasma brevidens………………………………………………………….. 168

Epioblasma capsaeformis……………………………………………………… 169

Lampsilis fasciola……………………………………………………………… 170

Algal Concentrations and Water Quality………………………………………. 171

Discussion ………………………………………………………………………………….. 172


Appendix A: Detailed Statistical Results……………………………………………………... 192

Appendix B: Species Comparisons within Temperature Treatments………………………… 198

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List of Tables

Chapter 1. Restoring the Endangered Oyster Mussel (Epioblasma capsaeformis) to the Upper

Clinch River, Virginia: An Evaluation of Population Reintroduction Techniques:

Table 1.

Species and numbers of native host fishes infested with E. capsaeformis

glochidia and released in the upper Clinch River, Virginia at Artrip (Site 3)

each year from 2007–2010.

49

Table 2. Numbers released, predicted abundance ( ) and survival (proportion of

released individuals that survived) of translocated adults and laboratory-

propagated sub-adults in the left-descending channel of Cleveland Islands,

Virginia (Site 1) by release year in 2011 and 2012.

50

Table 3. Survey sample size requirements to estimate predicted abundance and density

levels with a desired precision of 15% of the estimate (CV = SE/mean) in the

left-descending channel (Site 1) and right-descending channel (Site 2) of

Cleveland Islands, and at Artrip (Site 3) in the upper Clinch River, Virginia.

Abundance and density values represent number of surviving individuals

predicted from the Leslie matrix (i.e., reproductive values were not included

in projections).

51

Table 4. Sample size, proportion of area covered by quadrats, person-hours of

sampling effort, number of E. capsaeformis collected, and precision

(CV=SE/mean) of systematic sampling collections conducted in 2011 and

2012, sorted by reintroduction method, in the left-descending channel (Site 1)

and right-descending channel (Site 2) of Cleveland Islands, and at Artrip (Site

3) in the upper Clinch River, Virginia.

52

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Table 5. Estimated mean and standard errors (SE) of abundance and density with lower

and upper 95% confidence intervals for translocated adults, released

laboratory-propagated sub-adults, and newly recruited E. capsaeformis in the

left-descending channel of Cleveland Islands, Virginia (Site 1) in 2011 (n=388

quadrats) and 2012 (n=347 quadrats) using systematic quadrat sampling.

53

Appendix B: Sample Size Requirements (Statistical Analyses)

Table B. 1. Estimated number of samples (0.25-m2 quadrats) required to reach a desired

sampling precision assuming a predicted density of the target species.

92

Table B. 2. Sample size requirements (per group) to detect various effect sizes (d =

)

between two years or sites for assorted combinations of power (1-β) and

significance level (ɑ). Effect sizes 0.2, 0.5, and 0.8 are characterized as small,

medium, and large as defined in Cunningham et al. (2007). With a 0.16

sampling variance (σ = 0.4) for E. capsaeformis, detecting effect sizes of

0.0625 and 0.8 are proportionate to 0.025/m2 and 0.32/m

2 differences in

density between two groups.

93

Appendix C: Species List

Table C. 1. Species collected in the upper Clinch River, Virginia at each sampling site in

2011 and 2012.

96

Chapter 2. Evaluation of Systematic Quadrat and Capture-Mark-Recapture Survey Techniques:

Monitoring a Reintroduced Population of Oyster Mussels (Epioblasma capsaeformis) in the

Upper Clinch River, Virginia:

Table 1. Top models, model used for median ĉ GOF test, descriptions, and model 145

xii

summary statistics for E. capsaeformis at Cleveland Islands, Virginia in 2011

and 2012 using closed-capture models in Program MARK. Summary statistics

in bold indicate that the model was used (top model or in model averaging) to

describe the data set in that year.

Table 2. Population size and density estimates for E. capsaeformis, A. pectorosa, and

M. conradicus at Cleveland Islands, Virginia from closed-capture modeling in

Program MARK.

146

Table 3. Top models, model used for median ĉ GOF test, descriptions, and model

summary statistics for A. pectorosa at Cleveland Islands, Virginia in 2011 and

2012 from closed-capture modeling in Program MARK. Summary statistics in

bold indicate that the model was used (top model or in model averaging) to

describe the data set in that year.

147

Table 4. Top models, model used for median ĉ GOF test, descriptions, and model

summary statistics for M. conradicus at Cleveland Islands, Virginia in 2011

and 2012 from closed-capture modeling in Program MARK. Summary

statistics in bold indicate that the model was used (top model or in model

averaging) to describe the data set in that year.

148

Table 5. Contrasts of population size estimates between systematic quadrat and CMR

sampling methods, and between 2011 and 2012, for E. capsaeformis, A.

pectorosa and M. conradicus at Cleveland Islands, Virginia. Effect sizes are

defined as the mean difference in population size.

149

Table 6. Top models, descriptions, and model summary statistics for E. capsaeformis

at Cleveland Islands, Virginia in 2011 and 2012 from open-capture modeling

150

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(Cormack-Jolly-Seber) in Program MARK. Summary statistics in bold

indicate that the model was used to describe the data set.

Table 7. Summary of pros, cons, and recommendations regarding systematic quadrat

and capture-mark-recapture sampling approaches to monitoring freshwater

mussels.

151

Appendix B: Species List

Table B. 1. Species collected in the upper Clinch River at Cleveland Islands, Virginia

using systematic quadrat and capture-mark-recapture (CMR) sampling in

2011 and 2012.

158




Species, Wavyrayed Lampmussel (Lampsilis fasciola):

Table 1. Summary of gravid female mussel, host-fish collection, and host-fish

infestation methods at the Freshwater Mollusk Conservation Center (FMCC)

and Aquatic Wildlife Conservation Center (AWCC) in 2011 used to produce

juveniles in this study. All gravid females were collected from the lower

Clinch River, Tennessee.

186

Table 2. Experimental items and test conditions for culture temperature tests of E.

brevidens, E. capsaeformis, and L. fasciola juveniles at the FMCC, November

2011–April 2012.

187

Table 3. Final growth and survival (mean ± SE) of E. brevidens (EB), E. capsaeformis 188

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(EC), and L. fasciola (LF) juveniles cultured in one of five temperature

treatments. Values followed by different subscripts are significant (p<0.05);

z–w indicate differences in temperatures within a species, and v–t differences

among species within a temperature treatment. The final sampling event

occurred at 138, 138, and 141 days for EB, EC, and LF juveniles,

respectively.

Appendix A: Detailed Statistical Results

Table A. 1. Summary of growth (mm) and survival (%) ANOVA of fixed effects for E.

brevidens, E. capsaeformis and L. fasciola.

192

Table A. 2. Summary of growth and survival slicing of the F-test for treatments by

sampling event (time=days since start of experiment) for E. brevidens, E.

capsaeformis and L. fasciola.

193

Table A. 3. Contrasts of differences between treatment means for final growth and

survival estimates of E. brevidens at the last sampling event (day 138) with

95% confidence intervals. Effect size for growth is in millimeters (mm).

Effect size for survival (%) data has been arc-sine transformed to achieve

normality.

194

Table A. 4. Contrasts of differences between treatment means of final growth and survival

estimates of E. capsaeformis at the last sampling event (day 138) with 95%

confidence intervals. Effect size for growth is in millimeters (mm). Effect size

for survival (%) data has been arc-sine transformed.

195

Table A. 5. Contrasts of differences between treatment means for final growth and

survival estimates of L. fasciola at the last sampling event (day 141) with 95%

196

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confidence intervals. Effect size for growth is in millimeters (mm). Effect

size for survival (%) data has been arc-sine transformed.

Table A. 6. Analysis of variance summary for algae concentrations (µm3/mL) within

buckets (EUs) among treatment temperatures.

197

Appendix B: Species Comparisons within Temperature Treatments

Table B. 1. Comparing E. brevidens, E. capsaeformis and L. fasciola growth (mm) within

temperature treatments. Summary of fixed effects for 20, 22, 24, 26, and 28°C.

199

Table B. 2. Comparing E. brevidens, E, capsaeformis and L. fasciola survival (%) within


200

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List of Figures

Chapter 1. Restoring the Endangered Oyster Mussel (Epioblasma capsaeformis) to the Upper

Clinch River, Virginia: An Evaluation of Population Reintroduction Techniques:

Figure 1. Topographic map of 19.3-km designated population restoration reach for

Epioblasma capsaeformis in the upper Clinch River from Nash Ford to Carbo,

VA (yellow circles=towns) and locations of study sites (red stars).

54

Figure 2. Aerial view of translocation and release sites of E. capsaeformis (red stars)

and sampling areas (yellow polygons) in the left-descending channel (Site 1)

and right-descending channel (Site 2) of Cleveland Islands, VA.

55

Figure 3. Aerial view of release site of stream-side infested host fishes (red star) and

sampling area (yellow polygon) in the left-descending channel of Artrip,

Virginia (Site 3). Black polygon in river represents intermittent island.

56

Figure 4. Numbers of translocated adults and sex proportions per translocation year: A)

initially released for each year, and B) predicted to survive in 2011, and C)

2012 at Site 1 based on matrix transition probabilities presented in Jones et al.

(2012).

57

Figure 5. Numbers of laboratory-propagated sub-adults per each release year: A)

initially released for each year, B) predicted to survive in 2011, and C) 2012 at

Site 1 based on matrix transition probabilities presented in Jones et al. (2012).

58

Figure 6. Number of 8-week old laboratory-propagated juveniles per release year: A)

initially released for each year, B) predicted to survive to 2011, and C) 2012 at

Site 2, assuming 100% of the released juveniles successfully settled into

suitable substrate at the site based on matrix transition probabilities presented

59

xvii

in Jones et al. (2012).

Figure 7. Predicted numbers of excysted juveniles from each stream-side infestation of

host fishes: A) initially released for each year, and B) predicted to survive in

2011, and C) 2012 at Site 3 based on matrix transition probabilities presented

in Jones et al. (2012).

60

Figure 8. Estimated population sizes and densities (±95% CI) of translocated adults,

released laboratory-propagated sub-adults, and newly recruited E.

capsaeformis in the left-descending channel at Cleveland Islands (Site 1) in

2011 and 2012 using systematic quadrat sampling

61

Figure 9. Age-frequencies and sex-ratio distributions of translocated adult (sexed) and

laboratory-propagated sub-adult (unsexed) E. capsaeformis in: A) 2011 and

B) 2012 observed at Site 1 using systematic quadrat sampling. N = total

number of mussels collected in quadrat samples.

62

Figure 10. Observed length-class frequency distributions and sex-ratios of translocated

adult (sexed) and laboratory-propagated sub-adult (unsexed) E. capsaeformis

in: A) 2011 and B) 2012 at Site 1 using systematic quadrat sampling. N = total

number of mussels collected in quadrat samples.

63

Figure 11. Predicted length-class frequency distributions and sex-ratios for: A) 2011

without, B) with laboratory-propagated sub-adults, C) 2012 without, and D)

with laboratory-propagated sub-adults at Site 1.

64

Figure 12. Predicted survival estimates of: A) translocated adults (T) and laboratory-

propagated sub-adults (P) at Site 1, B) 8-week old laboratory-propagated

juveniles (J) at Site 2, and C) juveniles from stream-side infestations of host

65

xviii

fishes at Site 3 by release year over time.

Figure 13. Predicted abundance and density of translocated adults and released

laboratory-propagated sub-adults at Site 1 over time.

66

Figure 14. Predicted age-frequency and sex ratio distributions for translocated adult and

laboratory-propagated sub-adult E. capsaeformis surviving in: A) 2006, B)

2007, C) 2008, and D) 2009 at Site 1.

67

Figure 15. Predicted age-frequency and sex ratio distributions for translocated and

released E. capsaeformis surviving in: A) 2010 without laboratory-propagated

sub-adults (LPSA), B) 2010 with LPSA, C) 2011 without LPSA, D) 2011

with LPSA, E) 2012 without LPSA, and F) 2012 with LPSA at Site 1.

Predicted recruitment was not included in histograms.

68

Figure 16. Predicted abundance and density of 8-week old laboratory-propagated

juveniles at Site 2 over time, assuming 100% and 50% scenarios of the

released juveniles successfully settling into suitable substrate at the site.

69

Figure 17. Predicted age-frequency distributions of released 8-week old laboratory-

propagated juveniles from 2005–2012 at Site 2.

70

Figure 18. Predicted length-class frequency distributions of released 8-week old

laboratory-propagated juveniles at Site 2 in: A) 2011 and B) 2012 assuming a

1:1 sex ratio.

71

Figure 19. Predicted abundances and densities of juveniles released from stream-side

infested host fishes at Site 3 over time, under two scenarios (100% and 50%)

of the estimated average 22 viable juveniles excysted per infested host fish

successfully settled into suitable substrate at the site.

72

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Figure 20. Predicted age-frequency distributions of juveniles released from stream-side

infested host fishes from 2007–2012 at Site 3.

73

Figure 21. Predicted length-class frequency distributions of juveniles released from

stream-side infested host fishes to A) 2011 and B) 2012 at Site 3 assuming a

1:1 sex ratio.

74

Appendix A: Age-Class Categories and Matrices

Figure A. 1. Age-class categories, corresponding age, corresponding size ranges by sex,

and associated growing seasons for Epioblasma capsaeformis. Age 0–1 year

olds are referred to as age class 1 and represent newly transformed juveniles

during their first growing season. Predicted length-at-age based on estimated

von Bertalanffy growth curves presented in Jones et al. (2011).

75

Figure A. 2. Leslie matrix (L) of E. capsaeformis survival probabilities referenced in this

study analyses (from Jones et al. 2012).

76

Figure A. 3. Vector format for translocated adults, laboratory-propagated sub-adults, 8-

week old laboratory-propagated juveniles, and juveniles from stream-side

infestations released per sampling site.

77

Figure A. 4. Number and cohort structure at time of release of translocated adults (T) and

released laboratory-propagated sub-adults (P) released per year at Site 1 in

vector format.

78

Figure A. 5. Two scenarios (100% and 50%) representing the predicted number of 8-week

old laboratory-propagated juveniles (J) released per year that successfully

settled into suitable substrate at Site 2.

79

Figure A. 6. Two scenarios (100% and 50%) representing the predicted number of viable 80

xx

juveniles released from each stream-side infestation effort (I) that successfully

settled into suitable substrate after excystment from host fishes at Site 3.

Figure A. 7. Male and female age-class specific survival rates used in analyses (Leslie

matrix).

81

Figure A. 8. Example of how numbers, cohort structure, and lengths of E. capsaeformis

released per year were projected 1 to 6 years into the future depending on time

of translocation or release. Population vectors are provided to predict survival,

cohort structure, and length-frequency distribution of the population in 2011

and 2012.

82

Figure A. 9. Population projection vectors displaying the total number and cohort structure

of individuals from each release effort predicted to survive in 2011 and 2012

at Site 1 (T=translocated adults, P=laboratory-propagated sub-adults).

83

Figure A. 10. Predicted cohort structure and population size (N) of all translocated adults (T)

in 2011 and 2012 at Site 1.

85

Figure A. 11. Predicted cohort structure and population size (N) of laboratory-propagated

sub-adults (P) in 2011 and 2012 at Site 1.

86

Figure A. 12. Predicted cohort structure and population size (N) of all E. capsaeformis

(translocated adults and laboratory-propagated sub-adults) in 2011 and 2012

at Site 1.

87

Figure A. 13. Projected surviving number and cohort structure of 8-week old laboratory-

propagated juveniles (J) from each release effort in 2011 and 2012 at Site 2,

assuming 100% of the released juveniles successfully settlement into suitable

substrate at the site.

88

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Figure A. 14. Predicted cohort structure and population size (N) of released 8-week old

laboratory-propagated juveniles (J) in 2011 and 2012 at Site 2, assuming

100% of the released juveniles successfully settlement into suitable substrate

at the site.

89

Figure A. 15. Projected surviving number and cohort structure of juveniles released from

stream-side infested host fishes (I) from each release effort in 2011 and 2012

at Site 3, assuming 100% of the estimated average 22 viable juveniles

excysted per infested host fish successfully settled into suitable substrate at

the site.

90

Figure A. 16. Predicted cohort structure and population size (N) of juveniles released from

stream-side infested host fishes (I) in 2011 and 2012 at Site 3, assuming 100%

of the estimated average 22 viable juveniles excysted per infested host fish

successfully settled into suitable substrate at the site.

91

Appendix B: Sample Size Requirements (Statistical Analyses)

Figure B. 1. Effect size to detect as a function of power and total sample size for A–D

levels of significance (0.05, 0.10, 0.15, and 0.20) using a two-tailed t-test for

mean differences between two independent groups.

94

Figure B. 2. Significance level (ɑ) as a function of power and total sample size for A–E

effect sizes (0.1, 0.2, 0.3, 0.4, 0.5) using a two-tailed t-test for mean

differences between two independent groups.

95

xxii

Chapter 2. Evaluation of Systematic Quadrat and Capture-Mark-Recapture Survey Techniques:


Upper Clinch River, Virginia:

Figure 1. Comparison of capture-mark-recapture and systematic quadrat population size

estimates and associated 95% confidence intervals for: A) translocated adult

and B) laboratory-propagated sub-adult (LPSA) E. capsaeformis, C) A.

pectorosa, and D) M. conradicus at Cleveland Islands, Virginia in 2011 and

2012.

152

Figure 2. Capture (and recapture, p and c) probabilities and associated 95% confidence

intervals for E. capsaeformis per sampling occasion for translocated adults in:

A) 2011 and B) 2012, and released laboratory-propagated sub-adults (LPSA)

in: C) 2011 and D) 2012 at Cleveland Islands, Virginia using a closed-capture

model in Program MARK.

153

Figure 3. Capture (and recapture, p and c) probabilities and associated 95% confidence

intervals for A. pectorosa in: A) 2011 and B) 2012, and M. conradicus in C)

2011 and D) 2012 at Cleveland Islands, Virginia using a closed-capture model

in Program MARK.

154

Figure 4. Epioblasma capsaeformis recapture probabilities (p) and associated 95%

confidence intervals per sampling occasion for translocated adults in: A) 2011

and B) 2012, and released laboratory-propagated sub-adults (LPSA) in: C)

2011 and D) 2012 at Cleveland Islands, Virginia using an open-capture model

(Cormack-Jolly-Seber) in Program MARK.

155

xxiii

Appendix A: Cormack-Jolly-Seber Diagram and Program Mark Input Formatting

Figure A. 1. Cormack-Jolly-Seber open-capture model diagram for E. capsaeformis. Black

numbers in boxes represent encounter occasions; numbers 1–5 represent

2006–2010 annual release events (no searches=p fixed at 0), 11 represents

2011 release event (no search=p fixed at 0) that occurred between 2011 and

2012 capture-mark-recapture sampling, and boxes 6–10 and 12–16 represent

capture-mark-recapture active searches with 5 encounter occasions each in

2011 and 2012 (active searches=p time dependent). Red Phii (φi) values

represent survival probability parameters between successive occasions. Blue

pi’s represent recapture probability parameters during encounter occasions.

Black numbers above arrows represent the time in weeks between occasions.

156

Figure A. 2. A sample of Program MARK input formatting for E. capsaeformis open

population modeling (Cormack-Jolly-Seber model). The first two columns

represent the ID (tag) of an individual and its associated encounter history.

The last two columns represent the group the individual was classified under

(translocated adult or a laboratory-propagated sub-adult).

157




Species, Wavyrayed Lampmussel (Lampsilis fasciola):

Figure 1. Top view of recirculating downweller bucket culture system and chambers. 189

Figure 2. Mean growth versus time for: (a) E. brevidens, (b) E. capsaeformis, and (c) L. 190

xxiv

fasciola juveniles cultured in one of five temperature treatments. Growth

measurements were taken at 2-week intervals for 20 weeks to provide a total

of 10 sampling events.

Figure 3. Mean survival versus time for: (a) E. brevidens, (b) E. capsaeformis, and (c)

L. fasciola juveniles cultured in one of five temperature treatments. Survival

was assessed at 2-week intervals for 20 weeks to provide a total of 10

sampling events.

191

Appendix B: Species Comparisons within Temperature Treatments

Figure B. 1. Comparisons of E. brevidens, E. capsaeformis and L. fasciola growth (mm) at

each of the 5 temperature treatments (20, 22, 24, 26, and 28°C) over 10

sampling events.

201

Figure B. 2. Comparisons of E. brevidens, E. capsaeformis and L. fasciola survival (%) at

each of the 5 temperature treatments (20, 22, 24, 26, and 28°C) over 10

sampling occasions.

202

1

CHAPTER 1

Restoring the Endangered Oyster Mussel (Epioblasma capsaeformis) to the Upper Clinch

River, Virginia: An Evaluation of Population Reintroduction Techniques

2

ABSTRACT

In 2002, the Virginia Department of Game and Inland Fisheries designated an

approximately 19.3-km reach of the upper Clinch River in Virginia as a reach suitable for

population restoration of the federally endangered oyster mussel (Epioblasma capsaeformis).

From 2006–2011, four population reintroduction techniques were applied to three sites within

this reach, including three at Cleveland Islands (Sites 1 and 2), and one at Artrip (Site 3). These

techniques were: 1) translocation of adults (Site 1, N=1,418), 2) release of laboratory-propagated

sub-adults (Site 1, N=2,851), 3) release of 8-week old laboratory-propagated juveniles (Site 2,

N=9,501), and 4) release of stream-side infested host fishes (Site 3, N=1,116 host fishes, with an

estimated 24,552 newly-metamorphosed juvenile excysted from them). The objective of this

study was to evaluate the success of these four reintroduction strategies via population

monitoring at each release site using systematic 0.25-m2 quadrat sampling. Estimated abundance

and density of translocated adults at Site 1 were 577 (SE=155) individuals and 0.11 individuals

/m2 (SE=0.03) in 2011, and 645 (SE=110) individuals and 0.13 individuals /m

2 (SE=0.02) in

2012. Estimated abundance and density of laboratory-propagated sub-adults at Site 1 were 1,678

(SE=42) individuals and 0.33 individuals /m2 (SE=0.01) in 2011, and 1,700 (SE=229)

individuals and 0.33 individuals /m2 (SE=0.05) in 2012. No E. capsaeformis were collected at

sites where 8-week old laboratory-propagated juveniles (Site 2) and stream-side infested host

fishes (Site 3) were released. These results indicate that the translocation of adults and release of

laboratory-propagated sub-adults are the most effective techniques for restoring populations of E.

capsaeformis. I recommend that management efforts focus on the release of larger individuals

for purposes of augmenting vulnerable or reintroducing extirpated mussel populations.

3

KEYWORDS: Freshwater Mussels, Oyster Mussel, Epioblasma capsaeformis, Endangered

Species, Reintroduction, Population Restoration, Translocation of Adults, Release of Cultured

Individuals, Stream-side Infestation of Host Fishes

4

INTRODUCTION

Many freshwater mussel populations have declined significantly in the last 50 to 100

years, with over 70% of North America’s estimated 300 mussel species listed as extinct,

endangered, threatened, or of special concern (Williams et al. 1993; Neves et al. 1997).

Considered freshwater ecosystem engineers, mussels play vital ecological roles in their ability to

filter large portions of the water column through their gills and modify habitat. They provide

physical habitat and serve as a food source to other animals, supply nutrients to the water column

through nutrient cycling, stabilize substrates, and remove silt and pollutants in aquatic

ecosystems (Spooner and Vaughn 2006; Vaughn et al. 2008; Williams et al. 2008). In recent

years, reintroductions of species into historical habitats where they had become extirpated and

augmentations of extant but generally declining populations have been conducted to recover

imperiled species and mitigate future losses (Haag 2012). In addition to verifying suitable

habitats, restoring populations to previously occupied habitats requires adaptive management—

including assessments to identify the most efficient techniques for reintroducing and monitoring

restored populations. In this study, I evaluated four reintroduction techniques to determine the

most successful approach to reestablish viable populations of the federally endangered oyster

mussel (Epioblasma capsaeformis) in the upper Clinch River, Virginia (VA).

Federal and state recovery plans for listed mussel species have identified translocation of

adults and release of laboratory-propagated juveniles as approaches for increasing viability of

existing populations and for reintroducing species to historically occupied sites, thus facilitating

recovery (U.S. Fish and Wildlife Service [USFWS] 2003, 2004; Virginia Department of Game

and Inland Fisheries [VDGIF] 2010). Epioblasma capsaeformis is endemic to, and was once

widely distributed throughout, the upland portions of the Cumberland and Tennessee River

5

drainages, collectively known as the Cumberlandian Region (USFWS 2004; Williams et al.

2008). Historically, its range extended across six states, but now it occurs only in a few

fragmented stretches in these river drainages in Kentucky (KY), Tennessee (TN) and VA. The

species is considered extirpated from Alabama, Georgia, and North Carolina (NC) (USFWS

2004; Jones et al. 2006a).

The Clinch River is part of the upper Tennessee River drainage, flowing southwest

through southwestern VA into northeastern TN. In 2002, VDGIF designated an approximately

19.3-km reach of the upper Clinch River in VA as suitable for population restoration of E.

capsaeformis (Eckert and Pinder 2010; VDGIF 2010). In collaboration with VDGIF’s Aquatic

Wildlife Conservation Center (AWCC) near Marion, VA, Virginia Tech’s Freshwater Mollusk

Conservation Center (FMCC) has worked to restore E. capsaeformis within this reach from

2006–2012. Cleveland Islands (Clinch River Kilometer [CRKM] 435.8) and Artrip (CRKM

441.9) were chosen as reintroduction sites in the upper Clinch River for the project. The native

population of E. capsaeformis in the upper river has severely declined over the past 50 years to

the point of being essentially undetectable using normal sampling methodologies, if not

extirpated. This decline was due to various anthropogenic impacts, including poorly treated

wastewater effluent from municipal treatment facilities located along the river and many other

factors (Eckert and Pinder 2010; VDGIF 2010). However, over the past 10 to 20 years water

quality has improved, thus allowing Cleveland Islands and Artrip’s mussel and fish fauna to

begin to recover and for the sites to become suitable for restoration of this species (Eckert and

Pinder 2010).

The most recent survey of Cleveland Islands was conducted in 2008 by VDGIF (Eckert

and Pinder 2010). They found twenty-three live mussel species, including seven federally

6

endangered species (E. capsaeformis, shiny pigtoe [Fusconaia cor], fine-rayed pigtoe

[Fusconaia cuneolus], slabside pearlymussel [Pleuronaia dolabelloides], fluted kidneyshell

[Ptychobranchus subtentum], rough rabbitsfoot [Quadrula cylindrica strigillata], and purple

bean [Villosa perpurpurea]). All E. capsaeformis they encountered were tagged—indicating they

were from recent translocations. Prior to the start of reintroductions in 2006, the last E.

capsaeformis observed within this designated population restoration reach were found between

CRKM 436.6 and 439.4 (approximately 0.8–3.6 RKM upstream of Cleveland Islands and 2.1–

5.3 RKM downstream of Artrip) in 1985 by Drs. David Stansbery and Thomas Watters of Ohio

State University (Jones 2004, collection records), and at CRKM 437.1 (1.3 RKM upstream)

during surveys conducted in 1972–1975 by Bates and Dennis (1978). Additionally, no E.

capsaeformis were collected during recent qualitative and quantitative surveys of Artrip

conducted in 2003, 2004, and 2010 (Krstolic et al. 2013; B. Ostby, Virginia Tech, personal

communication).

As of 2010, four reintroduction techniques were applied to this reach by the FMCC and

AWCC: 1) translocation of adults, 2) release of laboratory-propagated sub-adults, 3) release of 8-

week old laboratory-propagated juveniles, and 4) release of stream-side infested host fishes. In

order to determine the success of the four restoration strategies, population monitoring was

conducted at each release site in 2011 and 2012 to estimate abundance and density of E.

capsaeformis. The purpose of my study was to determine which technique was most effective at

restoring populations of E. capsaeformis in the upper Clinch River.

7

METHODS

Study Area

Reintroduction techniques were implemented at three sites within the 19.3-km VDGIF

designated augmentation reach of the upper Clinch River, VA (Figure 1). Study sites were

located within a 6.1-km reach from CRKM 435.8–441.9, in Russell County, near the town of

Cleveland. Three of the reintroduction techniques were implemented at Cleveland Islands

(CRKM 435.8, Sites 1 and 2). At Cleveland Islands, translocated adults and laboratory-

propagated sub-adults were released together in the left descending channel (LDC, Site 1), and 8-

week old laboratory-propagated juveniles were released in the right descending channel (RDC,

Site 2) at Cleveland Islands. Owned by The Nature Conservancy, and cooperatively managed by

VDGIF, Cleveland Islands are characterized by four channels and three islands (Figure 2). In

recent years, flow has declined in the furthest-upstream right descending channel; this channel is

not used or referenced in my study. The three remaining channels are referred to as the right,

middle, and left descending channels. Each channel contains excellent water quality and flow

conditions, stable gravel substrates, and darter fish hosts utilized by E. capsaeformis and other

mussel species.

The fourth technique was implemented further upstream near Artrip (CRKM 441.9, Site

3) where stream-side infested host fishes were released. Artrip also has excellent water quality,

stable substrates, and fish hosts to support reproduction and recruitment of E. capsaeformis. It is

comprised of two main channels separated by a large island (Figure 3). Host fish collection and

releases of artificially infested fish hosts were conducted in the LDC. The LDC is characterized

by a shallow pool and run with stable gravel and sand substrates, followed by riffle habitat

composed of gravel, cobble, and sand.

8

Translocation of Adults and Release of Sub-Adults at Cleveland Islands (Site 1)

Over a five-year period from 2006–2010, adult E. capsaeformis were collected from the

lower Clinch River, TN, and translocated throughout the LDC of Cleveland Islands. Additional

laboratory-propagated sub-adult E. capsaeformis were released into the LDC by the AWCC in

2010 and 2011 (Site 1; Figure 2). Translocated adult and released sub-adult E. capsaeformis

were uniquely tagged, measured for length (mm), and sexed for identification purposes.

Generally, individuals <30 mm in size were not sexed because their shells were not yet sexually

dimorphic (i.e., the marsupial shell expansion characterizing females was undeveloped).

Translocation of adults and releases of laboratory-propagated sub-adults were randomly

distributed throughout the LDC.

Releases of Young Juvenile Mussels at Cleveland Islands (Site 2)

Over a four-year period from 2005–2008, juveniles were released into the RDC of

Cleveland Islands by the FMCC and AWCC (Site 2, Figure 2). Laboratory-propagated juveniles

were approximately 8-weeks old (0.5–1.0 mm) and were released in a 25-m2 area located

approximately 185 m upstream of where the RDC reconnects with the main river channel.

Release of Infested Host Fishes at Artrip (Site 3)

From 2007–2010, stream-side infestations of native fishes with E. capsaeformis glochidia

were conducted at Artrip (Site 3; Figure 3). Each stream-side infestation involved the collection

of native host fishes from Artrip, holding them in water tanks with an air source on site, infesting

them for 50 minutes with E. capsaeformis glochidia, and then returning them to the LDC riffle.

9

The number of juveniles assumed to have successfully transformed after stream-side infestations

was derived using a combination of empirical data from published works (Yeager and Saylor

1995; Jones and Neves 1998, 1999, 2001; Rogers et al. 2001; Jones et al. 2002; Jones 2004;

Liberty et al. 2005; Jones et al. 2006a) and unpublished fish host and mussel fecundity studies

conducted at the FMCC. From analysis of these data, I assumed that an average of 22 viable

juveniles excysted per fish.

Predicted Estimates of Population Parameters (All Sites)

Survival rates from one age-class to the next were obtained from data collected by Jones

and Neves (2011) and presented in Jones et al. (2012). An age-class survival transition Leslie

matrix was used to predict the number of individuals alive in 2011 and 2012 from each release

technique at each site (Appendix A, Figure A. 2). Because the focal objective of this study was to

evaluate the success of each reintroduction technique in terms of successful settlement and

survival of released individuals, and due to uncertainty concerning fecundity of translocated,

laboratory-propagated, and stream-side propagated mussels upon release (i.e., uncertainty of

magnitude of short term impact on reproduction; Sarrazin and Legendre 2000), fecundity values

were not included in my projections of current population size.

Using length measurements at time of release, translocated individuals were aged by

applying von Bertalanffy growth curves of predicted length-at-age for females and males from

the lower Clinch River, TN (Jones and Neves 2011). Laboratory-propagated sub-adults and 8-

week old juveniles were of known age when released. Age 0–1 year olds are referred to as age

class 1 and represent newly transformed juveniles during their first growing season. Age-class

10

categories, corresponding age, corresponding size ranges by sex, and associated growing season

are presented in Appendix A.

Numbers and ages of translocated adults, laboratory-propagated sub-adults, 8-week old

laboratory-propagated juveniles, and juveniles from stream-side infestations released per year

were put in vector format to compute expected survival and abundance over time at the

respective release sites. Using Jones et al. (2012) matrix transition probabilities in combination

with age at release, abundance was projected for 1–6 years from each release to 2011 and 2012

for all released mussels (e.g., abundance of mussels released in 2007 were projected for 4 years

to 2011 and 5 years to 2012). Two scenarios (50% and 100%) of successful settlement of

released 8-week old laboratory-propagated juveniles and excysted juveniles from stream-side

infestations within study sites were used in the matrix transition probabilities to predict survival.

Among the three different age 0–1 survival (i.e., probability of a viable newly-metamorphosed

juvenile surviving to the next year) scenarios (30, 35, and 42%), I fitted a 30% survival rate to

my analyses because Jones et al. (2012) concluded this would correspond to stable population

growth. Survival rates were assumed to be the same for females and males up until age-class 11

(10–11 year olds), where the maximum age was set at 10 and 12 years old for females and males,

respectively (e.g., females typically died after reaching 10 years old; Jones et al. 2012).

Numbers, ages, and lengths of E. capsaeformis released per year were projected for 1–6 years

(dependent on time of translocation or release) to predict survival (proportion of originally

released individuals that survived), abundance, density, cohort structure, and length-frequency

distribution of the population in 2011 and 2012 (Appendix A).

11

Habitat Measurements

The upstream and downstream boundaries of each reintroduction site were determined

prior to systematic quadrat sampling by a qualitative snorkel survey. Observation of live E.

capsaeformis or shells, presence of other mussels, substrate composition, and water depth and

flow were taken into consideration for establishing the upstream and downstream boundaries.

River width was measured at 5-m intervals along the total length of each reintroduction site

using a 100-m measuring tape. Area within each 5-m interval was calculated and summed to

determine the sampling area (m2) for each reintroduction site. Study area was used to determine

required intervals between sampling quadrats, and to convert estimates of abundance to density.

Banks were marked every 20 m with orange marking spray to serve as a location guide during

sampling.

Site 1

The LDC of Cleveland Islands is approximately 125 m in length with an average wetted

width of 14.8 m. The extended boundaries of this reintroduction site are approximately 35 m

upstream and 100 m downstream, with average wetted widths of 16.0 and 28.2 m, upstream and

downstream, respectively. The estimated total sample area at Site 1 was 5,085 m2.

Site 2

The 180-m reach of the RDC was primarily characterized by riffle and shallow run

habitat typically <0.5 m deep with gravel, cobble, and sand substrates. Because of the potential

for 8-week old laboratory-propagated juveniles placed at the upper end of the RDC to drift, the

entire length of the reach downstream from the release point at Site 2 was considered the

12

reintroduction survey site after taking dispersal within the channel into account. However, a 25-

m portion of this reach consisted of a deep run (~1 m deep) with a sand and bedrock bottom.

This habitat type is unsuitable for the oyster mussel and represented an area of low likelihood of

successful settlement and development of the released juvenile mussels. Therefore, this section

was not surveyed so that time and effort were focused on areas with the highest probability of

occurrence. Therefore, the actual survey length was 155 m with an average wetted width of 17.9

m. The estimated total sample area at Site 2 was 2,935 m2.

Site 3

The sampled portion in the LDC of Artrip was approximately 90-m in length with an

average wetted width of 29.6 m. The boundaries were approximately 25 m upstream and 65 m

downstream of the head of the riffle in the LDC. The estimated total sample area was 2,655 m2.

Quadrat Sampling

For the purpose of determining which reintroduction technique was most effective for

restoring populations of E. capsaeformis in this study, I estimated abundance and density of

individuals greater than 1-year old. Quadrat surveys were performed at each of the study sites

using 0.25-m2 sampling frames constructed of welded steel rebars. A systematic sampling design

was used to collect population demographic data on E. capsaeformis. Systematic sampling is a

probability-based survey method for assessing rare or clustered populations, is simple to execute

in the field, and offers effective spatial coverage (Christman 2000; Smith et al. 2001; Strayer and

Smith 2003). In addition, with probability-based sampling, I could estimate the probability that

the species is present at a specified mean density even if the target species were not detected

13

(Green and Young 1993; Strayer and Smith 2003). Estimation of basic population demographic

characteristics by quadrat sampling included species presence, abundance and density,

population growth rate, sex ratios, age-class structure, survival, and evidence of recruitment.

Systematic quadrat surveys were conducted at Site 1 in 2011 and 2012 and at Sites 2 and

3 in 2012. The required number of sampling units (0.25-m2 quadrat samples) needed to estimate

population density (mean mussels/m2) for a given level of precision was calculated using the

formula of Strayer et al. (1997):

where: n = number of quadrats searched,

m = mean number of E. capsaeformis per quadrat, and

CV = coefficient of variation.

As a function of my quadrat sampling unit and the predicted density of E. capsaeformis within a

site, the coefficient of variation (CV=SE/mean) is equivalent to the desired level of relative

precision of the estimate (i.e., 15% precision=true value falls within 15% of estimate) (Downing

and Downing 1992; Strayer et al. 1997). Prior to systematic sampling at Site 1, predicted E.

capsaeformis densities were obtained from the 2008 VDGIF (2010) survey, my 2011 mark-

recapture density data (see Chapter 2), and predicted abundance estimates of translocated adults

and released laboratory- propagated sub-adults (Appendix A). Initial estimations of E.

capsaeformis densities at Sites 2 and 3 were obtained from predicted abundance of total released

individuals to 2011 and 2012. I examined various combinations of target densities (0.01–

1.00/m2) and precision levels (SE/mean=0.05–0.50) to estimate the sample size requirements

needed to estimate density with varying levels of precision (Appendix B). Several scenarios were

taken into consideration to determine a sample size that provided a reasonable precision level

14

(e.g., 10–20% of the estimated density) and was logistically feasible (e.g., excavating 200–300

quadrats per 1–2 days).

A series of power analyses was conducted in GPower 3 to determine the number of

sampling units required to detect various standardized effect (d) sizes (0.06–0.80) between two

groups (i.e., between years or sites) for assorted combinations of Type I (ɑ=0.05–0.20, false

positive) and Type II (β=0.05–0.20, false negative) error rates (Cohen 1988; Cunningham and

McCrum-Gardner 2007; Appendix B). Standardized effect size (d) was defined as the difference

between two group means divided by the standard deviation. Standard deviation values were

pooled from previous quadrat surveys. The pooled variance used for d was 0.16 (σ=0.4). A d of

0.0625 and 0.2 correspond to a 0.1/m2

and 0.32/m2 mean density difference between two groups,

respectively. Considering the tradeoff between sample size and power, sample sizes required to

detect standardized effect sizes below 0.2 were not justifiable in terms of fieldwork efficiency

and costs. These sampling size estimates were combined with those obtained from the Strayer et

al. (1997) formula to justify sample sizes required to detect statistical differences that provided

good overall power and were logistically feasible to conduct at each site.

Quadrats were sampled using a systematic design with a minimum of three random starts.

Quadrats were spaced at regular intervals from each random starting point. Intervals between

quadrats were based on survey area, required sample size, and the number of random starts, as

calculated by the formula in Strayer and Smith (2003):

√

where: d = distance between units,

L = length of study site,

15

W = width of study site,

n = total number of quadrats, and

k = number of random starts.

Two random numbers were generated to determine the starting point at the downstream

boundary of each site for each random start (on the left descending bank at Sites 1 and 2, and the

right descending bank at Site 3). Utilization of a minimum of three random starts (k=3) allowed

for estimation of sampling variance without having to make any assumptions about E.

capsaeformis spatial distribution within the reintroduction sites (i.e., one random start assumes a

random distribution of mussels) (Hedayat and Sinha 1991; Smith et al. 2001; Strayer and Smith

2003). Each set of quadrats sampled within a random start constituted one systematic sample.

Each quadrat was carefully hand excavated to hardpan (approximately 15 cm below

surface) or to underlying bedrock, whichever was contacted first. All mussels sampled were

sexed (if possible), identified to species, measured for length (mm), and categorized as being

observed at the substrate surface or completely buried. It was assumed that all individuals in age

class 2 (i.e., 1 going on 2 years old) and older had a 100% probability of detection within a

quadrat. Due to their small size (<10–15 mm) and in the absence of sieving substrates from

quadrats in my study, juveniles <1 year old (i.e., young of year) were difficult to detect during

sampling and therefore were not included in population size estimations. Any untagged E.

capsaeformis were tagged and recorded, and examined for presence of glue on the shell

(indication of a previous tag), growth annuli (estimation of age), and photographed if determined

to be a new recruit. All mussels and excavated substrate were returned to their original collection

location.

16

Sample Size

Site 1.—Sample units required to estimate E. capsaeformis density at Site 1 with a desired

precision level of 15% of the estimated density ranged from 196–411 quadrats using predicted

density estimates of translocated adults, released laboratory-propagated sub-adults and the

combined total in 2011 and 2012 (Table 3). A minimum target sample size of 360 was chosen to

detect low density levels (≤0.25/m2) with a desired precision level of 15%, and to detect a small

standardized effect (d=0.2) between years with 0.10 significance (ɑ=0.10) and 85% power (1-

β=0.85) (Appendix B). Sampling at Site 1 was conducted with four random starts with a total of

388 and 347 quadrats in 2011 and in 2012, respectively. The area sampled by quadrats ranged

from 87–97 m2 and covered approximately 1.8% of the total sample site area. Quadrats were

flipped 14 times to achieve regular distance intervals of 7 m between sampling units.

Site 2.—Sample units required to estimate density at Site 2 with a desired precision of 15% of the

estimated density ranged from 189–284 quadrats using predicted density estimates of 8-week old

laboratory-propagated juveniles to 2011 and 2012 given 50 and 100% successful settlement into

suitable substrate (Table 3). A minimum target sample size of 191 was chosen to detect low to

moderate density levels (≈0.75/m2) with a desired precision level of 15%, and to detect a small to

moderate standardized effect (d=0.3) between sites with 0.10 significance and 90% power

(Appendix B). Sampling at Site 2 was conducted with five random starts with a total of 210

quadrats in 2012. The area sampled by quadrats was 52.5 m2 and covered approximately 1.8% of

the total sample site area. Quadrats were flipped 17 times to achieve regular distance intervals of

8.5 m between sampling units.

17

Site 3.—Sample units required to estimate density at Site 3 with a desired precision of 15% of the

estimated density ranged from 103–150 quadrats using predicted density estimates of stream-side

infested juveniles to 2011 and 2012, given scenarios of 50 and 100% successful settlement into

suitable substrate within the survey area (Table 3). Even though predicted densities were

moderate to high (>1/m2), a minimum target sample size of 191 was chosen to detect moderate

density levels (≥0.70/m2) with a desired precision level of 15%, and to detect a small to moderate

standardized effect size (d=0.3) between sites with 0.10 significance and 90% power (Appendix

B). Sampling at Site 3 was conducted with three random starts with a total of 194 quadrat units

in 2012. The area sampled by quadrats was 48.5 m2 and covered approximately 1.8% of the total

sample site area. Quadrats were flipped 13 times to achieve regular intervals of 6.5 m between

sampling units.

Estimation of Population Parameters

Abundance and Density

Abundance ( ) was defined as the total number of ≥1 year old E. capsaeformis in the

study area at a particular point in time. This was estimated by multiplying the average count per

systematic sample by the total number of possible systematic samples (M) in the study area

(Seber 1973; Smith et al. 2001; Strayer and Smith 2003):

∑

where: = abundance estimate,

M = number of possible systematic samples,

= count per systematic sample, and

m = number of systematic samples.

18

If a site had three random starts (k=3), there were three systematic samples (m=3). Dependent on

the area (A) of the site, the area of the sampling unit (a=0.25 m2), and the total number of

quadrats sampled (n), the total number of possible systematic samples (M) was calculated

following the formula in Smith et al. (2001):

∑

Variance for abundance was estimated by the formula (Smith et al. 2001; Strayer and Smith

2003):

( )

∑

For normally distributed sample data, the 95% confidence intervals for abundance were

calculated as:

√ ( )

Population density was defined as the total number of E. capsaeformis (>1 year old) per

m2 ). This was estimated by dividing abundance ( ) by the survey area (A) (Strayer and Smith

2003):

Variance for population density was calculated by dividing abundance variance ( ( ) by the

squared area (Strayer and Smith 2003):

( ) ( )

19

For normally distributed sample data, the 95% confidence intervals for density were calculated

as:

√ ( )

Data was assessed for normality. Occasionally, the traditional approach to calculating

confidence intervals utilizing the assumption of a normal distribution has been found to be

inaccurate for mussel population size and density estimations. Based on mussel population

sampling simulations, mussel population sizes (or density) tend to have a positively (right)

skewed distribution (Pooler and Smith, unpublished data, cited by Smith et al. 2001; Strayer and

Smith 2003). If normality tests revealed a departure from normality, data were log-transformed

and 95% confidence intervals were calculated for abundance by using a logarithmic

transformation of the estimate and a delta-method approximation of variance (Seber 1982; Smith

et al. 2001; Strayer and Smith 2003):

(

√ ( )

)

The 95% log-based confidence intervals for population density were calculated as:

(

√ ( )

)

Abundances, population densities, and their associated variances were estimated separately for

translocated adults and laboratory-propagated sub-adults.

Abundance and population density estimates at each reintroduction site, and from 2011–

2012 at Site 1, were compared using mixed-model analysis with each random start within a year

representing on sample. Heterogeneity of the data was assessed using residual plots and Levene’s

20

test for equality of variances (Levene 1960). If heterogeneity of variances was revealed, a

Satterthwaite’s approximation was used to account for unequal variances (Satterthwaite 1946).

The P-values were used to interpret whether a statistically significant difference (ɑ=0.05) in

abundance and density existed among reintroduction sites, or from 2011–2012 at Site 1.

However, the appropriate question was not just whether abundances or densities were different

between groups or years, but rather what was the magnitude of any difference (Gerrodette 1987;

Strayer and Smith 2003). Although P-values alone may confirm that an effect exists, they do not

provide information on the magnitude of the effect, what constitutes an important effect size, or

the precision of the effect-size estimate (Stefano 2004; Nakagawa and Cuthill 2007). It is

important to specify an effect size that is ecologically important a priori to the study. The effect

size for my study was defined as the mean difference in population density (Stefano 2004;

Cunningham and McCrum-Gardner 2007).

An effect that was considered ecologically important was determined a priori at a

magnitude of 0.08 individuals/m2 (i.e., a 0.08 E. capsaeformis/m

2 difference in density

constitutes an ecologically important difference between years or sites). Given a standard

deviation of 0.4 (based on previous survey data), a 0.08 individuals/m2 magnitude of an effect

would correspond with Cohen’s small standardized effect size of d=0.2. This magnitude was

judged acceptable based on the sample size required and the feasibility of conducting a field

survey to detect an effect change of this magnitude. Unpaired t-tests were used to calculate effect

sizes (unstandardized effect size=mean difference) and associated 95% confidence intervals

between sites and years. Results were used to provide statistical and biological inference to

whether density estimates differed. Analyses were conducted using SAS software (SAS Institute,

Inc., Cary, North Carolina, version 9.2).

21

RESULTS

Site 1: Translocated Adults and Release of Laboratory-Propagated Sub-Adults

Summary of Translocations and Releases 2006–2011

Over a five-year period from 2006–2010, a total of 1,418 adult E. capsaeformis were

collected from the lower Clinch River, TN, and translocated along the LDC of Cleveland Islands

(Site 1, Figure 4). An additional 2,501 and 350 laboratory-propagated sub-adult E. capsaeformis

were released into the LDC by the AWCC in 2010 and 2011, respectively (Figure 5). Sex ratio of

translocated adults was approximately 1:1. At the time of translocation, female adults ranged

from 23–47 mm and averaged 36 mm in size. Similarly, translocated male adults ranged from

19–47 mm and averaged 33 mm in size. Laboratory-propagated sub-adults were approximately

1–2 years old (age class 2), ranged from 11–31 mm, and averaged 21 mm in size.

Population Parameter Estimates and Sampling Observations in 2011 and 2012

A total of 44 E. capsaeformis were sampled in 2011, comprised of 11 translocated adults,

32 laboratory-propagated sub-adults, and 1 natural recruit. Similarly, 41 individuals were

sampled in 2012 and consisted of 11 translocated adults, 29 laboratory-propagated sub-adults,

and 1 recruit. Observed precision (CV) in number of translocated adults and released laboratory-

propagated sub-adults encountered among the four systematic samples were 0.54 and 0.05 in

2011, and 0.27 and 0.34 in 2012, respectively. Observed precision in total E. capsaeformis

encountered among the four systematic samples was 0.12 in 2011 and 0.26 in 2012.

Approximately 30 person-hours of effort were required to complete sampling of 388 quadrats in

2011 and 347 quadrats in 2012 (Table 4). A total of 440 and 380 individuals representing 20 and

18 species were encountered in 2011 and 2012, respectively (Appendix C).

22

Estimated abundances and densities of translocated adult E. capsaeformis were 577

(SE=155) individuals and 0.11/m2 (SE=0.03) in 2011, and 645 (SE=110) individuals and 0.13/m

2

(SE=0.02) in 2012 (Table 5; Figure 8). Normality tests did not indicate a departure from

normality. Standard deviations of the translocated adult 2012 abundance and density estimates

(625.07 and 0.12) were 1.37 times as large as the 2011 abundance and density standard

deviations (457.02 and 0.09), indicating that the homogeneity of variance assumption was not

violated. A Levene’s test also confirmed no violation of the homogeneity assumption (p=0.62)

and therefore data were not transformed for analysis. The magnitude of abundance and density

differences (effect sizes) between 2011 and 2012 were 68 (SE=190) individuals and 0.02/m2

(SE=0.04). Effect size confidence limits (ɑ=0.05) calculated around abundance [-397, 533] and

density [-0.07, 0.11] contained zero, indicating no significant difference in abundance (p=0.60)

or density (p=0.60) between years.

Estimated abundances and densities of laboratory-propagated sub-adult E. capsaeformis

were 1,678 (SE=42) individuals and 0.33/m2 (SE=0.01) in 2011, and 1,700 (SE=229) individuals

and 0.33/m2 (SE=0.05) in 2012 (Table 5; Figure 8). Normality tests did not indicate a departure

from normality. Standard deviations of the laboratory-propagated sub-adult 2012 abundance and

density estimates (982.80 and 0.19) were 5.38 times as large as the 2011 abundance and density

standard deviations (182.89 and 0.04), indicating a possible violation of the homogeneity of

variance assumption. Levene’s test of equal variances also indicated a potential violation of the

homogeneity of variances assumption (p=0.08); therefore a Satterthwaite’s approximation was -

used. The magnitude of abundance and density differences between 2011 and 2012 were 22

(SE=233) individuals and <0.01/m2 (SE=0.05). Effect size confidence limits (ɑ=0.05) calculated

23

around abundance [-719, 763] and density [-0.16, 0.16] contained zero, indicating no significant

difference in abundance (p=0.93) or density (p=1.00) between years.

Estimated abundance and density of E. capsaeformis recruits were 52 (SE=26)

individuals and 0.01/m2 (SE=0.01) in 2011, and 59 (SE=29) individuals and 0.01/m

2 (SE=0.01)

in 2012 (Table 5; Figure 8). Because plotted data and normality tests indicated a departure from

normality, data were log transformed. Standard deviations of the E. capsaeformis recruit 2012

abundance and density estimates (121 and 0.02) were 1.14 times as large as the 2011 abundance

and density standard deviations (106 and 0.02), indicating no violation of the homogeneity of

variance assumption. A Levene’s test indicated also confirmed no violation of the homogeneity

assumption (p=0.79). The magnitude of differences between 2011 and 2012 were 7 (SE=39)

individuals and <0.01/m2 (SE=0.01). Effect size confidence limits (ɑ=0.05) calculated around

abundance [-88, 102] and density [-0.03, 0.03] contained zero, indicating no significant

differences in abundance (p=0.86) or density (p=1.00) between years.

Age and Length-Frequency Distributions

Estimated ages at Site 1 ranged from 2–12 years (mean=3–4 years old; median=2–3 years

old) in 2011, and from 2–6 years (mean=3–4 years old; median=3–4 years old) in 2012 (Figure

9). Observed lengths ranged from 21.9–41.0 mm (mean=31.5 mm) in 2011 and from 27.0–39.5

mm (mean=32.9 mm) in 2012 (Figure 10). Lengths of the two individual recruits were 29.1 mm

in 2011 and 27.3 mm in 2012. The recruit collected in 2011 was estimated to be 2–3 years old,

and the recruit collected in 2012 was estimated to be 3–4 years old. Lengths from growth annuli

for the 2011 recruit were not recorded. Lengths measured from growth annuli corresponding to

0–1, 1–2, 2–3, and 3–4 years old for the 2012 recruit were 9.7, 16.5, 22.1, and 27.3 mm.

24

Predicted length-frequency distributions of translocated adults in 2011 and 2012 were negatively

(left) skewed. Including laboratory-propagated sub-adults, it was predicted that over half the

population was represented by the 30 and 34 mm length classes in 2011 and 2012, respectively

(Figure 11).

Predicted Estimates of Population Parameters

Predicted survival (the proportion of released individuals that survived) of adult mussels

at Site 1 translocated from 2006–2010 ranged from 29–91% in 2011, and 17–83% in 2012.

Predicted survival of laboratory-propagated sub-adults released from 2010 to 2011 ranged from

95–100% in 2011 and 90–95% in 2012. Predicted survival of all translocated adults and released

laboratory-propagated sub-adults was a function of age and ranged from 29–100% in 2011, and

17–95% in 2012; decreasing over time after mussels were released at the site (Table 2; Figure

12).

The adults predicted to survive, based on survival rates from Jones et al. (2012), from

each translocation effort from 2006–2010 were approximately 58, 107, 148, 320, and 366

individuals in 2011, and 34, 81, 120, 276, and 332 individuals in 2012 (Figure 4). The

laboratory-propagated sub-adults predicted to survive from the 2010 release were 2,376

individuals in 2011 and 2,257 individuals in 2012, and 333 individuals from the 2011 release

were predicted to survive in 2012 (Figure 5). Predicted abundances and densities of translocated

adults were approximately 1,000 individuals and 0.20/m2 in 2011, and 843 individuals and

0.17/m2 individuals in 2012. Predicted abundances and densities of laboratory-propagated sub-

adults were approximately 2,726 individuals and 0.54/m2 in 2011 (including the 350 individuals

released in 2011), and 2,590 individuals and 0.51/m2 in 2012, respectively. Predicted total

25

abundances and densities of E. capsaeformis were approximately 3,726 individuals and 0.73/m2

in 2011, and 3,433 individuals and 0.68/m2 in 2012, respectively (Table 2; Figure 13; Appendix

A).

At the time of release, the average ages of all translocated adults (T) and laboratory-

propagated sub-adults (P) were 4–5 and 1–2 years old, respectively. Predicted age distribution of

translocated adults shifted slightly from 2006–2009, as younger translocated individuals were

introduced on an annual basis (Figure 14). Age distribution was predicted to shift dramatically in

2010 when laboratory-propagated sub-adults were released into the population. Predicted

average age of surviving translocated adults and laboratory-propagated sub-adults were 5–6 and

2–3 years old in 2011, and 6–7 and 3–4 years old in 2012, respectively. Approximate average

age of all surviving released E. capsaeformis in the population was 2–3 years old in 2011 and 3-4

years old in 2012 (Figure 15; Appendix A).

Site 2: Release of 8-week Old Laboratory-Propagated Juveniles

Summary of Releases 2005–2008

Over a four-year period from 2005–2008, a total of 9,501 juveniles (approximately 8-

weeks old and 0.5–1.0 mm) were released into the RDC of Cleveland Islands (Site 2; Figure 6).


No live or dead E. capsaeformis were collected in 2012 from the RDC. A total of 194

individuals representing 13 other species was encountered (Appendix C). Approximately 16

person-hours of effort were required to complete sampling 210 quadrats (Table 4).

26


Although no live or dead E. capsaeformis were collected at Site 2 in 2012, given my

methodological assumptions, predicted survival of the 2005 to 2008 released juveniles should

have ranged from 20.8–27.1% in 2011, and 16.6–25.7% in 2012 (Table 2; Figure 12). Because

all individuals were approximately two months of age at release, survival over time would be the

same for each yearly release effort. Juveniles predicted to survive, based on survival rates in

Jones et al. (2012), from each reintroduction effort would have been 632, 390, 938, and 328

individuals to 2011, and 506, 331, 891, and 312 individuals to 2012 (Figure 6). Predicted

abundance and density would have been 2,289 individuals and 0.78/m2 in 2011, and 2,041

individuals and 0.70/m2 in 2012 (Figure 16; Appendix A). Assuming a scenario that only 50% of

the 9,501 juveniles successfully settled into suitable substrate within the survey area after

release, number of individuals predicted to survive to 2011 and 2012 would be half of the

predicted values above. Predicted age classes of individuals would have ranged from 3–4 years

old (mean=4–5 years old) in 2011, and 4–8 years old (mean=5–6 years old) in 2012 (Figure 17).

Assuming a 1:1 sex ratio, predicted average length class would have been 38 mm (±2) in 2011

and 2012 (Figure 18).

Site 3: Release of Stream-Side Infested Host Fish

Summary of Stream-Side Releases 2007–2010

From 2007–2010, eight separate (two per year) stream-side infestations of native fishes

with E. capsaeformis glochidia were conducted at Artrip (Site 3). A total of 1,116 fish were

collected, infested and released into the head of the LDC riffle over this four-year period (Table

27

1). Assuming that an average of 22 viable juveniles excysted per fish within the survey area, I

estimated approximately 24,552 juveniles were released at the site (Figure 7).


No live or dead E. capsaeformis were collected in 2012 from Artrip. A total of 289

individuals representing 16 other species were encountered (Appendix C). Approximately 14

person-hours of effort were required to complete sampling of 194 quadrats (Table 4).


Predicted survival of excysted juveniles from host fishes ranged from 26–30% in 2011

and 24–29% in 2012 (Table 2; Figure 12). Because all individuals were of the same age at time

of release (by year), survival as a function of time was the same for each release effort.

Assuming successful transformation, excystment, and settlement into suitable substrate, juveniles

predicted to survive from each release (2007–2010) were 1,205, 1,739, 2,201, and 1,716

individuals to 2011, and 1,145, 1,652, 2,091, and 1,630 individuals to 2012 (Figure 7). Projected

abundance and density would have been 6,861 individuals and 2.58/m2 in 2011, and 6,518

individuals and 2.46/m2 in 2012 (Figure 19; Appendix A). Assuming a scenario of only 50%

success, the number of individuals predicted to survive would be half the previous values.

Under these survival scenarios, the shape of predicted age-frequency distributions would

shift from 2007–2010 as stream-side infested juveniles were introduced on an annual basis. The

distribution would stabilize in 2011, followed by a right shift in 2012. Predicted age classes of

individuals would have ranged from 1–5 years old (mean=2–3 years old) in 2011, and 2–6 years

28

old (mean=3–4 years old) in 2012 (Figure 20). Assuming a 1:1 sex ratio, predicted average

length class would have been 30 mm (±2) in 2011, and 34 mm (±2) in 2012 (Figure 21).

DISCUSSION

The federal recovery plan for E. capsaeformis requires six distinct viable populations in

order to meet delisting criteria to threatened status (USFWS 2004). Currently there are only two

extant populations, each restricted to the upper Tennessee River drainage in the Clinch and

Nolichucky Rivers in eastern TN and southwestern VA. Biologists define a viable population as

a naturally reproducing population that contains enough individuals to maintain genetic diversity

to adapt and respond to environmental changes (Sarrazin and Barbault 1996; USFWS 2004;

Jones et al. 2006b). To meet delisting criteria, recovery plans for listed mussels recommend the

translocation of adults, release of laboratory-propagated individuals, and the release of

artificially-infested host fishes be used as methods to augment existing populations and

reintroduce species to historically occupied sites (USFWS 2003, 2004). Post-release monitoring

of demographic vital rates of reintroduced populations is essential to assessing reintroduction

success, improving method efficiency, providing biologists with data required for effective

management, and evaluating whether down- or delisting criteria have been met (Sarrazin and

Barbault 1996; Sarrazin and Legendre 2000; USFWS 2004; Jones and Neves 2011).

My study has shown that the translocation of adults and release of laboratory-propagated

sub-adults are effective techniques for re-establishing populations of E. capsaeformis among the

reintroduction techniques implemented in this project. Epioblamsa capsaeformis were only

detected at Site 1 where translocated adults and laboratory-propagated sub-adults were released.

Recruitment was also documented at Site 1 during both monitoring years, indicating that natural

29

reproduction and recruitment are occurring. Because no individuals were encountered in any of

the quadrats sampled at Sites 2 and 3, it can be concluded based on the total area sampled by

quadrats that if E. capsaeformis were to exist at these sites, they would occur at a density that

was essentially undetectable (e.g., <0.01/m2).

Although mussels have been reintroduced throughout the United States over the last

century as a management strategy to restore populations (Haag 2012), published literature with

detailed plan objectives, reintroduction methods, post-release monitoring data, and defined

success criteria are limited (Cope and Waller 1995). Of the published studies documenting

reintroductions, implementation concurrent with comparison of techniques is rare, and most

studies involved only translocations. Additionally, there is a general lack of consistency in

monitoring methods used to evaluate success, resulting in highly variable reporting

methodologies and results among studies (Sheehan et al. 1989; Cope and Waller 1995). Cope

and Waller (1995) compiled data from the mussel relocation literature to compare relocation

methods, subsequent monitoring programs, and mortality estimates. Of the 37 relocation projects

they assessed—30% of which were reintroductions to recolonize extirpated populations—only

three were available in the peer-reviewed literature, many lacked detailed explanations of

methods of relocation, monitoring, and assessment approaches, and estimates of mortality varied

greatly among projects. Furthermore, 60% of the projects had ≤1 year or no subsequent

monitoring, thus limiting the amount of data available to accurately evaluate methodologies and

assess success of reintroductions. Likewise, Sarrazin and Legendre (2000) expressed the need for

more detailed data-oriented monitoring studies of reintroduced populations in order to provide

information for future conservation plans.

30

Generally, studies reporting reintroduction efforts conclude with a statement regarding

continued or proposed monitoring of the site; however, they are seldom accompanied by or

followed with post-reintroduction observations or follow-up monitoring reports. Reports

conveying release numbers, collection sources, destination, and species are informative, and can

make conservation efforts appear productive, but they do not disclose anything about the relative

success of reintroduction efforts. As a result of the shortage of both short- and long-term data on

approach-specific reintroduction success, it is difficult for managers to make informed decisions

on which methods to employ for recovery projects (Sarrazin and Legendre 2000). My study

attempted to fill these knowledge gaps by providing a detailed reintroduction and monitoring

methodology, establishing criteria for success, and evaluating effectiveness of each technique.

By performing and reporting post-restoration population monitoring, several projects

have provided insight into the relative success of method-specific restoration efforts. From

1976–1978, almost 3,000 mussels of 16 species were reintroduced to historically occupied sites

in the North Fork Holston River in southwestern VA (Ahlstedt 1979). During subsequent

monitoring, Ahlstedt (1979) documented large variability among results of mussel translocation

efforts, with several reintroduced populations persisting >5 years and others washing out with

flood events soon after translocation (Sheehan et al. 1989). Others have also reported variable to

low recovery rates of translocated individuals (Sheehan et al. 1989), outcomes which contrast

with the generally high post-reintroduction recovery and survival of translocated adults and

released sub-adults observed in this study. However, results of a few projects are in agreement

with the high survival rates of translocated individuals observed in my study. For example,

Layzer and Scott (2006) documented (at 1-year intervals for five years) relatively high survival

of 18 species, and successful recruitment of one species, translocated over four years to the lower

31

French Broad River, TN. Likewise, in 2008, the Kentucky Department of Fish and Wildlife

Resources’ (KDFWR) Center for Mollusk Conservation observed 100% post-reintroduction

survival of 300 individuals—97 of which were E. capsaeformis—three months after

translocation to the Big South Fork Cumberland River, KY (KDFWR 2008). Increased success

in reintroduction and monitoring may reflect differences in use of technical improvements (e.g.,

refined site selection by better understanding species-specific habitat requirements, timing of

release, reduced stress from improved translocation methodologies, refined monitoring

methodologies), as well as more accurate determination of recovery versus survival rates.

Research in controlled propagation of freshwater mussels began over a century ago (Haag

2012). However, only in the past two decades has the technology been refined and used more

routinely for restoring populations, particularly the growing out of cultured individuals to sub-

adult stage, thereby avoiding the high levels of natural mortality among newly transformed

juveniles (Jones et al. 2006b). Correspondingly, there is little information on the immediate

survival of juveniles after settlement or documented success or failure of reintroduction methods

utilizing controlled propagation (Haag 2012). Layzer and Scott (2006) released 801 host fishes

infested with glochidia of pheasantshell (Actinonaias pectorosa), Cumberland moccasinshell

(Medionidus conradicus), and P. subtentum over three years, in addition to adult mussel

translocations of each species in the French Broad River, TN. Similar to results of my study, no

juveniles resulting from stream-side infestations were found during subsequent monitoring

(Layzer and Scott 2006). However, others have been able to observe some evidence of juvenile

mussel survival from infested host fish releases. From 1994–1998, the Tennessee Wildlife

Resource Agency released over 5,000 fish infested with threeridge (Amblema plicata) and

washboard (Megalonaias nervosa) glochidia into Kentucky Lake, TN to augment populations in

32

that reservoir (Hubbs 2000). In an attempt to document that stream-side infestations produced

mussels, they held a portion of the infested fish in cages at a control site. They recovered five

sub-adults two years after release, verifying that some mussels successfully transformed and

settled in cages after the infestation (Hubbs 2000). Since 2001, Higgins eye (Lampsilis higginsii)

glochidia have been infested on host fishes and released at reintroduction sites in the Wisconsin,

Iowa, Wapsipinicon, and Cedar Rivers in Wisconsin and Iowa by the Genoa National Fish

Hatchery (GNFH) (Eckert and Aloisi 2010; N. Eckert, USFWS, GNFH, personal

communication). As of 2013, a few dozen L. higginsii have been detected at sites in the

Wisconsin, Iowa (15 individuals) and Wapsipinicon Rivers (38 individuals), where the species

had been extirpated for many decades (N. Eckert, USFWS, GNFH, personal communication).

Using stream-side infestations, differing survival and recovery rates of transformed individuals

among species across studies may be due to: 1) differences in species-specific infestation

methods (e.g., infestation duration, temperature, degree of infestation, timing of release), 2)

condition (e.g., stress, disease, reproductive condition) and compatibility of host fish, 3)

condition of gravid mussels and maturity of glochidia used for infestation, 4) juvenile dispersal

through down-stream drift, 5) immigration as attached glochidia on host fishes, 6) excystment

over unsuitable habitat, or 7) release site variability (Waller et al. 1985; Roger et al. 2001; Jones

et al. 2005; Layzer and Scott 2006).

Due to recent advancements in controlled propagation over the last decade allowing

facilities to produce quantities of larger juveniles (>10 mm), it has become more feasible to

conduct reintroductions using laboratory-propagated mussels (Jones et al. 2005; Barnhart 2006;

Haag 2012). Research results support the assertion that larger and older individuals have a

significantly increased chance of survival when released in the wild relative to newly-

33

metamorphosed juveniles (Sarrazin and Legendre 2000; Hua et al. 2011). Newly-metamorphosed

juvenile mussels may fall prey to a suite of predators, including hydroids, dragonfly larvae,

dipteran larvae, crayfishes, and especially flatworms (Zimmerman et al. 2003; Klocker and

Strayer 2004). Further, because adult and sub-adults have more general micro-habitat

requirements than young juveniles, fewer factors work against their chances of post-

reintroduction survival, suggesting that release of larger individuals may be a promising

reintroduction method (Cosgrove and Hastie 2001).

For example, from 2009–2010, Virginia Tech’s FMCC released a total of 193 tagged

laboratory-propagated sub-adult Cumberlandian combshell (Epioblasma brevidens) into the

Powell River, TN—50 with passive integrated transponder (PIT) tags. Monitoring of these

individuals revealed high annual survival (>98%) and growth (mean=7.7 mm in 12 months)

(Hua et al. 2011). Additionally, in 2008, KDFWRs’ Center for Mollusk Conservation

documented survival and growth of laboratory-propagated fatmucket (Lampsilis siliquidea) sub-

adults (1.5 years old at time of release) one year after reintroduction to Elkhorn Creek, KY

(KDFWR 2008). Hence, continued and improved reporting and long-term monitoring of

reintroduction efforts contribute to the long-term success of reintroduction efforts.

While unpublished literature documenting reintroduction by controlled propagation is

available, such reports and data are not easily accessible. Data from planned monitoring efforts

are even harder to find outside of personal communications with mussel conservation

practitioners, conference proceedings, and agency reports (Sarrazin and Barbault 1996). The lack

of published studies in the peer-reviewed literature on mussel reintroductions using laboratory-

propagated juveniles (<10 mm) and stream-side infestations of host fishes may be due to short-

term difficulty in detection (i.e., individuals are too small to detect for first few years) and the

34

absence of long-term monitoring programs to document success. Peer-reviewed literature on

reintroduction efforts may also be scarce simply because survival—whether in the form of

encountering surviving individuals or observing consequent natural recruitment—has not

occurred. Because release of laboratory-propagated sub-adults is a relatively new reintroduction

technique, even fewer studies are available to provide data on long-term success (Haag 2012).

Foremost in post-reintroduction project evaluations is the need to clearly identify what

criteria constitute a successful reintroduction (Sarrazin and Barbault 1996). Quantitative and

qualitative criteria provide a reference point for comparisons to other projects. For example, an

ecological criterion of reintroduction success is establishment of a long-term viable population

(Griffith et al. 1989; Fischer and Lindenmayer 2000). A short-term measure of reintroduction

success is documentation of natural recruitment (Cope and Waller 1995; Sarrazin and Barbault

1996). Other reintroduction studies have evaluated success using three objectives: 1) settlement

of released individuals at a release site, 2) survival of individuals after release, and 3) natural

reproduction (i.e., mussels demonstrating natural recruitment) (Teixeira et al. 2007; Matějů et al.

2012).

I considered reintroduction of E. capsaeformis at Site 1 through translocation of adults

and release of laboratory-propagated sub-adults a short-term success because both high post-

reintroduction survival and natural recruitment (i.e., viable young produced from released

individuals) were documented. Abundance and density estimates in 2011 and 2012 obtained

from systematic quadrat sampling were generally lower than estimates predicted using the Leslie

matrix; however, most of the confidence limits for 2011 and 2012 abundance estimates contained

the predicted mean values from the matrix. This outcome signifies that translocated and released

individuals successfully settled into Site 1 and are surviving at rates similar to those reported for

35

E. capsaeformis in the lower Clinch River, TN (Jones et al. 2012). Although 2011 and 2012

density estimates at Site 1 were larger than 2008 quadrat sampling estimates, they do not signify

an increase in population size through natural recruitment. The increase in abundance from 2008

to 2011 and 2012 reflects the outcome of additional reintroduction efforts from 2008–2010.

Nonetheless, based on this project’s reintroduction intensity and time frame, it is too early in the

monitoring program to determine if recruitment is occurring at self-sustaining levels.

Reintroduction efforts at Sites 2 and 3—through release of 8-week old laboratory-

propagated juveniles and stream-side infested host fishes—were not successful in the context of

this study. Given the predicted abundance and size of juveniles at Sites 2 and 3 in 2011 and

2012, they would have been easily detectable using the sampling methods employed in this

study. This signifies that released 8-week old laboratory-propagated juveniles and juveniles from

stream-side infestations may not have successfully settled into Sites 2 and 3 or are not surviving

at rates similar to those reported for E. capsaeformis in the lower Clinch River (Jones et al.

2012). If any individuals did survive from reintroduction techniques employed at Sites 2 and 3,

they occurred at such low densities so as to be nearly undetectable or they dispersed from the

study area through down-stream drift or immigration as glochidia attached to host fishes.

Populations existing at low densities not only affect detectability but can reduce fertilization

success, further reducing the probability of the reintroduced population naturally reproducing

and recruiting at self-sustaining levels (Downing et al. 1993). Factors such as predation, host fish

death before glochidial transformation, settling into unsuitable substrates within the study area,

or unfavorable environmental conditions during excystment (e.g., high flow events) may have

contributed to the apparent failure of reintroductions at Sites 2 and 3 in this study.

36

Although habitat characteristics of the three sites in this study appeared to be very

similar—and no spatial replication of reintroduction techniques was conducted—it could be

hypothesized that subtle differences in habitat among the release sites contributed to the apparent

failures of reintroduction techniques implemented a Sites 2 and 3 in this study. However, given

the diversity and density similarities of native mussel species among the three sites, it is unlikely

that differences in habitat alone, if at all, explain the apparent failures at Sites 2 and 3. The four

reintroduction techniques in this study could not be implemented concurrently at each of the

three sites (to completely remove the habitat factor and replicate) because of the inability to

separate natural recruitment from the multiple releases of 8-week old laboratory-propagated

juveniles or juveniles from stream-side infestations (i.e., no identifiable markings or tags). A

larger-scale (temporally and spatially) experiment with replication is needed to investigate the

effects of habitat characteristics on the success of reintroduction techniques. However, based on

the variability in success of previous releases of newly-metamorphosed juveniles and stream-side

infested host fishes that have been implemented over a diversity of habitats, research supporting

the release of larger individuals (Sarrazin and Legendre 2000; Hua et al. 2011), and the results of

this study, it is in all likelihood that the release of larger individuals has a more reliable (i.e.,

predictable) and accelerated payoff for expediting the recovery of populations.

To improve success of mussel reintroductions, it is important to consider biotic and

abiotic factors that can hinder reintroductions. Several factors can influence reintroduction

success, including species- and size-specific suitability of the destination site (macro- and

microhabitat characteristics), timing of release, host fish presence and density, handling- and

transportation-related stressors, genetic variation among released individuals, condition of

released individuals, and environmental stochasticity (Griffith et al. 1989; Sheehan et al. 1989;

37

Cope and Waller 1995; Sarrazin and Barbault 1996; Fischer and Lindenmayer 2000; Sarrazin

and Legendre 2000; Layzer and Scott 2006; Jones et al. 2012). Additionally, intensity of the

reintroduction effort may be a key to success; as the number of individuals released annually is

increased, natural and stress-related mortality may be offset (Griffith et al. 1989; Sarrazin and

Barbault 1996; Fischer and Lindenmayer 2000; Sarrazin and Legendre 2000; Matějů et al. 2012;

S. Ahlstedt, U. S. Geological Survey, retired, personal communication).

In this study, the potential effects of these limiting factors on reintroduction success were

controlled by: 1) assessing sites for suitable habitat and water quality prior to reintroduction

efforts, 2) conducting reintroductions in late summer and early fall when the reproductive state

of individuals was low, 3) implementing optimal transportation and release protocols to reduce

stress, 4) confirming the presence of host fishes at reintroduction sites, and 5) fostering genetic

diversity. To maintain genetic diversity, translocated adults and those used for host fish stream-

side infestations were collected from multiple source populations within the Clinch River.

Additionally, the production of laboratory-propagated juveniles and sub-adults and the stream-

side infestation of host fish were conducted following controlled propagation policies and

guidelines and permitted by state and federal natural resource agencies (USFWS and National

Oceanic and Atmospheric Administration 2000; Jones et al. 2006b)

The data collected in my study also provided insight into the demographic characteristics

(i.e., age-specific survival rates) of E. capsaeformis. Estimates of abundance and density of

adults at Site 1 were slightly lower than predicted. Assuming the age-based matrix transition

probabilities presented in Jones et al. (2012) represent reasonably accurate year-to-year survival

rates of wild E. capsaeformis, the lower survival observed in this study may reflect higher initial

mortality occurring at time of release as a result of stress-induced mortality from handling (Cope

38

and Waller 1995; Sarrazin and Legendre 2000; Teixeira et al. 2007) or loss of individuals from

the site during high flow events. Additional sources of variability in survival could be attributed

to differences in habitat characteristics (biotic and abiotic) between the upper and lower Clinch

River, or an artifact of detection difficulty at low density rather than representing true differences

in survival rates.

The matrix transition probabilities used to calculate predicted abundance and density at

Sites 1, 2, and 3 were approximations of survival rates using best available data. Jones et al.

(2012) recognized the uncertainty in their Leslie matrix input sources and recommended

additional studies to improve estimates of age-class survival rates for E. capsaeformis. By

following unique individuals through time, I was able to estimate survival based on fates of

individuals captured. Although original aging of uniquely-marked translocated adults was

estimated using predicted age-at-length curves, my study was able to estimate annual survival

rates by combining capture histories with known time since release. Similarly, tagged laboratory-

propagated sub-adults were of known age at release and provided concrete age-specific data for

estimating annual survival rates. Despite the slight differences in survival based on predicted

versus observed data, the results of my study were in general agreement with previous

predictions of Jones et al. (2012) on annual survival for older age-classes—E. capsaeformis

adults and sub-adults exhibit moderate to high annual survival after making it past the vulnerable

age 0–1 stage, respectively, with increasing mortality as individuals approach maximum age.

Assuming introduced E. capsaeformis sub-adults at Site 1 from the 2010 release reached

sexual maturity in 2012 (i.e., based on size, >35 mm), and that environmental conditions have

been favorable for reproduction, it is likely that recruitment from these individuals can be

assessed as early as 2014 (1–2 year-old recruits). In accordance with the recovery plan for E.

39

capsaeformis (USFWS 2004), I suggest Site 1 at Cleveland Islands, VA be monitored every two

years (biennially) beginning in 2014 in order to assess population viability and to obtain

information for understanding age-1 class survival and recruitment rates of E. capsaeformis. An

assessment of genetic diversity should be conducted to further validate reintroduction success

(Jones et al. 2012). Future monitoring at Sites 2 and 3 to confirm if the absence of reintroduced

individuals and subsequent failure of released 8-week old laboratory-propagated juveniles and

stream-side infested fish hosts efforts in this study were accurate assessments of these

reintroduction techniques is not feasible. Since the completion of my study (September 2012),

several releases of laboratory-propagated sub-adult E. capsaeformis have been implemented at

Sites 2 and 3 (October 2012–2013)—making the differentiation between 8-week old laboratory-

propagated juveniles, juveniles from stream-side infestations, laboratory-propagated sub-adults,

and natural recruitment difficult. To invalidate the argument for confounding habitat variability

with reintroduction technique-specific success or failure in this study, and to further evaluate the

efficiency of releasing newly-metamorphosed laboratory-propagated and stream-side infested

host fishes, a spatially replicated experiment should be conducted.

I recommend that management efforts focus on translocation of adults and release of

laboratory-propagated sub-adults for maximizing success of population restoration projects.

Results of my study helped support previous conclusions of moderate to high annual survival

rates of E. capsaeformis adults; however, it may be too early in the monitoring program at

Cleveland Islands to gather sufficient data to assess age 0–1 survival and recruitment rates.

Although catch-curve and shell thin-sectioning analyses are useful techniques for calculating

broad estimates of survival for demographic models, their assumptions are rarely met by natural

populations (Miranda and Bettoli 2007; Jones et al. 2012). Given the chance to follow marked

40

cohorts of known age through time at reintroduction sites presented an opportunity to increase

knowledge of species-specific demographic rates rather than having to rely on catch-curve and

shell thin-sectioning data alone. Future reintroductions should continue to tag reintroduced

individuals in order to improve our understanding of species-specific demographic

characteristics while further refining survival criteria.

41

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49

Table 1. Species and numbers of native host fishes infested with E. capsaeformis glochidia and

released in the upper Clinch River, Virginia at Artrip (Site 3) each year from 2007–2010.

No. Infested Per Year

Species Common Name 2007 2008 2009 2010 Total

Etheostoma blennioides Greenside darter 18 35 70 18 141

E. camurum Bluebreast darter 0 10 79 30 119

E. denoncourti Golden darter 0 0 2 9 11

E. rufilineatum Redline darter 185 235 164 157 741

E. stigmaeum Speckled darter 0 0 0 1 1

E. zonale Banded darter 3 0 31 36 70

Percina evides Gilt darter 7 12 5 9 33

Total 213 292 351 260 1,116

50

Table 2. Numbers released, predicted abundance ( ) and survival (proportion of released

individuals that survived) of translocated adults and laboratory-propagated sub-adults in the left-

descending channel of Cleveland Islands, Virginia (Site 1) by release year in 2011 and 2012.

Predicted 2011 Predicted 2012

Year No.

Released

Survival

(%)

Survival

(%)

Translocated

adults 2006 201 58 29

34 17

2007 197 107 54

81 41

2008 218 148 68

120 55

2009 401 320 80

276 69

2010 401 366 91

332 83

Subtotal 1,418 1,000 71

843 59

Laboratory-

propagated

sub-adults

2010 2,501 2,376 95

2,257 90

2011 350 350 100

333 95

Subtotal 2,851 2,726 96

2,590 91

Total 4,269 3,726 87

3,433 80

51

Table 3. Survey sample size requirements to estimate predicted abundance and density levels

with a desired precision of 15% of the estimate (CV = SE/mean) in the left-descending channel

(Site 1) and right-descending channel (Site 2) of Cleveland Islands, and at Artrip (Site 3) in the

upper Clinch River, Virginia. Abundance and density values represent number of surviving

individuals predicted from the Leslie matrix (i.e., reproductive values were not included in

projections).

Source of preliminary density estimates

Abundance

(N-hat)

Density

(per m2)

0.25-m2

Sampling

Units Required

2008 VDGIF survey:

Site 1 Left descending channel 585 0.25 338

2011 Capture-mark-recapture survey:

Site 1 Left descending channel 1,569 0.31 303

Leslie matrix transition probabilities to 2011:

Site 1 Translocated adults 1,000 0.20 378

Laboratory-propagated sub-adults 2,726 0.54 228

Total E. capsaeformis 3,726 0.73 196

Site 2 8-week old laboratory-propagated

juveniles

2,289 0.78 189

Site 3 Juveniles from stream-side infested host

fishes

6,861 2.58 103

Leslie matrix transition probabilities to 2012:

Site 1 Translocated adults 843 0.17 411

Laboratory-propagated sub-adults 2,590 0.51 235

Total E. capsaeformis 3,433 0.68 204

Site 2 8-week old laboratory-propagated

juveniles

2,041 0.70 200

Site 3 Juveniles from stream-side infested host

fishes

6,518 2.45 105

52

Table 4. Sample size, proportion of area covered by quadrats, person-hours of sampling effort,

number of E. capsaeformis collected, and precision (CV=SE/mean) of systematic sampling

collections conducted in 2011 and 2012, sorted by reintroduction method, in the left-descending

channel (Site 1) and right-descending channel (Site 2) of Cleveland Islands, and at Artrip (Site 3)

in the upper Clinch River, Virginia.

Site Year Reintroduction

Method

Sample

Size

(0.25-m2)

Area

Covered

(%)

Person-

Hours

No.

Collected

Observed

Precision

(CV)

1 2011 Translocated adults

11 0.54

Laboratory-

propagated sub-adults 33 0.05

Recruits

1 1.00

Total 388 1.9 29 45 0.12

1 2012 Translocated adults

11 0.34

Laboratory-

propagated sub-adults 29 0.27

Recruits

1 1.00

Total 347 1.7 25 41 0.26

2 2012 8-week old

laboratory-propagated

juveniles

210 1.8 16 0 *

3 2012 Juveniles from

stream-side infested

host fishes

194 1.8 14 0 *

* = Precision was not calculated because no E. capsaeformis were collected at Sites 2 and 3.

53

Table 5. Estimated mean and standard errors (SE) of abundance and density with lower and

upper 95% confidence intervals for translocated adults, released laboratory-propagated sub-

adults, and newly recruited E. capsaeformis in the left-descending channel of Cleveland Islands,

Virginia (Site 1) in 2011 (n=388 quadrats) and 2012 (n=347 quadrats) using systematic quadrat

sampling.

2011 2012

Mean SE

95% C.I.

Mean SE

95% C.I.

Lower Upper Lower Upper

Population Size (N-hat)

Translocated Adults 577 155 83 1,070 645 110 295 995

Lab-Propagated Sub-Adults 1,678 42 1,543 1,812 1,700 229 970 2,430

Recruits 52 26 2 1,226 59 29 2 1,375

Total 2,307 134 1,880 2,733 2,403 313 1,409 3,398

Density (per m2)

Translocated Adults 0.11 0.03 0.02 0.21 0.13 0.02 0.06 0.20

Lab-Propagated Sub-Adults 0.33 0.01 0.30 0.36 0.33 0.05 0.19 0.48

Recruits 0.01 0.01 <0.01 0.24 0.01 0.01 <0.01 0.27

Total 0.45 0.03 0.37 0.54 0.47 0.06 0.28 0.67

54

Figure 1. Topographic map of 19.3-km designated population restoration reach for Epioblasma

capsaeformis in the upper Clinch River from Nash Ford to Carbo, Virginia (yellow

circles=towns) and locations of study sites (red stars).

55

Figure 2. Aerial view of translocation and release sites of E. capsaeformis (red stars) and

sampling areas (yellow polygons) in the left-descending channel (Site 1) and right-descending

channel (Site 2) of Cleveland Islands, Virginia.

56

Figure 3. Aerial view of release site of stream-side infested host fishes (red star) and sampling

area (yellow polygon) in the left-descending channel of Artrip, Virginia (Site 3). Black polygon

in river represents intermittent island.

57

Figure 4. Numbers of translocated adults and sex proportions per translocation year: A) initially

released for each year, and B) predicted to survive in 2011, and C) 2012 at Site 1 based on

matrix transition probabilities presented in Jones et al. (2012).

58

Figure 5. Numbers of laboratory-propagated sub-adults per each release year: A) initially

released for each year, B) predicted to survive in 2011, and C) 2012 at Site 1 based on matrix

transition probabilities presented in Jones et al. (2012).

59

Figure 6. Number of 8-week old laboratory-propagated juveniles per release year: A) initially

released for each year, B) predicted to survive to 2011, and C) 2012 at Site 2, assuming 100% of

the released juveniles successfully settled into suitable substrate at the site, based on matrix

transition probabilities presented in Jones et al. (2012).

60

Figure 7. Predicted numbers of excysted juveniles from each stream-side infestation of host

fishes: A) initially released for each year, and B) predicted to survive in 2011, and C) 2012 at

Site 3 based on matrix transition probabilities presented in Jones et al. (2012).

61

Figure 8. Estimated population sizes and densities (±95% CI) of translocated adults, released

laboratory-propagated sub-adults, and newly recruited E. capsaeformis in the left-descending

channel at Cleveland Islands (Site 1) in 2011 and 2012 using systematic quadrat sampling.

62

Figure 9. Age-frequencies and sex-ratio distributions of translocated adult (sexed) and

laboratory-propagated sub-adult (unsexed) E. capsaeformis in: A) 2011 and B) 2012 observed at

Site 1 using systematic quadrat sampling. N = total number of mussels collected in quadrat

samples.

63

Figure 10. Observed length-class frequency distributions and sex-ratios of translocated adult

(sexed) and laboratory-propagated sub-adult (unsexed) E. capsaeformis in: A) 2011 and B) 2012

at Site 1 using systematic quadrat sampling. N = total number of mussels collected in quadrat

samples.

64

Figure 11. Predicted length-class frequency distributions and sex-ratios for: A) 2011 without, B)

with laboratory-propagated sub-adults, C) 2012 without, and D) with laboratory-propagated sub-

adults at Site 1.

65

Figure 12. Predicted survival estimates of: A) translocated adults (T) and laboratory-propagated

sub-adults (P) at Site 1, B) 8-week old laboratory-propagated juveniles (J) at Site 2, and C)

juveniles from stream-side infestations of host fishes at Site 3 by release year over time.

66

Figure 13. Predicted abundance and density of translocated adults and released laboratory-

propagated sub-adults at Site 1 over time.

67

Figure 14. Predicted age-frequency and sex ratio distributions for translocated adult and

laboratory-propagated sub-adult E. capsaeformis surviving in: A) 2006, B) 2007, C) 2008, and

D) 2009 at Site 1.

68

Figure 15. Predicted age-frequency and sex ratio distributions for translocated and released E.

capsaeformis surviving in: A) 2010 without laboratory-propagated sub-adults (LPSA), B) 2010

with LPSA, C) 2011 without LPSA, D) 2011 with LPSA, E) 2012 without LPSA, and F) 2012

with LPSA at Site 1. Predicted recruitment was not included in histograms.

69

Figure 16. Predicted abundance and density of 8-week old laboratory-propagated juveniles at

Site 2 over time, assuming 100% and 50% scenarios of the released juveniles successfully

settling into suitable substrate at the site.

70

Figure 17. Predicted age-frequency distributions of released 8-week old laboratory-propagated

juveniles from 2005–2012 at Site 2.

71

Figure 18. Predicted length-class frequency distributions of released 8-week old laboratory-

propagated juveniles at Site 2 in: A) 2011 and B) 2012 assuming a 1:1 sex ratio.

72

Figure 19. Predicted abundances and densities of juveniles released from stream-side infested

host fishes at Site 3 over time, under two scenarios (100% and 50%) of the estimated average 22

viable juveniles excysted per infested host fish successfully settled into suitable substrate at the

site.

73

Figure 20. Predicted age-frequency distributions of juveniles released from stream-side infested

host fishes from 2007–2012 at Site 3.

74

Figure 21. Predicted length-class frequency distributions of juveniles released from stream-side

infested host fishes to A) 2011 and B) 2012 at Site 3 assuming a 1:1 sex ratio.

75

APPENDIX A: Age-Class Categories and Matrices

Age

Class

Age Female Size

Range (mm)

Male Size

Range (mm)

Growing

Season

1 0–1 0–19.3 0–20.5 1

2 1–2 19.3–26.5 20.5–27.2 2

3 2–3 26.5–32.1 27.2–31.5 3

4 3–4 32.1–36.3 31.5–34.4 4

5 4–5 36.3–39.5 34.3–36.3 5

6 5–6 39.5–41.9 36.3–37.5 6

7 6–7 41.9–43.8 37.5–38.3 7

8 7–8 43.8–45.2 38.3–38.9 8

9 8–9 45.2–46.3 38.9–39.2 9

10 9–10 46.3–47.1 39.2–39.5 10

11 10–11 47.1–47.8 39.5–39.6 11

12 11–12 47.8–48.3 39.6–39.7 12

>12 >12 >48.3 >39.7 >12

Figure A. 1. Age-class categories, corresponding age, corresponding size ranges by sex, and

associated growing seasons for Epioblasma capsaeformis. Age 0–1 year olds are referred to as

age class 1 and represent newly transformed juveniles during their first growing season.

Predicted length-at-age based on estimated von Bertalanffy growth curves presented in Jones et

al. (2011).

76

[

]

Figure A. 2. Leslie matrix (L) of E. capsaeformis survival probabilities referenced in this study

analyses (from Jones et al. 2012).

77

[

]

[

]

[

]

[

]

[

]

where TY = Translocated adults of year Y,

PY = released laboratory-propagated sub-adults of year Y,

JY = released 8-week old laboratory-propagated juveniles of year Y,

IY = released viable juveniles from year Y stream-side infestation,

N = total number of E. capsaeformis,

t = time (year),

TYx(t) = number of translocated adults (year Y) of age x at time t,

PYx(t) = number of laboratory-propagated sub-adults (release year Y) of age x at

time t,

JYx(t) = number of released 8-week old laboratory-propagated juveniles (release

year Y) of age x at time t,

IYx(t) = number of viable juveniles (stream-side infestation year Y) of age x at time

t, and

Nx(t) = number of individuals of age x at time t

Figure A. 3. Vector format for translocated adults, laboratory-propagated sub-adults, 8-week old

laboratory-propagated juveniles, and juveniles from stream-side infestations released per

sampling site.

78

[ ]

[ ]

[ ]

[

]

[

]

[

]

[

]

Figure A. 4. Number and cohort structure at time of release of translocated adults (T) and

released laboratory-propagated sub-adults (P) released per year at Site 1 in vector format.

79

100%

[

]

[

]

[

]

[

]

50%

[

]

[ ]

[

]

[ ]

Figure A. 5. Two scenarios (100% and 50%) representing the predicted number of 8-week old

laboratory-propagated juveniles (J) released per year that successfully settled into suitable

substrate at Site 2.

80

100%

[

]

[

]

[

]

[

]

50%

[

]

[

]

[

]

[

]

Figure A. 6. Two scenarios (100% and 50%) representing the predicted number of viable

juveniles released from each stream-side infestation effort (I) that successfully settled into

suitable substrate after excystment from host fishes at Site 3.

81

[

]

[

]

Figure A. 7. Male and female age-class specific survival rates used in analyses (Leslie matrix).

82

The number projected to be at Site 1 in 2007 after the 2007 yearly reintroduction was

calculated by adding the 2007 translocation vector to the product of the 2006 translocation vector

by the Leslie matrix.

[ ]

[

]

[

]

+

[

]

The ensuing years were calculated by adding the corresponding translocation or release vector (if

applicable) to the product of the previous year’s vector by the Leslie matrix:

[ ]

[ ]

[ ]

[ ]

[ ]

Figure A. 8. Example of how numbers, cohort structure, and lengths of E. capsaeformis released

per year were projected 1 to 6 years into the future depending on time of translocation or release.

Population vectors are provided to predict survival, cohort structure, and length-frequency

distribution of the population in 2011 and 2012.

83

[ ]

[

]

[ ]

[

]

[

]

[

]

[

]

[

]

[

]

[

]

Figure A. 9. Population projection vectors displaying the total number and cohort structure of

individuals from each release effort predicted to survive in 2011 and 2012 at Site 1

(T=translocated adults, P=laboratory-propagated sub-adults).

84

[

]

[

]

[

]

[

]

[

]

Figure A. 9. (continued) Population projection vectors displaying the total number and cohort

structure of individuals from each release effort predicted to survive in 2011 and 2012 at Site 1

(T=translocated adults, P=laboratory-propagated sub-adults).

85

[

]

[

]

Figure A. 10. Predicted cohort structure and population size (N) of all translocated adults (T) in

2011 and 2012 at Site 1.

86

[

]

[

]

Figure A. 11. Predicted cohort structure and population size (N) of laboratory-propagated sub-

adults (P) in 2011 and 2012 at Site 1.

87

[

]

[

]

Figure A. 12. Predicted cohort structure and population size (N) of all E. capsaeformis

(translocated adults and laboratory-propagated sub-adults) in 2011 and 2012 at Site 1.

88

[

]

[

]

[

]

[

]

[

]

[

]

[

]

[

]

Figure A. 13. Projected surviving number and cohort structure of 8-week old laboratory-

propagated juveniles (J) from each release effort in 2011 and 2012 at Site 2, assuming 100% of

the released juveniles successfully settlement into suitable substrate at the site.

89

[

]

[

]

Figure A. 14. Predicted cohort structure and population size (N) of released 8-week old

laboratory-propagated juveniles (J) in 2011 and 2012 at Site 2, assuming 100% of the released

juveniles successfully settlement into suitable substrate at the site.

90

[

]

[

]

[

]

[

]

[

]

[

]

[

]

[

]

Figure A. 15. Projected surviving number and cohort structure of juveniles released from stream-

side infested host fishes (I) from each release effort in 2011 and 2012 at Site 3, assuming 100%

of the estimated average 22 viable juveniles excysted per infested host fish successfully settled

into suitable substrate at the site.

91

[

]

[

]

Figure A. 16. Predicted cohort structure and population size (N) of juveniles released from

stream-side infested host fishes (I) in 2011 and 2012 at Site 3, assuming 100% of the estimated

average 22 viable juveniles excysted per infested host fish successfully settled into suitable

substrate at the site.

92

APPENDIX B: Sample Size Requirements (Statistical Analyses)

Table B. 1. Estimated number of samples (0.25-m2 quadrats) required to reach a desired

sampling precision assuming a predicted density of the target species.

Precision = CV (SE/mean)

0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.50

Den

sity

(per

m2)

0.01 12879 3648 1744 1033 688 494 373 293 236 195

0.05 5668 1605 767 455 303 217 164 129 104 86

0.10 3980 1127 539 319 213 153 115 90 73 60

0.15 3237 917 438 260 173 124 94 74 59 49

0.20 2795 792 378 224 149 107 81 63 51 42

0.25 2494 706 338 200 133 96 72 57 46 38

0.30 2273 644 308 182 121 87 66 52 42 34

0.35 2101 595 284 169 112 81 61 48 39 32

0.40 1963 556 266 157 105 75 57 45 36 30

0.45 1848 523 250 148 99 71 54 42 34 28

0.50 1752 496 237 140 94 67 51 40 32 27

0.55 1668 473 226 134 89 64 48 38 31 25

0.60 1596 452 216 128 85 61 46 36 29 24

0.65 1532 434 207 123 82 59 44 35 28 23

0.70 1475 418 200 118 79 57 43 34 27 22

0.75 1424 403 193 114 76 55 41 32 26 22

0.80 1378 390 187 111 74 53 40 31 25 21

0.85 1336 378 181 107 71 51 39 30 25 20

0.90 1298 368 176 104 69 50 38 29 24 20

0.95 1263 358 171 101 67 48 37 29 23 19

1.00 1230 348 167 99 66 47 36 28 23 19

93

Table B. 2. Sample size requirements (per group) to detect various effect sizes (d =

)

between two years or sites for assorted combinations of power (1-β) and significance level (ɑ).

Effect sizes 0.2, 0.5, and 0.8 are characterized as small, medium, and large as defined in

Cunningham et al. (2007). With a 0.16 sampling variance (σ = 0.4) for E. capsaeformis,

detecting effect sizes of 0.0625 and 0.8 are proportionate to 0.025/m2 and 0.32/m

2 differences in

density between two groups.

Effect Size (d)

(1-β) ɑ 0.0625 0.1 0.2 0.3 0.4 0.5 0.8

0.80 0.05 4020 1571 394 176 100 64 26

0.85 0.05 4598 1797 450 201 114 73 30

0.90 0.05 5381 2103 527 235 133 86 34

0.95 0.05 6655 2600 651 290 164 105 42

0.80 0.10 3436 1238 310 139 78 51 21

0.85 0.10 3995 1439 361 161 91 59 24

0.90 0.10 4759 1714 429 191 109 70 28

0.95 0.10 6013 2166 542 242 136 88 35

0.80 0.15 2891 1041 276 117 66 43 17

0.85 0.15 3406 1227 307 137 78 50 20

0.90 0.15 4114 1482 371 166 94 60 24

0.95 0.15 5286 1904 477 212 120 77 31

0.80 0.20 2502 901 226 101 57 37 15

0.85 0.20 2984 1075 269 120 68 44 18

0.90 0.20 3650 1314 329 147 83 53 21

0.95 0.20 4758 1714 429 191 108 69 28

94

Figure B. 1. Effect size to detect as a function of power and total sample size for A–D levels of

significance (0.05, 0.10, 0.15, and 0.20) using a two-tailed t-test for mean differences between

two independent groups.

95

Figure B. 2. Significance level (ɑ) as a function of power and total sample size for A–E effect

sizes (0.1, 0.2, 0.3, 0.4, 0.5) using a two-tailed t-test for mean differences between two

independent groups.

96

APPENDIX C: Species List

Table C. 1. Species collected in the upper Clinch River, Virginia at each sampling site in 2011

and 2012.

Cleveland Islands Artrip

Species Common name

Site 1

(2011)

Site 1

(2012)

Site 2

(2012)

Site 3

(2012)

Actinonaias ligamentina Mucket

X

Actinonaias pectorosa Pheasantshell X X X X

Cyclonaias tuberculata Purple wartyback X

X

Elliptio dilatata Spike X X X X

Epioblasma brevidensFE

Cumberlandian combshell X X

Epioblasma capsaeformisFE

Oyster mussel X X

Epioblasma triquetraFE

Snuffbox

X

Fusconaia barnesiana Tennessee pigtoe XA X

A X X

Fusconaia corFE

Shiny pigtoe XB X

B X X

Fusconaia cuneolusFE

Fine-rayed pigtoe XB X

B

X

Fusconaia subrotunda Longsolid XA X

A X

Lampsilis fasciola Wavy-rayed lampmussel X X X X

Lampsilis ovata Pocketbook X X

X

Lasmigona costata Flutedshell X X X X

Medionidus conradicus Cumberland moccasinshell X X X X

Pleurobema oviforme Tennessee clubshell XA X

A

X

Pleuronaia dolabelloidesFE

Slabside pearlymussel X X X

Ptychobranchus fasciolaris Kidneyshell X X X X

Ptychobranchus subtentumFE

Fluted kidneyshell X

X

Quadrula cylindrica

strigillataFE

Rough rabbitsfoot X X X

Villosa iris Rainbow X X X X

Villosa vanuxemensis Mountain creekshell X X X A = Fusconaia barnesiana, F. subrotunda, and Pleurobema oviforme individuals were pooled

due to lack of positive identification at this site. B = Fusconaia cor and F. cuneolus individuals were pooled due to lack of positive identification

at this site. FE

= Federally endangered species

97

CHAPTER 2

Evaluation of Systematic Quadrat and Capture-Mark-Recapture Survey Techniques:


Upper Clinch River, Virginia.

98

ABSTRACT

A total of 1,418 translocated adult and 2,851 laboratory-propagated sub-adult federally

endangered oyster mussels (Epioblasma capsaeformis) were reintroduced from 2006–2011 in the

upper Clinch River at Cleveland Islands, Virginia. The objective of this study was to estimate

population size of this restored population using two survey methods and assess the effectiveness

of these methods to estimate population parameters. Demographic data were collected in 2011

and 2012 by systematic quadrat and capture-mark-recapture sampling. Systematic quadrat

sampling of translocated adult E. capsaeformis estimated population sizes of 577 (SE=155)

individuals in 2011 and 645 (SE=110) individuals in 2012. Systematic quadrat sampling of

laboratory-propagated sub-adult E. capsaeformis estimated population sizes of 1,678 (SE=42)

individuals in 2011 and 1,700 (SE=229) individuals in 2012. With similar point estimates but

more precision than quadrat sampling, capture-mark-recapture sampling produced translocated

adult population size estimates of 451 (SE=97) in 2011 and 370 (SE=80) in 2012. Also similar in

point estimates but with less precision than quadrat sampling, capture-mark-recapture produced

laboratory-propagated sub-adult population size estimates of 1,938 (SE=1,088) in 2011 and

1,390 (SE=611) in 2012. My results indicate that systematic quadrat and capture-mark-recapture

sampling have useful applications in population monitoring, but the choice among them is

dependent on project objectives. I recommend that monitoring projects utilize systematic quadrat

sampling when the objective is to simply estimate and detect trends in population size of species

at moderate to higher densities (>0.2/m2). Capture-mark-recapture sampling should be used

when objectives include assessing a reintroduced population of endangered species, and

obtaining precise population demographic estimates such as survival and recruitment, or

estimating population size for species of low to moderate densities (0.1–0.2/m2).

99

KEYWORDS: Freshwater Mussels, Capture-Mark-Recapture Sampling, Systematic Quadrat

Sampling, Oyster Mussel, Epioblasma capsaeformis, Demographic Data

100

INTRODUCTION

The federal recovery plan for the oyster mussel (Epioblasma capsaeformis) identifies the

quantification of demographic characteristics— such as population size, age-class structure, and

survival rates—as key to assessing species recovery (USFWS 2004). Estimation of demographic

parameters of biological populations is vital to understanding species-specific population

dynamics and, ultimately, determining population viability (USFWS 2004; Jones et al. 2012). In

recent years, reintroductions of freshwater mussel species into historical habitats where they had

become extirpated, and augmentations of extant but generally declining populations, have been

conducted in order to recover imperiled species and to prevent future losses (Haag 2012). These

recovery efforts require post-release monitoring of demographic vital rates in order to assess

restoration success and evaluate whether down- or delisting criteria have been met. Data from

post-release monitoring studies aid biologists in making informed decisions for effective

management (Sarrazin and Barbault 1996; Sarrazin and Legendre 2000; USFWS 2004; Jones

and Neves 2011).

A common methodology that is employed to collect demographic data for population

assessments of mussels is the quantitative quadrat sampling approach. The quadrat method

involves systematically sampling 0.25-m2 or 1-m

2 quadrats using a complete census within each

quadrat at a study site and extrapolating the finding across a wider area. Population parameters

such as species diversity (i.e., richness and evenness), size and density, growth rate, sex ratios,

age-class structure, survival, and evidence of recruitment all can be estimated (see Chapter 1).

From the length data and shells collected during quadrat sampling, catch-curve and shell thin-

sectioning analyses can be used to calculate approximate estimates of survival rates for

demographic models. However, the assumptions of these techniques for survival analyses are

101

rarely met by natural populations (Miranda and Bettoli 2007; Jones et al. 2012). Alternately,

following uniquely marked individuals (or cohorts of known age) through time allows improved

estimates of annual survival rates based on the fates of individuals captured. Although capture-

mark-recapture (CMR) has a long history in wildlife ecology (Lincoln 1930; Young et al. 1952;

Seber 1962; Jolly 1963, 1965; Cormack 1964; Edwards and Eberhardt 1967; Otis et al. 1978)

and is commonly used in studies of many other taxa (Dussart 1991; Karanth 1995; Mowat and

Strobeck 2000; Silver et al. 2004), it has been seldom used to monitor and assess mussel

populations (Pollock et al. 1990; Strayer and Smith 2003; Villella et al. 2004).

In a CMR design, repeated sampling of the population is required. During the first

capture occasion, all individuals captured are uniquely marked, recorded, and released back into

the population. During repeated sampling occasions, all marked individuals encountered are

recorded, and all unmarked individuals captured are uniquely tagged and recorded during each

occasion (Otis et al. 1978). Based on the proportion of marked to unmarked individuals,

population size can be estimated (Petersen 1896; Lincoln 1930; Jolly 1965; Seber 1982; Strayer

and Smith 2003; Villella et al. 2004). Data from individuals monitored over time also can be

used to model and estimate detection (i.e., capture and recapture, or encounter probabilities),

survival rates, and recruitment probabilities for each sampling period (Cormack 1964; Jolly

1965; Pollock 1982; Seber 1982; Pollock et al. 1990; Villella et al. 2004).

Capture-mark-recapture models can be categorized into two broad classes: closed- and

open-population models. Closed-population models are used in CMR designs when capture-

recapture occasions occur over a relatively short period (days to weeks) to ensure that no births

or deaths (demographic closure), and no emigration or immigration (geographic closure) occur

during the census. Open-population models are used over a longer time frame (e.g., years) in

102

order to estimate recruitment, mortality or migration (Seber 1982; Strayer and Smith 2003). A

common assumption of both model types is that all animals–marked and unmarked–are equally

likely to be caught during any sampling occasion (i.e., The Equal Catchability Assumption)

(Seber 1962; Jolly 1963, 1965; Cormack 1964; Seber 1982; Pollock 1982). However, this

assumption is frequently violated in CMR field studies by the inherent variability in capture

probabilities of individuals due to heterogeneity, trap response, temporal emigration, time

effects, and combinations of these and other factors (Pollock 1982; Seber 1982). To cope with

capture variability, models have been developed for closed and open population designs which

allow for parameter estimates that incorporate such factors (Pollock 1975, 1981, 1982; Otis et al.

1978; Seber 1982; White et al. 1982; Pollock and Otto 1983).

The Clinch River is part of the upper Tennessee River drainage, flowing southwest

through southwestern, VA and into northeastern, Tennessee (TN). In 2002, the Virginia

Department of Game and Inland Fisheries (VDGIF) designated approximately 19.3-km of the

upper Clinch River in VA as an population restoration reach for E. capsaeformis (Eckert and

Pinder 2010; VDGIF 2010). In collaboration with VDGIF’s Aquatic Wildlife Conservation

Center (AWCC) near Marion, VA, Virginia Tech’s Freshwater Mollusk Conservation Center

(FMCC) has been working to increase the local population size and viability of E. capsaeformis

within this reach over the last seven years (2006–2012). Cleveland Islands, at Clinch River

kilometer (CRKM) 435.8, was chosen as the population restoration site in the upper Clinch River

for the project.

As of 2011, a total of 1,418 translocated adult and 2,851 laboratory-propagated sub-adult

E. capsaeformis have been released at Cleveland Islands, VA. In order to determine the success

of these reintroduction efforts and assess population viability, population monitoring was

103

conducted in 2011 and 2012 by systematic quadrat sampling (Chapter 1) and CMR. This

population restoration site presented an ecological opportunity to increase knowledge of species-

specific demographic rates because all E. capsaeformis released at this site were uniquely

marked, aged, and sexed, and therefore could be followed through time. The objectives of my

study were to monitor this restored population of E. capsaeformis and evaluate a potentially new

survey technique by estimating population parameters at Cleveland Islands, and to compare and

assess the effectiveness of CMR versus quadrat methods for estimating population parameters.

To further compare CMR population parameter estimates and their precision to those from

traditional quadrat techniques, I also monitored two non-listed, naturally occurring, relatively

common species—the pheasantshell (Actinonaias pectorosa) and the Cumberland moccasinshell

(Medionidus conradicus)—at Cleveland Islands.

METHODS

Study Area

Mussel reintroductions were conducted in the upper Clinch River at Cleveland Islands

(CRKM 435.8) in the left descending channel (LDC). Cleveland Islands is located in Russell

County, near the town of Cleveland, VA. Owned by The Nature Conservancy and cooperatively

managed by VDGIF, Cleveland Islands are characterized by four channels formed by three

islands. The LDC contains excellent water quality, suitable flow conditions, stable gravel

substrates, and darter fish hosts utilized by E. capsaeformis and other mussel species. The most

recent survey of Cleveland Islands was conducted in 2008 by VDGIF (Eckert and Pinder 2010).

They found 23 live mussel species, including 7 federally endangered species—E. capsaeformis,

shiny pigtoe (Fusconaia cor), fine-rayed pigtoe (Fusconaia cuneolus), slabside pearlymussel

104

(Pleuronaia dolabelloides), fluted kidneyshell (Ptychobranchus subtentum), rough rabbitsfoot

(Quadrula cylindrica strigillata), and purple bean (Villosa perpurpurea). All E. capsaeformis

they encountered were tagged—indicating they were from recent translocations.

Epioblasma capsaeformis Translocations and Releases

Over a five-year period from 2006 to 2010, a total of 1,418 adult E. capsaeformis were

collected from the lower Clinch River, Hancock County, TN, and translocated along the LDC of

Cleveland Islands. An additional 2,501 and 350 laboratory-propagated sub-adult E. capsaeformis

were released into the LDC by the AWCC in 2010 and 2011, respectively. Each of these

translocated adult and released sub-adult E. capsaeformis were uniquely tagged (shellfish tag;

Hallprint Inc., Holden Hill, New South Wales, Australia), measured for length (mm), and sexed

for identification purposes. Generally, individuals <25 mm in size were not sexed because their

shells were not yet sexually dimorphic (i.e., the marsupial shell expansions of females were

undeveloped). However, the sex of a few translocated individuals, as small as 19 mm, was

predicted using inference from estimated age (e.g., if female, a 3–4+ year old estimated from

growth annuli should have begun to develop marsupial expansion). Additionally, individuals

were sexed at recapture and compared to original release data to further validate sex.

Sex ratio of translocated adults was approximately 1:1. At the time of translocation,

female adults ranged from 23 to 47 mm and averaged 36 mm in size, and male adults ranged

from 19 to 47 mm and averaged 33 mm. Laboratory-propagated sub-adults were approximately

1–2 years old (age class 2), ranged from 11 to 31 mm and averaged 21 mm in size. Translocation

of adults and releases of laboratory-propagated sub-adults were randomly distributed throughout

an approximately 125 x 15-m reach of the LDC.

105

Habitat Measurements

Upstream and downstream boundaries of the sampling study area were determined prior

to sampling by a preliminary qualitative snorkel survey. Observation of live E. capsaeformis or

shells, presence of other mussels, substrate composition, water depth and flow, and specific

locations of releases were taken into consideration. The LDC of Cleveland Islands is

approximately 125 m in length with an average wetted width of 14.8 m. The extended

boundaries (i.e., where the LDC reconnects with the main channel) of this study site are

approximately 35 m upstream and 100 m downstream of the core 125 m LDC, with average

wetted widths of 16.0 and 28.2 m, upstream and downstream, respectively. The estimated total

sample study area (A) was 5,085 m2. Banks were marked every 20 m with orange marking spray

to serve as a location guide during sampling.

Quadrat Sampling

Field Methods

Population demographic data for A. pectorosa, E. capsaeformis, and M. conradicus from

systematic quadrat sampling were collected in the LDC at Cleveland Islands, VA, following the

methods given in Chapter 1.

Estimation of Population Size and Density

Population sizes ( ) were defined as the total number of ≥1 year olds in the study area at

a particular point in time. This was estimated by multiplying the average count per systematic

106

sample by the total number of possible systematic samples (M) in the study area (Seber

1982; Smith et al. 2001; Strayer and Smith 2003):

∑

where: = abundance estimate,

M = number of possible systematic samples,

= count per systematic sample, and

m = number of systematic samples.

Because the site had four random starts (k=4), there were four systematic samples (m=4).

Dependent on the area (A=5085 m2) of the site, the area of the sampling unit (a=0.25 m

2), and

the total number of quadrats sampled (n), the total number of possible systematic samples (M)

was calculated following the formula in Smith et al. (2001):

∑

Variances for population sizes were estimated by the formula (Smith et al. 2001; Strayer

and Smith 2003):

( )

∑

For normally distributed sample data, the 95% confidence intervals for abundance were

calculated as:

√ ( )

Data was assessed for normality. Occasionally, the traditional approach to calculating

confidence intervals utilizing the assumption of a normal distribution has been found to be

inaccurate for mussel population size and density estimations. Based on mussel population

107

sampling simulations, mussel population sizes (or density) tend to have a positively (right)

skewed distribution (Pooler and Smith, unpublished data, cited by Smith et al. 2001; Strayer and

Smith 2003). If normality tests revealed a departure from normality, data were log-transformed

and 95% confidence intervals were calculated for abundance by using a logarithmic

transformation of the estimate and a delta-method approximation of variance (Seber 1982; Smith

et al. 2001; Strayer and Smith 2003):

(

√ ( )

)

Capture-Mark-Recapture Sampling

Field Methods

According to Otis et al. (1978), closed-population models with capture probabilities

averaging at least 0.10 require 5–10 sampling occasions to obtain reasonable estimates of

population size (Pollock 1982). Closed-population sampling designs would typically have

sampling occasions occurring on days close together (3–7 days apart) to ensure geographic and

demographic closure. This assumption of complete closure is frequently violated in CMR field

studies. Completing sampling within such a restrictive time frame is not always feasible due to

field conditions or labor availability. However, if closed population studies are properly

designed, closed-population CMR model assumptions can be met approximately (Otis et al.

1978).

Due to the large study site and time required to complete one capture occasion in my

study, a closed-population design of five occasions within a four-month period was chosen to

estimate population. This design permitted me to complete one closed-capture study of

108

individual encounter occasion (EO) histories per year (2011 and 2012). Time between

consecutive sampling occasions was set at 2–3 weeks; however, sampling time was dependent on

feasibility of sampling in unfavorable river conditions.

The study area was divided into twelve 20-m transects that extended from bank to bank.

Each transect was given a label (A–L), with A being at the downstream and L being at the

upstream boundary of the reach. Surveying begin at the downstream end and moved upstream

through each transect. Each transect reach was sampled and timed individually, and divided into

equal-width lanes (approximately 1 m wide) that ran parallel to the flow across the width of the

transect. Multiple surveyors lined up systematically and began sampling at the downstream end

of each lane, continued upstream, and stopped at the top of the transect. Surveyors then would

start over at the downstream end of that transect until all lanes within the transect survey area

were completed. This ensured that the entire transect substrate area from bank to bank was

systematically and thoroughly searched by surveyors while snorkeling. Excavation was not

performed during the CMR sampling. All individual mussels seen at the surface were collected

and their location in the substrate marked with surveyor’s flags. After a transect had been

completed, all mussels were identified, measured for length (mm), sexed (if possible), tag

numbers recorded, and placed back into the substrate where they were found. Any untagged E.

capsaeformis found throughout the study period were tagged before returning them to the

substrate.

In case population parameter estimation was influenced by population density, the size of

individuals, or typical species-specific behavior at the substrate surface, I included two additional

species, A. pectorosa and M. conradicus, in the CMR study in order to further compare the point

estimates and precision of CMR relative to quadrat sampling. I choose these two non-listed,

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naturally occurring, relatively common species because they occur at moderate to high densities

(>0.2/m2) at Cleveland Islands, are characterized by different maximum sizes, and exhibit

different ‘availability for detection’ behavior when at the surface relative to E. capsaeformis.

Average maximum sizes of A. pectorosa, E. capsaeformis, and M. conradicus are 150, 48–55,

and 60 mm, respectively. ‘Availability for detection’ behavior at the substrate surface is defined

as the appearance of siphons or displays at the substrate surface that would influence surveyor

detection ability (e.g., larger siphons=individual more likely to be detected by surveyor). On

average, siphon appearance at the substrate surface of A. pectorosa, E. capsaeformis, and M.

conradicus are large, medium, and small, respectively. Additionally, female E. capsaeformis

possess a specialized behavior in which they display a whitish-blue mantle-lure to attract host

fish in the Spring to early Summer (Jones and Neves 2011). This display is very visible at the

substrate surface, thus increasing its likelihood for detection. All untagged A. pectorosa and M.

conradicus observed were tagged before returning them to the substrate beginning from the first

CMR EO in 2011 up to the fourth EO in 2012. Tagging was not necessary for the fifth EO in

2012 because it was the final sampling event of this study. All individual mussels of any species

seen at the surface during the first CMR sampling event (i.e., first EO) of each year (2011 and

2012) were collected; however, during all subsequent sampling events within each year, only A.

pectorosa, E. capsaeformis, and M. conradicus individuals were collected, measured, recorded,

and tagged if previously untagged.

Complete closure of A. pectorosa, E. capsaeformis, and M. conradicus populations were

assumed for within year sampling. Populations were defined as all individuals that were ≥1 year

old due to the difficultly of observing same-year recruitment (i.e., individuals <10 mm) in the

absence of excavating or sieving substrates, and thereby adhered to the assumption of no ‘births’

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(i.e., recruits) during the capture period (Negishi and Kayaba 2010). Based on previous

laboratory and field studies, mussel annual mortality was assumed to be minimal (<5%) for

individuals within each population (Hua et al. 2011; Jones et al. 2012; C. Carey, Virginia

Polytechnic Institute and State University, unpublished data). Concerning migration, adult

mussels are sedentary and usually spend their entire lives in the same location with limited

means of dispersal (Vaughn and Taylor 1999). Based on these inferences, and the absence of E.

capsaeformis observations for this area prior to translocations and releases, the likelihood that: 1)

any individuals were recruited into the defined population (>10 mm), 2) mortality was

significant, and 3) that any individuals permanently migrated in or out of the study area within a

four-month period was minimal—thereby allowing the study to adhere to demographic and

geographical closure.

Estimation of Population Parameters

Population Size, Density, and Capture Probabilities—Population size and capture (i.e.,

encounter or detection) probabilities of tagged mussels were modeled within sampling year

(2011 and 2012). Population size ( ) was defined as the number of individuals in a defined area.

The capture probability ( ) was defined as the probability that a mussel is encountered given that

it was alive and in the study area. Collected E. capsaeformis were recorded in two data sets (one

for each year), each having five EOs, two groups (translocated adults and laboratory-propagated

sub-adults), and one covariate (length at capture). Similarly, A. pectorosa and M. conradicus

each had two data sets (one for each year). Actinonaias pectorosa data sets were comprised of

five EOs, one group, and individual length covariate. Medionidus conradicus data sets were

comprised of four and five EOs in 2011 and 2012, one group, and individual length covariates.

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Individual covariates were added to the analysis to allow me to assess whether capture

probabilities of an individual(s) were a function of length at capture, and if so, whether this

variable applied to all three or just specific species.

I used Program MARK (White and Burnham 1999) to estimate population parameter

estimates with associated standard errors from EO histories of marked individuals within each

survey year. I used the Huggins’ Full Closed Captures with Heterogeneity model, which allows

for effects of time, behavior, and heterogeneity. The Huggins Closed Capture data type also

allows for the incorporation of individual covariates (e.g., length of individual) to be used to

model capture probabilities and to estimate population size. In contrast to the Closed Capture

model (i.e., full likelihood model), the Huggins Closed Capture model does not include

individuals in its likelihood that were never captured. Therefore population size ( ) is

conditioned out of the likelihood in the model (i.e., conditional likelihood model) and is

estimated as a derived parameter (Huggins 1989, 1991; Pledger 2000; White 2008; Cooch and

White 2012). The derived population size ( ) still is defined as the number of individuals in a

defined population, but is estimated, for data with no individual covariates, as

[ ]

where: = total number of individually-marked mussels captured at

least once,

= probability the mussel is captured at time (i) given that it is alive

and in the defined population, and

= probability of not being captured at time (i).

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Individuals have their own capture probabilities ( ) when using individual covariates in

the analysis. The derived population size ( ) with individual covariates is now estimated by

calculating:

[ ]

for each individual and then taking the sum (White 2008; Cooch and White 2012). A more

complex formula and explanation is discussed by Huggins (1989, 1991).

Program MARK provided top model population size estimates and associated variances.

If two or more models were competing, models were averaged in Program MARK to provide

model averaged population size estimates and associated unconditional variances. The lower and

upper bounds of the confidence intervals for model averaged population sizes were derived as

(Cooch and White 2012):

{ [ (

)]

}

Population densities were calculated by dividing the modeled averaged population sizes

( ) by the sampling study area (A):

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Variances for population densities were calculated by dividing the unconditional

variances ( ( ) by the squared area:

( )

The 95% log-based confidence intervals for population densities were calculated as

(Cooch and White 2012):

[ ( ( )

)]

Apparent Survival Rates

Apparent survival probabilities (φi) of tagged E. capsaeformis from year to year (2006–

2012) and recapture probabilities (pi) within a sampling year (2011 and 2012) were estimated by

Program MARK using Cormack-Jolly-Seber (CJS) models. Apparent survival (φ) is the

probability of surviving between EOs and being available for recapture given that the individual

has not permanently emigrated from the study area (the CJS model cannot distinguish between

mortality and losses due to permanent emigration) (Pledger et al. 2003; Villella et al. 2004).

Translocated and laboratory-propagated sub-adult E. capsaeformis were recorded in one data

input file with 16 EOs. The data set consisted of two groups, translocated adults and laboratory-

propagated sub-adults. Time intervals between successive EOs were set in weeks. Time intervals

1–5 ranged from 39–54 weeks apart (equivalent to approximately 9–12 months between release

occasions), 6–10 ranged from 1–4.5 weeks apart, 11 was 36 weeks, and 12–15 ranged from 2–

3.5 weeks apart. There were 15 recapture parameters (p2–p16) representing recapture probabilities

p during each occasion i that occurred after the 1st encounter history (2006), and 15 survival

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parameters (φ1–φ15) representing survival probabilities φ between each occasion i in the data set.

A CJS diagram and data set formatting can be found in Appendix A.

Encounter history occasions 1–5 and 11 represented translocation and release events and

not active CMR surveys (2006–2010 annual release events, and the 2011 release event that

occurred after 2011 CMR sampling). These release events were treated as missing occasions

shown as ‘dots’ in the 1st through 5

th and 11

th EOs, and corresponding recapture probabilities ( )

were fixed at zero (i.e., recapture parameters p2–p5 and p11) to indicate that individuals were not

searched for on those occasions. However, the presence of a ‘1’ in the 1st through 5

th and 11

th

EOs indicated that the individual was released in that year. Encounter occasions 6–10 and 12–16

represented active CMR survey EOs in 2011 and 2012, respectively. All recapture probabilities

corresponding to active surveys in 2011 and 2012 (p6–p10 and p12–p16) were allowed to be time-

dependent. Survival parameters φ6–φ10 and φ12–φ15 were held constant (.) within 2011 and within

2012 because the population was assumed to be closed demographically within years. A time-

dependent constraint (i.e., allowed to vary year to year) was imposed on survival probabilities for

survival parameters φ1–φ5 and φ11—representing survival between initial release and the first

survey year 2011 and between 2011 and 2012 surveys (Appendix A).

To test whether stream discharge influenced recapture probabilities, a constraint of mean

daily discharge was imposed on recapture parameters p6–p10 and p12–p16. Daily mean values of

discharge for the Clinch River were obtained at U.S. Geological Survey gage number 03524000,

which is located 200 m upstream of the study site. As with E. capsaeformis, demographic data

collected for A. pectorosa or M. conradicus within each active sampling year, 2011 and 2012,

were treated as closed. Survival was not be estimated between 2011 and 2012 for A. pectorosa

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and M. conradicus because with only two years of data, survival and recapture parameters are

not individually identifiable.

.

Program MARK Model Selection: Huggins Closed Capture & Cormack-Jolly-Seber Models

An a priori model candidate set based on species biology as well as selection models

from Otis et al. (1978), including a saturated model, were run in Program MARK. Given the

data, Program MARK uses Akaike’s Information Criterion (AIC) to select the most

parsimonious model—best fit with fewest parameters— to explain the variation in the data.

The AIC is an estimator of the difference between the unknown ‘true’ model that explains the

data and the given approximating model in our candidate set. To optimize precision and fit of the

model to the data, the AIC is calculated using the model likelihood and the number of parameters

in the model. The fit of the model is positively associated with model likelihood, thus as the

model fit increases, the AIC declines. More parameters in a given model indicates greater

uncertainty— thus as the precision decreases, models are penalized and AIC increases. In

otherwords, the model with the lowest AIC in the given candidate set is the ‘best’ model to

balance precision and fit and describe the data (i.e., model nearest to the unknown truth).

Specifically, Program MARK uses the corrected AIC (AICc) to account for small sample sizes.

To account for lack of fit between saturated and general models in a candidate set, the AICc is

adjusted to yield the quasi-likelihood adjusted AIC (QAICc) for model selection (Anderson et al.

2000; Boulanger et al. 2002; Johnson and Omland 2004; Cooch and White 2012).

Models are ranked in Program MARK by lowest–highest corrected or adjusted AIC

(AICc or QAICc, respectively). For each candidate model, the difference in AIC (ΔAIC) between

two models (the model with the lowest AIC and the given model) are provided. When ΔAIC<2

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between two models, Burnham and Anderson (1998) suggest that it is reasonable to conclude

that both models have approximately equal weight in the data (i.e., as ΔAIC increases, there is

evidence to suggest a real difference between models). For model selection, generally models

with ΔAIC<2 have support, models within 2<ΔAIC<7 have less support, and models ΔAIC>7

have no support (Burnham and Anderson 1998; Anderson et al. 2000; Cooch and White 2012).

For my analysis, I reported any models with a ΔAIC<7 of the most parsimonious model.

Program CAPTURE was used to test for violations of the closure assumption for each

closed capture data set. Because Program CAPTURE is limited to running 2,000 individual

encounter histories, and the number of individual A. pectorosa encountered exceeded this limit, I

chose three random samples of 2,000 A. pectorosa encounter histories among the data sets for

each year (2011 and 2012) in order to test closure. While this closure test is unaffected by

heterogeneity in capture probabilities, it is not appropriate for populations that may exhibit time

or behavior variation in capture probabilities or temporary migration during the study period

(Otis et al. 1978; Stanley and Burnham 1999).

Additional goodness-of-fit (GOF) testing was done to verify that my saturated (or

general) model adequately fits the data (i.e., test underlying model assumptions) and to assess

overdispersion in the data (Boulanger et al. 2002; Cooch and White 2012). Lack of model fit

indicates that the assumptions underlying the model not being met. This is assessed by

measuring overdispersion, or extra-binomial noise, in the data set—the degree to which the data

exhibit greater variability than is predicted by the model (Boulanger et al. 2002; Hinde and

Demétrio 1998; Cooch and White 2012). By measuring overdispersion in the data, a quasi-

likelihood parameter (variation inflation factor, ĉ) can be estimated and lack of fit can be

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corrected for. An estimate of ĉ=1 indicates the model fits the data, ĉ >1 indicates overdispersion,

and ĉ <1 indicates underdispersion (Cooch and White 2012).

To test lack of fit and produce a quasi-likelihood parameter (ĉ), I ran GOF tests on the

saturated (fully parameterized) model without individual covariates in Program MARK using the

χ2 test statistic that allows for time variation in capture probabilities. I used the median ĉ and

parametric bootstrapping approaches to test the closed-population and CJS models, respectively.

These approaches produced median ĉ values (closed-population model) and bootstrapped

simulation data (CJS model). If the logistic regression for the median ĉ test failed to run on the

fully saturated model, the next-most parameterized model was used. Bootstrapped simulation

data were used to estimate ĉ values by two approaches: 1) taking the ratio of the original data

deviance by the simulated mean deviance, and 2) taking the ratio of the observed ĉ (i.e., observed

deviance divided by the observed deviance degrees of freedom) by the simulated mean ĉ (Cooch

and White 2012). If overdispersion was detected (ĉ >1), the ĉ parameter was adjusted and QAICc

was used for model selection. If underdispersion was detected (ĉ<1), ĉ was left unadjusted at 1.

Likelihood ratio tests were utilized in Program MARK to compare nested models as

needed. Top models were chosen based on parsimony and biology, and top competing models

were then averaged to produce estimates of population size, capture probabilities, and survival

rates. A constraint was imposed on the final p in all models to ensure that it was identifiable (i.e.,

not confounded by final survival or recapture parameter; White 2008). To test the assumption

that survival is approximately 100% within a year (e.g., within 2011 and 2012 sampling

occasions), nested survival models were compared (i.e., time-dependent versus constant and/or

time fixed at zero model). I predicted E. capsaeformis capture probabilities to vary by group

(translocated adults versus un-sexed laboratory-propagated sub-adults), time, and to be positively

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related to size. Similarly, I expected A. pectorosa and M. conradicus capture probabilities to

have a time effect and relation to size. Based on previous studies (see Chapter 1), I predicted E.

capsaeformis to have relatively high (>80%) annual survival rates.

Comparisons of Sampling Methods and Population Size Estimates

To compare sampling method estimators and between year (2011 to 2012) population

size estimates, unpaired t-tests were used to calculate estimates of the magnitude of difference

(unstandardized effect size=mean difference) and associated 95% confidence intervals. Unequal

variance t-tests were used to handle unequal sample sizes and variances (Satterthwaite 1946;

Welch 1947; Ruxton 2006). Results provided statistical and biological inference to whether the

sampling method estimates significantly differed. Biological importance was assessed based on

the magnitude of effect and in the context of the species. For example, an effect size of 1,000

individuals for E. capsaeformis would be biologically important in this study because it would

cause two very different conclusions to be drawn about the survival of reintroduced individuals

and the effectiveness of population restoration efforts at this site. Alternatively, the same effect

size may not be biologically important for A. pectorosa or M. conradicus, both of which are

established, common species at this site occurring at moderate to high density levels. Analyses

were conducted using SAS software (SAS Institute, Inc., Cary, North Carolina, version 9.2).

Growth

Length data from live individuals and shells of E. capsaeformis were collected during

systematic quadrat and CMR sampling and used to determine absolute growth:

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where L1 is length at release, L2 is length at recapture, and t2-t1 is time between release and

recapture. The growth parameters asymptotic maximum length (L∞) and growth coefficient (k)

were estimated from absolute growth data using the NLIN procedure is SAS:

( )

where Lt is length at recapture and t is time between release and recapture.

RESULTS

Quadrat Sampling

A total of 44 E. capsaeformis were collected in 2011, comprised of 11 translocated

adults, 32 laboratory-propagated sub-adults, and 1 natural recruit. Similarly, 41 individuals were

collected in 2012, comprised of 11 translocated adults, 29 laboratory-propagated sub-adults, and

1 natural recruit.

Estimated abundances and densities of translocated adult E. capsaeformis were 577

(SE=155) individuals and 0.11/m2 (SE=0.03) in 2011, and 645 (SE=110) individuals and 0.13/m

2

(SE=0.02) in 2012. Estimated abundances and densities of laboratory-propagated sub-adult E.

capsaeformis were 1,678 (SE=42) individuals and 0.33/m2 (SE=0.01) in 2011, and 1,700

(SE=229) individuals and 0.33/m2 (SE=0.05) in 2012. Estimated abundances and densities of E.

capsaeformis recruits were 52 (SE=26) individuals and 0.01/m2 (SE=0.01) in 2011, and 59

(SE=29) individuals and 0.01/m2 (SE=0.01) in 2012. There was no significant difference

between the 2011 and 2012 estimates of abundance and density for translocated adults or

laboratory-propagated sub-adults (Figure 1).

Estimated abundance and density of A. pectorosa were 9,227 (SE=328) individuals and

1.81/m2 (SE=0.07) in 2011, and 7,972 (SE=482) individuals and 1.57/m

2 (SE=0.09) in 2012.

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Estimated abundance and density of M. conradicus were 4,404 (SE=203) individuals and

0.87/m2 (SE=0.04) in 2011, and 5,158 (SE=349) individuals and 1.01/m

2 (SE=0.07) in 2012

(Figure 1).

Approximately 40 person-hours of effort were required to complete sampling of quadrats

each year. A total of 440 and 380 individuals representing 20 and 18 species were encountered in

2011 and 2012, respectively (Appendix B; Chapter 1).

Capture-Mark-Recapture Sampling

Summary of Encounters in 2011 and 2012

A total of 255 individual E. capsaeformis were collected in 2011, comprised of 144

translocated adults, 110 laboratory-propagated sub-adults, and one natural recruit. The recruit

was 23.8 mm, male, and 2–3 years old; lengths corresponding to 0–1 and 1–2 years old growth

annuli were 8.6 and 15 mm. Likewise, 231 individuals were collected in 2012, comprised of 98

translocated adults and 132 laboratory-propagated sub-adults. Time between consecutive

sampling occasions within each year was approximately 19 days apart. On average, 0.5 min of

search effort was required to survey 1 m2 of substrate surface area, and approximately 172

person-hours of effort were required to complete CMR sampling each year. This included time

spent surveying substrate surface, collecting mussels, and processing (i.e., recording, measuring,

and tagging) all study species (A. pectorosa, E. capsaeformis, and M. conradicus). A total of

9,923 and 5,719 individuals (including approximately 100 shells each year) representing 25

species (some Fusconaia and Pleurobema species were pooled due to lack of positive

identification) were collected in 2011 and 2012, respectively (Appendix B).

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Closed Capture-Mark-Recapture Modeling: Population Size and Capture Probabilities

Epioblasma capsaeformis 2011—There were 293 captures (n) of 254 uniquely marked [M(t+1)]

E. capsaeformis in 2011, comprised of 144 translocated adults and 110 laboratory-propagated

sub-adults. Of the translocated adults, 83 were female and 61 were male. Of these 254

individuals, 33 were captured 2–3 time times (no individuals were encountered four or more

times). The closure test in CAPTURE was marginally significant (p=0.05), indicating a potential

violation of closure. A reduced model was used (pi[.]p[g*t]=c[g*t] in Table 1) for median ĉ GOF

testing in MARK due to nonsensical standard errors of parameter estimates for the full group x

time model. The median ĉ was 1.07 (SE=0.04), suggesting minimal overdispersion.

The candidate model set consisted of 22 starting (a priori) closed-capture models. I

removed ten of the starting models due to one or more nonsensical detection probabilities,

population size estimates, and associated standard errors that likely resulted from sparse data and

an inability to model certain parameters. Of these remaining 12 models, 2 had ΔQAICc<7.

Greater than 99% of parameter support came from these top two models, with 63% from the

most parsimonious model (Table 1). Model averaged population size and density estimates were

453 (SE=96) and 0.09/m2 (SE=0.02) for translocated adults and 1,915 (SE=1,030) and 0.38/m

2

(SE=0.20) for laboratory-propagated sub-adults (Table 2; Figure 1). There was support that

capture probabilities (p) varied by time and group and were a function of length (Table 1; Figure

2).

Epioblasma capsaeformis 2012—There were 254 captures (n) of 230 uniquely marked [M(t+1)] E.

capsaeformis in 2012, comprised of 98 translocated adults and 132 laboratory-propagated sub-

adults. Of the translocated adults, 66 were female and 32 were male. Of these 230 individuals, 21

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were captured 2–3 times (no individuals were encountered four or more times). The closure test

in CAPTURE was not significant (p=0.25), indicating the population was closed during the

sampling period (i.e., met closure assumptions). The median ĉ estimated generated in MARK

was 2.78 (SE=0.07), suggesting overdispersion of the data.

The candidate model set consisted of 22 starting (a priori models same as 2011) closed

capture models. I removed ten of the starting models due to one or more nonsensical detection

probabilities, population size estimates, and associated standard errors that likely resulted from

sparse data and an inability to model certain parameters. Of these remaining 12 models, 3 had

ΔQAICc<7 (making up >99% of parameter support), with over 72% from the most parsimonious

model (Table 1). Derived population size and density estimates were 372 (SE=132) and 0.07/m2

(SE=0.03) for translocated adults, and 1,390 (SE=1,018) and 0.27/m2 (SE=0.20) for laboratory-

propagated sub-adults (Table 2; Figure 1). Capture probabilities (p) varied by time and were a

function of length (Table 1; Figure 2).

Actinonaias pectorosa 2011—A total of 3,771 [M(t+1)] A. pectorosa were uniquely marked in

2011, of which 1,641 individuals were captured on multiple occasions. There were 6,140

captures (n) over five encounter occasions. The test for closure procedure in CAPTURE was

significant (p<0.01), indicating that the population is most likely not closed during sampling.

Median ĉ estimate generated in MARK was 1.93 (SE=0.19), indicating moderate overdispersion

of the data.

The candidate model set consisted of 18 starting (a priori models same as E.

capsaeformis but with no group models) closed capture models. I removed two of the starting

models due to nonsensical detection probabilities and associated standard errors that likely

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resulted from sparse data and an inability to model certain parameters. No models fell within

ΔQAICc<7 of the most parsimonious model, which >97% of parameter support came from the

most parsimonious model (Table 3). Derived population size and density estimates were 6,615

(SE=483) individuals and 1.30/m2 (SE=0.09) (Table 2; Figure 1). Capture probabilities (p) varied

by time and were a function of length (Table 3; Figure 3).

Actinonaias pectorosa 2012—A total of 2,471 [M(t+1)] A. pectorosa were uniquely marked in

2012, of which 722 individuals were captured on multiple occasions. There were 3,372 captures

(n) over the five encounter occasions. The test for closure procedure in CAPTURE was

significant (p<0.01), indicating that the population is most likely not closed during sampling.

Median ĉ estimate in MARK was 0.26 (SE=0.27), indicating underdispersion of the data.

The candidate model set consisted of 18 starting (a priori models same as 2011 A.

pectorosa models) closed capture models. I removed four of the starting models due to one or

more nonsensical detection probabilities, population size estimates, and associated standard

errors. Of these remaining models, three models had ΔAICc<7 (making up >99% of parameter

support), with >49% of parameter support from the most parsimonious model. These three

models were competing models, each within 2 ΔAICc of one another (Table 3). Model averaged

population size and density estimates were 4,729 (SE=203) individuals and 0.93/m2 (SE=0.04)

(Table 2; Figure 1). Capture probabilities (p) varied by time and were a function of length (Table

3; Figure 3).

Medionidus conradicus 2011—A total of 1,366 [M(t+1)] M. conradicus were uniquely marked in

2011, of which 338 individuals were captured on multiple occasions. There were 1,753 captures

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(n) over the four encounter occasions. The test for closure procedure in CAPTURE was

significant (p=0.03), indicating that the population was likely not closed during sampling.

Median ĉ in MARK was 4.67 (SE=0.17), indicating overdispersion of the data. The median ĉ

estimate likely was large due to sparse data (only four encounter occasions).

The candidate model set consisted of 18 starting (a priori) closed capture models. I

removed eight of the starting models due to one or more nonsensical detection probabilities,

population size estimates, and associated standard errors. The remaining ten models consisted of

three models with a ΔQAICc<7, and two competing models (ΔQAICc<2) with 55.3% of

parameter support from the most parsimonious model, followed by 32.9% for the next best

supporting model (Table 4). Model averaged population size and density estimates were 3,237

(SE=825) individuals and 0.67/m2 (SE=0.16) (Table 2; Figure 1). Capture probabilities (p) varied

as a function of length (Figure 3). There was some support (2<ΔQAICc<7) that capture

probabilities (p) varied over time (Table 4; Figure 3).

Medionidus conradicus 2012—A total of 1,088 [M(t+1)] M. conradicus were uniquely marked in

2012 (or encountered from 2011 and considered a new tag), of which 194 individuals were

captured on multiple occasions. There were 1,304 captures (n) over the five encounter occasions.

The test for closure procedure in CAPTURE was not significant (p=0.23), indicating the

population met closure assumptions. Median ĉ in MARK was 0.60 (SE=0.13), indicating

underdispersion of the data, and ĉ was left unadjusted at 1.0.

The candidate model set consisted of 18 starting (a priori) closed capture models. I

removed eight of the starting models due to one or more nonsensical detection probabilities,

population size estimates, and associated standard errors. Of these remaining ten models, six

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models had a ΔQAICc<7. The top two models were competing, with 59.8% of parameter support

from the most parsimonious model followed by 25.0% for the next best supporting model (Table

4). Model averaged population size and density estimates were 2,849 (SE=160) individuals and

0.56/m2 (SE=0.03) (Table 2; Figure 1). There was support that capture probabilities (p) varied

over time and as a function of length (Table 4; Figure 3).

Open Capture-Mark-Recapture Modeling: Survival and Recapture Probabilities

Epioblasma capsaeformis—There were a total of 4,841 captures (n) (includes initial releases) of

4,269 uniquely marked [M(t+1)] E. capsaeformis, comprised of 1,418 translocated adults and

2,851 laboratory-propagated sub-adults. Of these 4,269 individuals released, 3,828 were never

recaptured (i.e., not observed after release during CMR sampling in 2011 and 2012). There were

a total of 533 recaptures (i.e., captures after initial release) consisting of 441 unique individuals.

Of these 441 individuals, 365 were recaptured once, 62 were recaptured twice, 12 were

recaptured three times, and 2 were recaptured four times. No individuals were recaptured five or

more times over the 2011 and 2012 CMR sampling events.

Bootstrap parametric and median ĉ GOF tests may not be accurate due to the missing

encounter occasions in my data set. Parametric bootstrap simulations provided ĉ estimates of

0.13 and 0.67, indicating underdispersion. However, because the observed model deviance fell

below all deviances from the simulated data (p=1.00), I concluded that there is an adequate fit of

the model to the data. Median ĉ in MARK was 1.26 (SE=0.01), indicating moderate

overdispersion of the data. The ĉ parameter was adjusted to 1.26 for model selection.

The candidate model set consisted of 24 starting (a priori) CJS models incorporating

group, heterogeneity, time, and gage discharge covariate effects. I removed 20 of the starting

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models due to one or more nonsensical survival estimates, detection probabilities, and associated

standard errors that likely resulted from sparse recapture data relative to number of individuals

released and an inability to model certain parameters. Additionally, all starting models

incorporating gage discharge did not differ from corresponding models without gage discharge

and therefore were removed from model selection. Of these remaining four models, >99% of

parameter support came from the most parsimonious model. Apparent annual survival estimates

were >99% for translocated adults and laboratory-propagated sub-adults. There was support that

recapture probabilities (p) varied by time and group (Table 6; Figure 4).

Comparisons of Sampling Methods and Population Size Estimates

Statistically (p<0.05) and biologically significant differences were revealed between

estimator methods for A. pectorosa 2011 (p <0.01, effect size=2,612) and 2012 (p =0.01,effect

size=3,243), and M. conradicus 2012 (p =0.01, effect size=2,309) population size estimates.

Statistically significant differences were revealed between estimator methods for laboratory-

propagated sub-adult E. capsaeformis 2011 (p<0.01) and for native M. conradicus 2011 (p

=0.01) population size estimates; however, these differences were not considered biologically

significant at effect sizes of 237 and 1,167 individuals, respectively. No significant differences in

population size estimates were revealed by comparisons of estimator methods (Table 5).

Generally, CMR estimates were more precise than systematic quadrat estimates. The

CMR population size estimates were more precise than systematic quadrat estimates for

translocated adult E. capsaeformis, and for native A. pectorosa and M. conradicus in 2011 and

2012. Neither population size estimators produced consistent precise estimates of laboratory-

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propagated sub-adult E. capsaeformis, which may be related to their smaller size and subsequent

lower capture probabilities (Table 2; Table 5; Figure 1).

Actinonaias pectorosa population sizes estimated by CMR revealed a statistically

(p<0.01) and biologically significant difference between 2011 and 2012, with an effect size of

1,886 (SE=4) individuals that represented a density decline of 0.37/m2. Actinonaias pectorosa

population size estimated by systematic quadrats revealed a marginally statistical (p=0.08) and

biologically significant difference, with an effect size of 1,255 (SE=583) individuals, that

represented a density change of 0.25/m2 from 2011 to 2012. Statistical significance was detected

between 2011 and 2012 population sizes estimated by CMR for E. capsaeformis (p<0.01) and M.

conradicus (p<0.01). However, these differences were not considered biologically significant at

effect sizes of 81, 525, and 388 individuals for translocated adult and laboratory-propagated sub-

adult E. capsaeformis, and M. conradicus, respectively (Table 5).

None of the other within species comparisons (estimated by either survey method),

showed a significant decline or increase in population size between 2011 and 2012 (Table 5;

Figure 1). Population size of recruits (individuals captured that were neither translocated nor

laboratory-propagated) could not be modeled in Program MARK because of insufficient data

(i.e., only one individual was encountered on one occasion during CMR sampling), therefore no

comparisons to systematic quadrat estimates were made.

Growth

Recaptured E. capsaeformis length data indicate that growth (i.e., length from one year to

the next) of translocated adults and laboratory-propagated sub-adults can be estimated using the

equations:

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( ) for female translocated adults,

for male translocated adults, and

for unsexed, laboratory-propagated sub-adults.

DISCUSSION

My study has shown that E. capsaeformis population restoration efforts since 2006 were

successful in the upper Clinch River at Cleveland Islands, VA, and that CMR has useful

application in the inference of demographic parameters for mussels. Recruitment was

documented by systematic quadrat and CMR survey methods, indicating that natural

reproduction is occurring for this species. Estimated population parameters were similar between

systematic quadrat sampling and the CMR approach. Although CMR was nearly four times more

time intensive (person-hours effort) than quadrat sampling, I processed nearly three times the

number of mussels per person-hour effort and collected over 5 and 10 times the number of

individual laboratory-propagated sub-adult and translocated adult E. capsaeformis, respectively,

than I did from systematic quadrat sampling. Because CMR resulted in data from more

individuals, it provided more precise estimates of population size and density, and improved

estimates of survival, and growth rates. To my knowledge, this is the first study to

simultaneously conduct systematic quadrat and CMR surveys to estimate population parameters

for mussels and to determine monitoring design effectiveness for each method.

Estimating species-specific demographic vital rates is essential to assessing

reintroduction success, evaluating whether delisting criteria have been met, and for developing

effective management plans (USFWS 2004; Villella et al. 2004; Jones and Neves 2011; Meador

et al. 2011). The common methodology for collecting demographic data is the quantitative

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quadrat survey, which has been used in the Clinch River since the mid-1970s (Dennis 1985).

Systematic quadrat sampling is a probability-based survey method for assessing rare or clustered

populations, is simple to execute in the field, and offers effective spatial coverage (Christman

2000; Smith et al. 2001; Strayer and Smith 2003). In addition, with probability-based sampling,

the probability that a species is present at a specified mean density even if it were not detected

can be estimated (Green and Young 1993; Strayer and Smith 2003).

Because not all mussels are available at the surface for one quadrat survey point in time

(temporary emigration), population parameter estimates may be biased if quadrat excavation is

not executed (Amyot and Downing 1991; Smith et al. 2001). Even when excavation is applied to

minimize temporary emigration, it is unknown whether excavation disrupts substrate

composition and stability, causes increased mortality, disrupts reproduction, or causes significant

displacement of individuals (i.e., permanent emigration) (Smith et al. 1999). In addition to

possible biological disturbances, excavation can be resource intensive. Excavating or not,

quadrat sampling is often problematic to implement in deep water and high velocity habitats

(Meador et al. 2011). Although useful for estimating and detecting trends in abundance and

density, quadrat sampling provides only broad estimates of species diversity, sex ratios, length-

frequency distributions, growth rates, age-class structure, and periodic survival and recruitment

rates—particularly when target species are at low densities (Table 7).

Less commonly used to assess mussel populations, CMR-based designs track and collect

data on uniquely tagged individuals over time in order to model and estimate demographic

parameters. The assumption of equal catchability of marked and unmarked mussels during any

sampling occasion often is violated due to temporary emigration (i.e., vertical migration) and

leads to biased population parameter estimates. A properly designed and executed CMR study

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can model the effects of temporary emigration, providing more precise population parameter

estimates, and determine what factors are influencing survival and capture probabilities (Otis et

al. 1978; Pollock 1990; Villella et al. 2004; Meador et al. 2008).

Similar to quadrat surveys, CMR is useful for estimating and detecting trends in

abundance and density, but in addition, it can: 1) offer improved precision of species density

estimates (i.e., low to high), 2) provide information for species’ vital rates (i.e., annual survival,

recruitment) which are difficult to determine with quadrat methods, 3) investigate factors

influencing survival and capture, and 4) validate and improve species-specific demographic

models (Table 7; Cormack 1964; Jolly 1965; Seber 1982; Strayer and Smith 2003; Villella et al.

2004). Improved estimates of population parameters are partly a result of high numbers of

captures and recaptures of individuals, thus increasing sample size. Additionally, testing what

factors are important predictors of capture probabilities provides biologists with guidelines to the

conditions under which specific species are most likely to be encountered at any given time (i.e.,

under what specific temperature, discharge, reproductive condition, etc.), and to creating

efficient monitoring plans (Villella et al. 2004; Meador et al. 2011). Because of the increasing

importance of understanding and monitoring species-specific population dynamics for

conservation management, the use of CMR studies for mussels has been expanding (Villella et

al. 2004; Meador et al. 2011).

Mussels exhibit seasonal (time) and species-specific life requirement patterns of vertical

migration in substrate. Several factors can influence when mussels are available at the substrate

surface— and consequently influence their capture probability—to include age, size (length),

water temperature, stream discharge, day length, habitat type, and reproductive condition

(Amyot and Downing 1991, 1997; Balfour and Smock 1995; Watters et al. 2001; Villella et al.

131

2004; Meador et al. 2011). Some of these studies have shown that larger and older individuals

tend to be more epibenthic than juveniles and smaller individuals, even during warmer months.

Mussel size can positively influence surveyor ability to detect individuals at the substrate surface

simply because larger individuals are easier to detect visually.

A few mussel CMR studies have examined the factors influencing temporary emigration

and capture probabilities (Villella et al. 2004; Meador et al. 2011; B. Watson, VDGIF,

unpublished data). Villella et al. (2004) and Meador et al. (2011) found that vertical migration

patterns varied by species and season, and suggested it was associated with reproductive

behavior (e.g., actively spawning or releasing glochidia). They also determined that capture

probabilities were influenced by body size (i.e., shell length), water temperature, and habitat

type. Capture probabilities may also be affected by environmental sampling conditions, such as

discharge affecting visibility due to turbidity (B. Watson, VDGIF, unpublished data).

In agreement with those of Meador et al. (2011) and Villella et al. (2004), my results

indicate that capture probabilities varied by species and with time, and were positively associated

with shell length. Though my mean capture probability estimates for E. capsaeformis (2–6%)

were lower than those reported for other species during the warmer months by Meador et al. (8–

20%; 2011) and Villella et al. (7–19%; 2004), my mean capture probability estimates for A.

pectorosa (11–24%) and M. conradicus (7–15%) were similar. The lower capture rates of E.

capsaeformis may reflect the difficulty of detecting a species existing at much lower densities

(<0.4/m2) than my other two study species, or be related to species-specific behavior or smaller

body size. Actinonaias pectorosa had the highest capture probabilities, presumably because

individuals are larger. Interestingly, capture probability trends were similar for all three species

within each year. Capture probabilities were higher in mid-June, then declined by mid-July

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before steadily increasing through late September. Because all three species are long-term

brooders—spawning in late summer and autumn, gravid through winter, and releasing glochidia

the following summer—capture patterns may be related to vertical migration due to reproductive

behavior (Watters et al. 2001; Villella et al. 2004). Because mean capture probabilities were

different within species among years, but trends were similar within years among species,

environmental conditions (e.g., temperature, stream discharge) additionally may have played a

role.

Estimating vital rates and identifying factors influencing mussel survival is important for

developing effective conservation plans. Several factors have been found through CMR studies

that influence annual survival, including age, length, habitat type, and stream discharge (Villella

et al. 2004; Meador et al. 2011). Over a four-year CMR study using a CJS model, Villella et al.

(2004) estimated high annual survival rates for the Eastern elliptio (Elliptio complanata), and

suggested that survival was time- and size-dependent. Using a PIT-tag CMR methodology, Hua

et al. (2011) documented high annual survival (>98%) for the Cumberlandian combshell

(Epioblasma brevidens) in the Powell River, TN. Furthermore, during a one-year Robust Design

CMR study, Meador et al. (2011) reported survival variation among habitat type and a positive

association with shell length. In agreement with these other CMR findings of high adult annual

survival rates (>90%), my results indicated that E. capsaeformis exhibited very high annual

survival (>98%). By understanding these and other factors affecting survival, reintroduction plan

details such as habitat characteristics of the release site, and recommendations regarding age,

length and sex of released individuals can made to optimize annual survival, and ultimately,

restoration success.

133

In addition to providing important insights into the ecological relationships affecting vital

rates and capture probabilities (Villella et al. 2004), CMR studies can be used to validate

previous conclusions on vital rates estimated from other sampling approaches, such as

quantitative quadrats, length-at-age catch-curves or shell thin-sectioning analyses. By following

unique individuals through time, I was able to estimate survival rates based on fates of

individuals captured. Although original aging of uniquely marked translocated adults was

estimated using predicted age-at-length curves, my study was able to estimate annual survival

rates by combining capture histories with known time since release. Similarly, tagged laboratory-

propagated sub-adults were of known age at release and provided concrete age-specific data for

estimating annual survival rates. The results of my study were in agreement with previous

predictions—high annual survival for sub-adult and adult age-classes—estimated using shell

thin-sectioning analyses and length-at-age data of mussels collected from systematic quadrat

surveys in the Clinch River, TN (Jones et al. 2012).

Presently, a von Bertalanffy growth curve (von Bertalanffy 1938) could not accurately be

fitted to my growth data for E. capsaeformis for several reasons: 1) the ages of translocated

adults were estimates based on predicted length-at-age equations in Jones and Neves (2011), 2)

laboratory-propagated sub-adult growth data only represented younger age classes (≤ 3 years

old), and 3) shell thin-sectioning was not conducted. Using the available laboratory-propagated

sub-adult length-at-age data for 1–3 year olds likely would have resulted in biased estimates of

growth parameters. Nevertheless, the collection of more individuals through CMR sampling did

allow me to make inferences about growth of reintroduced individuals using absolute growth

(i.e., directly measured growth). Growth parameters from previously estimated von Bertalanffy

growth curves are not directly comparable to those from my study, but can be used as a base-line

134

for comparison. Jones and Neves (2011) reported higher growth coefficients (k) for naturally

reproduced female (0.27) and male (0.42) E. capsaeformis in the Clinch River, TN than the

translocated adult females (0.17) and males (0.13) evaluated in my study. In contrast to Jones

and Neves (2011), this likely is because my estimated growth parameters do not represent the

growth curve over the lifespan of E. capsaeformis, but represent growth after handling,

transportation, and release into a new habitat (i.e., growth earlier in life is not included). My

estimated growth coefficients are lower and may reflect growth variation because my data

represent only older age classes (≥ 3 years old). Younger age classes (< 3 years old) of E.

capsaeformis exhibit accelerated growth (larger k) before decreasing as individuals approach

older and maximum ages (Jones and Neves 2011); thus, the absence of the first years of growth

data for translocated adults would produce underestimates of growth coefficients.

Stress from a physical disturbance also may have influenced growth after release.

Mussels are known to lay down disturbance rings, which represent brief cessations of growth due

to factors such as handling (Haag and Commens-Carson 2008). Population restoration through

reintroductions or augmentations involves transportation and considerable physical handling; it is

unknown how much this may disrupt short- and long-term growth (Cope and Waller 1995).

Additional sources of variation in estimated growth parameters from my study relative to those

from Jones and Neves (2011) may include differences in site characteristics, such as water

chemistry and temperature (Haag and Rypel 2011), mean annual streamflow (Rypel et al. 2008),

or growth rate determination techniques (i.e., measuring absolute growth of marked individuals

versus shell thin-sectioning ) (Kesler and Downing 1997). Through shell thin-sectioning and

future monitoring at my study site, a complete length-at-age data set for introduced E.

capsaeformis can be compiled and a predicted length-at-age equation can be computed to

135

compare to that of Jones and Neves (2011). Consequently, further data from marked individuals

can be used to test the assumption of shell annuli formation for E. capsaeformis and accuracy

and precision of shell thin-sectioning.

Also of concern is whether sampling and monitoring efforts can cause declines in

abundance and density due to disturbance. It is unknown how much disturbance—through the

excavation of substrate or removal of mussels from substrate for processing—influences

mortality rates or increases displacement of individuals. In this study, CMR indicated a

significant decline in A. pectorosa abundance between 2011 and 2012. This decline was not

revealed by the systematic quadrat survey, presumably due to the larger variation in the quadrat

survey abundance estimator. It is likely that displacement from the site—rather than natural or

induced mortality—was responsible for this decline in abundance. Other studies suggested low

to no mortality from presumed similar levels of handling stress, nor did they reveal related

declines in abundance (Kesler and Downing 1997; Villella et al. 2004). Even though all mussels

were returned to where they were found in the substrate, the average size of A. pectorosa

individuals was larger than those of the other study species and these mussels may have had a

more difficult time re-burrowing into the substrate after handling. This in combination with

surveyors moving about the stream bed and high flow events after surveying may have displaced

some A. pectorosa individuals downstream out of the survey area, resulting in the decline in

abundance noted from the study area.

Despite the large number of mussel population restoration projects that have been

conducted over the last century (Haag 2012), few have determined the long-term success of these

efforts (Cope and Waller 1995). Detecting trends in population size, estimating species-specific

vital rates, identifying factors influencing capture and survival, and long-term monitoring are

136

essential to developing effective conservation plans and to determine long-term success of

reintroduction efforts (Sarrazin and Legendre 2000). By performing and reporting post-

restoration population monitoring, projects can provide insight into the relative success of

method-specific restoration efforts and population viability. Both systematic quadrat and CMR

sampling techniques have useful applications in population monitoring—and towards assessing

population viability—but are dependent on project objectives.

My results indicate that CMR is a more useful method than quadrat sampling for

monitoring population abundance and density trends. Employing CMR methodologies improves

our knowledge and precision of species-specific vital rates, and accounts for incomplete capture

of mussels, both shortcomings of quadrat sampling. However, CMR can be more resource

intensive depending on the scope of the project and sampling design. In addition to considering

project objectives and availability of resources, the selection of an appropriate mussel CMR

design should consider study site area and species distribution. If a study area is large (>2,000

m2), and target species distribution is random, biologists should sample a random sub-set of

strip-transects because it is less resource intensive and still provides precision in population

parameter estimates. If a study area is small (<500 m2), a CMR design surveying the entire

substrate surface area could easily be implemented in a cost-effective manner. Finally, if

monitoring is long-term (≥3 years), a Robust Design CMR study should be implemented to allow

estimation of recruitment—that which is crucial to evaluating population viability (Villella et al.

2004; Jones et al. 2012).

I recommend that monitoring projects utilize systematic quadrat sampling when the

objective is to simply estimate and detect trends in population size for established species, or

restored species, of moderate to larger densities (>0.2/m2). Capture-mark-recapture sampling

137

should be used when objectives include assessing restored populations of species reintroduced or

augmented at low to moderate densities, obtaining precise population demographics (e.g.,

survival and recruitment), or estimating population size for any species occurring at low to

moderate densities (<0.2/m2). Future mussel restoration efforts should continue to tag released

individuals and use CMR to improve our understanding of species-specific demographic

characteristics as well as assess likelihood of success of species restorations. Assuming released

E. capsaeformis laboratory-propagated sub-adults from the 2010 release will reach sexual

maturity in 2012 (i.e., based on size, >35 mm), and that environmental conditions are favorable

for reproduction, it is likely that recruitment from these individuals could be assessed as early as

2014 (1–2 year-old recruits). In accordance with the recovery plan for E. capsaeformis (USFWS

2004), I suggest Cleveland Islands, VA be monitored biennially beginning in 2014 to determine

age-1 class survival and recruitment rates, compute length-at-age equations, and assess

population viability of the species.

138

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Removal Methods for Sampling Closed Populations. Los Alamos National Laboratory.

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White, G.C. 2008. Closed population estimation models and their extensions in Program MARK.

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145

Table 1. Top models, model used for median ĉ GOF test, descriptions, and model summary

statistics for E. capsaeformis at Cleveland Islands, Virginia in 2011 and 2012 using closed-

capture models in Program MARK. Summary statistics in bold indicate that the model was used

(top model or in model averaging) to describe the data set in that year.

Model Description

QAICc

Rank ΔQAICc

AIC

Weight

(wi)

Model

Likelihood

No.

Parameters Deviance

2011

pi(0) p(t)=c(t) +

Length

Temporal variation in

detection probabilities &

length covariate

1 0.00 0.63 1.00 6 970.21

{pi(0)

p(g*t)=c(g*t)+Length}


detection probabilities for

each group & length

covariate

2 1.07 0.37 0.59 11 961.14

pi(.) p(h)=c(h) +

Length

Heterogeneity in capture

probabilities & length

covariate

3 10.62 0.00 0.00 3 986.88

pi(0) p(g)=c(g) +

Length

Group specific detection


covariate

4 12.00 0.00 0.00 3 988.26

A pi(0) p(g*t)=c(g*t) Temporal variation in

detection probabilities for

each group

5 15.54 0.00 0.00 10 977.65

2012

pi(0) p(t)=c(t) +

Length


capture/recapture


covariate

1 0.00 0.73 1.00 6 314.74

pi(0) p(t)=c(t) Temporal variation in

capture/recapture

probabilities

2 3.36 0.14 0.19 5 320.12

pi(0) p(t) c(.) Temporal variation in

capture probabilities,

constant recapture

probabilities with behavior

effect

3 5.02 0.06 0.08 6 319.76

pi(0) p(t) c(t) Temporal variation in

capture/recapture

probabilities with

behavioral effect

4 7.69 0.02 0.02 8 318.38

A pi(0) p(g*t) c(g*t) Temporal variation in

capture/recapture

probabilities for each group

with behavioral effect

12 18.14 0.00 0.00 16 312.48

AModel used for median ĉ GOF test

146

Table 2. Population size and density estimates for E. capsaeformis, A. pectorosa, and M. conradicus at Cleveland Islands, Virginia

from closed-capture modeling in Program MARK.

2011 2012

Mean SE

95% CI

Mean SE

95% CI

Lower Upper Lower Upper

Population Size (N-hat)

Epioblasma capsaeformis

Translocated adults 453 96 314 703

372 132 113 669

Laboratory-propagated sub-adults 1,915 1,030 747 5,220

1,390 1,018 313 5,057

Actinonaias pectorosa* 6,615 483 5,815 7,729

4,729 203 2,366 5,162

Medionidus conradicus 3,237 825 2,186 5,639

2,849 160 2,562 3,192

Density (per m2)


Translocated adults 0.09 0.02 0.06 0.13

0.07 0.03 0.04 0.15

Laboratory-propagated sub-adults 0.38 0.20 0.13 1.08

0.27 0.20 0.07 1.15

Actinonaias pectorosa* 1.30 0.09 1.13 1.50

0.93 0.04 0.86 1.01

Medionidus conradicus 0.67 0.16 0.39 1.05 0.56 0.03 0.50 0.63

*Statistically significant difference (p<0.05) detected between 2011 and 2012.

147


statistics for A. pectorosa at Cleveland Islands, Virginia in 2011 and 2012 from closed-capture

modeling in Program MARK. Summary statistics in bold indicate that the model was used (top

model or in model averaging) to describe the data set in that year.

Model Description

QAICc

Rank ΔQAICc

AIC

Weight

(wi)

Model

Likelihood

No.

Parameters Deviance

2011

pi(.)

p(t*h)=c(t*h)

+Length

Temporal variation and

heterogeneity in

capture/recapture probabilities &

length covariate

1 0.00 0.98 1.00 12 11267.51

pi(.) p(h) c(h)

+Length

Heterogeneity in

capture/recapture probabilities

with behavior effect & length

covariate

2 8.84 0.01 0.01 6 11288.37

pi(.)

p(h)=c(h)

+Length

Heterogeneity in


length covariate

3 9.84 0.01 0.01 4 11293.37

pi(0) p(t) c(.) Temporal variation in capture

probabilities, constant recapture

probabilities with behavior effect

& length covariate

4 13.54 0.00 0.00 7 11291.06

Api(0) p(t)

c(t)



with behavior effect

13 247.32 0.00 0.00 8 11522.85

2012

pi(0) p(t) c(.)

+ Length

Temporal variation in capture



& length covariate

1 0 0.49 1 7 12394.24

pi(0) p(t)=c(t)

+Length


probabilities & length covariate

2 1.01 0.30 0.60 6 12397.24

pi(0) p(t)

c(t)} +Length



& length covariate

3 1.70 0.21 0.43 9 12391.93

pi(.) p(h) c(h)

+Length

Heterogeneity in capture


& length covariate

4 138.15 0.00 0.00 6 12534.39

Api(0) p(t)

c(t)




9 182.46 0.00 0.00 8 12574.70


148


statistics for M. conradicus at Cleveland Islands, Virginia in 2011 and 2012 from closed-capture

modeling in Program MARK. Summary statistics in bold indicate that the model was used (top

model or in model averaging) to describe the data set in that year.

Model

Description

QAICc

Rank ΔQAICc

AIC

Weight

(wi)

Model

Likelihood

No.

Parameters Deviance

2011

pi(0) p(.)=c(.)

+Length

Constant capture/recapture


1 0 0.55 1.00 2 1237.71

pi(0) p(.) c(.)

+Length

Constant capture and recapture


& length covariate

2 1.04 0.33 0.59 3 1236.75

pi(0) p(t)=c(t)

+Length



3 4.42 0.06 0.11 5 1236.12

pi(0) p(t) c(.)

+Length




& length covariate

4 5.54 0.03 0.06 6 1235.23

Api(0) p(t)c(t) Temporal variation in



10 16.96 0.00 0.00 6 1246.66

2012

pi(0) p(t)=c(t)

+Length



length covariate

1 0.00 0.60 1.00 6 4847.33

pi(0) p(t)=c(t) Temporal variation in

capture/recapture probabilities 2 1.74 0.25 0.42 5 4851.08

pi(0) p(t)c(t)

+Length



with behavior effect & length

covariate

3 4.58 0.06 0.10 9 4845.89

pi(0) p(t) c(.)

+Length




& length covariate

4 5.12 0.05 0.08 7 4850.45

Api(0) p(t)c(t) Temporal variation in



5 6.21 0.03 0.04 8 4849.53


149

Table 5. Contrasts of population size estimates between systematic quadrat and CMR sampling

methods, and between 2011 and 2012, for E. capsaeformis, A. pectorosa and M. conradicus at

Cleveland Islands, Virginia. Effect sizes are defined as the mean difference in population size.

Contrasts

Effect

Size Error (SEp)

95% CI

Lower Upper

Population size between sampling methods


Translocated adults

2011 124 155 -369 617

2012 273 110 -77 623

Laboratory-propagated

sub-adults

2011* 237 51 111 363

2012 310 231 -426 1,046

Actinonaias pectorosa

2011* 2,612 328 1,567 3,657

2012* 3,243 482 1,710 4,776

Medionidus conradicus

2011* 1,167 204 519 1,815

2012* 2,309 349 1,199 3,419

Population size between 2011 and 2012

CMR sampling


Translocated adults* 81 4 74 88


sub-adults*

525 35 457 593

Actinonaias pectorosa* 1,886 4 1,879 1,894

Medionidus conradicus* 388 11 366 410

Systematic quadrat sampling


Translocated adults 68 190 -397 533


sub-adults

22 233 -720 764

Actinonaias pectorosa 1,255 583 -171 2,681

Medionidus conradicus 754 404 -234 1,742

*Statistically significant difference (p<0.05) detected.

150

Table 6. Top models, descriptions, and model summary statistics for E. capsaeformis at Cleveland Islands, Virginia in 2011 and 2012

from open-capture modeling (Cormack-Jolly-Seber) in Program MARK. Summary statistics in bold indicate that the model was used

to describe the data set.

Model Description

AICc

Rank ΔAICc

AIC

Weight

(wi)

Model

Likelihood

No.

Parameters Deviance

Phi(.) p(g+t)

with

constraints

Constant survival rates. Additive temporal and group

variation in recapture probabilities. Constraints: recapture

probabilties during releases

1 0.00 1.00 1.00 12 179.4058

Phi(.) p(t) with

constraints

Constant survival rates. Temporal variation in recapture

probabilities. Constraints: recapture probabilities during

releases

2 128.07 0.00 0.05 11 309.4906

Phi(.) p(t) with

constraints

Constant survival rates. Groupl variation in recapture

probabilities. Constraints: recapture probabilities during

releases

3 208.68 0.00 0.00 3 406.1427

Phi(.) p(g*t)

with

constraints

Constant survival rates. Temporal and group variation in

recapture probabilities. Constraints: recapture probabilties

during releases

4 323.66 0.00 0.00 20 486.9525

151

Table 7. Summary of pros, cons, and recommendations regarding systematic quadrat and

capture-mark-recapture sampling approaches to monitoring freshwater mussels.

Systematic quadrat sampling Capture-mark-recapture

Pros Offers good estimates of

population size for species that

occur at moderate to high densities

(>0.2/m2)

Offers improved precision of abundance

estimates for species that occur at low

to moderate (≤0.2/m2) densities

Useful for detecting trends in

density

Useful for detecting trends in

abundance and density

Relatively quick, simple, and cost

effective to implement in all sized

study sites

Relatively quick and simple to

implement in small (<500 m2) study

sites

Offers effective spatial coverage Spatial coverage can be customized to

project objectives (e.g., complete

surface area versus random strip-

transect sampling)

Can detect recruitment Can detect recruitment and obtain

reliable estimates of recruitment over

long-term study

Good for follow-up monitoring of

restored populations of moderate

to high densities (>0.2/m2)

Good for monitoring restored

populations regardless of density

Provides more precise estimates of

population demographics (population

size, density, growth rate; survival rates;

sex-ratios; growth; age-class structure)

and detecting species presence than

quadrat sampling

Useful for investigating factors that

influence survival and capture

probabilities

Cons Estimates of population

demographics for species of low to

moderate densities (≤0.2/m2) are

inaccurate and imprecise and

likely inaccurate

Complete study area substrate surface

coverage can be more resource

intensive to conduct than quadrat

sampling in large (>2,000 m2) study

sites

Excavation of quadrat samples is

difficult and time consuming to

implement in deep and high

velocity habitats

Additional costs incurred if using

shellfish tags

Biological disturbance caused by

excavation is unknown

Recommendations Should be conducted when survey

objective is to estimate or detect

trends in population size or density

for species that occur at moderate

to high densities (>0.2/m2)

Should be utilized when it is necessary

to obtain accurate and precise estimates

of population demographics and when

monitoring the status of restored

populations of endangered species and

other species at low densities

Use if study site is large, and

project has limited available

resources

If project has resource constraints and

target species is randomly distributed

within study area, sample random strip-

transects

152

Figure 1. Comparison of capture-mark-recapture and systematic quadrat population size

estimates and associated 95% confidence intervals for: A) translocated adult and B) laboratory-

propagated sub-adult (LPSA) E. capsaeformis, C) A. pectorosa, and D) M. conradicus at

Cleveland Islands, Virginia in 2011 and 2012.

153

Figure 2. Capture (and recapture, p and c) probabilities and associated 95% confidence intervals

for E. capsaeformis per sampling occasion for translocated adults in: A) 2011 and B) 2012, and

released laboratory-propagated sub-adults (LPSA) in: C) 2011 and D) 2012 at Cleveland Islands,

Virginia using a closed-capture model in Program MARK.

154

Figure 3. Capture (and recapture, p and c) probabilities and associated 95% confidence intervals

for A. pectorosa in: A) 2011 and B) 2012, and M. conradicus in C) 2011 and D) 2012 at

Cleveland Islands, Virginia using a closed-capture model in Program MARK.

155

Figure 4. Epioblasma capsaeformis recapture probabilities (p) and associated 95% confidence

intervals per sampling occasion for translocated adults in: A) 2011 and B) 2012, and released

laboratory-propagated sub-adults (LPSA) in: C) 2011 and D) 2012 at Cleveland Islands, Virginia

using an open-capture model (Cormack-Jolly-Seber) in Program MARK.

156

Appendix A: Cormack-Jolly-Seber Diagram and Program MARK Input Formatting

Figure A. 1. Cormack-Jolly-Seber open-capture model diagram for E. capsaeformis. Black

numbers in boxes represent encounter occasions; numbers 1–5 represent 2006–2010 annual

release events (no searches=p fixed at 0), 11 represents 2011 release event (no search=p fixed at

0) that occurred between 2011 and 2012 capture-mark-recapture sampling, and boxes 6–10 and

12–16 represent capture-mark-recapture active searches with 5 encounter occasions each in 2011

and 2012 (active searches=p time dependent). Red Phii (φi) values represent survival probability

parameters between successive occasions. Blue pi’s represent recapture probability parameters

during encounter occasions. Black numbers above arrows represent the time in weeks between

occasions.

157

/*ID Encounter History Trans group LPSA group semicolon*/

/*REDAA691*/ ...1.00100.00000 1 0 ;

/*REDAA692*/ ...1.00000.00010 1 0 ;

/*REDAA693*/ ...1.01000.00000 1 0 ;

/*REDAA694*/ ...1.00000.00000 1 0 ;

/*REDAA695*/ ...1.00000.00000 1 0 ;

/*REDAA696*/ ...1.00000.10000 1 0 ;

/*REDAA697*/ ...1.00000.00000 1 0 ;

/*REDAA698*/ ...1.01110.10000 1 0 ;

/*REDAA699*/ ...1.00000.00000 1 0 ;

Figure A. 2. A sample of Program MARK input formatting for E. capsaeformis open population

modeling (Cormack-Jolly-Seber model). The first two columns represent the ID (tag) of an

individual and its associated encounter history. The last two columns represent the group the

individual was classified under (translocated adult or a laboratory-propagated sub-adult).

158

APPENDIX B: Species List

Table B. 1. Species collected in the upper Clinch River at Cleveland Islands, Virginia using

systematic quadrat and capture-mark-recapture (CMR) sampling in 2011 and 2012.

Species Common name

Quadrats

(2011)

CMR

(2011)

Quadrats

(2012)

CMR

(2012)

Actinonaias ligamentina Mucket

X X X

Actinonaias pectorosa Pheasantshell X X X X

Amblema plicata Threeridge X

Cyclonaias tuberculata Purple wartyback X X

X

Elliptio crassidens Elephantear X

Elliptio dilatata Spike X X X X

Epioblasma brevidensFE

Cumberlandian combshell X X X X

Epioblasma capsaeformisFE

Oyster mussel X X X X

Epioblasma triquetraFE

Snuffbox

X X X

Fusconaia barnesiana Tennessee pigtoe XA X

A X

A X

A

Fusconaia corFE

Shiny pigtoe XB X

B X

B X

B

Fusconaia cuneolusFE

Fine-rayed pigtoe XB X

B X

B X

B

Fusconaia subrotunda Longsolid XA X

A X

A X

A

Lampsilis fasciola Wavy-rayed lampmussel X X X X

Lampsilis ovata Pocketbook X X X X

Lasmigona costata Flutedshell X X X X

Ligumia recta Black sandshell X X

Medionidus conradicus Cumberland moccasinshell X X X X

Plethobasus cyphyusFE

Sheepnose X X

Pleurobema oviforme Tennessee clubshell XA X

A X

A X

A

Pleuronaia dolabelloidesFE

Slabside pearlymussel X X X

Ptychobranchus fasciolaris Kidneyshell X X X X

Ptychobranchus subtentumFE

Fluted kidneyshell X X

X

Quadrula cylindrica

strigillataFE

Rough rabbitsfoot X X X

Villosa iris Rainbow X X X X

Villosa vanuxemensis Mountain creekshell X X X X A = Fusconaia barnesiana, F. subrotunda, and Pleurobema oviforme individuals were pooled

due to lack of positive identification at this site. B = Fusconaia cor and F. cuneolus individuals were pooled due to lack of positive identification

at this site. FE

= Federally endangered species

159

CHAPTER 3

Determining Optimum Temperature for Growth and Survival of Laboratory-Propagated

Juveniles of Two Federally Endangered Species, Cumberlandian Combshell (Epioblasma

brevidens) and Oyster Mussel (Epioblasma capsaeformis), and One Non-Listed Species,

Wavyrayed Lampmussel (Lampsilis fasciola)

Co-authors: J. Jones, E. Hallerman, and R. Butler

This is an Author’s Original Manuscript of an article whose final and definite form, the Version

of Record, has been publish in the North American Journal of Aquaculture, 25 September 2013,

copyright Taylor & Francis, available online at:

http://www.tandfonline.com/doi/pdf/10.1080/15222055.2013.826763#.UpNs1MTihZg

160

ABSTRACT

The effects of temperature on growth and survival of laboratory-propagated juvenile

freshwater mussels of two federally endangered species, the Cumberlandian combshell

(Epioblasma brevidens) and oyster mussel (Epioblasma capsaeformis), and one non-listed

species, the wavyrayed lampmussel (Lampsilis fasciola), were investigated to determine

optimum rearing temperatures for these species in smallwater-recirculating aquaculture systems.

Juveniles 4–5 months old were held in downweller buckets at five temperatures. Growth and

survival of juveniles were evaluated at 2-week intervals for 10 sampling events. At the end of the

20-week experiment, mean growth at 20, 22, 24, 26, and 28°C was, respectively, 0.75, 2.22, 3.27,

4.23, and 4.08 mm for E. brevidens; 1.35, 3.73, 3.81, 4.90, and 4.70 mm for E. capsaeformis;

and 2.09, 3.96, 4.99, 5.13, and 4.87 mm for L. fasciola juveniles. Generally, temperature was

positively correlated with growth of juveniles. Final mean maximum growth occurred at 26°C for

all three species, although no significant differences in growth were detected between 26°C and

28°C. The relationship between temperature and survival of juveniles was less clear. Final

survival was 82.5, 89.0, 91.0, 89.5, and 93.5% for E. brevidens; 73.0, 83.5, 78.0, 78.0, and

68.1% for E. capsaeformis; and 75.0, 89.5, 87.0, 86.5, and 89.5% for L. fasciola juveniles at the

five temperature treatments, respectively. Based on the species used in this study, results indicate

that 26°C is the optimum temperature for maximizing growth of juvenile mussels in downweller

bucket systems. The ability to grow endangered juveniles to larger sizes will improve survival in

captivity and upon release into the wild and will reduce time spent in hatcheries. As a result,

hatcheries can increase their overall production and enhance the likelihood of success of mussel

population recovery efforts by federal and state agencies and other partners.

161

KEYWORDS: Freshwater Mussels, Temperature, Growth, Survival, Laboratory-Propagated

Juveniles, Culturing Methods, Oyster Mussel, Cumberlandian Combshell, Wavyrayed

Lampmussel

162

INTRODUCTION

Because of significant declines of mussel populations in recent decades (Williams et al.

1993; Neves et al. 1997; Neves 1999), and with the culture and release of laboratory-propagated

mussels into the wild being applied as a recovery method (USFWS 2003, 2004; Jones et al.

2005; Jones et al. 2006; Eckert and Pinder 2010), there is a growing need to improve culture

methods, particularly grow-out of propagated juveniles. Water temperature is a vital

environmental parameter affecting growth and survival of juvenile mussels in captivity and in

the wild, also affecting various reproductive processes in adults, such as gametogenesis,

spawning, and larval brooding (Krebs 1972; Hastie et al. 2000; Zimmerman and Neves 2002;

Gosling 2003; Hastie et al. 2003; Zimmerman 2003; Jones et al. 2005; Negishi and Kayaba

2010; Pandolfo et al. 2010b). Temperature affects mussel developmental and physiological

processes, with specific effects on different life stages (Krebs 1972; Negishi and Kayaba 2010;

Pandolfo et al. 2010b). Efforts to propagate and culture mussels in captivity require an

understanding of the environmental factors that influence growth and survival at different life

stages. Thus, defining the optimum temperature for production of laboratory-propagated juvenile

mussels is critical for optimizing propagation and culture success and thereby has important

implications for their conservation.

In the past few years, it has become clear that larger and older laboratory-propagated

juveniles have a significantly increased chance of survival when released in the wild in

comparison to newly-metamorphosed juveniles (Sarrazin and Legendre 2000; Hua et al. 2011).

Although methods have been developed to produce thousands of newly-metamorphosed

juveniles, refinement of culture methods to grow these species to larger sizes is needed. The

ability to grow juveniles of imperiled species to larger sizes improves survival of individuals

163

while captive and upon release to the wild by decreasing the incidence of predation in both

settings. In addition, enhancing grow-out of cultured mussels increases detection probabilities for

subsequent monitoring, and most importantly, improves the likelihood of population recovery

(Zimmerman et al. 2003; Hua et al. 2011).

The purpose of my study was to determine the effect of temperature on the growth and

survival of juvenile (>4 months old and ≥1.5 mm) mussels of two federally endangered species,

Cumberlandian combshell (Epioblasma brevidens) and oyster mussel (Epioblasma

capsaeformis), and one non-listed species, wavyrayed lampmussel (Lampsilis fasciola), in

captivity. The intent of this research was to determine optimum rearing temperatures to

maximize growth and survival of juvenile mussels of these three mussel species in captivity.

METHODS

Gravid Mussel Collection

Juveniles were produced by the Freshwater Mollusk Conservation Center (FMCC) at

Virginia Polytechnic Institute and State University (Virginia Tech) in Blacksburg, and Virginia

Department of Game and Inland Fisheries’ Aquatic Wildlife Conservation Center (AWCC) near

Marion, Virginia (VA) following standard propagation and culture methods for these organisms.

Gravid females of each species were collected in May 2011 by snorkeling and using view scopes

in the lower Clinch River, Hancock County, Tennessee (TN). Gravid individuals were held and

transported to the FMCC and AWCC in coolers containing river water with aeration.

After arriving at the facilities, gravid females were placed in holding systems with

maintained water temperatures of 15°C in order to prevent early glochidial release before

infestation of host fishes could be conducted. The holding system at the FMCC contained 50–80

164

mm of river substrate (pebble, gravel) and water from the facility’s pond; the holding system at

the AWCC contained 50–80 mm of coarse limestone gravel substrate and water sourced from the

South Fork Holston River. Mussels were fed daily with a premixed commercial algae diet

(Nanno 3600 and Shellfish 105 Diet 1800 from Reed Mariculture, Campbell, California).

Host Fish Collection and Care

Based on the results of previous studies (Zales and Neves 1982; Yeager and Saylor

1995), black sculpin Cottus baileyi were used as the host fish for E. brevidens and E.

capsaeformis, and largemouth bass Micropterus salmoides were used as the host for L. fasciola.

Black sculpin were collected using a backpack electrofisher (Model LR24, Smith-Root,

Vancouver, Washington) and largemouth bass were obtained from a regional fish farm in

Arkansas.

Black sculpin were held and transported to each facility in 140-L coolers containing local

stream water. Salt was added to coolers to increase salinity to 0.7‰ in order to reduce fish stress

during transport. Water temperature was maintained at ambient stream levels during

transportation, and dissolved oxygen was maintained using an aerator. Transport time ranged

from 1 to 2 hours. After arrival at culture facilities, fish were acclimated to laboratory conditions,

regarding temperature and salinity, and were quarantined for 2–3 days at a salinity of 3.0‰ prior

to being infested with glochidia.

Infestation with Mussel Glochidia and Juvenile Mussel Collection

Host fish were infested with mussel glochidia following FMCC established non-lethal

laboratory protocols (Zale and Neves 1982; Neves 2004). At the FMCC, 180 black sculpin were

165

separated into groups of 45 fish which were placed into one of four 16-L containers with 3.5-L of

conditioned water at 21°C under continuous aeration. Glochidia from two gravid E.

capsaeformis were mixed into each of the four containers (eight gravid E. capsaeformis in total)

and allowed 45 minutes to attach to host fish. After infestation, host fish were moved into

recirculating aquaculture holding systems. Water quality parameters were monitored bi-weekly

in the host-fish holding systems. Similar host-fish infestation methods were used at AWCC to

produce juvenile mussels.

Once juveniles began to excyst from host fish, tank water was siphoned daily through

300-μm and 150-μm mesh sieves. Collected juveniles were rinsed into a petri dish, counted, and

placed into 18-L downweller bucket culture systems for growth and development (Barnhart

2006; Figure 1). Buckets were filled with 18 L filtered (<5 μm) pond (FMCC) or river (AWCC)

water, bucket water was exchanged once per week, and water temperatures were maintained

between 20 and 24°C. At each water exchange interval, buckets were cleaned and standard water

quality parameters were measured. Juveniles were fed continuously with a premixed commercial

algae diet. Because young juveniles experience a mortality bottleneck (4–8 weeks of age) and

are susceptible to flatworm predation at small sizes (Henley et al. 2001; Jones et al. 2005),

juveniles were cultured for 4–5 months to the desired initial size of 1–2 mm before the culture

experiment was initiated in order to remove any confounding factors. A summary of gravid

mussel collection, captive holding conditions, and host fish infestation protocols are given for

each species in Table 1.

166

Test Conditions

Juvenile mussels were acclimated to 20°C before testing and then allowed to acclimate to

treatment temperatures gradually over a 24-h period. Temperature was controlled by a water bath

surrounding the buckets and held constant (±0.5°C) through the use of heaters or chillers, and

monitored daily using a temperature data logger (Onset Computer Corporation, HOBO Pendant

Logger Model UA-001-08). Water quality in each bucket was conducted bi-weekly for ammonia

(salicylate method, Hach Method 8155), nitrite (diazotization method, Hach Method 8507),

nitrate (cadmium reduction method, Hach Method 8171), dissolved oxygen, pH, and specific

conductivity (YSI Professional Plus Multiparameter Meter). Total hardness (mg of Ca/L as

CaCO3 plus mg of Mg/L as CaCO3) via the titration method (Hach Method 8213) and total

alkalinity (Hach Model AL-AP; mg of phenolphthalein alkalinity/L plus mg total methyl orange

alkalinity/L as CaCO3) were measured on the source water once a week.

Mussels in each bucket were fed 500 mL daily (21 mL/h) of a premixed commercial

algae formula (mean cell concentration , about 1.0–2.0 x 106 μm

3/mL) delivered continuously

from a 1-L water bottle through a drip valve. Eighty water samples were taken randomly from

the buckets over the course of the experiment to quantify the algal cell concentrations using a

Coulter counter (Beckman Coulter, Multisizer 3) located at the AWCC. Algal concentrations

also were measured by a hemocytometer and compared to those from the Coulter counter.

Feeding bottles were cleaned, juvenile mussel holding chambers were rinsed, and bucket water

was completely exchanged once a week. Air bubbles were removed from culture chambers and

pumps, and power sources and water levels were checked daily, as per the FMCC protocols.

Testing of E. brevidens and E. capsaeformis juveniles began 18 November 2011 and

finished 4 April 2012. Testing of L. fasciola juveniles began 22 November 2011 and finished 12

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April 2012. Mussels in buckets were sampled at 2-week intervals for 20 weeks to provide a total

of 10 sampling events. Random samples of 10 of the 40 juveniles in each chamber were

measured under a microscope (Olympus American, Model SZ40) to assess mean growth (i.e.,

mean length at time t minus initial length). All live individuals and shells within a chamber were

counted to assess survival rates since the start of experiment. Shells of dead mussels were

removed and documented. A summary of test conditions is given in Table 2.

Experimental Design and Statistical Analyses

Five temperature treatments were tested (20, 22, 24, 26, and 28°C), covering the range of

normal (20–24°C) to upper (26–28°C) temperatures that mussels experience in the wild during

the warmer months of the annual growth period in the Clinch River. The test was conducted in

recirculating downweller bucket aquaculture systems, in which each bucket was independent of

others and served as one experimental unit (EU) (Barnhart 2006).

Following a power analysis that determined the appropriate sample size needed to

achieve a minimum of 80% power at α=0.05, each temperature treatment was assigned five

independent downweller buckets that served as replicates. Each bucket contained a total of six

juvenile mussel holding chambers. The three species were tested alongside one another within

buckets, with a single chamber containing juveniles of only one species, while the other three

chambers remained unoccupied (Figure 1). Juveniles of each species were randomly separated

into chambers containing 40 individuals and placed in 1 of the 25 buckets. The EUs were

randomly assigned to one of the five different treatment temperatures. A summary of the

experimental design is given in Table 2.

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Growth and survival of juveniles for each species were compared among temperature

treatments using a mixed-model analysis with repeated measures. Growth and survival of

juveniles within EUs were treated as random effects. Temperature treatment, time, and

temperature x time interaction were treated as fixed effects. Treatment means were estimated and

compared at each sampling event for further analyses. Survival data (proportion survival) for

each species was arcsine(x)-transformed before being compared among temperature treatments

in order to meet the normality assumption. Additionally, a one-way analysis of variance

(ANOVA) was used to determine whether algal concentrations differed among treatments.

Analyses were conducted using SAS software (SAS institute, Inc., Cary, North Carolina, version

9.2) and were considered significant at the ɑ≤0.05 level. Unless otherwise stated, all significant

results were p<0.01.

RESULTS

Epioblasma brevidens

For the five temperature treatment conditions tested in this study, final growth at 138

days for E. brevidens juveniles ranged from 0.75 to 4.23 mm, at 20°C and 26°C, respectively

(Table 3; Figure 2). Analysis of simple effects (i.e., separating the data by sampling events and

conducting one-way ANOVAs at each time-step) revealed significant differences in growth

between temperature treatments at each of the 10 sampling events. Results of the mixed model

analysis for growth indicated that the fixed effects of temperature, time, and temperature x time

interaction were all significant (Appendix A).

Contrasts of differences in treatment means (effect size ± SE) for final growth (i.e., mean

shell length at final sample minus initial mean shell length) revealed that growth at 20°C was

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significantly lower than that at all of the other treatment temperatures. Growth at 22°C was

significantly lower than growth at 24, 26, and 28°C, and growth at 24°C was significantly lower

than growth at 26°C and 28°C. No significant difference in juvenile growth was detected

between 26°C and 28°C (p=0.36) (Appendix A).

Epioblasma brevidens juvenile survival ranged from 82.5 to 93.5% (Table 3; Figure 3).

Examination of simple effects for temperature treatments at individual sampling events showed

some significance (p=0.05) of temperature on survival at the fourth (day 54) sampling event;

however, survival was not affected by treatment temperature at any other sampling event.

Survival was not affected by temperature (p=0.13), while the effects of time and temperature x

time interaction were significant. Contrasts of differences in treatment means for final survival

showed that survival was significantly lower at 20°C than at 24°C (p=0.05) and 28°C (p=0.03).

The remaining final survival estimates were not significantly different between other treatment

temperatures (Appendix A).


Final growth increment at 138 days for E. capsaeformis juveniles ranged from 1.35 to

4.90 mm across all temperature treatments (Table 3; Figure 2). Analysis of simple effects of

temperature at each sampling event revealed significant differences in growth between

temperature treatments at each of the 10 sampling events. Mixed-model analysis for growth

indicated that the effects of temperature, time, and temperature x time interaction were all

significant (Appendix A).

Contrasts of differences in treatment means for final growth revealed that growth at 20°C

was significantly lower than growth at all other treatment temperatures. Significantly lower

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growth also was observed for the 22°C and 24°C treatments when compared to growth at the

26°C and 28°C treatments. Growth was similar between 22°C and 24°C (p=0.63), and between

26°C and 28°C (p=0.25) (Appendix A).

Substantial mortality was observed in one of the EUs of the 28°C treatment during the

fourth sampling event (day 54), causing it to be a significant outlier for the survival analysis and

violating the homogeneity of variance assumption. It is unlikely that the mortality observed was

caused by temperature because: 1) no other EU within the treatment experienced similar

mortality, 2) mortality occurred in a single early sampling event, and 3) mortality ceased in this

bucket for all further sampling events (sample events 5–10). It is possible that this single

mortality event may have been induced by human error during sampling efforts (e.g., handling

stress). Data from this outlier unit were removed from the mixed-model analysis of survival from

the fourth sampling event forward (day 54 to 138) to reduce model variance and thereby to meet

the assumption of homogeneity of variance. Final survival of E. capsaeformis juveniles ranged

from 68.1 to 83.5% (Table 3; Figure 3). Analysis of simple effects for temperatures by time

under this model revealed no significant differences in survival between any temperature

treatments. Time was significant, whereas the effects of temperature (p=0.16) and temperature x

time interaction (p=0.71) were not significant. Contrasts of differences in treatment means for

final survival uncovered significantly lower survival at 22°C than at 28°C (p=0.03). Final

survival contrasts between all other temperature treatments were not significant (Appendix A).

Lampsilis fasciola

Final growth at 141 days for L. fasciola juveniles ranged from 2.09 to 5.13 mm (Table 3;

Figure 2). Analysis of simple effects for temperatures by time under this model revealed

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significant differences in growth between temperature treatments at each sampling event. All

fixed effects for growth were significant (Appendix A).

Contrasts of differences in treatment means for final growth revealed that growth at 20°C

was significantly lower than growth at all other temperature treatments. Significantly lower

growth was observed at 22°C than at 24, 26 and 28°C. Growth at 24°C did not differ statistically

from growth at 26°C (p=0.44) and 28°C (p=0.51). No significant differences in growth were

detected between 26°C and 28°C (p=0.15) (Appendix A).

Lampsilis fasciola juvenile final survival ranged from 75.0 to 89.5% (Table 3; Figure 3).

Statistically significant differences in survival between some temperature treatments were

detected at the second (day 29, p=0.04) and sixth (day 86, p=0.03) sampling events. No

significant differences in survival between temperature treatments at other sampling events were

revealed by examination of simple effects under this model. Survival was not affected by

temperature (p=0.10), while time and the temperature x time interaction effects were significant.

Contrasts of differences in treatment means for final survival revealed significantly lower

survival at 20°C than at 22°C (p=0.04) and 28°C (p=0.02). Final survival means were not

significantly different for any of the other temperature treatment comparisons (Appendix A).

Algal Concentrations and Water Quality

Algal cell concentrations within buckets ranged from 1.54 to 2.06 x 106 μm

3/mL

(mean=1.80 x 106 μm

3/mL) and did not differ among temperature treatments (p=0.23)

(Appendix A). Temperatures within treatments did not vary greatly from target temperatures

(±0.2°C). Ammonia, nitrite, and nitrate concentrations within buckets stayed within acceptable

levels, and averaged 0.01 mg/L as NH3, 0.005 mg/L as NO2, and 0.2 mg/L as NO3, respectively.

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Water in buckets had a mean dissolved oxygen concentration of 7.33 mg/L, pH of 8.46, and

specific conductivity of 393 µS/cm. Total hardness and alkalinity of source pond water ranged

from 193.76 to 209.09 mg/L with (mean=201.42 mg/L) as CaCO3, and 174.76 to 193.68 mg/L

(mean=184.22 mg/L) as CaCO3, respectively.

DISCUSSION

Previous experimental and observational studies have examined the direct effects of

numerous factors affecting growth and survival rates of freshwater bivalves in captivity and the

wild. Factors that have been found to correlate with mussel growth and survival rates include,

but are not limited to, substrate type and size (Hinch et al. 1986; Liberty et al. 2007), flows and

sediment load (Beaty 1997; Zimmerman 2003; Jones et al. 2005; Liberty et al. 2007; Rypel et al.

2008), toxicant exposure (Pandolfo et al. 2010b), mussel density (Hanson et al. 1988; Beaty

1997; Beaty and Neves 2004; Negishi and Kayaba 2009), food availability (Hanlon 2000),

sampling frequency (Beaty 1997; Zimmerman 2003; Liberty et al. 2007), maturity of larvae

(Jones et al. 2005), and temperature (Hanson et al. 1988; Buddensiek 1995; Beaty 1997; Hanlon

2000; Zimmerman and Neves 2002; Zimmerman 2003; Liberty 2004; Hanlon and Neves 2006;

Pandolfo et al. 2010a, 2010b; Negishi and Kayaba 2010). Results of these studies have helped

define requirements for mussel propagation and culture by better understanding factors affecting

growth and survival, and have shown that mussels are useful biological indicators of

environmental change. Providing optimal temperatures for laboratory-propagated mussels is

critical for propagation and culture success.

Due to their small size (<10–20 mm), juvenile mussels are difficult to detect in the wild,

restricting field investigations to the adult life stage and making it difficult to examine effects of

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temperature and other factors on early life stages (Negishi and Kayaba 2010). Although reports

of growth rates of juveniles from field-based studies are uncommon, researchers have begun to

close this knowledge gap by utilizing laboratory-propagated juveniles for experimental studies,

as I have done here. To my knowledge, no other studies have been published which directly

tested the effects of temperature on the growth and survival of older and larger (>4 months, ≥1.5

mm) laboratory-propagated endangered-species juveniles with the goal of determining an

optimal rearing temperature for maximizing culture success.

I found that temperature had a positive correlation with growth of E. brevidens, E.

capsaeformis, and L. fasciola juveniles, which agreed with conclusions from previous studies

regarding the effect of temperature on juvenile mussel growth (Buddensiek 1995; Beaty 1997;

Hanlon 2000; Hanlon et al. 2006). Further, the positive relationship between temperature and

growth and the magnitude of growth varied between juveniles of these three species. These

observed differences in juvenile mussel growth demonstrated that growth among these species

varies in relation to water temperature. In contrast to previous studies, temperature was neither

positively nor negatively associated with survival (Buddensiek 1995; Beaty 1997). The

relationship between temperature and survival of juveniles was less clear within the time-scale

and temperature treatments of this study. Even though survival did not differ statistically over

time between treatments for all three species of juveniles, a few significant treatment

comparisons between final survivals (at sampling event 10) were detected. Generally, it appeared

that lowest survival occurred at 20°C, although some pairwise comparisons were not significant.

Prior to my study, I set a biologically important effect size for final growth between

temperature treatments at 1 mm. For monitoring release and population success, juveniles are

individually tagged in the laboratory with a shellfish tag (Hallprint Inc., Holden Hill, New South

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Wales, Australia) before being released into the wild. This tagging procedure requires that

individuals be a minimum size of 10 mm because of the size of the tags (8 x 4 mm oval tag size).

Thus, a difference in 1 mm between individuals can influence how soon juveniles can be tagged

in the laboratory and then released to sites selected for population restoration. In addition to size

influencing when juveniles can be released, survival of overwintering juveniles may be directly

correlated with size, significantly improving the survival of individuals when released to the wild

(Buddensiek 1995; Hanlon 2000; Sarrazin and Legendre 2000; Hanlon and Neves 2007; Hua et

al. 2011). Greater size also will increase detection probability during monitoring efforts to detect

released individuals and enhance the overall likelihood of population recovery success (Hua et

al. 2011).

One goal of my study was to determine the optimum temperature for maximizing growth

of juveniles in captivity. I found that maximum growth in shell length after approximately 4.5

months for E. brevidens, E. capsaeformis, and L. fasciola juveniles occurred at 26°C. However,

growth at 26°C did not differ statistically nor biologically (difference < 1.0 mm) from growth at

28°C. Therefore, differences in final survival rates within species were assessed to make

evaluations between these two temperatures.

While E. brevidens and L. fasciola juveniles experienced highest final survival at 28°C,

E. capsaeformis juveniles had the lowest survival at this temperature treatment. It is not clear

whether high mortality at 28°C was due to approaching an upper thermal limit for E.

capsaeformis juveniles, sampling stress, or factors other than temperature. Sampling procedure

involves handling juveniles to obtain shell measurements and to estimate survival data—both

requiring short-term exposure to air—which can cause stress (Liberty et al. 2007). Several

studies have reported lower mortality in juveniles that were sampled less frequently (Beaty 1997;

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Zimmerman 2003; Liberty et al. 2007). Considering that differences in survival of juveniles over

time were not significant for the three species in my study, perhaps sampling frequency or other

factors contributed to final mortality rather than temperature alone. With no statistical or

biological difference detected between the 26°C and 28°C in growth and survival within species,

and due to the unknown source of additional mortality at 28°C for E. capsaeformis juveniles, I

incorporated conclusions of previous studies on water temperature relationships into our

assessment of optimum rearing temperature for these species.

Water temperature is one of the most important environmental parameters affecting

growth and survival of juvenile mussels in captivity (Zimmerman 2003; Jones et al. 2005;

Pandolfo et al. 2010a, 2010b). Several laboratory experiments have described the effects of

temperature on growth and survival of freshwater bivalves during early life stages (i.e., newly

transformed juveniles and < 1-year old juveniles) (Buddensiek 1995; Beaty 1997; Hanlon 2000;

Zimmerman 2003; Hanlon and Neves 2007; Pandolfo et al. 2010a, 2010b). Buddensiek (1995)

found that growth rates and mortality of freshwater pearl mussel Margaritifera margaritifera

juveniles were positively correlated with temperature. Similarly, Beaty (1997) reported a positive

relationship between temperature and growth and survival of newly transformed rainbow

mussels Villosa iris. Hanlon (2000) also reported a positive relationship between temperature

and growth in juvenile L. fasciola, but showed seasonal variation in survival that suggested that

temperature is negatively associated with mortality. Hanlon (2000) further suggested that the

relationship between temperature and survival is not always clear, and that studies with opposing

results may be due to resource availability at different experimental scales (i.e., streams are less

likely to be food-limiting at higher temperatures, in contrast to a laboratory-scale experiment).

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Two other studies examined temperature effects on survival during early life stages and

determined acute lethal temperatures (LT50s) for glochidia and laboratory-propagated juveniles.

The aims of these studies were to determine upper thermal limits of early life stages to provide

insight into any effects that rising maximum water temperatures—due to global climate

change—may have on mussel populations. Pandolfo et al. (2010b) reported acute lethal thermal

tolerances of eight species of glochidia and seven species of juveniles ranging in age from <1 to

8 weeks old. They reported that mean LT50s in 96-h tests were 34.7°C for juveniles, and 31.6°C

in 24-h tests for glochidia. Pandolfo et al. (2010b) concluded that the survival of these early life

stages can decline significantly with small increases in temperature. Dimock and Wright (1993)

also reported acute thermal tolerances for 1-week old juveniles of two freshwater mussel species

and reported LT50s between 31.5°C and 33°C. Because my study goal was to determine optimum

production temperatures, my experiment did not cover the upper temperature ranges (i.e., >30°C)

considered in these studies, suggesting why we likely did not observe a clear relationship

between temperature and survival for E. brevidens, E. capsaeformis, and L. fasciola juveniles.

Temperature has a significant effect on aquatic organism growth and survival rates in

hatchery settings due to its influence on physiological processes such as respiration, filtration,

and excretion rates (Zimmerman and Neves 2002; Spooner 2007; Spooner and Vaughn 2008;

Pandolfo et al. 2010a, 2010b; Fitzgibbon and Battaglene 2012). These metabolic activities of

mussels generally increase with higher water temperatures (i.e., within the natural range)

(Hanlon 2000; Spooner and Vaughn 2008; Vaughn et al. 2008). Typically, oxygen and food

resources can become limiting with increasing water temperature (Hanlon 2000). The availability

of dissolved oxygen in a system is negatively related to temperature, and dependent on the water

system (Hastie et al. 2003). The availability of food in a closed system is limited by the amount

177

of supplemental diet dispensed to individuals exhibiting higher feeding rates in systems cultured

at higher temperatures. Therefore, a combination of increased dissolved oxygen demand and

feeding rates with lower availability of these resources at higher temperatures can strongly

influence growth and survival. In addition, total ammonia concentrations have been shown to

increase with increased excretion rates of mussels due to higher temperatures (Spooner and

Vaughn 2008). Although ammonia toxicity (total ammonia) from increased water temperatures

is negligible between 3°C and 30°C for fish in freshwater systems, early life stages of mussels

are more sensitive to total ammonia concentrations than other aquatic organisms (USEPA 1998,

cited by Randall and Tsui 2002; Wang et al. 2007a, 2007b).

In healthy non-degraded streams, juveniles generally do not face limitation issues with

food and oxygen availability or ammonia toxicity—that which would increase mortality—

because of the continuous influx of freshwater and high turnover rate. Conversely, experiments

that are confined to small recirculating aquaculture systems have a higher likelihood of

encountering (if not managed properly) limited food and oxygen or increased ammonia levels at

higher temperatures in comparison to streams because of their lack of a continuous influx of

freshwater (Hanlon 2000). As a consequence, juveniles may experience increased levels of

mortality. Because of the possible occurrence of food and oxygen limitations and (sub)lethal

ammonia levels in small recirculating systems, researchers have been cautious about culturing

juveniles at higher temperatures. These general temperature relationships were taken into

consideration, even though food quantity was not a limiting factor in our experiment, and my

experimental culture systems did not experience any abnormally low dissolved oxygen or high

total ammonia levels.

178

Based on my analyses of final growth and survival, and previously described temperature

relationships, I believe that the optimal rearing temperature for maximum growth and survival in

captivity is around 26°C for E. brevidens, E. capsaeformis, and L. fasciola juveniles. I believe

my findings can be applied by researchers to improve laboratory culture methods for juveniles of

other species of mussels. Present culture temperatures for juvenile mussels are set based on

research manager discretion and source water temperatures, and sometimes overwintering

juveniles in captivity are held below growing temperatures (i.e., <15°C). Researchers also have

been cautious about culturing juveniles, particularly those of endangered species, at temperatures

consistently exceeding 24°C for concern of increased mortality. My results suggested that a

simulated winter season is not necessary for continued mussel growth or survival. However,

because some biologists believe laboratory-propagated mussels need to experience lower

overwintering temperatures in order to be better-adapted to natural conditions upon release,

further investigation is needed to determine whether long-term survival after release is affected

by the absence of a cooling period in captivity.

Determination of an optimum rearing temperature has clear implications for culturing of

laboratory-propagated juveniles, and ultimately for conservation efforts. The culture and release

of laboratory-propagated juveniles has been identified by federal species recovery plans and

other documents as an approach to increasing the viability of existing populations or

reintroducing species within their historical ranges (Williams et al. 1993; Neves et al. 1997;

Neves 1999; USFWS 2003, 2004). Optimizing temperature to maximize growth and survival of

mussels in hatchery settings reduces the length of time juveniles are held in the laboratory,

allowing biologists to grow endangered juveniles to larger sizes more quickly and maximizing

production levels relative to costs. Decreasing holding time is important because it reduces

179

mortality in captivity (i.e., subjects them to less handling stress) and frees up space in hatcheries,

thereby increasing the overall number of individuals produced for population recovery efforts by

resource managers.

Understanding the relationship between temperature and mussel growth and survival

across all life-stages is important for optimizing propagation and culture success—and by

extension, recovery of imperiled species. My findings support previous conclusions that higher

temperatures increase growth rates but neither supported nor contradicted conclusions on the

relationship between temperature and survival. Upper thermal limits (i.e., >50% mortality over

the duration of this experiment) were not observed for juveniles of any species in our study. This

experiment should be repeated with newly-transformed juveniles to determine whether

temperature affects growth and survival differently for younger and smaller juveniles.

Furthermore, additional testing of growth and survival of juveniles within these temperature

ranges (20–28°C) over a larger temporal scale, and at higher temperature ranges (>28°C), is

needed to reveal a the relationship between temperature and survival and to understand and

predict the potential effects of persistent high water temperatures on mussel populations due to

global climate change (Hastie et al. 2003; Pandolfo et al. 2010b).

180

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186

Table 1. Summary of gravid female mussel, host-fish collection, and host-fish infestation

methods at the Freshwater Mollusk Conservation Center (FMCC) and Aquatic Wildlife

Conservation Center (AWCC) in 2011 used to produce juveniles in this study. All gravid females

were collected from the lower Clinch River, Tennessee.

Species

Experiment details Cumberlandian

combshell

Oyster mussel Oyster mussel Wavyrayed

lampmussel

Facility AWCC AWCC FMCC AWCC

Mussel collection month June June May July

Mussel holding system 150-L circular

fiberglass tank

150-L circular

fiberglass tank

300-L living

stream

150-L circular

fiberglass tank

Host fish species Black Sculpin Black Sculpin Black Sculpin Largemouth Bass

Fish collection site Middle Fork

Holston, VA

Middle Fork

Holston, VA

South Fork

Holston, VA

Regional Fish

Farm, AR

Fish holding system AHABa AHAB

a Quarantine tank RPS

b

Infestation month June June May July

No. gravid mussels used 1 4 8 4

No. fish used 64 84 180 78

Infestation temperature (°C) 22–24 22–24 21 22–24

Duration of infestation (mins) 60 60 45 60

Infested fish recirculating

aquaculture holding system

AHABa AHAB

a 76-L Tanks

b RPS

c

Days to first excystment 12 12 13 12

No. provided for experiment 1000 500 500 1000 aAHAB=Aquatic Habitats, Inc. Z-Hab System

bA 2,000-L closed recirculating system made up of twenty 76-L tanks and two sumps

cRPS=Recirculating Propagation System

187

Table 2. Experimental items and test conditions for culture temperature tests of E. brevidens, E.

capsaeformis, and L. fasciola juveniles at the FMCC, November 2011–April 2012.

Experimental items Test conditions

Statistical analysis Randomized, repeated measures

Test system Downweller buckets with six chambers

Test duration 20 weeks

Test bucket volume 18 L

Water renewal Every 7 days

Initial age of juveniles E. brevidens: 4.5 months

E. capsaeformis: 5 months

L. fasciola: 4.5 months

Initial size (mm) of juveniles E. brevidens: 2.2 ± 0.03

E. capsaeformis: 1.5 ± 0.03

L. fasciola: 1.8 ± 0.03

Chambers/species/bucket 1

Juveniles/chamber 40

Buckets (replicates)/ treatment 5

Juveniles/treatment 200

Feeding (each bucket/day) 0.05 mL Nanno 3600: 0.15 mL Shellfish Diet 1800: 500 mL

conditioned water

Algal cell concentration (within bucket) Mean range of 1.0 – 2.0 x 106 um

3/mL

Flow Submersible pumpa, maximum flow=590 L/hour

Test water Pond water filtered to <5 µm, mean hardness=200 mg/L as

CaCO3, alkalinity=184 mg/L as CaCO3

Test temperatures 20, 22, 24, 26, or 28°C

Water quality Bi-weekly testing of ammonia, nitrite, nitrate, dissolved

oxygen, pH, and specific conductivity

Sampling interval (days) 14

Endpoints Growth (mean length at time t minus mean initial length) and

survival (proportion survival) aAquarium Systems Mini-Jet Model MN-606

188

Table 3. Final growth and survival (mean ± SE) of E. brevidens (EB), E. capsaeformis (EC), and

L. fasciola (LF) juveniles cultured in one of five temperature treatments. Values followed by

different subscripts are significant (p<0.05); z–w indicate differences in temperatures within a

species, and v–t differences among species within a temperature treatment. The final sampling

event occurred at 138, 138, and 141 days for EB, EC, and LF juveniles, respectively.

Temperature (°C)

Species 20 22 24 26 28

Growth (mm)

EB 0.75 ± 0.04 z v

2.22 ± 0.13 y v

3.27 ± 0.16 x v

4.23 ± 0.16 w v

4.08 ± 0.11 w v

EC 1.35 ± 0.09 z u

3.73 ± 0.10 y u

3.81 ± 0.14 y u

4.90 ± 0.10 x u

4.70 ± 0.22 x u

LF 2.09 ± 0.12 z t

3.96 ± 0.13 y u

4.99 ± 0.09 x t

5.13 ± 0.14 x u

4.87 ± 0.21 x u

Survival (%)

EB 82.50 ± 3.45 z v

89.00 ± 4.37 zy v

91.00 ± 3.76 y v

89.50 ± 1.70 zy v

93.50 ± 1.70 y v

EC 73.00 ± 2.00 zy v

83.50 ± 3.02 y v

78.00 ± 4.96 zy u

78.00 ± 6.02 zy u

68.13 ± 4.25 z u

LF 75.00 ± 8.44 z v

89.50 ± 3.20 y v

87.00 ± 3.98 zy vu

86.50 ± 3.10 zy vu

89.50 ± 2.89 y v

189

Figure 1. Top view of recirculating downweller bucket culture system and chambers.

190

Figure 2. Mean growth versus time for: (a) E. brevidens, (b) E. capsaeformis, and (c) L. fasciola

juveniles cultured in one of five temperature treatments. Growth measurements were taken at 2-

week intervals for 20 weeks to provide a total of 10 sampling events.

191

Figure 3. Mean survival versus time for: (a) E. brevidens, (b) E. capsaeformis, and (c) L. fasciola

juveniles cultured in one of five temperature treatments. Survival was assessed at 2-week

intervals for 20 weeks to provide a total of 10 sampling events.

192

APPENDIX A: Detailed Statistical Results

Table A. 1. Summary of growth (mm) and survival (%) ANOVA of fixed effects for E.

brevidens, E. capsaeformis and L. fasciola.

Species Fixed Effects Num DF Den DF F-value p-value

Growth

E. brevidens Temperature 4 20 97.07 <0.0001

Time 9 95.2 477.37 <0.0001

Temperature x Time 36 95.2 20.83 <0.0001

E. capsaeformis Temperature 4 20.3 138.94 <0.0001

Time 9 101 370.60 <0.0001

Temperature x Time 36 101 11.98 <0.0001

L. fasciola Temperature 4 20.1 67.71 <0.0001

Time 9 94.3 487.61 <0.0001

Temperature x Time 36 94.3 9.78 <0.0001

Survival

E. brevidens Temperature 4 23.9 1.98 0.1302

Time 9 173 12.14 <0.0001

Temperature x Time 36 173 2.25 0.0003

E. capsaeformis Temperature 4 22.2 1.82 0.1604

Time 9 167 11.24 <0.0001


L. fasciola Temperature 4 23.2 2.20 0.1003

Time 9 173 22.91 <0.0001


193

Table A. 2. Summary of growth and survival slicing of the F-test for treatments by sampling

event (time=days since start of experiment) for E. brevidens, E. capsaeformis and L. fasciola.

Species

Time

Growth Survival

F-value p-value F-value p-value

E. brevidens 12 2.74 0.0379

1.18 0.3345

26 20.04 <0.0001

1.38 0.2584

41 32.73 <0.0001

1.85 0.1395

54 35.84 <0.0001

2.69 0.0453

68 63.18 <0.0001

2.20 0.0870

82 81.04 <0.0001

2.31 0.0755

96 88.86 <0.0001

1.88 0.1335

110 102.60 <0.0001

2.30 0.0760

124 112.21 <0.0001

2.26 0.0801

138 151.20 <0.0001

1.65 0.1819

E. capsaeformis 12 4.32 0.0031

1.15 0.3516

26 13.19 <0.0001

1.66 0.1832

41 29.09 <0.0001

0.83 0.5148

54 44.08 <0.0001

1.40 0.2548

68 71.94 <0.0001

1.89 0.1358

82 82.50 <0.0001

1.79 0.1535

96 90.62 <0.0001

1.62 0.1928

110 112.87 <0.0001

1.66 0.1826

124 127.96 <0.0001

1.64 0.1872

138 135.31 <0.0001

1.63 0.1881

L. fasciola 15 2.53 0.0467

2.03 0.1110

29 10.80 <0.0001

2.75 0.0424

43 10.61 <0.0001

2.27 0.0800

58 25.21 <0.0001

1.87 0.1356

72 29.36 <0.0001

2.50 0.0588

86 29.95 <0.0001

3.07 0.0280

100 44.93 <0.0001

1.49 0.2241

114 49.67 <0.0001

1.14 0.3550

128 90.87 <0.0001

1.50 0.2228

141 96.16 <0.0001

1.84 0.1412

194

Table A. 3. Contrasts of differences between treatment means for final growth and survival

estimates of E. brevidens at the last sampling event (day 138) with 95% confidence intervals.

Effect size for growth is in millimeters (mm). Effect size for survival (%) data has been arc-sine

transformed to achieve normality.

Contrast

Effect

Size

Error

t-value

p-value

95% CI

Lower Upper

Growth Contrasts

22°C - 20°C 1.46 0.17 8.80 <0.0001 1.13 1.80

24°C - 20°C 2.51 0.17 15.12 <0.0001 2.18 2.85

26°C - 20°C 3.48 0.17 20.90 <0.0001 3.14 3.81

28°C - 20°C 3.32 0.17 19.98 <0.0001 2.99 3.66

24°C - 22°C 1.05 0.17 6.32 <0.0001 0.72 1.38

26°C - 22°C 2.01 0.17 12.11 <0.0001 1.68 2.35

28°C - 22°C 1.86 0.17 11.19 <0.0001 1.53 2.19

26°C - 24°C 0.96 0.17 5.79 <0.0001 0.63 1.30

28°C - 24°C 0.81 0.17 4.87 <0.0001 0.48 1.14

28°C - 26°C -0.15 0.17 -0.92 0.3614 -0.49 0.18

Survival Contrasts

22°C - 20°C 0.11 0.08 1.37 0.1791 -0.05 0.26

24°C - 20°C 0.16 0.08 2.07 0.0452 0.00 0.32

26°C - 20°C 0.08 0.08 1.05 0.3015 -0.08 0.24

28°C - 20°C 0.18 0.08 2.28 0.0283 0.02 0.33

24°C - 22°C 0.05 0.08 0.70 0.4867 -0.10 0.21

26°C - 22°C -0.02 0.08 -0.32 0.7498 -0.18 0.13

28°C - 22°C 0.07 0.08 0.91 0.3682 -0.09 0.23

26°C - 24°C -0.08 0.08 -1.02 0.3125 -0.24 0.08

28°C - 24°C 0.02 0.08 0.21 0.8360 -0.14 0.17

28°C - 26°C 0.10 0.08 1.23 0.2255 -0.06 0.25

195

Table A. 4. Contrasts of differences between treatment means of final growth and survival

estimates of E. capsaeformis at the last sampling event (day 138) with 95% confidence intervals.


transformed.

Contrast

Effect

Size

Error

t-value

p-value

95% CI

Lower Upper

Growth Contrasts

22°C - 20°C 2.38 0.17 13.87 <0.0001 2.04 2.72

24°C - 20°C 2.46 0.17 14.35 <0.0001 2.12 2.81

26°C - 20°C 3.55 0.17 20.67 <0.0001 3.21 3.89

28°C - 20°C 3.35 0.17 19.52 <0.0001 3.01 3.69

24°C - 22°C 0.08 0.17 0.48 0.6292 -0.26 0.42

26°C - 22°C 1.17 0.17 6.81 <0.0001 0.83 1.51

28°C - 22°C 0.97 0.17 5.65 <0.0001 0.63 1.31

26°C - 24°C 1.09 0.17 6.32 <0.0001 0.74 1.43

28°C - 24°C 0.89 0.17 5.16 <0.0001 0.55 1.23

28°C - 26°C -0.20 0.17 -1.16 0.2503 -0.54 0.14

Survival Contrasts

22°C - 20°C 0.13 0.07 1.83 0.0762 -0.01 0.28

24°C - 20°C 0.07 0.07 0.92 0.3646 -0.08 0.22

26°C - 20°C 0.01 0.07 0.09 0.9293 -0.14 0.16

28°C - 20°C -0.04 0.08 -0.51 0.6163 -0.19 0.12

24°C - 22°C -0.07 0.07 -0.91 0.3686 -0.22 0.08

26°C - 22°C -0.13 0.07 -1.74 0.0909 -0.28 0.02

28°C - 22°C -0.17 0.08 -2.27 0.0296 -0.33 -0.02

26°C - 24°C -0.06 0.07 -0.83 0.4125 -0.21 0.09

28°C - 24°C -0.11 0.08 -1.39 0.1733 -0.26 0.05

28°C - 26°C -0.04 0.08 -0.59 0.5580 -0.20 0.11

196

Table A. 5. Contrasts of differences between treatment means for final growth and survival

estimates of L. fasciola at the last sampling event (day 141) with 95% confidence intervals.


transformed.

Contrast Effect

Size

Error t-value p-value 95% CI

Lower Upper

Growth Contrasts

22°C - 20°C 1.88 0.18 10.24 <0.0001 1.51 2.24

24°C - 20°C 2.90 0.18 15.84 <0.0001 2.54 3.27

26°C - 20°C 3.05 0.18 16.62 <0.0001 2.68 3.41

28°C - 20°C 2.78 0.18 15.17 <0.0001 2.42 3.14

24°C - 22°C 1.03 0.18 5.60 <0.0001 0.66 1.39

26°C - 22°C 1.17 0.18 6.38 <0.0001 0.81 1.53

28°C - 22°C 0.90 0.18 4.93 <0.0001 0.54 1.27

26°C - 24°C 0.14 0.18 0.78 0.4356 -0.22 0.51

28°C - 24°C -0.12 0.18 -0.67 0.5050 -0.49 0.24

28°C - 26°C -0.27 0.18 -1.45 0.1501 -0.63 0.10

Survival Contrasts

22°C - 20°C 0.18 0.08 2.10 0.0428 0.01 0.35

24°C - 20°C 0.14 0.08 1.68 0.1023 -0.03 0.31

26°C - 20°C 0.08 0.08 0.91 0.3675 -0.09 0.25

28°C - 20°C 0.20 0.08 2.36 0.0235 0.03 0.37

24°C - 22°C -0.04 0.08 -0.42 0.6752 -0.21 0.14

26°C - 22°C -0.10 0.08 -1.19 0.2435 -0.27 0.07

28°C - 22°C 0.02 0.08 0.27 0.7917 -0.15 0.19

26°C - 24°C -0.06 0.08 -0.76 0.4504 -0.24 0.11

28°C - 24°C 0.06 0.08 0.69 0.4955 -0.11 0.23

28°C - 26°C 0.12 0.08 1.45 0.1551 -0.05 0.29

197

Table A. 6. Analysis of variance summary for algae concentrations (µm3/mL) within buckets

(EUs) among treatment temperatures.

Source DF

Sum of

Squares Mean Square F-value p-value

Model 4 7.54 x 1012

1.89 x 1012

1.43 0.232

Error 75 98.82 x 1012

1.32 x 1012

Corrected Total 79 106.36 x 1012

198

APPENDIX B: Species Comparisons within Temperature Treatments

At the 20, 22, and 24°C experimental temperatures, growth differed significantly among

the three species (Table 1). Final mean growth for L. fasciola was significantly larger than E.

brevidens at 20, 22, and 24°C, and was larger than those for E. capsaeformis at 20 and 24°C.

Epioblasma capsaeformis growth was significantly greater than that for E. brevidens at the 20,

22 and 24°C temperatures. Final growth among the three species ranged from 0.63–2.43 mm

with a mean of 1.40 mm at 20°C, 1.86–4.33 mm with a mean of 3.30 mm at 22°C, and 2.83–5.24

mm with a mean of 4.02 mm at 24°C, respectively (Figure 1).

Growth differed significantly among some of the comparisons among the three species at

the 26 and 28°C experimental temperatures (Table 1). Final mean growth for L. fasciola and E.

capsaeformis were significantly larger than those for E. brevidens at 26 and 28°C. Mean growth

for L. fasciola did not differ from that of E. capsaeformis at 26 or 28°C. Final growths among the

three species ranged from 3.79–5.52 mm with a mean of 4.75 mm at 26°C, and 3.41–5.68 mm

with a mean of 4.55 mm at 28°C (Figure 1).

Survival of all species was similar among the 20, 22, and 24°C experimental

temperatures, whereas survival of some species differed significantly at 26 and 28°C (Table 2).

Final survival of E. brevidens was significantly greater than E. capsaeformis at 24, 26, and 28°C.

Survival of L. fasciola was significantly greater than that of E. capsaeformis at 28°C. No

significant differences in survival were found between E. brevidens and L. fasciola at 26 and

28°C, and between L. fasciola and E. capsaeformis at 26°C. Final survival ranged from 51.6–

98.4% with a mean of 76.8% at 20°C, 75.1–100% with a mean of 87.3% at 22°C, 64.2–100%

with a mean of 85.3% at 24°C, 61.3–95.1% with a mean of 84.7% at 26°C, and 54.6–98.2% with

a mean of 83.7% at 28°C (Figure 2).

199

Table B. 1. Comparing E. brevidens, E. capsaeformis and L. fasciola growth (mm) within


Treatment Fixed Effects Num DF Den DF F-value p-value

20°C Species 2 12 18.85 0.0002

Time 9 61.3 129.61 <0.0001

Species x Time 18 61.3 8.82 <0.0001

22°C Species 2 12.7 82.58 <0.0001

Time 9 50.5 187.48 <0.0001

Species x Time 18 50.5 6.83 <0.0001

24°C Species 2 12 28.22 <0.0001

Time 9 58.7 553.16 <0.0001

Species x Time 18 58.7 11.74 <0.0001

26°C Species 2 12 4.58 0.0333

Time 9 58.9 521.98 <0.0001

Species x Time 18 58.9 4.00 <0.0001

28°C Species 2 12.4 6.01 0.0149

Time 9 60.2 159.90 <0.0001

Species x Time 18 60.2 2.49 0.0043

200

Table B. 2. Comparing E. brevidens, E, capsaeformis and L. fasciola survival (%) within


Treatment Fixed Effects Num DF Den DF F-value p-value

20°C Species 2 15.4 3.02 0.0781

Time 9 102 16.24 <0.0001

Species x Time 18 102 2.98 0.0003

22°C Species 2 13.8 1.95 0.1789

Time 9 105 12.10 <.0001

Species x Time 18 105 3.79 <.0001

24°C Species 2 13.1 2.58 0.1136

Time 9 107 8.26 <0.0001

Species x Time 18 107 3.13 0.0001

26°C Species 2 12.5 4.31 0.0377

Time 9 101 7.69 <0.0001

Species x Time 18 101 1.05 0.4177

28°C Species 2 14.6 21.36 <0.0001

Time 9 94.9 4.96 <0.0001

Species x Time 18 94.9 0.81 0.6819

201

Figure B. 1. Comparisons of E. brevidens, E. capsaeformis and L. fasciola growth (mm) at each

of the 5 temperature treatments (20, 22, 24, 26, and 28°C) over 10 sampling events.

202

Figure B. 2. Comparisons of E. brevidens, E. capsaeformis and L. fasciola survival (%) at each

of the 5 temperature treatments (20, 22, 24, 26, and 28°C) over 10 sampling occasions.

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