Prepared in cooperation with the Alabama Department of Conservation and Natural Resources, 

Alabama Power Company, U.S. Fish and Wildlife Service, and R.L. Harris Dam Adaptive 

Management Stakeholders

Adaptive Management of Flows from R.L. Harris Dam 

(Tallapoosa River, Alabama)—Stakeholder Process and 

Use of Biological Monitoring Data for Decision Making

Open-File Report 2019–1026

U.S. Department of the Interior

U.S. Geological Survey
Cover. Photograph showing collection of depth and velocity data at an electrofishing sample 

location. Taken at Hillabee Creek on May 26, 2010, by M. Martin, Alabama Cooperative Fish and 

Wildlife Research Unit.

Back cover. Photograph showing a prepositioned area electrofisher sample collection in action. 

Two workers hold a seine at the downstream end of the electrofisher and, after the electricity 

ceases, another worker (on right) visually inspects the area and kicks through the substrata 

to dislodge any fish that are entrained. Taken at Hillabee Creek on May 26, 2010, by M. Martin, 

Alabama Cooperative Fish and Wildlife Research Unit.
Adaptive Management of Flows from 

R.L. Harris Dam (Tallapoosa River, 

Alabama)—Stakeholder Process and Use 

of Biological Monitoring Data for Decision 

Making

Edited by Elise R. Irwin

Chapter A

Adaptive Management of a Regulated River—Process for Stakeholder Engagement and Consequences to Objectives

By Elise R. Irwin, Mary C. Freeman, James Peterson, Kathryn D.M. Kennedy, and 

M. Clint Lloyd

Chapter B

Long-Term Dynamic Occupancy of Shoal-Dwelling Fishes Above 

and Below a Hydropeaking Dam

By Elise R. Irwin, Mary C. Freeman, Kathryn D.M. Kennedy, and M. Clint Lloyd

Chapter C

Macroinvertebrate Community Structure in Relation to Variation in 

Hydrology Associated with Hydropower

By Kristie M. Ouellette Coffman, Ely Kosnicki, M. Clint Lloyd, Tom Hess, and Elise R. Irwin

Prepared in cooperation with the Alabama Department of Conservation and 

Natural Resources, Alabama Power Company, U.S. Fish and Wildlife Service, 

and R.L. Harris Dam Adaptive Management Stakeholders

Open-File Report 2019–1026

U.S. Department of the Interior

U.S. Geological Survey
U.S. Department of the Interior

DAVID BERNHARDT, Secretary

U.S. Geological Survey

James F. Reilly II, Director

U.S. Geological Survey, Reston, Virginia: 2019

For more information on the USGS—the Federal source for science about the Earth, its natural and living 

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Although this information product, for the most part, is in the public domain, it also may contain copyrighted materials 

as noted in the text. Permission to reproduce copyrighted items must be secured from the copyright owner.

Suggested citation:

Irwin, E.R., ed., 2019, Adaptive management of flows from R.L. Harris Dam (Tallapoosa River, Alabama)— 

Stakeholder process and use of biological monitoring data for decision making: U.S. Geological Survey 

Open-File Report 2019–1026, 93 p., https://doi.org/10.3133/ofr20191026. 

ISSN 2331-1258 (online)
iii

Acknowledgments

This research was funded by the U.S. Geological Survey, the U.S. Fish and Wildlife Service, the 

Alabama Department of Conservation and Natural Resources, and the Alabama Power Company. 

The Alabama Cooperative Fish and Wildlife Research Unit is jointly sponsored by the U.S. Geological Survey; the Alabama Department of Conservation and Natural Resources, Wildlife and 

Freshwater Fisheries Division; the Alabama Agricultural Experiment Station, Auburn University; 

the Wildlife Management Institute; and the U.S. Fish and Wildlife Service. The Oregon Cooperative Fish and Wildlife Research Unit is jointly sponsored by the U.S. Geological Survey; the 

Oregon Department of Fish and Wildlife; the Oregon Agricultural Experiment Station, Oregon 

State University; the Wildlife Management Institute; and the U.S. Fish and Wildlife Service.

The authors of chapter A thank the R.L. Harris Dam Stakeholders for assistance throughout 

the project by providing support ranging from funding to river access on their private lands. 

Stakeholders are: Alabama Rivers Alliance, Alabama Power Company, Alabama Department of 

Conservation and Natural Resources, Conservation Unlimited, Lake Wedowee Property Owners’ Association, Lake Watch of Lake Martin, Middle Tallapoosa River Conservation Association, Randolph County Commission, Randolph County Industrial Relations, Upper Tallapoosa 

Watershed Committee, The U.S. Fish and Wildlife Service, and the U.S. Parks Service. B. Boston 

and V. Herr were instrumental in helping to elicit objectives and implement governance for the 

stakeholders. F. Johnson and J. Dunham provided thoughtful reviews to refine the contents of 

the chapter.

The authors of chapter B thank the many students and staff that made the field, laboratory, and 

analytical work possible. Special thanks to J. Marshall for helping draft the R code to melt the 

data into the matrix that provided detection histories for our data. S. Yurek and S. Brewer provided helpful and insightful comments that improved chapter B. This study was completed under 

the auspices of Auburn University Institutional Animal Care and Use protocol no. 2006–2569.

The authors of chapter C would like to thank all the sponsors, stakeholders, and researchers 

who contributed time, financial support, and knowledge to the advancement of the program over 

the life of the project. We would like to extend a huge thank you to all the field hands and staff 

members who helped to collect these data, without whom this research would not be possible. 

I. Waite and T. Kennedy reviewed the chapter and their comments improved the product greatly.
Flotilla of boats parked on a gravel bar located in Hillabee Creek. Taken on October 26, 2005, by B. Martin, Alabama 

Cooperative Fish and Wildlife Research Unit.

Black redhorse (Moxostoma duquenei, 274 millimeters) captured and released from Hillabee Creek. Taken on 

September15, 2008, by M. Martin, Alabama Cooperative Fish and Wildlife Research Unit.
v

Contents

Acknowledgments........................................................................................................................................iii

Abstract...........................................................................................................................................................1

Introduction.....................................................................................................................................................1

Purpose and Scope ..............................................................................................................................1

Chapter A................................................................................................................................................1

Chapter B................................................................................................................................................2

Chapter C................................................................................................................................................2

Implications for Adaptive Management ...........................................................................................3

Chapter A. Adaptive Management of a Regulated River—Process for Stakeholder 

Engagement and Consequences to Objectives ..........................................................................5

Introduction.....................................................................................................................................................5

Adaptive Management of Regulated Rivers ....................................................................................5

Methods...........................................................................................................................................................6

Management Context...........................................................................................................................6

Stakeholder Objectives........................................................................................................................9

Development of a Decision Support Model .....................................................................................9

Hypothesized Faunal Response ................................................................................................9

Model Structure.........................................................................................................................10

Decision Nodes.................................................................................................................10

Uncertainty Nodes............................................................................................................12

Utility Nodes ......................................................................................................................16

Sensitivity Analysis.............................................................................................................................16

Monitoring............................................................................................................................................16

Results ...........................................................................................................................................................17

Modeling the Optimal Decision ........................................................................................................17

Sensitivity Analysis.............................................................................................................................17

Model Updates....................................................................................................................................18

Discussion.....................................................................................................................................................18

References Cited..........................................................................................................................................23

Appendix A1. Transcripts from the Adaptive Management Workshop, 

April 30–May 1, 2003 ......................................................................................................................26

Appendix A2. Initial Bayesian Belief Network (2005), Training Cases and 

Learned Networks (2005–16)........................................................................................................26

Appendix A3. Charter of the R.L. Harris Stakeholders Board ...........................................................26

Chapter B. Long-Term Dynamic Occupancy of Shoal-Dwelling Fishes Above and 

Below a Hydropeaking Dam.........................................................................................................27

Introduction...................................................................................................................................................27

Methods.........................................................................................................................................................29

Field Sampling Methods ....................................................................................................................29

Analysis ................................................................................................................................................29

Results ...........................................................................................................................................................34

Discussion.....................................................................................................................................................39

References Cited..........................................................................................................................................49

Appendix B....................................................................................................................................................53
vi

Chapter C. Macroinvertebrate Community Structure in Relation to Variation in 

Hydrology Associated with Hydropower ...................................................................................55

Introduction...................................................................................................................................................55

R.L. Harris Dam....................................................................................................................................56

Analysis of Historical Macroinvertebrate Collections .................................................................56

Methods.........................................................................................................................................................56

Macroinvertebrate Field Sampling Methods .................................................................................56

Sample Processing.............................................................................................................................58

Identification........................................................................................................................................58

Power Analysis....................................................................................................................................58

Subsampling Procedure ....................................................................................................................59

Selection of Sampling Years Based on Natural Variation in 

Hydrologic Extremes.............................................................................................................59

Community Analyses..........................................................................................................................61

Results ...........................................................................................................................................................65

Characterization of Regulated and Unregulated Communities...................................................66

Macroinvertebrate Community Response to Natural Variation i

n Hydrology.............................................................................................................................66

Nonmetric Multidimensional Scaling..............................................................................................69

Permutational Multivariate Analysis of Variance..........................................................................70

Density Similarity Percentage Analysis and Taxa Density Nonmetric 

Multidimensional Scaling Overlay......................................................................................70

Functional Feeding Group and Habit Vectors on Nonmetric 

Multidimensional Scaling ....................................................................................................70

Cluster Analysis...................................................................................................................................78

Presence-Absence Similarity Percentage Analysis.....................................................................78

Summary of Results.....................................................................................................................................81

Discussion.....................................................................................................................................................82

Macroinvertebrate Community Composition .................................................................................82

Macroinvertebrate Community Response to Natural Variation in Hydrology ..........................82

Nonmetric Multidimensional Scaling..............................................................................................83

Distribution Trends in Macroinvertebrate Functional Feeding and Habit Groups...................83

Cluster Analysis...................................................................................................................................84

Identification of Indicator Taxa for the Mid-Tallapoosa River System ......................................84

Summary........................................................................................................................................................85

References Cited..........................................................................................................................................85

Appendix C1. Standard Operating Procedures—Sorting Protocol .................................................89

Introduction...................................................................................................................................................89

Sorting Objectives........................................................................................................................................89

Materials .......................................................................................................................................................89

Detailed Procedures....................................................................................................................................89

Outline of Procedures .................................................................................................................................92

Appendix C2. Macroinvertebrate Data.................................................................................................93
vii

Figures

A1. Map showing location of study site in the Tallapoosa River Basin......................................7

A2. Graphs showing Tallapoosa River discharge measured at streamgage 02412000 

located near Heflin, Alabama, and streamgage 02414500 located in 

Wadley, Alabama, 22 kilometers below the dam.....................................................................8

A3. An influence diagram (with utility and decision nodes) showing the structure of 

the Bayes network and complexity of managing flows below R.L. Harris Dam...............11

A4. Tornado diagram from one-way sensitivity analysis of R.L. Harris Dam 

decision model ............................................................................................................................18

A5. Example plots from one-way response profile sensitivity analysis with each 

line representing a decision alternative .................................................................................19

A6. Graphs showing indifference curves for power generation utility ....................................20

A7. Tornado diagram of one-way sensitivity of fish population value to influencing 

variables.......................................................................................................................................21

B1. Map showing the locations of the 5 river reaches where fishes were collected 

from 5 shoals at each of the reaches between 2005–16......................................................30

B2. Graph showing relation between generation events and cumulative 

degree days .................................................................................................................................33

B3. Graph showing average fish density by reach for all combined sites in a reach ...........34

B4. Graph showing estimated mean probability of persistence for 38 fish species 

at each of 25 sites in the Tallapoosa River system, 2005–16, modeled using 

dynamic, multitaxa occupancy models...................................................................................35

B5. Graph showing estimated mean probability of colonization for 38 fish species 

at each of 25 sites in the Tallapoosa River system, 2005–16, modeled using 

dynamic, multitaxa occupancy models...................................................................................36

B6. Graph showing estimated effects of location in the flow-regulated reach on 

species-specific persistence modeled using dynamic, multitaxa occupancy 

models applied to data for 25 sites in the Tallapoosa River system, 2005–16 ..................37

B7. Graph showing estimated effects of location in the flow-regulated reach on 

species-specific colonization modeled using dynamic, multitaxa occupancy 

models applied to data for 25 sites in the Tallapoosa River system, 2005–16 ..................38

B8. Graph showing estimated effects on flow regulation, distance downstream 

from R.L. Harris Dam, and annual frequency of hydropower generation and 

cumulative time with temperatures meeting a threshold for spawning on 

persistence and colonization for 38 fish species, modeled using dynamic, 

multitaxa occupancy models applied to data for 25 sites or 15 sites in the 

Tallapoosa River system, 2005–16............................................................................................40

B9. Graph showing estimated mean probability of persistence for 38 fish species 

at each of 15 flow-regulated sites in the Tallapoosa River system, 2005–16, 

modeled using dynamic, multitaxa occupancy models .......................................................41

B10. Graph showing estimated mean probability of colonization for 38 fish species 

at each of 15 flow-regulated sites in the Tallapoosa River system, 2005–16, 

modeled using dynamic, multitaxa occupancy models .......................................................42

B11. Graph showing estimated mean probability of persistence for 38 fish species 

at 15 flow-regulated sites in the Tallapoosa River system, 2005–16, modeled 

using dynamic, multitaxa occupancy models and plotted in relation to annual 

number of generation events....................................................................................................43
viii

B12. Graph showing estimated mean probability of colonization for 38 fish species 

at 15 flow-regulated sites in the Tallapoosa River system, 2005–16, modeled 

using dynamic, multitaxa occupancy models and plotted in relation to annual 

number of generation events....................................................................................................44

B13. Graph showing estimated mean probability of persistence for 38 fish species 

at 15 flow-regulated sites in the Tallapoosa River system, 2005–16, modeled 

using dynamic, multitaxa occupancy models and plotted in relation to annual 

number of days meeting a temperature criterion for spawning.........................................45

B14. Graph showing estimated mean probability of colonization for 38 fish species 

at 15 flow-regulated sites in the Tallapoosa River system, 2005–16, modeled 

using dynamic, multitaxa occupancy models and plotted in relation to annual 

number of days meeting a temperature criterion for spawning.........................................46

C1. Map showing sampling locations of the R.L. Harris Dam Adaptive Management 

Project in the Piedmont region of Alabama ...........................................................................57

C2. Graphs showing species accumulation curves and associated standard 

deviation based on preliminary data for selected sites .......................................................60

C3. Graph showing monthly average discharge values for Heflin and Wadley 

streamgages and monthly average Palmer’s Modified Drought Index score 

for the Piedmont Region from 2005 through 2016..................................................................64

C4. Graph showing nonmetric multidimensional scaling ordination of the average 

estimated sample abundance at each site ............................................................................69

C5. Graph showing nonmetric multidimensional scaling bubble plot of the 

abundance of Chironominae observed at each site (year, reach, and shoal) 

over the ordination of all data...................................................................................................72

C6. Graph showing nonmetric multidimensional scaling bubble plot of the 

abundance of Orthocladiinae observed at each site (year, reach, and shoal) 

over the ordination of all data...................................................................................................73

C7. Graph showing nonmetric multidimensional scaling bubble plot of the 

abundance of Iswaeon observed at each site (year, reach, and shoal) 

over the ordination of all data...................................................................................................74

C8. Graph showing nonmetric multidimensional scaling bubble plot of the 

abundance of Hydropsychidae observed at each site (year, reach, and shoal) 

over the ordination of all data...................................................................................................75

C9. Graph showing nonmetric multidimensional scaling bubble plot of the 

abundance of Simuliidae observed at each site (year, reach, and shoal) 

over the ordination of all data...................................................................................................76

C10. Graph showing nonmetric multidimensional scaling ordination with 

significant functional feeding group class vectors indicating the relation 

between the ordination of macroinvertebrate community composition 

and percent abundance of each class of functional feeding group..................................77

C11. Graph showing nonmetric multidimensional scaling ordination with 

significant habit class vectors indicating the relation between the 

ordination of macroinvertebrate community composition and percent 

abundance of each class of habit............................................................................................78

C12. Diagram showing cluster analysis of the presence-absence taxa 

matrix for all sites (year-reach-shoal average) analyzed....................................................79
ix

Tables

A1. Stated a priori hypotheses (not exclusive) regarding how fishes and habitat 

will respond to specific flow conditions .................................................................................10

A2. Description of the three decisions and their alternatives for the R.L. Harris Dam 

Adaptive Management Project ................................................................................................12

A3. Conditional probability table for small fish abundance........................................................13

A4. Description of state variables, data sources, and ranges of values for the 

initial model parameterization ..................................................................................................14

A5. Summary of one-way response profile sensitivity analysis with number of optimal 

decisions across the range of state values, the decisions where the utilities 

of alternatives were equal (indifferent), and the decisions that do not change 

across range of node states (stochastically dominant).......................................................20

A6. Data used to conduct yearly model updates .........................................................................22

B1. Count data for 38 species captured in prepositioned area electrofishers at 

25 sites in 5 river reaches variably affected by R.L. Harris Dam ........................................31

B2. Parameter estimates from dynamic occupancy model fit to 12 years (2005–16) 

of observations of 38 fish species at 25 Tallapoosa River system sites ............................36

B3. Parameter estimates from dynamic occupancy model fit to 12 years (2005–16) 

of observations of 38 fish species at 15 flow-regulated sites .............................................39

B4. Parameter estimates from dynamic occupancy model fit to 12 years (2005–16) 

of observations of 38 fish species at 15 flow-regulated sites .............................................39

C1. Power analysis to estimate the number of Surber samples needed to test for a 

significant difference between regulated and nonregulated shoals of the 

R.L. Harris Dam study.................................................................................................................59

C2. Monthly average discharge values, with standard deviations in parentheses, 

for two U.S. Geological Survey streamgages, the Tallapoosa River at Wadley, Ala. 

and Tallapoosa River near Heflin, Ala., and the monthly reported Palmer’s 

Modified Drought Index values for the Piedmont Division of Alabama.............................62

C3. Percent composition of each identified order or group for each regulation type 

and for all observations, listed in order of greatest to least percent composition 

of the total observations............................................................................................................65

C4. Values of selected metrics for regulated and unregulated reaches .................................66

C5. List of the top five dominant taxa (order and taxon) in regulated and 

unregulated reaches based on percent of total observations............................................67

C6. List of the top five dominant orders and taxa in regulated and unregulated 

reaches for each year................................................................................................................68

C7. Results of permutational multivariate analysis of variance ................................................70

C8. Top 30 results of the similarity percentage analysis of the Wisconsin 

square root transformed average estimated species abundance per shoal 

species matrix .............................................................................................................................71

C9. Results of analysis of fit for functional feeding group and habit class vectors 

on the initial nonmetric multidimensional scaling.................................................................77

C10. Top 30 results of the similarity percentage analysis of the presence-absence 

taxa matrix ...................................................................................................................................80

C1.1. Estimate of sample sorted in 15 minutes ................................................................................90

C1.2. Estimate of total number of specimens in the sample..........................................................91

C1.3. Subsample estimator .................................................................................................................91

C2.1. Sum of total observations for each macroinvertebrate taxon at all sites.........................93
x

Conversion Factors

International System of Units to U.S. customary units

Multiply By To obtain

Length

centimeter (cm) 0.3937 inch (in.)

millimeter (mm) 0.03937 inch (in.)

meter (m) 3.281 foot (ft) 

kilometer (km) 0.6214 mile (mi)

meter (m) 1.094 yard (yd) 

Flow rate

cubic meter per second (m3/s) 70.07 acre-foot per day (acre-ft/d) 

cubic meter per second (m3/s) 35.31 cubic foot per second (ft3/s)

Temperature in degrees Celsius (°C) may be converted to degrees Fahrenheit (°F) as follows:

°F = (1.8 × °C) + 32.

Temperature in degrees Fahrenheit (°F) may be converted to degrees Celsius (°C) as follows:

°C = (°F – 32) / 1.8.

Supplemental Information

A water year is the 12-month period from October 1 to September 30 and is designated 

by the year in which it ends; for example, water year 2015 was from October 1, 2014, to 

September 30, 2015.

Abbreviations

AMP Adaptive Management Project

APC Alabama Power Company

BBN Bayesian Belief Network

CPT conditional probability table

EPT Ephemeroptera, Plecoptera, and Trichoptera

FERC Federal Energy Regulatory Commission

FFG functional feeding group

NMDS nonmetric multidimensional scaling

PAE prepositioned area electrofisher

PERMANOVA permutational multivariate analysis of variance

PHABSIM physical habitat simulation [model]

PMDI Palmer’s Modified Drought Index

SIMPER similarity percentage [analysis]

USGS U.S. Geological Survey
Adaptive Management of Flows from R.L. Harris Dam 

(Tallapoosa River, Alabama)—Stakeholder Process and 

Use of Biological Monitoring Data for Decision Making

Edited by Elise R. Irwin1,2

1

U.S. Geological Survey.

2

Alabama Cooperative Fish and Wildlife Research Unit.

Abstract

Adaptive management has been applied to problems with 

multiple conflicting objectives in various natural resources settings to learn how management actions affect divergent values 

regarding system response. Hydropower applications have 

only recently begun to emerge in the field, yet in the specific 

example reported herein, stakeholders invested in determining the best management alternatives for attainment of a suite 

of objectives outlined in a long-term adaptive management 

program below R.L. Harris Dam, a large, privately owned 

dam in Alabama. Stakeholders convened an objective-setting 

workshop to engage a governance structure and developed a 

decision support model to determine appropriate actions that 

optimized stakeholder values. The process led to implemented 

change in dam operation inclusive of incorporating hypothetical responses in system parameters to management. To 

account for the iterative loop of adaptive management, yearly 

monitoring of state variables that approximated many stakeholder objectives was performed from 2005 to 2016 and data 

collected were incorporated into the decision model. Specific 

analysis of fish and macroinvertebrate population responses 

indicated a less than satisfactory response for some stakeholders to the flow-management changes at the dam. Uncertainty 

regarding the best management to provide adequate hydrologic and thermal habitats for fauna and boatable days for 

recreationists still exists. The project led to a Federal Energy 

Regulatory Commission process for renewing the license to 

operate the dam (beginning in 2018); adaptive management 

could be a viable path forward to ensure stakeholder satisfaction related to new management options.

Introduction

Freshwater resources are a basic need of society and ecosystems. Because the management of water is multiobjective 

for diverse users, conflicts are inevitable when environmental 

concerns are pitted against economic interests. Adaptive management and decision analysis can account for multiple competing objectives identified by stakeholders, and these frameworks are applicable to water issues. Recently, there has been 

a call for large-scale flow manipulations in rivers to facilitate 

rapid learning so that theoretical frameworks can be transferred into knowledge of system function. Adaptive management is an iterative process that facilitates learning by making 

predictions relative to system uncertainty (decision support 

or other models), applying the scientific process to monitor 

effects of management actions that are applied to optimize 

resource objectives, and updating the knowledge base (relative 

to predicted responses) to improve future management. 

Purpose and Scope

This report describes the implementation and long-term 

application of adaptive management in a regulated river in 

the southeastern United States (Tallapoosa River, Alabama; 

R.L. Harris Dam, owned by Alabama Power Company). The 

implementation process included stakeholder involvement, 

development of flow prescriptions, predictions regarding system response, and design and implementation of a long-term 

monitoring program. We present the decision network used to 

assist stakeholders in prescribing initial flow manipulations, 

and 12 years of model updates from the monitoring program 

including assessment of the fish and macroinvertebrate communities at sites regulated by the dam and at unregulated sites.

Chapter A

Stakeholder engagement was key to implementation and 

long-term monitoring of potential system change related to 

management actions. Hypotheses regarding how the system 

state would change with increased base flow, better spawning conditions, and improved thermal conditions formed the 

underpinnings of the decision model. The model (Bayesian 

Belief Network) was constructed to (1) predict the consequences of different combinations of management actions that 
2 Adaptive Management of Flows from R.L. Harris Dam—Stakeholder Process and Use of Biological Monitoring Data

changed flows from the dam on multiple stakeholder objectives, (2) assist in selecting the decision that best satisfied 

equally weighted stakeholder objectives, and (3) incorporate 

data collected yearly to update probabilities (that is, Bayesian 

updating) quantifying how management actions contributed to 

meeting stakeholder objectives. A total of three management 

decisions were linked in the Bayesian Belief Network; flows 

from the dam either maintained the status quo or provided 

flows that variably matched unregulated flows in the river 

upstream from the reservoir impounded by the dam. A total 

of two other decisions were modeled: the provision of spring 

and summer, spring only, or summer only flows for spawning 

conditions and the provision (or not) of October boating flows.

Data to inform the model were supplied by Alabama 

Power Company (management decision, lake levels), collected 

through direct monitoring (fish population, temperature), or 

incorporated from U.S. Geological Survey streamgage data 

(reservoir inflows, boatable days). The initial decision was to 

implement the Alabama Power Company flow option, coupled 

with spring and summer periods for spawning (10 day, no 

discharge over 7,000 cubic feet per second), and October 

boating flows (discharge was 250–1,500 cubic feet per second 

on weekends). The portfolio of actions was termed the “Green 

Plan” and was implemented in 2005 along with a stakeholder 

governance structure to guide the project engagement. As the 

model was updated with the 12 years of monitoring and case 

data, the optimal decision changed, indicating uncertainty 

regarding the management options that best satisfied stakeholder objectives. The flow management options predicted to 

satisfy stakeholders most often were flows that matched the 

U.S. Geological Survey Tallapoosa River near Heflin, Ala. 

(U.S. Geological Survey streamgage 024112000, hereafter, 

“Heflin,” unregulated upstream), streamgage or the Green 

Plan, with the addition of spring and summer spawning flows 

and October boating flows. In practice, the Green Plan was 

always the flow decision, and with few exceptions, spring and 

summer spawning flow conditions were provided; however, 

October boating flows were never provided as a management 

option. Responses of fish populations were not positively 

related to changes in management contrary to model predictions. This finding further illustrated uncertainty regarding 

how management influenced stakeholder objectives and indicated that some stakeholder objectives were not satisfied.

Chapter B

A critical aspect of any adaptive management project is 

the monitoring of system response to the management protocols imposed. Based on the decision model, it was determined 

that the response of biological objectives to management was 

uncertain; reducing the uncertainty regarding the responses 

was an overall goal of the project. To reach this goal, we 

monitored dynamic occupancy (that is, two metapopulation 

processes: persistence and colonization) for 38 fish species 

(species for which we had more than 25 detections) at 25 sites 

above and below R.L. Harris Dam. Sites were selected in a 

stratified-random approach by surveying in shoal habitats in 

2 unregulated reaches (upper Tallapoosa River and Hillabee 

Creek) and 3 regulated reaches (main stem Tallapoosa River; 

from R.L. Harris Dam to the Malone Bridge, from Malone 

Bridge to Wadley Bridge, and the Horseshoe Bend area). We 

sampled shoals for fishes (and macroinvertebrates; chapter 

C) twice a year (summer and fall) for 12 years (2005–16) 

using prepositioned area electrofishers, resulting in occurrence records of over 81,900 individuals of 46 species. In 

general, fish density and species richness were depressed in 

the regulated versus the unregulated reaches. We modeled 

the effects of river regulation, distance from the dam, power 

generation, and temperature on probability of both persistence 

(species present on a shoal from year to year) and colonization 

(species present on a shoal where it was absent the previous 

year). Fishes persisted and colonized at lower probabilities at 

flow-regulated sites (downstream from R.L. Harris Dam) than 

at the unregulated sites in the upper Tallapoosa (Heflin) main 

stem and in Hillabee Creek. Estimated rates varied considerably among sites and among years; however, the estimated 

mean effects (across all species) of locations downstream from 

the dam were to lower the persistence by 81 percent and the 

probability of colonization by 65 percent. The effects of a site 

being located in the flow-regulated reach also varied among 

species for both persistence and colonization; however, only a 

single species (Ambloplites ariommus [shadow bass]) had an 

estimated effect that was positive.

We found evidence that both flow instability and 

depressed temperatures influenced fish persistence and colonization at the flow-regulated sites. Averaged over all taxa, fish 

persistence increased with greater distance from the dam and 

decreased during years with more generation events. Colonization was lower in years with more generation events; however, 

no clear increases in colonization were observed at the sites 

farthest from the dam. Persistence was not clearly related to 

warmer thermal regimes but increased at sites farthest from 

the dam. Fish colonization increased during years with warmer 

water temperatures.

Chapter C

Rapid changes in river stage and temperature are known 

to affect benthic invertebrate assemblage structure; research 

indicates that hydropeaking can reduce diversity and increase 

biomass of certain species. Some have suggested that management decisions should consider sensitive benthic species; 

adaptive management frameworks have been called upon to 

determine water release scenarios below dams that enhance 

invertebrate production and diversity.

In 2005, adaptive management was implemented to determine effects of flow prescriptions on state variables of interest 

to stakeholders in the regulated part of the Tallapoosa River 

below R.L. Harris Dam. Thus far, decisions have been based 

on projections of fish population responses to management. By 
Introduction 3

quantifying the relations between hydrology and the invertebrate assemblage structure in our study reaches, decisions 

regarding effective flow management could potentially be 

implemented more frequently because invertebrates should 

respond more rapidly than fishes to changes in management. 

Since sampling began in 2005, macroinvertebrates were 

collected as part of the adaptive management monitoring 

protocol. In 2016, a macroinvertebrate team began to sort and 

identify macroinvertebrate specimens within the stored collections. The prioritization of sample processing was designed to 

select for maximum variation in natural hydrologic variables 

as indicated by Palmer’s Modified Drought Index, a monthly 

metric available through the U.S. Geological Survey, to 

confirm macroinvertebrate communities display a response to 

alterations of hydrologic variables. We identified (to the lowest 

taxonomic level possible for each taxa) invertebrate specimens, calculated reach level community similarity metrics, 

and modeled the relations between faunal functional feeding 

group and habit, degree of river regulation, and yearly variation in hydrology.

Analyses based on the additive results of the identification of 4 samples from 3 shoals within each of 4 reaches 

(Main stem Tallapoosa River from R.L. Harris Dam to the 

Malone Bridge and from Malone Bridge to Wadley Bridge 

representing regulated reaches, and Upper Tallapoosa River 

and Hillabee Creek representing unregulated reaches) encompassing 5 years (2005, 2008, 2009, 2012, and 2014) indicate 

that the macroinvertebrate communities downstream from the 

R.L. Harris Dam have overall lower diversity, greater density 

driven by increased abundances of flow disturbance tolerant taxa, and the exclusion of some flow sensitive species 

from regulated reaches. Ordination of the macroinvertebrate 

community composition per shoal indicates a shift in community composition based on regulation, water year, and 

the interaction of year and regulation type (permutational 

multivariate analysis of variance results: probability [p] less 

than 0.001 for year, regulation type, and year×regulation type 

interaction). Among those taxa that contribute the most to the 

deviations in the similarities of the community composition 

as displayed on the ordination, flow-tolerant species display 

greater abundances within regulated reaches, and inclusions of 

large groups of these taxa are driving the similarities of closely 

ordinated regulated and unregulated sites. When analyzing 

the association of functional feeding group and habit with the 

ordination of the macroinvertebrate community, 4 of 6 functional feeding groups and 4 of 5 habits tested had a significant 

association with the ordination of macroinvertebrate shoal 

community composition; vectors for filterer/collectors, swimmers, and climbers ordinated towards regulated reaches, and 

vectors for predators, gatherer/collectors, clingers, and burrowers ordinated towards unregulated reaches, further illustrating the shift in community composition to flow-tolerant 

taxa in regulated reaches. Cluster analysis based on the 

presence/absence matrix of shoal community composition also 

indicated differences in the community composition between 

regulated and unregulated reaches, and a unique community 

composition in the downstream reach closest to the dam in all 

years analyzed, likely because of the regular absence of many 

flow sensitive species from regulated reaches. Results of the 

macroinvertebrate community composition in regulated and 

unregulated reaches of the mid-Tallapoosa River Basin suggest agreement with the standing published literature in which 

regulated reaches display a reduced richness and increase density of flow disturbance tolerant taxa. These results indicate 

that the Green Plan did not meet the stakeholder objective to 

restore and maintain macroinvertebrate community composition similar to unregulated reaches within the regulated portions of the river.

Peaking hydropower facilities that produce pulsed, highvelocity discharge create an unstable environment including 

adverse effects on hydraulic variables such as water velocity, 

depth, and temperature. The flow decision model and other 

analyses in this report indicate that stakeholder resource 

objectives, faunal communities, and fish and macroinvertebrate populations were negatively impacted by river regulation below the R.L. Harris Dam. Despite long-term (12 years) 

monitoring of fishes and macroinvertebrates in this adaptive 

management program, high levels of uncertainty regarding the 

ability to predict species-specific population response to management remain. One major impediment to defining specific 

flow and temperature regimes that would lessen the impacts 

of the dam on this socioecological system is engineering 

constraints in the design of the dam that make active adaptive 

management approaches difficult.

Implications for Adaptive Management

Despite potential obstacles, an adaptive management 

approach still holds substantial promise for improving the 

management of regulated rivers by allowing managers and 

scientists to address the uncertainty in predicting and measuring how river fauna will respond to flow-regime alterations. However, the recognition of uncertainty and the use of 

decision-analytic processes to reduce uncertainty attributed 

to flow management decisions with model updating through 

careful, replicated monitoring programs contribute to a viable 

path forward. Although monitoring data were used to assess 

stakeholder objectives using the decision model, the additional 

analysis to identify ecological responses to elements of dam 

operation could inform additional flow management regimes 

during relicensing. If flow and thermal alteration from the dam 

can be modified toward improving natural resource objectives, 

adaptive management processes and long-term monitoring 

could further reduce uncertainty related to biotic response 

to new Federal Energy Regulatory Commission licensing 

requirements.
Sampling site on the Tallapoosa River below R.L. Harris Dam. A prepositioned area electrofisher is visible in the lower right part of the photograph (white PVC 

frame). Taken on October 19, 2005, by B. Martin, Alabama Cooperative Fish and Wildlife Research Unit.
Chapter A

Adaptive Management of a Regulated River—Process for 

Stakeholder Engagement and Consequences to Objectives

By Elise R. Irwin,1,2Mary C. Freeman,1,3 James Peterson,1,4Kathryn D.M. Kennedy,2 and M. Clint Lloyd2

1

U.S. Geological Survey.

2

Alabama Cooperative Fish and Wildlife Research Unit.

3

Patuxent Wildlife Research Center.

4

Oregon Cooperative Fish and Wildlife Research Unit.

Introduction

Freshwater resources are a basic need for societal and 

ecosystem functions. Because the management of water 

involves multiple objectives and users, conflicts arise when 

environmental concerns are pitted against economic interests. 

Structured decision making and adaptive management can 

facilitate the resolution of conflicts by incorporating multiple stakeholder objectives into a framework relating system 

function and dynamics to water resource issues and decisions 

(Harwell, 1998; Irwin and Freeman, 2002; Poff and others, 

2003; Raadgever and others, 2008; Irwin, 2014). Several highprofile aquatic socioecosystems in the United States have been 

identified as candidates for adaptive management. Examples 

include the Florida Everglades (Harwell, 1998; Gunderson and 

Light, 2006), the Colorado River (National Research Council, 

1999), and southeastern regulated rivers (Irwin and Freeman, 

2002; Richter and Thomas, 2007; Irwin, 2014). In a review 

article highlighting the decade-long Alabama-Florida-Georgia 

“water war,” Poff and others (2003) call for large-scale river 

flow manipulations to facilitate rapid learning to improve 

management. Adaptive management is an iterative process that 

provides an appropriate context for meeting this call. Within 

the adaptive management framework, multiple hypotheses—

each represented by a model (or set of models) weighted by a 

plausibility or probability—predict system response to management actions. The optimal management decision is then 

selected based on the current system state and a prediction of 

the expected future state, taking into account various sources 

of uncertainty. For dynamic decision-making situations, 

model probabilities are updated by comparing model-specific 

predictions to observed (actual) future conditions (t+1; where 

t is a time step such as months or years). The adjusted model 

probabilities then are used to predict future conditions and, as 

a result, future optimal management decisions. A cyclical feedback loop explicitly provides for learning through time with 

the possibility of resolving competing hypotheses.

Adaptive Management of Regulated Rivers

Approaches to regulated river management, especially 

with respect to instream flow needs for biota, have historically focused on one-time negotiated flow settlements often 

related to the requirements of the Federal Energy Regulatory 

Commission (FERC; see Russo, 1999). In addition, the tools 

used to arrive at flow requirements have generally been based 

on models that do not explicitly incorporate uncertainty in 

physical, biological, or social components of socioecosystems. Monitoring of system responses to flow management 

has been rare once a settlement has been negotiated; there 

is usually no flexibility (or desire) to change management 

options. More recently, others have advocated the wider use of 

adaptive management to improve instream flow management 

(Castleberry and others, 1996; Van Winkle and others, 1997; 

Walters, 1997; Johnson, 1999; Irwin and Freeman, 2002; 

Poff and others, 2003; Irwin, 2014; Poff, 2017; McManamay 

and others, 2016), including several examples of adaptive 

management associated with FERC licenses (see below). 

Flow settlements at a few western U.S. dams have required 

additional scientific studies to address uncertainties and to 

support future adjustments to flow requirements (Castleberry 

and others, 1996; Van Winkle and others, 1997), and a few 

FERC licenses (Pearsall and others, 2005; Podolak and Yarnell, 2015) include an adaptive management process associated with the licenses. Yoccoz and others (2001) recognized 

adaptive management as an efficient framework for explicitly 

integrating management objectives, actions, and monitoring 

data for the purpose of improving management decision making. Coupling science-based monitoring with flow manipulations implemented to meet regulatory or societal requirements 

will allow for improved understanding of the functional 
6 Adaptive Management of Flows from R.L. Harris Dam—Stakeholder Process and Use of Biological Monitoring Data

relations among system biota and flow regime components 

(Irwin and Freeman, 2002; Irwin, 2014). Adaptive management focuses on the achievement of management goals with 

learning through monitoring, and emphasis on the reduction 

of system uncertainties that affect decision making (Williams 

and others, 2007). However, the key to the success of adaptive 

management of trust resources is stakeholder involvement and 

agreement (McLain and Lee, 1996). Stakeholders must agree 

on management objectives and governance for the framework 

to operate successfully (Williams and Johnson, 1995; Williams 

and others, 2007; Conroy and Peterson, 2013). Decisions 

with multiple competing objectives, decision alternatives, and 

complex system dynamics have the potential to become overly 

technical, thereby excluding the involvement by stakeholders 

throughout the process; therefore, reaching a starting point to 

begin adaptive management is not an easy process.

To overcome the threat of stakeholder abandonment, 

managers need a transparent process for identifying stakeholder objectives and establishing a governance structure for 

making decisions. Ideally, the process would involve scientists only as technical experts; neutral parties that develop the 

models linking potential management actions and stakeholder 

objectives. An ideal process also would include, to the extent 

possible, decision support models that are transparent and generally understandable by nontechnical stakeholders to facilitate 

understanding and buy-in to the process. Unfortunately, there 

are few examples of stakeholder-driven processes that have 

led to the successful implementation of adaptive management 

(Gregory and others, 2012; Conroy and Peterson, 2013). Here, 

we describe the development, implementation, and sustained 

prescription of adaptive management in the Tallapoosa River, 

Alabama, a process that included substantial stakeholder 

involvement, the development of a decision model used to aid 

determination of initial flow prescriptions, and model updating 

over an 11-year period (2005–16) to inform future management. Although the specific application is regional, we believe 

that the framework and lessons learned are applicable to managed ecological systems worldwide.

Methods

The Tallapoosa River below R.L. Harris Dam is a 

strongly flow-regulated reach in the Piedmont region of 

east-central Alabama (fig. A1). The system under study is a 

78-kilometer (km) reach of the Tallapoosa River beginning at 

R.L. Harris Dam and terminating in the headwaters of Lake 

Martin (not shown). R.L. Harris Dam was constructed primarily as a hydropower facility, with other potential benefits 

including flood control, recreational opportunities on the reservoir created by the dam, and economic growth associated with 

the reservoir. The generation capacity for the 2-turbine facility 

is 135 megawatts, which accounts for about 10 percent of the 

total capacity of the 11 privately owned hydropower dams in 

the eastern Mobile River Basin.

Management Context

Since going into service in 1983, R.L. Harris Dam has 

been operated primarily as a hydropeaking facility, such that 

water is released in pulses, usually 4–6 hours in duration, 

through one or two turbines, each with the capacity to pass 

226 cubic meters per second (m3/s). Historically, generation 

events were once or twice daily, 5 days a week, and usually 

included no generation on weekends (that is, “status quo” 

scenario in models below). As a result of the hydropeaking 

operation, the flow regime through the study reach typically 

fluctuated between extreme low flows and high flows corresponding to one- or two-turbine generation (fig. A2). Comparison of pre- and postdam hydrographs indicated changes in 

multiple aspects of the flow regime; for example, high flows 

were dampened, low flows were lower and more frequent, and 

seasonal shifts in flow magnitude were apparent (Irwin and 

Freeman, 2002). In addition to changes in the flow regime, the 

thermal regime below the dam has also been affected; during 

spring and summer months, temperature decreases as much as 

10 degrees Celsius (°C) during generation events (Irwin and 

Freeman, 2002). During nongeneration periods, the FERC 

license for R.L. Harris Dam requires that flow as recorded at 

the U.S. Geological Survey (USGS) streamgage at Wadley, 

Ala. (22 km downstream from the dam; U.S. Geological Survey streamgage 02414500 [U.S. Geological Survey, 2018]), 

is not to fall below the predam historical record low flow of 

1.27 m3/s.

The study reach represents one of the longest and highest-quality segments of Piedmont river habitat remaining in 

the Mobile River Basin, one of the most biologically diverse 

river drainages in North America (Lydeard and Mayden, 1995; 

Freeman and others, 2005). Extensive areas of shoal habitat, 

river features that typically support high faunal diversity and 

that have been replaced by impoundments throughout much 

of the Southeast, are characteristic along this part of the river. 

The native fish assemblage includes at least 57 species, including at least 7 species endemic to the Tallapoosa River system. 

Of the fish species, several are considered “at risk” by the 

U.S. Fish and Wildlife Service; however, no fish species are 

on the list of threatened and endangered species regulated by 

the Endangered Species Act of 1973. A total of 1 fish species, 

3 crayfish species, and 4 mussel species are listed as Greatest 

Conservation Need species by the State of Alabama (Wood, 

2015). Management needs for State Greatest Conservation 

Need fish and invertebrate species were considered when 

defining management options. One Federally listed unionid 

mussel species (Hamiota altilis [finelined pocketbook]) may 

have historically been in the river reach below R.L. Harris 

Dam; its current perceived absence also affected management options discussed by stakeholders, specifically in regard 

to providing habitat for reintroduction. Before construction 

of R.L. Harris Dam, the study reach also supported productive sport fisheries for native black basses (Micropterus spp.) 

and catfishes (primarily Ictalurus punctatus [channel catfish], 

and Pylodictis olivaris [flathead catfish]), as well as river 
Methods 7

Figure A1. Location of study site in the Tallapoosa River Basin. The river is regulated below R.L. Harris Dam and 

unregulated above Lake Wedowee. U.S. Geological Survey streamgages are maintained at Heflin and Wadley, 

Alabama (see figure B1 for sampling reach locations).

Hillabee Creek

Heflin

Wadley

R.L. HARRIS DAM Malone

Malone

Horseshoe

Bend

AL GA

FL

TN

MS

NC

Map area

GULF OF MEXICO

Watershed

boundary

Base from U.S. Geological Survey National Hydrography dataset, 2018

Lake Wedowee ALABAMA

GEORGIA

0 5 10 MILES

0 5 10 KILOMETERS

Watershed boundary

Little Tallapoosa River

Tallapoosa River

N

U.S. Geological Survey

streamgage 02412000

U.S. Geological Survey

streamgage 02414500

Tallapoosa River
8 Adaptive Management of Flows from R.L. Harris Dam—Stakeholder Process and Use of Biological Monitoring Data

boating activities (D. Catchings, Alabama Department of 

Conservation and Natural Resources, oral commun., 2002). 

A decline in sport fish populations and the loss of access to 

the river because of changes in flow regime have been major 

stakeholder concerns since construction of R.L. Harris Dam. 

Conversely, altering the peaking operation could threaten the 

power utility’s flexibility to provide and sell electricity on 

demand during periods of peak consumption. Changes in dam 

operation could also affect water levels and therefore values 

for users in the reservoirs, particularly at Harris Lake.

Management issues in the study reach below R.L. Harris 

Dam have therefore revolved around the effects of hydropower operation on values associated with power production 

needs; water availability for economic development, consumption, boating, angling and other recreational activities 

(upstream and downstream from the dam); and the general 

health of the Tallapoosa River ecosystem. Although these 

conflicting management objectives had been recognized for 

many years, the ability of stakeholders to reach consensus 

regarding implementation of changes in the flow regime had 

Figure A2. Tallapoosa River discharge measured at U.S. Geological Survey (USGS) streamgage 

02412000 (top panel—naturally occurring flows) located near Heflin, Alabama, and USGS streamgage 

02414500 (bottom panel—regulated by R.L. Harris Dam) located in Wadley, Alabama, 22 kilometers below 

the dam (May 5–12, 2014; U.S. Geological Survey, 2018; data are reported in cubic meters per second). 

[The Green Plan called for passing daily volumes of water equivalent to or greater than those recorded 

by the Heflin streamgage, which is located above the reservoir and measures flows that amount to 

about 50 percent of inflows into the reservoir.]
Methods 9

not been realized. During the 1990s, multiple stakeholders had 

communicated their desire for development of a plan of action. 

One option was to ask FERC to reopen the regulatory license 

and order an evaluation of the dam operation with respect to 

competing objectives. Reopening the license was not desirable to the power company, particularly in light of previous 

experiences where a reopened regulatory license resulted in a 

renegotiated flow regime developed without options to amend 

the license based on meeting (or not meeting) stakeholders’ 

objectives. Formal discussions with stakeholders and the publication of Irwin and Freeman’s (2002) framework provided a 

roadmap toward implementation of adaptive management in 

the system. The stakeholders recognized that quantification of 

system function during management would assist with reduction in uncertainty related to future FERC regulations—the 

license will be renewed in 2023.

Stakeholder Objectives

To begin the adaptive management process, we arranged 

a workshop to define stakeholder objectives. Our goal was to 

incorporate values associated with these conflicting objectives into a structured decision model to determine a starting 

point for adaptive management. From April 29 through May 1, 

2003, we held a workshop open to all stakeholders in the 

middle Tallapoosa Basin to introduce the concept of adaptive 

management and to create an open discussion for building 

consensus on management objectives and values (see appendix A1 for transcripts of the workshop).

Following a series of presentations by experts in the field 

of adaptive natural resource management, professional facilitators (https://groupsolutions.us; under contract with USGS) 

convened an interactive session, beginning with an open forum 

for all workshop participants to suggest and discuss potential 

values and objectives. Suggested objectives were judged in an 

electronic poll by 1 representative from each of 23 participating stakeholder groups. From the poll results, a tentative list 

of objectives (Clemen, 1997) was drawn for discussion among 

stakeholders. Objectives identified by stakeholders were as 

follows (order does not imply rank):

1. Maximize economic development, primarily for municipalities.

2. Maximize diversity and abundance of native fauna and 

flora.

3. Minimize bank erosion downstream from R.L. Harris 

Dam.

4. Maximize water levels in the reservoir.

5. Maximize boating and angling opportunities downstream 

from R.L. Harris Dam.

6. Minimize total cost to the power utility.

7. Maximize power utility operation flexibility.

8. Minimize river fragmentation (that is, no new dams).

9. Minimize consumptive use of water resources by 

municipalities and other users (that is, agriculture).

Stakeholders ultimately agreed upon these objectives 

as complete and representative of the interests of all parties 

involved. These higher level objectives are objectives defined 

as what the stakeholders want to accomplish, not the way in 

which they will accomplish them (that is, means objectives). 

In addition, stakeholders agreed to adopt the concept of adaptive management as a framework for future discussions and 

management decisions.

Development of a Decision Support Model

Objectives established at the workshop were then used 

in the development of a decision model to assist stakeholders in making the complex decisions necessary to change the 

flow regime below R.L. Harris Dam. To conduct the decision 

analysis, we followed the basic steps outlined by Clemen 

(1997): (1) formed hypothesized relations between flow and 

system response, (2) constructed a basic model outlining these 

hypotheses, (3) parameterized the model, (4) determined the 

optimal decision from the model results, and (5) completed 

sensitivity analyses to determine which components of the 

model had the greatest effect on the decision.

Hypothesized Faunal Response

Using existing knowledge, expert opinion, and empirical 

data, we constructed hypothesized relations of faunal dependence on flow regime. Studies in the system have contributed 

to our knowledge of how fauna may respond to specific flow 

features in the system. Irwin and Freeman (2002) hypothesized that (1) depleted low flows, (2) flow instability, and 

(3) thermal-regime alteration were the features most likely to 

affect faunal response in the system.

Published findings indicate that hydrologic alteration 

(fig. A2) in the river has affected various biological processes. 

Irwin and Hornsby (unpublished data) repeated Swingle’s 

1951 rotenone survey (Swingle, 1954) in the regulated river 

near Horseshoe Bend and reported a major shift in community 

composition (from specialists to generalists) and declines in 

overall fish numbers and biomass. In 1951, the community 

consisted of catfishes (46 percent) and minnows (47 percent), 

and in 1996, the community was dominated by black basses 

and sunfishes (51 percent); whereas, catfishes and minnows 

comprised 22 and 5 percent of the community, respectively. 

With the exception of the recorded absence of Lepomis megalotis (longear sunfish [Irwin and Hornsby, unpublished data]; 

Andress, 2002; Martin, 2008), fish species composition seems 

to be stable; yet, Freeman and others (2001) reported that 

persistence of fishes in the flow-regulated section depended, 

in part, on periodically stable shoal habitat conditions that 

allowed reproduction and juvenile survival. Irwin and others 
10 Adaptive Management of Flows from R.L. Harris Dam—Stakeholder Process and Use of Biological Monitoring Data

(1997), Andress (2002) and Martin (2008) reported disrupted 

spawning for sunfishes including Micropterus punctulatus (Alabama bass), M. tallapoosae (Tallapoosa bass), and 

L. auritus (redbreast sunfish). Nest success for redbreast 

sunfish was negatively related to both peaking power generation and depressed water temperatures (also caused by the 

dam; Andress, 2002; Martin, 2008). A list of some hypotheses 

related to flow features in the river is provided in table A1.

Model Structure

Once we established hypothesized relations between flow 

and biotic response, we incorporated these relations, along 

with various other management objectives, into an influence 

diagram (fig. A3) where relations among decision components 

are explicitly represented in graphical form (Clemen, 1997). 

In this figure, decision elements (or nodes) are represented by 

blue rectangles, consequence (or utility) nodes by pink hexagons, and uncertainty (or chance) nodes by yellow rectangles. 

Influence diagrams provide explicit representations of individual decision components and their dependencies.

State variables that were important for describing relations among outcomes representing high level objectives and 

flow regime were identified and incorporated into an influence 

diagram (state variables = uncertainty nodes, yellow rectangles; fig. A3) with casual relations (links) between management options (decision nodes, blue rectangles; fig. A3), state 

variables, and stakeholder values (utility nodes, pink hexagons; fig. A3). Using this structure, we developed a Bayesian 

belief network (Marcot and others, 2006), and a consequent 

Bayesian network model using Netica 1.12 (Norsys Software 

Corporation, 1998) to both quantify uncertainty regarding the 

response of the system to management actions and our understanding of system dynamics (hypotheses) relative to predicted 

response. A description of each model component (hereafter, 

“node”) represented in the model follows.

Decision Nodes

The decision alternatives included four differing primary 

flow regimes, the provision of spawning windows (periods 

during which flow fluctuations are minimized to allow for 

hypothesized increased spawning success), and increased 

weekend flows in October for recreational boating (table A2). 

Relations between flow and system response were modeled 

using probabilistic dependencies derived from long-term 

(1982–2004) empirical data from multiple projects and expert 

opinion; whereas, relations between system response and 

stakeholder satisfaction (that is, utility values) were based 

upon stakeholder opinion.

The primary set of decision alternatives concerned daily 

flow operations from the dam. Based on our hypotheses, 

increasing the flow level during nongeneration periods should 

have positive effects on the abundance, diversity, and growth 

of fishes (and likely other flow-dependent biota). Increased 

flow from the dam also should have positive benefits for river 

boaters and river landowners. We examined four alternatives 

to the primary decision set. The first was no change: keep 

the system at status quo (see details in description of study 

site above). The other three alternatives were based upon the 

concept of mimicking the flow regime recorded at the USGS 

streamgage in Heflin, at Wadley, 22 km below the dam. The 

Heflin streamgage measures flows in the unregulated upper 

portion of the Tallapoosa River (fig. A1); several stakeholders 

hypothesized that mimicking these flows at the dam would 

allow for some natural flow variability in the regulated portion 

of the river. The first of these alternatives was, in effect, modeled as a constant flow from the dam to maintain the Heflin 

target at Wadley (Heflin), which consisted of minimum flows 

plus any necessary generation flows. The second was similar, 

except the flow from the dam was to never reach levels below 

8.5 m3/s (Heflin 300). The third was an option proposed by the 

power utility, in which at least 75 percent of the Heflin target 

was maintained by 2–3 daily pulses, 1 at 0600 and 1 at 1200 

(Alabama Power Company [APC]). 

Table A1. Stated a priori hypotheses (not exclusive) regarding how fishes and habitat will respond to specific flow conditions.

[Developed using published literature and empirical data from the Tallapoosa River]

Hypothesized biotic response Hypothesized flow linkage 

Presence of fluvial specialists will be highest at unregulated sites. Unregulated flows provide more stable conditions for habitat 

specialists. 

Habitat persistence will be greatest at unregulated sites positively 

affecting recruitment processes. 

Highly regulated flows negatively affect persistence of habitats 

critical for fauna because of rapidly changing stage. 

Spawning success will be highest in years when spawning windows 

are provided. 

Stable flows provided for spawning will increase recruitment of 

multiple fish species. 

Filter-feeding invertebrate populations will respond positively to 

increased base flow. 

Increased base flow dampens magnitude of disturbance on shoal 

habitats and increases flow through pools. 
Methods 11

Figure A3. An influence diagram (with utility and decision nodes) showing the structure of the Bayes network and complexity of managing flows below R.L. Harris Dam. The blue rectangles are the decisions that were made about flow from the dam, the yellow rectangles are stakeholder objectives represented by measurable state variables, and the pink hexagons are consequence (utility) values of the different stakeholders. The initial flow management decision was the portfolio that maximized the satisfaction of the stakeholders (Alabama Power Company; sum of utility scores = 297.900).

Power generation

Fish population value

Small fish abundance

HighModerateLow

65.618.316.1

Bass recruitment

HighModerateLow

68.117.014.9

Degree days

HighModerateLow

30.539.030.5

Reservoir inflow

FloodWetNormalDryDrought

10.015.050.015.010.0

Reservoir user satisfaction

October flows?

YesNo

297.900

 0

River boater satisfaction River landowner satisfaction

Boatable days

HighModerateLow

 0 +

14.885.1

Windows of opportunity?

Status quoSpring sumSpringSummer

 0

297.900

 0 0

Flow-through pools

HighModerateLow

18.545.536.0

Erosion

HighModerateLow

33.333.333.3

Slow-cover amounts

HighModerateLow

35.556.67.95

Shallow-fast amounts

HighModerateLow

23.068.88.25

Lake levels

HighModerateLow

57.016.526.5

Flow?

Status QuoHeflinHef 300APC

 0 0 0

297.900

Redbreast sunfish spawning success

HighModerateLow

68.117.014.9

Model truth

YesNo

99.01.00
12 Adaptive Management of Flows from R.L. Harris Dam—Stakeholder Process and Use of Biological Monitoring Data

Table A2. Description of the three decisions and their alternatives for the R.L. Harris Dam Adaptive Management Project.

[m3/s, cubic meter per second; --, no data; APC, Alabama Power Company]

Flows from 

the dam Description Spawning conditions Description October flows Description

Status quo No change in current operations. Status quo No provision of spawning 

flows.

Yes Boatable flows of 

14.2–42.5 m3/s were 

provided on weekends.

Heflin Flows are constant to maintain the Heflin streamgage 

discharge at the Wadley 

streamgage.

Spring and summer 10-day window where 

flows did not exceed 

198.2 m3

/s in both 

spring and summer.

No No boatable flows were 

provided.

Heflin 300 Heflin scenario plus flow from 

dam did not drop below 

8.5 m3/s.

Spring 10-day window where 

flows did not exceed 

198.2 m3/s in spring 

only. 

-- --

APC 75 percent of the Heflin flow was 

maintained through pulsing 

2–3 times per day.

Summer 10-day window where 

flows did not exceed 

198.2 m3

/s in summer 

only.

-- --

A second set of decision alternatives concerned spawning 

or “flow” windows. Based on our hypotheses, periods of stable 

flow without hydropeaking should increase opportunities for 

fish to spawn and larvae to develop successfully. Alternative 

decisions included no change (status quo), spawning windows 

in both spring and summer, spring windows only, and summer 

windows only. The third decision set was whether or not to 

provide recreational boating flows on weekends in October, a 

traditionally popular time to float the river. The combination of 

decisions is equivalent to 32 possible decision portfolios in the 

Bayesian Belief Network.

Uncertainty Nodes

Relations among the uncertainty nodes (yellow rectangles; fig. A3) were modeled using probabilistic dependencies derived from empirical data and expert opinion. For 

each uncertainty node, a conditional probability table (CPT; 

table A3) was populated with probabilities of causal links 

among associated nodes. CPTs were populated using both 

expert opinion and available empirical data (Marcot and others, 2006) In practice, each causal link to a node represents a 

column in a CPT and probabilities associated with the causal 

links must be entered (table A3). For example, the node “small 

fish abundance” has four causal links: “shallow-fast habitat,” 

“degree days,” “flow through pools,” and the decision to 

provide “flow windows” (table A3). The probability of having 

high, moderate, or low small fish abundance was estimated 

for each of the 108 potential scenarios in this part of the BBN 

model and is represented on the associated CPT (table A3). 

For the “small fish abundance” node in our BBN, we had 

empirical data for two decisions (status quo and summer 

flow windows; Freeman and others, 2001). The other probabilities were estimated by expert opinion; for example, what 

is the probability of having low small fish abundance when 

conditions provide high amounts of shallow-fast habitat, high 

numbers of degree days, spring flow windows, and high flow 

through pools? As new data became available (for example, 

data associated with a new decision) from an associated 

monitoring program, CPTs were updated with new information that replaced the expert opinion. The nodes are described 

in detail below and the source and range for each are presented 

in table A4.

Reservoir inflow.—The input for reservoir inflow 

was based on the 10-percent, 25-percent, 75-percent, and 

90-percent exceedance flows for the combined Heflin (main 

stem Tallapoosa River upstream from Harris Reservoir) and 

Newell (USGS streamgage 02413300, Little Tallapoosa River 

near Newell, Ala.) streamgages for the period of record. The 

main stem Tallapoosa River and the Little Tallapoosa River 

are the two primary sources of inflow into Harris Reservoir. 

Using the period of record, we assigned conditional probabilities to each inflow condition as follows: flood, 0.10; wet, 0.15; 

normal, 0.50; dry, 0.15; drought, 0.10. Thus, flood conditions 

were equated to flows with greater than (>) 48.1 m3/s, wet 

conditions were flows between 42.5 and 48.1 m3/s, normal 

conditions were flows between 28.3 and 42.5 m3/s, dry conditions were flows between 17.0 and 28.3 m3

/s, and drought 

conditions had flows less than (<) 17.0 m3/s.
Methods 13

Table A3. Conditional probability table (CPT) for small fish abundance. The top 42 of 108 possible scenarios related to the decision to 

provide flow windows, availability of shallow-fast habitat, number of degree days (temperature component), and the amount of flow 

through pool habitat are shown. Probabilities were derived from data collected by prepositioned area electrofishing grids, by physical 

habitat simulation models, and from expert opinion.

[Nodes are listed as included in the Netica software CPT; bins of high, moderate, and low for each node are described in table A4]

Shallow-fast 

habitat

Degree days 

(temperature)1

Decision—Flow 

windows?

Flow through 

pools

Small fish abundance

High Moderate Low

High High Status quo High 0.3 0.5 0.2

High High Status quo Medium 0.3 0.5 0.2

High High Status quo Low 0.3 0.5 0.2

High High Spring and summer High 1.0 0.0 0.0

High High Spring and summer Medium 1.0 0.0 0.0

High High Spring and summer Low 1.0 0.0 0.0

High High Spring High 0.6 0.3 0.1

High High Spring Medium 0.6 0.3 0.1

High High Spring Low 0.6 0.3 0.1

High High Summer High 0.6 0.3 0.1

High High Summer Medium 0.6 0.3 0.1

High High Summer Low 0.6 0.3 0.1

High Moderate Status quo High 0.2 0.5 0.3

High Moderate Status quo Medium 0.2 0.5 0.3

High Moderate Status quo Low 0.2 0.5 0.3

High Moderate Spring and summer High 0.9 0.1 0.0

High Moderate Spring and summer Medium 0.9 0.1 0.0

High Moderate Spring and summer Low 0.9 0.1 0.0

High Moderate Spring High 0.4 0.4 0.2

High Moderate Spring Medium 0.4 0.4 0.2

High Moderate Spring Low 0.4 0.4 0.2

High Moderate Summer High 0.4 0.4 0.2

High Moderate Summer Medium 0.4 0.4 0.2

High Moderate Summer Low 0.4 0.4 0.2

High Low Status quo High 0.0 0.2 0.8

High Low Status quo Medium 0.0 0.2 0.8

High Low Status quo Low 0.0 0.2 0.8

High Low Spring and summer High 0.6 0.2 0.2

High Low Spring and summer Medium 0.6 0.2 0.2

High Low Spring and summer Low 0.6 0.2 0.2

High Low Spring High 0.2 0.6 0.2

High Low Spring Medium 0.2 0.6 0.2

High Low Spring Low 0.2 0.6 0.2

High Low Summer High 0.2 0.6 0.2

High Low Summer Medium 0.2 0.6 0.2

High Low Summer Low 0.2 0.6 0.2

Moderate High Status quo High 0.2 0.5 0.3

Moderate High Status quo Medium 0.2 0.5 0.3
14 Adaptive Management of Flows from R.L. Harris Dam—Stakeholder Process and Use of Biological Monitoring Data

Table A3. Conditional probability table (CPT) for small fish abundance. The top 42 of 108 possible scenarios related to the decision to 

provide flow windows, availability of shallow-fast habitat, number of degree days (temperature component), and the amount of flow 

through pool habitat are shown. Probabilities were derived from data collected by prepositioned area electrofishing grids, by physical 

habitat simulation models, and from expert opinion.—Continued

[Nodes are listed as included in the Netica software CPT; bins of high, moderate, and low for each node are described in table A4]

Shallow-fast 

habitat

Degree days 

(temperature)1

Decision—Flow 

windows?

Flow through 

pools

Small fish abundance

High Moderate Low

Moderate High Status quo Low 0.2 0.5 0.3

Moderate High Spring and summer High 0.9 0.1 0

Moderate High Spring and summer Medium 0.9 0.1 0

Moderate High Spring and summer Low 0.9 0.1 0

Moderate High Spring High 0.4 0.4 0.2

1

Degree days are described in the text.

Table A4. Description of state variables, data sources, and ranges of values for the initial model parameterization (see fig. A3).

[Erosion* is an uninformed node based on lack of data and resources to collect data; reservoir inflows** have five response levels versus three; m3/s, cubic meter 

per second; USGS, U.S. Geological Survey; >, greater than; d/yr, day per year; <, less than; APC, Alabama Power Company; --, no data; PHABSIM, physical 

habitat simulation (model); °C, degrees Celsius; %, percent]

State variable Brief description; source Range

Boatable days Number of consecutive weekend days of discharge between 12.7 and 56.6 m3

/s; USGS streamgage data

High Medium Low

>70 d/yr 40–70 d/yr <40 d/yr

Erosion* No data/uninformed node High Moderate Low

Lake levels Number of days/year that lake levels fall below rule 

curve; APC

High Moderate Low

<10 day 11–20 day >21 day

Reservoir inflows** Exceedance flows (m3

/s) for reservoir tributaries combined; USGS dataFlood Normal Dry

>48.1 m3

/s 28.3–42.5 m3/s 17.0–28.3 m3/s

Wet -- Drought

42.5–48.1 m3

/s -- >17.0 m3/s

Flow through pools Pool habitat percent with flow >20 m3

/s; expressed for 

different inflows using PHABSIM model

High Moderate Low

Normal year Normal year Normal year

>50% 20–50% <20%

Shallow-fast amounts Shallow (<45 m3

/s)-fast (>45 m3/s) habitat percent; 

expressed for different inflows using PHABSIM 

model

High Moderate Low

Normal year Normal year Normal year

60–100% 20–60% <20%

Slow-cover amounts Slow (<20 m3

/s)-cover (present) percent; expressed for 

different inflows using PHABSIM model

High Moderate Low

Normal year Normal year Normal year

50–100% 10–50% <10%

Degree days Number of 10-day periods where cumulative degree days 

exceeded 63 at 17.2 °C threshold; USGS data 

High Moderate Low

>120 100–119 <99

Small fish abundance Ratio of juvenile fish/100 samples×100 in regulated compared to unregulated sites; USGS dataHigh Moderate Low

>50 20–50 <20

Bass recruitment Number of juvenile bass in 100 samples; USGS data High Moderate Low

>20 20–10 <10

Redbreast sunfish 

spawning

Number of juvenile redbreast sunfish in 100 samples; 

USGS data

High Moderate Low

>60 30–60 <30
Methods 15

Lake levels.—These probabilities were derived from lake 

level data provided by APC and were tied to the number of 

days in a year that lake levels fell below the FERC rule curve. 

High lake levels were years that the lake fell below the rule 

curve less than 10 days, moderate lake levels were years when 

lake levels fell below the curve 11–20 days and low lake levels 

were greater than 21 days below the rule curve. For the period 

of record from 1983 to 2001, lake levels were high, moderate, 

and low for 57, 16.5, and 26.5 percent of the time, respectively. This node was dependent on reservoir inflow.

Boatable days.—Boatable days were based on the 

number of consecutive weekend days per year when flow 

was between 12.7 and 56.6 m3/s at the Wadley streamgage 

for the period of record through September 1974. Weekends 

were considered Saturday and Sunday but also included 

Columbus Day, Memorial Day, the Friday or Monday closest to July 4 (if on a Wednesday, the day within the flow 

bounds was chosen; if both Friday and Monday were within 

the flow bounds, Monday was chosen), and Labor Day. A 

high number of days was >70 days/year, a medium number 

of days was 40–70 days/year, and a low number of days 

was <40 days/year. For the period of record before the dam, 

80 percent of years had between 40 and 70 boatable days per 

year, 10 percent of years had more than 70 boatable days per 

year, and 10 percent of years had less than 40 boatable days 

per year.

Erosion.—The erosion node was parameterized with 

three levels: high, moderate, and low; however, because we 

had no data on erosion, we gave equal weight to the levels (all 

33.3 percent). This parameter was important to stakeholders, 

and probabilities will be updated pending the collection of 

additional data.

Shallow-fast habitat.—This node was directly dependent 

upon reservoir inflow. The probabilities we used to link these 

variables were based upon both the flow record since the dam 

was built and physical habitat simulation (PHABSIM) models 

developed by the USGS (Bowen and others, 1998). PHABSIM models were constructed at the Wadley site and depth/

flow measurements were recorded along transects at high and 

low flows. Therefore, we were able to estimate the percent 

of shallow-fast habitat (depth <45 centimeters [cm]; flow 

>45 centimeters per second [cm/s]) in the channel during a 

flood, wet, normal, dry, or drought year; we estimated habitat 

for April–July based on importance of habitat during spawning periods. For example, with all other variables unknown, in 

a normal year, there was a 20-percent chance of having high 

amounts (60–100 percent maximum habitat) of shallow-fast 

habitat, a 70-percent chance of having moderate amounts 

(20–60 percent maximum), and a 10-percent chance of having 

low amounts (<20 percent maximum).

Slow-cover habitat.—Again, this node was dependent 

on reservoir inflows and was calculated based on PHABSIM 

models. Percent slow-cover habitat (flow <20 cm/s; cover 

present) was estimated in the channel for flood, wet, normal, 

dry, and drought years, during April–July. For example, with 

all other variables unknown, in a normal year, there was a 

30-percent chance of having high amounts of shallow-fast 

habitat (50–100 percent maximum habitat), a 60-percent 

chance of having moderate amounts (10–50 percent maximum), and a 10-percent chance of having low amounts 

(<10 percent maximum).

Flow through pools.—This variable refers to maintenance of flowing water through the deep portions of the river 

channel, which can become nearly stagnant during prolonged 

nongeneration periods at the dam. We hypothesized that maintaining flow through pools would enhance fish abundances 

by increasing production by current-dependent macroinvertebrates (such as filter-feeding insects). We used PHABSIM 

to estimate flow in pool habitats during flood, wet, normal, 

dry, and drought years. As with the other habitat variables, 

flow through pools was dependent on reservoir inflow. The 

flow through pools variable was considered to be those pool 

habitats that had flows >20 cm/s. With all other variables 

unknown, in a normal year, there was a 15-percent chance of 

having a high proportion of flow through pools (>50 percent 

of all pool habitat), a 60-percent chance of having a moderate 

proportion (20–50 percent of pool habitat), and a 25-percent 

chance of having a low proportion of flow through pools 

(<20 percent of pool habitat).

Degree days.—This node was included because thermal 

effects were thought to be important for reproduction and 

development of certain faunal groups. Conditional probabilities in this CPT were based on data from Andress (2002). 

We parameterized this node based on the number of 10-day 

periods where cumulative degree days were 63 or greater. A 

degree day was calculated as:

Degree days = [(maximum daily temperature + minimum 

daily temperature)/2] – lower threshold temperature (1)

The lower threshold temperature used was 17 °C based on 

bluegill development data (Nakamura and others, 1971). 

Cumulative degree days (daily degree days added for each 

day from eggs to swim-up stage of fry) represent the amount 

of heat energy necessary for redbreast sunfish development to 

swim-up fry. Cumulative degree days must be around 63 for 

redbreast sunfish development from egg to swim-up stage 

of fry.

Small fish abundance.—We used our long-term (6 years) 

prepositioned electrofishing (grid) data to parameterize this 

node. This node was directly linked to shallow-fast habitat, 

degree days, and flow through pools (that is, production of 

food). We considered abundance to be high if >50 individuals were captured in 100 grids. Moderate abundance was 

20–50 individuals captured in grids, and low abundance 

was <20 individuals captured in grids. With all other variables unknown, in a normal year, the model predicted a 

31.5-percent chance of having a high abundance of small fish, 

a 30.8-percent chance of having a moderate abundance of 

small fish, and a 37.7-percent chance of having a low abundance of small fish.
16 Adaptive Management of Flows from R.L. Harris Dam—Stakeholder Process and Use of Biological Monitoring Data

Black bass recruitment.—We used backpack electrofishing data and expert opinion to parameterize this node, 

which was linked to slow-cover habitat, degree days, and 

flow through pools. High bass recruitment was equal to 

more than 20 juveniles collected in a sample, medium bass 

recruitment was 10–19 juveniles, and low bass recruitment 

equaled <10 juveniles. With all other variables unknown, in 

a normal year, the model predicted a 39.1-percent chance of 

high recruitment, a 26.2-percent chance of medium levels of 

recruitment, and a 34.7-percent chance of low recruitment.

Redbreast sunfish spawning success.—Similar to black 

bass recruitment, we used backpack electrofishing data and 

expert opinion to parameterize this node, which was linked 

to slow-cover habitat, degree days, and flow through pools. 

High success was equal to more than 60 juveniles collected 

in a sample, medium levels of success were 30–60 juveniles, 

and low levels equaled <30 juveniles. With all other variables 

unknown, in a normal year, the model predicted a 38.9-percent 

chance of high redbreast sunfish spawning success, a 

26.5-percent chance of moderate success, and a 34.6-percent 

chance of low success.

Utility Nodes

Following the example of Peterson and Evans (2003), 

the utility (or consequence) nodes were representative of the 

satisfaction of stakeholders involved in the decision. In this 

way, the model remains flexible as knowledge is gained and 

updated to determine stakeholder values related to objectives. In addition, stakeholder satisfaction provides a nonmonetary, equitable currency for optimizing the resource. We 

narrowed stakeholder satisfaction into five categories: river 

boater satisfaction, river landowner satisfaction, reservoir user 

satisfaction, fish population value, and power generation. The 

river boater, river landowner, and reservoir user satisfaction 

values were based upon feedback from individual stakeholders and ranged from 0 to 100 percent. River boaters were most 

satisfied with high numbers of boatable days (boaters were 

100 percent satisfied with number of days similar to predam 

conditions), river landowners with low rates of lateral bank 

erosion (100 percent), and reservoir users with lake levels at or 

above the established rule curve (100 percent). Each was less 

satisfied with the opposite and lowest result (0 percent). Fish 

population value (multiple stakeholders are represented here) 

increased with high incidences of small fish abundance, bass 

recruitment, and redbreast sunfish spawning success (100 percent satisfied) and decreased with low values (0 percent satisfied) of these influencing variables. The values incorporated 

for power generation were estimates of flexibility provided 

by the power utility; values ranged from 100 percent satisfied 

(status quo) to 83.5 percent (flow mimics Heflin streamgage 

but does not drop below 8.5 m3/s at the dam).

Sensitivity Analysis

The final step before deciding on a starting flow regime 

was to conduct a sensitivity analysis on the decision model. 

The sensitivity analysis examined the effect of model components on each utility value and, therefore, on the modeled 

decision(s). We used methods outlined in Clemen (1997) and 

used one-way sensitivity analyses and a one-way response 

profile sensitivity analysis. One-way sensitivity analyses was 

used to identify the components that have the greatest effect 

on the utility, and the one-way response profile sensitivity 

analysis was used to identify the components that had the 

greatest effect on the optimal decision. To address the influence of utility values on the decision, we developed an indifference curve by varying the weight of the power generation 

utility from 0.1 to 1 and plotted the results. See Conroy and 

Peterson (2013) for detailed methods.

Monitoring

The monitoring program was designed to collect data 

relative to predicted (or hypothesized) attainment of stakeholder objectives. Incorporation of new information collected 

over time was intended to reduce uncertainty with respect 

to the magnitude and direction of responses of objectives to 

management. A technical advisory group comprised of agency 

and power company biologists was appointed by the stakeholder board to determine the spatial and temporal array for 

data collection. Once it was developed, the monitoring plan 

was reviewed by several USGS scientists to ensure that the 

design was sound. Data regarding reservoir inflows and lake 

levels, number of boatable days, and provision of spawning 

conditions were calculated each year based on the analysis 

of the hydrology data provided by the USGS streamgages or 

collected by the power company as part of their FERC license 

requirement. Data on all state variables were collected each 

year for model updating; sources, nature, and range for data 

included in the initial model and the monitoring program are 

listed in table A4.

Biological sampling.—The response of the biological 

parameters was evaluated by design and implementation of 

monitoring to quantify variation in fish count data (ultimately, 

occupancy; see also chapter B) in relation to system covariates. The response of fish habitat variables was evaluated by 

seasonal application of the post management hydrograph to 

a PHABSIM model (Bowen and others, 1998) developed 

at two of the sampling sites. Sampling was executed twice 

yearly in spring/summer (May–August) and fall (September–

November) from 2005 to 2016. We randomly selected five 

shoals per sampling reach; the most downstream reach below 

R.L. Harris Dam was sampled only once per year and usually 
Results 17

in the fall (fig. A1). We used prepositioned area electrofishers (Bowen and others, 1998; Freeman and others, 2001) to 

sample fishes from 20 to 22 shoals per season. In 2005–7, 

we collected 20 prepositioned area electrofisher samples per 

shoal, and in 2008–16, we collected 10 prepositioned area 

electrofisher samples per shoal. The reduced effort was warranted based on an analysis of the data from the previous year 

and allowed us to spend more effort on field identification (see 

below).

Fishes.—In 2005–7, fishes were euthanized in MS–222, 

preserved in 10-percent formalin, and returned to the laboratory for processing. Larger specimens were field identified and 

released after total length (in millimeters) data were recorded. 

Fishes were transferred to 70-percent ethanol for long-term 

storage. In the laboratory, specimens were identified to species and total length (in millimeters) data were recorded. In 

2008–16, specimens were field identified, measured, and 

released; small individuals that could not be identified without 

a microscope were preserved and returned to the laboratory as 

described above.

Model updates.—Bayesian updating of probability distributions was completed yearly from 2005 to 2016 in Netica to 

learn how the management regime affected stakeholder objectives. The stakeholders were apprised of the results periodically and formally through board meetings and through other 

methods such as publications, presentations, and individual 

stakeholder briefings. Our initial decision model was based on 

the best available information. Because data are finite, and no 

single model can faithfully represent full truth (Burnham and 

Anderson, 2002), we wanted to be able to track the reliability 

of the initial decision model through time using monitoring 

data. Thus, we added an additional node, model truth, to the 

decision model representing the reliability of the model to 

predict the changes in small fish abundance, bass recruitment, 

and redbreast sunfish spawning success. The node contained 

two states, yes and no. For the yes node, the predicted changes 

in fish abundance, bass recruitment, and redbreast sunfish 

spawning success were based on the monitoring data, whereas 

the predictions under the no state were uniform distributions 

indicating that the changes in the three fish nodes in response 

to water management were unknown. The initial probability of 

the yes node was set at 0.99 and the no node at 0.01.

The relative reliability of the decision model was evaluated by comparing the model predictions under the yes and no 

states of the model truth node to the actual outcomes (that is, 

monitoring data) and calculating the likelihood of the observed 

outcome under each state. The posterior probabilities of yes 

and no states were then calculated using the prior node probabilities (that is, yes=0.99 and no=0.01 for the first time step) 

and the likelihood of the observed state under each alternative was calculated using Bayes theorem. The node updating 

process was repeated for each year of monitoring data using 

the posterior probabilities for model truth in t−1 as the prior 

probabilities for year t. (see appendix A2).

Results

Once the decision elements, state variables, and utility functions were related in the network and the CPTs were 

populated, we used the Netica software to find the optimal 

decision. In addition, we conducted sensitivity analysis and 

over time we conducted model updating to estimate new probabilities by incorporating field monitoring data. 

Modeling the Optimal Decision

The optimal decision (fig. A3) was determined by 

examining the expected value associated with each alternative decision, which was the sum of the probability-weighted 

utility values.

5 nj Utotal = Σj=1Σi=1 pijuij (2)

where 

n is the number of states for node j,

p is the probability of state i for node j, and 

u is the value of state i for node j. 

The modeled decision with the highest value was 

considered the optimal decision; therefore, to provide flows 

as measured at the USGS Wadley streamgage (22 km below 

R.L. Harris Dam) that matched flows at the USGS Heflin 

streamgage (located above Harris Reservoir), via pulsing 

operations supported by peaking generation, and to supply 

spring and summer spawning windows and October recreational boating flows (see fig. A3).

Sensitivity Analysis

One-way sensitivity analysis of the impact of uncertainty 

nodes on the overall utility values indicated that erosion and 

boatable day nodes were the most influential components 

of the model and the habitat nodes were the least influential 

(fig. A4). The power generation utility values ranged from 

83.5- to 100-percent satisfaction for the four flow decisions 

(see “Utility Nodes” section above) and indifference curves 

indicated that the optimal decision was unaffected by the 

relative value of the utility states (fig. A5). One-way response 

profile sensitivity analysis indicated that the erosion node 

illustrates stochastic dominance where the optimal decision does not change across the range of node states, and the 

reservoir inflow node illustrates an instance where the optimal 

decision changes four times across the range of node states 

(fig. A6; table A5).

The fish population value had several influencing variables in the model. One-way sensitivity analysis revealed sensitivity to the number of degree days (fig. A7). Similarly, the 
18 Adaptive Management of Flows from R.L. Harris Dam—Stakeholder Process and Use of Biological Monitoring Data

Figure A4. Tornado diagram from one-way sensitivity analysis of R.L. Harris Dam decision model. Each horizontal bar 

represents the range of utilities for optimal decision across a range of node states.

Flow through pools

Shallow-fast amounts

Slow-cover amounts

Model true

Degree days

Small fish abundance

Bass recruiment

Redbreast sunfish spawning success

Reservoir inflow

Lake levels

Boatable days

Erosion

Utility

240 280 300 320 340 360 400

Model node

260 380

fish population value was sensitive to reservoir inflow, which 

is likely an influencing variable on degree days.

Model Updates

Bayesian updating of applied decisions and results of 

monitoring were incorporated into the decision and uncertainty nodes in Netica (table A6; appendix A2). The implemented decision each year was usually the same; APC (Green 

Plan) pulsed flows from the dam with added 10-day spring and 

summer periods for spawning where discharge was no greater 

than 1 unit from the dam (about 198.2 m3/s). On three occasions (spring and summer 2005 and spring 2009), spawning 

periods of reduced generation were not provided. The third 

decision (provision of October boating flows) was never 

implemented. We incorporated cases of yearly results provided 

from APC (decision) and field collected and USGS streamgage 

data to update probabilities associated with the causal links 

in the Bayes network. See Conroy and Peterson (2013) for a 

description of the Bayesian paradigm that allowed us to use 

the rules of probability to make inference regarding the effects 

of management on objectives.

Overall, the highest utility scores for the best decision 

portfolios were associated with either the APC or Heflin flow 

regimes from the dam, along with spring and summer spawning periods and usually October boating flows (appendix A2). 

In general, utility scores did not change over time; however, 

probability distributions changed somewhat. The reservoir 

inflow distribution became more skewed toward drier conditions; however, lake levels had almost equal odds of being 

either high or low. The probabilities of small fish abundance, 

black bass recruitment, and redbreast sunfish spawning success converged to be nearly equal after probabilities were 

updated with 2008 field data.

Discussion

Use of adaptive management for solving resource dilemmas in multiple-use systems has become a goal for many 

agencies (Williams and Brown, 2011). However, procedures 

for implementation of the process are lacking; this project 

provided a template for adopting adaptive management for 

learning how multiple state variables representing stakeholder 

objectives in a regulated river system responded to manipulation of flow regimes. Historically, flow decisions have 

been made based on negotiations that often take years (for 

example, FERC relicensing), and although they are based on 

expert knowledge and data, one-time fixed flow regimes are 

often the net result. This approach does not allow for adjustment of management relative to system response and gained 
Discussion 19

knowledge, nor comparisons with a priori hypotheses (Irwin 

and Freeman, 2002). The development of an adaptive management template during this project was successful to the point 

that, based on the model developed by stakeholders, a decision 

was made to adjust flow at R.L. Harris Dam; adaptive management began in March 2005. The results of the decision model 

updates indicate that uncertainty regarding the fish population 

values has not been resolved. In addition, the decision portfolio that included the Green Plan (that is, APC pulsing from 

the dam) was not the decision portfolio that had the highest 

utility over time. Continuing adaptive management in tandem 

during the FERC relicensing process would be advantageous 

to include a specific assessment of long-term objectives of all 

stakeholders.

Failure to successfully implement adaptive management in natural systems is often caused by lack of stakeholder 

involvement (Johnson, 1999). Since 2000, we have attended 

dozens of meetings to engage stakeholders and discuss 

Figure A5. Example plots from one-way response profile sensitivity analysis with each line 

representing a decision alternative. The erosion node illustrates stochastic dominance where the 

optimal decision (top line) does not change across the range of node states, and the reservoir 

inflow node illustrates instances where the optimal decision changes four times across the range of 

node states.

A. Erosion

Node states

150

250

300

350

Low Moderate High

B. Reservoir inflow 

200

150

300

Drought Dry Normal Wet Flood

Expected utility

200

350

250
20 Adaptive Management of Flows from R.L. Harris Dam—Stakeholder Process and Use of Biological Monitoring Data

Figure A6. Indifference curves for power generation utility. The optimal decision (top line) does not change across 

the range of weights.

Weight of power generation utility

Utility

150

200

250

300

0 0.2 0.4 0.6 0.8 1.0

Table A5. Summary of one-way response profile sensitivity analysis with number of optimal decisions across the range of state 

values, the decisions where the utilities of alternatives were equal (indifferent), and the decisions that do not change across range of 

node states (stochastically dominant).

[--, no data]

Node Optimal 

decisions Indifferent decisions Stochastically dominant decisions

Bass recruitment 2 -- October flow, yes; spawning window, spring and summer.

Boatable days 2 October flows Flow, status quo; spawning window, spring and summer.

Degree days 2 -- October flow, yes; spawning window, spring and summer.

Erosion 1 -- October flow, yes; flow, Heflin; spawning window, spring and summer.

Flow through pools 2 -- October flow, yes; spawning window, spring and summer.

Lake levels 2 -- October flow, yes; spawning window, spring and summer.

Model true 2 Windows of opportunity October flow, yes.

Redbreast sunfish 

spawning success

2 -- October flow, yes; spawning window, spring and summer.

Reservoir inflow 3 October flows Spawning window, spring and summer.

Shallow-fast amounts 2 -- October flow, yes; spawning window, spring and summer.

Slow-cover amounts 2 -- October flow, yes; spawning window, spring and summer.

Small fish abundance 2 -- October flow, yes; spawning window, spring and summer.
Discussion 21

Figure A7. Tornado diagram of one-way sensitivity of fish population value to influencing variables.

0 5 10 15 20 25 30 35 40 45

Shallow-fast

Flow through pools

Slow-cover

Reservoir inflow

Degree-days

Fish population value

objectives and implementation of the adaptive management 

process. A workshop for stakeholders in the Tallapoosa River 

system catalyzed participation by providing a formal forum 

that prompted the development of a governance structure 

(that is, The R.L. Harris Stakeholders Board; hereafter, “the 

Board”) and set a standard for future stakeholder involvement 

(see http://rivermanagement.org/). The stakeholders determined and agreed that the Board would consist of one voting 

member of each of the stakeholder groups. The Board adopted 

a charter (appendix A3) that defined purposes and principles, 

membership, rules of engagement, and decision-making 

processes. Examples of specifics outlined in the charter were a 

long-term commitment (5–7 years) to the process; election of 

a board chair, if needed; and determination that professional 

facilitators would guide meetings of the Board. One extraordinary aspect of the charter establishes that “progress will not 

regress do to the entry of new Members.” New stakeholders 

were expected to familiarize themselves with the process to 

date and contribute to the discussion from their point of entry. 

All previous minutes and presentations were posted on the 

web page. The process defined in the charter has been strictly 

adhered to primarily because of the professional facilitators. 

Over the course of the study, the Board was polled to determine if a change in flow management was desired. No changes 

were made based on apparent satisfaction with the process.

This project provided the framework for incorporating 

stakeholders’ objectives and values into decisions regarding 

flow modification in regulated river systems. During elicitation, stakeholders commented on the presence of commonalities in their objectives and values. In addition, objectives 

that were of value to individual stakeholders were included 

in the model and were transparent (that is, nodes were 

specific and visible), including objectives for which parameterization was not readily possible (for example, erosion). 

Stakeholders seemingly grasped the concept that information gained through monitoring would ultimately improve 

the effectiveness of the model for representation of system 

processes. The modeling process that we used was easy for 

the stakeholders to understand based on the visual nature of 

the Netica software. These characteristics of the process were 

important during stakeholder meetings where new knowledge 

was incorporated, thereby reducing system uncertainty. In the 

future, a reevaluation of stakeholder objectives will be important because stakeholder perspectives have likely evolved 

based on learning and (or) changes in institutional structure 

(Williams, 2011).

Freeman and others (2001, p. 189) called for flow 

manipulations in an adaptive management context, coupled 

with continued biological monitoring to “elucidate how hydrologic variation affects species persistence.” Although alterations in flow regimes affect fish populations and communities, 

functional relations among flow parameters (for example, 

frequency, duration, magnitude) and fish populations are not 

well defined. Key uncertainties relative to how dam operations affect aquatic communities still need to be resolved. Our 

hypotheses revolved around the following flow features: base 

flow (Travnichek and others, 1995), periods of stable flow 

(Freeman and others, 2001), and thermal regime (Andress, 

2002). The governance structure that was developed by the 

stakeholders allowed for Technical Advisory Groups (appendix A3). The Board appointed a Science Technical Advisory 

Group and tasked them with developing a monitoring plan 

for the adaptive management project. These groups served in 

advisory capacity and reported to the Board so that appropriate 

actions were taken relative to decisions. The Science Technical Advisory Group developed a monitoring plan based on 

many of the uncertainty nodes in the decision support model 

and implemented it in spring 2005. Decisions regarding what 
22 Adaptive Management of Flows from R.L. Harris Dam—Stakeholder Process and Use of Biological Monitoring Data Table A6. Data used to conduct yearly model updates. See table A3 for a description of parameter calculations. Parameter 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 Lake levels bin Low Low Low High High High High Low Moderate Low Low Low Inflows Normal Dry Drought Drought Dry Wet Dry Dry Normal Normal Normal Normal Spawning window, spring No Yes Yes Yes No Yes Yes Yes Yes Yes Yes Yes Spawning window, summer No Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Boatable days bin Moderate Low Low Low Low Low Low Low Low Low Low Low Degree days bin Low Low Moderate Moderate Low Moderate High High Low Moderate Moderate High Small fish Low Low Low Low Low Low Moderate Low Low Low Low Low Bass recruitment Low Low Moderate Moderate Low Low Low Moderate Low Low Low Low Redbreast sunfish recruitment Low Low Low Low Low Low Moderate Low Low Low Low Low

parameters to monitor were made dependent on (1) relevance 

to stakeholders’ objectives and (2) ability of the scientists 

to collect adequate (that is, meaningful) and reliable (for 

example, bias minimized) data to use in updating model 

probabilities. Results of the monitoring and subsequent model 

updates indicated that the decision model updates (with one 

exception) appeared to reflect stakeholder values. The exception was the predicted responses of fish populations to flow 

management where the model predicted high fish abundances; 

whereas, when monitoring data were compiled, the fish population responses were overwhelmingly low (abundance and 

recruitment).

Specific analysis of colonization and persistence parameters (that is, in a metapopulation framework; Royle and Kéry, 

2007) of fishes (see chapter B) indicated that thermal alteration and generation frequency negatively affected the occupancy of most fish species below the dam. Although degree 

days were included in the decision model, specific hydrology 

related to generation frequency was not. In addition, habitat 

conditions were predicted to be favorable for positive fish 

population responses under the Green Plan. Habitat availability for fishes increased under the Green Plan management 

(Irwin and others, 2011), but the improved conditions did not 

improve recruitment processes for species of interest. Continued monitoring to evaluate the effects of the flow and thermal 

regimes on aquatic communities is needed, and inclusion of 

other habitat parameters (for example, persistence) is potentially needed.

We used sensitivity analysis to assist the managers 

(that is, water and natural resource) relative to allocation of 

resources for monitoring because the analysis identified what 

variables affected stakeholder satisfaction. For example, under 

flow conditions that match the Heflin streamgage target, managers can expect the satisfaction of reservoir users to remain 

constant or even decline during a normal water year and to 

change consistent with changes in reservoir inflow. On the 

part of river recreationists, managers must not expect boaters to be satisfied to the degree they would have been under 

predam conditions, but they will likely experience positive 

feedback from this stakeholder group under the alternate 

flow conditions. Over the course of the study, boater satisfaction remained low; the provision of boating flows may be a 

management strategy for the future. Sensitivity analysis was 

also beneficial for fisheries management in that, given the 

empirical data, periods of stable flows appeared to be most 

beneficial for the integrity of the fish populations; however, 

the spawning windows that were provided during the study did 

not seem to improve recruitment processes. In addition, the 

habitat data that were included in the model were habitat availability data. Other work has indicated that habitat persistence 

may be a better predictor of fish population parameters in the 

Tallapoosa River (Freeman and others, 2001). Consequently, 

reexamination of the linkages among the model nodes and the 

specific data used to parameterize the conditional relations 

is warranted. In addition, the associated monitoring program 

collected data on the macroinvertebrate community structure 
References Cited 23

at sites below the dam. Adding aspects of macroinvertebrate ecology into the decision framework may inform the 

development of different flow prescriptions or impact decisions already developed and analyzed. We hypothesized that 

enhancing flow through pools would benefit reproduction by 

unionid mussels (for example, Hamiota altilis); this aspect of 

mussel ecology could be defined and assessed with the existing decision model.

Although adaptive management was successfully 

implemented in the Tallapoosa River, other systems may have 

impediments preventing the process. One of the things that 

became apparent in the decision analysis process was inflexibility in the flow delivery mechanism. Specifically, the dam 

was not engineered to release finite amounts of water continuously, nor is there a mechanism to change water temperature. 

Consequently, delivery of base flow in the system is provided 

by pulsing flows from the turbines. When applying this 

template to other systems, decision elements regarding flow 

from dams will have to incorporate facility-specific engineering constraints. Because the flow management option did not 

result in the attainment of all stakeholder objectives, these 

constraints will need to be fully vetted to determine the next 

portfolio of adaptive management flows. In addition, the lack 

of delivery options and fine-scale control of flow and temperature regimes limited the experimental degree of the project and 

allowed for passive adaptive management only. Passive adaptive management focuses on achieving management objectives, and reducing uncertainty, and, therefore, learning is a 

byproduct of the approach (Williams and others, 2007). In the 

future, more emphasis on active adaptive management utilizing experimental learning could reduce uncertainty related to 

impacts of flow and thermal regimes on the natural resources 

in this system.

Overall, the development of the template for applying 

adaptive management and decision support was successful in 

the case of the Tallapoosa River, thus far. Key elements for 

success were (1) use of a professional and neutral facilitator 

to engage stakeholders in objective and value identification 

(see Belton and Jackson-Smith, 2010), (2) use of a visual 

decision support model that allowed for stakeholder input 

and optimization of values associated with various decisions, 

(3) development of a governance structure for future involvement and ownership in the process, and (4) recognition of a 

long-term commitment to learning the effects of management 

through system monitoring and adjustment of management 

regimes. The monitoring program associated with the project 

was labor intensive, both in terms of field and laboratory days, 

and is therefore costly. However, without the monitoring data, 

the stakeholders would not be informed regarding the lack 

of attainment of natural resource objectives. Cash and others 

(2003) describe processes to effectively manage how knowledge can be linked to action for sustainability. They state that 

institutional mechanisms (such as stakeholder groups) can 

improve communication, translation, and mediation toward 

understanding the science and technology that lend salience, 

credibility, and legitimacy to action. Future success during the 

FERC process will depend upon these elements and continued 

stakeholder involvement and support.

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26 Adaptive Management of Flows from R.L. Harris Dam—Stakeholder Process and Use of Biological Monitoring Data

Appendix A1. Transcripts from the Adaptive Management Workshop, 

April 30–May 1, 2003

This appendix contains transcripts developed by the R.L. Harris Stakeholders at a workshop (April 30–May 1, 2003). This 

document was prepared under contract to the U.S. Geological Survey and is owned by the U.S. Geological Survey. Appendix A1 

is available at https://doi.org/10.3133/ofr20191026.

Appendix A2. Initial Bayesian Belief Network (2005), Training Cases and 

Learned Networks (2005–16)

Figures within depict the model probability updating during the 12-year project. The initial model suggested the best management option (blue rectangles) and monitoring data were used to inform the model. Monitoring data were entered as training 

cases and after data were incorporated, the learned network approximated updated probabilities for the next time step. Appendix A2 is available at https://doi.org/10.3133/ofr20191026.

Appendix A3. Charter of the R.L. Harris Stakeholders Board

This appendix contains the Charter developed by the R.L. Harris Stakeholders Board. This document was prepared 

under contract to the U.S. Geological Survey and is owned by the U.S. Geological Survey. Appendix A3 is available at 

https://doi.org/10.3133/ofr20191026.
Chapter B

Long-Term Dynamic Occupancy of Shoal-Dwelling Fishes Above 

and Below a Hydropeaking Dam

By Elise R. Irwin,1,2 Mary C. Freeman,1,3 Kathryn D.M. Kennedy,2 and M. Clint Lloyd2

1

U.S. Geological Survey.

2

Alabama Cooperative Fish and Wildlife Research Unit.

3

Patuxent Wildlife Research Center.

Introduction

Because of the anthropogenic alteration of large river 

systems, faunal assemblages native to these systems mostly 

persist in river fragments that may be free flowing (that is, 

unimpounded) yet are variously affected by flow modification 

caused by upstream dams and reservoirs. This fragmentation and alteration of riverine habitat demand management 

options that explicitly address restoration and conservation of 

native aquatic biota and fisheries in flow-altered river reaches 

downstream from dams. Management options have usually 

been standard one-time negotiated flow plans without flexibility for change and generally begun and (or) governed by 

either the Federal Energy Regulatory Commission (FERC) 

or by the laws set forth in the Endangered Species Act. These 

historically one-time negotiated flow scenarios have hampered 

our ability to define relations between faunal processes (for 

example, population parameters) and system variability. Irwin 

and Freeman (2002) proposed that an adaptive approach could 

be used to manage riverine fish faunas in the southeast United 

States and elsewhere and specifically described the example 

of the Tallapoosa River, Alabama, where depleted low flows, 

altered thermal regimes, and flow instability were hypothesized to adversely affect fishes below the R.L. Harris Dam.

Adaptive management of river systems in the United 

States has been implemented in multiple river basins primarily 

in response to the protection of species under the Endangered 

Species Act (Jacobson and Galat, 2008; Cross and others, 

2011). However, the value of adaptive management processes 

to non-Endangered Species Act management scenarios has 

been recognized to explicitly include multiple stakeholder 

objectives and to reduce uncertainty in the response of system 

parameters associated with management actions along with 

subsequent monitoring programs and decision updating 

(Williams and Brown, 2012 McGowan and others, 2011; 

Williams and Brown, 2014). For the purpose of this project, 

we adopted the National Research Council’s (2004, p. 1–2) 

definition of adaptive management as a decision process with 

“flexible decision making that can be adjusted in the face of 

uncertainties as outcomes from management actions and other 

events become better understood. Careful monitoring of these 

outcomes both advances scientific understanding and helps 

adjust policies or operations as part of an iterative learning 

process.” The process involves a planning phase to frame 

the problem, identify objectives and management options, 

construct predictive models that relate consequences of 

management to objectives, and define monitoring programs to 

capture project-relevant information to help improve decision 

making. The subsequent iterative phase begins at a decision 

point and uses monitoring data to update resource status relative to management during assessment (Williams and Brown, 

2014). Adaptive management is well suited for situations 

where objectives are conflicting (even contentious) yet clearly 

defined by stakeholders, but uncertainty regarding how management actions affect those objectives is high.

The Tallapoosa River (Alabama) below R.L. Harris Dam 

was the subject of extreme stakeholder disagreement until 

spring 2005 when adaptive flow management changes were 

implemented at the dam (http://www.RiverManagement.org; 

Kennedy and others, 2006), consistent with the adaptive management framework described by Williams and Brown (2014). 

Leading up to the project, Irwin and Freeman (2002) provided 

a roadmap for applying adaptive decision making to address 

restoration and management of a strongly flow-regulated 

reach of the Tallapoosa River as an experimental system for 

determining the effects of managing for multiple stakeholder 

objectives including the conservation and management of 

populations of shoal-dwelling fish species below the dam. The 

deliberative or planning phase of the process (Williams and 

Brown, 2014) began by engaging stakeholders during a 3-day 

workshop (April 30–May 1, 2003) where they developed a set 

of objectives to define their values and desired outcomes associated with the management of the river and both reservoirs 
28 Adaptive Management of Flows from R.L. Harris Dam—Stakeholder Process and Use of Biological Monitoring Data

(upstream Lake Harris and downstream Lake Martin; see 

chapter A of this report and Irwin [2014]) for a more detailed 

description of stakeholder engagement). The stakeholders also 

developed a governance structure that consisted of a governing board with voting representation of all stakeholders. 

Next, flow management alternatives were identified based on 

hypothesized system responses (for example, periods of stable 

flows would increase recruitment for many fishes or flows 

between 14.2 and 34.0 cubic meters per second [m3/s] and 

would provide conditions suitable for boating) and evaluated 

for their feasibility by technical teams appointed by the governing board; teams were usually comprised of engineers and 

biologists. Finally, because stakeholder objectives were often 

competing (for example, maximize power utility operation 

flexibility versus maximize diversity and abundance of native 

fauna and flora), a decision model that predicted the consequences of management portfolios on stated objectives was 

compiled and parameterized (chapter A; Irwin, 2014). Stakeholder objectives were equally weighted, and the management 

portfolio that was the most satisfactory to all the stakeholders 

was adopted as the starting point for adaptive management 

(portfolio hereafter termed the “Green Plan”; chapter A; Kennedy and others, 2006; Irwin, 2014).

During the iterative phase of the project, a long-term 

(12 years) monitoring program to quantify system responses to 

the Green Plan management changes of flow regimes began in 

2005. The decision model was updated with monitoring data 

derived from multiple sources (chapter A); however, specific 

responses of fish populations to management were a primary 

interest to many stakeholders, and intensive fish monitoring began coincident with the Green Plan. The monitoring 

framework for fishes was a spatially and temporally replicated 

survey on randomly selected shoals (sites) that were either 

unregulated (that is, in free-flowing segments upstream from 

the dam or in direct tributaries) or variably regulated downstream from the dam (that is, most severe near the dam). This 

design allowed for the assessment of colonization and persistence (that is, 1-extinction) parameters in a metapopulation 

framework (Royle and Kéry, 2007). These vital rates were 

estimated for most of the fish species in regulated and unregulated river reaches of the middle Tallapoosa River Basin.

Temporal changes in metapopulation dynamics of fishes 

have been modeled to evaluate changes in persistence and 

colonization and (or) in relation to particular drivers in several 

southeastern river systems (Peterson and Shea, 2015; Shea and 

others, 2015; Freeman and others, 2017). Here, we are applying similar approaches to quantify differences in the processes 

underlying species assemblage composition in flow-regulated 

and unregulated sites and to evaluate evidence that flow and 

thermal regimes affect fish persistence and colonization in 

the flow-regulated reach below the dam. Metapopulations are 

spatial arrangements of distinct populations of species that 

occasionally intermix and differ with respect to their vital 

rates—specifically, extinction and colonization (Hanski and 

Simberloff, 1997). Dynamic occupancy models are hierarchical and are the product of submodels that account for latent 

detection/nondetection and latent occupancy process (Royle 

and Kéry, 2007). This class of models is beneficial because it 

estimates metapopulation rates through time and allows for 

the inclusion of random effects and covariates that may help 

explain temporal patterns in vital rates.

This chapter describes a multispecies assessment of 

persistence (that is, probability that a site remains occupied 

by a species from one time step to the next) and colonization 

(that is, exists at a site where it was previously absent) for a 

suite of 38 fish species over a 12-year period on shoals in the 

middle Tallapoosa River Basin. The monitoring and analytical framework were in support of the adaptive management of 

flows below a peaking hydropower dam and allowed for the 

estimation of detection probabilities to account for incomplete 

detection during monitoring (MacKenzie and others, 2003, 

Royle and Kéry, 2007). In addition, a long-term assessment 

of metapopulation dynamics on shoals in relation to temperature and hydrologic factors was completed. Specifically, we 

analyzed year-to-year variation in the probability that a species 

would persist or colonize against year-to-year variation in 

covariates that described different annual flow and thermal 

regimes in the basin. Because of the adaptive management 

project, the Tallapoosa River presented an opportunity to 

determine the effects of flow management on fish populations. 

Findings should be directly transferable to other similarly 

fragmented, flow-managed rivers that harbor shoal-dwelling 

Greatest Conservation Need species, such as the Coosa and 

(or) Tennessee Rivers.

The overall purpose of this study was to evaluate the 

effects of experimental flows on the persistence and colonization of shoal-dwelling fishes in the Tallapoosa River. Specific 

analyses were conducted (1) to evaluate evidence that fishes 

occupying shoals downstream from R.L. Harris Dam were less 

likely to persist or colonize from year to year than at sites with 

unregulated flow regimes and (2) to estimate the influences 

of flow fluctuations and of lowered water temperature on the 

persistence and colonization of shoal fishes downstream from 

R.L. Harris Dam. Based on a priori stated hypotheses, colonization rates should increase with the provision of spawning 

windows (that is, periods of semistable flows) below the dam, 

increase with distance from the dam, and be higher in unregulated reaches. Likewise, persistence rates should be lower at 

unregulated sites, be lower closest to the dam, and be unaffected by spawning windows. Hypotheses related to flow fluctuations included low expected persistence and colonization 

during years with more flow fluctuations than during years 

with fewer fluctuations. Hypotheses related to thermal effects 

predicted that colonization and persistence would increase 

downstream from the dam based on inflows of warmer water 

and dilution effects. The analysis provided for a communitywide assessment that included species of interest to stakeholders (that is, Greatest Conservation Need species [Wood, 

2015] or sportfishes) and allowed for reduction in uncertainty 

regarding how management actions influenced stakeholder 

objectives related to fish populations outlined in the adaptive 

management project (chapter A, this report).
Methods 29

Methods

The regulated study reach, beginning at R.L. Harris Dam 

and terminating 78 kilometers (km) downstream in the headwaters of Lake Martin, represents one of the longest and highest quality segments of unimpounded Piedmont river habitat 

remaining in the Mobile River Basin (Lydeard and Mayden, 

1995; Mettee and others, 1996; Neves and others, 1997; 

fig. B1). The native fish assemblage in the Piedmont section 

of the Tallapoosa River Basin includes at least 57 species 

including 7 species endemic to the Tallapoosa system (Mettee 

and others, 1996; Boschung and Mayden, 2004). During the 

adaptive management project, there were 2 main changes to 

flows from R.L. Harris Dam—increase in base flow and provision of spawning windows—and monitoring below the dam 

incorporated shoals in 3 reaches from near the dam (Malone, 

which was the closest at 2.6 km downstream) to shoals just 

upstream from Lake Martin (Horseshoe Bend, which was the 

farthest at 70.23 km downstream from the dam, and included 

sites at Peter’s Island). The Wadley reach was in between these 

two reaches (beginning 11.98 km below the dam). A total of 

two unregulated river reaches (Hillabee Creek and the upper 

Tallapoosa River, hereafter, Heflin) were monitored to assess 

how persistence and colonization fluctuated independent of 

regulated flows (that is, under “natural” conditions).

Field Sampling Methods

Sampling was conducted in summer (May–June) and fall 

(September–November) 2005–16 in 5 sampling reaches in 

the Piedmont Tallapoosa River Basin; 5 shoals per sampling 

reach were randomly selected from all shoals in each reach 

(25 shoals total; 15 below the dam; hereafter, “sites”; fig. B1). 

We used prepositioned area electrofishers (PAEs; Bain and 

others, 1985; Bowen and others, 1998; Freeman and others, 

2001) to sample fishes from sites. We deployed 1.5-meter 

(m) × 6-m PAEs in nearshore (less than [<] 1 m from bank) 

and offshore (greater than [>] 1 m from bank) shallow water 

habitats (<0.75 m in depth) during daylight hours. PAEs were 

electrified with alternating current by a 3,500 W generator 

and Smith-Root 7.5 generator powered pulsator unit. Before 

sampling, all PAEs were set and left undisturbed for 20 minutes (see Bain and others, 1985). Each PAE was oriented with 

its long side parallel to shore and to downstream flow; two 

workers held a 3- × 2-m seine (2-millimeter [mm] mesh size) 

at the downstream end to capture drifting stunned fish. One 

worker kicked through the PAE to dislodge any fish trapped in 

vegetation or substratum; then, PAEs were visually inspected 

for additional fish.

In 2005–7 we collected 20 PAE samples per site, and in 

2008–16 we collected 10 PAE samples per site. The reduced 

effort was warranted based on the analysis of all data from the 

previous year indicating that 10 samples were sufficient for 

estimation of parameters of interest related to the response of 

fish populations to management. Prior to 2008, most fish were 

euthanized in MS–222, and fixed in 10-percent formaldehyde 

in the field; large specimens (>200 mm) were generally identified, measured [in millimeters total length] with a measuring 

board or soft measuring tape, and released live in the river. 

Specimens remained in formaldehyde for 2 weeks, then underwent several water soak and rinse cycles. Finally, specimens 

were stored in 70-percent ethanol and identified and measured 

(in millimeters total length) in the laboratory. Beginning in 

2008, fish were identified and measured in the field, except 

where uncertainty regarding species identity was high (for 

example, juvenile Cyprinidae); those specimens were prepared 

using the above described protocol for pre-2008 samples.

Collections and ensuing count data from individual seasons, dates, sites, and PAEs were kept separate to facilitate the 

creation of detection/nondetection histories for each species 

in each year. The detection histories (0, 1) were incorporated 

into a matrix that included site, season, species, and year for 

25 sites, 2 seasons (summer and fall), 38 species (see results 

below), and 12 years of data.

Analysis

We tested for differences in persistence and colonization 

of selected fish species between flow-regulated and unregulated sites within the study system. These vital rates represent 

two main aspects of the metapopulation dynamics of this fish 

community. Persistence was defined as the probability that a 

species that was present in a site during a given year will also 

be present in the next year. Colonization was defined as the 

probability that a species that is absent at a site in a given year 

will be present in the next year. To address our two objectives, 

we fit models to our sampling data, which consisted of annual 

observations for 12 years (2005–16) of fish species occurrences at as many as 25 sites (15 downstream from R.L. Harris Dam [3 reaches, Malone (upper, nearest the dam), Wadley 

(middle), and Horseshoe Bend (lowest)], 5 in the free-flowing 

Tallapoosa River main stem upstream from the dam and Lake 

Harris, and 5 in Hillabee Creek—a large, unregulated, and 

free-flowing tributary to the Tallapoosa River). Of the 52 fish 

species that we detected during monitoring for all years, 38 

were the most common species and had >25 detections over 

all sites and years (table B1).
30 Adaptive Management of Flows from R.L. Harris Dam—Stakeholder Process and Use of Biological Monitoring Data

Figure B1. Locations of the 5 river reaches where fishes were collected from 5 shoals at each of the reaches 

between 2005–16.

Hillabee Creek

Heflin

Wadley

R.L. HARRIS DAM Malone

Malone

Horseshoe

Bend

AL GA

FL

TN

MS

NC

Map area

GULF OF MEXICO

Watershed

boundary

Base from U.S. Geological Survey National Hydrography dataset, 2018

Lake Wedowee ALABAMA

GEORGIA

0 5 10 MILES

0 5 10 KILOMETERS

Watershed boundary

Little Tallapoosa River

Tallapoosa River

N

U.S. Geological Survey

streamgage 02412000

U.S. Geological Survey

streamgage 02414500

Tallapoosa River

River reach

Heflin

Malone

Wadley

Horseshoe Bend

Hillabee

EXPLANATION
Methods 31

Table B1. Count data for 38 species captured in prepositioned area electrofishers at 25 sites in 5 river reaches variably affected by 

R.L. Harris Dam (5 per reach; sampling years 2005–16).

[Horseshoe Bend is the lowest reach below the dam; Heflin and Hillabee are unregulated; PAE, prepositioned area electrofisher]

Species Horseshoe Wadley Malone Heflin Hillabee

Ambloplites ariommus 29 48 49 24 61

Campostoma oligolepis 153 518 599 4,120 1,963

Cottus tallapoosae 1 4 5 69 5

Cyprinella callistia 2,568 3,196 602 3,115 4,444

Cyprinella gibbsi 280 117 20 766 2,989

Cyprinella venusta 165 210 128 516 1,075

Dorosoma petenense 2 2 124 0 0

Etheostoma chuckwachatte 941 3,064 1,750 3,031 5,114

Etheostoma stigmaeum 118 492 284 2,938 936

Etheostoma tallapoosae 183 296 498 314 217

Fundulus bifax 0 0 2 14 171

Fundulus olivaceus 0 2 3 112 33

Gambusia affinis 1 0 0 280 30

Hybopsis lineapunctata 1 2 3 3 22

Hypentelium etowanum 349 301 158 1,593 336

Ictalurus punctatus 67 86 43 121 64

Lepomis auritus 64 210 655 363 389

Lepomis cyanellus 0 4 16 5 3

Lepomis macrochirus 4 10 162 28 53

Luxilus chrysocephalus 7 54 48 138 290

Macrhybopsis aestivalis 1 0 0 0 401

Micropterus henshalli 88 29 45 76 84

Micropterus tallapoosae 74 32 33 10 48

Minytrema melanops 0 0 0 0 69

Moxostoma duquesnei 30 0 4 241 187

Moxostoma poecilurum 6 1 7 74 69

Notropis baileyi 2 116 20 6 3

Notropis stilbius 413 420 96 438 1,585

Notropis texanus 5 2 2 4 58

Noturus funebris 16 46 28 98 202

Noturus leptacanthus 26 25 13 434 145

Percina kathae 14 8 5 2 76

Percina palmaris 1,397 4,022 2,011 2,254 2,773

Percina smithvanizi 317 1,104 315 905 664

Phenacobius catostomus 8 0 2 247 165

Pimephales vigilax 32 0 0 3,014 2,234

Pylodictis olivaris 16 13 2 25 13

Semotilus atromaculatus 0 0 0 25 5

Total individuals 7,378 14,434 7,732 25,403 26,976

Richness 33 30 33 35 37

Total PAE samples 660 1,451 1,431 1,100 1,010

Mean number/PAE 11.18 9.95 5.40 23.09 26.71
32 Adaptive Management of Flows from R.L. Harris Dam—Stakeholder Process and Use of Biological Monitoring Data

For both objectives, we estimated persistence and colonization using hierarchical, dynamic occupancy models defined 

by Royle and Kéry (2007). These models were hierarchical in 

that they separated the ecological (occupancy) and observation (detection/nondetection) processes that result in the data 

(Kéry and Schaub, 2012) and therefore allowed for the possibility that a species may have been present at a site and yet 

not detected in a particular sample. We modeled occupancy 

through time at each site, beginning with the first sample, in 

which we allowed each species to have an (unknown) initial 

probability of occurrence. In the model comparing dynamics 

between flow-regulated and unregulated sites (objective 1), 

the initial species-specific occurrence was allowed to differ 

between sites downstream from the dam (flow-regulated sites), 

and sites in unregulated reaches:

z1ij~Bernoulii (Psii.regulated × Regj+Psii.unregulated × (1−Regj) (1)

where 

z1ij is 1 or 0 for presence or absence of species i

at site j in year 1; 

Psii.regulated is the probability of species i occurrence in 

a flow-regulated site; 

Psii.unregulated is the probability of species i occurrence in 

an unregulated site; and 

Regj is 1 if site j is flow regulated and 0 

otherwise. 

Then, in each subsequent year, a species was assumed 

to be present at a site with a probability that equaled either 

persistence (if the species was present in the previous year) or 

colonization (if the species was absent in the previous year). 

We modeled persistence and colonization as functions of 

objective-specific covariates, described below.

We accounted for imperfect detection by explicitly modeling observed detections and nondetections of each species 

in each site sample as a random draw from a species-specific 

probability of detection, conditional on the species being truly 

present; that is,

ytijl~Bernoulli (ptij × ztij) (2)

logit (ptij)=pi+εtjl (3)

where 

ytijl is the observed detection (1) or nondetection 

(0) in year t of species i in site j during 

replicate l (l=1 or 2, representing as many 

as 2 samples in each site and year); 

ptij is the probability of detection in year t of 

species i in site j; 

ztij is 1 if species i is present in year t in site j and 

is 0 otherwise; 

pi is the mean probability of detection of 

species i; and

εtjl is a normally distributed random effect with 

a mean of 0, accounting for variation 

in detection among surveys caused by 

unmodeled factors (for example, water 

level, weather).

We did not include explicit covariates on detection to 

simplify the models because identifying sources of variation 

in detection was not an objective. The observation process 

that resulted in the actual observed detection (1) or nondetection (0) of each species in each sample was represented as a 

random draw from a species-specific detection probability 

distribution, conditional on the species being truly present. To 

account for imperfect detection, sites were sampled as much 

as twice annually, and we used the summer and fall samples 

within a given year as replicates to estimate detection probabilities, assuming that any species detected in either sample 

was present and available to be detected in both samples. We 

also assumed that detection could differ among surveys (for 

example, because of differences in water level, instream habitat, and weather). Variation was represented as a survey-level 

random effect (normally distributed around a mean of 0) added 

to species-specific detection for each survey. Models were fit 

with a Markov chain Monte Carlo software JAGS (Plummer, 

2003), run using package “rjags” in R (Plummer, 2014; R 

Core Team, 2014). All code is included in appendix B1.

To address the first objective (comparison of persistence 

and colonization between regulated and unregulated sites), we 

modeled persistence and colonization as additive functions of 

random effects representing variation among species, years, 

and sites and an additional potential effect of flow regulation:

logit ([persistence or colonization]tij)=b0 + bi.reg × Regj+εt+εi+εj

where 

persistencetij and colonizationtij are probabilities that species i 

persists (if present at t−1) and colonizes 

(if absent at t−1) site j in year t; 

b0 is the intercept; 

bi.reg is the effect of site j being in a flow-regulated 

reach on persistence or colonization for 

species i; and 

εt, εi, and εj are normally distributed random effects (each 

with a mean of 0) representing unmodeled 

variation in persistence and colonization 

rates among years, species, and sites, 

respectively. 

We therefore assumed that fish species persistence and 

colonization in the study sites differed from year to year in 

response to unmeasured factors (such as regional variation 

in rainfall and temperature regimes; random year effect) and 

that local factors additionally affected year-to-year species 

turnover in individual sites (random site effect). We used this 

model to test for differences in species persistence, colonization, or both at sites downstream from R.L. Harris Dam 

compared to other sites (represented by the fixed effect, breg, 

for site location in the flow-regulated reach). We would expect 

fishes to have lower persistence, colonization, or both at sites 

(4)
Methods 33

downstream from R.L. Harris Dam if dam operations reduce 

fish survival or reproduction and, therefore, population sizes. 

The additive model allowed species to have differing average persistence and colonization rates (that is, random species 

effect on intercepts) and to differ in their responses to flow 

regulation (that is, random slopes), but the year effects were 

assumed to be similar at all sites (and, conversely, sites differed similarly in all years).

For our second objective (that is, relation of persistence/

colonization [below dam only] to generation events and water 

temperatures), we fit a similar mixed-effects model to fish 

observation data for the 15 flow-regulated sites downstream 

from R.L. Harris Dam. As in the model using data for all sites, 

we included random effects on the intercepts for persistence 

and colonization that represented unexplained, additive 

variation among year, species, and sites. We additionally 

included three fixed effects in the model for persistence and 

colonization at the flow-regulated sites: annual hydropeaking 

frequency, seasonal cumulative water temperature relative 

to spawning requirements, and site-specific distance from 

R.L. Harris Dam. The first two variables specifically addressed 

our objective of estimating effects of dam operations on 

fish population dynamics through effects on flow instability 

(caused by hydropeaking) and on water temperatures (because 

released water typically is cooler than in unregulated sites during spring and summer, when fishes typically reproduce). The 

variable for distance from the dam tested the extent to which 

effects of dam operation on either persistence or colonization 

may ameliorate downstream through the regulated reaches. 

Because our objective was to estimate variable influences on 

metapopulation rates (rather than to identify the best-fitting 

model), and to simplify interpretation, we did not include variable interactions.

Covariates were calculated using the following methods. Distance from the dam was simply the linear distance 

(1.0 km) from the dam to a given site. The yearly number 

of generation events was derived by counting the number of 

days during the months of March–September that maximum 

daily discharge exceeded 198.2 m3/s. Discharge data were 

obtained from the U.S. Geological Survey for the Tallapoosa 

River at Wadley, Ala., streamgage (U.S. Geological Survey streamgage 02414500; U.S. Geological Survey, 2018). 

The number of 10-day cumulative degree day periods that 

exceeded 63 cumulative degree day periods at a threshold 

of 17 degrees Celsius (°C) during March–September were 

enumerated for each year. This was based on the temperature 

requirements for successful hatching of a common centrarchid 

(Lepomis auritus [redbreast sunfish]; Andress, 2002). Temperature data were obtained for the Wadley reach using temperature logger data provided by the Alabama Power Company. 

Each covariate was scaled to a mean of 0 and standard deviation of 1 for analysis. We also plotted the relation between 

the number of generation events and cumulative degree day 

periods (fig. B2).

Figure B2. Relation between generation events and cumulative degree days; both variables were standardized 

to a mean of 0 and a standard deviation of 1. The outlier was 2006 when temperatures were cool and generation 

events were average. The correlation coefficient (r) is −0.49 and includes the outlier.

0.5

–0.5

–1.0

–1.5

–2.5

–2.0

0

1.5

1.0

Number of generation events (scaled)

Number of degree days (scaled)

–1.5 –1.0 –0.5 0 0.5 1.0 1.5 2.0 2.5
34 Adaptive Management of Flows from R.L. Harris Dam—Stakeholder Process and Use of Biological Monitoring Data

Results

We included 81,923 individual fish detections from 

5,652 PAE samples taken in the 5 river reaches (25 sites total; 

15 downstream from R.L. Harris Dam) over the 12 years. 

Overall, mean fish density (number of individuals/PAE) was 

lowest near the dam (5.40 fish/PAE, Malone) and highest in 

Hillabee Creek (26.71 fish/PAE; fig. B3; table B1). Although 

46 species were captured during the study, we narrowed the 

list to 38 to include only species with greater than 25 detections. Overall richness was highest at the unregulated reaches, 

35 and 37 for upper Tallapoosa (Heflin) and Hillabee Creek, 

respectively. The regulated reaches had the lower richness 

values; 33, 30, and 33 for Horseshoe Bend, Wadley and 

Malone, respectively. Many species at the regulated sites were 

represented by five or fewer specimens for the study period 

(table B1).

Fishes persisted and colonized at lower rates (that is, 

probabilities) at flow-regulated sites (downstream from 

R.L. Harris Dam) than at the unregulated sites in the upper 

Tallapoosa (Heflin) main stem and in Hillabee Creek. Estimated rates differed considerably among sites and among 

years; however, the estimated mean effects (across all species) of location downstream from the dam were to lower the 

probability of persistence by 81 percent (95-percent credible 

interval: −43 percent to −94 percent) and the probability 

of colonization by 65 percent (−18 percent to 86 percent; 

figs. B4 and B5; table B2). The effects of a site being in the 

flow-regulated reach also differed among species for both 

persistence and colonization. For persistence, 23.7 percent of 

species had an estimated positive effect; however, for colonization only two species (Ambloplites ariommus [shadow 

bass] and Etheostoma chuckwachatte [lipstick darter]) had an 

estimated effect that was positive (figs. B6 and B7).

We determined that both flow instability and depressed 

temperatures influenced fish persistence and colonization at 

the flow-regulated sites (tables B3 and B4; fig. B8). Models 

for the 15 sites downstream from R.L. Harris Dam resulted 

in somewhat higher estimates of persistence and colonization 

than when all 25 sites were modeled together, particularly 

persistence at the 5 sites that were located farthest (>60 km) 

downstream from the dam (figs. B9 and B10). This is likely 

due to removing the additive effect of flow regulation that was 

included when we compared unregulated to regulated sites. 

Averaged over all taxa, fish persistence increased with greater 

distance from the dam and decreased during years with more 

generation events (fig. B11). Colonization was lower in years 

with more generation events; however, no clear increases 

in colonization were observed at the sites farthest from the 

dam (fig. B12). Persistence was not clearly related to warmer 

thermal regimes but increased at sites farthest from the dam 

(fig. B13). Fish colonization increased during years with 

warmer water temperatures (fig. B14).

Figure B3. Average fish density (mean number of fish/PAE) by reach for all combined sites in a reach. Data 

from 2005 to 2016 were combined. High-low bars represent standard error.

0

5

10

15

20

25

30

35

40

Horseshoe Bend Wadley Malone Heflin Hillabee

Mean number of fish per prepositioned area electrofisher (PAE)

Site
Results 35

Figure B4. Estimated mean probability of persistence for 38 fish species at each of 25 sites in the Tallapoosa 

River system, 2005–16, modeled using dynamic, multitaxa occupancy models. Values for each year represent 

estimated mean species persistence from the previous year. [Sites with unregulated flow regimes are plotted 

in blue (Hillabee Creek) and green (Heflin). Flow-regulated sites are plotted in red (Malone), orange (Wadley), 

black (Horseshoe Bend), and gray (Peters Island). Horseshoe Bend and Peters Island together comprised the 

Horseshoe Bend reach. Estimated means for unregulated and regulated sites are plotted as bold dashed and 

solid lines, respectively.]

EXPLANATION

Year

Persistence probability

2006 2008 2010 2012 2014 2016

0.4

0.6

0.8

1.0

Heflin

Hillabee

Malone

Wadley

Horseshoe Bend

Peters Island (Horseshoe Bend) 
36 Adaptive Management of Flows from R.L. Harris Dam—Stakeholder Process and Use of Biological Monitoring Data

Figure B5. Estimated mean probability of colonization for 38 fish species at each of 25 sites in the Tallapoosa River 

system, 2005–16, modeled using dynamic, multitaxa occupancy models. Values for each year represent estimated 

mean species persistence from the previous year. [Sites with unregulated flow regimes are plotted in blue (Hillabee 

Creek) and green (Heflin). Flow-regulated sites are plotted in red (Malone), orange (Wadley), black (Horseshoe 

Bend), and gray (Peters Island). Estimated means for unregulated and regulated sites are plotted as bold dashed 

and solid lines, respectively.]

Year

2006 2008 2010 2012 2014 2016

0

0.2

0.4

0.6

Colonization probability

EXPLANATION

Heflin

Hillabee

Malone

Wadley

Horseshoe Bend

Peters Island (Horseshoe Bend) 

Table B2. Parameter estimates from dynamic occupancy model fit to 12 years (2005–16) of observations of 38 fish species 

at 25 Tallapoosa River system sites. Model includes additive fixed effect of site location downstream from R.L. Harris Dam 

(flow-regulation effect), and random effects for sites, taxa (intercept and slope), and years.

[Values are means on logit scale, with 95-percent credible intervals in parentheses. The bold numbers indicate that there was an effect measured 

for the parameter (the credible interval did not include zero).]

Parameter Persistence Colonization

Mean 2.84 (1.97, 3.77) −1.16 (−2.18, −0.25)

Flow-regulation effect −1.64 (−2.75, −0.57) −1.05 (−2.03, −0.20)

Variance terms

Among sites 0.33 (0.03, 0.92) 0.21 (0.02, 0.59)

Among taxa (mean) 3.15 (1.27, 6.63) 3.42 (1.48, 7.11)

Among taxa (flow-regulation effect) 6.12 (2.44, 14.12) 2.52 (0.55, 6.47)

Among years 0.72 (0.17, 2.11) 0.70 (0.13, 2.38)
Results 37 Figure B6. Estimated effects of location in the flow-regulated reach on species-specific persistence modeled using dynamic, multitaxa occupancy models applied to data for 25 sites in the Tallapoosa River system, 2005–16. Values are plotted on the logit scale and show 95-percent credible intervals. The horizontal line at 0 marks no effect; negative values indicate lower persistence in the flow-regulated reach compared to unregulated sites.

Flow regulation effect, persistence

−10

−2−4−6−8

86420

10

Cot_tal Cyp_gib Fun_oli Pim_vig Mox_poe Gam_aff Not_lep Phe_cat Sem_atr Min_mel Mox_duq Mac_aes Hyb_lin Lux_chr Not_tex Hyp_eto Fun_bif Per_smi Eth_sti Cam_oli Mic_hen Not_sti Cyp_ven Lep_aur Per_kat Cyp_cal Ict_pun Lep_cya Eth_tal Not_bai Not_fun Dor_pet Per_pal Lep_mac Pyl_oli Mic_tal Eth_chu Amb_ari

Species
38 Adaptive Management of Flows from R.L. Harris Dam—Stakeholder Process and Use of Biological Monitoring DataFigure B7. Estimated effects of location in the flow-regulated reach on species-specific colonization modeled using dynamic, multitaxa occupancy models applied to data for 25 sites in the Tallapoosa River system, 2005–16. Values are plotted on the logit scale and show 95-percent credible intervals. The horizontal 

line at 0 marks no effect; negative values indicate lower colonization in the flow-regulated reach compared to unregulated sites.

Cot_tal

Cyp_gib

Fun_oli

Pim_vig

Mox_poe

Gam_aff

Not_lep

Phe_cat

Sem_atr

Min_mel

Mox_duq

Mac_aes

Hyb_lin

Lux_chr

Not_tex

Hyp_eto

Fun_bif

Per_smi

Eth_sti

Cam_oli

Mic_hen

Not_sti

Cyp_ven

Lep_aur

Per_kat

Cyp_cal

Ict_pun

Lep_cya

Eth_tal

Not_bai

Not_fun

Dor_pet

Per_pal

Lep_mac

Pyl_oli

Mic_tal

Eth_chu

Amb_ari

Flow regulation effect, colonization −2

−4−8

420

Species

−6
Results 39

Table B3. Parameter estimates from dynamic occupancy model fit to 12 years (2005–16) of observations of 38 fish 

species at 15 flow-regulated sites. Model includes additive fixed effects of distance downstream from R.L. Harris 

Dam and annual number of generation events, an interaction between distance and number of generation events, 

and random effects for sites, taxa, and years.

[Values are means on logit scale, with 95-percent credible intervals in parentheses. The bold numbers indicate that there was an effect 

measured for the parameter (the credible interval did not include zero).]

Parameter Persistence Colonization

Mean 2.51 (1.23, 3.88) −3.03 (−4.30, −1.62)

Distance from the dam 0.94 (0.39, 1.71) −0.95 (−1.94, −0.03)

Annual number of generation events −0.67 (−1.48, 0.16) −1.63 (−2.85, −0.25)

Number of generation events × distance −0.28 (−0.93, 0.36) −1.20 (−2.16, −0.18)

Variance terms

Among sites 0.19 (0.00, 0.82) 0.30 (0.00 1.09)

Among taxa 7.40 (2.66, 17.81) 6.59 (2.11, 15.90)

Among years 1.00 (0.02, 3.94) 1.34 (0.02, 5.77)

Table B4. Parameter estimates from dynamic occupancy model fit to 12 years (2005–16) of observations of 38 fish 

species at 15 flow-regulated sites. Model includes additive fixed effects of distance downstream from R.L. Harris 

Dam and annual number of days above a cumulative degree day threshold for fish reproduction (number of 10-day 

periods greater than [>] 63 cumulative degree days), an interaction between distance and number of days > 

threshold, and random effects for sites, taxa, and years.

[Values are means on logit scale, with 95-percent credible intervals in parentheses. The bold numbers indicate that there was an effect 

measured for the parameter (the credible interval did not include zero).]

Parameter Persistence Colonization

Mean 2.01 (0.69, 3.40) −2.38 (−3.63, −1.12)

Distance from the dam 0.78 (0.23, 1.52) −0.07 (−0.62, 0.48)

Annual number of days > degree day threshold −0.24 (−1.20, 0.72) 1.12 (0.07, 2.58)

Number of days > threshold × distance −0.79 (−1.57, −0.17) 0.16 (−0.72, 1.13)

Variance terms

Among sites 0.19 (0.00, 0.77) 0.25 (0.00 1.10)

Among taxa 9.83 (3.67, 22.23) 6.44 (2.07, 16.87)

Among years 1.90 (0.20, 8.10) 2.15 (0.23, 8.51)
40 Adaptive Management of Flows from R.L. Harris Dam—Stakeholder Process and Use of Biological Monitoring Data

Figure B8. Estimated effects on flow regulation, distance downstream from R.L. Harris Dam, and annual frequency of 

hydropower generation (generation events) and cumulative time with temperatures meeting a threshold for spawning

(degree days) on persistence and colonization for 38 fish species, modeled using dynamic, multitaxa occupancy models 

applied to data for 25 sites (flow regulation effect) or 15 sites (distance, generation, and degree days) in the Tallapoosa 

River system, 2005–16. Values are plotted on the logit scale and show 90-percent credible intervals. The horizontal line 

at 0 marks no effect; negative and positive values indicate, respectively, lower and higher persistence or colonization in 

relation to the covariate.

phi

gamma

EXPLANATION

Model parameter estimates, logit scale

Flow regulation Distance below dam Generation events Degree days

−3

−2.5

−2

−1.5

−1

−0.5

0

0.5

1

1.5

2

2.5

3
Results 41

Figure B9. Estimated mean probability of persistence for 38 fish species at each of 15 flow-regulated sites in 

the Tallapoosa River system, 2005–16, modeled using dynamic, multitaxa occupancy models. Values for each year 

represent estimated mean species persistence from the previous year. [Sites farther from the dam are plotted in black 

(Horseshoe Bend) and gray (Peters Island); sites nearer the dam are plotted in red (Malone) and orange (Wadley).]

EXPLANATION

Year

Persistence probability

2006 2008 2010 2012 2014 2016

0.4

0.6

0.8

1.0

Malone

Wadley

Horseshoe Bend

Peters Island (Horseshoe Bend) 
42 Adaptive Management of Flows from R.L. Harris Dam—Stakeholder Process and Use of Biological Monitoring Data

Figure B10. Estimated mean probability of colonization for 38 fish species at each of 15 flow-regulated sites in 

the Tallapoosa River system, 2005–16, modeled using dynamic, multitaxa occupancy models. Values for each year 

represent estimated mean species persistence from the previous year. [Sites farther from the dam are plotted in 

black (Horseshoe Bend) and gray (Peters Island); sites nearer the dam are plotted in red (Malone) and orange 

(Wadley).]

Year

2006 2008 2010 2012 2014 2016

0

0.2

0.4

0.6

Colonization probability

EXPLANATION

Malone

Wadley

Horseshoe Bend

Peters Island (Horseshoe Bend) 
Results 43

Figure B11. Estimated mean probability of persistence for 38 fish species at 15 flow-regulated sites in the Tallapoosa 

River system, 2005–16, modeled using dynamic, multitaxa occupancy models and plotted in relation to annual number of 

generation events (standardized to mean of 0 and standard deviation of 1). [Sites farther from the dam are plotted in black 

(Griffin Shoals) and gray (Peters Island); sites nearer the dam are plotted in red (Malone) and orange (Wadley).]

−1.5 −0.5 0.0 0.5 1.0 1.5 2.5

Number of annual generation events (standardized)

0.4

0.5

0.6

0.7

0.8

0.9

1.0

Persistence probability

EXPLANATION

Malone

Wadley

Horseshoe Bend

Peters Island (Horseshoe Bend) 

−1.0 2.0
44 Adaptive Management of Flows from R.L. Harris Dam—Stakeholder Process and Use of Biological Monitoring Data

Figure B12. Estimated mean probability of colonization for 38 fish species at 15 flow-regulated sites in the Tallapoosa River 

system, 2005–16, modeled using dynamic, multitaxa occupancy models and plotted in relation to annual number of generation 

events (standardized to mean of 0 and standard deviation of 1). [Sites farther from the dam are plotted in black (Horseshoe 

Bend) and gray (Peters Island); sites nearer the dam are plotted in red (Malone) and orange (Wadley).]

EXPLANATION

Malone

Wadley

Horseshoe Bend

Peters Island (Horseshoe Bend) 

−1.5 −1.0 −0.5 0.0 0.5 1.0 1.5 2.0 2.5

0

0.1

0.2

0.3

0.4

0.5

Number of annual generation events (standardized)

Colonization probability
Results 45

Figure B13. Estimated mean probability of persistence for 38 fish species at 15 flow-regulated sites in the 

Tallapoosa River system, 2005–16, modeled using dynamic, multitaxa occupancy models and plotted in relation 

to annual number of days meeting a temperature criterion for spawning (standardized to mean of 0 and standard 

deviation of 1). [Sites farther from the dam are plotted in black (Horseshoe Bend) and gray (Peters Island); sites 

nearer the dam are plotted in red (Malone) and orange (Wadley).]

0.4

0.5

0.6

0.7

0.8

0.9

1.0

Persistence probability

EXPLANATION

Malone

Wadley

Horseshoe Bend

Peters Island (Horseshoe Bend) 

−2.5 −2.0 −1.5 −1.0 −0.5 0.0 0.5 1.0

Number of annual days meeting temperature criterion (standardized)
46 Adaptive Management of Flows from R.L. Harris Dam—Stakeholder Process and Use of Biological Monitoring Data

Figure B14. Estimated mean probability of colonization for 38 fish species at 15 flow-regulated sites in the 

Tallapoosa River system, 2005–16, modeled using dynamic, multitaxa occupancy models and plotted in relation 

to annual number of days meeting a temperature criterion for spawning (standardized to mean of 0 and standard 

deviation of 1). [Sites farther from the dam are plotted in black (Horseshoe Bend) and gray (Peters Island); sites 

nearer the dam are plotted in red (Malone) and orange (Wadley).]

EXPLANATION

Malone

Wadley

Horseshoe Bend

Peters Island (Horseshoe Bend) 

−2.5 −2.0 −1.5 −1.0 −0.5 0.0 0.5 1.0

0

0.1

0.2

0.3

0.4

0.5

Number of annual days meeting temperature criterion (standardized)

Colonization probability
Discussion 47

Discussion

Maximizing conservation potential in free-flowing sections of rivers will require, at minimum, clear evidence for 

effects of the current and future alternative regulated flow 

regimes on river biota. Development of flow-ecology relations 

is a critical element in prescribing ecological flow regimes. 

Process rate approaches, such as the one used in our study, 

allow for a better understanding of underlying demographic 

mechanisms that define flow-ecology relations (Poff, 2017; 

Wheeler and others, 2018). The estimation of two demographic processes, persistence and colonization (that is, 

1-extinction) probabilities, was robust and elucidated differences in vital rates in relation to river regulation, distance from 

the dam, and interannual differences in hydrology and thermal 

regimes. Monitoring of fauna over the 12-year study period 

has allowed us to quantify how metapopulation processes 

for many species responded to aspects of flow management 

in the Tallapoosa River downstream from R.L. Harris Dam. 

Although we observed increased density, persistence, and 

colonization along a downstream gradient from R.L. Harris 

Dam, richness did not differ appreciably at sites below the 

dam and all parameters were depressed relative the unregulated reaches. Additionally, densities below the dam were 

depressed relative to the unregulated sites, and usually, species 

contributed to richness values with less than five individuals 

over the course of the study. The alteration of fish diversity 

and abundance downstream from peaking hydroelectric dams 

has been reported for river systems, including our study site 

(Quinn and Kwak, 2003; Travnichek and Maceina, 1994). 

Kinsolving and Bain (1993) quantified downstream fish community changes below Thurlow Dam on the lower Tallapoosa 

River and reported that fluvial specialist populations were 

suppressed at sites nearest the dam prior to flow restoration. 

After increased base flows from the dam, fish species richness 

doubled and community composition shifted toward more 

fluvial specialists near the dam (Travnichek and others, 1995). 

In addition, positive temporal responses in persistence and 

colonization of fishes were not observed over the course of 

the study (see also Counihan and others, 2018; chapter A, this 

report), further indicating that the Green Plan flow management portfolio may not satisfy stakeholder objectives related 

to maximizing diversity and abundance of riverine fauna in the 

flow-regulated reaches.

Dams have altered flow and temperature regimes globally, potentially contributing to declines in abundance and 

early survival of fishes (Poff and others, 1997). Water released 

from hydroelectric dams is typically colder than surface 

temperatures, which alters downstream temperatures (Walker, 

1985; Humphries and Lake, 2000). Although it has long been 

recognized that temperatures are altered below R.L. Harris 

Dam, specific inference regarding the influence on biotic processes has been lacking until this study, which clearly relates 

colonization rates (that is, recruitment of a species to a site) 

to increased thermal energy in the river. In addition, our data 

indicate that there is no downstream recovery for colonization 

processes such that colonization rates did not increase with 

distance from the dam. Conversely, persistence rates were 

higher at more downstream sites, indicating that instream 

forces that affect these metapopulation parameters may change 

variably with distance from the dam. Our state monitoring of juvenile fish density response to flow management at 

R.L. Harris Dam indicated that recruitment across species was 

low in most years (see chapter A, this report); however, findings were not consistent with predictions of positive responses 

related to increased base flows and periods of reduced generation to allow for spawning (chapter A, this report).

Abiotic factors (for example, physical habitat, physiological constraints) influence faunal distribution by changing 

population rates such as mortality or recruitment (Poff, 1997). 

Many studies have reported the negative influences of dams on 

recruitment and survival of early life-history stages of fishes 

caused by altered flow and temperature regimes (Poff and 

others, 1997; Connor and others, 2003 Clarkson and Childs, 

2000; Humphries and Lake, 2000; Rolls and others, 2013). 

Matthews and others (1994) suggested that floods affected 

juvenile fish but not adults. In addition, short-term (hours, 

days) and long-term (seasonal) influences of river regulation 

are apparent in the ensuing flow (Poff and others, 1997) and 

thermal regimes (Caissie, 2006); local species distribution and 

biological responses to these regimes are also regulated by 

instream conditions (Poff, 1997; McManamay and Frimpong, 

2015). Our data suggest that adults of most species can persist 

below R.L. Harris Dam; however, colonization rates may not 

result in growing populations under the Green Plan management regime (Rolls and others, 2013; chapter A).

Thermal regimes are extremely complex downstream 

from dams (Webb and Walling, 1997; Caissie, 2006) and are 

intrinsically related to discharge magnitude and duration and 

the thermal characteristics of the receiving water (that is, 

volume, tributary inflows; Toffolon and others, 2010). Despite 

the recognition that alterations of the hydrologic and thermal 

regimes are consequences of river regulation, the inherent 

relations between the two have received little attention in the 

assessment of environmental flows (Olden and Naiman, 2010; 

Poff, 2017). Downstream from R.L. Harris Dam, water temperatures have been measured to decrease as much as 10 °C 

during generation events (Irwin and Freeman, 2002). Thermal 

alteration below dams associated with dam releases have 

been implicated in lack of recruitment of fishes (Patton and 

Hubert, 1996; Rolls and others, 2013; Shea and others, 2015); 

however, changes in dam management have successfully 

mitigated for thermal effects to fishes (Shea and others, 2015). 

Rolls and others (2013) reported that the lack of recruitment 

of fish in their systems was related to the dual effect of colder 

temperatures and lack of prey availability because of prolonged increased base flow. They proposed that managing for 

low flow periods would increase recruitment of fish species 

(see also Freeman and others, 2001). Although we have not 

accounted for prey availability in this study, data regarding depressed growth rates of fishes (Nash and Irwin, 1999; 

Sakaris and others, 2006), growth abnormalities expressed on 
48 Adaptive Management of Flows from R.L. Harris Dam—Stakeholder Process and Use of Biological Monitoring Data

otoliths (Irwin and others, 1997; Goar, 2013), low abundance 

of forage fish (Freeman and others, 2001; Counihan and others, 2018; this study), and altered invertebrate communities 

(chapter C) have been quantified below R.L. Harris Dam relative to unregulated river reaches in the basin. Based on these 

studies, investigations of flow regimes and temperature on 

fish forage (abundance and availability) are warranted below 

R.L. Harris Dam.

Ecological responses of fishes to flow alteration are 

overwhelmingly negative with respect to diversity, abundance, 

or population life-history parameters (Poff and Zimmerman, 

2010) and vital rates of metapopulations (this study; Shea 

and others, 2015). McManamay and others (2013, p. 30) 

reported that as little as 10-percent change in flow could result 

in “very large ecological responses.” However, theoretical 

underpinnings for development of flow-ecology hypotheses 

that can explicitly inform management are lacking (Poff and 

Zimmerman, 2010; Poff, 2017). Our long-term monitoring 

data are a step toward informing management at R.L. Harris Dam; however, the full range of flow alteration below the 

dam was not experienced during the study—flood and wet 

years were underrepresented and dry and drought years were 

more common during the study period. Freeman and others 

(2001) hypothesized that periods of lower flow consistent with 

droughts and years where inflows are low in the fall were conducive to recruitment of a suite of common fishes. In the current study, warmer thermal conditions increased colonization 

probability downstream from R.L. Harris Dam in some years; 

however, the overall probability of persistence and colonization was negatively affected by river regulation for almost all 

species. Analysis of temperature time-series data from below 

R.L. Harris Dam indicated poor thermal conditions for conspecifics of many warm-water species of fish below R.L. Harris 

Dam (Irwin and Freeman, 2002). Because of engineering constraints at the dam, it is unlikely that thermal conditions could 

be improved under any flow regime provided by pulsing at the 

dam to provide enhanced flows between generation events (for 

example, the Green Plan). Therefore, any investigation of thermal effects on fishes would need to be carefully considered 

relative to engineering constraints and the ability of the utility 

to provide for additional experimental flow portfolios below 

the dam. Toffolon and others (2010) assessed the thermal and 

hydrologic wave dynamics downstream from R.L. Harris Dam 

and inferred that the thermal and discharge waves did not 

decouple in the reach from below the dam to the headwaters of 

Lake Martin. Further analysis of complex hydrothermal data 

under different flow management regimes below R.L. Harris 

Dam is warranted. Olden and Naiman (2010) proposed that 

by viewing environmental flows together with various aspects 

of water quality (including temperature), the chances of 

long-term success in achieving ecologically sustainable water 

management are increased.

Disruption of riverine flows from impoundment and 

river regulation are primary reasons for high levels of imperilment of fishes and mollusks in Alabama and throughout the 

southeast. The primary cause of major extinction events for 

mollusks (mussels and gastropods) was the impoundment of 

shoal and riffle habitat (Neves and others, 1997). Similarly, 

53 percent of fishes inhabiting medium-sized rivers and creeks 

in the southeast United States are in jeopardy (Etnier, 1997). 

This is likely because fish communities in these habitats 

are dependent on shoals and riffles for at least part of their 

life history (Etnier, 1997). One of the objectives of adaptive 

flow management from R.L. Harris Dam was to maximize 

the diversity of native fauna and flora downstream from the 

dam, and hypothesized responses of the Green Plan related 

increased shoal habitat persistence, decreased magnitude of 

disturbance, and increased flow stability (associated with 

spawning windows; Martin, 2008) to positive fish population responses. Irwin and others (2011) reported an increase 

in shoal habitat persistence associated with the Green Plan; 

however, positive population responses have not ensued (see 

chapter A). Bradford and others (2011) reported similar findings for a long-term study of Pacific salmon in British Columbia; despite predictions of increased fish abundance based on 

improved habitat conditions (habitat did improve), fish populations in terms of density did not respond to flow manipulation. 

Our long-term metapopulation data provide evidence that 

suggests broadscale negative influences of the dam on species 

persistence and colonization parameters. Specifically, generation frequency and cool thermal regimes negatively affected 

fish persistence and colonization, respectively. Shea and others 

(2015) reported similar findings for a suite of darters in the Elk 

River, Tennessee. In the case of the Elk River, river temperatures were increased by limiting or ceasing generation at the 

dam during spring and summer periods.

Historical and current data from below R.L. Harris 

Dam indicate that for many species, occupancy rates below 

R.L. Harris Dam have remained at low levels despite the 

continued persistence of species (Irwin and others, 2011; 

Kennedy, 2015; this study). Counihan and others (2018) 

analyzed differences in assemblage structure among most of 

the historical reaches used in this study and reported substantial differences among sites (that is, Upper Tallapoosa reach 

and the reaches below the dam). They reported that differences were greatest between the Upper Tallapoosa and the 

Malone reach (58.2 percent) and noted that Bullhead Minnow, 

Largescale Stoneroller, Alabama Shiner, and Speckled Darter 

each contributed greater than 10 percent of the dissimilarity 

between reaches. In addition, the two most abundant fishes, 

Lipstick Darter and Bronze Darter, contributed greater than 

10 percent to dissimilarities between assemblages below the 

dam. They reported no temporal component to assemblage 

structure during the time series they analyzed (2005–13). 

However, historical fish assemblage structure has changed 

most likely in response to river regulation (from predam 

Ictalurid/Cyprinid dominant to postdam Centrarchid dominant; 

Swingle, 1954; E.R. Irwin, unpublished data). More specific 

analysis of temporal series of species-specific occupancy data 

may elucidate responses to interannual differences in flow and 

thermal regimes. In addition, species trait analysis may provide insights into predicted responses of species with similar 
References Cited 49

life-history strategies to proposed future management (Freeman and others, 2001; Craven and others, 2010; McManamay 

and Frimpong, 2015; Poff, 2017).

Fish assemblage diversity presents a challenge for 

management of flow-regulated systems; therefore, further 

identification of the biotic and abiotic factors that influence 

demographic rates of these species may benefit conservation efforts below dams. Most studies of biotic responses to 

flow management are focused on a single species (salmonids) 

or a few species (for example, Shea and others, 2015) and 

fewer have been cited as “successful” (Poff, 2017), but when 

considering regulation and thermal impacts to the number 

of species covered in this report, the future flow and thermal 

prescription could likely be extremely complex and trend 

toward “designer flows” (Acreman and others, 2014). Recent 

publications have been realistic regarding the feasibility of this 

approach (Poff, 2017; Acreman and others, 2014); however, 

the use of decision frameworks and adaptive management with 

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consequences of flow management on multiple objectives in a 

proactive way to elucidate regulatory engineering and biological constraints in FERC licensing procedures.

When Irwin and Freeman (2002) described an iterative 

structured process for reducing uncertainty and learning how 

carefully imposed management influenced fish populations, 

they were unaware of any FERC regulated projects with adaptive management formally defined in the license. Since then, 

several FERC relicensing processes and subsequent licenses 

have adaptive management programs tied to the license to 

better understand how natural resources and other stakeholder 

values respond to the management of dams (see Pearsall and 

others, 2005; Jacobson and Galat, 2008; Cross and others, 

2011; Podolak and Yarnell, 2015). Despite potential obstacles 

(Irwin and Freeman, 2002), an adaptive management approach 

holds substantial promise for improving management of regulated rivers by allowing managers and scientists to address 

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long-term monitoring of fishes in adaptive management 

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can further be used to identify additional changes in dam 

operations to evaluate during relicensing and beyond. If flow 

and thermal alteration from the dam can be modified toward 

improving natural resource objectives, adaptive management 

processes and long-term monitoring could further reduce 

uncertainty related to biotic response to new FERC licensing 

requirements.

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Appendix B 53

Appendix B

This appendix includes the R code used to conduct metapopulation analyses; it is separated into code specific to the two 

objectives described in the text. The R code can be downloaded from https://doi.org/10.3133/ofr20191026.
Sampling crew capturing fishes downstream from R.L. Harris Dam on the Tallapoosa River. Taken July 2, 2011, by E. Broder, Alabama Cooperative Fish and Wildlife 

Research Unit.
Chapter C

Macroinvertebrate Community Structure in Relation to Variation 

in Hydrology Associated with Hydropower

By Kristie M. Ouellette Coffman,1Ely Kosnicki,1 M. Clint Lloyd,1 Tom Hess,1 and Elise R. Irwin1,2

1

Alabama Cooperative Fish and Wildlife Research Unit.

2

U.S. Geological Survey.

Introduction

Hydropeaking dams can provide many valuable anthropogenic services, including flood and drought control, and the 

generation of electricity; however, hydrologic alteration from 

the daily operation of hydroelectric dams affects virtually 

every aspect of the riverine habitat because of changes to the 

natural flow and thermal regime. In general, river regulation 

changes the seasonal flow and temperature patterns of naturally flowing rivers by causing an increase in the number and 

frequency of flow events, an increase in the magnitude of normal daily thermal fluctuations, and a decrease in the variability 

and magnitude of flows (Graf, 2006; McMahon and Finlayson, 

2003; Olden and Naiman, 2010). For hydropeaking dams, a 

dual shock of hydraulic sheer stress followed by thermal stress 

can be experienced by the downstream habitats, which can 

potentially take days to fully recover (Toffolon and others, 

2010). Additionally, the availability and persistence of habitat 

area can be greatly altered by the daily scouring of substrate 

by hydropeaking releases, an overall reduction of median flow, 

and a general decrease in habitat heterogeneity (Freeman and 

others, 2001; Rehn, 2009; Tupinambás and others, 2016).

Over the last several decades the published literature 

has well documented the effects of changes in hydrology on 

the downstream communities, including aquatic macrophyte, 

macroinvertebrate, and fish communities (Bejarano and others, 

2017; Poff and Zimmerman, 2010). Since the emergence of 

the River Continuum Concept (Vannote and others, 1980), 

the response of macroinvertebrate communities to changes in 

the environment have been well documented in the literature 

(Wallace and Anderson, 1996; Barbour and others, 1999; 

Poff and others, 2006). Additionally, with the development of 

the Serial Discontinuity Concept (Ward and Stanford, 1982), 

researchers began observing changes in the macroinvertebrate 

community because of anthropogenic effects on the natural 

ecosystem, including the effects of dams and reservoirs on 

downstream communities (Poff and Zimmerman, 2010). 

Whether it be through community analysis (for example, taxa; 

Rader and Ward, 1988; Holt and others, 2015), physiological 

traits (for example, functional feeding group [FFG] and habit; 

Kennedy and others, 2016; Tupinambás and others, 2016; 

White and others, 2017), or quantitative sensitivity metrics 

(for example, Index of Biotic Integrity; Gore and others, 

2001; Rehn, 2009), there is a growing database of association of macroinvertebrate community changes with altered 

flow regimes.

Macroinvertebrates are useful for detecting immediate 

(minutes to days) and long-term (weeks to years) effects. Macroinvertebrates demonstrate sensitivity to changes in water 

chemistry and flow conditions by initiating drift upon reaching 

a tolerance threshold or leaving the habitat immediately by 

intentionally entering the water column and floating downstream from the disturbance (Wallace and Anderson, 1996). 

Additionally, many macroinvertebrates are sessile or weak 

swimmers that are unable to migrate back upstream immediately after initiating drift, so recolonization can take longer for 

macroinvertebrates versus fish, which are able to swim back 

into the habitat once the threat has passed (Barbour and others, 

1999; Gore and others, 2001; Wallace and Anderson, 1996; 

Bruno and others, 2016; Miller and Judson, 2014; Timusk and 

others, 2016). Rapid decreases in flow rates and river height 

because of hydropeaking can cause an increase in mortality 

from stranding and desiccation (Kennedy and others, 2016; 

Meile and others, 2016). Additionally, shifts in annual flow 

and thermal maxima and minima can disrupt breeding cues for 

many species (Freeman and others, 2001; McManamay and 

Frimpong, 2015). The consensus has been a general decrease 

in richness and diversity in favor of generalist species that can 

thrive well in disturbed flow conditions.
56 Adaptive Management of Flows from R.L. Harris Dam—Stakeholder Process and Use of Biological Monitoring Data

R.L. Harris Dam

R.L. Harris Dam is a hydroelectric facility located on 

river kilometer 223.9 of the Tallapoosa River in Randolph 

County, Alabama (U.S. Army Corps of Engineers, 2015; 

fig. C1). Beginning operation on April 20, 1983, the facility contains two 65,500-kilowatt generators that release 

approximately 368.1 cubic meters per second (m3/s) at best 

gate release (U.S. Army Corps of Engineers, 2015). The dam 

is generally operated on a daily hydropeaking schedule for 

electricity production in addition to meeting demands for 

water supply and flood control. In an effort to improve flow 

conditions in the section of the Tallapoosa River downstream 

from R.L. Harris Dam, the operators agreed to seek and test 

alternative flow plans that would better meet the objectives of 

the various stakeholders involved in the project, including ecological objectives of improving the conditions for downstream 

communities.

Since its induction, the R.L. Harris Dam Adaptive 

Management Project (AMP; “Harris AMP,” hereafter) has 

collected macroinvertebrate samples in conjunction with the 

collection of fish samples as part of the monitoring program. 

Thus far, reported faunal responses to flow management in the 

Tallapoosa River included a decrease in fish diversity below 

the dam potentially related to a reduction in shoal habitat persistence and a delay of thermal cues for breeding (Irwin and 

others, 2011). Although the biological emphasis of the Harris 

AMP has been to improve population persistence of fish species, the objective of maximizing the diversity and abundance 

of the native flora and fauna includes other faunal groups, such 

as macroinvertebrates. Understanding the response of the macroinvertebrate communities to river management is essential in 

terms of provision of productive food sources for fish populations (Gore and others, 2001). 

Analysis of Historical Macroinvertebrate 

Collections

Historical macroinvertebrate collections from the Harris AMP were systematically analyzed to determine if the 

macroinvertebrate community of the middle Tallapoosa River 

demonstrated responses to regulation type under different 

natural hydrologic conditions and identify which group of 

taxa or suite of traits could be used to determine species or 

community responses to flow management. To meet these 

goals, our specific objectives were to (1) characterize the 

macroinvertebrate communities in regulated and unregulated 

reaches; (2) characterize the macroinvertebrate communities 

during extreme natural variation in hydraulic conditions, or 

water years preceded by prolonged drought or wet conditions; 

(3) examine the distribution trends for FFG and habit traits 

among regulated and unregulated reaches; and (4) identify 

taxa that may be useful for future monitoring efforts on the 

Tallapoosa River. Based on results of published literature, we 

hypothesize that (1) macroinvertebrate communities would 

demonstrate a shift in community composition to more generalist species in regulated reaches versus unregulated reaches, 

(2) macroinvertebrate communities would demonstrate a 

response to natural hydrologic conditions in unregulated 

reaches and that response may be muted in regulated reaches 

because of a regulated flow regime, (3) FFG and habits would 

reflect a generalist community in regulated reaches versus 

taxa specialized for burrowing or more stable flows found 

in unregulated reaches, and (4) taxa commonly noted within 

the literature will indicate disturbed flow conditions, such as 

Baetidae and Hydropsychidae having a greater association 

with disturbed reaches, whereas taxa favoring stable flows, 

such as burrowers and predators, will have a greater association with unregulated reaches.

Methods

Macroinvertebrate Surber sampling was completed concurrent with fish sampling on many occasions except where 

daylight or staffing hours may have reduced the daily field 

work hours available for safe working conditions. Sampling 

was completed in summer (May–July) and fall (September–

November) of 2005–17 in 5 river reaches; 3 regulated by 

R.L. Harris Dam (Malone, Wadley, and Horseshoe Bend) and 

2 unregulated reaches (Upper Tallapoosa [“Heflin,” hereafter] 

and Hillabee Creek [“Hillabee,” hereafter]; fig. C1). A total of 

five shoals (A–E) were randomly selected within each reach, 

and attempts were made to visit every shoal once during each 

sampling season. Refer to chapter B of this report for details 

on fish sampling methods and results.

Macroinvertebrate Field Sampling Methods

A total of 10 Surber samples were collected randomly 

from shoal habitat with a water depth of at least 27 centimeters 

(cm) and no more than 40 cm, proceeding from downstream 

to upstream to avoid collection of drifting invertebrates from 

upstream sampling in subsequent downstream samples. The 

Surbers were standard 500-micrometer (μm) mesh Surbers 

with a 30.5-cm x 30.5-cm base opening. After placing the 

Surber, large rocks and debris were cleared of macroinvertebrates before agitating the substrate 8-cm deep or to bedrock 

for about 30 seconds. Samples were promptly emptied into 

5-gallon buckets about half full of clean river water. Again, 

large rocks and debris were visually inspected for macroinvertebrates before being returned to the shoal habitat. Remaining 

sample material was then elutriated through a 0.25-millimeter 

(mm) mesh sieve repeatedly until all organic material was 

removed from the bucket and no movement was noted upon 

close visual inspection (usually four or five times). Material 

left in the bucket after elutriation was returned to the shoal 

habitat. Specimens and material elutriated into the sieve were 

collected and stored in high-density polyethylene jars along 

with a printed label that recorded date, reach, shoal, and 
Methods 57

Figure C1. Sampling locations of the R.L. Harris Dam Adaptive Management Project in the Piedmont region of 

Alabama.

Hillabee Creek

Heflin

Wadley

R.L. HARRIS DAM Malone

Malone

Horseshoe

Bend

AL GA

FL

TN

MS

NC

Map area

GULF OF MEXICO

Watershed

boundary

Base from U.S. Geological Survey National Hydrography dataset, 2018

Lake Wedowee ALABAMA

GEORGIA

0 5 10 MILES

0 5 10 KILOMETERS

Watershed boundary

Little Tallapoosa River

Tallapoosa River

N

U.S. Geological Survey

streamgage 02412000

U.S. Geological Survey

streamgage 02414500

Tallapoosa River

Sampling location

Heflin

Malone

Wadley

Horseshoe Bend

Hillabee

EXPLANATION
58 Adaptive Management of Flows from R.L. Harris Dam—Stakeholder Process and Use of Biological Monitoring Data

random (1–10) Surber number, then filled with 70-percent ethanol until sample material was completely covered. Samples 

were stored in fire-proof storage cabinets until samples were 

processed.

Sample Processing

The sorting procedures developed for this project are 

detailed in appendix C1. The objective of the protocol was to 

maximize the number of species sorted from a Surber sample 

within 2 hours. Samples were processed using a subsampling 

technique with a target count of 100–150 individuals, accompanied by a qualitative post hoc visual inspection of remaining 

unsorted material to account for additional taxa not detected 

in the timed sort (similar to the P2 protocol in Stark and others 

[2001]). The percentage of material that was sorted to meet 

the target count was recorded and used to estimate the total 

number of individuals in the sample based on sorted proportions. See appendix C1 for details. Sorted samples were placed 

in glass vials, labeled, and stored in 70-percent ethanol.

Identification

Specimens were identified using stereo microscopes 

(75–640×, Olympus SZH, Tokyo), and a compound microscope (40×; 100×; 400×; 1,000×; Motic BA210, China) for 

confirmation of smaller taxonomic features where required. 

All nondipteran insects were identified to the lowest taxonomic level (genus or species) using Epler (2010), Merritt 

and others (2012), and Morse and others (2017). All dipterans 

were identified to the family level at minimum, except for 

Chironomidae, which were identified to subfamily, using the 

guide by Epler (2001). Cheumatopsyche were identified to a 

select group of easily identified morphotypes as designated by 

Burington (2011): Cheumatopsyche A/B (no notch on the frontoclypeus and long setae on the pronotal margin), Cheumatopsyche D (small notch on the frontoclypeus with long pronotal 

setae), Cheumatopsyche E (large notch in the frontoclypeus 

with two large sclerites in the prosternum), and Cheumatopsyche (all others—short pronotal setae and various frontoclypeus shapes). Noninsects were identified to the order or 

family level using available guides (Kathman and Brinkhurst, 

1998) and communication with local experts.

Taxonomists followed a “no head, no count” rule in 

which a present and identifiable head (or mouthpart for annelids) must be present (either attached or detached) for a specimen to be considered alive at the time of capture and counted. 

All terrestrial specimens (beetles, isopods) were excluded 

from the analysis. Aerial adults, including those of the orders 

Diptera, Ephemeroptera, Plecoptera, and Trichoptera, were 

not included in the analysis. Pupae, exuviae, empty shells, and 

cases were not counted. All zooplankton, including copepods 

and cladocerans, were considered microinvertebrates and 

excluded from the analysis.

Adult riffle beetles (Elmidae) were considered part of 

the general aquatic community assemblage and recorded as a 

separate taxon from larval Elmid beetles (for example, Elmidae_adult), but other adult aquatic beetles (including rarely 

caught surface skaters) were not included. Elmid beetles are 

prominent within the study system and larvae are commonly 

noted in large numbers. Larval and adult Elmidae have different morphologies and, therefore, have different diets, drift 

periods, and hydrologic and thermal tolerances (Elliott, 2008). 

Adult Elmidae beetles are still representative of the immediate 

habitat area because they have been known to travel short distances after pupation before returning to the water after which 

they will not leave the river gain for the rest of their lives, and 

some adults never fly at all and remain in the water after pupation (Elliott, 2008). For those reasons, we have included adult 

Elmidae beetles in the analysis, but they are noted as separate 

taxa from the Elmidae larvae.

Many samples contained large groups of early instar 

individuals, especially from the families Baetidae, Heptageniidae, Hydropsychidae, and Perlidae. During these early instar 

phases, many individuals lacked properly developed features 

that are typically used for identification (for example, gills 

of Heptageniidae and ocelli of Perlidae). These observations 

were recorded at the lowest possible identification level, generally at the family level. For analysis at the lowest taxonomic 

level, all early instar individuals are labeled as taxon* (for 

example, Hydropsychidae*).

Power Analysis

Although the established sampling protocol for the Harris 

AMP called for 10 samples to be collected at each shoal, there 

were several sampling occasions that did not have a full set of 

10 samples available for that site. The availability of an appropriate Surber sampling area was generally determined by river 

conditions and was occasionally limited by excessive flood 

or drought conditions during sampling periods. Additionally, 

the number of samples available per shoal was diminished 

after storage because of desiccation and decay within some 

samples. With further restrictions on time and availability of 

trained personnel for macroinvertebrate identifications, it was 

decided that finding an appropriate sample size to represent 

the shoal community that would allow for even sampling 

effort across all shoals, reaches, and years would be the best 

option moving forward.

To determine adequate sample sizes for characterizing 

community structure on river shoals, six macroinvertebrate 

samples per shoal were randomly selected from available 

presorted material from the 2005 and 2014 sampling years, 

the first year sampled and the last year sampled on hand at the 

time. After samples were sorted, the data from these 2 years 

were analyzed to identify the minimum number of samples 

needed to represent macroinvertebrate communities at the 

shoal level. Fall samples from four reaches were used: the 

Heflin and Hillabee reaches represented unregulated reaches, 
Methods 59

and the Malone and Wadley reaches represented regulated 

reaches.

A power analysis is used to determine the number of 

samples needed to detect whether a result differs significantly 

from the null hypothesis given the parameters of power, or the 

significance criterion, and alpha (α), or the rate of rejecting a 

true null hypothesis (Cohen, 1988). Power analysis was completed on metrics derived from Barbour and others (1999) to 

determine the number of samples needed to distinguish among 

shoals. Replicate Surber samples were combined to represent a single shoal. Shoal-level power analysis (power=0.8, 

α=0.05) was completed on a 1:1 basis (1 regulated shoal 

versus 1 unregulated shoal), as well as with 3 shoals combined 

for several shoals (2005 shoals included Heflin E, Malone B, 

Hillabee A, and Wadley A; 2014 shoals included Heflin B, 

Wadley E, and Hillabee B). Species accumulation curves were 

also generated to support the results of the power analysis.

A total of 50 percent of the 12 power analyses indicated that 3 samples were sufficient to illustrate differences 

between regulated and nonregulated shoals; alternatively, the 

other 50 percent indicated 2 to 3 times the number would be 

required to demonstrate differences between regulated and 

nonregulated shoals (table C1). Subsequent species accumulation curves (fig. C2) indicated that additional samples 

would minimally increase species richness values; however, 

the objective of these analyses is to assess the structure of the 

macroinvertebrate community at a single shoal to estimate 

reach-level differences in the macroinvertebrate community, 

not to perform a complete biological survey of the macroinvertebrate community. Based on the results of the power 

analysis and species accumulation curves, it was determined 

four samples per shoal was adequate to detect reach-level differences in macroinvertebrate community composition.

Subsampling Procedure

Based on the power analysis, subsampling of macroinvertebrate samples collected during the Harris AMP proceeded 

by randomly selecting 3 shoals to represent each reach per 

year (see below) and randomly selecting 4 Surber samples per 

shoal. Fall samples were processed from four reaches; Heflin 

and Hillabee represented unregulated reaches, and Malone and 

Wadley represented regulated reaches.

Selection of Sampling Years Based on Natural 

Variation in Hydrologic Extremes

We prioritized identifying additional years based on 

maximizing differences in hydrologic regimes in the Tallapoosa Basin as measured by the National Oceanography and 

Atmospheric Administration. The National Oceanic and Atmospheric Administration maintains regional historical drought 

records for each State based primarily on Palmer’s Drought 

Severity Index (Palmer, 1965). This method considers a waterbalance measure generated with historical precipitation and 

Table C1. Power analysis to estimate the number of Surber samples needed to test for a significant 

difference between regulated and nonregulated shoals of the R.L. Harris Dam study. Power, or the 

probability of accepting a false positive where the real difference is equal to the minimum effect size, 

was set to 0.8 and α, or the significance level at which the null hypothesis is rejected, was set to 

0.05. The results of the power analysis estimate how many Surber samples are necessary to test for 

differences among shoals.

[Hef, Heflin; Mal, Malone; --, no data; Hill, Hillabee; Wad, Wadley]

Metrics Year Shoal 1 Shoal 2 Shoal 3 Power Surbers

Percent burrower 2005 Hef E Mal B -- 0.99 3

Percent burrower 2005 Hill A Mal B -- 0.99 3

Percent clinger 2014 Hef B Wad E -- 0.99 3

Percent clinger 2014 Hef B Wad E Hill B 0.99 3

Percent gatherer 2014 Hef B Wad E -- 0.8 8

Percent gatherer 2014 Hill B Wad E -- 0.778 13

Percent scraper 2014 Hef B Wad E -- 0.86 7

Percent scraper 2014 Hill B Wad E Hef B 0.87 7

Richness 2005 Hill A Wad A Mal B 0.95 3

Richness 2005 Hill A Wad A -- 0.91 3

Richness 2005 Hef E Wad A -- 0.852 7

Richness 2005 Hef E Wad A -- 0.78 6
60 Adaptive Management of Flows from R.L. Harris Dam—Stakeholder Process and Use of Biological Monitoring Data Figure C2. Species accumulation curves and associated standard deviation based on preliminary data for selected sites.

1 2 3 4 5

50

353025201510

A. Species accumulation Heflin B, 2014

Total number of samples

Total number of taxa

1 2 3 4 5

50

353025201510

B. Species accumulation Hillabee C, 2014

Total number of samples

Total number of taxa

1 2 3 4 5

50

353025201510

C. Species accumulation Malone B, 2005

Total number of samples

Total number of taxa

1 2 3 4 5

50

353025201510

D. Species accumulation Wadley A, 2005

Total number of samples

Total number of taxa
Methods 61

temperature data, soil moisture storage, potential evapotranspiration, and runoff to generate climate-dependent coefficients that assist in determining the deviation of the measured 

precipitation to the expected precipitation based on historical 

climate conditions while considering the cumulative water 

demands, which are normalized to values that are comparable 

across different climactic regions. Palmer’s Modified Drought 

Index (PMDI) is the updated working model that considers 

the previous drought or wet period in addition to the current 

drought or wet period (Heddinghaus and Sabol, 1991).

Historical monthly PMDI values for the Piedmont Plateau Division (division 5) of Alabama were obtained online 

through the National Oceanic and Atmospheric Administration 

(National Climate Data Center, 2016). Historical discharge 

data were obtained through the U.S. Geological Survey online 

database for the two U.S. Geological Survey streamgages, 

Tallapoosa River at Wadley, Ala. (U.S. Geological Survey 

streamgage 05414500; “Wadley streamgage,” hereafter; 

U.S. Geological Survey, 2018), and Tallapoosa River near 

Heflin, Ala. (U.S. Geological Survey streamgage 02412000; 

“Heflin streamgage,” hereafter; U.S. Geological Survey, 

2018), representing discharge for regulated and unregulated 

reaches of the Tallapoosa River, respectively. Monthly average values for PMDI and discharge for both the Wadley and 

Heflin streamgages were graphed in Microsoft Excel (Microsoft Office 365 ProPlus, version 1708; table C2, fig. C3). 

There were two extensive drought periods during the AMP: 

one from the beginning of 2006 to mid-2008, and another 

from mid-2010 to the beginning of 2013. Drought period 1 

(2006–08) included the minimum PMDI value, which was 

recorded in September of 2007 (−5.48 monthly average) and 

low average discharge for the preceding winter (December 

2006 through February 2007) and spring (March 2007 through 

May 2007) months during which the average discharge for 

Heflin was less than [<] 15.9 m3/s and average discharge for 

Wadley was <76.4 m3/s. Drought period 2 (mid-2010–12) was 

characterized by greater monthly average discharge during 

the winter and spring months (monthly averages for Heflin 

were <36.5 m3/s and monthly averages for Wadley were 

<150.1 m3/s during drought period 2) and a greater minimum 

PMDI (−4.18 for August 2011) compared to drought period 1. 

Conversely, a period of positive PMDI values was recorded 

from winter 2008–9 to mid-2010, with the greatest positive 

value for the PMDI (5.86) reported in December 2009. This 

period also contains the greatest monthly average discharge 

for the Wadley reach (monthly average for December 2009 

was 238.5 m3/s) but only the fourth greatest discharge value 

noted for the Heflin reach (after December 2013, July 2005, 

and December 2015 when average discharge was 196.1 m3/s, 

199.5 m3/s, and 166 m3/s, respectively) from the years 

2005–15.

Additional years for sample processing and identification were selected based on the observations on the Piedmont 

PMDI values and availability of samples. The fall of 2008 

samples were chosen to represent the culmination of drought 

period 1 as insufficient samples were available from the fall of 

2007 because of extremely low water levels and inaccessibility of sampling sites (table C2). Fall of 2012 was selected as it 

represents the culminations of drought period 2 and sufficient 

samples were available. The fall of 2009 was chosen to represent a wet year from the 2009 to 2010 wet period.

A total of 228 Surber samples were included in the final 

analyses, with samples from each shoal summed to form 

57 testable shoals from 5 years; 2005 (number of samples 

[n]=48) and 2014 (n=48) represent “bookend” years (the first 

and last year on hand when processing began), 2008 (n=44) 

and 2012 (n=40) represent “drought” conditions, and 2009 

(n=48) represents “wet” conditions. Each shoal was represented by the sum of four randomly selected Surber samples 

from the available samples of sufficient quality (for example, 

no decay, no desiccation, and so on). Each reach is represented 

by 3 shoals, except for 2 reaches where sufficient sampling 

days did not take place that year: Heflin in 2008 (2 shoals) and 

Malone in 2012 (1 shoal). Because both of these occasions 

took place during drought years, it is likely that there were 

insufficient flows on most days of the designated sampling 

period to complete field work (see table C2).

Community Analyses

Analyses were completed on the sum of 4 Surbers from 

each of 3 randomly selected shoals from 4 reaches, 2 regulated 

(Malone and Wadley) and 2 unregulated (Heflin and Hillabee), 

for fall samples from 5 years (see above). Percent composition of order and family within each regulation type overall 

and percent composition of dominant orders and taxon for 

each year and regulation type were calculated using Microsoft 

Excel (Microsoft Office 365 ProPlus, version 1708). Density 

and richness metrics for the community, Ephemeroptera, 

Plecoptera, and Trichoptera (EPT) taxa, and all non-EPT taxa 

were calculated for the complete dataset using R statistical 

software (R Core Team, 2016) using functions included in 

the rich (Rossi, 2011) and vegan (Oksanen and others, 2017) 

packages.

Nonmetric multidimensional scaling (NMDS) utilizing 

Bray-Curtis dissimilarity scores (k=2 and try=999 where k is 

the number of dimensions and try is the minimum number of 

iterations the program is required to complete before returning a result) was used to visualize the dissimilarities in the 

macroinvertebrate density data for each shoal with regards 

to regulation type and years. A permutational multivariate 

analysis of variance (PERMANOVA) test was completed on 

the sample-taxa density matrix using Bray-Curtis dissimilarity indices (permutations=999 where permutations are the 

minimum number of iterations to complete before returning a 

result) to test the significance of the differences in community 

dissimilarity scores based on site, year, and site:year interactions (McArdle and Anderson, 2001; Reiss and others, 2010).

A similarity percentage (SIMPER) analysis was completed on the sample-taxa density matrix using Bray-Curtis 

dissimilarity indices (permutations=999), and the results were 
62 Adaptive Management of Flows from R.L. Harris Dam—Stakeholder Process and Use of Biological Monitoring Data

Table C2. Monthly average discharge values, with standard deviations in parentheses, for two U.S. Geological Survey streamgages, 

the Tallapoosa River at Wadley, Alabama, and Tallapoosa River near Heflin, Ala. (U.S. Geological Survey streamgages 02414500 and 

02412000, respectively), referred to as “Wadley” and “Heflin,” respectively, within the table, and the monthly reported Palmer’s Modified 

Drought Index (PMDI) values for the Piedmont Division of Alabama. Data are from U.S. Geological Survey (2018) and National Oceanic 

and Atmospheric Administration (National Climate Data Center, 2016).

Month

Average discharge and standard deviation 

of discharge, in cubic meters per second PMDI

Heflin Wadley

2005

January 15.9 (5.9) 71.5 (90.2) −0.02

February 21.1 (8.1) 118.2 (126.5) −0.34

March 29.8 (19.3) 145.3 (133.4) 0.53

April 34.3 (31.7) 152.5 (151) 1.31

May 14.1 (6.7) 73.8 (97.4) 1.00

June 14.7 (7.6) 72.9 (101.8) 1.52

July 51.2 (53.3) 199.5 (199.9) 3.28

August 16.1 (7.2) 79.8 (103.4) 3.62

September 6.3 (2) 40.1 (67) 2.03

October 5 (1.7) 26.9 (45.9) 1.12

November 8.2 (9.4) 47 (74.7) 0.65

December 14.9 (8.1) 62.3 (79.8) −0.29

2006

January 26.9 (27.4) 102.1 (120.6) −0.54

February 42.4 (41.2) 144.1 (142.3) −0.55

March 29.4 (23) 117.6 (121.8) −1.46

April 18.3 (3.7) 46.3 (58.7) −1.93

May 12.6 (5) 61.7 (82.7) −1.90

June 5.8 (2.2) 25.8 (41.9) −2.27

July 2.9 (0.8) 22.4 (44.2) −2.78

August 2.6 (0.8) 25.5 (48.5) −3.19

September 4.4 (3.5) 13.6 (24.3) −2.71

October 4.2 (4.1) 27.9 (45.9) −1.44

November 14.6 (26.2) 74.3 (122) −0.19

December 7.2 (2.6) 36.8 (60.2) −1.24

2007

January 15.9 (14.5) 76.4 (97) −1.27

February 11.6 (4.8) 53.1 (76.7) −2.27

March 10.5 (5.3) 41.5 (66.9) −3.29

April 7 (1.2) 15.7 (10.3) −3.45

May 3.5 (1.1) 10.8 (19.6) −4.17

June 1.2 (0.4) 15.2 (39) −4.50

July 2.3 (1.8) 18.5 (42) −4.66

August 0.7 (0.5) 10.8 (26.2) −5.36

September 0.5 (0.4) 10.6 (28.7) −5.48

October 0.2 (0.2) 7.2 (18.1) −5.20

November 0.7 (0.7) 5.2 (1.4) −5.17

December 2.2 (2.3) 6.2 (2.4) −4.95

Month

Average discharge and standard deviation 

of discharge, in cubic meters per second PMDI

Heflin Wadley

2008

January 5 (1.9) 8.5 (14.7) −4.76

February 17.6 (16.9) 73.1 (102.5) −3.67

March 17 (12.1) 69 (87.8) −4.15

April 13.8 (8.2) 46.8 (67.6) −3.99

May 10.2 (5.5) 50.8 (67.2) −2.99

June 2.6 (1.4) 20.2 (41) −3.49

July 2.1 (2.3) 18.4 (38.7) −3.49

August 2.3 (3.2) 26.5 (44.6) 0.69

September 1.1 (0.5) 20.5 (44.7) −0.37

October 1.1 (0.7) 18.8 (41.1) 1.09

November 1.5 (1.2) 13.2 (28.4) 0.80

December 12.7 (19.4) 61.7 (103.3) 1.04

2009

January 14 (20.5) 62.7 (90.7) 0.39

February 11.6 (24.2) 51.3 (93.7) 0.26

March 27.6 (30.5) 127.1 (126.9) 1.09

April 19.4 (10) 97.3 (95.7) 0.61

May 18.1 (9.8) 103.6 (122.3) 2.00

June 5.2 (2.1) 33.9 (60.6) 0.69

July 3.4 (2.7) 27.7 (52.2) 0.51

August 6.5 (6.7) 18.6 (32.3) 1.36

September 15.9 (20.1) 87.7 (124.4) 2.70

October 25 (19.7) 155.3 (134.3) 4.67

November 38.2 (50) 182 (172.6) 4.93

December 48.9 (36.7) 238.5 (134.9) 5.86

2010

January 39.2 (44) 176.9 (147.9) 5.51

February 43.3 (37) 187.8 (149.3) 4.67

March 47.6 (51.4) 211 (172.8) 4.02

April 21.3 (11.2) 55 (62.4) 2.13

May 24 (21.4) 106.6 (135.1) 2.58

June 11.1 (5.5) 60.7 (88.9) 1.34

July 5.7 (2.1) 32.1 (56.7) −0.96

August 2.2 (0.8) 17.8 (36.1) −2.01

September 0.7 (0.3) 12.8 (27.2) −2.65

October 1.1 (1.4) 27.3 (53.6) −2.53

November 2.1 (1.2) 35.5 (45.8) −2.37

December 4.9 (2.7) 32.4 (39.6) −2.74
Methods 63

Table C2. Monthly average discharge values, with standard deviations in parentheses, for two U.S. Geological Survey streamgages, 

the Tallapoosa River at Wadley, Alabama, streamgage and Tallapoosa River near Heflin, Ala. streamgage (U.S. Geological Survey 

streamgages 02414500 and 02412000, respectively), referred to as “Wadley” and “Heflin,” respectively, within the table, and the monthly 

reported Palmer’s Modified Drought Index (PMDI) values for the Piedmont Division of Alabama. Data are from U.S. Geological Survey 

(2018) and National Oceanic and Atmospheric Administration (National Climate Data Center, 2016).—Continued

Month

Average discharge and standard deviation 

of discharge, in cubic meters per second PMDI

Heflin Wadley

2011

January 6.6 (2.5) 40.1 (51.5) −3.00

February 11.3 (8.6) 62 (80.5) −3.01

March 36.5 (44.8) 150.1 (141.5) −1.58

April 18.5 (9.7) 79.9 (75.7) −2.16

May 5.1 (1.5) 32.5 (39.6) −2.88

June 2.8 (2) 25.1 (43.3) −3.14

July 3.6 (2.7) 16.5 (25) −3.11

August 1.9 (0.5) 16.3 (33.4) −4.18

September 2 (1.4) 9.2 (10.1) −3.15

October 1.2 (0.8) 23.3 (43.2) −3.52

November 5.1 (7.2) 57.6 (81.4) −3.05

December 8.9 (5.7) 56.4 (75.7) −2.95

2012

January 16.8 (22) 115.9 (135.7) −2.40

February 9 (1.6) 65.8 (70) −2.78

March 18.1 (18.8) 145.4 (134.7) −3.09

April 6.4 (2.2) 13.1 (19) −3.65

May 5.9 (3.7) 13.5 (15.4) −3.26

June 2.2 (0.7) 18.9 (23.7) −3.34

July 2.3 (2) 12.7 (12) −3.67

August 1.6 (0.5) 7.9 (8.5) −2.91

September 1.4 (0.5) 7.4 (7.7) −3.19

October 1 (0.7) 12.5 (20.8) −3.04

November 1 (0.2) 22.9 (39.9) −3.46

December 8.5 (10.6) 60.9 (97.6) −2.20

2013

January 23.9 (30.7) 110.5 (123.3) −1.73

February 35.6 (28.2) 159.5 (135.7) 1.05

March 20.7 (12) 112.8 (108.1) 0.07

April 25.1 (19.5) 84.2 (85.4) 0.40

May 24.7 (20.8) 160.5 (226.8) 1.35

June 12.2 (6) 79.7 (87.5) 1.75

July 23.6 (22.9) 127.7 (126.5) 2.74

August 16.7 (12) 78 (86.2) 3.17

September 12.9 (14) 44.7 (62.3) 2.67

October 6.6 (3.7) 58.7 (76.2) 1.98

November 6.9 (5.6) 56 (68.9) 1.14

December 52.1 (64) 196.1 (158.4) 2.95

Month

Average discharge and standard deviation 

of discharge, in cubic meters per second PMDI

Heflin Wadley

2014

January 21.5 (17.1) 107 (114.2) 1.89

February 21.7 (7.5) 115.3 (90.9) 1.47

March 25.3 (11.6) 111.9 (96.3) 1.06

April 41.5 (43.7) 165.9 (176.9) 2.47

May 19.8 (7.3) 79.6 (77.2) 2.38

June 15.9 (7.9) 68.9 (74.6) 2.54

July 6.8 (3.8) 35 (43.2) 1.19

August 4.8 (3) 23.3 (29.8) 0.13

September 2.4 (0.9) 13.3 (15.3) −0.87

October 3 (2.2) 23.6 (31.1) −0.69

November 10.3 (13.5) 51.2 (74.9) −0.87

December 18 (22.6) 87.9 (131.9) −0.44

2015

January 34.5 (55.1) 122.9 (142.4) −0.76

February 17.9 (6.5) 82.9 (83.1) −0.72

March 19 (6.6) 74.9 (71.2) −1.38

April 41.4 (20.2) 125.2 (116.8) −0.47

May 14.8 (4.6) 55.7 (60.6) −0.27

June 10.6 (4.1) 31.5 (40.7) −0.56

July 6.2 (3.3) 24.6 (36.6) −1.34

August 2.9 (1.6) 16.9 (19.3) −0.33

September 2.4 (0.7) 9.8 (11.2) −1.14

October 4.1 (1.8) 26.2 (41.9) −1.24

November 22 (16.5) 95.4 (99) 0.77

December 50.7 (68.8) 166 (215) 2.66
64 Adaptive Management of Flows from R.L. Harris Dam—Stakeholder Process and Use of Biological Monitoring Data Figure C3.

Monthly average discharge values for Heflin and Wadley streamgages (U.S. Geological Survey streamgages 02412000 and 02414500, respectively) and monthly 

average Palmer’s Modified Drought Index (PMDI) score for the Piedmont Region (Piedmont Division of Alabama) from 2005 through 2016. Data are from U.S. Geological Survey (2018) and National Oceanic and Atmospheric Administration (National Climate Data Center, 2016).

Palmer’s Modified Drought IndexHeflinWadley

EXPLANATION

Palmer’s Modified Drought Index

86420–2–4–6–8 0

50

100150200250

2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

, in cubic meters per second egr ahcsi D

Year
Results 65

used to determine which taxa contributed the most to the differences in the dissimilarity scores. To visualize taxa-specific 

responses to river regulation, the taxa that contribute the most 

to the cumulative deviation between regulation types were 

represented in the NMDS bubble plot using the estimated 

average density of each taxa as the size variable for the plotting function.

To determine if classes of FFG or habit had significant 

association with the NMDS results, the log transformed 

(log x+1) percent composition of the estimated average densities of each class for each site were fit on the NMDS ordination. Results were plotted over the NMDS results to visualize 

the associations of FFG and habit class vectors to the NMDS 

ordination of shoal over average estimated macroinvertebrate 

density.

To further illustrate similarities among macroinvertebrate 

communities at the reach scale, we completed a cluster analysis on the presence-absence taxa matrix of the taxa data using 

Ward’s method of Hierarchical Clustering (Ward, 1963) with 

Euclidian distances. A SIMPER analysis of the sample-taxa 

presence-absence taxa matrix (permutations=999) was used to 

determine which taxa contribute the most to the dissimilarities 

between regulation type.

All statistical analyses as well as NMDS and cluster plots 

were completed using R statistical software (R Core Team, 

2016) using functions included in the ggplot2 (Wickham, 

2009), ggrepel (Slowikowski, 2016), reshape2 (Wickham, 

2007), rich (Rossi, 2011), and vegan (Oksanen and others, 

2017) packages.

Results

A total of 164 taxa were identified from the 230 samples 

selected from the fall sampling season for the years of 2005, 

2008, 2009, 2012, and 2014 (appendix C1). Dipterans alone 

represented more than a quarter of all specimens observed 

(26.72 percent, table C3), whereas EPT orders represented 

41.27 percent of all specimens observed (19.96 percent 

Ephemeroptera, 4.03 percent Plecoptera, and 17.28 percent 

Trichoptera). Mollusks accounted for 7.9 percent of all specimens (3.91 percent Neotaenioglossa, 2.94 percent Veneroida, 

and 1.05 percent Basommatophora), Tubificida 6 percent, and 

Trobidiformes 3.07 percent.

Table C3. Percent composition of each identified order or group for each regulation type (regulated and unregulated) and for all (total) 

observations, listed in order of greatest to least percent composition of the total observations. The number of families represents the 

number of families positively identified within each order or group (not counting groups of immature individuals).

[--, no data]

Order or group Regulated, in percent Unregulated, in percent Total, in percent Number of families

Diptera 21.85 31.16 26.72 13

Ephemeroptera 22.80 17.37 19.96 10

Trichoptera 21.85 13.13 17.28 10

Coleoptera 8.96 12.33 10.72 7

Tubificida 6.79 5.28 6.00 1

Plecoptera 1.91 5.97 4.03 7

Neotaenioglossa 3.93 3.89 3.91 2

Trombidiformes 3.24 2.92 3.07 1

Veneroida 2.55 3.30 2.94 2

Odonata 1.30 1.46 1.38 6

Nematoida 1.52 0.90 1.20 1

Basommatophora 1.07 1.04 1.05 3

Turbellaria 1.75 0.38 1.04 1

Megaloptera 0.46 0.69 0.58 2

Lepidoptera 0.00 0.14 0.07 2

Hirudinea 0.04 0.03 0.04 1

Total 100 100 100 --
66 Adaptive Management of Flows from R.L. Harris Dam—Stakeholder Process and Use of Biological Monitoring Data

Diptera was the most diverse order, representing 13 positively identified families (table C3). Overall, the EPTs were 

represented by 27 positively identified families. Regulated 

reaches lacked positive identifications for many macroinvertebrate families documented in this study, including Plecoptera 

(1 family identified in regulated reaches out of 7 total families 

documented in the study, or 1 of 7), Lepidoptera (0 of 2), 

Diptera (7 of 13) and Odonata (4 of 6), whereas unregulated 

reaches lacked 1 family of Trichoptera (9 of 10) and 2 Dipterans (11 of 13) out of all the macroinvertebrate families identified in this study.

Characterization of Regulated and Unregulated 

Communities

Regulated reaches were characterized by greater density 

(total density, EPT density, and non-EPT density), and unregulated reaches were characterized by greater richness (total 

richness and EPT richness; table C4). Except for the minimum 

EPT richness and the standard deviation of non-EPT richness, 

the unregulated reaches had greater mean, standard deviation, 

minimum, and maximum richness than regulated reaches. 

Except for minimum EPT density, minimum non-EPT density, 

and maximum non-EPT density, regulated reaches had greater 

mean, standard deviation, minimum, and maximum density 

values than unregulated reaches. The EPT density is notably 

larger in regulated reaches, with mean density more than three 

times greater than in unregulated reaches and maximum density more than five times greater than in unregulated reaches.

The community composition of regulated and unregulated reaches are different at the order, family, and taxon level 

(table C5). Regulated reaches were dominated by Ephemeroptera (29.75 percent), Diptera (27.57 percent), and Trichoptera (18.11 percent). The response for Ephemeroptera was 

largely driven by large abundances of Baetidae (19.82 percent) because of large abundance of Iswaeon (7.56 percent) 

observed in regulated samples. The response of Dipterans 

was driven by a large proportion of Chironomidae (22.82 percent), which was composed of nearly half Chironominae and 

half Orthocladiinae. The high rank for Trichoptera in regulated reaches was primarily because of large abundances of 

Hydropsychidae* (early instar, 6.97 percent), which made up 

more than half of the total observations of Hydropsychidae 

(13.01 percent).

Unregulated reaches were dominated by Diptera 

(40.15 percent), Ephemeroptera (14.44 percent), and Coleoptera (10.15 percent) orders (table C5). Most of the Dipterans 

observed were from the family Chironomidae (32.57 percent), 

which was composed of more Chironominae (17.82 percent) 

than Orthocladiinae (11.75 percent). Simuliidae also made 

up a large proportion of the Diptera specimens observed in 

unregulated reaches, accounting for 6.79 percent of both the 

family and taxa level observations. Coleoptera was the third 

ranking dominant order within unregulated reaches, driven 

by large abundances of the Elmidae (8.55 percent) family, of 

which more than half were Stenelmis (4.39 percent).

Macroinvertebrate Community Response to 

Natural Variation in Hydrology

The response of the community at the order and taxon 

level was assessed for each year within each regulation type 

(table C6). Regulated years from all years analyzed were 

dominated by Diptera, Ephemeroptera, and Trichoptera orders, 

with Diptera ranking first in the 2005 (32.25 percent) and 2009 

(29.55 percent) years, and Ephemeroptera ranking first in 2008 

(37.93 percent), 2012 (29.14 percent), and 2014 (38.83 percent). The high ranking of Diptera in regulated reaches was 

Table C4. Values of selected metrics for regulated and unregulated reaches. Density is the average number of organisms per square 

foot per site (shoal). EPT are those taxa belonging to the Ephemeroptera, Plecoptera, and Trichoptera orders. Non-EPT are those taxa 

which are of the class Insecta but do not belong to the order Ephemeroptera, Plecoptera, or Trichoptera, which, in this study, are the 

orders Coleoptera, Diptera, Lepidoptera, Odonata, and Megaloptera. References for macroinvertebrate response to disturbance include 

Barbour and others (1999), Cortes and others (2002), Bednarek and Hart (2005), and Holt and others (2015).

[min, minimum; max, maximum; --, no data]

Metric name Disturbance 

response

Regulated Unregulated

Total Mean Standard 

deviation Min Max Total Mean Standard deviation Min Max

Richness Negative 84 30.04 6.98 10 44 112 32.07 7.28 18 46

EPT richness Negative 47 14.89 3.70 9 24 58 15.41 5.05 7 25

Non-EPT richness Positive 24 8.75 2.20 5 13 41 10.79 2.09 7 15

Density Positive -- 375.78 335.49 58.00 1,613.00 -- 217.04 203.00 34.50 1,009.00

EPT density Positive -- 197.66 230.46 0.25 1,157.50 -- 59.98 54.09 6.25 229.25

Non-EPT density Positive -- 115.76 97.48 11.25 403.50 -- 106.43 94.07 17.00 431.25
Results 67

driven by large abundances of Chironominae, Simuliidae, 

and Orthocladiinae in 2005 (14.56 percent, 9.10 percent, and 

6.91 percent, respectively), and Orthocladiinae and Chironominae in 2009 (14.02 percent and 10.18 percent, respectively). 

The high rank for Ephemeroptera in regulated reaches was 

driven by large abundances of both Iswaeon and Acerpenna

in the 2008 (14.04 percent and 5.61 percent, respectively) and 

2014 years (13.86 percent and 6.65 percent, respectively), 

whereas Acerpenna alone dominated the 2012 year (15.88 percent). Trichoptera ranked third in abundance within regulated 

reaches for all years except 2005, where it is ranked second. 

Hydropsychidae* is among the top five most abundant taxa in 

regulated reaches in the 2005 (7.14 percent), 2009 (9.28 percent), and 2012 (8.99 percent) years. Tubificida ranks fourth in 

regulated reaches in 3 of 5 years analyzed because of a large 

proportion of Naididae (5.99 percent to 11.92 percent).

The community composition in unregulated reaches 

indicates more diversity within the top five dominant orders 

and taxa across all years analyzed (table C6). Diptera was the 

dominant order in unregulated reaches for all years (32.86 percent to 55.62 percent) except 2014, where it is ranked second 

(15.51 percent) after Coleoptera (31.43 percent). Coleopterans are common in both regulated and unregulated reaches, 

ranking within the top five dominant taxa for both regulation 

types in 2005 and 2008 and unregulated reaches only in 

2012 and 2014. The Coleoptera population was detected in 

much larger proportions in unregulated reaches in 2014, and 

these results were driven by the top-ranking taxon Stenelmis

(17.60 percent) as well as fifth-ranking taxon Optioservus

(5.72 percent). The same year Coleopterans dominated the 

unregulated reaches (2014) was also the only year in which 

Ephemeroptera was not among the five dominant orders in 

unregulated reaches. The 2005 year saw more than half of 

the individuals from the unregulated reaches were Dipterans 

(55.62 percent) because of a large proportion of Chironominae 

(27.26 percent) and Orthocladiinae (20.08 percent). Plecoptera 

were among the top five most common orders in unregulated 

reaches in 2005 (5.65 percent), 2012 (16.03 percent), and 2014 

(10.64 percent), with Capniidae/Leuctridae complex driving 

results for this order in 2012 (9.84 percent) and Neoperla in 

2014 (6.71 percent). Neotaenioglossa was also common in 

unregulated reaches, with large proportions in 2009 (8.25 percent) as a result of large proportions of Hydrobiidae (6.55 percent) and in 2014 (13.77 percent) as a result of large proportions of Pleuroceridae (10.04 percent). Corbicula fluminea 

were within the top five dominant taxa only in unregulated 

reaches during drought years (rank 5 at 5.14 percent in 2008 

and rank 5 at 6.07 percent in 2012).

Table C5. List of the top five dominant taxa (order and taxon) in regulated and unregulated reaches 

based on percent of total observations.

Rank Regulated Unregulated

Name Percent composition Name Percent composition

Order

1 Ephemeroptera 29.75 Diptera 40.15

2 Diptera 27.57 Ephemeroptera 14.44

3 Trichoptera 18.11 Coleoptera 10.15

4 Tubificida 7.58 Trichoptera 7.89

5 Coleoptera 5.27 Plecoptera 7.14

Family

1 Chironomidae 22.82 Chironomidae 32.57

2 Baetidae 19.82 Elmidae 8.55

3 Hydropsychidae 13.01 Baetidae 7.01

4 Naididae 7.58 Simuliidae 6.79

5 Heptageniidae 5.73 Naididae 6.06

Taxon

1 Chironominae 11.60 Chironominae 17.82

2 Orthocladiinae 10.48 Orthocladiinae 11.75

3 Iswaeon 7.56 Simuliidae 6.79

4 Naididae 7.56 Naididae 6.00

5 Hydropsychidae* 6.97 Stenelmis 4.39

*

Indicates early instar individual.
68 Adaptive Management of Flows from R.L. Harris Dam—Stakeholder Process and Use of Biological Monitoring Data Table C6. List of the top five dominant orders and taxa in regulated and unregulated reaches for each year. Rank Regulated Unregulated Regulated Unregulated Order Percent composition Order Percent composition Taxon Percent composition Taxon Percent composition 2005

1 Diptera 32.25 Diptera 55.62 Chironominae 14.56 Chironominae 27.262 Trichoptera 19.54 Ephemeroptera 14.58 Simuliidae 9.10 Orthocladiinae 20.083 Ephemeroptera 18.84 Trichoptera 9.89 Naididae 7.83 Iswaeon 4.954 Coleoptera 9.10 Plecoptera 5.65 Hydropsychidae* 7.14 Cheumatopsyche 4.085 Tubificida 7.90 Coleoptera 5.35 Orthocladiinae 6.91 Tanypodinae 3.64

2008

1 Ephemeroptera 37.93 Diptera 32.86 Iswaeon 14.04 Chironominae 12.232 Diptera 22.43 Ephemeroptera 22.12 Chironominae 11.33 Naididae 7.783 Trichoptera 17.34 Coleoptera 8.59 Orthocladiinae 7.49 Simuliidae 7.634 Tubificida 5.98 Tubificida 8.09 Naididae 5.99 Orthocladiinae 7.405 Coleoptera 5.71 Trichoptera 7.06 Acerpenna 5.61 Corbicula fluminea 5.14

2009

1 Diptera 29.55 Diptera 48.08 Orthocladiinae 14.02 Chironominae 18.302 Ephemeroptera 26.06 Ephemeroptera 14.29 Chironominae 10.18 Simuliidae 15.303 Trichoptera 18.82 Neotaenioglossa 8.25 Hydropsychidae* 9.28 Orthocladiinae 11.154 Tubificida 6.72 Tubificida 7.36 Naididae 6.68 Naididae 7.365 Trombidiformes 5.31 Trichoptera 7.12 Acerpenna 6.32 Hydrobiidae 6.55

2012

1 Ephemeroptera 29.14 Diptera 33.26 Acerpenna 15.88 Chironominae 14.842 Diptera 27.87 Plecoptera 16.03 Chironominae 13.57 Orthocladiinae 12.723 Trichoptera 18.22 Ephemeroptera 11.83 Orthocladiinae 11.95 Capniidae/Leuctridae complex 9.844 Tubificida 11.92 Tubificida 9.49 Naididae 11.92 Naididae 9.475 Trombidiformes 5.17 Coleoptera 8.69 Hydropsychidae* 8.99 Corbicula fluminea 6.07

2014

1 Ephemeroptera 38.83 Coleoptera 31.43 Iswaeon 13.86 Stenelmis 17.602 Diptera 25.54 Diptera 15.51 Orthocladiinae 11.06 Chironominae 10.943 Trichoptera 15.74 Neotaenioglossa 13.77 Chironominae 8.70 Pleuroceridae 10.044 Tubificida 6.47 Plecoptera 10.64 Acerpenna 6.65 Neoperla 6.715 Veneroida 3.59 Trichoptera 10.29 Naididae 6.47 Optioservus 5.72

*Indicates early instar individual.
Results 69

Nonmetric Multidimensional Scaling

NMDS plots of the sample-taxa density matrix of 

macroinvertebrate taxa data ordinate on either side of the 

NMDS1 axis based on regulation type, with some exceptions 

(fig. C4, 2D stress=0.2247). All but one regulated shoal was 

ordinated towards the positive NMDS2 axis, whereas 23 of 

the 29 unregulated shoals were ordinated towards the negative NMDS2 axis. Regulated reaches cluster together with the 

exclusion of a notable outlier (2005_Malone_C). Unregulated 

reaches have a greater range for the ordination of abundance 

data on both the NMDS1 and NMDS2 axes, and, therefore, 

more regular deviation in the similarities of the community 

composition within unregulated reaches compared to regulated 

reaches.

Individual years show a great amount of overlap in the 

regulated reaches, with almost all shoals ordinated within the 

positive NMDS2 range (fig. C4). The regulated sites from 

the 2005 and 2008 years seem to ordinate towards the central 

NMDS1 axis (with the noted exception of the 2005_Malone_C 

outlier and 2008_Malone_A), whereas the 2012 and 2014 

years are mostly ordinated in the negative NMDS1 direction, 

and the 2009 wet year ordinates throughout the regulated 

cluster. Individual years within the unregulated reaches also 

show some overlap as well as a distribution gradient along the 

NMDS1 axis. Most of the unregulated shoals from the 2012 

and 2014 years seem to ordinate towards the positive NMDS1 

axis and farthest from regulated reaches (with exceptions for 

2012_Heflin_B and C), whereas shoals from the 2008 and 

2008 years ordinate towards the center of the NMDS1 axis 

(with exceptions for 2005_Hillabee_A, 2005_Heflin_C and 

2008_Heflin_E), and shoals from the 2009 year ordinate in the 

negative NMDS1 or positive NMDS2 directions.

Several regulated and unregulated sites are ordinated in 

such a manner as to seem to overlap regulation type. Specifically, 2009_Hillabee_C and D sites are the most positively 

ordinated unregulated sites, followed by 2014_Hillabee_C, 

2005_Hillabee_D, and 2008_Hillabee_D. The regulated sites 

most positively ordinated on the NMDS1 axis are the 2008_

Wadley_C and 2005_Wadley_D sites which ordinate between 

the aforementioned unregulated reaches that are positively 

ordinated on the NMDS2 axis. Inspection of the community 

composition at these sites reveals large abundances of Heptageniidae*, Hydrobiidae, and Iswaeon noted at all these sites.

Figure C4. Nonmetric multidimensional scaling (NMDS) ordination of the average estimated sample abundance at each site. 

2D stress=0.2247.

2014_Malone_E

2008_Malone_B

2009_Hillabee_C

2009_Hillabee_D

2008_Wadley_C

2005_Wadley_D

2014_Hillabee_C

2012_Wadley_D

2009_Wadley_C

2008_Wadley_B

2008_Wadley_D

2009_Wadley_E

2005_Wadley_A

2014_Wadley_D 2014_Wadley_E 2014_Wadley_A

2008_Wadley_C

2014_Malone_A

2012_Malone_A

2005_Wadley_E

2014_Malone_B

2008_Malone_A 2012_Wadley_C

2009_Malone_C 2009_Malone_A

2012_Wadley_B

2009_Malone_E

2005_Malone_E

2009_Wadley_B

2005_Malone_A

2008_Hillabee_D

2005_Hillabee_D

2005_Hillabee_A

2014_Hillabee_B

2014_Hillabee_A

2005_Heflin_C

2014_Heflin_B

2014_Heflin_C

2012_Heflin_A

2014_Heflin_A

2012_Hillabee_A

2012_Hillabee_C 2012_Hillabee_B

2005_Heflin_B

2008_Hillabee_C

2005_Hillabee_B

2009_Heflin_E

2012_Heflin_B

2009_Hillabee_B

2008_Heflin_E

2009_Heflin_B

2009_Heflin_A

2012_Heflin_C

2008_Hillabee_A 2005_Heflin_E

2008_Heflin_D

2005_Malone_C

0

–0.4

0.4

NMDS2

NMDS1

–0.5 0 0.5 1.0

2005

2008

2009

2012

2014

Regulated

Unregulated

EXPLANATION

Regulation type

Year

–1.5 –1.0

0.8

–0.8
70 Adaptive Management of Flows from R.L. Harris Dam—Stakeholder Process and Use of Biological Monitoring Data

Permutational Multivariate Analysis of Variance

Results of the PERMANOVA indicated that the macroinvertebrate communities were different with regards to year 

(PERMANOVA, probability [p]<0.001), regulation (PERMANOVA, p<0.001), as well as the interaction between year 

and regulation (PERMANOVA, p<0.001) (table C7). The 

effect of year was significant (p<0.001) for both regulation 

types. The coefficient of determination (R2) was greatest for 

the effect of year (R2=0.12867), second greatest for the effect 

of regulation type (R2=0.10855), and least for the interaction 

term (R2=0.08569). The residual R2 value is 0.6671.

Table C7. Results of permutational multivariate analysis of 

variance.

[DF, degrees of freedom; pseudo-F, variation in the distance matrix, analogous 

to the F statistic in an analysis of variance, or ANOVA (Reiss and others, 

2010); R2, coefficient of determination; p-value, probability value; reg, regulation type (regulated or unregulated); --, no data]

Metric DF Pseudo-F R2 p-value

Year 4 2.233 0.12867 <0.001

Reg 1 7.535 0.10855 <0.001

Year × reg 4 1.487 0.08569 <0.001

Residuals 47 -- 0.67709 --

Density Similarity Percentage Analysis and 

Taxa Density Nonmetric Multidimensional 

Scaling Overlay

Results of the SIMPER analysis of macroinvertebrate 

taxa data indicated that 30 taxa out of 164 taxa analyzed 

contributed 84.8 percent of the variation in the macroinvertebrate communities (table C8). Chironominae contributed 

the greatest value to the percent contribution of differences 

in the SIMPER analysis and were found in larger abundances 

in regulated sites than unregulated sites. Graphing the previous NMDS with symbols indicating the abundance of the 

taxon, the shoals with a large abundance of Chironominae are 

ordinated towards the center of the NMDS1 axis, with shoals 

ordinated towards the extreme positive or negative NMDS1 

axis having few or no Chironominae reported (fig. C5). Those 

unregulated sites from the 2012 and 2014 years that ordinated 

towards the positive NMDS1 axis show low abundances of 

Chironominae. 

Another subfamily within the Chironomidae, Orthocladiinae, contributes the second most to the deviations in the 

SIMPER analysis of abundances (table C8). Graphing the 

abundance of Orthocladiinae as symbol size on the original 

NMDS ordination shows just a few of the sites, mostly from 

the regulated reaches, have large abundances of Orthocladiinae, especially from 2009_Malone_A and C sites, and 

2008_Malone_A site (fig. C6). 

The NMDS-abundance graph of the third-ranking 

taxon on the SIMPER analysis, Iswaeon (fig. C7), shows 

that all abundances greater than (>) 2,000 ordinate towards 

the positive NMDS2 axis. All unregulated reaches ordinated 

on the positive NMDS2 axis have notable Iswaeon population sizes (about [~] 2,000 individuals); whereas, among 

regulated reaches, all three Wadley reaches from 2008 

and Malone_A_2008 have notably large population sizes 

(>3,000 individuals). 

Large populations of Hydropsychidae* (SIMPER rank 4, 

fig. C8) are mostly within regulated reaches and are especially 

large in the 2009 and 2012 years but do not show a clear distribution pattern. In contrast, Simuliidae (rank 5, fig. C9) does 

show a distribution pattern, clearly driven by large abundances 

in the 2 unregulated reaches ordinated most towards the 

NMDS2 axis (2009_Hillabee shoals C and D) and, to a lesser 

extent, the 2 regulated reaches ordinated most towards the 

NMDS1 axis (2008_Wadley C and 2005_Wadley_D).

Functional Feeding Group and Habit Vectors on 

Nonmetric Multidimensional Scaling

Of seven FFG classes fit to the NMDS, four had significant association with the NMDS results (table C9): 

filterer/collectors (R2=0.3269, p<0.001), gatherer/collectors 

(R2=0.7023, p<0.001), predators (R2=0.3297, p<0.001), and 

scrapers (R2=0.5439, p<0.001). Plotting of the FFG vectors 

over the NMDS (fig. C10) indicated that filterer/collectors 

ordinated towards the NMDS2 axis. The scrapers vector 

ordinated towards the positive NMDS1 and positive NMDS2 

axes, whereas gatherer/collectors were ordinated towards the 

negative NMDS1 and negative NMDS2 axes. The vector for 

predators was ordinated towards the center of the unregulated reaches in the positive NMDS1 and negative NMDS2 

direction.

Of five habit vectors fit to the NMDS, four had significant association with the NMDS results (table C9): burrowers (R2

=0.5650, p<0.001), climbers (R2=0.1073, p=0.047), 

clingers (R2=0.4863, p<0.001), and swimmers (R2=0.3603, 

p<0.001). Plotting of the vectors over the NMDS indicated 

swimmers and climbers have an association with regulated 

reaches as they both ordinate in similar positive NMDS2 

and negative NMDS1 directions, with swimmers being the 

stronger of the two vectors (fig. C11). Burrowers ordinate 

in a negative NMDS1 and negative NMDS2 direction, like 

the gatherer/collector FFG vector (fig. C10), and clingers in 

a positive NMDS1 and negative NMDS2 direction with the 

unregulated sites, similar to the predator FFG vector.
Results 71

Table C8. Top 30 results of the similarity percentage analysis of the Wisconsin square root transformed average estimated species 

abundance per shoal species matrix. The mean column shows the mean number observed in each regulation type, and the cumulative 

sum of deviation is the total deviation that taxon contributes to the overall similarity of the community observed at each site.

Rank Taxon

Mean

Cumulative sum of deviation

Regulated Unregulated

1 Chironominae 58.40793 53.03962 0.09489

2 Orthocladiinae 55.23785 30.36702 0.17857

3 Iswaeon 37.15852 9.45902 0.2369

4 Hydropsychidae* 40.18503 4.57040 0.29484

5 Simuliidae 19.75184 28.99499 0.35262

6 Naididae 32.97029 21.34779 0.40415

7 Acerpenna 37.93869 6.95384 0.45448

8 Acari 22.27980 8.59109 0.48468

9 Hydrobiidae 14.48402 12.49385 0.51472

10 Stenelmis 14.89303 9.89813 0.54413

11 Corbicula fluminea 6.22158 20.10891 0.57028

12 Baetis 18.41108 2.77428 0.59631

13 Heptageniidae* 15.85268 5.70873 0.62205

14 Hydropsyche 13.96072 2.65254 0.64398

15 Cheumatopsyche 17.31121 3.52870 0.6651

16 Pleuroceridae 6.65688 8.73726 0.68453

17 Baetidae* 10.71289 1.27670 0.70163

18 Tanypodinae 3.55907 10.41234 0.71789

19 Maccaffertium 13.83591 3.07144 0.73316

20 Cheumatopsyche E 11.99595 0.11533 0.74681

21 Isonychia 11.56421 3.42566 0.75951

22 Capniidae/Leuctridae complex 0.00000 6.68505 0.77175

23 Neoperla 2.01330 4.39741 0.78273

24 Ephemerellidae* 7.92330 0.27337 0.79363

25 Microcylloepus pusillus 6.20459 1.49031 0.80354

26 Nectopsyche 5.55427 2.08587 0.81318

27 Dubiraphia 0.08627 7.86792 0.82237

28 Teloganopsis deficiens 11.84292 0.05126 0.83111

29 Optioservus 2.00329 4.33796 0.83986

30 Turbellaria 5.39962 0.70763 0.84839

*Indicates early instar individual.
72 Adaptive Management of Flows from R.L. Harris Dam—Stakeholder Process and Use of Biological Monitoring Data

Figure C5. Nonmetric multidimensional scaling (NMDS) bubble plot of the abundance of Chironominae observed at each site 

(year, reach, and shoal) over the ordination of all data.

0

–0.4

0.4

NMDS2

NMDS1

–1.5 –1.0 –0.5 0 0.5 1.0

0.8

–0.8

Regulation type

Regulated

Unregulated

Year

2005

2008

2009

2012

2014

Abundance of Chironominae

0

2,000

4,000

6,000

EXPLANATION

2014_Malone_E

2008_Malone_B

2009_Hillabee_C

2009_Hillabee_D

2008_Wadley_C

2005_Wadley_D

2014_Hillabee_C

2012_Wadley_D

2009_Wadley_C

2008_Wadley_B

2008_Wadley_D

2009_Wadley_E

2005_Wadley_A

2014_Wadley_D 2014_Wadley_E 2014_Wadley_A

2008_Wadley_C

2014_Malone_A

2012_Malone_A

2005_Wadley_E

2008_Malone_A 2014_Malone_B

2012_Wadley_C

2009_Malone_C

2009_Malone_A 2012_Wadley_B

2009_Malone_E

2005_Malone_E

2009_Wadley_B

2005_Malone_A

2008_Hillabee_D

2005_Hillabee_D

2005_Hillabee_A

2014_Hillabee_B

2014_Hillabee_A

2005_Heflin_C

2014_Heflin_B

2014_Heflin_C

2012_Heflin_A

2014_Heflin_A

2012_Hillabee_A

2012_Hillabee_C 2012_Hillabee_B

2005_Heflin_B

2008_Hillabee_C

2005_Hillabee_B

2009_Heflin_E

2012_Heflin_B

2009_Hillabee_B

2008_Heflin_E

2009_Heflin_B

2009_Heflin_A

2012_Heflin_C

2008_Hillabee_A

2005_Heflin_E

2008_Heflin_D

2005_Malone_C
Results 73

Figure C6. Nonmetric multidimensional scaling (NMDS) bubble plot of the abundance of Orthocladiinae observed at each 

site (year, reach, and shoal) over the ordination of all data.

Regulation type

Regulated

Unregulated

Year

2005

2008

2009

2012

2014

Abundance of Orthocladiinae

0

2,500

5,000

10,000

EXPLANATION

0

–0.4

0.4

NMDS2

NMDS1

–1.0 –0.5 0 0.5 1.0

2014_Malone_E

2008_Malone_B

2009_Hillabee_C

2009_Hillabee_D

2008_Wadley_C

2005_Wadley_D

2014_Hillabee_C

2012_Wadley_D

2009_Wadley_C

2008_Wadley_B

2008_Wadley_D

2009_Wadley_E

2005_Wadley_A

2014_Wadley_D 2014_Wadley_E 2014_Wadley_A

2008_Wadley_C

2014_Malone_A

2012_Malone_A

2005_Wadley_E

2008_Malone_A 2014_Malone_B

2012_Wadley_C

2009_Malone_C

2009_Malone_A 2012_Wadley_B

2009_Malone_E

2005_Malone_E

2009_Wadley_B

2005_Malone_A

2008_Hillabee_D

2005_Hillabee_D

2005_Hillabee_A

2014_Hillabee_B

2014_Hillabee_A

2005_Heflin_C

2014_Heflin_B

2014_Heflin_C

2012_Heflin_A

2014_Heflin_A

2012_Hillabee_A

2012_Hillabee_C

2012_Hillabee_B

2005_Heflin_B

2008_Hillabee_C

2005_Hillabee_B

2009_Heflin_E

2012_Heflin_B

2009_Hillabee_B

2008_Heflin_E

2009_Heflin_B

2009_Heflin_A 2012_Heflin_C

2008_Hillabee_A

2005_Heflin_E

2008_Heflin_D

2005_Malone_C

7,500

–1.5

0.8

–0.8
74 Adaptive Management of Flows from R.L. Harris Dam—Stakeholder Process and Use of Biological Monitoring Data

Figure C7. Nonmetric multidimensional scaling (NMDS) bubble plot of the abundance of Iswaeon observed at each site 

(year, reach, and shoal) over the ordination of all data.

0

–0.4

0.4

NMDS2

NMDS1

–1.5 –1.0 –0.5 0 0.5 1.0

0.8

–0.8

Regulation type

Regulated

Unregulated

Year

2005

2008

2009

2012

2014

Abundance of Iswaeon

0

1,000

3,000

4,000

EXPLANATION

2014_Malone_E

2008_Malone_B

2009_Hillabee_C

2009_Hillabee_D

2008_Wadley_C

2005_Wadley_D

2014_Hillabee_C

2012_Wadley_D

2009_Wadley_C

2008_Wadley_B

2008_Wadley_D

2009_Wadley_E

2005_Wadley_A

2014_Wadley_D 2014_Wadley_E

2014_Wadley_A

2008_Wadley_C

2014_Malone_A

2012_Malone_A

2005_Wadley_E

2008_Malone_A 2014_Malone_B

2012_Wadley_C

2009_Malone_C

2009_Malone_A 2012_Wadley_B

2009_Malone_E

2005_Malone_E

2009_Wadley_B

2005_Malone_A

2008_Hillabee_D

2005_Hillabee_D

2005_Hillabee_A

2014_Hillabee_B

2014_Hillabee_A

2005_Heflin_C

2014_Heflin_B

2014_Heflin_C

2012_Heflin_A

2014_Heflin_A

2012_Hillabee_A

2012_Hillabee_C

2012_Hillabee_B

2005_Heflin_B

2008_Hillabee_C

2005_Hillabee_B

2009_Heflin_E

2012_Heflin_B

2009_Hillabee_B

2008_Heflin_E

2009_Heflin_B

2009_Heflin_A 2012_Heflin_C

2008_Hillabee_A

2005_Heflin_E

2008_Heflin_D

2005_Malone_C

2,000
Results 75

Figure C8. Nonmetric multidimensional scaling (NMDS) bubble plot of the abundance of Hydropsychidae* (* indicates early 

instar) observed at each site (year, reach, and shoal) over the ordination of all data.

0

–0.4

0.4

NMDS2

NMDS1

–1.5 –1.0 –0.5 0 0.5 1.0

0.8

–0.8

Regulation type

Regulated

Unregulated

Year

2005

2008

2009

2012

2014

Abundance of Hydropsychidae*

0

1,000

2,000

4,000

EXPLANATION

2014_Malone_E

2008_Malone_B

2009_Hillabee_C

2009_Hillabee_D

2008_Wadley_C

2005_Wadley_D

2014_Hillabee_C

2012_Wadley_D

2009_Wadley_C

2008_Wadley_B

2008_Wadley_D

2009_Wadley_E

2005_Wadley_A

2014_Wadley_D 2014_Wadley_E

2014_Wadley_A

2008_Wadley_C

2014_Malone_A

2012_Malone_A

2005_Wadley_E

2014_Malone_B 2008_Malone_A

2012_Wadley_C

2009_Malone_C 2009_Malone_A

2012_Wadley_B

2009_Malone_E 2005_Malone_E

2009_Wadley_B

2005_Malone_A

2008_Hillabee_D

2005_Hillabee_D

2005_Hillabee_A

2014_Hillabee_B

2014_Hillabee_A

2005_Heflin_C

2014_Heflin_B

2014_Heflin_C

2012_Heflin_A

2014_Heflin_A

2012_Hillabee_A

2012_Hillabee_C

2012_Hillabee_B

2005_Heflin_B

2008_Hillabee_C

2005_Hillabee_B

2009_Heflin_E

2012_Heflin_B

2009_Hillabee_B

2008_Heflin_E

2009_Heflin_B

2009_Heflin_A 2012_Heflin_C

2008_Hillabee_A

2005_Heflin_E

2008_Heflin_D

2005_Malone_C

3,000
76 Adaptive Management of Flows from R.L. Harris Dam—Stakeholder Process and Use of Biological Monitoring Data

Figure C9. Nonmetric multidimensional scaling (NMDS) bubble plot of the abundance of Simuliidae observed at each site (year, 

reach, and shoal) over the ordination of all data.

0

–0.4

0.4

NMDS2

NMDS1

–1.5 –1.0 –0.5 0 0.5 1.0

0.8

–0.8

Regulation type

Regulated

Unregulated

Year

2005

2008

2009

2012

2014

Abundance of Simuliidae

0

2,500

5,000

10,000

EXPLANATION

2014_Malone_E

2008_Malone_B

2009_Hillabee_C

2009_Hillabee_D

2008_Wadley_C

2005_Wadley_D

2014_Hillabee_C

2012_Wadley_D

2009_Wadley_C

2008_Wadley_B

2008_Wadley_D

2009_Wadley_E

2005_Wadley_A

2014_Wadley_D

2014_Wadley_E 2014_Wadley_A

2008_Wadley_C

2014_Malone_A

2012_Malone_A

2005_Wadley_E

2014_Malone_B 2008_Malone_A

2012_Wadley_C

2009_Malone_C

2009_Malone_A

2012_Wadley_B

2005_Malone_E 2009_Malone_E

2009_Wadley_B

2005_Malone_A

2008_Hillabee_D

2005_Hillabee_D

2005_Hillabee_A

2014_Hillabee_B

2014_Hillabee_A

2005_Heflin_C

2014_Heflin_B

2014_Heflin_C

2012_Heflin_A

2014_Heflin_A

2012_Hillabee_A

2012_Hillabee_C

2012_Hillabee_B

2005_Heflin_B

2008_Hillabee_C

2005_Hillabee_B

2009_Heflin_E

2012_Heflin_B

2009_Hillabee_B

2008_Heflin_E

2009_Heflin_B

2009_Heflin_A 2012_Heflin_C

2008_Hillabee_A 2005_Heflin_E

2008_Heflin_D

2005_Malone_C

7,500
Results 77

Table C9. Results of analysis of fit for functional feeding group and 

habit class vectors on the initial nonmetric multidimensional scaling.

[R2, coefficient of determination; p-value, probability value]

Class vector R2 p-value

Functional feeding group

Filterer/collector 0.3269 <0.001

Gatherer/collector 0.7023 <0.001

Omnivore 0.0547 0.214

Predator 0.3297 <0.001

Scraper 0.5439 <0.001

Shredder 0.0171 0.644

Habit

Burrower 0.5650 <0.001

Climber 0.1073 0.047

Clinger 0.4863 <0.001

Sprawler 0.0362 0.377

Swimmer 0.3603 <0.001

Figure C10. Nonmetric multidimensional scaling (NMDS) ordination with significant functional feeding group class vectors indicating 

the relation between the ordination of macroinvertebrate community composition and percent abundance of each class of functional 

feeding group.

0.0

–0.4

0.4

NMDS2

NMDS1

–1.5 –1.0 –0.5 –0.0 0.5 1.0

0.8

–0.8

Regulation type

Regulated

Unregulated

Year

2005

2008

2009

2012

2014

EXPLANATION

Gatherer/collector

Filterer/collector

Predator

Scraper
78 Adaptive Management of Flows from R.L. Harris Dam—Stakeholder Process and Use of Biological Monitoring Data

Figure C11. Nonmetric multidimensional scaling (NMDS) ordination with significant habit class vectors indicating the relation 

between the ordination of macroinvertebrate community composition and percent abundance of each class of habit.

Burrower

Climber

Clinger

Swimmer

0.0

–0.4

0.4

NMDS2

NMDS

–1.5 –1.0 –0.5 –0.0 0.5 1.0

0.8

–0.8

Regulation type

Regulated

Unregulated

Year

2005

2008

2009

2012

2014

EXPLANATION

Cluster Analysis

Cluster analysis of the presence-absence taxa matrix 

(fig. C12) clearly shows two groupings divided primarily 

by regulation type after the first cluster divide, with a few 

exceptions for each: 2008_Hillabee_D and 2014_Hillabee_C 

cluster with the regulated reaches, and 2005_Malone_C and 

2009_Malone_C cluster amongst the unregulated reaches. 

Among the regulated reaches, all observations of Malone_A 

(shoal closest to the dam) clustered on their own branch after 

the second split, along with the 2005_Malone_E and 2008_

Malone_B shoals, indicating a unique taxa composition in the 

Malone_A reach even amongst all regulated sites, especially 

in the 2012 and 2014 years. Similarly, all 2009_Heflin sites, 

along with the 2009_Heflin_E and 2005 and 2009 Malone_C 

sites, cluster on the opposite end of the graph after the third 

split, indicating a somewhat unique composition amongst 

unregulated sites. Most unregulated sites from 2012 share a 

branch after the fourth split (except 2012_Heflin_A).

Presence-Absence Similarity Percentage 

Analysis

Results of the SIMPER analysis of the presence-absence 

taxa matrix of the taxa analyzed within this study indicated 

that, of the 164 taxa identified, 30 taxa contributed to 43 percent of the variation between the communities of each regulation type (table C10). Among those taxa contributing the most 

variation in the presence-absence taxa matrix of the data, those 

most associated with regulated reaches were Cheumatopsyche

morphotype E, Turbellaria, Hydrobiidae, and Lepidostoma, 

among others; and those taxa most associated with unregulated 

reaches were Hexagenia, Ceratopogonidae, Neoperla, Oulimnius, and Perlidae*, among others (see table C10).
Results 79Figure C12. Cluster analysis of the presence-absence taxa matrix for all sites (year-reach-shoal average) analyzed.

86420

1210

Euclidian distance

2012 _ Malone _ A

2014 _ Malone _ A

2008 _ Malone _ B

2005 _ Malone _ A

2008 _ Malone _ A

2009 _ Malone _ A

2005 _ Malone _ E

2008 _ Wadley _ B

2009 _ Malone _ E

2012 _ Wadley _ B

2008 _ Wadley _ C

2008 _ Hillabee _ D

2005 _ Wadley _ D

2012 _ Wadley _ C

2012 _ Wadley _ D

2014 _ Hillabee _ C

2014 _ Malone _ B

2014 _ Wadley _ D

2009 _ Wadley _ D

2014 _ Malone _ E

2009 _ Wadley _ C

2009 _ Wadley _ E

2008 _ Wadley _ D

2014 _ Wadley _ E

2005 _ Wadley _ A

2014 _ Wadley _ A

2008 _ Malone _ C

2005 _ Wadley _ E

2014 _ Hillabee _ A

2014 _ Heflin _ B

2014 _ Hillabee _ B

2005 _ Heflin _ C

2005 _ Heflin _ B

2005 _ Hillabee _ B

2012 _ Heflin _ A

2005 _ Hillabee _ A

2008 _ Heflin _ D

2005 _ Heflin _ E

2008 _ Hillabee _ C

2005 _ Hillabee _ D

2008 _ Hillabee _ A

2009 _ Hillabee _ B

2009 _ Hillabee _ C

2009 _ Hillabee _ D

2012 _ Heflin _ C

2014 _ Heflin _ A

2014 _ Heflin _ C

2012 _ Hillabee _ A

2012 _ Hillabee _ C

2012 _ Heflin _ B

2012 _ Hillabee _ B

2008 _ Heflin _ E

2009 _ Heflin _ A

2009 _ Heflin _ B

2005 _ Malone _ C

2009 _ Malone _ C

2009 _ Heflin _ E

Site
80 Adaptive Management of Flows from R.L. Harris Dam—Stakeholder Process and Use of Biological Monitoring Data

Table C10. Top 30 results of the similarity percentage analysis of the presence-absence taxa matrix. The mean column shows the 

mean number of positive identifications for that taxon for each regulation type, and the cumulative sum of deviation is the total deviation 

that taxon contributes to the overall similarity of the community observed.

Rank Family

Mean

Cumulative sum of deviation

Regulated Unregulated

1 Cheumatopsyche E 0.85714 0.06897 0.02204

2 Turbellaria 0.78571 0.24138 0.04029

3 Hexagenia 0.00000 0.58621 0.05669

4 Ceratopogonidae 0.25000 0.65517 0.07273

5 Neoperla 0.39286 0.75862 0.08831

6 Hydrobiidae 0.71429 0.41379 0.10346

7 Oulimnius 0.46429 0.82759 0.11845

8 Perlidae* 0.46429 0.82759 0.13338

9 Lepidostoma 0.57143 0.20690 0.14797

10 Micrasema 0.42857 0.65517 0.16253

11 Ephemerellidae* 0.57143 0.27586 0.17706

12 Leptoceridae* 0.57143 0.34483 0.19151

13 Microcylloepus pusillus 0.67857 0.48276 0.20575

14 Empididae 0.57143 0.34483 0.21999

15 Elmidae* 0.46429 0.51724 0.23395

16 Stenelmis_adult 0.53571 0.51724 0.24788

17 Oecetis 0.60714 0.55172 0.26167

18 Optioservus 0.53571 0.72414 0.27542

19 Ferrissia 0.25000 0.48276 0.28892

20 Acerpenna 0.78571 0.55172 0.30232

21 Baetis 0.82143 0.55172 0.31571

22 Tricorythodes 0.42857 0.37931 0.32903

23 Tipulidae 0.32143 0.41379 0.34208

24 Baetidae* 0.92857 0.55172 0.35511

25 Cheumatopsyche A/B 0.42857 0.34483 0.36802

26 Nematoida 0.78571 0.58621 0.38078

27 Cheumatopsyche 0.67857 0.68966 0.39329

28 Capniidae/Leuctridae complex 0.00000 0.44828 0.40576

29 Nectopsyche 0.82143 0.62069 0.41808

30 Hydropsyche 0.96429 0.58621 0.43033

*

Indicates early instar individual.
Summary of Results 81

Summary of Results

• Most families are identified in samples from both 

regulation types; however, regulated reaches lack a 

substantial part of the Plecoptera and Coleoptera diversity detected in unregulated reaches, as well as several 

Odonata and Dipteran taxa, whereas unregulated 

reaches lack a single Ephemeroptera and two Dipteran 

families detected in the regulated reaches.

• Regulated reaches were characterized by greater 

density, whereas unregulated reaches were characterized by greater richness. These results reflect those 

commonly reported in the literature (Fuller and others, 

2010; Robinson, 2012; Holt and others, 2015).

• Ephemeroptera dominate regulated reaches, driven 

by the large abundance of Iswaeon, and Chironomidae dominate unregulated reaches because of large 

abundances of the taxa Chironominae (all years) and 

Orthocladiinae (all except 2014; table C5).

• Regulated reaches were dominated by Ephemeroptera 

in most years because of a large abundance of Iswaeon

and Acerpenna, whereas unregulated reaches were 

dominated by Dipterans in most years, driven by a 

large presence of Chironominae (within the top five for 

all years), Orthocladiinae (all but 2014), and Simuliidae (2008 and 2009).

• Based on total observed density of macroinvertebrates, 

many of the dominant macroinvertebrate families are 

shared between regulated and unregulated reaches, 

as would be expected for geographically similar sites 

(table C6); however, Plecoptera (especially Capniidae/

Leuctridae complex in 2012 and Neoperla in 2014) 

and Coleoptera (notably Stenelmis in 2014) were much 

more common in unregulated reaches, indicating habitat conditions that are unique to unregulated reaches 

that allow these taxa to thrive.

• NMDS ordination of the species over site data (2D 

stress=0.2247, fig. C4) shows a tight clustering of 

regulated reaches towards the positive NMDS2 axis 

and unregulated reaches towards the negative NMDS2 

axis, highlighting consistent differences in the composition of the macroinvertebrate community between 

regulated and unregulated reaches.

• NMDS ordination shows a much smaller range for 

the scattering of regulated sites, indicating much less 

variation in community composition among sites and 

years.

• A gradient of water years along the NMDS1 axis is 

present for unregulated years, with the latter 2012 and 

2014 years ordinated towards the positive NMDS1 axis 

and other years ordinated more towards the center of 

the NMDS1 axis. Regulated reaches have more overlap 

within water years, indicating homogeneity of community composition across most samples.

• The points that ordinate closer to the opposite regulation type are the regulated sites 2008_Wadley_C 

and 2005_Wadley_D, which are the regulated sites 

most positively ordinated on the NMDS1 axis, and 

the unregulated sites 2009_Hillabee_C and 2009_

Hillabee_D, which are the most positively ordinated 

unregulated sites along the NMDS2 axis. This ordination pattern is likely driven by abnormally large 

abundances of Simuliidae documented at these sites 

(fig. C9).

• The PERMANOVA results indicated significant differences among the Euclidian distances of the taxa 

composition among regulation type, year, as well as 

the response of water years in each regulation type 

(p<0.001 for all parameters).

• SIMPER analysis indicated that 30 of 164 taxa contributed to 84.8 percent of the variation in the Euclidian 

distances of the Wisconsin square root transformed 

taxa density between regulated and unregulated sites.

• The taxa Chironominae, Orthocladiinae, Iswaeon, 

Hydropsychidae*, and Simuliidae contribute the most 

to the cumulative deviation between regulated and 

unregulated sites (table C8). Whereas the graph of 

Chironominae site abundance (fig. C5) is somewhat 

unclear, Orthocladiinae (fig. C6) and Simuliidae 

(fig. C9) have clearly been detected in large abundances in a handful of sites each. Iswaeon (fig. C7) 

and Hydropsychidae* (fig. C8) show a preference for 

regulated reaches.

• Fitting FFG and habit classes to NMDS ordination, 

4 of 7 FFGs (filterer/collectors, gatherer/collectors, 

omnivores, and scrapers) and 4 of 5 habits (burrowers, 

climbers, clingers, and swimmers) indicate a significant association with the ordination of samples over 

macroinvertebrate space, indicating that biological 

traits may be useful in describing broad changes within 

the community composition.

• The vector for swimmers and climbers ordinates 

towards the positive NMDS2 axis along with the 

regulated sites (fig. C11); whereas gatherer/collectors, 

predators, burrowers, and clingers ordinate to the negative NMDS2 axis (figs. C10 and C11).

• Cluster analysis shows that the underlying taxonomic 

assemblage detected is different between regulated 

and unregulated sites, with regulated and unregulated 

reaches mostly dividing on the first cluster split with a 

few exceptions among each group. Among the regulated samples, all five Malone A years plus two other 
82 Adaptive Management of Flows from R.L. Harris Dam—Stakeholder Process and Use of Biological Monitoring Data

Malone sites separated on the second split, indicating a 

unique taxonomic assemblage at Malone A (site closest 

to the dam) among all the regulated shoals. Similarly, 

all 2009_Heflin sites share a branch with a couple 

other sites after the fifth split, indicating a somewhat 

unique taxonomic assemblage at these sites.

• SIMPER analysis utilizing macroinvertebrate presenceabsence data indicated 30 taxa contributed to 43 percent of the variation in Euclidian distances between 

regulated and unregulated macroinvertebrate communities.

• The taxa Cheumatopsyche morphotype E and Turbellaria are more common in regulated sites and contribute the most to the cumulative deviation in the Euclidian distances between the regulated and unregulated 

communities (table C10). Alternatively, Hexagenia, 

Ceratopogonidae, Neoperla, Oulimnius, and Perlidae* 

are more common in unregulated reaches versus regulated reaches.

Discussion

This study has demonstrated, through the analysis of 

taxonomic assemblage, NMDS of macroinvertebrate density 

data with supporting PERMANOVA tests, and cluster analysis 

of the presence-absence taxa matrix, that the macroinvertebrate communities downstream from R.L. Harris Dam are 

significantly different from communities that are not subject 

to flow regulation. Macroinvertebrate communities in reaches 

subject to flow regulation have regularly been characterized 

in the literature by lower richness (Barbour and others, 1999; 

Robinson, 2012; Holt and others, 2015), greater overall density (Barbour and others, 1999; Cortes and others, 2002; Holt 

and others, 2015), and more tolerant taxa (Cortes and others, 

2002; Bednarek and Hart, 2005) versus unregulated reaches. 

Observations of the mid-Tallapoosa River system reflect these 

results (table C4), indicating a disturbed community composition downstream from R.L. Harris Dam as a result of flow 

regulation.

Macroinvertebrate Community Composition

Samples from all reaches had large proportions of their 

populations dominated by the Diptera, Ephemeroptera, and 

Trichoptera orders. Many samples from regulated reaches had 

a larger proportion of early instar Ephemeroptera, especially 

from the genera Iswaeon and Acerpenna, and Trichoptera, 

especially from the family Hydropsychidae, whereas unregulated reaches generally had larger proportions of Dipterans. 

These taxa are common in riverine systems, maturing quickly 

and reproducing several times per year, especially in the 

warmer regions of the southern United States (Mackay, 1992; 

Wallace and Anderson, 1996). They are also commonly noted 

as some of the first taxa to colonize riverine habitats subject to 

flow disturbances (Clifford and others, 1992; Mackay, 1992; 

Robinson, 2012) and have been recorded as present in both 

regulated and unregulated reaches (Camargo and Voelz, 1998; 

Bruno and others, 2016; Ellis and Jones, 2016).

These taxa are equipped with some adaptations to aid 

in coping with spates of high flow. Baetidae are commonly 

cited as strong swimmers and can potentially find refuge during spates of high flow (Clifford and others, 1992; Mackay, 

1992; Robinson, 2012; Timusk and others, 2016; Vinson, 

2001). Hydropsychidae and some Chironomidae (for example, 

Rheotanytarsus) use silken retreats that allow them to hold fast 

during turbulent flow conditions (Clifford and others, 1992; 

Mackay, 1992); furthermore, Hydropsychidae prefer smooth 

stones free of algae for oviposition, and Baetidae have been 

reported to feed on thin epilithic films (Mackay, 1992). These 

strategies are likely the reason that these taxa dominate in 

reaches that are regularly exposed to scouring flows.

Macroinvertebrate Community Response to 

Natural Variation in Hydrology

Daily hydropeaking activities in regulated reaches can 

restrict many taxa from establishing in downstream reaches, 

resulting in large populations of generalists (Clifford and 

others, 1992). In the neighboring Chattahoochee River system 

(not shown), Holt and others (2015) reported no difference 

in the macroinvertebrate community composition among 

water years in reaches directly below the Buford Dam. Our 

results agree with their generalization, with Ephemeroptera, 

Diptera, and Trichoptera orders dominating regulated reaches 

consistently in all water years, whereas unregulated reaches 

had more variability in dominant orders, including some 

years with large proportions of Coleoptera, Plecoptera, and 

Neotaenioglossa.

Dipterans dominated both regulated and unregulated 

reaches in 2005 and 2009, led primarily by large abundances 

of Chironominae except in 2009 where Orthocladiinae 

composed 14 percent of regulated samples. The 2005 year 

included inclement weather events, including Hurricanes 

Dennis and Katrina affecting the Gulf Coast, which may have 

increased flow events, input large trees and other debris into 

the system, and temporarily increased pollutant runoff into the 

river system, potentially increasing the composition of Chironominae and Orthocladiinae in the unregulated reaches. The 

2009 year saw the beginning of a wet period (fig. C3) where 

increased hydrologic stress from flooding events may have 

excluded many predators and allowed both Chironominae and 

Simuliidae to thrive. The increased observations of these taxa 

were not permanent, and we would need to analyze additional 

samples for the 2006 and 2007 years to begin to describe the 

mechanisms in colonization and persistence parameters during 

these years.
Discussion 83

Nonmetric Multidimensional Scaling

Ordination of the estimated average macroinvertebrate 

densities indicates grouping of the data based on regulation 

type (fig. C4). Regulated reaches have a much smaller range 

for the ordination of points on the NMDS, reflecting the 

homogenization and predictability of flows in reaches subject 

to regulation. These data support the idea of the antidrought 

condition proposed by McMahon and Finlayson (2003), as 

well as increased predictability of flows (Graf, 2006; Olden 

and Naiman, 2010) where extreme conditions, such as drought 

or flood, are not experienced in flow regulated reaches because 

of the practice of mitigation of flow conditions that prevent 

these extreme flow events (except where mitigation measures 

require emergency release or retention of flows from the dam 

to meet upstream and downstream water demands). Unregulated reaches, on the other hand, are subject to the natural 

variation in hydrology, including annual or seasonal droughts 

and floods, and, therefore, can support greater variability 

within the macroinvertebrate community composition.

Several sites ordinate in a manner that indicates similarity 

in the communities at these regulated and unregulated sites. It 

is notable that the unregulated sites are all from the Hillabee 

reach, which has a higher water table than the other unregulated reach (Heflin), and that the two sites ordinated most in 

the positive NMDS2 directions are from the wet (2009) year. 

Upon closer inspection of the communities at these sites, a 

common feature among all these sites are large abundances 

of Iswaeon (fig. C7), Simuliidae (fig. C9), Heptageniidae*, 

and Hydrobiidae. Based on the distribution of abundances of 

Iswaeon and Simuliidae, these ordinations could be primarily driven by observations of large groups of these taxa. As 

noted in previous sections, the Baetidae are strong swimmers; 

however, the affinity of Iswaeon to regulated reaches indicates 

a particularly successful life-history strategy for dealing with 

spates of high flow used by this taxon as opposed to other 

Baetidae taxa. Simuliidae are also commonly noted as successful during high flow events because they utilize hooks and 

silk on the posterior abdominal segment to maintain attachment to the substrate (Merritt and others, 2012). The Baetidae 

and Simuliidae taxa are also commonly noted as drifting during extreme flow conditions (Bruno and others, 2016; Timusk 

and others, 2016), indicating that conditions were acceptable 

for breeding in the days before sampling to create these large 

clusters of individuals, and no catastrophic drift events had 

taken place in the several days that would have encouraged 

these individuals to drift.

Distribution Trends in Macroinvertebrate 

Functional Feeding and Habit Groups

The ordination of the FFG and habit vectors agreed with 

many generalizations reported in the literature. Gatherer/

collectors are commonly generalist species that are typically 

noted as the first taxa to colonize after hydrologic disturbances 

(Clifford and others, 1992). Our findings agreed as these taxa 

ordinated strongly towards wet years, indicating that these 

taxa were able to thrive and dominate in the wet years.

Although it seems contradictory for burrowers to also 

be strongly associated with flood years as scouring flows 

generally eliminate burrowing substrate from the habitat, the 

persistence of Hexagenia, a burrowing mayfly that requires 

fine substrate for burrowing, indicated that flood years may not 

have affected the substrate and habitat composition in unregulated reaches the same way as daily pulses from a hydropeaking dam affect the regulated reaches. Additionally, many of the 

observations for the burrowing group were of the common and 

quickly reproducing taxa Orthocladiinae, which are known to 

thrive in regulated streams and are resistant to flood disturbances (Rader and Ward, 1988; Mackay, 1992). Many of these 

taxa are also strongly associated with drought years in the 

regulated reaches, which follows as burrowing taxa are more 

likely to survive spates of dewatering by retreat into the sediments for cooler temperatures and wetter conditions. Although 

burrowers were not eliminated from regulated reaches (that 

is, Orthocladiinae, Corbicula fluminea), taxa requiring fine 

burrowing sediments (that is, Hexagenia) were not observed in 

the regulated reaches. The absence of these taxa and the dominance of generalist feeders may reflect the change in substrate 

composition below the dam, where regular sheer stress from 

hydropeaking releases likely eliminates suitable habitat and 

potential foraging resources for other functional feeding and 

habit groups from the reaches.

Scrapers and clingers were strongly ordinated towards 

the positive x-axis. Scrapers include the gastropods, Elmidae, 

Baetidae, and Heptageniidae, among others. These taxa are 

prevalent in both regulated and unregulated reaches, reflecting the ordination of the scraper vector. The clinger vector, on 

the other hand, is more closely associated with unregulated 

reaches. Many taxa in the clinger group have adaptations to 

handle spates of high flows but may begin catastrophic drift 

during serious disturbances such as flood (Wallace and Anderson, 1996; Robinson, 2012). Many clinger taxa are Heptageniidae, Elmidae, and Simuliidae taxa, which are well known to 

thrive in disturbed conditions, whereas other taxa (Cortes and 

others, 2002; Robinson, 2012; Timusk and others, 2016), such 

as Perlidae and Elmidae do not thrive as well under high sheer 

stress conditions (Gore and others, 2001; Fuller and others, 

2010). With many sensitive taxa composing the clinger habit, 

the clinger vector is more closely associated with “normal” 

water conditions in unregulated reaches versus regulated 

reaches or disturbed years for unregulated reaches.

Sprawlers and predators have a strong ordination towards 

the negative NMDS2 axis and unregulated drought/normal 

years (fig. C10 and fig. C11). Many sprawlers are from the 

Baetiscidae, Caenidae, and Capniidae/Leuctridae complex 

families, and predators include the water mites, Megaloptera, Odonata, Plecoptera, and a few Trichoptera genera. 

These taxa are not noted as resistant to flow disturbances, 

likely because of lacking adaptations to successfully thrive in 

reaches that experience frequent pulses, or because of longer 
84 Adaptive Management of Flows from R.L. Harris Dam—Stakeholder Process and Use of Biological Monitoring Data

lifespans and fewer opportunities to reproduce compared to 

multivoltine taxa.

Climbers and swimmers were both strongly associated 

with regulated reaches. Climbers commonly observed included 

the disturbance intolerant Trichoptera families Leptoceridae 

and Lepidostomatidae. Several large clusters of these taxa 

(>10 individuals) were observed, mostly in the 2012 and 2014 

years (drought and bookend years, respectively), after periods 

of low flow with few or no peaking events in the weeks before 

sampling. These taxa could be taking advantage of available 

habitat area in regulated reaches during spates of low flow 

through either egg deposition, drift, or migration, and it is 

likely these large groups detected within regulated reaches are 

driving the ordination of the climber vector. Swimmers, such 

as Baetidae, are well adapted for thriving in regulated reaches 

(see discussion above). These results reflect the dominance of 

Baetidae and flow resistant taxa in the regulated reaches, and 

the inaccessibility of potential habitat in regulated reaches to 

disturbance sensitive taxa during normal peaking operations.

Cluster Analysis

The results of our cluster analysis of macroinvertebrate 

presence-absence data indicated that regulation was one of 

the primary factors influencing community composition. 

The analysis also indicates a unique community composition 

at the Malone_A shoal even among regulated reaches. The 

Malone_A shoal is closest to the dam and experiences the 

highest flow ramping rates because of hydropeaking (Meile 

and others, 2011; Toffolon and others, 2010). Samples from 

Malone_A were generally restricted to a large number of a few 

taxa because of daily catastrophic pulses eliminating many 

individuals from the immediate habitat area (Meile and others, 

2011; Timusk and others, 2016). The restriction in community composition is likely the primary cause for regulated and 

unregulated reaches forming individual clusters at the first 

split, as well as for Malone_A reaches clustering together early 

in the dendrogram.

Identification of Indicator Taxa for the 

Mid-Tallapoosa River System

The results from the SIMPER analysis of the average estimated species abundance per shoal matrix identified 

that half of the deviation between regulation types could be 

explained by less than 10 taxa (table C8). Most of these taxa 

include those commonly noted by taxonomists as increasing 

in response to disturbances, such as Chironomidae, Baetidae, 

and Hydropsychidae taxa (Clifford and others, 1992; Mackay, 

1992; Holt and others, 2015). The bubble plot NMDS clearly 

shows greater abundance within the regulated reaches for 

Iswaeon (fig. C7) and Hydropsychidae* (fig. C8), where these 

taxa are commonly detected in large groups of early instar 

individuals. Observations for Orthocladiinae (fig. C6) and 

Simuliidae (fig. C9) indicate a few sites with large abundances 

of these taxa and are likely responsible for driving the ordination of these sites further from their group. For example, the 

large proportion of Simuliidae in the 2009_Hillabee_C and 

D shoals is likely the reason these two points ordinated so 

strongly to the positive NMDS2.

The results of the SIMPER analysis of the presenceabsence taxa matrix list the taxa that are least similar with 

regards to their presence or absence in different regulation 

types and highlight where some taxa can thrive, whereas other 

taxa may be excluded because of habitat constraints. The topranking taxon for deviation in similarities of sites based on the 

presence-absence taxa matrix (table C10), Cheumatopsyche

morphotype E (associated with C. etrona; Burington, 2011), 

is detected in greater proportions in regulated reaches versus 

unregulated reaches (table C8, rank 20). Cheumatopsyche

morphotype E is also one of the easiest Cheumatopsyche morphotypes to identify as it has unique morphological characteristics (large vetromental plates and a large excision from the 

frontoclypeus) not seen in any other Cheumatopsyche morphotype or Hydropsyche genus. Similar to Cheumatopsyche

morphotype E, Cheumatopsyche morphotypes A/B (table C10, 

rank 25) and Hydropsyche sp. (table C10, rank 30) were also 

more prevalent in regulated reaches. Other Cheumatopsyche

sp. were identified in similar proportions in regulated reaches 

and unregulated reaches (table C10, rank 27); however, both 

Hydropsyche and Cheumatopsyche E were detected in greater 

abundances (table C8, ranks 14 and 20, respectively) and proportions (table C10, ranks 30 and 1, respectively) in regulated 

reaches versus unregulated reaches. Our results indicated that 

many Hydropsychidae taxa were more associated with regulated reaches both in terms of presence-absence and density 

and would be useful indicators of habitat quality.

Hexagenia, the third ranking taxa contributing to deviations between the similarities of regulation types based on the 

presence-absence taxa matrix (table C10), are burrowers that 

may be excluded from regulated reaches because of lack of 

favorable habitat of fine or sandy substrate. Habitat in the regulated reaches can be characterized as large patches of scoured 

bedrock with occasional pockets of vegetation and gravel that 

lack deep layers of sediment, sand, gravel, and coarse rocks 

that are more favorable for burrowing taxa. Similarly, Neoperla (table C10, rank 5), Perlidae* (table C10, rank 8) and 

other sprawlers are less common in regulated reaches, perhaps 

because of the lack of consistent flows generally favored by 

these long-living taxa (Cortes and others, 2002). Turbellaria 

(table C10, rank 2) and Hydrobiidae (table C10, rank 6) were 

both present in more samples from regulated reaches than 

unregulated reaches, perhaps because of high flow tolerance, 

decreased exposure during low flow periods, increased feeding 

opportunities, or decreased predation (Fisher and LaVoy, 1972; 

Richards and others, 2014).
References Cited 85

Summary

The R.L. Harris Dam Adaptive Management Project 

began in 2005 as a management method that would continually monitor the outcome of the agreed upon new flow 

management plan, referred to as “the Green Plan,” report any 

findings of changes in the macroinvertebrate or fish populations as a result of the new flow management scheme, and 

suggest management actions to consider as future management alternatives. The team set forth to establish a protocol 

for analyzing the historical macroinvertebrate collections that 

included identifying the minimum number of samples required 

to reflect the macroinvertebrate community at each shoal to 

estimate reach-level differences in the macroinvertebrate community, establishing a subsampling protocol for processing 

macroinvertebrate samples in the laboratory, and selecting several years and seasons to analyze in which the potential maximum variability of macroinvertebrate communities was based 

on natural variation in water availability. The goal was to 

characterize the macroinvertebrate communities in regulated 

and unregulated reaches of the Tallapoosa River and Hillabee 

Creek tributary, characterize the macroinvertebrate community 

in both regulation types during different natural hydraulic conditions, examine the distribution trends in macroinvertebrate 

functional feeding and habit groups, and identify taxa that will 

be useful for monitoring changes in the macroinvertebrate 

community in both regulated and unregulated reaches of the 

Tallapoosa River.

The results of this study indicate that the macroinvertebrate community downstream from the R.L. Harris Dam in the 

regulated river reaches near Malone, Alabama, and Wadley, 

Ala., of the Tallapoosa River are different from those macroinvertebrate communities observed in both the unregulated 

Tallapoosa River reaches upstream from R.L. Harris Dam in 

Heflin, Ala., and the unregulated Hillabee Creek Tributary 

that intersects with the Tallapoosa River south of the Wadley 

reaches. Although the effects of changes to flow and temperature as a result of hydropeaking are less severe as distance 

from the dam increases, our results indicate that flow and 

temperature remain in a nonnatural state in regulated reaches 

of the Tallapoosa River from R.L. Harris Dam downstream to 

Wadley, Ala., as a result of hydropeaking that the community 

in regulated reaches show many dissimilarities to communities 

from unregulated river reaches. Fewer dissimilarities between 

the macroinvertebrate community composition in the regulated 

reaches from the first and last years analyzed than between the 

regulated and unregulated reaches of the last year to be analyzed indicate that the Green Plan did not improve the quality 

of flow conditions to shift the macroinvertebrate community 

in regulated reaches to more closely reflect the community 

composition found in unregulated river reaches.

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Detailed Procedures 89

Appendix C1. Standard Operating Procedures—Sorting Protocol

Introduction

Benthic macroinvertebrates are diverse and ubiquitous 

organisms documented in many freshwater habitats and are 

commonly used as indicators of habitat quality and stability. The objective of sorting macroinvertebrates is to obtain a 

representative of the sample that can be used to characterize 

the community structure from which the sample was taken 

without spending an excessive amount of effort in picking 

out every specimen. By the end of your training, you should 

be able to (1) determine how long a sample should take to 

process, (2) estimate the proportion of the sample that needs 

to be picked to effectively characterize the sample community, (3) identify higher orders of select macroinvertebrates, 

and (4) understand how to properly label and document your 

work effort.

Sorting Objectives

Our target count for the number of specimens per sample 

is 100–150 if that many specimens are in the sample. It is 

likely that some samples may not contain half as many specimens, and for those samples, our goal will be to pick out every 

specimen. In addition to specimen counts, our goal is to pick 

our target number of specimens (or the whole sample) within 

2 hours. This protocol should help guide you in making the 

decisions that will enable you to achieve these objectives. You 

should plan on spending about 3 hours for each sample. Keep 

in mind, once you start a sample, you should try to finish it 

rather than leaving it unfinished for another time.

Materials

• Sorting pans—2

• Sieve (250 micrometer)

• Spoon and (or) putty knife

• Petri dishes—6

• Stereo scope

• Light source

• Benthic freshwater macroinvertebrate identification 

sheet

• Sorting record sheet

• Pencil

• 75 percent ethanol

• Waste bucket

• Funnel

• Magnifying lens

• Shell vials and caps

• Data labels

• Timer

Detailed Procedures

Locate a sample from a crate in the main laboratory to 

be sorted. Wash the sieve and pans with water and a brush. 

Also wash the Petri dishes if they look like they are not clean. 

Place the funnel in the waste bucket and place the sieve on top 

of the funnel. Unscrew the sample cap and pour the contents 

of the sample into the sieve while ensuring that the sieve is 

secure by holding onto it. Remove the sieve from the funnel and place over a large sorting pan. Use water to wash the 

remaining contents of the sample cup through the sieve. Turn 

the water on before aiming it into the sample cup to make sure 

that the water does not flow out too fast, causing material and 

specimens to splash outside of the sieve. Also rinse the sample 

cap over the sieve and check both the sample cup (especially 

around the lip) and cover to ensure that no specimens remain. 

Once all the material from the cup is washed into to the sieve, 

wash the contents through the sieve with water to remove 

very small particles. During this part of the process, locate 

the sample label that was inside the sample cup, wash it with 

water over the sieve to remove excess material, and record its 

information on the sorting record sheet, benthic freshwater 

macroinvertebrate identification sheet, and vial labels for your 

sort of this sample. On the sorting record sheet, record today’s 

date (date picked), the sample date, reach, sample, start time, 

and scientist (your initials). The rest of the information will 

be filled out at the end of the sort. On the benthic freshwater 

macroinvertebrate identification sheet, record the collection 

date (same as the sample date), reach, sample, sorted by (your 

initials), and sort date. Additional information will be filled 

out at the end of your sort. Find the premade label representing the collection reach of your sample and record the sample/

collection date and sample number (for example, B05) for six 

labels. On each of the six labels, include one of the following: 

“EP” for Ephemeroptera and Plecoptera, “T” for Trichoptera, 
90 Adaptive Management of Flows from R.L. Harris Dam—Stakeholder Process and Use of Biological Monitoring Data

“Chi” for chironomids and other dipterans, “Gen” for general 

insects that do not fit into the previous categories, “Other” for 

taxa that are not insect but included in the benthic fauna (for 

example, mollusks), and “No Count” for specimens that may 

be counted but either are not identifiable in their current condition or lack an identifiable head or mouthpart. Place each label 

into a separate vial with the written information facing out.

After you have washed the sample, place all the contents 

into a sorting tray using as little water as possible. Spread the 

sample material out so that it is “homogenized.” Use the putty 

knife or spoon to place a manageable amount of material into 

a Petri dish. A manageable amount should consist of about 

a single layer of material or less. Place the same amount of 

material into the remaining Petri dishes. If the sample contains an amount material that will fill more than 6 Petri dishes, 

divide the homogenized material into equal portions within the 

sorting tray so that 1 portion could fit into 4–6 Petri dishes.

Set your timer for 15 minutes and begin sorting material 

from one of the Petri dishes under the stereoscope. Systematically sort through material as instructed so that you can 

estimate the proportion of material sorted from the Petri dish 

after 15 minutes has expired. Use the counter to record each 

specimen that you pick as belonging to the groups represented 

by each label (you will receive additional training to recognize 

different aquatic invertebrate groups). After 15 minutes expire, 

use the number of specimens you sorted and the proportion of 

the Petri dish and number of Petri dishes to estimate the total 

number of specimens expected from this sample (tables C1.1–

C1.3); however, if your specimens consist mostly of mollusks, 

do not include their numbers to determine the amount of material to subsample. For example, if you have split the sample 

into 5 Petri dishes, and you sorted 10 specimens (“bugs”) 

from 40 percent of your first Petri dish, then you have sorted 

8 percent of the sample in the first 15 minutes (table C1.1), 

and the estimated number of specimens for the sample is 125 

(table C1.2), which falls in our target range of 100–150 specimens, indicating that you should be prepared to sort all the 

material from the sample (table C1.3). At this point, finish 

picking specimens from this Petri dish and continue sorting all 

contents of each Petri dish or the number of Petri dishes for 

the estimated proportion of material needed to reach our target 

count. If 100 specimens are picked before all Petri dishes are 

Table C1.1. Estimate of sample sorted in 15 minutes. Find the estimated “percent of sample” sorted after 15 minutes based on the 

portion sorted in one Petri dish and total number of Petri dishes the sample is divided into.

[%, percent]

Portion 

of dish 

picked in 

15 minutes

Number of Petri dishes you split the sample into

1 2 3 4 5 6 7 8 9 10

5% 5% 3% 2% 1% 1% 1% 1% 1% 1% 1%

10% 10% 5% 3% 3% 2% 2% 1% 1% 1% 1%

15% 15% 8% 5% 4% 3% 3% 2% 2% 2% 2%

20% 20% 10% 7% 5% 4% 3% 3% 3% 2% 2%

25% 25% 13% 8% 6% 5% 4% 4% 3% 3% 3%

30% 30% 15% 10% 8% 6% 5% 4% 4% 3% 3%

35% 35% 18% 12% 9% 7% 6% 5% 4% 4% 4%

40% 40% 20% 13% 10% 8% 7% 6% 5% 4% 4%

45% 45% 23% 15% 11% 9% 8% 6% 6% 5% 5%

50% 50% 25% 17% 13% 10% 8% 7% 6% 6% 5%

55% 55% 28% 18% 14% 11% 9% 8% 7% 6% 6%

60% 60% 30% 20% 15% 12% 10% 9% 8% 7% 6%

65% 65% 33% 22% 16% 13% 11% 9% 8% 7% 7%

70% 70% 35% 23% 18% 14% 12% 10% 9% 8% 7%

75% 75% 38% 25% 19% 15% 13% 11% 9% 8% 8%

80% 80% 40% 27% 20% 16% 13% 11% 10% 9% 8%

85% 85% 43% 28% 21% 17% 14% 12% 11% 9% 9%

90% 90% 45% 30% 23% 18% 15% 13% 11% 10% 9%

95% 95% 48% 32% 24% 19% 16% 14% 12% 11% 10%

100% 100% 50% 33% 25% 20% 17% 14% 13% 11% 10%
Detailed Procedures 91

Table C1.2. Estimate of total number of specimens in the sample. Use the “percent of sample” from table C1.1 and the number of specimens (bugs) picked during the 15-minute sort to estimate the total number of specimens in the sample.[no., number; %, percent]

No. of 

bugs

Percent of sample

1% 2% 3% 4% 5% 6% 7% 8% 9% 10% 11% 12% 13% 14% 15% 16% 17% 18% 19% 20% 21% 22% 23% 24% 25%

5 500 250 167 125 100 83 71 63 56 50 45 42 38 36 33 31 29 28 26 25 24 23 22 21 20

10 1,000 500 333 250 200 167 143 125 111 100 91 83 77 71 67 63 59 56 53 50 48 45 43 42 4015 1,500 750 500 375 300 250 214 188 167 150 136 125 115 107 100 94 88 83 79 75 71 68 65 63 6020 2,000 1,000 667 500 400 333 286 250 222 200 182 167 154 143 133 125 118 111 105 100 95 91 87 83 8025 2,500 1,250 833 625 500 417 357 313 278 250 227 208 192 179 167 156 147 139 132 125 119 114 109 104 10030 3,000 1,500 1,000 750 600 500 429 375 333 300 273 250 231 214 200 188 176 167 158 150 143 136 130 125 12035 3,500 1,750 1,167 875 700 583 500 438 389 350 318 292 269 250 233 219 206 194 184 175 167 159 152 146 14040 4,000 2,000 1,333 1,000 800 667 571 500 444 400 364 333 308 286 267 250 235 222 211 200 190 182 174 167 16045 4,500 2,250 1,500 1,125 900 750 643 563 500 450 409 375 346 321 300 281 265 250 237 225 214 205 196 188 18050 5,000 2,500 1,667 1,250 1,000 833 714 625 556 500 455 417 385 357 333 313 294 278 263 250 238 227 217 208 200

sorted, finish sorting through your current Petri dish and stop 

(even if your estimate was to do more Petri dishes). Record 

the total proportion of material you sorted from the sample 

and record the number of specimens that were picked on the 

sorting record sheet and the benthic freshwater macroinver-

tebrate identification sheet. The sorting process should take 

about 2 hours.

Table C1.3. Subsample estimator. Based on the total number of 

specimens estimated in table C1.2, determine how much of the 

sample you will likely need to sort.

[>, greater than]

Estimated number of bugs in sample Suggested proportion

<1501

150–300 3/4

300–450 1/2

450–600 1/4

600–750 1/6

750–1,000 1/8

1,000+ 1/10

If you reached your target count without finishing the 

sample, make a new data label with the sample site informa-

tion and name it “last pick.” Combine the remaining unsorted 

material into a sorting pan and use the magnifying glass to 

search for as many different specimens as you can find within 

10 minutes (in addition to the 2 hours used for the timed 

sort). It is acceptable to pick several specimens that look the 

same, but do not spend all your time picking the same type 

of specimen. For example, if a sample contains hundreds of 

snails, pick about 5–10 of these during your “last pick” but try 

to spend more time looking for other invertebrates, especially 

ones that look different from what you have already picked 

during the timed pick. Place these specimens into the “last 

pick” vial and do not count them.

After you are finished sorting, place all the contents 

back into the original sample cup with a new inside label and 

enough 75 percent ethanol to cover more than 150 percent of 

the material. Place all the sample vials and the original sample 

label into a Ziploc bag with the reach, date, and sample infor-

mation written on the outside of the bag with a Sharpie pen. 

Place the sample bag into the bin labeled with the same reach 

and place the benthic freshwater macroinvertebrate identifica-

tion sheet in the in the appropriate bin. Wash the sieve, Petri 

dishes, and sorting pans with water, using a brush.
92 Adaptive Management of Flows from R.L. Harris Dam—Stakeholder Process and Use of Biological Monitoring Data

Outline of Procedures

1. Locate the sample cup and sorting materials; make sure 

Petri dishes, trays, and sieve are clean.

2. Pour contents into the sieve and wash contents.

3. Locate the sample label and record information on the 

sorting record sheet, benthic freshwater macroinvertebrate identification sheet, and vial labels.

4. Divide the sample evenly into several Petri dishes or into 

proportions that can accommodate a set of Petri dishes.

5. Complete the 15-minute sort and use tables C1.1 to C1.3 

to estimate how much of the sample needs to be sorted.

6. Sort material until you reach 100 specimens; you must 

complete the Petri you are sorting even if it ends up 

being more than 100 specimens.

7. Record the number of specimens and proportion of 

sample sorted.

8. If necessary, perform a 10-minute “last pick” on the proportion of material that was not sorted with the magnifying glass.

9. Place the sorted material back into the original sample 

container with a new label.

10. Place the sorted specimens into a labeled Ziploc bag 

with the original sample label.
Appendix C2. Macroinvertebrate Data 93

Appendix C2. Macroinvertebrate Data

The sum of total observations for each macroinvertebrate taxon at all sites, listed alphabetically by class, order, family, and 

taxon, is provided in table C2.1 (available at https://doi.org/10.3133/ofr20191026).

Table C2.1. Sum of total observations for each macroinvertebrate taxon at all sites, listed alphabetically by class, order, family, and 

taxon.
Sampling site on Hillabee Creek. Taken July 1, 2011, by B. Martin, Alabama Cooperative Fish and Wildlife Research Unit.

For more information about this publication, contact:

Alabama Cooperative Fish and Wildlife Research Unit

School of Forestry and Wildlife Sciences 

Auburn University 

602 Duncan Dr.

Auburn, AL 36849-5418

334–844–4796

For additional information, visit: https://www.coopunits.org/Alabama/

Publishing support provided by the 

Lafayette and Rolla Publishing Service Centers

Irwin—Adaptive Management of Flows from R.L. Harris Dam—Stakeholder Process and Use of Biological Monitoring Data—Open-File Report 2019–1026

ISSN 2331-1258 (online)

https://doi.org/10.3133/ofr20191026