Comparison of Random and 

Systematic Site Selection for 

Assessing Attainment of Aquatic 

Life Uses in Segments of the Ohio 

River

R E S E A R C H A N D D E V E L O P M E N T

 EPA 600/R-06/089

September 2006 

www.epa.gov

Comparison of Random and Systematic 

Site Selection for Assessing Attainment 

of Aquatic Life Uses in Segments of the 

Ohio River 

Karen Blocksom1, Erich Emery2, and Jeff Thomas2

1

U.S. Environmental Protection Agency 

Ecological Exposure Research Division 

National Exposure Research Laboratory 

Cincinnati, OH 45268 

2

Ohio River Valley Water Sanitation Commission 

5735 Kellogg Avenue 

Cincinnati, OH 45228 

Notice: Although this work was reviewed by EPA and approved for publication, it may not necessarily 

reflect official Agency policy. Mention of trade names and commercial products does not 

constitute endorsement or recommendation for use. 

U.S. Environmental Protection Agency 

Office of Research and Development 

Washington, DC 20460 
NOTICE 

The research described in this document has been funded in part by the U.S. 

Environmental Protection Agency. This report has been subjected to the 

Agency’s peer and administrative review process and has been approved for 

publication as an EPA document. 

Mention of trade names or commercial products does not constitute endorsement 

or recommendation for use.

ii
EXECUTIVE SUMMARY 

Introduction 

The Clean Water Act (CWA) section 305(b) requires that states report biennially 

on the water quality standards (WQS) attainment status of all waters. The United 

States Environmental Protection Agency (USEPA) has recognized that biological 

assessment data are very valuable and should serve as a core indicator in 

determining aquatic life use attainment status (USEPA 2002). However, great 

(or large-floodplain) rivers, such as the Ohio River, are one type of aquatic 

resource for which biological assessment data typically have been deficient, 

primarily due to difficulty in sampling (Emery et al. 2003). The Ohio River Valley 

Water Sanitation Commission (ORSANCO), a compact of eight states and the 

federal government is responsible for assessment of the Ohio River. 

Recognizing the abundance of stresses on this system and the lack of biological 

data for the river, ORSANCO developed a bioassessment program several years 

ago. In 1990, ORSANCO began a Long-Term Intensive Survey (LTIS) of the 

Ohio River to provide high sample density (one sample every 3.2-6.4 km) from 

selected reach segments of the river. Beginning with this effort, a fish-based 

index of biotic integrity (IBI) for the Ohio River was developed (Emery et al. 

2003). Although the LTIS approach provided valuable data for the development 

of a fish IBI and biocriteria, this approach was very resource-intensive and timeconsuming and was not feasible for routine sampling of the entire river. To 

support the development of a cost-effective program for routine monitoring, the 

minimal amount of effort required to produce an adequate assessment needed to 

be determined. 

Methods 

A probability-based sampling design, in which sites are randomly selected from 

the population of possible sites, is an effective way to reduce effort and still 

collect data representative of an entire resource. By simulating a probability 

design using the intensive survey data already collected, the number of sites 

required to adequately represent the condition of a pool was determined. Two 

approaches were used to analyze the simulation data, and both assume that the 

intensive survey data provide the most accurate representation of condition 

available. The first approach was to determine the minimum number of sites that 

provided a similar condition estimate to that of the full set of sites, indicating 

some stability in the estimate. This was achieved by calculating variables for 

various subsets of sites and identifying the number of sites at which distributions 

of variables no longer differed from those of the full set of sites. Simultaneously, 

the number of sites required to obtain an estimate of condition with a specific 

level of precision was determined. A second and more commonly used 

approach to determining the adequacy of sampling was based on the relative 

proportion of species collected. The number of 500 m reaches required to collect 

iii
80-90% of the observed taxa in the pool was calculated as a way to determine 

the number of sites at which a sufficient level of effort has been expended to 

estimate biological condition. By examining the results of both approaches for 

this study, a probability sampling design was developed that will still be rigorous 

and will provide known confidence around any estimate of condition for Ohio 

River navigational pools while reducing the level of effort (i.e., number of sites 

sampled) in the field. 

Results and Recommendations 

This research indicated that 15 sites (compared to 20-32 sites sampled in the 

LTIS) may be adequate to draw conclusions about the overall condition of a 

navigational pool. However, in some cases, additional sites may need to be 

sampled to achieve the desired level of precision around the condition 

assessment. Because there are a large number of pools in the Ohio River, an 

approach that allows ORSANCO to sample and assess more pools each year 

will result in a more robust assessment of the river for the 305(b) report. A 

suggested approach is to sample all of the navigational pools of the Ohio River 

over a 5-year period. In each pool, an initial sampling of 15 sites is carried out, 

and additional sampling is completed only if required to make a definitive 

assessment of the pool. This approach would limit the resources required to 

assess an individual pool, such that additional effort would only be required in 

those pools that are of more marginal condition. In addition, this approach will 

help ORSANCO to identify those pools in which biological condition is most 

impacted and to prioritize any mitigation or restoration efforts. Additional 

sampling of individual sites may be required to determine causes of impairment 

within pools but may be guided by the data acquired through the random 

sampling design.

iv
TABLE OF CONTENTS 

NOTICE ................................................................................................................ii

EXECUTIVE SUMMARY.....................................................................................iii

LIST OF TABLES................................................................................................vi

LIST OF FIGURES .............................................................................................vii

APPENDIX FIGURES........................................................................................viii

ACKNOWLEDGMENTS.......................................................................................x

1. INTRODUCTION ...........................................................................................1

2. METHODS.....................................................................................................3

2.1. Study Area..............................................................................................3

2.2. Sampling Protocols.................................................................................4

2.2.1. Fish..................................................................................................4

2.2.1.1. Electrofishing............................................................................4

2.2.1.2. Fish Sample Processing...........................................................5

2.2.2. Habitat.............................................................................................5

2.3. Data Analysis..........................................................................................7

2.3.1. IBI and Metrics ................................................................................7

2.3.1.1. Simulations...............................................................................8

2.3.1.2. Condition Measures .................................................................9

2.3.2. Species Richness..........................................................................10

3. RESULTS....................................................................................................11

3.1. IBI and Metrics......................................................................................11

3.2. Species Richness .................................................................................20

4. DISCUSSION ..............................................................................................22

5. RECOMMENDATIONS ...............................................................................23

6. LITERATURE CITED ..................................................................................24

APPENDIX A......................................................................................................27

v
LIST OF TABLES 

Table 1. The five pools used for this study, along with sampling information 

associated with the LTIS and selected background information. ..........................3

Table 2. Selected potential influences on water quality (from ORSANCO 

1994) in study pools..............................................................................................4

Table 3. The metrics included in the ORFIn and their expected response to 

stress. ...................................................................................................................8

Table 4. Frequency of samples in each habitat class by pool..............................8

Table 5. Metrics used in analyses for each pool are denoted by an (X) for 

that metric. Metrics were excluded if they had a maximum of less than 5 for 

richness metrics and DELT anomalies and less than 10 for percentage 

metrics. ...............................................................................................................12

Table 6. Number of occurrences in 500 simulations for which the K-S test of 

differences in distributions was significant (p < 0.20), shown for the ORFIn 

scores and metrics by pool and number of sites. If a metric was not 

evaluated for a particular pool (see Table 3), (NA) is listed for that cell..............18

vi
LIST OF FIGURES 

Figure 1. Locations of intensive survey pools used in this study (highlighted 

in red) along the Ohio River. .................................................................................6

Figure 2. Cumulative distribution functions with 95% confidence bounds for 

ORFIn scores in R.C. Byrd pool based on bootstrapped subsets of sites (red 

lines) and the full set of sites (black lines)...........................................................13

Figure 3. Cumulative distribution functions with 95% confidence bounds for 

ORFIn scores in Hannibal pool based on bootstrapped subsets of sites (red 

lines) and the full set of sites (black lines)...........................................................14

Figure 4. Cumulative distribution functions with 95% confidence bounds for 

ORFIn scores in McAlpine pool based on bootstrapped subsets of sites (red 

lines) and the full set of sites (black lines)...........................................................15

Figure 5. Cumulative distribution functions with 95% confidence bounds for 

ORFIn scores in Newburgh pool based on bootstrapped subsets of sites (red 

lines) and the full set of sites (black lines)...........................................................16

Figure 6. Cumulative distribution functions with 95% confidence bounds for 

ORFIn scores in Smithland pool based on bootstrapped subsets of sites (red 

lines) and the full set of sites (black lines)...........................................................17

Figure 7. Confidence interval (CI) lengths for estimates of proportion of river 

km failing in each pool as a function of the number of sites sampled. The 

horizontal dotted line represents the desired maximum CI length of 0.25, and 

the vertical solid lines represent the approximate number of sites for which 

the desired CI length is achieved........................................................................19

Figure 8. Estimates of the proportion of river km failing with 90% confidence 

bounds, based on bootstrap randomizations. The dotted line represents the 

threshold delineating failure of the entire pool (0.25). .........................................20

Figure 9 . Mean bootstrap species richness as a function of sample size, 

based on 1000 bootstrap randomizations of sites. The horizontal dotted lines 

represent 80% and 90% of the maximum number of species and the vertical 

lines represent the numbers of sites at which these are attained. ......................21

vii
APPENDIX FIGURES 

Figure A-1. Cumulative distribution functions for (a) species richness, (b) 

sucker species richness, (c) centrarchid species richness, and (d) % simple 

lithophils in R.C. Byrd pool for random subsets and the full set of sites..............28

Figure A-2. Cumulative distribution functions for (a) % detritivores, (b) % 

invertivores, (c) % piscivores, and (d) CPUE in R.C. Byrd pool for random 

subsets and the full set of sites...........................................................................29

Figure A-3. Cumulative distribution functions for (a) species richness, (b) 

sucker species richness, (c) intolerant species richness, and (d) % tolerant 

individuals in Hannibal pool for random subsets and the full set of sites. ...........30

Figure A- 4. Cumulative distribution functions for (a) % simple lithophils, (b) 

% non-native individuals, (c) % detritivores, and (d) % invertivores in 

Hannibal pool for random subsets and the full set of sites..................................31

Figure A-5. Cumulative distribution functions for (a) % piscivores, (b) DELT 

anomalies, and (c) CPUE in Hannibal pool for random subsets and the full 

set of sites...........................................................................................................32

Figure A-6. Cumulative distribution functions for (a) species richness, and 

(b) sucker species richness in McAlpine pool for random subsets and the full 

set of sites...........................................................................................................33

Figure A-7. Cumulative distribution functions for (a) centrarchid species 

richness, and (b) intolerant species richness in McAlpine pool for random 

subsets and the full set of sites...........................................................................34

Figure A-8. Cumulative distribution functions for (a) % simple lithophils, and 

(b) % detritivores in McAlpine pool for random subsets and the full set of 

sites. ...................................................................................................................35

Figure A-9. Cumulative distribution functions for (a) % invertivores, and (b) 

% piscivores in McAlpine pool for random subsets and the full set of sites. .......36

Figure A-10. Cumulative distribution functions for (a) DELT anomalies, and 

(b) CPUE in McAlpine pool for random subsets and the full set of sites. ............37

Figure A-11. Cumulative distribution functions for (a) species richness, (b) 

sucker species richness, (c) centrarchid species richness, and (d) % simple 

lithophils in Newburgh pool for random subsets and the full set of sites.............38

viii
Figure A-12. Cumulative distribution functions for (a) % non-natives, (b) % 

detritivores, (c) % invertivores, and (d) % piscivores in Newburgh pool for 

random subsets and the full set of sites..............................................................39

Figure A-13. Cumulative distribution functions for CPUE in Newburgh pool 

for random subsets and the full set of sites.........................................................40

Figure A-14. Cumulative distribution functions for (a) species richness, and 

(b) centrarchid species richness in Smithland pool for random subsets and 

the full set of sites. ..............................................................................................41

Figure A-15. Cumulative distribution functions for (a) intolerant species 

richness, and (b) % simple lithophils in Smithland pool for random subsets 

and the full set of sites. .......................................................................................42

Figure A-16. Cumulative distribution functions for (a) % detritivores, and (b) 

% invertivores in Smithland pool for random subsets and the full set of sites.....43

Figure A-17. Cumulative distribution functions for (a) % piscivores, and (b) 

DELT anomalies in Smithland pool for random subsets and the full set of 

sites. ...................................................................................................................44

Figure A-18. Cumulative distribution functions for CPUE in Smithland pool 

for random subsets and the full set of sites.........................................................45

ix
ACKNOWLEDGMENTS 

We thank Mark Barath, U.S. EPA Region III in Philadelphia, PA, and Tony 

Olsen and Dave Bolgrien, U.S. EPA Office of Research and Development in 

Corvallis, OR, and Duluth, MN, respectively, for comments on an earlier 

draft of this report. The data used in this research could not have been 

collected without the tireless efforts and skill of ORSANCO field crews. 

x
1. INTRODUCTION 

The Clean Water Act (CWA) section 305(b) requires that states report biennially 

on the water quality standards (WQS) attainment status of all waters. Currently, 

an Integrated Report is prepared to fulfill requirements related to sections 303(d), 

305(b), and 314 of the Clean Water Act. The United States Environmental 

Protection Agency (USEPA) has recognized that biological assessment data are 

very valuable and should serve as a core indicator in determining aquatic life use 

attainment status (USEPA 2002). Biological data can reflect overall ecological 

condition (i.e., chemical, physical, and biological), and they provide a spatially 

and temporally integrated measure of the aggregate effect of stressors and 

prevailing environmental conditions. USEPA suggests that biological criteria, or 

biocriteria, in WQS are most effective as numerical values, which set ranges of 

indicators representing acceptable conditions for attainment of the Aquatic Life 

Use designation of a water body (USEPA 2002). In practice, however, state 

WQS often include only narrative biological criteria, although numeric thresholds 

of biological indicators are often used to interpret the narrative criteria. In any 

case, the ability to report on the condition of all waters over which a state has 

jurisdiction is often limited because of inadequate assessment of certain types of 

water bodies. 

Great (or large-floodplain) rivers are one type of resource for which biological 

assessment data typically have been deficient, primarily due to difficulty in 

sampling (Emery et al. 2003). However, the data that are available suggest that, 

despite reductions in chemical and organic pollution, the biological condition of 

running waters of the United States, including large and great rivers, has 

continued to decline since the inception of the Clean Water Act (Karr and Chu 

1999). Great rivers are distinctive in that they are few in number but comprise a 

large and highly visible component of lotic resources in terms of volume. They 

are disproportionately degraded by many human actions that “pollute” them, 

including water withdrawals, overharvesting of fish, impoundments, and changes 

in the landscape that can affect natural processes (Emery et al. 2003, Karr and 

Chu 1999). Recognizing such stresses on the system and the lack of biological 

data, a bioassessment program was developed for the Ohio River. Assessment 

of the Ohio River is the responsibility of the Ohio River Valley Water Sanitation 

Commission (ORSANCO), a compact of eight states and the federal government. 

In 1990, ORSANCO began a Long-Term Intensive Survey (LTIS) of the Ohio 

River to provide high sample density (one sample every 3.2-6.4 km) from 

selected reach segments of the river. From these 741 fish assemblage samples, 

efforts began in 1991 to develop numeric biocriteria for the Ohio River (Simon 

and Emery 1995). A fish-based index of biotic integrity (IBI) that incorporates 

river kilometer (rkm) has since been developed for the Ohio River (Emery et al. 

2003). Recently, biocriteria have been refined to account for habitat type and 

sampling date as well. 

1
Although the intensive survey approach provided valuable data for the 

development of a fish IBI and biocriteria, this approach is very resource- and 

time-consuming and is not feasible for sampling the entire river on a regular 

basis. To support the development of a cost-effective program for routine 

monitoring, the minimal amount of effort required to produce an adequate 

assessment needed to be determined. A probability-based sampling design, in 

which sites are randomly selected from the population of possible sites, is an 

effective way to reduce effort and still collect data representative of an entire 

resource. By simulating a probability design using the intensive survey data 

already collected, the number of sites required to adequately represent the 

condition of a pool can be determined. Although the LTIS samples do not 

represent a complete sampling of a pool because they only consist of a 500 m 

reach every few km, the systematic nature of the sampling effort allows for the 

assumption that the data adequately characterize actual distributions in a given 

pool. Two approaches were used to analyze the simulation data, and both 

assume that the intensive survey data provide the most accurate representation 

of condition available. 

The first approach was to determine the minimum number of sites that provided a 

similar condition estimate to that of the full set of sites, indicating some stability in 

the estimate. This was achieved by calculating variables for various subsets of 

sites and identifying the number of sites at which distributions of variables no 

longer differed from those of the full set of sites. Simultaneously, the number of 

sites required to obtain an estimate of condition with a specific level of precision 

was determined. 

A second and more commonly used approach to determining the adequacy of 

sampling was based on the relative proportion of species collected. Previous 

studies have examined the level of effort necessary to produce a representative 

sample based on the proportion of the available species captured by that method 

in wadeable streams (Dauwalter and Pert 2003, Reynolds et al. 2003, Lyons 

1992) and nonwadeable rivers (Meador 2005, Hughes et al. 2002, Lyons et al. 

2001). These studies have been focused on determination of the appropriate 

continuous sampling distance at a single location, which may represent an entire 

stream or river. However, ORSANCO already had set the electrofishing distance 

at 500 m, based on work by Simon and Sanders (1999), who determined that 

500 m was a sufficient distance to characterize biological integrity. For this 

study, the number of 500 m reaches required to collect 80-90% of the observed 

taxa in the pool was calculated as a way to determine the number of sites at 

which a sufficient level of effort has been expended to estimate biological 

condition. By examining the results of both approaches for this study, a 

probability sampling design was developed that will still be rigorous and will 

provide known confidence around any estimate of condition for Ohio River 

navigational pools while reducing the level of effort (i.e., number of sites 

sampled) exerted in the field. 

2
2. METHODS 

2.1. Study Area 

The Ohio River begins in Pittsburgh, Pennsylvania, at the confluence of the 

Monongahela and Allegheny rivers (rkm 0) and flows southwesterly for 

approximately 1579 km through six states to the confluence with the Mississippi 

River (Figure 1). Currently, there are 18 high-lift and 2 low-head dams on the 

Ohio River, each providing a minimum of 2.75 m depth for commercial 

navigation. These dams define major pools on the river and significantly limit fish 

populations to a specific pool. For this reason, and because any watershed 

management actions would likely be carried out at this scale, the pool was 

viewed as the appropriate level of assessment in the Ohio River for this study. In 

addition, probability designs are not intended for assessment of individual sites 

but for larger areas. Thus, the pool is the most well-defined unit of assessment 

using a probability sampling design in the Ohio River. 

From the LTIS dataset, data were selected from five pools, each sampled in one 

of four different years. Table 1 provides information on the pools and the 

samples collected in them, and Table 2 characterizes factors that may influence 

water quality in these pools. These five pools were selected because they 

represent the most intensive sampling, with a sample frequency of more than 

one site every 4 km (2.5 mi). Variations in condition were expected both across 

pools and among years, although the goal of this study was to identify patterns 

with sample size and not to specifically evaluate the condition of individual pools. 

Table 1. The five pools used for this study, along with sampling information 

associated with the LTIS and selected background information. 

Pool Year of 

sampling 

Dam location 

(river km) 

Length 

(km) 

Average 

width (m) 

Number of sites 

sampled 

R.C. Byrd 2002 449.3 67.1 352 24 

Hannibal 1996 203.4 58.3 345 20 

McAlpine 1997 976.5 118.3 622 32 

Newburgh 1994 1249.0 89.2 755 25 

Smithland 1996 1478.2 116.7 1255 30 

 

3
Table 2. Selected potential influences on water quality (from ORSANCO 1994) in 

study pools. 

Pool 

Tributaries 

(drainage >200 mi2

(518 km2)) 

No. 

permitted 

discharges 

General influences 

R.C. Byrd 2 30 Intersected by heavily industrialized 

Kanawha River 

Hannibal 2 52 Many small-moderate sized towns, 

industrial sites and associated barge 

traffic 

McAlpine 2 44 Includes city of Louisville, Kentucky 

Newburgh 2 53 Smaller industry, small towns, one 

moderate-sized city, minimal barge 

traffic or heavy industry 

Smithland 3 10 Intersected by agriculture-influenced 

Wabash River, few towns, no heavy 

industry 

2.2. Sampling Protocols 

2.2.1. Fish 

2.2.1.1. Electrofishing 

All sampling was conducted during the low-flow, stable conditions of July through 

October and during water conditions meeting sampling criteria (i.e., minimum 

secchi depths of 38 cm; water levels within 61 cm of normal-flat-pool). 

Procedures for electrofishing followed that described by Emery et al. (2003). At 

each site, a 500 m reach was electrofished with a 5.5 m jon boat outfitted with an 

onboard generator. Electrofishing was conducted at night, as this is the 

established protocol used by ORSANCO and has been documented to provide 

for a more representative sample of the resident fauna in deeper rivers when 

compared to day electrofishing (Sanders 1992). The onboard generator supplied 

AC power to 150-W floodlights on the bow of the boat, and to a Smith-Root Type 

VI-A alternator-pulsator used to convert the AC generator output to DC and then 

regulate the output for electrofishing. A single stainless steel ball suspended 

from a bow-mounted retractable aluminum boom served as the anode, with the 

aluminum boat hull serving as the cathode. 

Each site was electrofished proceeding downstream along the shoreline at a 

speed equal to, or slightly greater than the prevailing current velocity. The 

electrofishing time at each site generally ranged from 1800 to 5000 seconds 

depending on the current velocity, available cover, and the number of fish 

encountered. Efforts were made to capture every fish sighted by the crew. 

4
5

2.2.1.2. Fish Sample Processing 

Upon capture, fish were placed in an aerated, recirculating on-board live well for 

processing. Particular care was used in handling species of special concern. 

The majority of captured fish were identified to species, examined for external 

anomalies, weighed, measured for total length, and released in the field. Those 

requiring laboratory identification were preserved in buffered 10% formalin and 

later identified using regional ichthyological keys (e.g., Fishes of Ohio (Trautman 

1981), Fishes of Missouri (Pflieger 1997), and Fishes of Tennessee (Etnier and 

Starnes 1993)). Fish measuring less than 20 mm in length (e.g., larval fish) were 

not recorded as they are difficult to identify accurately and offer data of 

questionable value to an assemblage assessment (Angermeier and Karr 1986). 

The occurrence of external DELT (deformities, eroded fins and body parts, 

lesions, and tumors) anomalies was recorded following procedures outlined by 

Ohio EPA (1989) and refined by Sanders et al. (1999). The frequency of DELT 

anomalies has been shown to be a good indication of stress caused by chronic 

agents, intermittent stresses, and chemically contaminated sediments. As a 

result, it is a commonly used metric for assessment of rivers throughout the 

United States (Emery et al. 2003). 

2.2.2. Habitat 

Substrate information used in data analysis was collected in 2000 for sites 

sampled prior to that year, with the assumption that the basic characteristics of 

the habitat at a site have not changed over time. For sites sampled in 2000 and 

beyond, habitat was sampled within 4 months of fish sampling (i.e., during the 

fish sampling index period). At each site, the shoreline of each 500 m sampling 

zone was divided into five 100 m segments, creating 6 points of reference for the 

zone along the shoreline (i.e., 0 m, 100 m, 200 m, 300 m, 400 m, and 500 m). At 

each interval, a 6 m copper pole was used to characterize the substrate at 11 

points. The first measurement was taken at the shoreline with subsequent 

measurements at 3 m intervals towards mid-channel (total distance = 30 m). 

This resulted in a total of 66 point measurements within each 500 m fishing zone. 

Substrate was recorded as boulder, cobble, gravel, sand, fines, hardpan, or as a 

combination of these substrate types and used to estimate the percentage of 

each sediment-type within the 500 m sample area. Habitat data for some sites 

was unavailable. 

 
Figure 1. Locations of intensive survey pools used in this study (highlighted in red) along the O
2.3. Data Analysis

There were three main objectives of the analysis. The first was to use a 

multimetric index of fish assemblage condition and the raw values of its 

component metrics to compare the intensive survey sampling design with 

subsets of those sites. The second was to compare pool-level condition 

estimates and precision using all of the sampling sites and subsets of sites. For 

this portion of the analysis, the condition estimate represented the proportion of 

sites in a pool for which the multimetric index score failed to meet a minimum 

threshold value. The final objective was to determine the number of sites in each 

pool at which 80% and 90% of fish species were captured, as an additional way 

to gauge representativeness of sampling in the pool. For all three objectives, 

only the sample from the first visit to each site was used in analyses. 

2.3.1. IBI and Metrics 

Analyses for this study involved the use of an existing multimetric index 

specifically developed for the Ohio River fish assemblage. The Ohio River Fish 

Index (ORFIn) was originally developed using LTIS data and consists of 13 

metrics describing various characteristics of the fish assemblage (Emery et al. 

2003) (Table 3). As part of this process, least disturbed (reference) sites were 

identified and defined as being at least 1 km upstream or downstream from 

navigational dams, at least 1.61 km downstream from any point source 

discharge, and at least 500 m from the mouth of any tributary (Emery et al. 

2003). 

Subsequent to the ORFIn’s development, three habitat-based classes were 

developed that rely on substrate composition (ORSANCO, unpublished data): 

(A) cobble >= 14%; (B) cobble < 14% and sand < 70%; and (C) cobble <14% and 

sand >= 70%. Within each of these habitat classes, the 25th percentile ORFIn 

score among least disturbed sites was set as the minimum score required to be 

considered passing (unimpaired). This threshold for determining impairment was 

selected following the logic of Yoder and Rankin (1995), because the reference 

condition represents only least disturbed and not truly pristine conditions. The 

three habitat-specific thresholds for ORFIn scores were quite different from one 

another. For habitat A, this threshold was 39, and for habitat B, it was 33 (of a 

possible score of 65). For habitat C, considered to be sand flats, the condition 

assessment was determined to be dependent on sampling date, with higher 

ORFIn scores obtained later in the sampling index period. Thus, the threshold 

was adjusted for Julian day of the sample collection based on a 25th percentile 

regression using a quantile regression method (Koenker and Bassett 1978). This 

adjustment resulted in the following formula for the minimum adjusted score 

required to “Pass”: (0.12*Julian Day - 2.4) (unpublished data). 

7
For this study, each site was evaluated with regard to impairment thresholds 

using the ORFIn. First, ORFIn metrics and index scores were calculated for 

each sample. Then, each site was classified into habitat A, B, or C using the 

criteria above. Each site was assessed as passing or failing (impaired) based on 

the ORFIn score, habitat classification, and Julian day (when relevant). For sites 

lacking habitat data, ORFIn scores were compared to thresholds for all 3 habitat 

types. If the score exceeded the minimum required to pass for all habitat types, 

the site condition was designated as ‘PASS’. If the score fell below the threshold 

for all 3 habitats, its condition was designated as ‘FAIL’. Those sites with mixed 

results were assessed as ‘UNKNOWN’ condition. 

Table 3. The metrics included in the ORFIn and their expected response to stress. 

Metric Expected response 

to stress 

Number of species Decrease 

Number of sucker species Decrease 

Number of centrarchid species Decrease 

Number of great-river species Decrease 

Number of intolerant species Decrease 

% Tolerant individuals Increase 

% Simple lithophilic individuals Decrease 

% Non-native individuals Increase 

% Detritivore individuals Increase 

% Invertivore individuals Decrease 

% Piscivore individuals Decrease 

Number of DELT anomalies Increase 

Catch per unit effort (CPUE) Decrease 

Table 4. Frequency of samples in each habitat class by pool. 

Pool A (cobble) B (mixed) C (sand) Unavailable Total 

Byrd 4 19 1 0 24 

Hannibal 3 7 2 8 20 

McAlpine 8 13 5 6 32 

Newburgh 0 10 13 2 25 

Smithland 3 12 6 9 30 

2.3.1.1. Simulations 

Bootstrap methods were used to simulate the random selection of sites within 

pools. A bootstrap approach randomly draws sites with replacement from the 

original dataset and assumes that the set of sites in this original dataset 

adequately reflects the distribution of conditions in the pool. Sampling with 

replacement means that during each random draw, each site has an equal 

probability of being selected. For example, when a subset of five sites is 

selected with replacement, it is possible for any particular site to be drawn all five 

times. This methodology is appropriate for these data because, although pools 

were sampled intensively for LTIS, they were not sampled in their entirety. Thus, 

8
although the systematic nature of the sampling likely resulted in a set of samples 

representative of the distribution of conditions in the pool, all of the possible 

sample locations in the pool were not specifically included in the dataset. By 

creating a large number of sample sets and subsets (of sites) using 

bootstrapping, almost any population parameter and its variance can be 

estimated robustly from the original dataset (Chernick 1999). 

In this analysis, the general process was as follows for each pool. A number of 

sites equal to that in the full set of LTIS sites (e.g., 24 sites in R.C. Byrd pool) 

were selected with replacement from the set of LTIS sites (original set) to create 

a bootstrap set of sites (full set). The first five of those were combined and used 

to estimate certain characteristics related to condition. Then the next five were 

added to the first five and the characteristics re-estimated for that set of ten. This 

process was repeated with five additional sites added each time until the full set 

of bootstrap sites was included. The full set of bootstrap sites was treated during 

each simulation as the true measure of condition in the pool, and the 

characteristics for each subset of sites was compared to this full set. The entire 

process was repeated 500 times to obtain 95% confidence intervals for each 

statistic at each sample size. 

2.3.1.2. Condition Measures 

The cumulative distribution function (CDF) of a variable indicates the probability 

that any particular observation of that variable will be at or below a specified 

value (Sokal and Rohlf 1995). By comparing CDFs between subsets of sites and 

the full set of bootstrap sites, the minimum sample size at which the distributions 

strongly overlap and provide very similar information can be determined. For 

each pool, empirical cumulative distribution functions (CDFs) were calculated for 

each metric and the ORFIn based on various subsets of sites and for the full set 

of bootstrap sites. For example, for R.C. Byrd pool, there were 24 sites in the full 

set of data, and CDFs were generated using 5, 10, 15, 20, and 24 sites for each 

of the 500 simulation runs. Calculation of the empirical CDF consisted of 

determining the following percentiles for each run on each set of sites: 0, 1, 5, 

10, 25, 50, 75, 90, 95, 99, and 100. Then, the 2.5th, 50th, and 97.5th quantiles 

were determined for each percentile across all 500 runs. These values provide 

the median CDF and its 95% confidence intervals (CI) for plotting. For each 

metric or the ORFIN scores and each subset size, the median CDF and its 95% 

CI was plotted, along with the CDF for the full set of bootstrap sites for 

comparison. Visual comparisons of CDFs and confidence intervals were used to 

evaluate the degree of similarity between the full set of sites and subsets of 

different sizes. 

In addition to line plots of CDFs, distributions of metrics or the ORFIn were 

directly compared between subsets and the full set of sites using a 

nonparametric Kolmogorov-Smirnov (K-S) two-sample test. The test was 

performed using PROC NPAR1WAY in SAS with Monte Carlo estimation of 

9
exact p-values (v. 9.1, SAS Institute, Cary, NC). The K-S test assumes that the 

two samples being compared are independent of one another, but this 

assumption was not met because one sample was simply a subset of the other. 

This would tend to lead to an actual Type I error rate much smaller than the 

nominal value. To improve the ability to detect differences between distributions 

(power) and to achieve an actual Type I error rate closer to 0.05, a p-value of 

0.20 or less was used to identify significant differences between distributions, 

rather than the preferred significance level of 0.05. The number of runs out of 

500 in which the distributions differed significantly (p < 0.20) was recorded for 

each metric and sample size. 

The habitat-specific ORFIn thresholds described previously were used to 

evaluate each sample as passing or failing, and the pool-level condition was 

determined as the proportion of samples (river km) failing. For each run, an 

estimate of the proportion of river km failing (Pfail) in the pool was calculated, and 

over the 500 runs, 95% confidence intervals were estimated for that proportion. 

This was done for each of the subsets of sites, and the full set of sites from the 

intensive survey was viewed as providing the ‘true’ assessment of the pool. For 

these estimates of condition, the desired precision was within 12.5 percentage 

points, or a total confidence interval length of 25 percentage points. A proportion 

of 0.25 of river km failing has been set tentatively as the threshold for assessing 

an entire pool as failing (for placement on the 303(d) list of impaired waters). 

The selection of this criterion value stemmed from the decision to set the samplelevel threshold for failing at the 25th percentile of the reference distribution. 

Based on this threshold, it would be possible for up to 25% of sites within a pool 

to be considered failing, even within a pool made up entirely of reference sites 

(as defined by ORSANCO). Whether this variation among reference sites is due 

to natural factors or actual differences in disturbance level, this approach would 

tend to limit only the Type I error rate (i.e., incorrectly assessing a pool as failing) 

when assessing a pool and not the Type II error rate (incorrectly assessing a 

pool as passing). As a way to limit both Type I and Type II errors, a definitive 

assessment of a pool is attained only if the confidence interval for the proportion 

failing does not include the threshold. This means that proportions either above 

or below the threshold by a small amount may not provide a sufficient 

assessment of the pool as a whole. 

2.3.2. Species Richness 

To estimate the number of samples required to obtain approximately 80% or 90% 

of the total species collected within a pool, EstimateS software was used to 

model the species richness as a function of the number of samples (Colwell 

2005). For each simulation run in EstimateS, a sample was selected from the full 

set of samples with replacement. The bias-adjusted bootstrap estimate of 

species richness (Sboot) was calculated based on Smith and van Belle (1984) as: 

10
∑

=

−+=

Sobs

k

m

boot SS obs pk

1

)1(

where Sobs is the number of species observed in the pooled samples, pk is the 

proportion of samples containing species k, and m is the total number of 

samples. From this calculation, it can be shown that the value of Sboot is 

maximized when each species occurs in only one sample, and Sboot equals Sobs

when each species occurs in all samples from a pool. After calculating Sboot, 

another sample was selected with replacement and combined with the first 

sample, and the species richness was again estimated. This process continued 

until a number of samples equivalent to the original set had been selected and 

combined, with species richness estimated each time using the bias-adjusted 

bootstrap approach. The entire procedure was repeated 1000 times, and the 

average value and standard deviation across runs was determined. From this 

information, the minimum sample size (i.e., number of sites) required to collect 

an average of 80% or 90% of observed taxa within a pool was determined. 

3. RESULTS 

3.1. IBI and Metrics 

Those component metrics with very small ranges tend to reflect more rare 

components of the fish population and contribute very little variation to the overall 

ORFIn score. Thus, metrics with limited ranges of values were excluded from 

plotting and comparing distributions (Table 5). Richness metrics with a maximum 

of 5 or greater and percentage metrics with a maximum of 10 or greater were 

included in analyses. The DELT anomalies metric was used only if the maximum 

value was 5 or greater. As a result of these criteria, one or more metrics was 

excluded from each analysis, and the metric for great-river species richness was 

excluded from all analyses. The ORFIN score was used in analyses for all pools. 

The CDFs of ORFIn scores for subsets of pools were usually very similar to 

those for the full set of bootstrap sites (Figures 2-6). The confidence bounds for 

the CDFs narrowed predictably with increasing numbers of sites relative to the 

maximum number of sites sampled within a pool. Within a sample size of 15 

sites for Byrd, Hannibal, and Newburgh pools and 20 sites for McAlpine and 

Smithland pools, the confidence bounds were closely aligned between the subset 

and the full set of sites. Plots of CDFs of metrics can be found in Appendix A. 

11
Table 5. Metrics used in analyses for each pool are denoted by an (X) for that 

metric. Metrics were excluded if they had a maximum of less than 5 for richness 

metrics and DELT anomalies and less than 10 for percentage metrics. 

Metric R.C. Byrd 

Pool 

Hannibal 

Pool 

McAlpine 

Pool 

Newburgh 

Pool 

Smithland 

Pool 

Number of species X X X X X 

Number of sucker 

species X X X X 

Number of centrarchid 

species X X X X 

Number of great-river 

species 

Number of intolerant 

species X X X 

% Tolerant individuals X 

% Simple lithophilic 

individuals X X X X X 

% Non-native individuals X X 

% Detritivore individuals X X X X X 

% Invertivore individuals X X X X X 

% Piscivore individuals X X X X X 

Number of DELT 

anomalies X X X 

Catch per unit effort 

(CPUE) X X X X X 

 

12
0

5 sites

0

20

40

60

80

100

10 sites

Subset 95% CI

Subset median

Full set 95% CI

Full set median

15 sites

ORFIn Score

0 10 20 30 40 50

Cumulative Percent

0

20

40

60

80

20 sites

0 10 20 30 40 50 60

5 sites

0

20

40

60

80

100

10 sites

Subset 95% CI

Subset median

Full set 95% CI

Full set median

15 sites

ORFIn Score

0 10 20 30 40 50

Cumulative Percent

0

20

40

60

80

20 sites

10 20 30 40 50 60

Figure 2. Cumulative distribution functions with 95% confidence bounds for 

ORFIn scores in R.C. Byrd pool based on bootstrapped subsets of sites (red lines) 

and the full set of sites (black lines, N=24). 

13
5 sites

Cumulative Percent

0

20

40

60

80

100

10 sites

10 20 30 40 50 60

Subset 95% CI

Subset median

Full set 95% CI

Full set median

15 sites

ORFIn Score

0 10 20 30 40 50 60

0

20

40

60

80

5 sites

Cumulative Percent

0

20

40

60

80

100

10 sites

10 20 30 40 50 60

Subset 95% CI

Subset median

Full set 95% CI

Full set median

15 sites

ORFIn Score

0 10 20 30 40 50 60

0

20

40

60

80

Figure 3. Cumulative distribution functions with 95% confidence bounds for 

ORFIn scores in Hannibal pool based on bootstrapped subsets of sites (red lines) 

and the full set of sites (black lines, N=20). 

14
30 sites

0 10 20 30 40 50 6

5 sites

0

20

40

60

80

100

10 sites

Subset 95% CI

Subset median

Full set 95% CI

Full set median

15 sites

Cumulative Percent

0

20

40

60

80

20 sites

25 sites

ORFIn Score

0 10 20 30 40 50

0

20

40

60

80

30 sites

0 10 20 30 40 50 6

5 sites

0

20

40

60

80

100

10 sites

Subset 95% CI

Subset median

Full set 95% CI

Full set median

15 sites

Cumulative Percent

0

20

40

60

80

20 sites

25 sites

ORFIn Score

0 10 20 30 40 50

0

20

40

60

80

0

Figure 4. Cumulative distribution functions with 95% confidence bounds for 

ORFIn scores in McAlpine pool based on bootstrapped subsets of sites (red lines) 

and the full set of sites (black lines, N=32). 

15
0

5 sites

0

20

40

60

80

100

10 sites

Subset 95% CI

Subset median

Full set 95% CI

Full set median

15 sites

ORFIn Score

0 10 20 30 40 50

Cumulative Percent

0

20

40

60

80

20 sites

0 10 20 30 40 50 60 

5 sites

0

20

40

60

80

100

10 sites

Subset 95% CI

Subset median

Full set 95% CI

Full set median

15 sites

ORFIn Score

0 10 20 30 40 50

Cumulative Percent

0

20

40

60

80

20 sites

10 20 30 40 50 60 

Figure 5. Cumulative distribution functions with 95% confidence bounds for 

ORFIn scores in Newburgh pool based on bootstrapped subsets of sites (red 

lines) and the full set of sites (black lines, N=25). 

16
Figure 6. Cumulative distribution functions with 95% confidence bounds for 

ORFIn scores in Smithland pool based on bootstrapped subsets of sites (red 

lines) and the full set of sites (black lines, N=30). 

igure 6. Cumulative distribution functions with 95% confidence bounds for Cumulative Percent

25 sites

ORFIn Score

0 10 20 30 40 50 60

0

20

40

60

80

5 sites

0

20

40

60

80

100

10 sites

Subset 95% CI

Subset median

Full set 95% CI

Full set median

15 sites

0

20

40

60

80

20 sites

25 sites 10 20 30 40 50 60

ORFIn Score

0 10 20 30 40 50 60

0

20

40

60

80

5 sites

0

20

40

60

80

100

0

20

40

60

80

10 sites

Subset 95% CI

Subset median

Full set 95% CI

Full set median

15 sites 20 sites

10 20 30 40 50 60

17
Table 6. Number of occurrences in 500 simulations for which the K-S test of differences in distributions 0.20), shown for the ORFIn scores and metrics by pool and number of sites. If a metric was not evaluatepool (see Table 3), (NA) is listed for that cell. Pool No. sites ORFIn Species Suckers Centrarch. Grt River Intol. % Toler. % Simple Lith % Non- native % Detr. % Invert. 

5 29 30 31 27 NA NA NA 24 NA 30 26 

10 7 3 5 7 NA NA NA 2 NA 5 4 15 0 0 1 0 NA NA NA 0 NA 0 0 

R.C. Byrd 20 0 0 0 0 NA NA NA 0 NA 0 0 

5 8 13 18 NA NA 13 13 8 15 13 9 

10 3 0 0 NA NA 1 0 0 1 2 1 

Hannibal 15 0 0 0 NA NA 0 0 0 0 0 0 

5 30 47 39 44 NA 45 NA 37 31 32 50 

10 16 24 21 27 NA 16 NA 17 20 16 13 15 5 0 3 6 NA 6 NA 5 9 1 2 20 0 0 0 0 NA 0 NA 0 1 0 0 25 0 0 0 0 NA 0 NA 0 0 0 0 

McAlpine 

30 0 0 0 0 NA 0 NA 0 0 0 0 

5 26 28 25 20 NA NA NA 25 21 29 25 

10 11 4 8 9 NA NA NA 5 10 4 3 15 1 0 0 0 NA NA NA 0 0 0 0 

Newburgh 20 0 0 0 0 NA NA NA 0 0 0 0 

5 44 36 NA 38 NA 28 NA 33 NA 49 28 

10 9 3 NA 7 NA 12 NA 11 NA 12 7 15 0 2 NA 1 NA 4 NA 0 NA 1 1 20 0 0 NA 0 NA 0 NA 0 NA 0 0 

Smithland 

25 0 0 NA 0 NA 0 NA 0 NA 0 0 
The K-S tests of differences between distributions of subsets of sites and the full 

set of sites for each pool were rarely significant, typically only for subsets of 5 or 

10 sites (Table 6). 

Confidence intervals around Pfail varied with the number of sites in the subset, but 

for 15 or more sites, the estimate itself was usually very close to that for the full 

set (Figures 7 and 8). The confidence interval length around Pfail tended to 

decrease with increasing sample size, but this pattern was not entirely 

consistent. Approximately 25 sites were required for Smithland and Newburgh 

pools and 20 sites for Hannibal pool to reach the desired 90% CI length of 25 

points (0.25). This target CI length was never reached for the other two pools, 

even when the CI was based on bootstrapping with the full number of sites 

(Figure 7). Only in McAlpine pool did the confidence bounds not include the 0.25 

threshold for at least some sample sizes. 

0 10 20 30 40

Number of sites

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

90% CI length

Smithland

Newburgh

McAlpine

Hannibal

R.C. Byrd

0 10 20 30 40

Number of sites

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

90% CI length

Smithland

Newburgh

McAlpine

Hannibal

R.C. Byrd

Figure 7. Confidence interval (CI) lengths for estimates of proportion of river km 

failing in each pool as a function of the number of sites sampled. The horizontal 

dotted line represents the desired maximum CI length of 0.25, and the vertical 

solid lines represent the approximate number of sites for which the desired CI 

length is achieved. 

19
McAlpine pool

0 5 10 15 20 25 30 35

Proportion km failing

0.0

0.2

0.4

0.6

0.8

1.0

90% confidence bounds

Median

Condition threshold

Smithland pool

Number of sites

0 5 10 15 20 25 30 35

0.0

0.2

0.4

0.6

0.8

1.0

R.C. Byrd pool

0 5 10 15 20 25

0.0

0.2

0.4

0.6

0.8

1.0

Newburgh pool

0 5 10 15 20 25 30

0.0

0.2

0.4

0.6

0.8

1.0

Hannibal pool

4 6 8 10 12 14 16 18 20 22

0.0

0.2

0.4

0.6

0.8

1.0

McAlpine pool

0 5 10 15 20 25 30 35

Proportion km failing

0.0

0.2

0.4

0.6

0.8

1.0

90% confidence bounds

Median

Condition threshold

Smithland pool

Number of sites

0 5 10 15 20 25 30 35

0.0

0.2

0.4

0.6

0.8

1.0

R.C. Byrd pool

0 5 10 15 20 25

0.0

0.2

0.4

0.6

0.8

1.0

Newburgh pool

0 5 10 15 20 25 30

0.0

0.2

0.4

0.6

0.8

1.0

Hannibal pool

4 6 8 10 12 14 16 18 20 22

0.0

0.2

0.4

0.6

0.8

1.0

Figure 8. Estimates with 90% confidence bounds of the proportion of river km 

failing, based on bootstrap randomizations. The dotted line represents the 

threshold delineating failure of the entire pool (0.25). 

3.2. Species Richness 

On average, 80% of the total observed species richness in a pool were collected 

within 10 sites and 90% of species within approximately 15 sites (Figure 9). The 

two longest pools, McAlpine and Smithland, tended to require more samples than 

shorter pools to reach these benchmarks. Variability around species richness 

estimates only decreased slightly with increasing sample size. 

20
R.C. Byrd

0 5 10 15 20 25 30

10

15

20

25

30

35

Hannibal

0 5 10 15 20 25

10

20

30

40

50

McAlpine

0 5 10 15 20 25 30 35

10

20

30

40

50

Bootstrap species richness (+/- SD)

Newburgh

0 5 10 15 20 25 30

10

20

30

40

50

Smithland

0 5 10 15 20 25 30 35

Number of sites

10

20

30

40

50

60

R.C. Byrd

0 5 10 15 20 25 30

10

15

20

25

30

35

Hannibal

0 5 10 15 20 25

10

20

30

40

50

McAlpine

0 5 10 15 20 25 30 35

10

20

30

40

50

Bootstrap species richness (+/- SD)

Newburgh

0 5 10 15 20 25 30

10

20

30

40

50

Smithland

0 5 10 15 20 25 30 35

Number of sites

10

20

30

40

50

60

Figure 9 . Mean bootstrap species richness as a function of sample size, based 

on 1000 bootstrap randomizations of sites. The horizontal dotted lines represent 

80% and 90% of the maximum number of species and the vertical lines represent 

the numbers of sites at which these are attained. 

21
4. DISCUSSION 

The use of random sampling is clearly advantageous for assessing the Ohio 

River. By using random sampling, many fewer samples per pool would be 

required for an adequate assessment, compared to the number of samples 

collected using the non-random intensive survey design. This reduced effort per 

pool would reduce the resources required to adequately assess a pool and could 

allow for more pools to be sampled each year. In addition, a random design 

brings with it certain statistical properties that result in known confidence levels 

around estimates of condition (Diaz-Ramos et al. 1996). The results of this study 

show that a random subset of samples may be able to represent condition in the 

pool adequately. The CDFs of both ORFIn scores and component metrics were 

very similar for all but the smallest subset sizes of 5 and 10 sites. The 

confidence intervals around the CDFs did vary with sample size, as expected, 

but generally were not excessively large even for sample sizes of only 10. The 

analysis of species richness also showed that 90% of species observed poolwide could be captured within 10-15 samples. In addition, estimates of Pfail

generally were very close to the estimate based on the full set for 15 sites or 

more. 

In contrast, the confidence intervals around the estimates of Pfail for a given pool 

were generally larger than desired, sometimes even for the full set of sites. 

Under ideal circumstances for drawing sound conclusions about a pool’s 

condition, the 90% confidence interval of the condition estimate would not include 

the threshold of 0.25. In such a situation, the size of the CI would be irrelevant, 

and one could assess the pool definitively as impaired or unimpaired with some 

known level of confidence. Based on the Pfail estimated from the original set of 

data, which can be viewed as the best representation of the true condition in the 

pools, only two of the five pools (i.e., Byrd and McAlpine) would be considered 

failing. However, the closer the actual Pfail is to the threshold, the more likely it is 

that the CI will include the threshold. For example, in R.C. Byrd pool, with a Pfail

of 0.20, even the CI based on the full set of 24 sites included the threshold of 

0.25. Thus, in cases with an estimate close to the threshold, more samples 

would be required to make definitive statements about the condition of the pool. 

On the other hand, in cases where Pfail was either very large or very small, a 

relatively small number of sites was sufficient to avoid inclusion of the 0.25 

threshold in the CI (e.g., McAlpine pool, 10 sites). 

Although some of the analyses may lead to different conclusions, the patterns 

that emerge from the data generally are consistent across pools, regardless of 

the level of variability within a pool. The five pools included in this study varied in 

potential impacts to water quality and in habitat diversity, but patterns in species 

richness and in variability associated with the ORFIn were similar across or 

seemingly unrelated to these differences. Habitat classified as a mixture of sand 

and cobble (habitat B) was common in all five pools, but the presence of cobble 

and sand habitats varied across pools (Table 4). For example, Newburgh pool 

22
had no cobble (habitat A) and mostly sandy (habitat C) sites, whereas Byrd and 

McAlpine pools tended to have more cobble than sand sites. Smithland had 

more sandy than cobble sites, and Hannibal was approximately evenly divided 

between cobble and sandy sites. The influences in each pool that might affect 

water quality (e.g., contribution by tributaries, discharges) also differed across 

pools (Table 2), with varying levels of industry and sizes of towns along the 

banks of each pool. Still, these differences do not seem to directly affect the 

patterns seen. 

Overall, the ability of ORSANCO to report on the biological condition of the Ohio 

River should improve with the use of a random sampling design. Although the 

typical recommended minimum sample size for this type of design is usually 

around 50 sites (U.S. EPA Aquatic Resources Monitoring web site, 

http://www.epa.gov/nheerl/arm/surdesignfaqs.htm), a much smaller number of 

samples has the potential to provide enough information for assessment in some 

pools of the river. This study indicated that 15-20 sites may be adequate to draw 

conclusions about the overall condition of a navigational pool. However, the 

sufficiency of this smaller number of sites depends on the variation in condition of 

the fish assemblage within a given pool. The more consistent water and habitat 

quality are throughout a pool, the fewer samples that are needed to characterize 

the biological condition of that pool. Because there are a large number of pools 

in the Ohio River, an approach that allows ORSANCO to sample and assess 

more pools each year will result in a more robust assessment of the river for the 

Integrated Report. 

5. RECOMMENDATIONS 

In order to be consistent with surrounding states and maximize the data available 

for the Integrated Report, a five-year rotational sampling approach is highly 

desirable, as data up to 5 years old may be used in the report. An ideal 

approach would allow ORSANCO to sample all navigational pools over a 5-year 

period and provide an adequate assessment for each pool. Tentatively, 

biocriteria require less than 25% of river km in a pool be considered failing in 

order to consider the pool in attainment of its Aquatic Life Use designation. A 

requirement designed to ensure that an adequate assessment has been 

performed is that the confidence interval around the estimate of Pfail does not 

include the threshold of 0.25. By sampling only 15 randomly selected sites per 

pool in a single year, there is potential for an adequate and conclusive 

assessment of a pool. If, after 15 sites, a definitive assessment of the pool 

cannot be made, an additional 15 sites from the same design would be sampled 

the following year and combined with the first 15 sites. This may shift the Pfail but 

will also reduce the length of the CI so that it may no longer include the 

threshold. If the CI still includes the threshold, an additional 15 sites should be 

sampled the third year and combined with the first 30 sites. If the CI still includes 

the threshold at this point, the estimate of condition based on 45 samples could 

23
be used to assess the pool as above or below the 0.25 threshold, regardless of 

the CI. The result would be a sample size close to the recommended sample 

size of 50 and should lead to desirable CI lengths. This approach would limit the 

resources required to assess an individual pool, such that additional effort would 

be necessary only in those pools that are of more marginal condition. In addition, 

this approach would help ORSANCO to rapidly identify those pools in which 

biological condition is most impacted and to prioritize any mitigation or restoration 

efforts. To ensure that an adequate number of sites would be available to meet 

potential sampling needs in a given pool, an initial random draw of 60-80 sites 

per pool is appropriate. This approach should provide enough oversample to 

account for non-target, inaccessible, or otherwise unsampleable sites and still 

obtain up to 45 samples for assessment of condition. Finally, additional sampling 

of individual sites may be required to determine causes of impairment within 

pools but may be guided by the data acquired through the random sampling 

design. 

 

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Colwell, R.K. 2005. EstimateS: Statistical estimation of species richness and 

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applications published at http://purl.oclc.org/estimates. 

Dauwalter, D.C. and E.J. Pert. 2003. Electrofishing Effort and Fish Species 

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Diaz-Ramos, S., D.L. Stevens, Jr., and A.R. Olsen. 1996. EMAP Statistical 

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Thomas. 2003. Development of a Multimetric Index for Assessing the 

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24
Etnier, D.A. and W.C. Starnes. 1993. The Fishes of Tennessee. The University 

of Tennessee Press. Knoxville. 681 pp. 

Hughes, R.M., P.R. Kaufmann, A.T. Herlihy, S.S. Intelmann, S.C. Corbett, M.C. 

Arbogast, and R.C. Hjort. 2002. Electrofishing distance needed to 

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Karr, J.R., and E.W. Chu. 1999. Restoring Life in Running Waters: Better 

Biological Monitoring. Island Press, Covelo, California. 206 pp. 

Koenker, R. and G. Bassett. 1978. Regression Quantiles. Econometrica. 

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Lyons, J. 1992. The length of stream to sample with a towed electrofishing unit 

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Lyons, J., R.R. Piette, and K.W. Niermeyer. 2001. Development, validation, and 

application of a fish-based index of biotic integrity for Wisconsin’s large 

rivers. Transactions of the American Fisheries Society 130:1077-1094. 

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sampling distance. Transactions of the American Fisheries Society 

134:59-67. 

Ohio EPA. 1989. Addendum to: Biological Criteria for the Protection of Aquatic 

Life: Volume II: Users Manual for Biological Field Assessment of Ohio 

Surface Waters. Ohio EPA. Columbus, OH. 

ORSANCO. 1994. Ohio River Water Quality Fact Book. Ohio River Valley 

Water Sanitation Commission, Cincinnati, Ohio. 

Pflieger, W.L. 1997. The Fishes of Missouri. Missouri Department of 

Conservation. Jefferson City, MO. 

Reynolds L., A.T. Herlihy, P.R. Kaufmann, S.V. Gregory, and R.M. Hughes. 

2003. Electrofishing Effort Requirements for Assessing Species Richness 

and Biotic Integrity in Western Oregon Streams. North American Journal 

of Fisheries Management. 23: 450-461. 

Sanders, R.E. 1992. Day Versus Night Electrofishing Catches from Near-Shore 

Waters of the Ohio and Muskingum Rivers. Ohio Journal of Science. 

92(3): 51-59. 

Sanders, R.E., R.J. Miltner, C.O. Yoder, and E.T. Rankin. 1999. The Use of 

External Deformities, Erosions, Lesions, and Tumors (DELT Anomalies) in 

25
Fish Assemblages for Characterizing Aquatic Resources: A Case Study of 

Seven Ohio Streams. Pages 225-248 in T.P. Simon, editor. Assessing 

the Sustainability and Biological Integrity of Water Resources using Fish 

Communities. CRC Press, Boca Raton, Florida. 

Simon, T.P. and E.B. Emery. 1995. Modification and Assessment of an Index of 

Biotic Integrity to Quantify Water Resource Quality in Great Rivers. 

Regulated Rivers: Research and Management. 11: 283-298. 

Simon, T.P. and R.E. Sanders. 1999. Applying an Index of Biotic Integrity 

Based on Great River Fish Communities: Considerations in Sampling and 

Interpretation. Pages 475-506 in T.P. Simon, editor. Assessing the 

Sustainability and Biological Integrity of Water Resources using Fish 

Communities. CRC Press, Boca Raton, Florida. 

Smith, E.P., and G. van Belle. 1984. Nonparametric estimation of species 

richness. Biometrics 40:119-129. 

Sokal, R.R., and F.J. Rohlf. 1995. Biometry. W.H. Freeman and Company, 

New York. 887pp. 

Trautman, M.B. 1981. The Fishes of Ohio. Ohio State University Press. 782 

pp. 

U.S. Environmental Protection Agency (USEPA). 2002. Consolidated 

Assessment and Listing Methodology: Toward a Compendium of Best 

Practices, 1st edition. U.S. Environmental Protection Agency, Office of 

Water, Washington, D.C. http://www.epa.gov/owow/monitoring/calm.html. 

Yoder, C.O. and E.T. Rankin. 1995. Biological criteria program development 

and implementation in Ohio. Pages 109-144 in W.S. Davis and T.P. 

Simon, editors. Biological Assessment and Criteria. Lewis Publishers, 

Boca Raton, Florida. 

 

26
27

APPENDIX A 

Plots of CDFs and 95% CI of ORFIn metrics for subsets of sites (red lines), 

with the CDF and 95% CI for the full set of sites (black lines). 
0 0

0 0

5 sites

0

20406080

100

10 sites

15 sites

Species richness

0 5 10 15 20

Cumulative Percent

0

20406080

20 sites

5 10 15 20 25

5 sites

0

20406080

100

10 sites

95% MedFull 

15 sites Full 

012345

0

20406080

20 sites

Sucker species richness

123456

5 sites

Cumulative Percent 02040

6080

100

10 sites

15 sites

012345

0

20406080

20 sites

Centrarchid species richness

123456

5 sites

0

20406080

100

10 sites

15 sites

0 10 20 30 40

0

20406080

20 sites

% Simple lithophils

0 0 10 20 30 40 50

0 0

5 sites

0

20406080

100

10 sites

15 sites

Species richness

0 5 10 15 20

Cumulative Percent

0

20406080

20 sites

5 10 15 20 25

5 sites

0

20406080

100

10 sites

95% MedFull 

15 sites Full 

012345

0

20406080

20 sites

Sucker species richness

123456

5 sites

Cumulative Percent 02040

6080

100

10 sites

15 sites

012345

0

20406080

20 sites

Centrarchid species richness

123456

5 sites

0

20406080

100

10 sites

15 sites

0 10 20 30 40

0

20406080

20 sites

% Simple lithophils

10 20 30 40 50

Figure A-1. Cumulative distribution functions for (a) species richness, (b) sucker species richness, (c) centrarchid and (d) % simple lithophils in R.C. Byrd pool for random subsets and the full set of sites. 
0

0

5 sites

Cumulative Percent 02040

6080

100

10 sites

15 sites

0 2 4 6 8 10 12

0

20406080

20 sites

% Detritivores

0 2 4 6 8 10 12 14

5 sites

0

20406080

100

10 sites Subset 

Subset Full set Full set 

15 sites

0 20 40 60 80

0

20406080

20 sites

% Invertivores

20 40 60 80 100

5 sites

Cumulative Percent 02040

6080

100

10 sites

15 sites

0 10 20 30 40

0

20406080

20 sites

% Piscivores

10 20 30 40 50

5 sites

0

20406080

100

10 sites

15 sites

01000200030004000

0

20406080

20 sites

CPUE

010002000300040005000 0

0

5 sites

Cumulative Percent 02040

6080

100

10 sites

15 sites

0 2 4 6 8 10 12

0

20406080

20 sites

% Detritivores

0 2 4 6 8 10 12 14

5 sites

0

20406080

100

10 sites Subset 

Subset Full set Full set 

15 sites

0 20 40 60 80

0

20406080

20 sites

% Invertivores

20 40 60 80 100

5 sites

Cumulative Percent 02040

6080

100

10 sites

15 sites

0 10 20 30 40

0

20406080

20 sites

% Piscivores

10 20 30 40 50

5 sites

0

20406080

100

10 sites

15 sites

01000200030004000

0

20406080

20 sites

CPUE

010002000300040005000

Figure A-2. Cumulative distribution functions for (a) % detritivores, (b) % invertivores, (c) % piscivores, and (d) CPfor random subsets and the full set of sites. 
5 sites

Cumulative Percent 02040

6080

100

10 sites

5 10 15 20 25 15 sites

Species richness

0 5 10 15 20 25

0

20406080

5 sites

0

20406080

100

10 sites

2 4 6 8 10

Subset 95% CISubset medianFull set 95% CIFull set median

15 sites

Sucker species richness

0 2 4 6 8 10

0

20406080

5 sites

0

20406080

100

10 sites

2 4 6 8 10 15 sites

Intolerant species

0 2 4 6 8 10

Cumulative Percent

0

20406080

5 sites

0

20406080

100

10 sites

5 10 15 20 25

15 sites

% Tolerant individuals

0 5 10 15 20 25

0

20406080

5 sites

Cumulative Percent 02040

6080

100

10 sites

5 10 15 20 25 15 sites

Species richness

0 5 10 15 20 25

0

20406080

5 sites

0

20406080

100

10 sites

2 4 6 8 10

Subset 95% CISubset medianFull set 95% CIFull set median

15 sites

Sucker species richness

0 2 4 6 8 10

0

20406080

5 sites

0

20406080

100

10 sites

2 4 6 8 10 15 sites

Intolerant species

0 2 4 6 8 10

Cumulative Percent

0

20406080

5 sites

0

20406080

100

10 sites

5 10 15 20 25

15 sites

% Tolerant individuals

0 5 10 15 20 25

0

20406080

Figure A-3. Cumulative distribution functions for (a) species richness, (b) sucker species richness, (c) intolerant s(d) % tolerant individuals in Hannibal pool for random subsets and the full set of sites. 
5 sites

Cumulative Percent 020

406080

100

10 sites

15 sites 20 40 60 80 100

% Simple lithophils

0 20 40 60 80 100

0

20406080

5 sites

0

20406080

100

10 sites

5 10 15 20 25

Subset 95% CISubset medianFull set 95% CIFull set median

15 sites

% Non-native

0 5 10 15 20 25

0

20406080

5 sites

Cumulative Percent 02040

6080

100

10 sites

15 sites 5 10 15 20 25

% Detritivores

0 5 10 15 20 25

0

20406080

5 sites

0

20406080

100

10 sites

15 sites 20 40 60 80 100

% Invertivores

0 20 40 60 80 100

0

20406080

5 sites

Cumulative Percent 020

406080

100

10 sites

15 sites 20 40 60 80 100

% Simple lithophils

0 20 40 60 80 100

0

20406080

5 sites

0

20406080

100

10 sites

5 10 15 20 25

Subset 95% CISubset medianFull set 95% CIFull set median

15 sites

% Non-native

0 5 10 15 20 25

0

20406080

5 sites

Cumulative Percent 02040

6080

100

10 sites

15 sites 5 10 15 20 25

% Detritivores

0 5 10 15 20 25

0

20406080

5 sites

0

20406080

100

10 sites

15 sites 20 40 60 80 100

% Invertivores

0 20 40 60 80 100

0

20406080

Figure A- 4. Cumulative distribution functions for (a) % simple lithophils, (b) % non-native individuals, (c) % detritinvertivores in Hannibal pool for random subsets and the full set of sites. 
5 sites

Cumulative Percent 020

406080

100

10 sites

15 sites 10 20 30 40 50

% Piscivores

0 10 20 30 40 50

0

20406080

5 sites

0

20406080

100

10 sites

2 4 6 8 10

Subset 95% CISubset medianFull set 95% CIFull set median

15 sites

DELT anomalies

0 2 4 6 8 10

0

20406080

5 sites

0

20406080

100

10 sites

15 sites 100 150 200 250 300

CPUE

50 100 150 200 250 300

Cumulative Percent

0

20406080

5 sites

Cumulative Percent 020

406080

100

10 sites

15 sites 10 20 30 40 50

% Piscivores

0 10 20 30 40 50

0

20406080

5 sites

0

20406080

100

10 sites

2 4 6 8 10

Subset 95% CISubset medianFull set 95% CIFull set median

15 sites

DELT anomalies

0 2 4 6 8 10

0

20406080

5 sites

0

20406080

100

10 sites

15 sites 100 150 200 250 300

CPUE

50 100 150 200 250 300

Cumulative Percent

0

20406080

Figure A-5. Cumulative distribution functions for (a) % piscivores, (b) DELT anomalies, and (c) CPUE in Hannibal 

subsets and the full set of sites. 
0

5 sites

0

20406080

100

10 sites

15 sites

Cumulative Percent

0

20406080

20 sites

25 sites

Species richness

0 5 10 15 20

0

20406080

30 sites

5 10 15 20 25

5 sites

0

20406080

100

10 sites

15 sites

0

20406080

20 sites

25 sites

Sucker species richness

0123456

0

20406080

30 sites

0123456

Subset 9Subset mFull set Full set 

0

5 sites

0

20406080

100

10 sites

15 sites

Cumulative Percent

0

20406080

20 sites

25 sites

Species richness

0 5 10 15 20

0

20406080

30 sites

5 10 15 20 25

5 sites

0

20406080

100

10 sites

15 sites

0

20406080

20 sites

25 sites

Sucker species richness

0123456

0

20406080

30 sites

0123456

Subset 9Subset mFull set Full set 

Figure A-6. Cumulative distribution functions for (a) species richness, and (b) sucker species richness in McAlpinsubsets and the full set of sites. 
0

5 sites

0

20406080

100

10 sites

15 sites

Cumulative Percent

0

20406080

20 sites

25 sites

Centrarchid species richness

0246

0

20406080

30 sites

2468

30 sites

02468

5 sites

0

20406080

100

10 sites

Subset Subset Full set Full set 

15 sites

0

20406080

25 sites

Intolerant species

0246

0

20406080

20 sites

0

5 sites

0

20406080

100

10 sites

15 sites

Cumulative Percent

0

20406080

20 sites

25 sites

Centrarchid species richness

0246

0

20406080

30 sites

2468

30 sites

02468

5 sites

0

20406080

100

10 sites

Subset Subset Full set Full set 

15 sites

0

20406080

25 sites

Intolerant species

0246

0

20406080

20 sites

Figure A-7. Cumulative distribution functions for (a) centrarchid species richness, and (b) intolerant species richnfor random subsets and the full set of sites. 
0

5 sites 0

20406080

100

10 sites

15 sites 0

20406080

20 sites

25 sites

% Detritivores

0 20 40 60

0

20406080

30 sites

0 20 40 60

5 sites

0

20406080

100

10 sites

15 sites

Cumulative Percent

0

20406080

20 sites

25 sites

% Simple lithophils

0 20 40 60

0

20406080

30 sites

0 20 40 60 80

5 sites 0

20406080

100

10 sites

15 sites 0

20406080

20 sites

25 sites

% Detritivores

0 20 40 60

0

20406080

30 sites

0 20 40 60

5 sites

0

20406080

100

10 sites

15 sites

Cumulative Percent

0

20406080

20 sites

25 sites

% Simple lithophils

0 20 40 60

0

20406080

30 sites

20 40 60 80

Figure A-8. Cumulative distribution functions for (a) % simple lithophils, and (b) % detritivores in McAlpine pool foand the full set of sites. 
0 0

5 sites

0

20406080

100

10 sites

15 sites

Cumulative Percent

0

20406080

20 sites

25 sites

% Invertivores

0 20 40 60 80

0

20406080

30 sites

20 40 60 80 100

5 sites

0

20406080

100

10 sites

15 sites

0

20406080

20 sites

25 sites

% Piscivores

0 20 40 60

0

20406080

30 sites

20 40 60 80

Subset Subset Full seFull se

0 0

5 sites

0

20406080

100

10 sites

15 sites

Cumulative Percent

0

20406080

20 sites

25 sites

% Invertivores

0 20 40 60 80

0

20406080

30 sites

20 40 60 80 100

5 sites

0

20406080

100

10 sites

15 sites

0

20406080

20 sites

25 sites

% Piscivores

0 20 40 60

0

20406080

30 sites

20 40 60 80

Subset Subset Full seFull se

Figure A-9. Cumulative distribution functions for (a) % invertivores, and (b) % piscivores in McAlpine pool for randfull set of sites. 
0

5 sites

0

20406080

100

10 sites

SubsSubsFull sFull s

15 sites

Cumulative Percent

0

20406080

20 sites

25 sites

DELT anomalies

0 2 4 6 8 10 12

0

20406080

30 sites

0 2 4 6 8 10 12 14

5 sites

0

20406080

100

10 sites

15 sites

0

20406080

20 sites

25 sites

CPUE

0 100 200 300

0

20406080

30 sites

0 100 200 300 400

5 sites

0

20406080

100

10 sites

SubsSubsFull sFull s

15 sites

Cumulative Percent

0

20406080

20 sites

25 sites

DELT anomalies

0 2 4 6 8 10 12

0

20406080

30 sites

0 2 4 6 8 10 12 14

5 sites

0

20406080

100

10 sites

15 sites

0

20406080

20 sites

25 sites

CPUE

0 100 200 300

0

20406080

30 sites

100 200 300 400

Figure A-10. Cumulative distribution functions for (a) DELT anomalies, and (b) CPUE in McAlpine pool for random 

set of sites. 
0 0

0

5 sites

0

20406080

100

10 sites

15 sites

Species richness

0 5 10 15 20

Cumulative Percent

0

20406080

20 sites

5 10 15 20 25

5 sites

0

20406080

100

10 sites

Subset Subset Full seFull se

15 sites

Sucker species richness

0123456

0

20406080

20 sites

0123456

5 sites

0

20406080

100

10 sites

15 sites

Centrarchid species richness

02468

Cumulative Percent

0

20406080

20 sites

2 4 6 8 10

5 sites

0

20406080

100

10 sites

15 sites

% Simple lithophils

0 20 40 60

0

20406080

20 sites

0 0 20 40 60 80

0

5 sites

0

20406080

100

10 sites

15 sites

Species richness

0 5 10 15 20

Cumulative Percent

0

20406080

20 sites

5 10 15 20 25

5 sites

0

20406080

100

10 sites

Subset Subset Full seFull se

15 sites

Sucker species richness

0123456

0

20406080

20 sites

0123456

5 sites

0

20406080

100

10 sites

15 sites

Centrarchid species richness

02468

Cumulative Percent

0

20406080

20 sites

2 4 6 8 10

5 sites

0

20406080

100

10 sites

15 sites

% Simple lithophils

0 20 40 60

0

20406080

20 sites

20 40 60 80

Figure A-11. Cumulative distribution functions for (a) species richness, (b) sucker species richness, (c) centrarchand (d) % simple lithophils in Newburgh pool for random subsets and the full set of sites. 
0

0 0

5 sites

0

20406080

100

10 sites

15 sites

% Non-natives

0 20 40 60

Cumulative Percent

0

20406080

20 sites

20 40 60 80

5 sites

0

20406080

100

10 sites Subse

SubseFull seFull se

15 sites

% Detritivores

0 5 10 15 20

0

20406080

20 sites

5 10 15 20 25

5 sites

0

20406080

100

10 sites

15 sites

% Invertivores

0 10 20 30 40

Cumulative Percent

0

20406080

20 sites

10 20 30 40 50

5 sites

0

20406080

100

10 sites

15 sites

% Piscivores

0 20 40 60 80

0

20406080

20 sites

0 0 20 40 60 80 1000 0

5 sites

0

20406080

100

10 sites

15 sites

% Non-natives

0 20 40 60

Cumulative Percent

0

20406080

20 sites

20 40 60 80

5 sites

0

20406080

100

10 sites Subse

SubseFull seFull se

15 sites

% Detritivores

0 5 10 15 20

0

20406080

20 sites

5 10 15 20 25

5 sites

0

20406080

100

10 sites

15 sites

% Invertivores

0 10 20 30 40

Cumulative Percent

0

20406080

20 sites

10 20 30 40 50

5 sites

0

20406080

100

10 sites

15 sites

% Piscivores

0 20 40 60 80

0

20406080

20 sites

0 20 40 60 80 100

Figure A-12. Cumulative distribution functions for (a) % non-natives, (b) % detritivores, (c) % invertivores, and (d) Newburgh pool for random subsets and the full set of sites. 
5 sites

0

20406080

100

10 sites Subset 95% CI

Subset medianFull set 95% CIFull set median

15 sites

CPUE

02004006008001000

Cumulative Percent

0

20406080

20 sites

020040060080010001200

5 sites

0

20406080

100

10 sites Subset 95% CI

Subset medianFull set 95% CIFull set median

15 sites

CPUE

02004006008001000

Cumulative Percent

0

20406080

20 sites

020040060080010001200

Figure A-13. Cumulative distribution functions for CPUE in Newburgh pool for random subsets and the full set of 
5 sites

0

20406080

100

10 sites

SubseSubseFull seFull se

15 sites

Cumulative Percent

0

20406080

20 sites

25 sites 5 10 15 20 25

Species richness

0 5 10 15 20 25

0

20406080

5 sites

0

20406080

100

10 sites

15 sites

0

20406080

20 sites

25 sites 2 4 6 8 10

Centrarchid species richness

0 2 4 6 8 10

0

20406080

5 sites

0

20406080

100

10 sites

SubseSubseFull seFull se

15 sites

Cumulative Percent

0

20406080

20 sites

25 sites 5 10 15 20 25

Species richness

0 5 10 15 20 25

0

20406080

5 sites

0

20406080

100

10 sites

15 sites

0

20406080

20 sites

25 sites 2 4 6 8 10

Centrarchid species richness

0 2 4 6 8 10

0

20406080

Figure A-14. Cumulative distribution functions for (a) species richness, and (b) centrarchid species richness in Sm

random subsets and the full set of sites. 
5 sites

0

20406080

100

10 sites

15 sites

Cumulative Percent

0

20406080

20 sites

123456

25 sites

Intolerant species richness

0123456

0

20406080

5 sites

0

20406080

100

10 sites

SubseSubseFull seFull se

15 sites

0

20406080

20 sites

10 20 30 40 50

25 sites

% Simple lithophils

0 10 20 30 40 50

0

20406080

5 sites

0

20406080

100

10 sites

15 sites

Cumulative Percent

0

20406080

20 sites

123456

25 sites

Intolerant species richness

0123456

0

20406080

5 sites

0

20406080

100

10 sites

SubseSubseFull seFull se

15 sites

0

20406080

20 sites

10 20 30 40 50

25 sites

% Simple lithophils

0 10 20 30 40 50

0

20406080

Figure A-15. Cumulative distribution functions for (a) intolerant species richness, and (b) % simple lithophils in Sm

random subsets and the full set of sites. 
5 sites

0

20406080

100

10 sites

15 sites

Cumulative Percent

0

20406080

20 sites

5 10 15 20

25 sites

% Detritivores

0 5 10 15 20

0

20406080

5 sites

0

20406080

100

10 sites

SubseSubseFull seFull se

15 sites

0

20406080

20 sites

20 40 60 80

25 sites

% Invertivores

0 20 40 60 80

0

20406080

5 sites

0

20406080

100

10 sites

15 sites

Cumulative Percent

0

20406080

20 sites

5 10 15 20

25 sites

% Detritivores

0 5 10 15 20

0

20406080

5 sites

0

20406080

100

10 sites

SubseSubseFull seFull se

15 sites

0

20406080

20 sites

20 40 60 80

25 sites

% Invertivores

0 20 40 60 80

0

20406080

Figure A-16. Cumulative distribution functions for (a) % detritivores, and (b) % invertivores in Smithland pool for rthe full set of sites. 
5 sites

0

20406080

100

10 sites

15 sites

Cumulative Percent

0

20406080

20 sites

25 sites 20 40 60 80 100

% Piscivores

0 20 40 60 80 100

0

20406080

5 sites

0

20406080

100

10 sites

SubsSubsFull sFull s

15 sites

0

20406080

20 sites

2 4 6 8 10

25 sites

DELT anomalies

0 2 4 6 8 10

0

20406080

5 sites

0

20406080

100

10 sites

15 sites

Cumulative Percent

0

20406080

20 sites

25 sites 20 40 60 80 100

% Piscivores

0 20 40 60 80 100

0

20406080

5 sites

0

20406080

100

10 sites

SubsSubsFull sFull s

15 sites

0

20406080

20 sites

2 4 6 8 10

25 sites

DELT anomalies

0 2 4 6 8 10

0

20406080

Figure A-17. Cumulative distribution functions for (a) % piscivores, and (b) DELT anomalies in Smithland pool for the full set of sites. 
Figure A-18. Cumulative distribution functions for CPUE in Smithland pool for random subsets and the full set of 

5 sites

0

20406080

100

10 sites

Subset 95% CISubset medianFull set 95% CIFull set median

Cumulative Percent 15 sites

0

20406080

20 sites

100 200 300 400

25 sites

CPUE

0 100 200 300 400

0

20406080

5 sites

0

20406080

100

10 sites

Subset 95% CISubset medianFull set 95% CIFull set median

Cumulative Percent 15 sites

0

20406080

20 sites

100 200 300 400

25 sites

CPUE

0 100 200 300 400

0

20406080
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