Assessing the distribution 

and habitat needs of the 

Least Darter and sympatric 

species of the Ozark and 

Arbuckle Mountain 

ecoregions

S. K. Brewer1

D. Swedberg2

R. Mollenhauer2

J. Dattilo2

1 U.S. Geological Survey, Oklahoma Cooperative Fish and Wildlife 

Research Unit, Oklahoma State University, Stillwater, OK

2 Oklahoma Cooperative Fish and Wildlife Research Unit, Oklahoma State 

University, Stillwater, OK

Cooperator Science Series # 146-2020
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This research is a contribution of the Oklahoma Cooperative Fish and Wildlife Research Unit 

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Recommended citation:

Brewer, S. K., D. Swedberg, R. Mollenhauer, and J. Dattilo. 2020. Assessing the distribution 

and habitat needs of the Least Darter and sympatric species of the Ozark and Arbuckle 

Mountain ecoregions. U.S. Department of Interior, Fish and Wildlife Service, Cooperator 

Science Series FWS/CSS-145-2020, Washington, D.C. https://doi.org/10.3996/css59114986

For additional copies or information, contact:

Shannon Brewer

U.S. Geological Survey

Alabama Cooperative Fish and Wildlife Research Unit

Auburn University

Auburn, Alabama 36849

E-mail: skbrewer@usgs.gov
Assessing the distribution and habitat needs of the Least Darter 

and sympatric species of the Ozark and Arbuckle Mountain 

ecoregions

October 1, 2017-September 30, 2020

 * Image by Dusty Swedberg (July 2018) 

S. K. Brewer1*, D. Swedberg2, R. Mollenhauer2, and J. Dattilo2

1 U.S. Geological Survey, Oklahoma Cooperative Fish and Wildlife Research Unit, 

Oklahoma State University 007 Agriculture Hall, Stillwater, OK 74078

2 Oklahoma Cooperative Fish and Wildlife Research Unit, Oklahoma State 

University 007 Agriculture Hall, Stillwater, OK 74078

*Current address: Alabama Cooperative Fish and Wildlife Research Unit, Auburn 

University, Auburn, AL 36849; skb0064@auburn.edu
2

Executive Summary

Limited information is known about factors driving the distribution of Least Darter in 

Oklahaoma. The Least Darter occurs in the Ozark Highland and Arbuckle Uplift ecoregions of 

Oklahoma which represent the southern extent of its range. Least Darter was historically recorded 

in Oklahoma from groundwater-fed streams. Our study objectives were to determine the 

distribution of Least Darter and a subset of congeners across the two ecoregions of Oklahoma and 

assess factors driving patch occupancy of Least Darter at a fine spatial scale. We used temporally 

replicated snorkel surveys conducted in July through October 2018-2019 to determine occupany by

Least Darter. We snorkeled and seined for four species in each reach including two life stages of 

Smallmouth Bass (subadult and adult). We sampled 153 sites (i.e., riffle-pool complexes) nested 

within 61 stream reaches (i.e., 200-500-m long) in the Arbuckle Uplift and Ozark Highland 

ecoregions. Detection probability was similar between ecoregions. Least Darter was detected at 

more sites when snorkeling compared to seining (24 versus 18). Smallmouth Bass, Redspot Chub 

and Southern Redbelly Dace were typically 2-3 times more likely to be detected by snorkeling than 

by seining. We found relationships between occupancy and habitat parameters that were both 

shared among species but also species-specific. Least Darter occurrence probability in the Ozark 

Highlands was lower than in the Arbuckle Uplift. Occurrence probability was higher for subadult 

Smallmouth Bass and Southern Redbelly Dace in 2018 compared to 2019. Occurrence 

probabilities of both Least Darter and Southern Redbelly Dace were higher in cooler habitat 

patches. Southern Redbelly Dace was negatively associated with a higher proportion of pool 

habitat across a reach. Lastly, subadult Smallmouth Bass and Redspot Chub were more likely to 

occur in deeper pools and in larger streams (i.e., drainage area). We sampled one study reach

(~150-m long with shallow riffles or a waterfall on each end) in the Arbuckle Uplift (winter and 
3

summer sampling) and Ozark Highland (winter sampling) ecoregions to determine fine-scale 

habitat selection during the thermally harsh seasons. We developed transects across the reaches to 

quantify depth, velocity, substrate, cover and water temperature. We found Least Darter used 

higher water column velocities and shallower water depths with little vegetation during the winter. 

The average water depth used was similar during summer and winter (~ 20 cm deep). Least Darter 

used denser vegetation during the summer and tended to avoid coarse substrates in both seasons. If 

the conservation of Least Darter is a management goal, actions to mitigate increasing stream water 

temperatures (e.g., protection of springs and riparian corridors) and protecting stream 

morphologies that facilitate species separations (i.e., allow for a wide range of water depth and 

velocities) may be beneficial (e.g., fencing cattle from streams, promoting natural bankful flows 

during spring). 
4

Background

The Least Darter (Etheostoma microperca) is an isolated spring-obligate species with 

patchy southern populations in the Ozark Highlands and Arbuckle Uplift ecoregions (Burr 1977; 

Pflieger et al. 1997; Wagner et al. 2012). Least Darter populations also occur in the stream and 

lakes of the northern United States (Figure 1), but southern and northern populations are

genetically divergent and isolated (Echelle et al. 2015). Thus, the two populations may benefit 

from different conservation and management strategies. In the southern extent of the range, 

historic collections of Least Darter suggest the species has an affinity for smaller, calm headwater 

streams and springs, but also occupies the vegetated margins of larger stream runs and pools (Burr 

1977; Burr and Page 1979; Pflieger et al. 1997; Bergey et al. 2008; Hargrave and Johnson 2003). 

However, historic sampling efforts were conducted without consideration of sampling detection 

which could bias our understanding of both habitat relationships and distributions (Mackenzie et 

al. 2002). Though Least Darter is considered a spring-obligate species, landscape and other 

stream-level factors could be important drivers of occupancy, and management or monitoring 

plans could benefit from a more thorough evaluation of the species needs. The purpose of this 

project is to improve the conservation and management of Least Darter and other sympatric 

species by identifying drivers of the split distribution and habitat needs. Our objectives were to 1) 

model the factors related to the distribution of Least Darter and other sympatric fishes in the 

Arbuckle Mountain and Ozark Mountain ecoregions; and 2) identify the fine-scale habitat factors 

related to species occupancy, emphasizing temperature.

Study area

Our study took place in both the Ozark Highlands and Arbuckle Mountain ecoregions. Both 

ecoregions are characterized by karst topography and spring flow characteristics (Woods et al. 
5

2005); however, the springs of the Arbuckle Mountain ecoregion are typically isothermic (Osborn 

2009; Christenson et al. 2011) and often located on small spring branches. The Ozark Highlands 

region is humid (102-122 cm precipitation annually), limestone dominated, and comprises mixed 

deciduous forest with lowland areas converted to pasture in many areas (Woods et al. 2005). 

Likewise, the Arbuckle Mountain ecoregion is humid (96-109 cm precipitation annually), 

dominated by both limestone and granite, and comprises tall grass prairie and oak savannas, with 

lowland areas converted to both cropland and pasture (Woods et al. 2005). Small and large 

impoundments, and other human alterations (e.g., mining, (Christenson et al. 2011); poultry 

pollution (Olsen et al. 2012), and accelerated streambank erosion are common to both ecoregions 

(Woods et al. 2005). 

Objective 1. Model the factors related to the distribution of Least 

Darter in the Arbuckle Mountain and Ozark Mountain 

ecoregions.

Study design

We sampled fish and habitat from streams of the Arbuckle Uplift and Ozark Highland 

ecoregions (Figure 2); all sampling was conducted from July through October in 2018 and 2019 

under base-flow conditions (0.00-5.00 m3/s) and relatively warm water temperatures (16.1-28.9 

°C). Our sites were riffle-pool complexes nested within stream reaches approximately 200-500 

m long; thus, multiple sample sites shared reach-scale attributes. Sample locations were 

selected to include 1) locations previously documented to support Least Darter, and 2) 

previously unsampled stream reaches or reaches where prior sampling did not detect Least 

Darter (Figure 2 and 3). Historical records revealed locations where Least Darter was 

previously collected or observed, but it was important to sample locations where previous 

detections were not reported to avoid sampling bias.
6

At each site (riffle-pool complex), two temporally replicated surveys were conducted to 

account for imperfect sampling detection (Mackenzie et al. 2002; Tyre et al. 2003). Because Least 

Darter is patchily distributed but also considered locally abundant (Pflieger et al. 1997), we

anticipated an average detection probability of 0.50 (i.e., the species was equally likely to be

observed as not observed) when designing the study. Given our detection estimate, two surveys at 

each site would be adequate to account for average detection, thereby allowing more sampling to 

be devoted to different sites rather than increasing the number of surveys. Increasing the number of 

sites is more important when sampling rare species and disjunct populations (Guillera-Arroita et al. 

2010).

Target species

Least Darter was the primary focus of this study, but we also documented conditions for a 

select group of sympatric species. We determined occurrence probabilities for Least Darter and 

Southern Redbelly Dace Chrosomus erythrogaster, Redspot Chub Nocomis asper, and Smallmouth 

Bass Micropterus dolomieu. Species were chosen based on 1) the hypothesized importance of 

spring habitats to species occurrences, and 2) the species ecological and economic importance. 

Southern Redbelly Dace and Redspot Chub were selected because they are considered spring 

associates (Seilheimer and Fisher 2010). We also sampled adult and sub-adult Smallmouth Bass 

because of their importance as a sportfish and top-level predator (Brewer and Orth 2014). 

Additionally, Smallmouth Bass offers an interesting comparison because the species is native in 

the Ozark Highlands ecoregion and non-native in the Arbuckle Uplift ecoregion (Miller and 

Robison 2004). Including a few sympatric species added perspective on ecological relationships 

for this assemblage and increased the robustness of our resulting ecological models.
7

Fish sampling

We used both snorkeling and seining to sample Least Darter and our other target fishes.

Seining and snorkeling are commonly used to sample Least Darter (Burr and Page 1979; Bergey et 

al. 2008; Wagner et al. 2012) and snorkeling is successfully used to sample Smallmouth Bass 

(Brewer and Ellersieck 2011; Mollenhauer et al. 2018) and Redspot Chub (Mollenhauer et al. 

2019) from clear, warmwater streams. Using two sampling approaches allowed for more sampling 

flexibility because it was not possible to sample all sites with both methods (i.e., too shallow to 

snorkel or too deep to seine). Lastly, sampling with two gears allowed us to identify and account 

for sources of detection variability with both approaches.

Sites were sampled using two temporally replicated snorkel surveys on separate days to 

both minimize disturbance on habitat and biota and ensure heterogeneity in the detection

probability estimates (MacKenzie et al. 2006; Dunham et al. 2009). The snorkeling approach 

followed the methods of Dunham et al. (2009). Snorkel surveys were conducted when the 

horizontal visibility was > 1 m and between 0800 hours and 1800 hours (i.e., when daylight was 

most conducive to sampling, Spyker and Vandenberghe 1995). Snorkel surveys were completed by 

1-2 people (depending on channel dimensions and complexity, Thurow 1994). Each snorkeler was 

randomly assigned to a snorkel lane. Snorkel lanes varied in width depending on water depth and 

habitat complexity (i.e., narrower lanes in complex habitat and shallow water). Snorkelers swam 

upstream in their designated lanes at approximately 2 m/min, spending more time in complex 

habitats. In areas too shallow for completely submerging their mask, snorkelers walked slowly 

upstream and visually scanned the stream bottoms. If a target species was observed using abovewater observation, the identity was confirmed by partially submerging the mask and viewing the 

individual. When snorkelers encountered a target species, they recorded the species, enumerated 
8

individuals and referenced the channel unit (see below) used on a polyvinyl chloride (PVC) writing 

cuff.

On the day of each snorkel survey, we resampled wadeable habitat at each site using a 

standardized seining protocol (Rabeni et al. 2009). Seining was completed following the 

temporally replicated snorkel surveys to ensure independent surveys (MacKenzie and Royle 2005). 

Seining began at the downstream end of each site. Similar to snorkel surveys, only Least Darter 

and other target species were counted via each seine haul. Only one seining event (multiple seine

hauls) through each site was completed because seining was intrusive (e.g., removal of vegetation); 

therefore, detection probability would be expected to change if we used a second pass on the same 

day.

Environmental data collection

Channel units were classified as riffles, runs, pools and backwaters following the general 

classification of Rabeni and Jacobson (1993). Riffles were characterized by higher gradients, faster 

velocities, and coarser substrates compared to the surrounding habitat. Runs were transitional 

habitats of intermediate depths and velocities with lower gradients. Pools were depositional 

habitats under base-flow conditions and were typified by slower velocities, low gradients, and finer 

substrates. Similarly, backwaters shared pool characteristics (i.e., depositional, slack water habitat 

with fine substrates) but were located off the main channel.

We measured site (i.e., riffle-pool complex) covariates hypothesized to relate to the 

detection of Least Darter and sympatric species (Table 1). Velocity and substrate were measured 

following methods of Dodd et al. (2008). Average water-column velocity (0.1 m/s, 0.6 of water 

depth at depths < 1.0 meters, Gordon et al. 2004) of each site was measured along three, evenly 

spaced transects perpendicular to streamflow using a Marsh McBirney Flo-mate (Hach, Loveland, 

Colorado). Depth (1.0 cm) was measured at the same points along the transects. The number of 
9

velocity and depth points measured depended on channel width (~1 measurement for every 1-2 m 

wide). Velocity and depth measurements were averaged to represent the general conditions at each 

site. Coarse substrate was estimated as a percent of the available substrate ≥ 90-mm diameter 

(Wentworth 1922). Percent coverage of coarse wood (1.0 m2; i.e., circumference > 10 cm, Dodd et 

al. 2008) was visually estimated at each site. We also estimated the percent cover of floating and 

emergent vegetation at each site and during each survey. Because water clarity is related to fish 

detection (Thurow 1994), we measured horizontal clarity using a Secchi disk. The Secchi disk was 

positioned downstream of a snorkeler, and clarity was determined by the maximum distance (to the 

nearest 0.1 m) at which the snorkeler could distinguish the bands on the disk (Tyler 1968). A single 

value was applied to multiple sites if they occurred within the same reach because we did not 

expect or observe clarity varying between nested sites.

We quantified both site and reach-scale occupancy covariates to determine the multiscale 

factors associated with species occurrence (Table 1). First, the surface area (1.0 m2) of each 

channel unit (i.e., pool, riffle, run and backwater) at each site was estimated by measuring wetted 

width and length. Additionally, we quantified percent of sand and silt at each site because Least 

Darter has been associated with finer substrates (Burr and Page 1979). Percent coarse wood, 

percent vegetation, and average site velocity were quantified as described for the detection 

covariates (previous paragraph). Residual pool depth (RPD) of each site was measured as 

described by Lisle (1987), where the difference between channel depth at the riffle crest and the 

deepest point of the downstream pool were quantified. A temperature logger was placed 

approximately mid pool within each reach to account for mean daily stream temperature (0.1 oC) 

over the same 2-week period. The same water temperature value was applied to each site within 

the reach. Discharge (0.1 m3/sec) was measured at the downstream and upstream end of each site 

with a Marsh McBirney Flo-mate (Hach, Loveland Colorado) using the velocity-area method 
10

(Gordon et al. 2004). Groundwater contribution was quantified using seepage runs following Zhou 

et al. (2018). The seepage contributions or losses (to the nearest 0.01 m3/sec) were calculated by 

taking the difference between the downstream and upstream discharge calculations to estimate a 

net gain or loss (gaining or losing stream discharge) for each site (Riggs 1972). Lastly, percent 

vegetation and percent coarse wood were calculated from the detection covariates by averaging the 

values from the two surveys.

Existing geospatial data were used to calculate several reach-scale covariates and were 

applied to multiple sites if they occurred within the same reach (i.e., nested). We calculated 

drainage area for each reach as it is a primary factor structuring fish distributions (Schlosser 1995; 

Fausch et al. 2002). We calculated the drainage area (km2) upstream of each reach using the 

software NHDPlus version 2 (Dewald et al. 2016) to help determine the position of the stream 

within the network (headwater or higher stream orders). A landscape development intensity index 

(hereafter LDI) was calculated using the 2016 National Land Cover Dataset (Homer et al. 2015) to 

represent a cumulative disturbance upstream from each site in the catchment. The disturbance 

index was calculated by simplifying the landscape development intensity index (LDI) of Brown 

and Vivas (2005) to include only the four land cover categories occurring in our study area (see 

Mouser et al. 2019): developed (coefficient = 7.31), cultivated crops (4.54), hay/pasture (2.99), and 

forested/wetland (1.00). For instances where Brown and Vivas (2005) have multiple categories for 

a land-use type such as hay/pasture: woodland pasture (2.02), pasture without livestock (2.77), low 

intensity pasture (3.41), high intensity pasture (3.74), these category values were averaged and 

assigned the same average value for each land category (see Mouser et al. 2019). We also 

designated all forested or wetland areas as being in a relatively “natural state.” The final 

coefficients for a site could range 0-10 where a higher LDI coefficient reflected increased land 

cover disturbance; however, in our data, the values only ranged from 1-3. 
11

Occupancy modeling and validation

We developed a single-season, multispecies occupancy model for our five target species to 

determine relationships related to both detection and site-level occupancy as described by 

MacKenzie et al. (2002). Repeat survey data are needed to account for species detection 

probability associated with habitat covariates (Mackenzie et al. 2002; Tyre et al. 2003). We used 

data where fish were both detected and not detected, allowing us to relate detection probability to 

the covariates measured at each site (MacKenzie et al. 2006). Occupancy modelling requires four 

assumptions: 1) occupancy state does not change at a site over the study season 2) constant 

occurrence probability across sites 3) constant detection probability among surveys, and 4) 

independence between detection histories. The first assumption was met by limiting our study 

season between July and October after spring floods and before water temperatures cooled during 

late autumn. The second and third assumptions were satisfied by using covariates hypothesized to 

explain differences in occurrence and detection probabilities (Mackenzie et al. 2002). The fourth 

assumption was met using temporally replicated surveys instead of multiple seining or snorkeling 

events on the same day (Mackenzie et al. 2002).

We made several data transformations and checked correlations among our detection 

covariates. Coarse substrate, velocity and depth were log-transformed because they were rightskewed. We checked the continuous detection covariates for high correlations (|r| > 0.50) using the 

Pearson correlation coefficients (Table 2). If two covariates were correlated, we retained only one 

to avoid redundancy. However, detection covariates showed no significant correlations (|r| < 0.26). 

Also, categorical covariates were examined for independence by determining the frequency at 

which covariates occurred together across sites. Our most complex detection model contained a 
12

quadratic depth term and several continuous detection covariates: percent coarse substrate, average 

water column velocity, water clarity, water temperature, and depth.

First, we built a detection model accounting for some species-specific relationships, but 

also more general relationships expected to be shared among species. We fit species-specific 

relationships with gear and ecoregion to determine how each species differed between ecoregions 

and with gear. We assumed species would have similar detection relationships with continuous 

covariates to avoid an overly complex detection model (i.e., place the emphasis on occupancy). 

Our most complex model included several continuous covariates and a gear and ecoregion factor 

where seining and the Arbuckle Uplift ecoregion were references. We included interactions with 

each of the continuous covariates and gear to account for a gear effect with differing habitat 

conditions. Additionally, we used stream reach as a grouping factor to account for unexplained 

variation and spatial correlation of the sites nested within a stream reach (Gelman and Hill 2006; 

Wagner et al. 2006).

After the most complex detection model was fitted, we simplified the model using an 

iterative process where we first tested interactions and then main effects by removing any 

covariates overlapping zero via the 95% highest-density intervals (HDIs: Kruschke 2014; Kery and 

Royle 2015). First, two-way interactions overlapping zero were removed, followed by rerunning 

the model and removing any main effects overlapping zero or not included in a critical (not 

overlapping zero) two-way interaction. All significant interactions (i.e., not overlapping zero) and 

corresponding main effects were retained in the detection model. The model was then refitted to 

determine if the 95% highest-density intervals of the main effects overlapped zero. The final 

detection model covariates were then included in the occupancy models to determine which 

environmental factors related to species occurrence.
13

We made several data transformations and checked correlations among occurrence 

covariates (Table 3). Percent fine, discharge, RPD, drainage area, percent vegetation, average twoweek temperature, and percent coarse wood were all log transformed due to skewness. All 

continuous occupancy covariates were standardized for each survey to a mean of zero and a 

variance of one to improve model interpretation and convergence (MacKenzie and Royle 2005). 

We chose drainage area over two other highly correlated variables (discharge and total area, Table

3) to minimize redundancy between variables. 

Next, we fit the most complex occupancy model, while including the detection 

relationships in our model. Incorporating the detection portion of the model allowed us to interpret 

physicochemical relationships at sites without species specific occurrence. The most complex 

occurrence model contained the following continuous covariates: drainage area, average two-week 

temperature, percent fine substrate, catchment-scale LDI, proportion pool area, RPD, seepage run, 

percent vegetation, and percent coarse wood. We additionally included three interaction terms we

hypothesized could be important: average water temperature and residual pool depth, total 

vegetation and residual pool depth, and a total vegetation-pool area interaction. We hypothesized 

occurrence probability of smaller-bodied species (i.e., Southern Redbelly Dace and Least Darter) 

would be higher in shallow areas with warmer water temperature because larger- bodied predators 

tend to have lower thermal tolerances and occupy moderate depths (Peck et al. 2013). We 

predicted that occurrence probability of the smaller-bodied species would be independent of 

residual pool depth at cooler water temperatures. Next, we hypothesized occurrence probability in 

areas of high vegetation (i.e., refuge from a variety of predators) would remain relatively constant 

across pool depths, whereas low vegetation in shallow water could reduce occupancy due to 

increased avian predation and lack of suitable habitat. Deeper water, however, even with limited 

vegetation reduces the risk of avian predation (Savino and Stein 1982; Rozas and Odum 1988). 
14

Lastly, we hypothesized occurrence probability in highly vegetated areas would be consistent 

regardless of pool area. However, if vegetation occurs in low quantities, occurrence should be 

lower for the small bodied species in larger pools because of the increased likelihood of predators 

(Burr and Page 1979a; Johnson and Hatch 1991; Hargrave and Johnson 2003).

We allowed each species to be modeled around the group mean (i.e., all species) and 

interpreted the results as the deflection of individual species from the group mean with covariates. 

This model structure shifts the attention to individual species rather than differences among species 

(the reference approach), similar to a “random-slopes” model (Jamil et al. 2013). Additionally, a 

grouping factor of stream reach was included in our model to account for the hierarchical structure 

of streams, with multiple sites within the same reach. Adding grouping factors accounts for the 

inherent pseudoreplication between nested sites (Wagner et al. 2006). 

We used a backward selection approach to simplify our overall model. First, we fit our

most complex model including all three-way interactions. We retained only significant three-way 

interactions (i.e., 95% HDIs that did not overlap zero) and then refit the model and examined twoway interactions. We again omitted any non-significant interactions by examining HDIs and 

removing non-significant interactions. Lastly, we fit a model that included significant three-way, 

and two-way interactions, and all main effects. In the last iteration, we retained only significant 

main effects and those variables that were part of a higher order interaction.

Priors were used to give the models starting points to begin estimation for posterior 

distributions used to assess model fit and estimate parameters. Broad uniform priors were used for 

main effects and species covariates and vague gamma priors for associated standard deviations 

(Kery and Royle 2015). The use of broad and vague priors is common when previous research 

gives no useful initial estimates (Kruschke and Liddell 2018). Broad priors follow a distribution 

(i.e., normal) and gives the model a basis for estimating parameters. Because the range of the prior 
15

is wide, its effect on the model outcome is minimal. Posterior distributions for covariates were 

estimated using Markov chain Monte Carlo methods with 60,000 iterations (first 10,000 = burn-in). 

Convergence was determined by applying the Brooks-Gelman-Rubin statistic (Gelman and Rubin 

1992), for which values <1.1 indicate adequate mixing of chains for all parameters.

We used MacKenzie and Bailey (2004) chi-squared goodness-of-fit test to assess the fit of 

our final model, where ĉranging from 1.00 to 1.02 is considered acceptable (Kery and Royle 

2015). The chi-squared goodness-of-fit test uses a factor to account for overdispersion and helps

yield more reliable inferences when using overdispersed data that is common for occupancy 

models (MacKenzie and Bailey 2004). Models were fit using the program JAGS (Plummer 2003) 

called from the statistical software R (version 3.5.3; R Developments Core Team 2019) using the 

package jagsUI (Kellner 2019). Detection and occurrence probability were determined by using the 

inverse logit of a parameter, while holding all other parameters at mean levels.

Objective 2. Identify the fine-scale habitat factors related to 

species occupancy, emphasizing temperature.

Design

We chose two study reaches (~150-m long with shallow riffles or a waterfall on each end), 

one in the Arbuckle Uplift ecoregion and one in the Ozark Highland ecoregion (Figure 4). The two 

reaches were selected because they had relatively high abundances of Least Darter (based on 

Objective 1 sampling), and we had access to these privately-owned lands. The reach in the 

Arbuckle Uplift was an unnamed headwater tributary of the Blue River (3rd order stream, Strahler 

1957) containing numerous artesian springs and consisting of multiple pool-riffle complexes. The 

Ozark Highlands study reach was a riffle-pool complex located on Snake Creek (3rd order, Strahler 

1957). The respective coordinates were 34°27'23.4"N 96°39'58.8"W and 36°09'02.1"N 

95°10'11.6"W.
16

Habitat availability

Relatively homogenous (~ 2 m2) habitat patches (hereafter, sampling units) were 

mapped across the study reaches prior to each seasonal sampling (February 2019, August 2019, 

and December 2019). We established transects both perpendicular and parallel to streamflow to 

quantify habitat conditions (Figure 5). We measured water depth (0.1 m) at the center of each 

sampling unit. Average water-column velocity (0.1 m/s) was measured at 60% of depth using a 

Marsh-McBirney Flo-Mate (Hach, Loveland Colorado) (Gordon et al. 2004). We visually 

estimated dominant substrate within each sampling unit using a modified Wentworth scale 

(Wentworth 1922). We then simplified dominant substrate into two classes: fine (i.e., bedrock, 

silt and sand, ≤ 2.0 mm), and coarse (i.e., gravel, pebble, cobble, >2.0 mm). The presenceabsence of coarse wood (> 10 cm diameter, Dodd et al. 2008) and percent aquatic vegetation 

cover was also estimated in each sampling unit.

We quantified water temperature in each habitat patch by measuring temperature 

continuously over each sampling period. Temperature was quantified using a fiber-optic 

distributed temperature sensing (FO-DTS) cable (Lios, Cologne, Germany), which measures 

temperature by sending a laser pulse down the cable and measuring the return speed and 

backscatter of the signal (Selker et al. 2006). To calibrate the instrument, the cable was run 

through a series of two or three differing temperature baths (cold, ambient, and hot) equipped 

with temperature loggers. Temperature at each point along the cable was calibrated using signal 

backscatter based on differences between the uncalibrated temperatures and known temperature 

from the calibration baths (Selker et al. 2006). We used these data to determine an average 

temperature for each patch along the cable (e.g., Selker et al. 2006; Westhoff et al. 2010). We

laid the cable on the stream bottom parallel to streamflow and anchored it with a rock or 

polyvinyl chloride (PVC) cylinder filled with cement in the center of each of our sampling 
17

units. The fiber optic cable had markings printed every meter so temperature measurements 

could be spatially referenced to each patch during each sampling event (see below).

Habitat use

We determined habitat use by Least Darter during both winter (February 2019) and 

summer (August 2019) in the Arbuckle Uplift (Figure 4). The Ozark Highlands reach was only 

sampled during winter (December 2019) because a suitable stream reach was not identified 

until summer 2019 (Figure 4). We anticipated groundwater upwelling would have the most 

influence on water temperatures during summer and winter rather than spring or autumn 

(Hubbs 1995; Constantz 1998). We hypothesized Least Darter would use patches of water 

temperature cooler than the median temperature available during summer and warmer than the 

median temperature during winter.

Fish location and associated habitat use were quantified during summer and winter. 

During each season, we conducted one snorkel survey daily for five or six days in winter and 

summer, respectively. We alternated between morning (~9:00) and afternoon (~15:00) survey 

times on consecutive days. Snorkeling followed the approach described for Objective 1 (see 

Fish Sampling). Briefly, snorkelers swam upstream in their designated lanes at approximately 2 

m/min, spending more time in complex habitats. Observers carefully examined habitat patches 

to locate fish, including under coarse substrate and within dense vegetation. Upon identification 

of Least Darter, a numbered, weighted fluorescent flag was placed on the substrate near the 

fish’s location. Flag number and fish count were recorded on a PVC wrist cuff. If the fish’s 

behavior appeared to be altered by the snorkeler, that habitat-use observation was omitted. We 

determined which spatially referenced sampling unit was nearest each fish observation at each

sampling event. We were then able to assign habitat use data to the fish use-point based on the 

habitat availability data (see previous section).
18

Analyses

To improve model interpretation and function, we transformed several predictor 

variables. Average water column velocity and vegetation were right-skewed, so they were logtransformed. Depth was still right-skewed following log transformation, so we had to square 

root transform these values. The deviation from the ecoregions median temperature was used in 

place of average temperature for each sample unit to normalize temperature across each 

ecoregion and season. We standardized all our covariates by subtracting the mean of the 

covariate from each value and dividing by the standard deviation to improve model 

interpretation.

We determined our final variable set by examining correlations and considering factors 

affecting Least Darter detection. We checked correlations among continuous covariates using 

the Pearson coefficient correlation. Categorical covariates were checked for correlations by 

determining the frequency of co-occurrence in our data and none were correlated (Table 4).

Correlations between our standardized continuous covariates were |r|  0.58, so all covariates 

could be used for model development (Table 4). We omitted water clarity because it was > 5 m 

and was not likely to affect Least Darter detection. Our final model set contained the following 

variables: deviation from the site median-temperature, square root of depth, log of percent 

vegetation, log of average water column velocity, four binary variables representing substrate 

(coarse or fine; fine was the reference), coarse wood (present or absent; absent = reference), 

season (winter or summer; summer = reference), and ecoregion (Arbuckle Uplift or Ozark 

Highlands; Ozark Highlands = reference).

We determined habitat selection by Least Darter using a generalized linear mixed 

model. The use-nonuse approach uses data from both occupied and unoccupied patches to 

strengthen habitat-use relationships (Johnson 1980). First, we built our most complex 

microhabitat model. In addition to the main effects, we considered reasonable interaction 
19

terms: deviation from median temperature × depth, vegetation × depth, and vegetation × 

deviation from median temperature. Patch use of Least Darter would be higher in shallow areas 

with more temperature deviation because larger-bodied predators tend to use moderate depths 

(Peck et al. 2013). We predicted patch use by Least Darter would be independent of residual 

pool depth at lower temperature deviations (Hetrick et al. 1998). Next, we hypothesized Least 

Darter would use patches with high vegetation coverage consistently across water depths 

because it would offer refuge from fish piscivores; whereas, fish would be less likely to use 

patches with low vegetation coverage in shallow water due exposure to avian predation. Deeper 

water without vegetation, however, acts as cover and reduces the risk of avian predation 

(Savino and Stein 1982; Rozas and Odum 1988). Finally, we hypothesized Least Darter would 

be more likely to use sample units with little vegetation if the water temperature was constant 

(i.e., no major deviation in heating extremes) because thermal refugia is considered important 

for Least Darter (Seilheimer and Fisher 2010). In areas of high vegetation coverage, we

hypothesized Least Darter patch use would be independent of temperature deviation. To 

account for variation between ecoregions, we included ecoregion as a nuisance factor to 

improve model fit and convergence. We also included a sampling-day grouping factor to 

account for unexplained temporal variation due to concurrent sampling days in each season. 

Lastly, we also included a grouping factor for patches at each site because of anticipated spatial 

autocorrelation (Gelman and Hill 2006; Wagner et al. 2006). 

The final overall model was selected using the same backward selection approach used 

in Objective 1. First, we fit a model containing interactions and removed any two-way 

interactions where the 95% HDI overlapped zero (i.e., not considered significant). The model 

was then refitted with main effects and only significant main effects were retained. The final 

model included only significant main effects and interactions (including associated main 

effects). Models were fit using the program JAGS (Plummer 2003) called from the statistical 
20

software R (version 3.5.3; R Developments Core Team 2019) using the package jagsUI 

(Kellner 2019). We used broad uniform priors for species coefficients and main effects and 

vague gamma priors for associated standard deviations (Kery and Royle 2015). Posterior 

distributions for coefficients were estimated using Markov chain Monte Carlo methods with 

100,000 iterations after a 50,000 iteration burn-in phase. Posterior predictive distributions were 

in the range of our data and were used to assess model fit. Convergence was determined by 

applying the Brooks-Gelman-Rubin statistic. Values of the Brooks-Gelman-Rubin statistic <1.1 

indicate adequate mixing of chains for all parameters (Gelman and Rubin 1992).

RESULTS

Objective 1. Model the factors related to the distribution of 

Least Darter in the Arbuckle Mountain and Ozark Mountain 

ecoregions.

Fish sampling

We sampled 153 sites nested within 61 stream reaches in the Arbuckle Uplift and Ozark 

Highland ecoregions (Table 5; Figure 2) during 2018-2019. Of the 153 sites, 42% (n = 64) 

were in the Arbuckle Uplift ecoregion, whereas 58% (n = 89) were in the Ozark Highlands 

ecoregion (Table 5). During the two summers of sampling, we conducted 284 seining surveys 

and 264 snorkel passes across all sites (Table 5). During summer 2018, 69 sites were sampled: 

26 sites in the Arbuckle Uplift and 43 in the Ozark Highlands (Table 5). During summer 2019, 

we sampled 84 sites: 38 sites sampled in the Arbuckle Uplift and 46 sites sampled in the Ozark

Highlands.

Commonness of our target species differed by ecoregion, and some species were easier

to sample using one of the two gears (Table 6). Least Darter was the rarest of the four target 
21

species and two life stages of Smallmouth Bass. Redspot Chub was common across both 

ecoregions. Both Smallmouth Bass life-history stages (adult and subadult) were relatively 

uncommon in the Arbuckle Uplift, but common in the Ozark Highlands. Least Darter was 

detected at more sites in the Arbuckle Uplift ecoregion (n = 15) than in the Ozark Highlands 

ecoregion (n = 3), whereas Redspot Chub, adult Smallmouth Bass and sub-adult Smallmouth 

Bass occurred at more than twice as many sites in the Ozark Highlands compared to the 

Arbuckle Uplift. Least Darter was detected at a comparable number of sites when seining or 

snorkeling. Smallmouth Bass, Redspot Chub and Southern Redbelly Dace were typically 2-3 

times more likely to be detected by snorkeling than by seining, regardless of ecoregion (Table 

6). In the Arbuckle Uplift, Redspot Chub and subadult Smallmouth Bass were exceptions as 

detection was comparable when using either gear (Table 6).

Physicochemical Conditions

The physicochemical conditions associated with our surveys varied across sites but 

were similar between ecoregions and sample year (Table 7). Sites in both ecoregions had 

moderate amounts (~25%) of coarse substrates and coarse wood (~15%). Average temperature 

(~23 °C), depth (~0.30 m), and average water column velocity (~ 0.17 m/s) were comparable

across sites in each ecoregion. Percent vegetation and water clarity were more variable at sites 

in the Arbuckle Mountain ecoregion. Additionally, sites in the Arbuckle Uplift tended to have 

higher percentages of vegetation and lower water clarity than those in the Ozark Highlands.

Site-level occupancy covariates were variable across sites and between the two 

ecoregions but comparable among sample years (Table 8). Water temperature, seepage runs, 

and LDIs were, on average, comparable between ecoregions. The most notable differences 

between sites in the two ecoregions was the percent of fine substrates (Arbuckle Uplift, 39%; 

Ozark Highlands, 10%). Similar to our detection covariates, average percent vegetation was 
22

higher in the Arbuckle Uplift (25%) than in the Ozark Highlands (15%). Lastly, residual pool 

depth was greater in the Ozark Highlands (0.74 m) than in the Arbuckle Uplift (0.54 m). 

Average physicochemical conditions between 2018 and 2019 were comparable with little 

variation except average water- column velocities were slightly higher in 2019 (0.21 m/s) than 

in 2018 (0.11 m/s).

Occupancy modeling and validation

The final occupancy model had appropriate model fit and adequate mixing of chains. 

The final model had an average ĉof 1.0 indicating appropriate model fit (Kery and Royle 

2015). Additionally, all model parameters successfully converged with an effective sample size 

of at least 7,847 suggesting the model had appropriate mixing.

Relationships between our target species and detection covariates were often shared 

among species, but some relationships were species-specific. The final model included water 

clarity and water depth interactions with gear (two-way interactions) as the only common slope 

among species, and the HDIs did not overlap zero for any predictor variable (Table 8). 

Southern Redbelly Dace, Redspot Chub and Smallmouth Bass had higher average detection 

probabilities when snorkeling compared to seining; however, detection was similar between the 

two gears when sampling Least Darter (Table 8). Detection increased with water clarity while 

snorkeling (i.e., seining as the reference) (Table 8, Figure 6). Similarly, detection probability 

was higher in deeper water when snorkeling (Table 8, Figure 7). Detection probability was not 

significantly different between ecoregions, but we retained an ecoregion factor in our model to 

account for unexplained spatial variation (Table 8).

Our occurrence model indicated both common (i.e., shared among species) and speciesspecific occurrence relationships with our predictor variables (Table 9). Occurrence probability 

of Least Darter in the Ozark Highlands was significantly lower than in the Arbuckle Uplift, 
23

whereas the reverse was seen for Smallmouth Bass sub-adults (Table 9). Occurrence 

probability was higher for Smallmouth Bass sub-adult and Southern Redbelly Dace in 2018 

than in 2019 (Table 9). Occurrence probabilities of both Least Darter and Southern Redbelly 

Dace increased with cooler water temperatures (Figure 8). Redspot Chub and both Smallmouth 

Bass life-stages were associated with larger drainage areas (Figure 9). Southern Redbelly Dace 

was negatively associated with sites having a higher proportion of pool habitat (Figure 10). 

Lastly, occurrence probabilities of Smallmouth Bass sub-adults and Redspot Chub were higher 

in deeper pools (Figure 11).

Objective 2. Identify the fine-scale habitat factors related to 

species occupancy, emphasizing temperature.

Fish sampling

Occupied patches by Least Darter varied by site and season. Least Darter was present in 

more microhabitat patches in the Arbuckle Uplift during winter (28%, 157 of 570 patches) 

when compared to the Ozark Highlands (6%, 52 of 870 patches). At the Arbuckle Uplift site, 

patch occupancy by Least Darter during summer was higher (36%, 281 of 786 patches) than 

during the winter (28%, 157 of 570 patches).

Habitat availability

There were some seasonal differences in habitat availability at our sites. In the Arbuckle 

Uplift, water temperatures were cooler during the winter compared to summer, though 

deviation from the median water temperature was similar between the two seasons. Fine 

substrates were common during both seasons and both ecoregions. During the winter, habitat 

patches of the Ozark Highlands tended to be less homogenous than patches of the Arbuckle 

Uplift (Table 10). Average temperatures during winter were cooler in the Ozark Highlands than 
24

the Arbuckle Uplift. There was also less variation from median water temperature at our site in 

the Ozark Highlands compared to the Arbuckle Uplift. Habitat patches were deeper with slower 

water velocities in the Ozark Highlands. Vegetation (% coverage) was higher at our site in the 

Ozark Highlands compared to Arbuckle Uplift during the winter. Lastly, coarse wood was 

present in more habitat patches of the Ozark Highlands compared to Arbuckle Uplift (data not 

shown in Table 10 as these data were collected as presence or absence at each patch).

Habitat use

Our final microhabitat model had appropriate model fit and adequate mixing of chains. 

The final model consisted of 10 significant terms (HDIs did not overlap zero). All model 

parameters successfully converged.

Least Darter was more common in habitat patches of the Arbuckle Uplift ecoregion, 

and habitat use varied between winter and summer (significant two-way interaction between 

use of depth, velocity, and vegetation and season, Table 11). During winter, Least Darter used 

higher water column velocities (0.12 m/s versus 0.06 m/s in the summer) and shallower habitat 

patches with less aquatic vegetation (Figure 12). Average water depth used was 19.6 cm during 

winter and 20.8 cm during summer. Vegetation was consistently used in both seasons but use

of low amounts of vegetation was higher during winter compared to summer. Lastly, patch use

was negatively associated with coarse substrates regardless of season (Table 11).

DISCUSSION & MANAGEMENT IMPLICATIONS

Objective 1

The historical distribution of Least Darter differed between the two ecoregions, and the 

arrangement of springs may be related to those observations. In the Arbuckle Uplift, Least 
25

Darter was presumed to only occur in a relatively small section of river on or adjacent to the 

mainstem Blue River where springs erupt regularly (Seilheimer and Fisher 2010). This differs 

from the Ozark Highlands, where Least Darter was historically broadly distributed (Figure 3). 

The western Ozark Highlands boasts karst topography but tends to have patchy cool water

upwelling rather than the isothermic springs visible in the main channel of the Arbuckle Uplift.

The arrangement of springs and the spatial and temporal influence of the associated cooler 

water temperatures may be key to current distribution of Least Darter where a certain length of 

stream with cooler water temperatures may be necessary for population persistence. 

We detected Least Darter at three new locations in the Arbuckle Uplift and two new 

locations in the Ozark Highlands during the summers of 2018 and 2019. Two of the locations 

in the Arbuckle Uplift were further upstream in the Blue River than previously documented 

(see Figure 2; 34°35'47.0"N 96°42'28.5"W and 34°37'20.8"N 96°46'26.8"W). Both occurrences 

were detected in early July when water temperatures were cooler but were not detected on the 

following visit at the end of July. We found no noticeable spring during our site visits. The 

third new detection of Least Darter in the Arbuckle Uplift was on the Nature Conservancy’s 

Oka’ Yanahli Nature Preserve (west portion; 34°27'03.2"N 96°39'17.8"W). To our knowledge, 

this is the first documentation of the species from that section of Blue River. The two new 

localities in the Blue River headwaters suggests isolated populations or metapopulations could 

occur in other areas of the Blue River and may contribute to the overall population (Falke and 

Fausch 2010). However, locating these small, isolated populations is difficult due to low 

sampling detection (MacKenzie et al. 2006). The two new localities within the Ozark 

Highlands both occurred adjacent to reaches with previous documented use by Least Darter: 1) 

Rock Creek (36°58'57.9"N 94°37'13.5"W), a second order tributary to Fivemile Creek, and 2) 

Snake Creek (36°09'07.2"N 95°10'11.9"W). A previous collection was made on Fivemile 

Creek (Echelle et al. 2015) upstream of the confluence with Rock Creek. However, no records 
26

of Least Darter have been documented for Rock Creek in Oklahoma or Missouri. We also 

detected Least Darter in a backwater approximately 70 m from the mainstem of Snake Creek 

downstream of a previous location at the Highway 82 bridge south of Locust Grove, 

Oklahoma. At the time of sampling, the backwater was completely disconnected from the 

mainstem river, but hyporheic flow was keeping the water cool (Arrigoni et al. 2008; Zhou et 

al. 2018). The section of Snake Creek near Highway 82 has several historical Least Darter 

collections and could be a reliable monitoring location. Future research could benefit from 

examination of seasonal habitat shifts by this species as the overall range may be broader based 

on seasonal trends. However, before we can begin to understand seasonal shifts, we first need 

to understand the overall distribution and how it might change annually.

The apparent decrease in suitable habitats for Least Darter at several of the known 

historic locations is likely related to many factors outlined for all North American fish species 

in Jelks et al. (2008) including altered flow regimes (Poff et al. 1997; Lynch et al. 2018), 

climate change (Hu et al. 2005), or introduced species (Rahel et al. 2008). The Least Darter 

was likely more widespread in the Ozarks as suggested by historical data. Populations of Least

Darter may have become more isolated and rarer after human landscape changes (TejerinaGarro et al. 2005) that affected spring-flow volume and stream morphologies (i.e., channel 

depth). Sand mines have become common in the Arbuckle Uplift area, disrupting baseflows

including disconnecting surface water and groundwater of the Arbuckle Simpson Aquifer 

(Christenson et al. 2011). Least Darter and other groundwater-associated species rely on the 

groundwater contribution in the upper Blue River drainage (Seilheimer and Fisher 2010) likely 

due to the importance of water temperature. Future efforts focused on understanding the spatial 

distribution of springs, and the relationships between springs and spatial thermal dynamics 

could be beneficial to developing detailed management actions. Best management practices 

associated with land-uses that affect groundwater-surface water are available in several states 
27

though the focus is often placed on groundwater contamination rather than flow volume or 

connections (see https://www.pca.state.mn.us/sites/default/files/wq-gw1-08.pdf).

Habitat alterations due to landscape changes not captured by our disturbance index may 

be of concern for species in both ecoregions including Least Darter (Seilheimer and Fisher 

2010; Christenson et al. 2011). The Ozark Highland sites were more disturbed based on our

LDI values (land use coefficients), and human landscape activities can intensify patchy 

distributions at range edges (Sagarin et al. 2006). The LDI coefficients used in this study were 

derived from land-use types in Florida based on energy consumption (Brown and Vivas 2005) 

and could benefit from improvement for applications in the Great Plains. Mouser et al. (2019) 

used a similar method and found occurrence probability of Faxonious spp. were negatively 

related to higher LDI coefficients (i.e., more disturbed) in the Ozark Highlands. Watershed 

characteristics (geology or topography), groundwater withdrawals, and surface runoff were not 

represented by our LDI coefficient but may be important to species that rely on cooler-water 

temperatures and relatively stable flows often associated with springs (Labbe and Fausch 2000; 

Duncan et al. 2010; Seilheimer and Fisher 2010). Future research might benefit from direct 

examination of these landscape perturbations, and perhaps the development of a similar land 

disturbance index focused on stressors affecting streams of the southern United States.

Our findings support the overall importance of accounting for incomplete detection if 

underlying ecological relationships are the focus. Accounting for incomplete detection is 

important to prevent Type I errors (Reid and Dextrase 2017; Reid and Haxton 2017; 

Mollenhauer et al. 2018). For example, Gwinn et al. (2015) documented several examples 

where incomplete detection led to erroneous conclusions about the underlying ecological 

relationships. Our results support snorkeling as the preferred and most reliable method in 

species occurrence assessments for warmwater fishes in clear groundwater-fed streams (Brewer 

and Ellersieck 2011; Chamberland et al. 2014; Mollenhauer and Brewer 2018).
28

Multiscale studies of spring-associated and other lotic warmwater species are important 

for developing conservation plans (Labbe and Fausch 2000; Wang et al. 2001; Torgersen et al. 

2006). For example, occupancy relationships of Least Darter and Southern Redbelly Dace 

could be used in management plans to identify areas of critical habitats for other species that 

rely on groundwater contributions and cooler water temperatures (Caissie 2006; Brewer 2013b; 

Mollenhauer et al. 2019). Human pressures are changing stream function and structure that is 

affecting the unique thermal regimes of groundwater habitats and their associated fish 

assemblages (Hynes 1975; Ward 1989; Fausch et al. 2002; Caissie 2006). Longitudinal stream 

management is important for groundwater-associated species because headwater streams are 

often cooler and contribute flow and nutrients to downstream reaches (Moore and Richardson 

2003). Protection of headwater streams could also provide refugia for small bodied fishes from 

predators and extreme temperatures (Schlosser 1995; Peterson and Rabeni 1996; Torgersen et 

al. 1999) and help maintain habitat complexity (cover, deeper pools, etc.). Restoration of 

riparian habitats and application of best management practices to catchments would reduce 

thermal pollution from runoff (Nelson and Palmer 2007) and agriculture pollutants that could 

contaminate an already limited and patchy environment (Osborne and Kovacic 1993; Johnson 

et al. 1997). Additionally, riparian habitats help mitigate the effects floods have on species by 

decreasing water column velocities and providing refugia during high flows (Swanson et al. 

1998; Tockner and Stanford 2002). Catchment disturbances can also lead to stream channels 

widening and pools becoming shallower resulting in an increase in stream temperatures 

(Harvey et al. 2003; Poff 2018). Conservation of groundwater-associated species could benefit 

from regional efforts to protect critical aquifers by regulating groundwater withdrawals (Labbe 

and Fausch 2000; Seilheimer and Fisher 2010). Protecting base flows could also protect against

stream channels drying and reducing hyporheic exchange resulting in increased stream 

temperatures (Cardenas 2009). The consistent water temperatures associated with springs
29

minimizes extreme fluctuations, creates thermal refugia and helps maintain baseflows 

(Matthews et al. 1985; Peterson and Rabeni 1996; Torgersen et al. 1999; Schaefer et al. 2003; 

Bergey et al. 2008).

Objective 2

Least Darter relationship with habitat use of water depth, water velocity, and vegetation 

shifted from summer to winter. Similar seasonal shifts in habitat use have been documented for 

other darter species often in response to reproduction cues or food resources (Wynes and 

Wissing 1982; Hubbs 1985; Harding et al. 1998). Least Darter at the Arbuckle Uplift site 

shifted to slightly higher water velocities and shallower water during winter when compared to 

the summer. The wintertime shift to shallower water is contrary to the observation that, in 

Minnesota, the species moved to deeper pools during the winter (Johnson and Hatch, 1991). 

This discrepancy might reflect the warmer winter temperature (i.e., air and groundwater 

temperature) in the southern portion of the range or the effects of springs moderating 

temperature extremes. Further, ice coverage in the northern portion of the range may force the 

species into deeper water during winter. Johnson and Hatch (1991), recorded that Least Darter 

moved to vegetated run margins after spawning in July and August. Given generally warmer 

stream temperatures in our study area, Least Darter may be occupying run margins throughout 

the winter. The slight decrease in vegetation use by Least Darter in the winter is interesting 

because all studies have documented strong relationships with vegetation for the species, for 

example in Illinois (Burr and Page 1979), Minnesota (Johnson and Hatch 1991), and Arkansas 

(Hargrave and Johnson 2003). However, these studies have not documented Least Darter 

habitat use during the winter. The decrease in vegetation use in winter could reflect lower 

vegetation availability in the winter causing shifts to other cover types, such as detrital material.

Finally, seasonal shifts could be a function of spawning activity. Winn (1958) observed 
30

seasonal movement in response to reproduction for Least Darter. Previous records of Least 

Darter in Oklahoma suggest that they enter breeding condition in February (Burr and Page 

1979), which matches our observations during February sampling.

Least Darter uses patches characterized by fine sediments (silt or sand) regardless of 

season (Burr and Page 1979; Johnson and Hatch 1991; Hargrave and Johnson 2003; Seilheimer 

and Fisher 2010; our results). Although the functional reasons are unknown, fine substrates

may be related to spawning habitat (Winn 1958) or foraging. Fine substrates are often used for 

foraging by prey species consumed by small bodied fishes like Least Darter (Angermeier 1985; 

Gilliam et al. 1989). Some degree of increase in fines has been shown to increase fish densities 

in other studies (e.g., Smallmouth Bass, Brewer and Rabeni 2011), although a threshold 

response is hypothesized for excessive fines due to land uses or other human effects that may 

have undesirable consequences (Quinn and Hickey 1990). Moreover, fine substrates can 

represent areas of suitable spawning due to the high amount of vegetation often located in high 

silty areas (Burr and Page 1979; Johnson and Hatch 1991). We found no correlation between

fine sediments and vegetation in our study, suggesting that vegetation and fine substrate act 

independently regarding Least Darter patch use.

We found temperature at a fine scale was not a significant factor related to habitat use 

by Least Darter. This result is interesting because, at the reach scale, temperature was the only 

factor that we found associated with Least Darter occupancy (See Objective 1). It is common 

for habitat relationships to manifest themselves or become indiscernible at different spatial 

scales (Frissell et al. 1986; Wiens 1989; Levin 1992). For example, temperature is an 

environmental factor affecting fish distributions but might not be important at fine spatial 

scales (Coutant 1976; Baltz et al. 1987; Buisson et al. 2008; Comte et al. 2013). Limited 

variation in temperature has been observed in other spring systems (Hubbs 1995). Likewise, 

our data also suggested little variation in temperature among patches at our sites (deviation 
31

from the median ranged -1.15 to 2.68 °C). However, small changes could be important for 

patch use across seasons (Kollaus and Bonner 2012); this relationship may be more important 

in streams with less spring or hyporheic influence though it is questionable whether the Least 

Darter would occupy those reaches (see Objective 1), particularly given the high summer air 

temperatures at this latitude. Our finding emphasizes the importance of springs in determining 

occupancy of stream reaches. It is unlikely that smaller seeps at finer patches are sufficient for 

maintaining populations. Management actions directed at maintaining cool stream temperatures 

(e.g., protecting spring and riparian buffers) could be worth considering if maintaining these 

populations is a conservation goal. 

Our findings support existing habitat-use patterns reported for Least Darter, but with 

some important winter habitat-use differences. Least Darter in the Arbuckle Uplift of 

Oklahoma selected habitat patches with finer substrates and lower water velocities during 

summer (Seilheimer and Fisher 2010). However, to our knowledge, our study is unique for the 

southern United States in noting a seasonal shift in habitat from summer to winter. Preparation

for spawning by Least Darter is expected by early spring (Burr and Page 1979; Johnson and 

Hatch 1991). We did find slight differences in depth use in our December (22.5 cm) and 

February (19.5 cm) sampling. Further, we found individuals in breeding condition during our 

February sampling (not December sampling), suggesting this is likely when spawning activity 

begins. Though seasonal habitat shifts did occur, the shifts seem to be rather minimal. Summer 

to winter shifts in darter habitat use are not uncommon. Fantail Darter Etheostoma flabellare

and Rainbow Darter Etheostoma caeruleum in Ozark Highland streams used riffle, run, or pool

habitats more indiscriminately in winter than in summer (Rettig and Brewer 2011). This might 

reflect seasonal temperature, thus metabolic differences, making summer selection of habitats

more important (Gillette et al. 2006). Additionally, it is common for groundwater-associated 

species to exhibit seasonal habitat shifts in response to spawning, rearing, and overwintering
32

(Wynes and Wissing 1982; Hubbs 1985; Harding et al. 1998; Wolf et al. 2019). Many studies 

focus on spring and summer habitat use, but relatively few have focused on winter habitat. 

Such studies may be important in light of climate change, increasing water demands, and other 

human perturbations.

Acknowledgements

This research is a contribution of the Oklahoma Cooperative Fish and Wildlife Research Unit 

(U.S. Geological Survey, Oklahoma Department of Wildlife Conservation, Oklahoma State 

University, and Wildlife Management Institute cooperating). This project was funded by the 

Oklahoma Department of Wildlife Conservation (F17AF01254 (T-99-R-1). This study was 

performed under the auspices of Oklahoma State University Animal Care and Use Protocol 

AS-18-13. We thank Skylar Wolf, Doug Zentner, Sean Kelly, Matt Smith, Josh Goff, Paul

Stafford, and Sarah Barnes for technical assistance and Steve Sammons and Amanda 

Rosenberger for providing helpful comments on an earlier draft. Any use of trade, firm, or 

product names is for descriptive purposes only and does not imply endorsement by the U.S. 

Government.

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44

Table 1. The detection and occupancy covariates and associated spatial scale (Scale) of each variable. Hypothesized effects on Least Darter: 

negative (─), positive (+), or null (∅) indicating no expected effect. The rationale for inclusion of each variable in the modeling process is provided 

under the justification column. RPD = residual pool depth

Detection

Scale Covariate Hypothesized effect Justification

Site Gear type Snorkeling > seining Detection probabilities can be different between the two 

methods.1,2

Site Water velocity (m/s) ─ Detection probability is reduced with increasing flow.3

Site Structure (+/-) ─ Presence of structure can lower detection.4

Site Substrate (%) ─ Larger substrates can reduce detection of stream fishes.5

Site Temperature (°C) ─ Warmer water temperatures make fish increasingly active 

and harder to detect

Site Vegetation (%) ─ Vegetation reduces detectability.6

Reach Water clarity (m) ─ Lower water clarity can reduce detection.7

Occupancy

Scale Covariate Hypothesized effect Justification

Reach Temperature (°C) ─ Least Darter most commonly documented in cooler 

waters.8,9,10
45

 

1. Goldstein 1978

Reach Ecoregion ∅ Used to account for unexplained variation between 

ecoregions

Reach Year ∅ Account for unexplained variation between years

Reach Landcover/Use ─ Lack of riparian and the presence of disturbed landscape 

can alter fish assemblages.12

Reach Drainage Area (km2) ─ Least Darter reported more commonly from smaller 

headwater areas.10

Reach Discharge (m/s3) ─ Least Darter documented from smaller streams (i.e. lower 

discharge streams).10,13,14

Site Seepage Run (m/s3) + Net gain of water from spring input represents occurrence 

of a cold-water spring.8,9,10

Site Silt/Sand (%) + Least Darter often observed over fine substrate.10,13,14

Site Percent Structure 

(%)

+ Least Darter often observed in relation to some 

structure10,13,14

Site Percent Vegetation 

(%)

+ Documented Least Darter prefers vegetated areas.10,13,14

Site Channel Unit Area 

(m2)

+ Least Darter have an affinity for pools but also use 

runs.10,13,14

Site RPD (m) + Stream permanence for spawning has been documented for 

Least Darter.14
46

2. Hagler et al. 2011

3. Mcmanamay et al. 2014

4. Thurow et al. 2004

5. Thurow et al. 2006

6. Bayley and Austen 2002

7. Mollenhauer et al. 2018

8. Bergey et al. 2008

9. Burr 1977

10. Pflieger 1997

11. Woods et al. 2005

12. Jones et al. 1999

13. Burr and Page 1979

14. Johnson and Hatch 1991
37

Table 2. The correlation matrix (Pearson’s correlation coefficients) for the continuous detection 

variables included in the detection model. Percent coarse wood, percent coarse substrate, 

percent vegetation, average water column velocity, average water depth, and water temperature 

were all measure at the site level (i.e., riffle-pool complex). Water clarity (clarity) was 

measured at the reach where multiple riffle-pool complexes were assumed to share similar 

conditions.

Coarse Wood Substrate Vegetation Velocity Depth Clarity

Substrate -0.07

Vegetation -0.53 -0.37

Velocity -0.06 -0.06 -0.20

Depth 0.43 0.01 -0.45 0.10

Clarity -0.34 -0.31 0.15 -0.23 -0.42

Temperature 0.04 0.06 -0.16 -0.37 -0.15 -0.32
38

Table 3. The correlation matrix (Pearson’s correlation coefficients) for the continuous variables 

that were considered in the occupancy model. The variables that were measured at the reach 

scale were water temperature (Temp, the two-week average water temperature), land use 

disturbance index (LDI), drainage area (Drain), and seepage run (Seep). Fine substrates (Fines), 

coarse wood (Wood), and vegetation (Veg) were expressed as percent coverage for each site. 

Pool area (Pool) was the proportion of total area (Total) represented by pool habitat. Discharge 

(Q) was quantified at the reach scale. The residual pool depth (RPD) was quantified for each site

(i.e., riffle-pool complex).

Temp LDI Drain Seep Fine Wood Veg Pool RPD Total

LDI 0.19

Drain 0.21 0.10

Seep -0.32 0.07 0.05

Fines -0.18 -0.91 -0.27 -0.19

Wood -0.03 -0.06 0.09 -0.26 0.04

Veg -0.19 -0.45 -0.43 -0.07 0.54 -0.51

Pool -0.37 -0.32 -0.23 -0.13 0.17 0.29 -0.31

RPD -0.24 -0.06 0.50 -0.13 -0.17 0.39 -0.53 0.56

Total -0.12 -0.08 0.82 0.34 -0.14 -0.05 -0.34 0.04 0.56

Q 0.00 0.10 0.89 0.35 -0.29 0.11 -0.53 -0.16 0.46 0.86
39

Table 4. The correlation matrix (Pearson’s correlation coefficients) for the continuous 

variables that were considered in the habitat use model. Deviation from the median 

water temperature (temp), water depth (depth), coarse wood (wood), percent vegetation 

(vegetation), and average water column velocity (velocity) were measure at each patch

(~ 2 m2).

Temp Depth Wood Vegetation

Depth -0.19

Wood -0.07 0.24

Vegetation -0.03 0.14 -0.03

Velocity 0.15 -0.58 -0.14 -0.36
40

Table 5. The number of sites (i.e., riffle-pool complexes) and reaches (200-500-m long) 

sampled in 2018 and 2019 in the Arbuckle Uplift (Arbuckles) and Ozark Highlands 

(Ozarks) ecoregions. The total is the combined number of reaches and sites from the two 

sampling years. 

Ecoregion 2018 

reaches

2018 

sites

2019 

reaches

2019 

sites

Total 

reaches

Total 

sites

Arbuckles 12 26 13 38 25 64

Ozarks 18 43 18 46 36 89

Total 30 69 31 84 61 153
41

Table 6. Species detections by sampling method (i.e., snorkeling or seining surveys) in the 

Arbuckle Uplift (Arbuckle) and Ozark Highlands (Ozarks) ecoregions. The numbers associated 

with each species and ecoregion reflect the frequency of surveys at each site where the species 

was detected, whereas occupied sites refers to the number of stream reaches where each species 

was detected (i.e., there were multiple gear surveys within each site). Smallmouth bass is 

referenced as SMB. 

Species Ecoregion Seine surveys Snorkel surveys Occupied sites

Least Darter Ozarks 3 2 3

Least Darter Arbuckles 15 22 15

Redspot Chub Ozarks 35 130 72

Redspot Chub Arbuckles 32 30 20

Adult SMB Ozarks 4 96 55

Adult SMB Arbuckles 0 13 8

Subadult SMB Ozarks 29 97 59

Subadult SMB Arbuckles 8 8 10

Dace Ozarks 37 57 36

Dace Arbuckles 15 26 18
42

Table 7. The summary statistics of covariates included in the detection or occupancy models for 

A) Ozark Highlands ecoregion, detection model; B) Ozark Highlands ecoregion, occupancy 

model; C) Arbuckle Uplift ecoregion, detection model; and D) Arbuckle Uplift, occupancy 

model. Abbreviations used for summary statistics are: N is sample size, SD is standard deviation, 

min is minimum, and max is the maximum value. Covariate abbreviations are: LDI, the land 

disturbance index; average temperature (occupancy covariate) refers to the 2-week average

temperature; and temperature (detection covariate) was quantified as a point measurement at the 

time of sampling.

N Mean SD Min Max

A)

Coarse Wood (%) 1805 16.00 16.51 0.00 75.00

Coarse Substrate (%) 1805 28.00 20.40 5.00 85.00

Vegetation (%) 1805 14.00 18.60 0.00 95.00

Average Velocity (m/s) 1805 0.16 0.13 0.00 0.64

Depth (m) 1805 0.27 0.14 0.06 0.72

Clarity (m) 1805 4.40 1.61 1.60 11.20

Temperature (°C) 1805 23.84 2.73 17.00 28.80

B)

Fine Substrate (%) 470 10.00 11.78 0.00 85.00

Residual Pool Depth (m) 470 0.74 0.53 0.02 2.20

Seepage Run (m/s3) 470 0.03 0.15 -0.24 1.26
43

Average Temp (°C) 470 23.15 2.52 16.14 27.77

Drainage Area (km2) 470 92.56 94.91 15.82 543.90

LDI 470 1.99 0.33 1.12 2.50

Proportion Pool 470 0.54 0.26 0.00 0.96

Vegetation (%) 470 14.73 18.63 0.00 90.00

Coarse Wood (%) 470 15.67 16.13 0.00 70.00

C)

Coarse Wood (%) 955 14.00 13.18 0.00 65.00

Coarse Substrate (%) 955 25.00 18.71 0.00 70.00

Vegetation (%) 955 25.00 29.17 0.00 95.00

Average Velocity (m/s) 955 0.17 0.11 0.00 0.47

Depth (m) 955 0.30 0.16 0.09 0.97

Clarity (m) 955 2.70 2.08 0.20 11.50

Temperature (°C) 955 22.96 3.87 14.20 30.80

D)

Fine Substrate (%) 295 39.00 26.65 0.00 90.00

Residual Pool Depth (m) 295 0.54 0.40 0.00 1.95

Seepage Run (m/s3) 295 0.02 0.09 -0.14 0.45

Average Temp (°C) 295 23.40 3.56 17.07 28.85

Drainage Area (km2) 295 73.65 110.25 1.00 329.08

LDI 295 1.42 0.18 1.06 1.76

Proportion Pool 295 0.59 0.24 0.00 0.98
44

Vegetation (%) 295 25.00 29.39 0.00 93.00

Coarse Wood (%) 295 14.00 12.56 0.00 52.00
45

Table 8. The significant coefficients (i.e., those with HDIs not overlapping zero) retained in 

the final detection model. The species by gear relationships were estimated using seining as 

the reference, whereas ecoregion was referenced to the Arbuckle Uplift and was retained in 

the detection model to account for unexplained variation. Water depth and clarity were 

modeled as common relationships among all species and are reported as the mean with the 

lower (Low) and upper (High) 95% credibility intervals.

Mean Low High

Species by gear

Least Darter 0.76 -0.29 1.82

Redspot Chub 2.57 2.04 3.13

Smallmouth Bass Adult 4.49 3.52 5.63

Smallmouth Bass Sub-adult 2.34 1.76 2.96

Southern Redbelly Dace 1.41 0.79 2.06

Species by ecoregion

Least Darter -0.61 -2.73 0.57

Redspot Chub -0.44 -1.04 0.14

Smallmouth Bass Adult 0.20 -0.77 1.31

Smallmouth Bass Sub-adult 0.14 -0.64 1.06

Southern Redbelly Dace 0.34 -0.32 1.06

Detection intercept by gear

Depth 0.61 0.28 0.95

Clarity 0.65 0.37 0.93
46

Table 9. The significant coefficients (i.e., those with HDIs not overlapping zero) retained in the 

final occupancy model (deflections from the group mean). Ecoregion and year are categorical 

covariates using Arbuckle Uplift and 2018 as references. The two-week average water 

temperature, drainage area, proportion pool, and residual pool depth are covariates that had at 

least one species-specific relationship. All covariates are reported as the mean occurrence 

probability with the lower (Low) and upper (High) 95% credibility intervals. Values are reported 

on the logit scale.

Mean Low High

Ecoregion

Least Darter -2.77 -5.29 -0.17

Redspot Chub 0.79 -1.01 2.50

Smallmouth Bass Adult 1.45 -0.47 3.41

Smallmouth Bass Sub-adult 2.09 0.40 3.82

Southern Redbelly Dace 0.37 -1.28 2.04

Year

Least Darter -1.13 -2.61 0.32

Redspot Chub -0.84 -2.16 0.57

Smallmouth Bass Adult -0.98 -2.38 0.43

Smallmouth Bass Sub-adult -1.61 -3.15 -0.33

Southern Redbelly Dace -1.26 -2.64 -0.01

2-week average temperature

Least Darter -1.38 -2.58 -0.34

Redspot Chub 0.36 -0.50 1.20
47

Smallmouth Bass Adult 0.23 -0.84 1.27

Smallmouth Bass Sub-adult 0.93 -0.06 1.95

Southern Redbelly Dace -2.53 -4.31 -1.41

Drainage area

Least Darter -0.18 -1.37 0.98

Redspot Chub 0.98 0.01 2.11

Smallmouth Bass Adult 3.09 1.25 5.91

Smallmouth Bass Sub-adult 1.44 0.41 2.63

Southern Redbelly Dace -0.02 -1.11 1.21

Proportion pool area

Least Darter -0.36 -1.18 0.44

Redspot Chub -0.41 -1.08 0.22

Smallmouth Bass Adult 0.75 -0.16 1.77

Smallmouth Bass Sub-adult 0.07 -0.56 0.70

Southern Redbelly Dace -0.77 -1.55 -0.07

Residual pool depth

Least Darter 0.39 -0.50 1.08

Redspot Chub 0.61 0.04 1.24

Smallmouth Bass Adult 0.59 -0.04 1.27

Smallmouth Bass Sub-adult 0.66 0.08 1.36

Southern Redbelly Dace 0.40 -0.44 1.01
48

Table 10. The summary statistics (sample size [N], mean, standard deviation [SD], minimum 

[min] and maximum [max]) of habitat patch covariates considered in the Least Darter 

microhabitat model. Temperature (temp) was not included in any of our models but 

summarized here for reference. Deviation from median was the difference between patch water 

temperature and the median reach water temperature in each season (i.e., all patches 

combined). The Ozark Highlands reach was only sampled during winter (December 2019) 

because a suitable stream reach was not identified until summer 2019 (see methods). 

N Mean SD Min Max

Arbuckle Uplift summer

Water temperature (°C) 786 18.52 0.85 16.54 24.29

Deviation from median temp (°C) 786 0.11 0.44 -2.53 2.09

Depth (cm) 786 17.32 7.54 4 41

Aquatic vegetation (%) 786 24.12 24.24 0 100

Average water column velocity (m/s) 786 0.15 0.16 0 0.65

Arbuckle Uplift winter

Water temperature (°C) 570 16.88 0.62 13.57 17.91

Deviation from median (°C) 570 0.10 0.52 -1.15 2.68

Depth (cm) 570 18.03 7.94 3 39

Aquatic vegetation (%) 570 7.53 12.86 0 75

Average water column velocity (m/s) 570 0.18 0.20 0 0.79

Ozark Highlands winter
49

Water temperature (°C) 870 12.46 1.98 9.52 17.44

Deviation from median (°C) 870 -0.02 0.46 -1.34 2.05

Depth (cm) 870 31.96 13.44 5 60

Aquatic vegetation (%) 870 35.86 30.31 0 100

Average water column velocity (m/s) 870 0.0001 0.020 0 0.20
50

Table 11. The final Least Darter microhabitat model results containing all coefficients. 

Substrate was a binary variable (coarse or fine, where fine was the reference). Ecoregion was a 

binary variable (Ozark Highlands or Arbuckle Uplift, where Ozark Highlands was the 

reference). All covariates contain lower (Low) and upper (High) 95% credibility intervals 

where no overlap indicates significance. References for binary variables are in parentheses: 

substrate (fine), reach/ecoregion (Ozark Highlands ecoregion), and season (summer).

Parameter Mean Low High

Intercept -4.16 -5.57 -2.84

Substrate (fine) -0.67 -1.16 -0.19

Depth 1.13 0.59 1.75

Vegetation 0.68 0.29 1.09

Water velocity -0.64 -0.88 -0.43

Season (summer) -0.26 -1.18 0.62

Ecoregion (Ozarks) 3.10 1.97 4.33

Season × depth -1.29 -1.98 -0.65

Season × vegetation -0.71 -1.24 -0.18

Season × velocity 0.42 0.14 0.70
51

Figure 1. Least Darter collection records for the United States and Canada. Data were obtained 

through literature review, online databases, and professional correspondence in 2017. See 

Appendix A for references. The state and U.S. border data were obtained by the authors from 

the USDA, NRCS geospatial data gateway 

(https://datagateway.nrcs.usda.gov/GDGHome.aspx). River layers were obtained from the 

USGS NHDPlus (https://www.usgs.gov/core-science-systems/ngp/nationalhydrography/access-national-hydrography-products).
52

Figure 2. Reaches sampled (black circles) in summer 2018 (30 reaches) and 2019 (31 reaches) 

and Least Darter detections (black triangles) in the Arbuckle Uplift ecoregion (dark grey, 

Woods et al. 2005) and Ozark Highland ecoregion (light grey, Woods et al. 2005) of 

Oklahoma. The state and U.S. border data were obtained by the authors from the USDA, 

NRCS geospatial data gateway (https://datagateway.nrcs.usda.gov/GDGHome.aspx). River 

layers were obtained from the USGS NHDPlus (https://www.usgs.gov/core-sciencesystems/ngp/national-hydrography/access-national-hydrography-products).
53

Figure 3. Historic Least Darter collection in Oklahoma during four time periods: (A) Pre 1970, 

(B)1970 to 1990, (C) 1990 to 2000, and (D) 2000 to current. See Appendix A for fish data 

sources. The state and U.S. border data were obtained by the authors from the USDA, NRCS 

geospatial data gateway (https://datagateway.nrcs.usda.gov/GDGHome.aspx). River layers were 

obtained from the USGS NHDPlus (https://www.usgs.gov/core-science-systems/ngp/nationalhydrography/access-national-hydrography-products).
54

Figure 4. Sample location within the Ozark Highlands ecoregion (black square) and Arbuckle 

Uplift ecoregion (black triangle). The ecoregions within Oklahoma are shaded gray: Arbuckle 

Uplift ecoregion (dark gray, Woods et al. 2005) and Ozark Highland ecoregion (light gray, 

Woods et al. 2005). The site within the Arbuckle Uplift was sampled using snorkeling during 

both summer and winter 2019, whereas the Ozark Highland site was only sampled during 

winter 2019. The state and U.S. border data were obtained by the authors from the USDA, 

NRCS geospatial data gateway (https://datagateway.nrcs.usda.gov/GDGHome.aspx). River 

layers were obtained from the USGS NHDPlus (https://www.usgs.gov/core-sciencesystems/ngp/national-hydrography/access-national-hydrography-products).
55

Figure 5. Image shows microhabitat grid used for Least Darter microhabitat sampling. Snorkelers start at the downstream end and 

work upstream underneath marking string that represent the longitudinal transects. The fiber optic cable was placed parallel to 

flow beneath the water’s surface to complete the grid network. Photo credit: Dusty Swedberg (February 2019). 
56

Figure 6. The relationship between mean detection probability (dashed lines are 95% 

credibility intervals) among target fishes (i.e., group detection) and water clarity when 

sampling via snorkeling (i.e., group detection) The relationship shown is in reference to 

seining.
57

Figure 7. The relationship between mean detection probability (dashed lines are 95% 

credibility intervals) among target fishes (i.e., group detection) and water depth when sampling 

via snorkeling (i.e., group detection) The relationship shown is in reference to seining.
58

Figure 8. The relationships between mean occurrence probability (deflection from the group 

mean with 95% credibility intervals) of Southern Redbelly Dace (black line) and Least Darter 

(gray line) and 2-week average stream water temperature (Temperature).
59

Figure 9. The relationships between drainage area (Area) and mean occurrence probability 

(deflections from the group mean with 95% credibility intervals) of Redspot Chub (black line), 

sub-adult Smallmouth Bass (light gray line), and adult Smallmouth Bass (dark gray line)
60

Figure 10. The relationships between mean occurrence probability (deflections from the group 

mean with 95% credibility intervals) of Southern Redbelly Dace (black line), and proportion 

of pool area at a site (Proportion Pool).
Figure 11. The relationships between mean occurrence probability (deflections from the 

group mean with 95% credibility intervals) of Redspot Chub (black line) and Southern 

Redbelly Dace (gray line) and residual pool depth. The x-axis is cutoff at 0.30 m because 

occurrence probability remained high thereafter. 
Figure 12. The mean probability of Least Darter use (with 95% credibility intervals) for 

the three significant covariate interactions with season, where summer was the reference: 

depth (top panel), vegetation (middle panel), and velocity (bottom panel). Relationships 

are shown with other significant covariates held constant at mean levels.
63

Appendix A. Sources of historical collection data obtained from agencies or databases (in 

parentheses). State agencies and museums were contacted in autumn 2017. The two 

databases, GBIF (Global Biodiversity Information Facility) and BISON (Biodiversity 

Information Serving Our Nation), were accessed in October 2017.

Scope Agency 

Oklahoma Oklahoma Water Resource Board 

Oklahoma Sam Noble Museum of Natural History

Oklahoma Oklahoma Department of Wildlife Conservation

Missouri Missouri Department of Conservation

Illinois Illinois Natural History Survey

Iowa Iowa Department of Natural Resources

Michigan Michigan Department of Natural Resources

Arkansas Arkansas Game and Fish Commission 

Minnesota Minnesota Department of Natural Resources 

Indiana Indiana Department of Natural Resources 

Wisconsin Wisconsin Department of Natural Resources 

Ohio Ohio State Museum of Biodiversity 

Michigan Michigan Department of Natural Resources

United States U.S. Geological Survey; BISON 

*International GBIF

* GBIF is an international data infrastructure funded by multiple governments