U.S. Department of the Interior

U.S. Geological Survey

Open-File Report 2020–1100

Prepared in cooperation with U.S. Fish and Wildlife Service

Modeling Occupancy of Rare Stream Fish Species in the 

Upper Cumberland and Kentucky River Basins 
Focal species in this study: Cumberland arrow darter (top), blackside dace (middle), Kentucky arrow darter (bottom). Photos by Dr. 

Matthew Thomas, Kentucky Department of Fish and Wildlife Resources.
Modeling Occupancy of Rare Stream Fish 

Species in the Upper Cumberland and 

Kentucky River Basins 

By Nathaniel P. Hitt, Karli M. Rogers, Karmann Kessler, and Hannah Macmillan

Prepared in cooperation with U.S. Fish and Wildlife Service

Open-File Report 2020–1100

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: 2020

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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:

Hitt, N.P., Rogers, K.M., Kessler, K., and Macmillan, H., 2020, Modeling occupancy of rare stream fish species 

in the upper Cumberland and Kentucky River Basins: U.S. Geological Survey Open-File Report 2020–1100, 22 p., 

https://doi.org/10.3133/ofr20201100.

ISSN 2331-1258 (online)
iii

Acknowledgments

We thank Mike Compton (Office of Kentucky Nature Preserves), Michael Floyd, Peggy Shute 

(U.S. Fish and Wildlife Service), Matthew Thomas (Kentucky Department of Fish & Wildlife 

Resources), Stephen Faulkner, Thomas O’Connell, and Daniel Fitzgerald (U.S. Geological Survey) 

for assistance with this research.

v

Contents

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

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

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

Methods...........................................................................................................................................................2

Data Collection ......................................................................................................................................2

Occupancy Modelling ..........................................................................................................................4

Results .............................................................................................................................................................6

Discussion.....................................................................................................................................................10

References Cited..........................................................................................................................................13

Figures

1. Photos showing focal species included in this analysis: blackside dace, 

Cumberland arrow darter, Kentucky arrow darter, and Cumberland darter ......................2

2. Map showing location of Cumberland and Kentucky River Basins in Virginia 

and Kentucky with sampling sites .............................................................................................3

3. Correlation matrix for detection-level covariates in the upper Cumberland River 

Basin and the upper Kentucky River Basin ..............................................................................7

4. Correlation matrix for occurrence-level covariates in the upper Cumberland 

River Basin and upper Kentucky River Basin ..........................................................................8

5. Graphs showing blackside dace predicted occurrence and detection 

probabilities and 95-percent confidence intervals for covariates in the top 

three models ...............................................................................................................................10

6. Graphs showing Cumberland arrow darter predicted occurrence and detection 

probabilities and 95-percent confidence intervals for covariates in the top 

three models ................................................................................................................................11

7. Graphs showing Kentucky arrow darter predicted occurrence and detection 

probabilities and 95-percent confidence intervals for covariates in the top 

three models ................................................................................................................................12

Tables

1. Environmental covariates for modeling species detection probability in the 

upper Cumberland River Basin and upper Kentucky River Basin ........................................4

2. Site covariates for modeling species occurrence probability in the upper 

Cumberland River Basin and upper Kentucky River Basin ....................................................5

3. Description of the top three occupancy models for blackside dace, Cumberland 

arrow darter, and Kentucky arrow darter .................................................................................9

4. Top model coefficients for blackside dace, Cumberland arrow darter, and 

Kentucky arrow darter .................................................................................................................9

5. Estimated detection probability and occurrence probability for top three 

occupancy models for blackside dace, Cumberland arrow darter, and Kentucky 

arrow darter. ...............................................................................................................................12
vi

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)

kilometer (km) 0.5400 mile, nautical (nmi)

meter (m) 1.094 yard (yd)

Area

square meter (m2) 0.0002471 acre

hectare (ha) 2.471 acre

square hectometer (hm2) 2.471 acre

square kilometer (km2) 247.1 acre

square centimeter (cm2) 0.001076 square foot (ft2)

square meter (m2) 10.76 square foot (ft2)

square centimeter (cm2) 0.1550 square inch (ft2)

square hectometer (hm2) 0.003861 section (640 acres or 1 square mile)

hectare (ha) 0.003861 square mile (mi2)

square kilometer (km2) 0.3861 square mile (mi2)

Volume

cubic meter (m3) 6.290 barrel (petroleum, 1 barrel = 42 gal)

liter (L) 33.81402 ounce, fluid (fl. oz)

liter (L) 2.113 pint (pt)

liter (L) 1.057 quart (qt)

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.

Datum

Vertical coordinate information is referenced to North American Vertical Datum of 1988 

(NAVD 88).

Horizontal coordinate information is referenced to the North American Datum of 1983 (NAD 83).

Elevation, as used in this report, refers to distance above the vertical datum.
Modeling Occupancy of Rare Stream Fish Species in the 

Upper Cumberland and Kentucky River Basins 

By Nathaniel P. Hitt, Karli M. Rogers, Karmann Kessler, and Hannah Macmillan

Abstract

Biological conservation often requires an understanding 

of how environmental conditions affect species occurrence 

and detection probabilities. We used a hierarchical framework 

to evaluate these effects for several Appalachian stream fish 

species of conservation concern: Chrosomus cumberlandensis (BSD; blackside dace), Etheostoma sagitta (CAD; 

Cumberland arrow darter), and Etheostoma spilotum (KAD; 

Kentucky arrow darter). Etheostoma susanae (Cumberland 

darter) also is present in the study area but was too rare to 

model in this analysis. In this study, conducted by the U.S. 

Geological Survey in cooperation with the U.S. Fish and 

Wildlife Service, fish and habitat data were collected from 

205 randomly selected stream sites in the upper Cumberland 

and Kentucky River Basins (120 and 85 sites, respectively) 

of Kentucky and Tennessee. Sites were sampled with 10 

spatial replicates (2 meter x 5 meter electrofishing zones) to 

enable estimation of detection probabilities and environmental 

effects. The best models (that is, lowest Akaike information 

criterion scores) showed the effects of agriculture (negative) 

on occurrence of BSD and stream conductivity (negative) on 

occurrence of CAD and KAD. These effects were statistically 

more important than measures of basin area, elevation, and 

substrate size. Conductivity and agriculture showed nonlinear 

effects on species occurrence, and effects of conductivity were 

more precise above 400 microsiemens per centimeter than 

below this threshold. Models incorporated detection-level 

effects of electrofishing time (positive), flow velocity (negative), sand substrate (positive), and gravel/cobble substrate 

(negative). Models accounting for detection of BSD estimated 

occupancy rates similar to the observed proportion of occupied sites (0.10), but the best-supported models for CAD and 

KAD increased expected occupancy by about 4 percent for 

each species (from 0.17 to 0.21 for CAD and from 0.07 to 0.11 

for KAD). Results of this study provide new inferences for 

modeling stream fish occurrence and detection processes and 

highlight the importance of continued monitoring and assessment of rare fish species in Appalachian headwater streams.

Introduction

Biological conservation often requires an understanding 

of environmental controls on species occurrence and detection probabilities (MacKenzie and others, 2002). Chrosomus 

cumberlandensis (Blackside dace; BSD), Etheostoma sagitta

(Cumberland arrow darter; CAD), Etheostoma susanae

(Cumberland darter; CD), and Etheostoma spilotum (Kentucky 

arrow darter; KAD) (fig. 1) are high-priority species because 

they involve Endangered Species Act (ESA) conservation 

planning by the U.S. Fish and Wildlife Service (FWS). The 

U.S. Geological Survey (USGS) conducted this study in 

cooperation with the FWS to evaluate environmental predictors of species occurrence while jointly modeling the detection 

process in a hierarchical framework.

BSD is a headwater fish species endemic to the upper 

Cumberland River Basin in Tennessee and Kentucky (Starnes 

and Starnes, 1978; FWS, 1987), with recent expansions into 

the Kentucky River Basin in Kentucky and the Clinch and 

Powell River Basins in Virginia (Skelton, 2013). It inhabits 

small upland streams characterized by low turbidity and fine 

substrates, and low conductivity levels (Starnes and Starnes, 

1981; Eisenhour and Strange, 1998; Black and others, 2013a, 

2013b; Hitt and others, 2016). Prior research identified conductivity thresholds associated with reduced BSD abundance 

at about 240 microsiemens per centimeter (μS/cm) (Black and 

others, 2013a) and about 340 μS/cm (Hitt and others, 2016). 

BSD was listed as a threatened species under the ESA in 1987 

(FWS, 1987). A recovery plan was developed in 1988 (FWS, 

1988), and the FWS continues conservation planning for this 

species (for example, FWS, 2015a).

CAD and KAD also are endemic to the study area, with 

CAD restricted to the upper Cumberland River Basin (FWS, 

2012) and KAD restricted to the upper Kentucky River Basin 

(FWS, 2010a). These closely related species are distinguished 

by genetic and morphological differences (Kuehne and Bailey, 

1961). Both species inhabit moderate- to high-gradient headwater streams and are obligate invertivores; adult diets include 

larval mayflies and other invertebrates (Thomas, 2007, 2008; 

FWS, 2010a, 2012). Both species also apparently have been 

extirpated from some locations. Rangewide surveys conducted 

over the last several decades have not detected CAD in 43 of 

128 historically inhabited streams (34 percent) (FWS, 2015b) 
2 Occupancy of Rare Stream Fish in the Upper Cumberland and Kentucky River Basins

and have not detected KAD in 36 of 74 historically inhabited 

streams (49 percent) (FWS, 2016). In part based on these 

survey data, the FWS determined CAD did not constitute a 

threatened species under the ESA (FWS, 2015b), but KAD 

did (FWS, 2016). Genetic analysis further indicated effects 

of recent isolation and fragmentation of KAD populations 

(Blanton and others, 2019) that exacerbates local extirpation 

risks (Fagan, 2002).

CD is also an endemic species within the upper 

Cumberland River Basin, but it has a much smaller range 

than BSD or CAD. Known occurrences are limited to 14 sites 

within 12 streams (FWS, 2010b). Ecological requirements 

of the species are not fully understood (FWS, 2019), but the 

species has been observed in streams with width ranging 

from 4 to 9 meters (m) and within pools and shallow runs 

(O’Bara, 1991; Thomas, 2007). Their diet is probably like 

that of a closely related species (Etheostoma nigrum, Johnny 

darter) (FWS, 2011) and consists primarily of benthic macroinvertebrate larvae (Etnier and Starnes, 1993). Based on its 

geographic rarity and threats from degraded water quality and 

physical habitat, CD was recognized as an endangered species 

under the ESA in 2011 (FWS, 2011).

This report presents applied hierarchical modeling techniques to estimate environmental effects on species occurrence while modeling their detection probabilities. Imperfect 

detection of individuals may bias predicted occurrence rates 

(MacKenzie and others, 2002), and this potential problem is 

widely recognized for interpretation of species survey data 

(Bailey and others, 2014), including for the focal species (see 

FWS, 2016). The objectives of the study were to (1) model 

species occurrence and detection probabilities from environmental data and (2) estimate the potential importance of 

the detection process by comparing model predictions that 

account for detection against the observed proportion of occupied sites.

Methods

Data Collection

Fish and habitat data collected from 205 stream sites in 

the upper Cumberland River Basin (CU) and upper Kentucky 

River Basin (KE) in the southeastern United States were evaluated (fig. 2; appendix table 1.1). The landscape of the study 

area is characterized by highly dissected forested watersheds 

of the Cumberland Plateau physiographic region. Land use 

includes mining, forestry, and agricultural development with 

some urbanization in lower elevations. The CU study area is 

upstream from Cumberland Falls, and the KE study area is 

upstream from a series of locks and dams managed by the U.S. 

Army Corps of Engineers near Lexington, Ky.

Stream sampling was conducted by State and Federal 

wildlife biologists led by Mike Compton (Office of Kentucky 

Nature Preserves, OKNP) and Michael Floyd (FWS) during 

summer base-flow conditions in 2012, 2013, and 2015. CU 

sites were sampled in 2012 and 2015 (n = 120), and KE sites 

were sampled in 2013 (n = 85). Site locations were selected at 

random to facilitate interpretation of results across the study 

area. Sampling occurred between June and September each 

year, and most sites were sampled during August.

Sampling was conducted using spatial replicates (quadrats) to model the detection process (Charbonnel and others, 

2014). Within each site, 10 quadrats were sampled using a 

systematic randomized design to represent available mesohabitat types (pool, riffle, run). Quadrats measured 2 m x 5 m 

with the long side parallel to stream flow and were separated 

by a minimum of 5 m. Backpack electrofishing techniques 

were used to collect all fish within each quadrat (Reynolds and 

Kolz, 2012) using a Smith-Root LR24 backpack electrofishing 

unit with dipnets at 200–350 volts, 60 megahertz, and 15–20 

percent duty cycle. Blocknets were not used. Captured fish 

Figure 1. Focal species included in this analysis: blackside dace (top left), Cumberland arrow darter (top right), Kentucky 

arrow darter (bottom left), and Cumberland darter (bottom right). Photos by Dr. Matthew Thomas, Kentucky Department of Fish 

and Wildlife Resources.
Methods 3

38°

37°30'

37°

36°30'

84°30' 84° 83°30' 83° 82°30'

4,147

548

Elevation, in feet (Earth Gravitational Model of 1996)

Cumberland Falls

Sampling site

EXPLANATION

Base from ESRI, 2020

Hydrology from U.S. Geological Survey, National Hydrography Dataset, 2020

Digital elevation data from U.S. Geological Survey, EROS, 2020

WGS 1984 Web Mercator Projection, North American Datum of 1983 

KENTUCKY

WEST VIRGINIA

VIRGINIA

TENNESSEE

Kentucky River Basin

Cumberland River Basin

0 25 50 KILOMETERS

0 25 50 MILES

Location of study area

Figure 2. Location of Cumberland and Kentucky River Basins in Virginia and Kentucky with sampling sites. Sampling sites are shown 

as points and are listed in appendix table 1.1. 
4 Occupancy of Rare Stream Fish in the Upper Cumberland and Kentucky River Basins

were identified to species, counted, and released downstream 

after each quadrat was sampled. Sampling proceeded in an 

upstream direction.

Environmental covariates were measured in each quadrat 

(table 1), including measures of sampling effort (electrofishing 

time), flow velocity, stream depth, and substrate size. Substrate 

size categories followed Wentworth (1922) with pebble, 

gravel, and cobble categories combined. Stream depth and 

substrate size class were measured at the corners and center of 

each quadrat (five samples). Flow velocity was estimated visually and scored on a scale of 1 (no flow) to 4 (fast flow), following Albanese and others (2007). A total of 2,050 quadrats 

was sampled in the study area, including 1,200 samples in the 

CU area (120 sites) and 850 samples in the KE area (85 sites).

Site-level covariates include measures of water quality, stream volume, and land use (table 2). Conductivity was 

measured with a calibrated YSI Professional Plus multiparameter meter prior to fish sampling. We calculated elevation, 

upstream basin size, and stream gradient from 30-m digital 

elevation models with a geographic information system (ESRI 

Arc Hydro tools). Land cover was expressed as the percent 

of upstream watershed area classified as forest, agriculture, 

barren land, or developed land as defined by the 2016 version 

of the National Land Cover Database (see Wickham and others, 2014).

Occupancy Modelling

The R package “unmarked” version 0.13-2 (Fiske and 

Chandler, 2011) was used to model species detection and 

occurrence probabilities in a hierarchical framework as

zi~Bernoulli(Ψi) (1)

yij zi~Bernoulli(zi pij) (2)

where

zi is the state variable defining the presence 

or absence of BSD, CAD, or KAD 

within site i,

Ѱi (psii) is the probability of species presence 

within site i,

pij is the probability of species detection in 

quadrat j within site i, and

yij is the observed presence or absence of the 

target species in quadrat j within site i.

Model (1) represents the process of species occurrence 

among sites, and model (2) represents the process of detection within a quadrat when a species is present in a given 

site. Within each site, the sequence of observed presence 

and absence records across quadrats represents a detection history with a likelihood contingent on true presence or 

absence (z). Percent variables and continuous variables with 

arcsine square-root and ln-transformations, respectively, were 

Table 1. Environmental covariates for modeling species detection probability in the upper Cumberland River Basin and upper Kentucky 

River Basin. Samples were observed at the quadrat level (that is, 10 quadrats per site).

 [CU, Cumberland River Basin, n=1,200; KE, Kentucky River Basin, n=850. Codes are indexed with “d” to indicate detection-level covariates in subsequent 

tables and figures. SD, standard deviation]

Covariate Code Unit Basin Mean SD Range

Electrofishing time dET Second CU 66 22 6–182

KE 66 20 18–171

Stream depth dSD Meter CU 0.15 0.13 0.01–1.00

KE 0.10 0.11 0.01–0.84

Flow velocity dFV Index CU 1.9 0.6 1.0–4.0

KE 2.1 0.5 1.0–4.0

Fine substrates dFI Percent CU 9 18 0–100

KE 4 11 0–100

Sand substrates dSA Percent CU 16 22 0–100

KE 14 21 0–100

Gravel/cobble substrates dGC Percent CU 61 32 0–100

KE 58 33 0–100

Boulder substrates dBO Percent CU 7 14 0–80

KE 6 12 0–80

Bedrock substrates dBE Percent CU 8 20 0–100

KE 19 32 0–100
Methods 5

transformed, and all covariates were scaled to a mean of 0 

and standard deviation of 1. Logit link functions were used to 

relate covariates to psi and p on a 0–1 probability scale.

This modeling structure provides a hierarchical framework because the observed data (y) are modeled jointly with a 

detection process and a higher-level occupancy process. The 

underlying Bernoulli probability distributions assume three 

conditions: (1) there are only two possible outcomes (species 

presence or absence and species detection or non-detection), 

(2) species occurrence or detection in one sample unit does 

not affect occurrence or detection in others, and (3) the true 

occurrence state (z) does not change during the period of data 

collection (Bailey and others, 2014). The rapid collection of 

quadrat-level data in the current study (that is, sampled within 

a single day) gives high confidence for satisfying the latter 

condition. We assumed that electrofishing did not affect the 

spatial distribution of fish among quadrats (see “Discussion” 

section).

The dataset was split by basin for modeling species occupancy, yielding 120 sites (1,200 quadrats) for analysis of BSD 

and CAD, and 85 sites (850 quadrats) for analysis of KAD. 

First-order combinations of all covariates at the detection and 

occurrence levels were evaluated using Akaike Information 

Criterion scores (AIC) scores to identify the best performing models for each species (117 models per species). Model 

goodness-of-fit was evaluated using bootstrapped chi-squared 

statistics with 1,000 samples; all possible combinations of 

covariates were not evaluated because higher-order models 

generally lacked sufficient goodness-of-fit for interpretation. 

The expected probability of detection and occurrence (p and 

psi) was evaluated for top-performing models with covariates 

effects held at mean-effect levels.

Table 2. Site covariates for modeling species occurrence probability in the upper Cumberland River Basin and upper Kentucky River 

Basin. 

 [Covariates were observed at the site level. CU, Cumberland River Basin, n=120; KE, Kentucky River Basin, n=85. Codes are indexed with “o” to indicate 

occurrence-level covariates in subsequent tables and figures. μS/cm, microsiemens per centimenter; <, less than; SD, standard deviation]

Covariate Code Units Basin Mean SD Range

Conductivity oCO μS/cm CU 401 360 15–2,171

KE 473 484 29–2,175

Basin area oBA Hectare CU 3007 6756 103–37,907

KE 1471 2789 189–14,617

Elevation above sea-level oEL Meter CU 391 91 282–769

KE 308 54 219–488

Barren land cover oBR Percent CU < 1 1 0–6

KE 1 3 0–15

Forest land cover oFO Percent CU 83 16 26–100

KE 82 17 13–100

Agricultural land cover oAG Percent CU 2 5 0–31

KE 3 7 0–44

Developed land cover oDE Percent CU 3 4 0–29

KE 5 3 0–18

Fine substrates oFI Percent CU 9 13 0–75

KE 4 5 0–22

Sand substrates oSA Percent CU 16 16 0–94

KE 14 15 0–65

Gravel/cobble substrates oGC Percent CU 61 23 2–98

KE 58 23 6–98

Boulder substrates oBO Percent CU 7 8 0–42

KE 6 7 0–37

Bedrock substrates oBE Percent CU 8 14 0–64

KE 19 26 0–90
6 Occupancy of Rare Stream Fish in the Upper Cumberland and Kentucky River Basins

Results

The fish dataset includes 16,717 individuals, of which 

the focal species constituted a small fraction. A total of 96 

individual BSD were observed within 23 quadrats across 12 

sites in the CU study area (naive occupancy = 0.10). In sites 

where BSD were observed, they were detected on average in 

1.9 quadrats (19 percent) with a maximum observed presence 

within 5 quadrats in one site (DOW02036606 in table 1.1). A 

total of 52 individual CAD were observed within 37 quadrats 

across 20 sites in the CU study area (naive occupancy = 0.17). 

As with BSD, CAD were observed on average in 1.9 quadrats (19 percent) within sites where they were detected, and 

the maximum observed presence was 5 quadrats in one site 

(DOW02013601 in table 1.1). Thirteen KAD were observed 

within 10 quadrats across six sites within the KE study area 

(naive occupancy = 0.07). In sites where KAD were observed, 

they were detected on average in 1.7 quadrats (17 percent) 

with a maximum observed presence within 3 quadrats in 

one site (DOW04052401 in table 1.1). CD was observed in 

only one site (13 individuals within 5 quadrats); therefore, it 

was excluded from further analysis because it lacked enough 

observations for modeling.

Environmental covariates measured at the quadrat level 

exhibited substantial variation (table 1) and similar patterns 

of correlation within the CU and KE study areas (fig. 3). 

Sampling effort as indexed by electrofishing time (dET 

in table 1) ranged from 6 to 182 seconds per quadrat, and 

mean values were equivalent across study areas (66 seconds; table 1). Quadrat depths ranged from 0.01 m to 1.00 m 

(table 1) with a grand mean of 0.13 m. Substrates in both study 

areas were primarily gravel/cobble (61 percent and 58 percent 

in CU and KE, respectively; table 1) and secondarily sand (16 

percent and 14 percent in CU and KE, respectively; table 1). 

Bedrock was more abundant in KE sites than in CU sites (19 

percent and 8 percent, respectively; table 1).

Electrofishing time increased with quadrat depth (fig. 3). 

Flow velocity was inversely related to the percent fine substrates and positively related to percent gravel/cobble substrates within quadrats (fig. 3). Percent sand and gravel/cobble 

substrates were inversely related, and gravel/cobble substrates were inversely related to the percent bedrock (fig. 3). 

Detection covariates showed similar correlations in both study 

areas, but flow velocity generally showed stronger relations to 

substrate size in CU than in KE, and bedrock showed stronger 

relations within KE than CU (fig. 3).

Site-level covariates included measures of water quality, 

stream volume, land use, and substrate size (table 2). Stream 

conductivity ranged from 15 to 2,175 μS/cm, and mean values 

were not different between study areas (t = -1.2, p = 0.25). 

Conductivity decreased with forest cover and increased with 

barren land cover in both study areas (fig. 4). By comparison, 

developed land cover showed relatively weak correlations 

with conductivity (fig. 4), indicating the importance of sulfates 

from surface mine runoff rather than chlorides from road 

salts (Cormier and others, 2013). Conductivity was positively 

related to the percent gravel/cobble and negatively related 

to the percent sand within CU sites but not KE sites (fig. 4). 

Conductivity increased with the percent fine substrates in KE 

but not in CU (fig. 4).

Upstream basin areas range from 103 hectares (ha) to 

nearly 38,000 ha (table 2) and were larger on average in CU 

than in KE (t = 2.3, p = 0.02). Site elevations ranged from 282 

m to 769 m (table 2) and were higher on average in CU than 

in KE (t = 8.1, p < 0.001). Forest dominated land cover in both 

study areas (83 percent and 82 percent in CU and KE, respectively), whereas agriculture and developed areas constituted 

less than 5 percent of land cover in all cases (table 2). Barren 

land was rare in the land-cover dataset, accounting for 1 percent of the KE basins on average and less than 1 percent of the 

CU basins on average (table 2).

Basin area and elevation generally showed stronger correlations with land use and substrate size among CU sites than 

among KE sites (fig. 4). Percent agriculture and developed 

areas were positively associated, and each showed negative 

associations with percent forest cover. The percent barren 

land was positively associated with developed land in CU but 

not in KE (fig. 4). Elevation showed a positive correlation 

with percent bedrock in CU but a negative correlation in KE 

(fig. 4). Percent agriculture showed a positive association with 

percent fine substrate in CU but not in KE (fig. 4).

Bootstrapped chi-squared statistics indicated sufficient 

goodness-of-fit for the top three models for each species 

(appendix table 1.2). The top three models (that is, lowest AIC 

scores) for BSD (table 3) show a negative effect of agriculture on occurrence probability and detection-level effects of 

electrofishing time (positive), flow velocity (negative), and 

gravel/cobble substrate (negative) (table 4; fig. 5). Uncertainty 

of the predicted effect of agriculture generally decreased 

with increasing agricultural land cover (that is, decreasing 

confidence intervals with increasing covariate values; fig. 5). 

Conductivity exhibited a negative relation with BSD occurrence but was not included in the top three models.

A single model best described CAD occupancy given 

the observed data (that is, AIC cumulative weight = 1 for the 

top model; table 3). This model showed a negative effect for 

stream conductivity on occurrence probability and simulated 

the detection process with a positive effect for electrofishing time (table 4; fig. 6). The predicted (negative) effect of 

conductivity on CAD occurrence probability was more precise 

at greater than mean values than less than mean values in 

the CU study area (about 400 μS/cm threshold; table 2). In 

contrast, predicted effects of barren land cover (negative) and 

forest cover (positive) on CAD occurrence probability showed 

decreasing precision at low and high covariate values (fig. 6).

The top three models for KAD (table 3) included negative 

effects of conductivity and barren land cover on occurrence 

probability (table 4; fig. 7). Covariates on KAD detection 

probability included effects of sand substrate (positive) and 

electrofishing time (positive). As with CAD, uncertainty in the 

predicted (negative) effect of conductivity on KAD occurrence 

diminished with increasing conductivity values. In contrast, 
Results 7

dET

dSD

dFV

dFI

dSA

dGC

dBO

dBE

0.4

−0.14

0.07

0.05

−0.04

0.04

−0.03

−0.18

0.22

0.09

−0.23

0.08

−0.01

−0.32

−0.24

0.29

0.05

0

0.09

−0.38

−0.17

−0.15

−0.59

−0.11

−0.02

−0.17

−0.4 −0.08

Cumberland

0.4

−0.11

−0.01

−0.11

0.23

0.13

−0.18

−0.23

0.12

0

0.19

−0.21

−0.16

−0.07

0.11

0.06

−0.03

0.04

−0.14

−0.05

−0.14

−0.35

−0.03

−0.23

−0.13

−0.64 −0.18

Kentucky

dET

dSD

dFV

dFI

dSA

dGC

dBO

dBE

0.18

−1

−0.8

−0.6

−0.4

−0.2

0

0.2

0.4

0.6

0.8

1

−1

−0.8

−0.6

−0.4

−0.2

0

0.2

0.4

0.6

0.8

1

EXPLANATION

Figure 3. Correlation matrix for detection-level covariates in the upper Cumberland River Basin and the upper Kentucky River Basin. 

Cumberland River Basin, n=1,200 quadrats; Kentucky River Basin, n=850 quadrats. Lower diagonal cells give Spearman correlation 

coefficients, and upper diagonal cells represent correlation direction (color) and magnitude (circle size). Codes are given in table 1, and 

study sites are shown in figure 2. 
8 Occupancy of Rare Stream Fish in the Upper Cumberland and Kentucky River Basins

oCO

oBA

oEL

oBR

oFO

oA

oDE

oFI

oSA

oGC

oBO

oBE

oCO

oBA

oEL

oBR

oFO

oAG

oDE

oFI

oSA

oGC

oBO

oBE

0.15

0.03

0.39

−0.35

0.05

0.21

0

−0.3

0.23

−0.05

−0.13

−0.31

0.43

−0.25

0.43

0.18

0.11

0.07

−0.13

0.07

−0.13

0.02

0.19

−0.6

−0.33

−0.34

−0.26

0.24

0.29

0.25

−0.45

0.26

0.42

−0.01

0.02

−0.07

0.17

−0.11

−0.37

−0.43

−0.05

−0.01

0

0

0.2

0.51

0.19

0.21

−0.23

−0.22

−0.12

0.12

0.15

−0.18

−0.14

−0.03

0.24

−0.23

−0.4

−0.24

−0.75

−0.24

−0.01

0.06

−0.32 0.11

G

0.06

0.19

0.59

−0.48

−0.22

0.04

0.25

0.18

0.09

−0.07

−0.21

−0.25

0.18

−0.07

0.14

−0.02

0.1

0

−0.03

0.13

−0.15

0.27

0.06

−0.61

0.02

0.06

0.19

0.14

0.08

−0.35

−0.57

−0.12

0.28

0.08

0.17

0.13

0.17

−0.3

−0.23

−0.31

−0.13

−0.19

−0.07

−0.13

0.12

0.42

0

−0.19

0.07

−0.06

0.12

0.1

0.15

0.26

0.02

−0.29

0.26

0.08

−0.2

−0.26

−0.29

0.01

−0.37

0.05

−0.6 −0.22

Cumberland

Kentucky

−0.8

−0.6

−0.4

−0.2

−1

0

0.2

0.4

0.6

0.8

1

−1

0

0.2

0.4

0.6

0.8

1

−0.8

−0.6

−0.4

−0.2

EXPLANATION

Figure 4. Correlation matrix for occurrence-level covariates in the upper Cumberland River Basin, n = 120, and upper Kentucky River 

Basin, n = 85. Lower diagonal cells give Spearman correlation coefficients, and upper diagonal cells represent correlation direction 

(color) and magnitude (circle size). Codes are given in table 2, and study sites are shown in figure 2.
Results 9

the predicted effect of barren land cover on KAD occurrence 

became less precise at high values, indicating that conductivity 

is a more important predictor in this regard (fig. 7).

Estimated occurrence probabilities for BSD in the top 

three models were similar to the naive occupancy rate (0.10), 

and estimated detection probabilities ranged from 0.123 to 

0.170 (table 5). Estimated occurrence probability for CAD was 

0.209 in the top model, an increase of 4.2 percent from the 

naive occupancy rate of 0.167 (table 5), and detection probability was 0.057 in the top model for this species. Estimated 

occurrence probabilities for KAD ranged from 0.029 to 0.112 

in the top three models, and detection probabilities ranged 

from 0.035 to 0.071 in the top three models (table 5). Greater 

detection probabilities were estimated for BSD than CAD or 

KAD in the top three models for each species: the maximum 

estimated detection probabilities for CAD (0.062) and KAD 

(0.071) were less than the minimum detection probability for 

BSD (0.123) (table 5).

Table 3. Description of the top three occupancy models for blackside dace, Cumberland arrow darter, and Kentucky arrow darter. 

 [Covariates for detection probability (ꞵp) and occurrence probability (ꞵpsi) are defined in table 1 and table 2. The relative difference in Akaike Information 

Criterion scores (ΔAIC), AIC weights (AICw), cumulative AIC weights (AICwc), and number of parameters (nP) are given for each model]

Model βp βpsi nP ΔAIC AICw AICwc

Blackside dace

1 dET oAG 4 0.00 0.10 0.10

2 dFV oAG 4 0.77 0.07 0.18

3 dGC oAG 4 1.44 0.05 0.23

Cumberland arrow darter

1 dET oCO 4 0.00 1.00 1.00

2 dET oBR 4 17.94 0.00 1.00

3 dET oFO 4 18.48 0.00 1.00

Kentucky arrow darter

1 dSA oCO 4 0.00 0.20 0.20

2 dSA oBR 4 2.01 0.07 0.27

3 dET oCO 4 3.06 0.04 0.31

Table 4. Top model coefficients for blackside dace, Cumberland arrow darter, and Kentucky arrow darter.

 [Coefficients are identified in table 3. Cells show standard errors (SE) and type-1 error rates (p) from standardized z-scores. <, less than]

Level Covariate Estimate SE z p

Blackside dace

Occurrence Intercept -2.16 0.45 -4.79 < 0.001

oAG -1.15 0.72 -1.61 0.108

Detection Intercept -1.97 0.34 -5.88 < 0.001

dET 0.76 0.30 2.53 0.011

Cumberland arrow darter

Occurrence Intercept -1.33 0.44 -3.05 0.002

oCO -2.21 0.70 -3.14 0.002

Detection Intercept -2.81 0.31 -9.16 < 0.001

dET 1.41 0.28 4.96 < 0.001

Kentucky arrow darter

Occurrence Intercept -2.07 0.99 -2.10 0.036

oCO -2.59 1.91 -1.36 0.175

Detection Intercept -3.32 0.67 -4.94 < 0.001

dSA 0.74 0.28 2.68 0.007
10 Occupancy of Rare Stream Fish in the Upper Cumberland and Kentucky River Basins

Discussion

Our analysis provides several inferences for monitoring and assessment of rare stream fishes in Appalachia. We 

showed that (1) quadrat-based spatial replicates can provide 

a useful framework for modeling stream fish occupancy; (2) 

sampling effort, flow velocity, and substrate size can affect 

species detection probabilities; (3) agriculture decreased 

occurrence probability for BSD, and conductivity decreased 

occurrence probabilities for CAD and KAD; (4) predicted 

effects of conductivity and agriculture became more precise 

as their values increased; and (5) maximum potential occupancy rates (that is, accounting for imperfect detection) were 

relatively low in all cases, highlighting the importance of 

continued monitoring and assessment of these rare stream 

fish species.

The best-performing models for CAD and KAD included 

negative effects of conductivity on species occurrence 

(table 4), and similar effects have been observed from independent datasets in the study area (Black and others, 2013b; 

Hitt and others, 2016) and elsewhere in Appalachia (Palmer 

and others, 2010; Hitt and Chambers, 2014; Merovich and 

others, in press). Conductivity was clearly the most important 

covariate to model CAD occurrence (that is, AIC cumulative 

weight = 1.0; table 3). Conductivity was also 

included in the best model for KAD, but other 

variables were closer in their performance for 

KAD than for CAD (table 3). However, the 

next-best models for KAD included barren 

land cover (table 3, fig. 7), which is correlated with stream conductivity (fig. 3) and 

therefore may represent the same underlying 

mechanisms. Even though stream volume 

and temperature are primary determinants of 

stream fish distributions (Burton and Odum, 

1945; Sheldon, 1968; Vannote and others, 

1980), our indices of stream volume (basin 

area) and stream temperature (elevation) were 

unimportant in occurrence models relative to 

the overriding effect of conductivity.

Analysis contributed a new inference 

on conductivity: the predicted effects became 

more precise as observed conductivity values 

increased (fig. 6 and fig. 7). Specifically, predicted effects on CAD occurrence were more 

precise at greater than the mean observed 

value within the CU area (about 

400 μS/cm) than below this threshold. This 

pattern is consistent with the wedge-shaped 

relation between abundance and conductivity 

reported previously for KAD (Hitt and others, 

2016), implying a limiting effect of water 

quality at high conductivity values and other 

limiting effects at low conductivity values (see 

Schmidt and others, 2012). Moreover, conductivity showed nonlinear relations to CAD 

and KAD occurrence such that models predicted more change 

at less than mean conductivity values than at greater than 

mean conductivity (fig. 6 and fig. 7). For instance, the steepest 

changes in predicted occurrence were near the conductivity 

benchmark established by the U.S. Environmental Protection 

Agency (EPA) for protection of aquatic life downstream from 

mining operations in Appalachia (300 μS/cm; EPA, 2011). 

Predicted effects of conductivity on CAD and KAD were consistent with the hypothesis that conductivity affects growth and 

survival of invertivorous fishes by altering the benthic macroinvertebrate prey base available for consumption (see Hitt and 

others, 2016). Moreover, we attribute observed conductivity 

effects to sulfates from mining activity rather than chlorides 

from road salts (Cormier and others, 2013) because conductivity was weakly related to developed land but strongly related 

to “barren” land associated with surface mining (fig. 4).

Agriculture was more important than conductivity for 

modeling BSD occurrence in this analysis (table 3, table 4) 

even though their occurrence was limited to low conductivity 

sites. Because agriculture is more prevalent in lower elevation 

sites (fig. 4), unmeasured effects of water temperature or other 

conditions that vary by elevation may influence the observed 

effect of agriculture in these models. Nonetheless, agriculture 

was associated with increasing fine substrates and decreasing 

0.0

0.2

0.4

0.6

0.8

1.0

−2 −1 2

0.0

0.2

0.4

0.6

0.8

1.0

Standardized variable

Predicted value

oAG

dFV

dET

dGC

0 1 −2 −1 0 1 2

Figure 5. Blackside dace predicted occurrence and detection probabilities (black 

lines) and 95-percent confidence intervals (grey lines) for covariates in the top 

three models. Top three models are described in table 3. Covariates for occurrence 

(“o”) and detection (“d”) probabilities are defined in tables 1 and 2. 
Discussion 11

gravel/cobble substrates in the CU study area (fig. 4), and the 

negative effects of turbidity and siltation on BSD populations 

are well known (FWS, 1987). Similar agricultural effects were 

reported for Percina oxyrhynchus (sharpnose darter) in the 

upper Kentucky River Basin (Hopkins and Roush, 2013). BSD 

spawning is often associated with spawning mounds constructed by Semotilus atromaculatus (creek chub) (Mattingly 

and Black, 2013), and such nest associations can enable 

long-term persistence of fish species in agriculturally affected 

streams (Hitt and Roberts, 2012), which indicates the presence 

of other mechanistic effects of land use besides siltation and 

egg mortality. Although agriculture was not included in the top 

three models for CAD or KAD (table 3), it was included for 

models ranked 12 of 117 for CAD (top 10 percent) and 42 of 

117 for KAD (top 36 percent) (results not shown). All potentially interactive effects were not evaluated (owing to higherorder models lacking sufficient goodness-of-fit), but we expect 

combined effects of land use as shown for benthic macroinvertebrate communities (Merriam and others, 2011) and a BSD 

congener in Virginia (Chrosomus sp. cf. saylori, Clinch dace) 

(Moore and others, 2017).

Our estimated detection probabilities ranged from 0.035 

(KAD) to 0.170 (BSD) (table 5); these values are comparable 

to other rare stream fishes in the southeastern United States. 

For example, Pteronotropis welaka (bluenose 

shiner) detection rates ranged from 0.03 to 

0.08 (Albanese and others, 2007), and the 

detection rate for Percina aurolineata (goldline darter) was 0.20 (Albanese and others, 

2013) using seine hauls as spatial replicates. 

Electrofishing and snorkeling surveys also 

revealed low detection rates for Erimystax 

insignis (blotched chub) in southern 

Appalachian streams (0.11 and 0.09, respectively; Albanese and others, 2011). In contrast, 

common stream fish species in this region can 

show detection rates of nearly 90 percent (for 

example, Albanese and others, 2007).

Higher detection probabilities were 

found for BSD than KAD or CAD (table 5); 

this may be due to differences in local abundance. BSD showed greater mean abundance 

than CAD or KAD at the site level (8.0, 2.6, 

and 2.2 fish per occupied site, respectively), 

consistent with prior research (Black and others, 2013a; Hitt and others, 2016). Likewise, 

BSD showed greater densities at the quadrat 

level than CAD or KAD (4.2, 1.4, and 1.3 fish 

per occupied quadrat, respectively), and fish 

density therefore may be related to the detection process (see Royle and Nichols, 2003). 

Future studies on the focal species therefore 

may benefit by limiting quadrat samples to 

targeted microhabitats rather than sampling 

all available habitats as implemented in 

this study.

We found that sampling effort (that is, 

electrofishing time) increased detection rates for all species 

(table 3), as expected. However, the precision of the predicted 

effects was greater for CAD than KAD (that is, confidence 

intervals for dET in fig. 6 and fig. 7), whereas BSD showed an 

intermediate response (fig. 5). The results therefore underscore 

the importance of sampling effort for rare species detection, 

as shown previously (Green and Young, 1993). We further 

note that the effect of electrofishing effort was not simply a 

function of observed density because BSD was most abundant 

but exhibited an intermediate response to electrofishing effort 

(fig. 5). Instead, the observed effect of electrofishing time in 

our study may indicate that more effort was expended after the 

first individual of a target species was observed within a quadrat (M. Compton, OKNP, oral commun.). Future studies with 

blocknetted quadrats or repeat samples are needed to evaluate 

this effect empirically.

Flow velocity affects fish detection rates in many lotic 

ecosystem types (Gwinn and others, 2016), and our results 

showed this. However, flow velocity was found to be more 

important for BSD than CAD or KAD (table 3); this may be 

due to variation in mesohabitat use and body morphology 

between species. Specifically, pelagic stream fishes such as 

BSD typically exhibit laterally compressed body shapes that 

oBR

dET

oCO

0.0

0.2

0.4

0.6

0.8

1.0

−2 −1 2

0.0

0.2

0.4

0.6

0.8

1.0

Standardized variable

Predicted value

0 1 −2 −1 0 1 2

oFO

Figure 6. Cumberland arrow darter predicted occurrence and detection 

probabilities (black lines) and 95-percent confidence intervals (grey lines) for 

covariates in the top three models. Top three models are described in table 3. 

Covariates for occurrence (“o”) and detection (“d”) probabilities are defined in 

table 1 and table 2.
12 Occupancy of Rare Stream Fish in the Upper Cumberland and Kentucky River Basins

are expected to be more sensitive to changes in flow velocity than dorsally compressed benthic fishes such as CAD and 

KAD (Sagnes and Statzner, 2009). Similarly, Albanese and 

others (2007) report negative effects of flow velocity for detection of another rare, pelagic stream fish species in Appalachia 

(bluenose shiner). We also found that smaller substrates (that 

is, sand) increased detection probability for BSD and KAD 

(table 3), as reported by Albanese and others (2011).

Our inferences on conductivity were constrained by 

the sampling design. High conductivity levels can affect 

electrofishing efficiency (Hill and Willis, 1994; Hense and 

others, 2010; Dean and others, 2019), but 

we could not directly evaluate this effect 

because conductivity was measured at the site 

level (that is, invariant at the quadrat level). 

Incorporation of temporal replicates would be 

necessary to quantify this effect (for example, 

Hayer and Irwin, 2008), but we are not 

confident that seasonal differences in conductivity downstream from mining operations in 

Appalachia (see Lindberg and others, 2011) 

would provide enough variation to permit 

modeling. Nonetheless, observed conductivity 

thresholds for electrofishing efficiency (Dean 

and others, 2019) exceed threshold effects of 

conductivity on stream fish populations and 

assemblages (Black and others, 2013b; Hitt 

and Chambers, 2014; Hitt and others, 2016); 

therefore, conductivity is expected to be more 

important for the occurrence process than 

the detection process for the focal species 

studied here.

This study was constrained by the 

spatial-replicate sampling design within sites. 

We assumed that the sampling process was 

independent among quadrats within a site, as 

required for statistical analysis. However, fish 

escapement from electrofishing may exceed 

the minimum quadrat spacing distance in this 

study (5 m), particularly within pool environments (for example, Hitt and others, in press). Kendall and 

White (2009) report that such non-independence in spatial 

replicates can bias species occupancy estimates. They further 

demonstrate that sampling quadrats with replacement can 

overcome this potential bias (Kendall and White, 2009). In 

our analysis this would require a randomization process to 

select spatial quadrats within study sites from which all spatial 

units could be selected in each sampling iteration (that is, 

sampling with replacement). Future research may benefit from 

this approach, as this could maintain the logistical benefits of 

Standardized variable

Predicted value

oCO

dSA

oBR

dET

0 1 0 1

0.0

0.2

0.4

0.6

0.8

1.0

−2 −1 2

0.0

0.2

0.4

0.6

0.8

1.0

−2 −1 2

Figure 7. Kentucky arrow darter predicted occurrence and detection probabilities 

(black lines) and 95-percent confidence intervals (grey lines) for covariates in 

the top three models. Top three models are described in table 3. Covariates for 

occurrence (“o”) and detection (“d”) probabilities are defined in tables 1 and 2.

Table 5. Estimated detection probability and occurrence probability for top three occupancy models for blackside dace, Cumberland 

arrow darter, and Kentucky arrow darter. 

[Models hold covariates at mean-effect levels and are listed in table 3. BSD, blackside dace; CAD, Cumberland arrow darter; KAD, Kentucky arrow darter; —, 

no data; p, detection probability; psi, occurrence probability]

BSD CAD KAD

Model p psi p psi p psi

Naive — 0.100 — 0.167 — 0.071

1 0.123 0.103 0.057 0.209 0.035 0.112

2 0.142 0.100 0.059 0.278 0.051 0.029

3 0.170 0.094 0.062 0.271 0.071 0.075
References Cited 13

spatial replicates (Srivathsa and others, 2018) by reallocating 

sampling effort with single site visits rather than requiring 

multiple site visits.

We found that imperfect detection is unlikely to explain 

the observed rarity of the focal species. Models accounting 

for detection of estimated BSD occupancy rates are similar 

to the observed proportion of occupied sites (0.10), and the 

best-supported models for CAD and KAD increased expected 

occupancy by about 4 percent for each species (from 0.17 to 

0.21 for CAD and from 0.07 to 0.11 for KAD). Our results 

therefore support prior research demonstrating the rarity of 

the focal species (Thomas, 2007, 2008; FWS, 2010a, 2015a, 

2010b) and highlight the importance of their continued 

monitoring and assessment. A strength of this study is that 

sites were selected at random; therefore, results can inform 

expectations for species occupancy and detection across the 

study area.

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16 Occupancy of Rare Stream Fish in the Upper Cumberland and Kentucky River Basins

Appendix 1
 17

Table 1.1. Sampling sites in the upper Cumberland River Basin and upper Kentucky River Basin. 

[Site coordinates are given in decimal degrees. CU, Cumberland River Basin, n=120; KE, Kentucky River Basin, n=85. Fish and habitat sampling were coordinated by M. Compton (Office of Kentucky Nature Preserves) and M. Floyd (U.S. Fish and Wildlife Service)]

Station Latitude Longitude Stream Date

Cumberland River Basin

DOW02036601 36.90598 -83.72948 BRICES CREEK 7/17/2012

DOW02030601 36.65717 -84.16132 WOLF CREEK 6/28/2012

DOW02037603 36.66455 -83.64608 CRANES CREEK 7/27/2012

DOW02041601 36.70291 -83.52935 BROWNIES CREEK 7/27/2012

DOW02044601 36.88399 -83.02526 CLOVER FORK CUMBERLAND 7/26/2012

DOW02044603 36.8753 -82.93544 CLOVER FORK CUMBERLAND 7/26/2012

DOW02044602 36.85611 -83.26593 CLOVER FORK CUMBERLAND 7/26/2012

DOW02041617 36.68467 -83.59329 HANCES CREEK 7/25/2012

DOW02041605 36.7805 -83.52426 PUCKETT CREEK 7/25/2012

DOW02044604 36.92651 -83.04314 LEFT FORK FUGITT 7/24/2012

DOW02041607 36.72028 -83.56627 ELK BRANCH 7/24/2012

DOW02042602 36.82159 -83.36319 EWING CREEK 7/23/2012

DOW02041603 36.69983 -83.42879 BROWNIES CREEK 7/18/2012

DOW02036605 36.96339 -83.73792 MILLS CREEK 7/17/2012

DOW02041606 36.75569 -83.46034 PUCKETT CREEK 7/18/2012

DOW02041602 36.69646 -83.45905 BROWNIES CREEK 8/1/2012

DOW02040601 36.73694 -83.71306 CLEAR CREEK 8/1/2012

DOW02032606 36.90901 -83.94677 POPLAR BRANCH 8/31/2012

DOW02040604 36.73206 -83.69544 UT-196 7/30/2012

DOW02042603 36.80444 -83.41106 CAMP BRANCH 7/30/2012

DOW02043604 36.79032 -83.15156 CRANKS CREEK 7/31/2012

DOW02043603 36.76968 -83.16811 GRANT BRANCH 7/31/2012

DOW02040602 36.67733 -83.82357 CANEY CREEK 8/1/2012

DOW02032601 36.99611 -83.8814 RICHLAND CREEK 8/30/2012

DOW02035602 36.63828 -83.94102 PINE CREEK 9/14/2012

DOW02031603 36.77212 -83.91212 SUGAR TREE BRANCH 9/14/2012

DOW02032602 36.87435 -83.90479 RICHLAND CREEK 9/10/2012

DOW02043602 36.77945 -83.2153 LONG BRANCH 9/11/2012

DOW02040603 36.65038 -83.79216 LITTLE CLEAR CREEK 9/11/2012

DOW02039605 36.89312 -83.57455 CAMP BRANCH 9/13/2012

DOW02039603 36.82549 -83.63101 LEFT FORK STRAIGHT CREEK 9/13/2012

DOW02031601 36.78969 -83.94464 LITTLE POPLAR CREEK 9/12/2012

DOW02031602 36.77274 -83.91222 HUBBS CREEK 9/12/2012

DOW02034602 36.73361 -83.80891 CENTER'S BRANCH 9/12/2012

DOW02037601 36.69687 -83.68401 CANNON CREEK 9/12/2012

DOW02046601 37.05023 -82.79206 FRANKS CREEK 9/17/2012

DOW02044606 36.88248 -83.19489 UT-67 8/23/2012

DOW02015602 36.68263 -84.22676 PAINT CREEK 8/17/2012

DOW02031604 36.81115 -84.04119 MEADOW CREEK 8/17/2012

DOW02013601 36.82457 -84.3847 LAUREL FORK 8/16/2012

DOW02018602 36.79557 -84.24338 MIDDLE FORK SANDERS 8/16/2012
18 Occupancy of Rare Stream Fish in the Upper Cumberland and Kentucky River Basins

Table 1.1. Sampling sites in the upper Cumberland River Basin and upper Kentucky River Basin. —Continued

[Site coordinates are given in decimal degrees. CU, Cumberland River Basin, n=120; KE, Kentucky River Basin, n=85. Fish and habitat sampling were coordinated by M. Compton (Office of Kentucky Nature Preserves) and M. Floyd (U.S. Fish and Wildlife Service)]

Station Latitude Longitude Stream Date

DOW02018601 36.76049 -84.28849 ARCHERS CREEK 8/16/2012

DOW02017601 36.81052 -84.10201 JACKS FORK 8/15/2012

DOW02034603 36.79533 -83.85919 LITTLE BRUSH CREEK 8/21/2012

DOW02031606 36.68153 -83.92248 POPLAR CREEK 8/21/2012

DOW02034601 36.72989 -83.80033 GREASY CREEK 8/21/2012

DOW02044605 36.90649 -83.06894 BEAR BRANCH 8/23/2012

DOW02042601 36.8301 -83.36998 EWING CREEK 8/22/2012

DOW02043606 36.77204 -83.24889 LICK BRANCH 8/23/2012

DOW02042604 36.82518 -83.41264 TERRY FORK 8/23/2012

DOW02031608 36.88628 -83.97385 DEMPS HOLLOW 8/27/2012

DOW02031607 36.81023 -84.06365 UT-60 8/28/2012

DOW02031605 36.82652 -84.04601 MEADOW CREEK 8/28/2012

DOW02030602 36.65313 -84.157 LITTLE WOLF CREEK 6/28/2012

DOW02037602 36.66847 -83.66283 YELLOW CREEK 8/20/2012

DOW02039602 36.80714 -83.64741 LEFT FORK STRAIGHT CREEK 8/24/2012

DOW02045601 36.87145 -83.31072 POOR FORK CUMBERLAND RIVER 8/22/2012

DOW02045602 36.88417 -83.28529 POOR FORK CUMBERLAND RIVER 8/22/2012

DOW02043601 36.77607 -83.24201 MARTINS FORK CUMBERLAND RIVER 7/31/2012

DOW02013602 36.81386 -84.46021 COGUR FORK 7/23/2012

DOW02014604 36.65868 -84.40472 CAT CREEK 8/29/2012

DOW02014603 36.66594 -84.36974 CLEAR CREEK 8/29/2012

DOW02015604 36.60236 -84.31311 ROCK CREEK 8/15/2012

DOW02015603 36.63893 -84.31179 UT-RYANS CREEK 8/24/2012

DOW02036602 36.90945 -83.60419 ALEX CREEK 8/16/2012

DOW02039604 36.89095 -83.3674 STRAIGHT CREEK 8/16/2012

DOW02037604 36.64438 -83.65958 SUGAR RUN 7/24/2012

DOW02036603 36.84575 -83.70913 LEFT FORK MOORE CREEK 8/2/2012

DOW02039601 36.83207 -83.67007 RIGHT FORK CANEY CREEK 9/12/2012

DOW02037605 36.64899 -83.57877 SHILLALAH CREEK 9/21/2012

DOW02036604 36.90733 -83.75924 HALE FORK 8/2/2012

DOW02043605 36.68058 -83.46429 MARTINS FORK CUMBERLAND RIVER 9/20/2012

DOW02014602 36.76416 -84.37695 HENS NEST CREEK 7/3/2012

DOW02032605 37.03444 -83.8686 RICHLAND CREEK 8/16/2012

DOW02036606 36.93514 -83.6008 PAINT GAP BRANCH 8/28/2012

DOW02045603 37.06947 -82.74626 POOR FORK CUMBERLAND RIVER 7/5/2012

DOW02015605 36.62646 -84.24312 CRISCILLIS BRANCH 9/11/2012

DOW02014605 36.65385 -84.42559 PERKINS CREEK 9/20/2012

DOW02041604 36.70994 -83.54005 COAL STONE BRANCH 9/20/2012

DOW02035601 36.65200 -83.87141 LAUREL FORK 9/19/2012

DOW02015601 36.68715 -84.27884 JELLICO CREEK 8/28/2012

DOW02014601 36.78823 -84.35911 MARSH CREEK 8/30/2012

Primary 1 36.56581 -83.81561 SUGAN CREEK 8/1/2015
 19

Table 1.1. Sampling sites in the upper Cumberland River Basin and upper Kentucky River Basin. —Continued

[Site coordinates are given in decimal degrees. CU, Cumberland River Basin, n=120; KE, Kentucky River Basin, n=85. Fish and habitat sampling were coordinated by M. Compton (Office of Kentucky Nature Preserves) and M. Floyd (U.S. Fish and Wildlife Service)]

Station Latitude Longitude Stream Date

Primary 2 36.57804 -84.34507 MIKE BRANCH 8/19/2015

Primary 3 36.52047 -84.30369 BEAR BRANCH 8/25/2015

Primary 5 36.56393 -84.37943 UT GUM FORK 8/19/2015

Primary 12 36.45307 -84.30542 COONTAIL BRANCH 8/26/2015

Primary 13 36.56353 -84.04906 UT CLEAR FORK 8/6/2015

Primary 14 36.57555 -84.22761 TRAMMEL BRANCH 8/20/2015

Primary 15 36.53909 -84.38879 UT JELLICO CREEK 8/21/2015

Primary 16 36.43894 -84.30947 ELK CREEK 8/26/2015

Primary 17 36.48133 -84.11278 ROCK CREEK 8/6/2015

Primary 20 36.49043 -83.97393 LITTLE TACKETT CR. 8/10/2015

Primary 21 36.54494 -84.30111 TRAMMEL BRANCH 8/18/2015

Primary 23 36.54988 -83.86082 VALLEY CREEK 8/2/2015

Secondary 24a 36.52085 -84.21069 LITTLE ELK CREEK 8/27/2015

Primary 25 36.57605 -84.23817 HATFIELD CREEK 8/19/2015

Primary 26 36.46268 -84.05355 DAVIS CREEK 8/10/2015

Primary 27 36.49928 -83.9553 LITTLE TACKETT 8/11/2015

Primary 28 36.52367 -84.39598 JELLICO CREEK 8/20/2015

Primary 29 36.57056 -83.80809 BURRELL CREEK 8/1/2015

Primary 31 36.47584 -84.01652 DAVIS CREEK 8/10/2015

Primary 32 36.44670 -84.29517 ELK CREEK 8/26/2015

Primary 33 36.53685 -83.90038 STRAIGHT CREEK 8/2/2015

Primary 35 36.46874 -84.14506 JIM BRANCH 8/6/2015

Primary 36 36.57643 -84.2695 CAPUCHIN CREEK 8/25/2015

Primary 37 36.49582 -84.06539 DAVIS CREEK 8/5/2015

Secondary 38a 36.57146 -83.91659 CLEAR FORK 8/27/2015

Primary 39 36.50621 -84.13808 STINKING CREEK 8/5/2015

Primary 40 36.50000 -84.13157 STINKING CREEK 8/5/2015

Primary 41 36.55595 -83.9659 CLEARFORK 8/2/2015

Primary 42 36.56521 -84.00356 TACKETT CREEK 8/3/2015

Secondary 1 36.55688 -83.98358 ROSE CREEK 8/3/2012

Secondary 2 36.52740 -83.93635 ROCK CREEK 8/2/2015

Secondary 5 36.54557 -84.09405 UT LAUREL 8/11/2015

Secondary 7 36.47255 -84.19511 UT STINKING CK. 8/6/2015

Secondary 8 36.47799 -84.29189 LICK FORK 8/26/2015

Secondary 9 36.51407 -84.23458 BARLEY BRANCH 8/26/2015

Secondary 10 36.57671 -84.36054 CHILDERS BRANCH 8/20/2015

Secondary 11 36.54827 -84.2631 BAIRD CREEK 8/20/2015

Kentucky River Basin

DOW04038401 37.49732 -83.83042 GRANNY DISMAL CREEK 6/20/2013

DOW04038402 37.41188 -83.8299 UT STURGEON CREEK (ROCK SPRINGS) 6/26/2013

DOW04038403 37.50179 -83.8511 GRANNY DISMAL CREEK 8/2/2013

DOW04038404 37.43681 -83.84544 STURGEON CREEK 9/10/2013
20 Occupancy of Rare Stream Fish in the Upper Cumberland and Kentucky River Basins

Table 1.1. Sampling sites in the upper Cumberland River Basin and upper Kentucky River Basin. —Continued

[Site coordinates are given in decimal degrees. CU, Cumberland River Basin, n=120; KE, Kentucky River Basin, n=85. Fish and habitat sampling were coordinated by M. Compton (Office of Kentucky Nature Preserves) and M. Floyd (U.S. Fish and Wildlife Service)]

Station Latitude Longitude Stream Date

DOW04039401 37.54149 -83.72368 LONG BRANCH 6/19/2013

DOW04039402 37.58561 -83.71268 SILVER CREEK 6/20/2013

DOW04039403 37.61643 -83.74454 RIGHT FORK CONTRARY CREEK 7/16/2013

DOW04044401 37.44506 -83.64023 BEAR RUN 6/20/2013

DOW04044402 37.41258 -83.68385 WHITE OAK CREEK 7/17/2013

DOW04044404 37.28516 -83.85613 OPOSSUM TROT BRANCH 8/22/2013

DOW04044405 37.29567 -83.75351 CRADLEBOW BRANCH 8/22/2013

DOW04044406 37.30911 -83.73415 UPPER FORK COOL SPRING BRANCH 8/22/2013

DOW04044407 37.29664 -83.70576 LOWER TEGES CREEK 8/22/2013

DOW04044408 37.36016 -83.58179 LUCKY FORK 8/25/2013

DOW04044409 37.40905 -83.67178 WHITE OAK CREEK 7/17/2013

DOW04044410 37.41268 -83.69508 WHITE OAK CREEK 9/11/2013

DOW04044411 37.41041 -83.70264 WHITE OAK CREEK 9/11/2013

DOW04044412 37.40899 -83.7147 WHITE OAK CREEK 8/25/2013

DOW04045401 37.32835 -83.50723 SQUABBLE CREEK 8/28/2013

DOW04046401 37.26333 -83.50521 LEATHERWOOD CREEK 8/8/2013

DOW04046402 37.26028 -83.50119 NEWBERRY FORK 8/8/2013

DOW04046403 37.22697 -83.43024 HELL FOR CERTAIN CREEK 8/27/2013

DOW04047401 37.67855 -83.46541 MANDY HOLLAND FORK 7/18/2013

DOW04047402 37.66894 -83.41686 HURST FORK 7/18/2013

DOW04047403 37.63549 -83.38718 LOWER NEGRO BRANCH 7/18/2013

DOW04047404 37.60892 -83.55217 BRUSH CREEK 7/18/2013

DOW04047405 37.57276 -83.68169 BLAINES BRANCH 7/16/2013

DOW04047406 37.67855 -83.65406 WALKER CREEK 8/30/2013

DOW04048401 37.31157 -83.20725 FIRST CREEK 6/7/2013

DOW04048403 37.39196 -83.32555 CANEY CREEK 8/27/2013

DOW04049401 37.59481 -83.22408 HUNTING CREEK 9/6/2013

DOW04049402 37.57578 -83.09102 HAWES FORK 8/28/2013

DOW04049403 37.54995 -83.23978 SULPHUR SPRINGS FORK 9/6/2013

DOW04049404 37.52342 -83.02912 PRATER BRANCH 8/28/2013

DOW04049405 37.52876 -83.25871 SOUTH FORK QUICKSAND CREEK 9/6/2013

DOW04049406 37.51577 -82.97854 SPRING FORK QUICKSAND CREEK 8/21/2013

DOW04050401 37.40995 -82.96258 MILL BRANCH 7/16/2013

DOW04050402 37.46055 -83.23601 FUGATE FORK 7/18/2013

DOW04050403 37.34294 -83.16306 PIGEONROOST BRANCH 7/16/2013

DOW04050404 37.30779 -82.94965 TRACE FORK 7/16/2013

DOW04050405 37.30609 -82.92903 TROUBLESOME CREEK 7/15/2013

DOW04050406 37.3083 -83.07702 CLEAR CREEK 7/15/2013

DOW04050407 37.33969 -82.93541 MILL CREEK 7/18/2013

DOW04050408 37.45782 -83.32144 MILL BRANCH 8/27/2013

DOW04051401 37.20612 -83.74454 JACKS BRANCH 7/3/2013

DOW04051402 37.19313 -83.8833 UT TANYARD BRANCH 7/3/2013
 21

Table 1.1. Sampling sites in the upper Cumberland River Basin and upper Kentucky River Basin. —Continued

[Site coordinates are given in decimal degrees. CU, Cumberland River Basin, n=120; KE, Kentucky River Basin, n=85. Fish and habitat sampling were coordinated by M. Compton (Office of Kentucky Nature Preserves) and M. Floyd (U.S. Fish and Wildlife Service)]

Station Latitude Longitude Stream Date

DOW04051403 36.99118 -83.79731 HORN BRANCH 7/2/2013

DOW04051404 36.97298 -83.78805 SPRUCE PINE BRANCH 7/2/2013

DOW04051405 37.10643 -83.84928 EAST FORK PIGEON ROOST BRANCH 7/3/2013

DOW04051406 37.08695 -83.70726 SEVIER BRANCH 7/25/2013

DOW04051407 37.02646 -83.84806 BULL CREEK 8/6/2013

DOW04051408 37.0677 -83.79964 COLLINS FORK 8/6/2013

DOW04051409 37.10643 -83.84928 WEST FORK PIGEON ROOST BRANCH 9/5/2013

DOW04051410 37.07975 -83.73809 SAPLINGS FORK 9/5/2013

DOW04051411 37.12211 -83.78849 HORSE CREEK 9/5/2013

DOW04052401 37.03937 -83.47185 BOWEN CREEK 8/29/2013

DOW04052402 37.07147 -83.56499 FLAT CREEK 8/29/2013

DOW04052403 37.1669 -83.5146 BOBS FORK 8/29/2013

DOW04052404 37.18257 -83.49213 BOBS FORK 8/29/2013

DOW04053401 37.16959 -83.33263 FLACKEY BRANCH 9/5/2013

DOW04054401 37.13542 -83.33733 HURRICANE CREEK 7/25/2013

DOW04054402 37.13655 -83.39836 SHORT CREEK 7/25/2013

DOW04054403 37.13503 -83.41166 SHORT CREEK 7/25/2013

DOW04054404 36.93154 -83.30248 RIGHT FORK BILL BRANCH 8/1/2013

DOW04054405 36.95212 -83.4084 BIG BRANCH 8/1/2013

DOW04054406 37.11005 -83.38237 MUNCY CREEK 8/1/2013

DOW04054407 37.12391 -83.38228 MUNCY CREEK 8/1/2013

DOW04054408 37.03643 -83.41328 MIDDLE FORK KENTUCKY RIVER 8/6/2013

DOW04054409 36.97415 -83.40526 BEECH FORK 8/6/2013

DOW04054410 37.05801 -83.42224 TRACE BRANCH 9/4/2013

DOW04054411 37.00126 -83.33668 BRITTON BRANCH 9/4/2013

DOW04055401 37.09106 -82.99763 LINE FORK 9/9/2013

DOW04055402 37.28816 -83.15596 LOTTS CREEK 9/3/2013

DOW04055405 37.09267 -82.98618 WHITAKER BRANCH 9/4/2013

DOW04055406 37.06433 -82.96947 BIG BRANCH 9/4/2013

DOW04055407 37.21628 -83.18648 BUFFALO CREEK 8/28/2013

DOW04055408 37.22045 -83.17941 BUFFALO CREEK 8/28/2013

DOW04057401 37.22125 -82.98826 SMITH BRANCH 6/27/2013

DOW04057402 37.27342 -82.84293 MEADOW BRANCH 7/18/2013

DOW04057403 37.24020 -82.92062 LITTLE CARR FORK 9/9/2013

DOW04059401 37.16956 -82.90687 BLAIR BRANCH 6/27/2013

DOW04059402 37.11942 -82.79408 CRAFTS COLLY CREEK 6/26/2013

DOW04059403 37.05655 -82.91555 KINGS CREEK 6/26/2013

DOW04059404 37.14162 -82.96789 ROCKHOUSE CREEK 6/27/2013

DOW04059405 37.21926 -82.66298 WRIGHT FORK 6/26/2013
22 Occupancy of Rare Stream Fish in the Upper Cumberland and Kentucky River Basins

Table 1.2. Goodness-of-fit of the three best occupancy models for blackside dace, Cumberland arrow darter, and Kentucky arrow 

darter. 

[BSD, blackside dace; CAD, Cumberland arrow darter; KAD, Kentucky arrow darter. Cells show the type-1 error rates from bootstrapped chi-squared tests 

(1,000 replicates). Small error rate values indicate inadequate model fit in this context. See table 3 for model summaries]

Model BSD CAD KAD

1 0.106 0.637 0.533

2 0.782 0.263 0.401

3 0.681 0.470 0.347
For additional information, contact:

Director, Leetown Science Center

U.S. Geological Survey

11649 Leetown Road

Kearneysville, WV 25430

Or visit our website at:

https://usgs.gov/centers/lsc

Publishing support provided by the

West Trenton Publishing Service Center
Hitt and others—Occupancy of Rare Stream Fish in the Upper Cumberland and Kentucky River Basins—OFR 2020–1100

ISSN 2331-1258 (online)

https://doi.org/10.3133/ofr20201100