Assessing the spawning 

movement and habitat needs 

of riverine Neosho 

Smallmouth Bass

Shannon K. Brewer1

Andrew Miller2

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 # 145-2020
ii

About the Cooperator Science Series:

The Cooperator Science Series was initiated in 2013. Its purpose is to facilitate the archiving and 

retrieval of research project reports resulting primarily from investigations supported by the U.S. Fish 

and Wildlife Service (FWS), particularly the Wildlife and Sport Fish Restoration Program. The online 

format was selected to provide immediate access to science reports for FWS, state and tribal 

management agencies, the conservation community, and the public at large.

All reports in this series have been subjected to a peer review process consistent with the agencies 

and entities conducting the research. For U.S. Geological Survey authors, the peer review process 

(http://www.usgs.gov/usgs-manual/500/502-3.html) also includes review by a bureau approving 

official prior to dissemination. Authors and/or agencies/institutions providing these reports are solely 

responsible for their content. The FWS does not provide editorial or technical review of these reports. 

Comments and other correspondence on reports in this series should be directed to the report authors 

or agencies/institutions. In most cases, reports published in this series are preliminary to publication, 

in the current or revised format, in peer reviewed scientific literature. Results and interpretation of 

data contained within reports may be revised following further peer review or availability of additional 

data and/or analyses prior to publication in the scientific literature.

The Cooperator Science Series is supported and maintained by the FWS, National Conservation 

Training Center at Shepherdstown, WV. The series is sequentially numbered with the publication year 

appended for reference and started with Report No. 101-2013. Various other numbering systems have 

been used by the FWS for similar, but now discontinued report series. Starting with No. 101 for the 

current series is intended to avoid any confusion with earlier report numbers. 

The use of contracted research agencies and institutions, trade, product, industry or firm names or 

products or software or models, whether commercially available or not, is for informative purposes 

only and does not constitute an endorsement by the U.S. Government.

Contractual References:

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). Funding was provided by the Oklahoma Department of 

Wildlife Conservation (F15AF00102).

Recommended citation:

Brewer, S. K. and A. Miller. 2020. Assessing the spawning movement and habitat needs of riverine 

Neosho Smallmouth Bass. U.S. Department of Interior, Fish and Wildlife Service, Cooperator Science 

Series FWS/CSS-145-2020, Washington, D.C. https://doi.org/10.3996/css43006037

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
FINAL REPORT 

January 31, 2020

Assessing the spawning movement and habitat needs of riverine Neosho Smallmouth Bass

 Photo credit: S. Brewer

Shannon K. Brewer1, and Andrew Miller2

1 U.S. Geological Survey, Oklahoma Cooperative Fish and Wildlife Research Unit; 2Oklahoma 

Cooperative Fish and Wildlife Research Unit, Oklahoma State University
2

Principal Investigator

Shannon K. Brewer, Ph.D. – Assistant Unit Leader-Fisheries, U.S. Geological Survey, 

Oklahoma Cooperative Fish & Wildlife Research Unit, Oklahoma State University, 007 

Agriculture Hall, Stillwater, OK 74078

405-744-9841 (P) | 405-744-5006 (F) | shannon.brewer@okstate.edu

Executive Summary

Stream fishes are vulnerable to a variety of natural and anthropogenic stressors. Information on 

fish movements and habitat use is essential to conserve and manage populations, particularly at 

the edges of distributions and novel habitats. The Neosho Smallmouth Bass Micropterus 

dolomieu velox is endemic to the southwestern Ozark Highlands ecoregion, where the riverscape 

is highly dissected by impoundments. Our study objectives were to determine the pre-spawn, 

spawn, and post-spawn movements of adult, radio-tagged Neosho Smallmouth Bass, and identify 

the habitat factors at multiple spatial scales related to suitable spawning habitat. Movements by 

tagged fish in the Elk River of Oklahoma and Missouri and two Oklahoma tributaries draining to 

lower Elk River and Grand Lake O’ the Cherokee were greatest during the spring spawning 

period and were positively related to discharge and fish size; however, we observed considerable 

individual and stream-specific variability. Temperature and fish movement rate in the Elk River 

were positively related in all seasons except for winter, although temperature was less important 

for Smallmouth Bass movement in the smaller streams. Tagged fish were never detected using 

active or passive telemetry in the reservoir or reservoir-river interface except during periods of 

lotic character (i.e., the reservoir was not pooled above the Buffalo Creek-Elk River confluence). 

Nests were typically located at intermediate depths (mean = 0.8 m; SD = 0.3) and in low velocity 

habitats (0.0–0.2 m/s). Most of the nests examined comprised gravel substrates; however, 2.5% 

of the nests observed were located on full or partial bedrock substrate. We also documented nest 

clustering behavior by Neosho Smallmouth Bass (i.e., adjacent nests within 2 m of each other); 

66 nest clusters were identified across 22 stream reaches. Cluster presence was more prevalent in 

warmer stream reaches with wide, shallow channels, and less likely in groundwater-gaining 

reaches, whereas overall nest abundance was greater in warmer streams and reaches with deeper 

pools. We showed the importance of both warmer streams and deep pools of small streams for 
3

Smallmouth Bass rearing (i.e., young-of-year abundance). We found negative relationships 

between floods and first-year juvenile survival and show the importance of stream network 

position (i.e., adjacency to larger streams) for mitigating the negative effects of July floods. Our 

analyses of both nesting and young-of-year habitat use suggest small streams, typically not 

considered important to many fisheries, are responsible for a proportion of Neosho Smallmouth 

Bass production. Further, consideration of management actions restricting take during the early 

spawning season may be warranted due to the unique nesting behavior (i.e., clustering) exhibited 

by this subspecies.
4

Background 

Riverine ecosystems experience a wide range of human stressors. Among the most well-known 

and detrimental of these stressors are land development for agriculture and urbanization, and the 

impoundment of free-flowing rivers for flood control or hydroelectric generation (Vörösmarty et 

al. 2010). Agricultural and urban landscapes often generate nonpoint sources of pollution that 

result in excessive influxes of nutrients, sediment, and contaminants to flowing waters (Allan 

2004). Furthermore, these land-use conversions typically affect soil infiltration capacity, which 

in turn leads to altered hydrology (e.g., larger floods, lower base flows) and instream habitat 

(Peterson and Kwak 1999; Wang et al. 2001). Impoundments fragment riverine habitat and 

create discontinuities in longitudinal environmental gradients along the river corridor (Ward and 

Stanford 1983). For example, dams alter flow regimes (Poff et al. 1997), thermal patterns 

(Caissie 2006), and instream habitat (Ligon et al. 1995) in ways that typically favor tolerant, 

generalist fish species at the expense of endemic, locally adapted species (Poff and Zimmerman 

2010).

A disproportionately high degree of fish biodiversity is concentrated in freshwater 

ecosystems, but several major threats put this diversity at risk. Globally, 40% of fish species are 

found in freshwater, despite these systems covering less than 2% of the Earth’s surface 

(Lundberg et al. 2000; Allan and Castillo 2007). This diversity of fishes provides value to 

humans via fisheries and ecosystem services (e.g., nutrient subsidies), and confers resistance and 

resilience to biota in dynamic environments (Schindler et al. 2010). Projected biodiversity 

declines across all taxa are greatest in freshwater ecosystems (Sala et al. 2000). Among the 

abiotic threats to freshwater biodiversity are habitat loss and flow modification (Richter et al. 

1997; Dudgeon et al. 2006). Species with specialized habitat needs and limited physiographic 

ranges are particularly vulnerable to extirpation (Moyle and Leidy 1992; Angermeier 1995). 

Altered flow regimes drastically change the physicochemical environment, influence the quantity 

and quality of stream habitat, and disrupt the timing of key life-history events in fishes (Poff et 

al. 1997; Bunn and Arthington 2002). Despite this, basic ecological information (e.g., natural 

history, migrations, and flow-ecology relationships) is often lacking for vulnerable species such 

as narrow-range endemics (Jelks et al. 2008; Cooke et al. 2012). 
5

Smallmouth Bass Micropterus dolomieu is an important ecological and recreational 

component of many warmwater systems in North America. Smallmouth Bass plays a critical role 

in aquatic food webs by acting as a top predator and conferring top-down effects (Power et al. 

1985; Vander Zanden et al. 1999; MacRae and Jackson 2001), making them highly problematic 

invasive species in some regions (Whittier and Kincaid 1999; Carey et al. 2011). The species is a 

common target species of recreational anglers (Long et al. 2015), known for strong fighting 

ability relative to body size (Henshall 1881). Fisheries including combination of Smallmouth 

Bass subspecies and unique strains are economically important and are found in rivers, lakes, and 

impoundments (Martin and Fisher 2008; Carey et al. 2011). 

Populations of Smallmouth Bass in the southwestern Ozark Highlands ecoregion are of 

special interest to scientists and managers due to their unique genetics and importance as 

sportfish, but they also face several threats. First described by Hubbs and Bailey (1940), the 

Neosho subspecies M. d. velox (hereafter “Neosho Smallmouth Bass”) is one of three 

genetically-distinct clades of Smallmouth Bass (Stark and Echelle 1998). Neosho Smallmouth 

Bass is endemic to streams in the Ozark Highlands of Oklahoma, Missouri, Arkansas, and 

Kansas, comprising the extreme southwestern extent of the native range of Smallmouth Bass 

(Brewer and Long 2015). The subspecies is a popular angler target both regionally and nationally 

(i.e., “bass slams”) and experience particularly high catch and harvest rates (Martin and Fisher 

2008; Long et al. 2015; Taylor et al. 2019). Annual economic benefits of angling in eastern 

Oklahoma streams, where 70% of licensed anglers pursue black bass (i.e., Smallmouth Bass, 

Spotted Bass M. punctulatus, and Largemouth Bass M. salmoides), were approximately $24 

million in 1993 (Fisher et al. 2002). Throughout much of the distribution of the Neosho 

subspecies, reservoirs fragment the landscape (Brewer and Long 2015) and create challenges for 

the persistence of these populations (e.g., reduced gene mixing, Taylor et al. 2018). Furthermore, 

Ozark streams are naturally flashy (Leasure et al. 2016), and many smaller streams containing 

Neosho Smallmouth Bass are seasonally disconnected (e.g., Hafs et al. 2010). This loss of 

habitat connectivity at multiple scales may force diminutive populations in some stream 

fragments to use suboptimal habitat conditions during critical life events (e.g., use of bedrock 

substrate for spawning; S. K. Brewer and J. Burroughs, personal observations). 

Relative to the well-studied northern subspecies (see review by Brewer and Orth 2015), 

we lack general information on many aspects of Neosho Smallmouth Bass life history (e.g., 
6

seasonal movements, life history diversity, habitat needs, and growth; Brewer and Long 2015). 

The role of connected habitats is also of interest given the many smaller, seasonally disconnected 

streams and reservoir-connected systems throughout the subspecies range. Information on 

seasonal movement patterns, and an improved understanding of the hierarchical influences on 

nesting and juvenile habitat will allow for more relevant management actions to be developed 

(e.g., protection and enhancement of important spawning and rearing habitats, defining 

management objectives for different streams, and delineation of important movement corridors).

Therefore, our study objectives were to determine the pre-spawn, spawn, and post-spawn 

movements of riverine Neosho Smallmouth Bass, and identify the habitat factors at multiple 

spatial scales related to suitable spawning habitat.

Objective 1. Assess the movements of riverine Neosho Smallmouth Bass before and 

following the spawning period

Background

Much of what is known about spawning by Smallmouth Bass relates to studies of the northern 

subspecies (Micropterus dolomieu dolomieu). Spawning occurs within the littoral zone of lakes 

and nearshore habitat of lotic systems from April to mid-July at southern latitudes (e.g., 

Missouri, Pflieger 1975). Adult Smallmouth Bass migrate to spawning sites in spring when the 

temperature is approximately 15 oC (Coble 1975). Larger males spawn first, and repeat nesting 

may occur if nest failure is perceived by fish due to flooding or weather events (Shuter et al. 

1980; Lukas and Orth 1995; Larimore 2002; Orth and Newcomb 2002; Cooke et al. 2006). 

Larger males account for the highest production of free-swimming larvae and make the most renesting attempts (Lukas and Orth 1995; Knotek and Orth 1998). Male Smallmouth Bass guard 

saucer-shaped nests (depressions in coarse substrates, Pflieger 1966). Many males exhibit nestsite fidelity, nesting within 20 m of their previous year’s nest site (Ridgway et al. 1991; Barthel 

et al. 2008). Larvae become free-swimming 4-16 days after hatching and are 8.1–10.1 mm total 

length (Ridgway and Friesen 1992; Warren 2009). Males provide parental care during early 

ontogeny until juveniles begin to disperse (see Brewer and Orth 2015 for a complete review). 

The free-swimming larvae are referred to as ‘black fry’ due to their pigmentation. Young-of-year 
7

(YOY) have the body shape and pigmentation of adults at approximately 24 mm total length 

(TL; Brewer and Orth 2015). The black fry school near the nest and guarding male, slowly 

expanding their foraging area during the day but remaining near the stream bottom at night 

(Brewer and Orth 2015). Although research efforts historically tended to focus on the northern 

subspecies, conservation and management agencies recognize the importance of the subspecies 

(i.e., Neosho subspecies M. dolomieu velox) and much more attention is being paid to this and 

other endemic bass (Taylor et al. 2019). 

Migration patterns by Smallmouth Bass are not well studied in highly modified 

riverscapes (i.e., connected rivers and reservoirs). Potamodromous behavior is evident for many 

warmwater fishes in natural systems (e.g., Golden Perch Macquaria ambigua and Silver Perch 

Bidyanus, Mallen-Cooper and Stuart 2003; Shoal Bass M. cataractae, Sammons 2015). For 

example, Smallmouth Bass in a connected lotic-lentic system in Ontario, Canada, overwinter in a 

lake, but spawn in either the lake or connected river habitats (Barthel et al. 2008). The use of 

connected habitats in human-modified systems (e.g., impoundments) is less clear, though such 

modifications appear to create barriers to movement by native fishes in some cases (Herbert and 

Gelwick 2003; Pelicice et al. 2015). Understanding migration patterns between these connected 

systems would allow for more relevant management actions to be developed (e.g., when to 

sample and how to define management units and seasons). 

Study area

We tracked radio-tagged adult Smallmouth Bass from three populations occupying different 

lotic-lentic interfaces: 1) a small creek (Sycamore Creek) that terminates at Grand Lake O’ the 

Cherokees (hereafter Grand Lake), 2) a tributary (Buffalo Creek) that terminates at the 

confluence of a larger river-transient reservoir interface, and 3) a fifth-order river population 

situated between a low-head dam and Grand Lake (Elk River; Figure 1). Sycamore Creek is a 

third-order stream (Strahler 1952) that flows 18 km, draining a 133-km2catchment, before 

reaching Grand Lake, Oklahoma. A road crossing 4 km upstream of the reservoir creates a 

barrier to fish movement on Sycamore Creek. Buffalo Creek is also a third-order stream and 

flows 42 km southwest from Missouri to Oklahoma and drains a 293-km2catchment before 

terminating in the transient river-reservoir interface of Grand Lake and the Elk River. The fifth-
8

order Elk River flows west for 41 km from Pineville, Missouri to Grand Lake, draining a 2,524-

km2catchment. A low-head dam in Noel, Missouri disconnects the lower 23 km of the Elk 

River, though this barrier may be passable during higher flows (i.e., ~2-year return interval 

discharge, R. Horton, Missouri Department of Conservation, pers. comm.). These streams are 

characteristic of the Ozark Highlands ecoregion, and contain distinct riffle-pool sequences and 

some off-channel habitats. Groundwater inflows are common but spatially variable (Brewer 

2013a; Zhou et al. 2018). Substrates are predominately gravel, pebble, and cobble with some 

exposed bedrock. 

Field collection and tagging

We conducted initial capture and tagging of Smallmouth Bass in May 2016. All collection and 

tagging procedures were conducted under the auspices of Oklahoma State University Animal 

Care and Use Protocol number AG-16-8. We used boat and tow-barge (Stealth Mini-Boat; 

Midwest Lake Management, Polo, Missouri) electrofishing and angling to sample 99 

Smallmouth Bass weighing ~400 g or greater across the three study streams (n = 40 in Buffalo 

Creek, n = 30 in Elk River, n = 29 in Sycamore Creek). Boat electrofishing was conducted from 

a 4.3-m boat with a Smith-Root 5.0 generator-powered pulsator and one boom-mounted anode 

using pulsed-DC electricity. For each of the three study streams, tagging occurred in 4–6 

locations of varying distance from major confluences and the river-reservoir interface (Figure 1). 

We anesthetized fish by immersing them in a 30-mg/L bath of Aqui-S 20E (Aqui-S, Lower Hutt, 

New Zealand) until fish lost equilibrium, typically 2–5 min, and then recorded TL (1 mm), sex 

(where possible), and weight (1 g). Anesthetized fish were placed ventral side up on a V-shaped 

surgical table fish and had a low-dose (15 mg/L) of Aqui-S 20E was continuously flushed across 

the gills. We made a ~15 mm incision slightly lateral to the ventral midline and inserted MCFT2 

radio transmitters (Model 3BM: 11 × 43 mm, 8 g in air, and Model 3EM: 12 × 53 mm, 10 g in 

air; Lotek Wireless, Newmarket, Ontario) into the coelom (Cooke and Bunt 2001). Estimated tag 

life was 723 and 860 days, respectively. We trailed the antenna out of a secondary incision to 

reduce risk of tag loss (Ross and Kleiner 1982) and trimmed the antenna to the caudal fin to 

reduce the potential for biofouling (Thorstad et al. 2001). The incision was closed using 

absorbable material (Unify PGA; AD Surgical, Sunnyvale, California) and a pair of simple 
9

interrupted sutures (Cooke and Bunt 2001). Following surgeries, fish were placed in flowthrough containers in a shaded portion of the stream and allowed to recover for a minimum of 30 

min prior to release near the site of capture.

We conducted additional tagging of Smallmouth Bass in October 2016 and March–April 

2017 using transmitters recovered during tracking efforts. If recovered transmitters were in good 

condition, they were cleaned and sterilized prior to implantation into new fish. We performed 

tagging in autumn and spring to avoid warmer water temperatures associated with greater 

handling stress, mortality, and transmitter loss (Bunnell and Isely 1999; Walsh et al. 2000). 

These tagging efforts combined with initial tagging brought the total number of tagged fish to 

152 over the study period (n = 62 in Buffalo Creek, n = 50 in Elk River, n = 40 in Sycamore 

Creek).

Movement and habitat use 

Location accuracy associated with tracking tagged fish was determined prior to fish tagging. 

Tags were hidden in numerous stream reaches and relocated by each team member without prior 

knowledge of the tag location. Location accuracy was within ~1.5 m based on dummy tag 

relocations. 

We actively tracked radio-tagged Smallmouth Bass in our three tracking reaches from 

June 2016 to February 2018. Fish tracking did not occur until after a minimum of 48 h postsurgery. We tracked approximately weekly from March–October and monthly from November–

February (Appendix A, Table A1). All tracking was conducted during daylight hours (~0700-

1900). Manual tracking occurred via foot or from a kayak or on foot using an SRX800 VHF 

receiver (Lotek Wireless) and a three-element Yagi antenna. We tracked in a downstream 

direction covering the lower 9.3 km of Sycamore Creek, the lower 14.3 km of Buffalo Creek, 

and the lower 23.4 km of the Elk River (Figure 1). Following the initial detection of a tagged 

fish, we slowly approached the area of strongest signal and gradually reduced receiver gain to 

improve directionality (i.e., homing; Koehn et al. 2012; Westhoff et al. 2016; Heim et al. 2018). 

We were able to visually confirm tagged fish presence for 23% (505/2,233) of relocations.

In addition to tracking our regularly designated stream reaches, we also actively tracked 

fish into the reservoir on multiple occasions. Each tracking event started at the downstream end 
10

of either the Sycamore Creek reach or the Elk River. From Sycamore Creek, we tracked to a 

location near Council Cove (36.717886, -94.738625). From the Buffalo Creek-Elk River 

confluence, we also actively tracked downriver into Grand Lake O’ the Cherokee at S 678 Road 

(36.646781, -94.666359). 

Tagged fish using coded radio tags can be individually identified and located either 

actively (with an antenna as described above) or with passive receivers (Kuechle and Kuechle 

2012). Both methods of tracking were used in this study with the passive receivers used 

primarily to determine if tagged fish left the active tracking area and moved into the reservoir.

Three receiver stations with fixed-position antennas were used to help monitor tagged fish in the 

study area. The stations were placed at the confluence of Elk River and Buffalo Creek, the 

Sycamore Creek-reservoir interface, and the Elk River-reservoir confluence downriver of 

Buffalo Creek (36.650083, -94.650809). The stations were placed near major confluences or 

reservoir interfaces to detect fish leaving the rivers or crossing the reservoir interface with 

Buffalo Creek during the spring when the reservoir backed up. Each receiver station consisted of 

two, three-element Yagi antennas (i.e., one facing upstream and one downstream) positioned 3–4 

m high on the bank (i.e., in a nearby tree) and angled toward the river. The three stations

continuously scanned the radio frequencies used and recorded the frequency, identification 

number, and time stamp of each detection. Each receiver was powered by a marine battery 

connected to a solar panel. Data collected by the receivers were downloaded approximately 

every 7-10 days.

We obtained GPS coordinates and recorded habitat characteristics at each fish relocation. We recorded channel unit type as riffle, run, pool, or slackwater based on current 

velocity, depth, gradient, and substrate (simplified from Rabeni and Jacobson 1993). We also 

measured focal depth (0.01 m), water-column velocity (0.6× depth, 0.1 m/s; FP111 Flow Probe, 

Global Water, Sacramento, California), and water temperature (0.1 ̊C; Ultrapen PT1, Myron L, 

Carlsbad, California). We noted the nearest cover type within the channel unit each fish was 

relocated: woody debris, boulder, rootwad, undercut bank or bedrock, vegetation, man-made 

cover, and combinations of these types. Man-made cover consisted of miscellaneous structures in 

the stream (e.g., bridge pilings, broken concrete). We also quantified distance to cover (0.5 m) 

for each location; fish were considered using cover if cover occurred within 1 m of their location 

(Probst et al. 1984). We visually estimated dominant substrate class within a 1-m2area 
11

surrounding each fish location using a modified Wentworth scale (Bain 1999): silt (< 0.06 mm), 

sand (> 0.06–2 mm), gravel (> 2–16 mm), pebble (> 16–64 mm), cobble (> 64–256 mm), 

boulder (> 256 mm) and bedrock (Brewer 2011). If a fish demonstrated a fright response to 

tracking efforts (indicated by a sudden, drastic decrease in received signal strength), we recorded 

a waypoint at the initial fish location but did not collect microhabitat information.

After evaluating fish location data, we summarized fish movement in two ways and 

estimated their home range. We plotted fish locations in ArcMap 10.3.1 (ESRI, Redlands, 

California) and used field-collected information on recovered transmitters and visual 

confirmation of tagged fish to evaluate the validity of each fish location (Schwarz and Arnason 

1990). For example, if a transmitter was recovered or a fish was found in the same location over 

multiple consecutive tracking events, we used prior locations and visual confirmations of tagged 

fish to identify and remove spurious “fish” locations. We combined the updated fish location 

waypoints and manually digitized stream lines in ArcMap and used the “Locate Features Along 

Routes” tool to measure the distance (1 m) along the stream between relocations. We calculated 

daily movement rate as the distance moved between consecutive locations divided by the number 

of days between locations. This metric accounted for varying amounts of time between fish 

locations and was particularly useful for evaluating the role of daily changes in environmental 

conditions (i.e., water temperature, stream discharge; see analyses section). We also calculated 

home ranges for each fish as the distance between the maximum upstream and downstream 

locations (Hill and Grossman 1987; Young 1994).

Analyses

Several variable calculations, transformations, and standardizations were necessary prior to 

modeling to meet model assumptions and improve interpretation. The response variable, 

movement rate, was natural-log transformed to account for skewness; we added a constant (i.e., y 

+ 1) to all values to facilitate the transformation in cases of no apparent movement. We used 

stream and season as categorical predictors with three and four levels, respectively. Water 

temperatures were used to define the following seasons as spring (rising between 10–20°C; 

approximately March–May), summer (> 20°C; approximately June–September), autumn 

(decreasing between 20–10°C; approximately October–November), and winter (< 10°C; 

approximately December–February). We quantified both mean water temperature and the change 
12

in water temperature over the period between consecutive fish locations. To account for 

differences in stream size, discharge data were first scaled by the median streamflow values for 

each stream during the study (McCune and Grace 2002): Buffalo Creek (0.43 m3/s), Elk River 

(6.68 m3/s) and Sycamore Creek (0.38 m3/s). We then calculated both mean discharge and the 

coefficient of variation (CV: σ

μ

× 100) of discharge for the period between successive locations. 

Lastly, we included fish TL as a predictor. We natural-log transformed mean discharge and fish 

TL to account for skewness and standardized all continuous predictors (mean = 0; SD = 1) to 

improve model interpretation (McCune and Grace 2002).

We built linear mixed models (LMMs) and used a model selection approach to determine 

the relationship among Smallmouth Bass daily movement, stream, season, environmental 

conditions, and fish size. All analyses were conducted in R version 3.5.1 (R Core Team 2018). 

We considered all subsets of the model containing terms for stream, season, mean water 

temperature, change in water temperature, mean discharge, CV of discharge, fish TL, and the 

following interactions: stream × mean temperature, stream × mean discharge, season × mean 

temperature, season × mean discharge, and season × stream. Pairwise correlations between 

predictor variables were < |0.44|, so all combinations were included in the candidate model set (n

= 563 models). All models included a random effect for individual fish to account for unequal 

sampling and the lack of independence among measurements (Otis and White 1999; Wagner et 

al. 2006). We assumed a normal distribution N(0, τ2) for random effects, where τ2represents the 

population variance among random effect levels (e.g., among individual fish). Candidate LMMs 

were coded using the ‘lme4’ package (Bates et al. 2015) in R. We used the R package 

‘AICcmodavg’ (Mazerolle 2017) to rank our models using Akaike’s information criterion 

adjusted for small sample size (AICc, Sugiura 1978). For this and all subsequent model-selection 

efforts, we did not consider models within 2 AICc of the top model to have support if they 

contained more parameters than the top model; this approach avoids unnecessary model 

complexity and reduces the inclusion of uninformative predictors (Arnold 2010). We evaluated 

residual and Normal Q-Q plots for the top model to ensure homogeneity of variance and the 

normal distribution of residuals and random effects. To assess the relative amount of variation 

explained by fixed and random effects in the top model, we calculated marginal and conditional 

R

2

as described by Nakagawa and Schielzeth (2013) using R package ‘MuMIn’ (Bartoń 2018). 
13

Marginal R

2

(R

2

m) describes the variance explained by fixed effects, whereas conditional R

2

(R

2

c) 

reflects the variance explained by fixed and random effects (Vonesh et al. 1996). 

We evaluated and summarized habitat use in relation to season, stream, fish sex, and fish 

TL. Our initial plots suggested depth and velocity related to fish length, whereas cover and 

substrate plots revealed little association. Based on apparent seasonal, among-stream, and 

individual differences in depth and velocity use, we modeled predictors of both depth and 

velocity use via separate candidate LMM sets that differed only in their response term. We 

natural-log transformed velocity values (i.e., ln (y + 0.001)) to address skewness. Categorical 

predictors included stream (three levels), season (four levels), and fish sex (three levels, i.e., 

male, female, unknown). We natural-log transformed and standardized (mean = 0; SD = 1) the 

continuous predictor, TL. Each candidate model set included all additive combinations of the 

fixed effect predictors (n = 16 models). We added a random individual fish effect to all candidate 

models to address correlation between measurements and unequal sampling of individuals (Otis 

and White 1999; Wagner et al. 2006). We ranked candidate models using AICc (Sugiura 1978). 

We assessed model fit and assumptions of our most supported model using residual and normal 

Q-Q plots and calculated the amount of variation explained by the model using marginal and 

conditional R

2

(Nakagawa and Schielzeth 2013). Because the use of different cover and substrate 

types appeared to vary only with categorical predictors, we calculated proportional use of cover 

types and substrate classes for each season and stream. 

Objective 2. Determine the habitat factors at multiple spatial scales that relate to suitable 

spawning habitat, particularly in regions influenced by reservoirs

Background

Rheophilic fishes are managed under a variety of landscape characteristics, many of which have 

been altered. Some landscape characteristics constrain the ability of populations to succeed under 

a variety of management scenarios. For example, Smallmouth Bass populations thrive under 

some natural landscape conditions that vary spatially (Brewer et al. 2007) and are less likely to 

benefit from improved land-use conditions when natural soil conditions relate to high runoff 

potential (Brewer and Rabeni 2011). Understanding the constraints imposed by both natural and 
14

human-induced landscape changes allows managers to apply more realistic regulations or 

expectations (e.g., trophy fishery designations in feasible locations) on populations. Without this 

knowledge, management activities may not be successful, and the observed patterns may not 

even relate to the designated management activities. Understanding how different life stages 

benefit (i.e., grow, reproduce, survive) from the use of different ecosystem components would 

help direct management actions across the landscape. 

Little information is available on habitat use by Neosho Smallmouth Bass during the 

spawning period (Brewer and Long 2015). The only study conducted on the subspecies occurred 

during a below average discharge year and indicated spawning occurred in low velocity, 

nearshore habitats located close to adjacent cover (Dauwalter and Fisher 2007). The number of 

Smallmouth Bass occupying these locations reflected some of the highest nest densities ever 

reported (i.e., (147 nests/km and 100 nests/km at Baron Fork upstream and downstream reaches, 

respectively). The cause for these high densities is unknown but two possibilities exist: 1) 

spawning habitat was limited, and 2) there are factors that interact with these very local 

combinations of depth, velocity and cover that create suitable conditions for Smallmouth Bass. 

We anticipate both factors are interactive because other, more coarse-scale variables are likely 

responsible for the creation of suitable spawning habitat (e.g., groundwater, soils, etc). Other 

studies on Smallmouth Bass indicate coarse features of the landscape are significantly related to 

use of local habitat features (Brewer et al. 2007; Brewer 2013b). Unfortunately, studies on 

Smallmouth Bass nesting have focused primarily on fine-scale habitat conditions and this applies 

to not only the Neosho subspecies, but also to the nominal species.

Study area

We conducted surveys for Smallmouth Bass nests (Figure 2) and YOY juveniles (Figure 3) in 

the known range of Neosho Smallmouth Bass in the southwest Ozark Highlands ecoregion. This 

portion of the ecoregion includes tributaries to several major impoundments, including Grand 

Lake, Spavinaw Lake, Lake Hudson, Fort Gibson Lake, and Tenkiller Lake. Sampled stream 

reaches were located within second-to-fifth-order tributaries. Many of these streams, particularly 

the Elk River, Illinois River, and their tributaries, support popular Smallmouth Bass fisheries. 
15

Nest surveys

The primary targets of our surveys were Smallmouth Bass nests or nest aggregations (hereafter 

clusters, where at least two actively guarded nests or nests containing eggs or larvae were located 

within 2 m of one another). We only considered nests that were guarded or that contained eggs or 

fry, as individual Smallmouth Bass sometimes excavate multiple nests in close proximity before 

choosing their nesting location (Cleary 1956; Mraz 1964). The minimum nest distance criteria 

we used to define a cluster was based on the minimum distance observed between lotic 

Smallmouth Bass nests in some interior Ozark Highland streams (~1.1 m; Pflieger 1966). 

However, the distances between neighboring nests of spawning bass are usually far greater (e.g., 

~78 m, Winemiller and Taylor 1982; Wiegmann et al. 1992). Smallmouth Bass nests are 

typically defined by depressions made in gravel-cobble substrates in low-velocity areas (Warren 

2009). However, anecdotal observations suggest the Neosho subspecies may also spawn on solid 

bedrock where a depression or fanned area may not be obvious (S. K. Brewer and Brandon 

Brown, personal communications). Thus, we used multiple characteristics to define nests: the 

presence of a guarding male, and the presence of eggs or fry. 

We surveyed nesting Neosho Smallmouth Bass (i.e., nest and nest clusters) from 120 

stream reaches representative of the study area during late spring and early summer (May–early 

July) of 2016–2018 (Figure 2). Because the majority of properties were owned by private 

landowners, we could not realistically randomly select our sample locations. Sampling began 

after water temperatures reached ~15°C (Pflieger 1966; Graham and Orth 1986; Dauwalter and 

Fisher 2007) and continued throughout the spawning period until fry dispersed from nests. The 

beginning and end dates of sampling each year depended on spring and summer hydrologic 

patterns because bass may abandon and (or) re-nest following flood events (Warren 2009). At 

least one flood event, for example, prevented our sampling for ~7 d each sampling year and 

frequent high flows resulted in continued spawning activity by fish into July. 

We surveyed each sample reach with a crew of 2–5 snorkelers (depending on wetted 

width and complexity) during daylight hours (0900-1700 hours) to maximize visibility. 

Snorkelers slowly moved upstream in parallel lanes (e.g., left, middle, right), carefully searching 

complex habitats for guarding male bass or groups of eggs or fry in small depressions or near 

cover. Communication between observers was required to prevent double counting nests. We 
16

uniquely marked individual nests with numbered flags for later microhabitat measurements 

(Dunham et al. 2009; Brewer 2011) unless we were planning to conduct a second pass (i.e., for 

sampling efficiency estimates). We adjusted lane widths to reflect visibility and habitat 

complexity; in areas with reduced visibility or more-complex habitat, narrower lanes were used,

and more snorkelers were used (Hillman et al. 1992; Thurow et al. 2012). After each snorkeling 

pass, we measured water clarity (0.1 m), as the distance an observer could distinguish a bassshaped silhouette underwater (Dunham et al. 2009). Observers were trained on models (i.e., cut 

outs of different sizes that resembled Smallmouth Bass) varying from 150–305 mm to assist with 

estimating the TL of guarding bass on marked nests.

We conducted a second snorkeling pass at 60 sample reaches to assess nest detection 

efficiency (i.e., the proportion of nests detected among those present, sensu Peterson and Paukert

2009). We allowed a minimum of one hour between the start of passes one and two so the stream 

could return to a relatively undisturbed state (O’Neal 2007; Thurow et al. 2012). If the same 

snorkelers surveyed the second pass, each observer switched lanes so there would be no prior 

information on fish from the previous survey. Count data were not shared between the observers 

after pass one to prevent biasing counts on the second pass. The second survey was completed 

using the aforementioned approach and individual nests were marked with numbered flags. 

Habitat

We quantified physicochemical conditions associated with nesting habitat at multiple 

spatial scales. First, we measured focal conditions at each individual nest and cluster within each 

reach. Then, we quantified reach-scale attributes describing conditions among reaches where fish 

chose to nest. Because of the inherent nested structure of streams, we also quantified attributes 

associated with stream segments (i.e., section of stream between tributary confluences) because 

multiple stream reaches were nested within stream segments (see also analyses). Lastly, we 

measured conditions associated with the catchment draining to each stream segment (i.e., 

hereafter catchment) due to the influence of the upstream drainage on local conditions (Hynes, 

1975). 

Microhabitat characteristics were quantified at encountered nest clusters beginning in 

2016 (n =120 reaches), whereas individual nest microhabitats were quantified in 2017-2018 (n = 
17

88 reaches). For each nest observed, we measured water depth (0.01 m) and velocity at the nest 

surface (0.1 m/s, focal location at estimated nest center). We also estimated guarding male total 

length (TL) to the nearest 5-mm increment (i.e., to offset estimated error from our training with 

bass silhouettes). For each cluster, we counted the number of nests and measured length and 

width to calculate approximate area of the cluster (0.1 m2). Lastly, we measured the center-tocenter distance of each nest in the cluster to the nearest neighboring nest. 

We measured additional physicochemical characteristics to determine the relative 

importance of reach-scale factors on nest abundance (Table 1). We used a thermometer (Ultrapen 

PT1, Myron L, Carlsbad, CA) to measure water temperature (0.1 °C) at mid-pool depth. Because 

water temperature represented only a point measure at the sampling event and there was a natural 

break in these data, we converted stream temperature to a factor with two levels (cool: ≤ 19.5°C, 

warm: > 19.5°C). We classified channel units using a simplified version of Rabeni and Jacobson 

(1993) and calculated percent area of each (Thurow 1994). Briefly, riffles were erosional units 

with relatively high gradient and coarse substrate, pools were depositional units in the main 

channel, runs were transitional habitats of intermediate depth and velocity, and off-channel 

habitats were low-velocity and located adjacent to the main channel (i.e., backwaters or 

forewaters). Mean wetted channel width (0.1 m) was calculated when measuring channel units 

due to its expected influence on sampling efficiency (Schill and Griffith 1984; Hicks and Watson 

1985) and its relationship with land use (Clifton 1989). We measured residual pool depth (RPD; 

0.01 m) as the difference between maximum pool depth and the downstream riffle crest (Lisle 

1987). Thalweg depth (0.01 m) was measured every 50 m and averaged across each reach. We 

quantified high-flow characteristics of channel hydrology by measuring bankfull width-depth

ratios (Gordon et al. 2004) and by estimating median substrate size (D50) as a proxy for channel 

shear stress (Shields 1936). To estimate D50, we measured intermediate diameters (1 mm) of 

≥100 substrate particles collected evenly along transects across a run channel unit and the 

associated floodplain (Wolman 1954; Leopold 1970). We also conducted seepage runs (Harvey 

and Wagner 2000; Zhou et al. 2018) using an acoustic Doppler current profiler (RiverSurveyor 

M9, SonTek, San Diego, CA) to quantify discharge (0.01 m3/s) at the upstream and downstream 

ends of each reach; we used the difference in discharge to calculate reach-scale groundwater 

flow. We categorized groundwater flow into three categories based on longitudinal gain or loss 

of streamflow relative to discharge measurement uncertainty (~0.03 m3/s; Zhou et al. 2018): 
18

losing streams (net loss of flow), neutral streams (no change) and gaining streams (net gain of 

flow).

We used terrain analysis and existing geospatial data in ArcMap 10.3.1 (ESRI, Redlands, 

California) to calculate characteristics describing each stream segment (i.e., section of stream 

between tributary confluences, n = 69; Table 1). Using a 30-m resolution raster digital elevation 

model (USGS National Elevation Dataset [NED]), we defined overland flow-direction pathways 

(O’Callaghan and Mark 1984; Jenson and Domingue 1988) and upstream catchment areas (Betz 

et al. 2010) for each stream segment. We calculated instream flow direction using rasterized 

1:100,000 flowlines (USGS National Hydrography Dataset [NHD]) and overlaid this grid on the 

landscape flow-direction grid to ensure that flow pathways agreed with mapped streams (Betz et 

al. 2010). We used the instream flow direction raster to calculate two metrics of stream topology: 

link magnitude (Shreve 1966) and downstream link (Osborne and Wiley 1992). Link magnitude 

describes stream size, whereas downstream link describes segment location within the stream 

network (e.g., a similar-sized stream may be located in the headwaters or adjacent to a large 

mainstem river).

We calculated metrics describing landscape disturbance, topography, soils, geology, 

runoff, and baseflow conditions for each catchment (Table 1). We calculated an index of 

landscape disturbance using land cover data from the 2011 National Land Cover Dataset 

(NLCD; Homer et al. 2015) and disturbance coefficients modified from Brown and Vivas (2005) 

and Mouser et al. (2019) (Table 2). The lowest index coefficient (1.00) described undisturbed 

habitats (e.g., forests, wetlands) whereas habitats subject to greater degrees of disturbance were 

assigned larger coefficients (e.g., 7.92, high-intensity development). Brown and Vivas (2005) 

used land-cover categories of a finer-resolution than the NLCD, so we averaged their coefficients 

to make them applicable to the NLCD (see Mouser et al. 2019). Then, we calculated an areaweighted average of these coefficients across the upstream contributing area for each stream 

segment. We characterized topography using an instream measure of stream gradient from the 

NHDPlus V2 and calculated average slope in the upstream catchment of each stream segment. 

We calculated percent hydrologic soil group D and percent carbonate lithology for each 

catchment using existing data (Stoeser et al. 2005). Hydrologic soils groups (A-D) reflect soil 

permeability characteristics and would be highly correlated among one another (i.e., total 100% 

across groups). Thus, we quantified soil group D because of previous relationships described 
19

with Smallmouth Bass (Brewer et al. 2007). Likewise, lithology data would also be highly 

correlated, so we chose to quantify carbonate lithology because of its relationship with karst 

topography and springs. Lastly, we used data from Hill et al. (2016) to summarize variation in 

average annual runoff and baseflow contributions (i.e., groundwater) across the upstream 

contributing area for each segment.

Analyses

Nest clusters

We used a mixed-effects logistic regression model to evaluate the multiscale habitat features 

associated with nest cluster presence. First, we modeled the presence of nest clusters at n = 120 

stream reaches in relation to a subset of predictors based on hypothesized explanations for 

clustering behavior (e.g., habitat limitation, groundwater influence, progression of nesting 

season; Table 3). We also included late-summer YOY densities collected from the same reach in 

the same year (See “young-of-year sampling”, this report) as a model predictor to assess whether 

nest-clustering behavior was related to differences in offspring survival. Because we lack YOY 

sample data from all stream reaches, we calculated mean values of YOY density in place of 

missing observations (n = 71 sites). We included a sample-area term in the model to account for 

differences in snorkeling effort among stream reaches. We natural-log (continuous variables) or 

logit-transformed proportions and standardized predictors to reduce skewness and simplify 

interpretation of model terms (Warton and Hui 2011). We also included random effects for 

stream segment and year to account for non-independent observations and address unexplained 

variability among reaches (Wagner et al. 2006; Gelman and Hill 2007). We used a parametric 

bootstrapping procedure with 1,000 replications to ensure more robust coefficient estimates. 

Logistic regression modeling and bootstrapping were implemented in R package ‘lme4’ (Bates et 

al. 2015). Lastly, we calculated marginal R

2

and conditional R

2

(Vonesh et al. 1996; Nakagawa 

and Schielzeth 2013) to evaluate model fit. Logistic regression modeling and R

2

calculations 

were implemented in R packages ‘lme4’ (Bates et al. 2015) and ‘MuMIn’ (Bartoń 2018), 

respectively.
20

Nest abundances among reaches 

First, we evaluated the influence of water clarity, wetted channel width, and thalweg depth on 

sampling efficiency (p) among sites (Royle 2004) using the ‘unmarked’ package in Program R 

(Version 3.5.1, R Core Team 2018). We scaled wetted width by the number of observers for each 

sample reach to account for differences in personnel among sites. We natural-log (continuous 

variables) or logit-transformed (proportions; +0.001 for channel units) and standardized all 

predictors to mean = 0 and SD = 1 to help with model convergence and simplify interpretation 

(Warton and Hui 2011). We used the ‘unmarked’ package (Fiske and Chandler 2011) to build a 

zero-inflated Poisson (ZIP) model with a three-way interaction among detection covariates and 

four univariate abundance predictors (area sampled, drainage area, water temperature, and 

amount of pool habitat; Table 4). We evaluated model fit by calculating overdispersion (ĉ, where 

values > 1 indicate overdispersion) using the chi-square test described by MacKenzie and Bailey 

(2004) with 1,000 bootstrap replications in the R package ‘AICcmodavg’ (Mazerolle 2017). We 

used model coefficients and empirical Bayes methods to adjust nest abundance estimates for 

variable detection efficiency among sites (Royle and Dorazio 2008; Fiske et al. 2017) so that 

these adjusted counts could be evaluated in a mixed-model framework (not currently possible in 

‘unmarked’, A. Royle, pers. comm.). Empirical Bayes methods use observed data to estimate 

unknown prior parameters to determine the posterior distribution of random variables, such as 

latent detection efficiency (Carlin and Louis 1996).

We evaluated the predictor variable distribution and reasonable generalized linear mixed 

model (GLMM) forms for comparing adjusted Smallmouth Bass nest abundances and habitat. 

We first transformed all continuous (natural log) and proportion (logit) variables as needed to 

reduce skew (Warton and Hui 2011) and then standardized all independent variables (mean = 0 

and SD = 1) to improve model convergence and simplify interpretation. We performed all 

transformations and standardizations with the ‘MuMIn’ (Bartoń 2018) and ‘psych’ (Revelle 

2018) packages in R. A natural break in our data led us to convert stream temperature to a factor 

with two levels (≤ 19.5°C and > 19.5°C). Initial evaluation of our nest count data suggested 

overdispersion and zero inflation. We modeled adjusted nest abundance (scaled by area sampled) 

and zero inflation in relation to 16 predictors (Table 1) using several model forms (i.e., Poisson, 

negative binomial, ZIP, ZINB). We included random effects for stream segment and sample year 
21

in these models to address unexplained variability among reaches and the non-independence 

between observations (Wagner et al. 2006; Gelman and Hill 2007). Comparison of model forms 

revealed that a ZINB model best fit the data (AICc weight > 0.99).

We used model ranking to reduce our set of predictor variables. We compared a set of 

ZINB GLMMs with each univariate predictor variable (n = 18) and ecologically sensible twoway interaction terms (n = 46) to a random-intercept only (null) model using AICc (Sugiura 

1978; Hurvich and Tsai 1989). If ∆AICc < 2 compared to the intercept-only model, we retained 

that model predictor (Burnham and Anderson 2002). We also filtered our variable set by 

removing covariates with pairwise correlations ≥ |0.7|, retaining only the predictor variable with 

greater model support (Dormann et al. 2013). The reduced set of variables consisted of 12 

univariate predictors and 31 two-way interaction terms (Table 1).

We used the reduced variable set to create a candidate set of nest abundance models. This 

exploratory approach used AICc to determine the set of predictors with the most support from an 

ecologically sensible candidate model set (Dochtermann and Jenkins 2011; Grueber et al. 2011). 

Model comparisons during variable reduction indicated that three predictors (i.e., stream 

temperature and two interaction terms: stream temperature × residual pool depth [RPD] and 

stream temperature × drainage area) were especially important. Using these three terms and an 

additive combination of the interaction terms, we built a base set of four models. We built our 

full candidate set of 172 GLMMs by adding the remaining univariate and two-way interaction 

terms from the reduced set (see Table 1) to this base model set. To ensure appropriate degrees of 

freedom for model performance and avoid overfitting, we capped fixed-effect predictors at 11 

per model (in addition to one random effect; Peduzzi et al. 1995; Harrell 2001). We included a 

random effect for stream segment in all models to address the nested structure of reach-scale 

observations (Wagner et al. 2006; Gelman and Hill 2007). We assumed the random effect was 

normally distributed N(0, τ2), where τ2 represented population variance among levels of a 

random effect (e.g., among stream segments). We built all ZINB models in the R package 

‘glmmTMB’ (Magnusson et al. 2018) and conducted model selection (AICc) using the ‘bbmle’ 

package (Bolker 2017).

We assessed model fit of our top model by calculating marginal R

2

and conditional R

2

(Vonesh et al. 1996; Nakagawa and Schielzeth 2013). We performed these calculations in the 

‘sjstats’ (Lüdecke 2018) package in R.
22

Young-of-year sampling

We conducted snorkel surveys for YOY Smallmouth Bass in 120 stream reaches (i.e., 20 times 

average channel width) nested within 70 stream segments (i.e., stream area between two tributary 

junctions; Frissell et al. 1986) in late summer and early autumn 2015–2017 (Figure 3). Our 

sample timing was chosen to avoid periods of significant, but expected mortality (e.g., larval 

bass; Brewer et al. 2019) and major changes in sampling efficiency due to water clarity 

(minimum 1.5 m using a fish silhouette, Goldstein 1978; Dunham et al. 2009), water 

temperature, and fish activity (Oliver et al. 1979). All surveys were conducted during daylight 

hours (approximately 0900–1700 hours) to maximize visibility. At each stream reach, a team of 

2–5 snorkelers moved upstream in parallel lanes spending additional time searching complex or 

deep habitats and communicating to prevent double-counting of individual fish (Dunham et al. 

2009; Brewer 2011). Lanes were defined by both visibility and habitat complexity where areas of 

lower visibility and highly complex habitat resulted in much narrower sample lanes (Hillman et 

al. 1992; Thurow et al. 2012). 

At a subset of stream reaches (n = 78), we snorkeled a second pass to assess sampling 

efficiency (i.e., the proportion of individuals detected among those present; Peterson and Paukert 

2009) of YOY Smallmouth Bass. Observers did not discuss fish counts between each pass, and 

observers changed survey lanes in the stream between passes. We waited a minimum of one hour 

between the start of each pass to allow the stream to return to a reasonably undisturbed state 

(O’Neal 2007; Thurow et al. 2012). The second pass was conducted in the same manner as the 

first snorkel pass and on the same day to avoid changes due to emigration or immigration. 

Habitat

We quantified habitat characteristics at each stream reach immediately following our fish 

survey(s) (Table 5). Discharge (0.01 m3/s) was measured at each site using the velocity-area 

method (Gordon et al. 2004). Channel units were classified following a simplified version of 

Rabeni and Jacobson (1993) and quantified by percent area (Thurow 1994). Briefly, pools were 

depositional units, riffles were high-gradient erosional habitats with swift water and coarse 
23

substrate, runs were transitional habitats of moderate velocity, and off-channel habitats included 

both forewaters and backwaters. Average width of the channel (0.1 m) was also calculated while 

mapping channel units due to hypothesized influences of stream morphology on fish abundance 

(Jowett et al. 1996; Rosenfeld et al. 2000). We measured residual pool depth (0.01 m) as the 

maximum pool depth minus depth of downstream riffle crest (Lisle 1987) and thalweg depth 

(0.01 m) at 50-m intervals along the study reach. We measured water temperature (0.1 °C) at 

mid-pool depth with a thermometer (Ultrapen PT1, Myron L, Carlsbad, CA). Because we only 

had a point measure from the time of sampling and these data reflected a natural break in 

temperature conditions, we converted stream temperature to a categorical variable with two 

levels (≤ 19.5 °C and > 19.5 °C).

We combined terrain analysis with existing data using Spatial Analyst Tools in ArcMap 

10.3.1 (ESRI, Redlands, California) to summarize habitat attributes for each stream segment (n = 

70; Table 5). Following the methods of Jenson and Domingue (1988), we delineated overland 

flow-direction pathways (O’Callaghan and Mark 1984) and upstream contributing areas (Betz et 

al. 2010) for each segment using a 30-m resolution raster digital elevation model (U.S. 

Geological Survey [USGS] National Elevation Dataset [NED]). We restricted in-stream flow 

pathways by calculating flow direction within the rasterized 1:100,000 stream network (USGS 

National Hydrography Dataset [NHD]) and overlaying this grid on the landscape-flow direction 

grid (Betz et al. 2010). Using our instream flow-direction grid, we calculated two topology 

metrics: link magnitude (Shreve 1966) and downstream link (Osborne and Wiley 1992). The first 

metric reflected stream size, whereas the latter was useful for understanding spatial position 

within the stream network. 

We then calculated metrics of landscape disturbance, topography, soils, geology, annual 

runoff, and baseflow contribution for the upstream contributing area of each stream segment 

(Table 1). To quantify landscape disturbance, we calculated a disturbance index that was 

modified from Brown and Vivas (2005) and Mouser et al. (2019) (see complete description 

under Habitat for Nest Surveys). The resulting coefficients ranged from 1.00 describing 

relatively undisturbed habitats (e.g., forests, wetlands) to 7.92 for high intensity development. 

We averaged the coefficients across the upstream contributing area so one value represented 

each stream segment. We also calculated area-weighted percent impervious cover using 30-m 

imperviousness data from Xian et al. (2011); we considered impervious cover as a predictor 
24

given its disproportionate negative influence on stream biota in many systems (Allan 2004; 

Brewer and Rabeni 2011; King et al. 2011). We calculated the local slope for each 30-m raster 

cell across the landscape and used the area-weighted average of these cells to characterize 

topography for each stream segment. We calculated area-weighted measures of soil conditions 

(i.e., percent hydrologic soil group D, soil permeability) and geology (i.e., percent carbonate 

lithology) using existing data (Stoeser et al. 2005; Hill et al. 2016; NRCS 2017). Variability in 

annual runoff and baseflow contributions (i.e., groundwater) was quantified by averaging data 

from Hill et al. (2016) across the upstream contributing area of each stream segment. 

Annual hydrology

We characterized annual hydrology (i.e., 2015–2017) as a proxy for the flow conditions during 

the spawning and early growth period of Smallmouth Bass. Because many streams we sampled 

did not have stream gages, we summarized available data using 11 representative stream gages 

(USGS gages: 07189100, 07188653, 07188838, 07188885, 07189540, 07189542, 07191222, 

071912213, 07196000, 07197000, and 07197360; Table A2). Using daily streamflow data, we 

calculated mean discharge from April–July for each year (2015–2017). We also calculated 

monthly (April–July) maximum daily discharge and corresponding flow exceedance probability 

(EP, the relative frequency of a given streamflow magnitude based on historical flows) for each 

gage. Because we averaged discharge across all streams in each sample year, we used monthly 

flow EP due to its relative independence from stream size. Lastly, we calculated the average 

frequency of higher flow events (> 10 m3/s) and the average number of days (duration) with 

mean daily streamflow above this threshold from April–July of each year. We used 10 m3/s as a 

threshold given its association with Smallmouth Bass nest failure (Lukas and Orth 1995) and 

with observed reductions in spawning activity in streams of similar size (A. D. Miller and S. K.

Brewer, unpublished data). In addition to these streamflow metrics, we also summarized April–

July precipitation trends across the study area for each year using data from the Oklahoma 

Mesonet (https://www.mesonet.org/, accessed April 2018; Table A3).
25

Analyses

We used data from 78 sites with two snorkel surveys to assess our sampling efficiency (Royle 

2004). Our multiple-survey data showed high concordance at each site (Pearson’s r = 0.97), 

suggesting our Smallmouth Bass counts were precise. With the large number of sites examined, a 

lack of variability in repeated counts also suggests high sampling efficiency, with the correlation 

coefficient between passes providing a rough estimate of average real-world detection 

probability (Kéry and Royle 2015). We, therefore, proceeded with our habitat analyses using 

unadjusted counts. We used the higher counts at the sites where multiple surveys were 

conducted.

We assessed the distribution of our predictor variables and compared generalized linear 

mixed models (GLMMs) with different probability distributions prior to evaluating the 

relationship between YOY Smallmouth Bass abundance and habitat. To reduce skewness, we 

transformed continuous (natural log) and proportion (logit) variables (Warton and Hui 2011). All 

independent variables were then standardized to mean = 0 and SD = 1 to ease model 

interpretation and improve model convergence (Gelman and Hill 2007). Transformations and 

standardizations were done using the ‘MuMIn’ (Bartoń 2018) and ‘psych’ (Revelle 2018) 

packages in Program R (Version 3.5.1, R Core Team 2018). Examination of our count data 

suggested both overdispersion and zero inflation. We compared several model forms (i.e., 

Poisson, negative binomial, zero-inflated Poisson, zero-inflated negative binomial [ZINB]) for 

modeling abundance (scaled by area sampled) as a function of 11 predictors (Table 5). These 

models included random effects for both stream segment and sample year to account for 

unexplained variability among reaches and lack of independence among observations (Wagner et 

al. 2006; Gelman and Hill 2007). Model comparison indicated that a ZINB model with zero 

inflation modeled in relation to stream temperature provided the best model fit (AICc weight > 

0.99). 

To reduce the number of possible explanatory variables, we first compared ZINB models 

with each univariate predictor variable (n = 25) and two-way interaction term of a priori interest 

(n = 22) to a random-intercept only (null) model using Akaike’s information criterion adjusted 

for small sample size (AICc; Sugiura 1978; Hurvich and Tsai 1989). We retained terms from all 

models with ∆AICc < 2 relative to the null model (Burnham and Anderson 2002). To minimize 
26

issues with interpretation, we reduced any remaining covariates with pairwise correlations ≥ |0.7| 

by retaining the predictor variable with better model fit (Dormann et al. 2013). The reduced 

variable set contained 10 univariate predictors and 11 two-way interaction terms (Table 5).

Using our reduced variable set, we developed a set of candidate models. Initial model 

comparisons suggested that July flow EP and several two-way interaction terms (i.e. July flow 

EP × drainage area, July flow EP × downstream link, July flow EP × stream temperature, and 

drainage area × residual pool depth) were particularly influential predictors. We used all additive 

combinations of these terms to build a base set of 17 models. We then added the remaining 

univariate and two-way interaction terms (Table 5) to this base model set to build our candidate 

set of 261 models. We limited the number of fixed-effect predictors to 10 (in addition to two 

random effects) to maintain appropriate degrees of freedom for model performance (Peduzzi et 

al. 1995; Harrell 2001). All candidate models included stream-segment and year random effects 

to account for the non-independence among reach-scale observations (Wagner et al. 2006; 

Gelman and Hill 2007). We assumed a normal distribution for all random effects as N(0, τ2), 

where τ2 was the population variance among levels of a random effect (e.g., among stream 

segments). All models were built in the R package ‘glmmTMB’ (Magnusson et al. 2018), and we 

used the ‘bbmle’ package (Bolker 2017) to calculate AICc for our candidate set to determine the 

variable set that had the most support. This approach, though exploratory, limited candidate 

models to those that were ecologically sensible (Dochtermann and Jenkins 2011; Grueber et al. 

2011). 

We calculated the amount of variation explained by the top model using marginal R

2

and 

conditional R

2

(Vonesh et al. 1996; Nakagawa and Schielzeth 2013). For zero-inflated mixed 

models, this formula considers only the conditional (abundance) model and ignores the zeroinflation model (B. Bolker, personal communication, 2018). These calculations were performed 

using the ‘sjstats’ (Lüdecke 2018) package in R.

RESULTS

Objective 1. Assess the movements of riverine Neosho Smallmouth Bass before and 

following the spawning period
27

Movement and habitat use

The size of tagged fish varied within and among study systems, with the largest fish were 

generally tagged in the larger streams (Appendix A, Table A4). Fish that were tagged following 

capture from the Elk River had a mean TL of 349 mm (range: 290–464 mm) and a mean weight 

of 600 g (range: 368–1,603 g). Comparatively, fish sampled and tagged in Buffalo Creek were 

shorter (mean TL: 327 mm; range: 278–408 mm) and lighter (mean weight: 518 g; range: 370–

1,010 g). Smallmouth Bass sampled and tagged from Sycamore Creek were the smallest in both 

TL (mean: 322 mm; range: 290–370 mm) and weight (mean: 481 g; range: 376–765 g). Over the 

duration of the study, we recovered 71 transmitters; we re-implanted 53 into new fish (n = 22 in 

Buffalo Creek, n = 20 in Elk River, and n = 11 in Sycamore Creek, Appendix A, Tables A5, A6). 

Tracking efforts from the 21-month study period resulted in n = 2,362 individual locations from 

n = 130 fish (i.e., includes only tagged fish redetected on more than two occasions). 

Discharge conditions and water temperature during the study followed typical seasonal 

patterns and reflected groundwater variability of these Ozark Highland streams (Figure 4). High 

flows typically occurred during spring for all three streams. There was one major flood event in 

late April 2017 representing a substantial deviation (> 10 times) from median flow values and 

approaching the maximum flow recorded over the period of record at the USGS stream gauges 

(i.e., 80 years for the Elk River gauge). In late summer and autumn 2016 and 2017, low-flow 

conditions were common in both Buffalo and Sycamore creeks. Stream drying of riffles and pool 

isolation were common across many reaches of both streams during these periods. Over the 

study period, discharge was greatest in Elk River (median: 6.68 m3/s) and similar for Buffalo 

Creek (median: 0.43 m3/s) and Sycamore Creek (median: 0.38 m3/s). Mean water temperatures 

during the study period were warmer in Elk River (19.0 °C) than in Sycamore Creek (17.0 °C) 

and Buffalo Creek (16.9 °C). Thermal variability was greatest in Sycamore Creek (CV: 38.2) and 

lowest in Elk River (CV: 30.2). 

Tagged fish were never detected using either active or passive telemetry in the reservoir 

or reservoir-river interface except during periods of lotic character (i.e., the reservoir was not 

pooled above the Buffalo Creek-Elk River confluence). Fish detected via the passive stations 

were also detected during active tracking efforts and thus, events were not duplicated in the 

analyses. During our active tracking events into Grand Lake O’ the Cherokee from the lower Elk 
28

River and lower extent of Sycamore Creek, we did not detect any of our tagged Smallmouth 

Bass. 

Movements by tagged Smallmouth Bass were highly variable among seasons, streams, 

and individuals. Movement rates were greatest (median: 3.8 m/d) and most variable (CV: 450.7) 

during spring, lowest (median: 0.3 m/d) in winter, and least variable (CV: 184.0) in autumn. 

Tagged fish tended to be more mobile in Elk River (median: 5.2 m/d), followed by Buffalo 

Creek (median: 2.3 m/d) and Sycamore Creek (median: 1.3 m/d). The greatest individual 

movements were an order of magnitude greater in Elk River (17.0 km) and Buffalo Creek (12.9 

km) compared to movements of tagged fish in Sycamore Creek (1.7 km). Of 73 individual 

movements > 1.0 km, 43 (59%) occurred during the spring. Movement rates > 1,000 m/d were 

observed on nine occasions in Elk River (56%) and Buffalo Creek (44%) and were concentrated 

during the spring season (89%). We did not observe any tagged fish using Grand Lake or its 

associated river-reservoir interface zones during the study, but three tagged fish moved between 

Elk River and Buffalo Creek when reservoir levels were low and there was flowing water at the 

confluence.

The top model for movement rate included several interaction terms (season × mean 

discharge, season × mean temperature, season × stream, stream × mean temperature) and two 

main effects: CV of discharge and fish TL (Table 6). Movement rate was positively associated 

with increasing discharge during spring, but this relationship was negligible over the smaller 

range of flows that occurred during summer (Figure 5). Predicted movement rates were 

consistent and comparatively low during relatively homogenous discharge conditions associated 

with both autumn (mean: 3.4 m/d) and winter (mean: < 0.1 m/d). The effect of mean water 

temperature on movement rate varied by stream and by season (Figure 6). Movement rates in Elk 

River were positively related to mean temperature in all seasons except winter, and this 

relationship was particularly evident in spring. Movement rates of tagged fish in Buffalo Creek 

were similar regardless of mean water temperature in spring and autumn. Interestingly, 

movement rates of these fish also decreased as temperature increased in both summer and winter. 

In Sycamore Creek, a small but positive relationship between mean temperature and movement 

rate was observed in all seasons except winter. Across all streams and seasons, larger fish were 

predicted to display greater movement rates (Figure 7). Increasing flow variability, as measured 

by the CV of discharge, had a small but positive effect on movement rate. The fixed effects in 
29

this model explained 16% of the variation in movement rate (R

2

m = 0.16), and the addition of the 

random individual effect led to a total of 29% of the variation being explained (R

2

c = 0.29).

Several aspects of habitat use varied among streams and seasons (Table 7). Modelpredicted depth use varied by stream, with deeper habitats used in Buffalo Creek. Season also 

affected depth use, with greater depth use in winter, followed by spring, autumn, and summer. 

The fixed effects of stream and season explained 10% of the variability in depth use (R

2

m = 

0.10), and the addition of a random individual effect increased the variance explained by the 

model to 41% (R

2

c = 0.41). The top model for velocity use included stream and season predictors 

(Table 7). Modeled velocity use was greater in Elk River regardless of season and lower during 

autumn across all streams. These fixed effects explained 7% of the variation in velocity use (R

2

m

= 0.07), and the random effect for individual brought total variance explained to 15% (R

2

c = 

0.15). Qualitative plots indicated proportional use of different substrate and cover types was 

slightly different among streams and seasons (Figure 8). A greater proportion of tagged 

Smallmouth Bass in the Elk River used bedrock substrates compared to fish in the smaller 

streams and tagged fish in all streams used gravel substrates to a greater degree during spring. 

Observations of fish not associated with any cover type were common in all study streams, 

particularly the Elk River, and the use of woody debris was more frequent in Buffalo Creek. No 

seasonal differences were apparent regarding use of different cover types.

Objective 2. Determine the habitat factors at multiple spatial scales that relate to suitable 

spawning habitat, particularly in regions influenced by reservoirs

Nest surveys

Nest surveys

We observed Smallmouth Bass nests in 77 of 120 stream reaches (64%). An additional 15 

reaches were occupied by age-1+ Smallmouth Bass even though no nests were present. Non-zero 

nest counts ranged from 1 to 79 (mean: 11.5; SD: 13.7), and non-zero nest densities varied from 

1.2 to 90.0 ha-1(mean: 19.4 ha-1; SD: 16.3). Nest counts per stream length varied from 2 to 140

km-1(mean = 26.8 km-1; SD = 26.7) at these sites.
30

Nest clusters and microhabitat

Habitat conditions varied among nest survey sites (Table 1). Temperature varied spatially and 

temporally among sampled reaches, but warm streams were more common (n = 85 warm, n = 35 

cool). Pool habitat was typically abundant but variable among sample reaches (mean = 64%; SD 

= 17, range: 3–92%). Channel morphology also varied considerably among streams, particularly 

residual pool depth (range: 0.35–3.14 m) and bankfull width-depth ratio (range: 7.9–122.7). 

Groundwater flow was emblematic of karst topography (range: -0.70–1.13 m3/s) and indicated a 

mix of losing (n = 48), neutral (n = 33), and gaining (n = 39) stream reaches. Coarse-scale habitat 

reflected a wide range of stream sizes, spatial metrics, and catchment characteristics. Drainage 

area of our sample sites varied from 18.0 to 886.6 km2. Downstream link varied from 3 to 874, 

indicating some reaches were adjacent to larger streams, whereas others were in more isolated, 

upstream portions of the river network. Catchment slope varied from 1 to 15% and the amount of 

hydrologic soil group D varied from 6% to 70% across the study area. Disturbance index values 

were low to moderate (range: 1.5–3.9; mean = 2.3; SD = 0.4).

The model developed describing nest cluster habitat indicated relationships with stream 

temperature, groundwater flow, sample timing, and channel morphology on cluster presence 

(Table 3). The presence of nest clusters was less likely in cooler stream segments (≤ 19.5°C) and 

in gaining stream reaches. Clusters were more likely to be present earlier in the sampling period, 

and in reaches with wide, shallow channels. Sampling effort (i.e., area sampled) was not a 

significant predictor of nest cluster presence (95% CI: -0.10–1.60). Nest clusters at the reach 

scale were not associated with higher or lower offspring densities when compared to nonclustered nest behavior (95% CI: -1.71–4.49). Fixed effects explained 72% of the variability in 

nest cluster presence (marginal R

2 = 0.72), and random stream segment effect did not explain any 

additional variability (conditional R

2 = 0.72).

Nest cluster microhabitat was measured for n = 66 clusters in n = 22 stream reaches. Nest 

clusters contained 2–6 nests (mean = 2.4; SD = 0.8). Center-to-center spacing between clustered 

nests ranged from 0.5–2.0 m (mean = 1.5 m; SD = 0.3). Average area encompassed by nest 

clusters was 2.4 m2(SD = 2.6, range = 0.5–14.0 m2).

Nest microhabitat measures (n = 646 nests in n = 55 reaches) were similar for nests 

regardless of cluster inclusion, and reflected variability in nesting Smallmouth Bass size, a range 
31

of nesting depths and predominate use of low-velocity areas with gravel substrates. In total, 160 

of the 646 nests measured (25%) were part of a nest cluster. Male Smallmouth Bass guarding 

nests varied in size from 130 to 400 mm TL, and average TL of male fish associated with and 

without nest clusters was 269 mm (SD = 45) and 253 mm (SD = 46), respectively. Nests were 

constructed in 0.26–1.85-m deep water; average water depth of nest clusters and single nests 

were 0.84 m (SD = 0.29) and 0.83 m (SD = 0.31), respectively. Nest-surface water velocities 

were 0.0–0.2 m/s, though most cluster nests (96%) and regular nests (96%) occurred in zerovelocity habitats. Nest substrate was predominately gravel (98% of clustered nests, 97% of 

individual, non-clustered nests), though use of bedrock (2% of clustered nests, 3% of nonclustered nests) and other substrates (i.e., roots, < 1% of clustered nests) was observed. 

Nest abundances among reaches 

Average sampling efficiency of our nest surveys was relatively high (72%) and varied across 

sites in response to water clarity, depth, and lane width surveyed per observer (Table 8). As 

expected, nest detection efficiency was positively related to water clarity and average stream 

depth. Sampling efficiency also decreased in reaches where snorkelers were responsible for 

surveying wider lanes. Adjusted non-zero nest counts varied from 1 to 86 (mean = 14.7; SD = 

16.0) and mean adjusted nest density in reaches with nests was 24.1 ha-1(SD = 17.6). 

The top-ranked model for Smallmouth Bass nest abundance included a zero-inflation 

term, an interaction, and two main effects that highlight the importance of multiple reach-scale 

habitat features, though there was similar support for several models (Table 9). Zero inflation in 

the top model suggests that zero nest counts are expected at 13% of stream reaches, though no 

single predictor variable explained this phenomenon. Nest abundance was positively related to 

residual pool depth and this relationship depended on stream temperature (Figure 9). Although 

the relationship between nest abundance and increasing pool depth was similar in magnitude and 

direction between the two temperature categories, nest abundances were lower for a given 

residual pool depth in cooler stream reaches. A main effect of the proportion of pool habitat was 

included in the top model of Smallmouth Bass nest abundance and indicated a positive 

relationship between nest abundance and the quantity of pool habitat. Similarly, a main effect for 

bankfull width-depth ratio in the top model showed a positive relationship between nest 
32

abundance and relatively wide, shallow stream reaches. The fixed effects in the top model 

explained 80% of the variability in nest abundance (marginal R

2 = 0.80), and the random stream 

segment effect did not explain any additional variability in nest abundance (conditional R

2 = 

0.80). There was additional support based on AICc for simpler forms of the top-ranked model, 

one without the bankfull width-depth ratio main effect and another without the percent pool 

habitat main effect. Similarly, there was some support for a more complex model containing the 

residual pool depth × stream temperature interaction from the top-ranked model along with a 

percent pool habitat × median substrate size interaction. In the latter interaction, nest abundance 

had a positive relationship with pool habitat when substrate was small-to-average (i.e., reflecting 

low-average shear stress), with this relationship changing to slightly negative when substrate was 

larger than average (i.e., reflecting greater shear stress at higher flow).

Young-of-year sampling

Young-of-year Smallmouth Bass occurrence and abundance varied among study reaches. We 

observed YOY Smallmouth Bass in 103 of 120 surveyed stream reaches, with an average of 

181.3 ± 182.5 ha-1(mean ± SD) at occupied sites (range: 1–1,347 ha-1). YOY Smallmouth Bass 

were disproportionately absent from streams we classified as cold (≤ 19.5 °C). Stream 

temperature explained some absences of YOY Smallmouth Bass, with model-predicted zeroinflation probabilities of 29.5% for cold streams and 8.0% for warm streams. We observed YOY

Smallmouth Bass in both very small streams and larger rivers within our study area (drainage 

area range: 16.9–772.7 km2).

Habitat and annual hydrology

Habitat conditions and hydrology varied considerably among sites and years (Table 5, Appendix 

A, Table A2). As expected, pools made up the majority of habitat in sampled reaches (mean ± 

SD: 73.7 ± 15.8%), though residual pool depths differed considerably across study streams 

(range: 0.20–2.37 m). Sample reaches differed greatly in their spatial position relative to larger 

streams, with downstream link values ranging from 4 to 694. Thus, some reaches were adjacent 

to large mainstem rivers, but others were located in upstream, more isolated portions of the river 
33

network. Flows during the nesting period were particularly high and variable in 2015 and 2017, 

though the timing of flood events varied. In 2015, high flows occurred May–July but in 2017, 

high flows occurred earlier in the season (April–May). In contrast, precipitation was lower and 

flow conditions more benign, on average, in 2016 (Appendix A, Tables A2, A3).

YOY Smallmouth Bass abundance

The top model explaining conditional abundance of YOY Neosho Smallmouth Bass (i.e., after 

accounting for zero inflation in cold study reaches) contained several interaction terms for reach 

and segment-scale predictors and hydrology (Table 10). Late-summer abundances of YOY

Smallmouth Bass were positively associated with higher July flow EP (i.e., more benign July 

flows), though this relationship was more pronounced in reaches located further up in the river 

network (i.e., not adjacent to larger rivers (Figure 10). The influence of residual pool depth on 

YOY Smallmouth Bass abundance depended on stream size (Figure 11). In smaller streams, 

deeper pool habitat was associated with greater YOY Smallmouth Bass abundance. There was no 

clear relationship between abundance and pool depth in average-size streams (~117.0 km2), 

whereas abundance and pool depth were inversely related in larger streams. The relationship 

between YOY Smallmouth Bass abundance and downstream link magnitude was minimal in 

warm (> 19.5 °C) stream reaches, but a positive trend was observed for cold streams (Figure 12). 

Thus, although cold stream reaches generally had lower numbers of YOY Smallmouth Bass, this 

relationship was less apparent in reaches adjacent to larger streams. Collectively, fixed effects in 

our model explained 57% of the variability in YOY Smallmouth Bass abundance (marginal R2 = 

0.57). No additional variability was explained by random stream segment or year effects in the 

final model (conditional R2 = 0.57), with all remaining unexplained variability at the residual 

(i.e., reach) level.
34

DISCUSSION

Fish movement 

Neosho Smallmouth Bass exhibit a considerable degree of heterogeneity in seasonal movement 

behavior. As expected, tagged fish exhibited greater mobility associated with spawning during 

the spring and reduced movement during winter (Todd and Rabeni 1989; Lyons and Kanehl 

2002; Barthel et al. 2008). We did not observe a seasonal concentration of movements to 

overwinter habitats; however, movements to overwinter habitats sometimes occur over a 

protracted period (i.e., summer–autumn, Robbins and MacCrimmon 1977). Movement 

magnitudes were not as large as those observed in some northern populations. For example, 

Langhurst and Schoenike (1990) observed radio-tagged Smallmouth Bass movements of 69–87 

km from a tributary to downstream mainstem habitat prior to the overwinter period. We would 

not expect movements of this magnitude given the dissected study river-reservoir landscape. The 

only way tagged fish could move such distances would be if they traversed the reservoir or riverreservoir interface. 

We did not observe any evidence of fish using Grand Lake or transient river-reservoir 

interfaces during the study suggesting lentic habitats act as a barrier to movement for the Neosho 

subspecies. In fact, when the pool level of Grand Lake rose 3.1 m in spring 2017 and flooded the 

lower 1.7 km of Buffalo Creek, tagged fish in the affected area (n = 3) moved just far enough 

upstream to remain in lotic habitat. The avoidance of lentic habitat by the subspecies is further 

supported by the high degree of genetic uniqueness within the smaller stream populations of the 

Ozarks of Oklahoma and Missouri including the Elk River basin (Taylor et al. 2018). Some 

mixing of genetics with the Tennessee strain of Smallmouth Bass is evident in the Elk River and 

thought to be related to angler movement of fish to Grand Lake (Taylor et al. 2018). 

Interestingly, Smallmouth Bass use both lotic and lentic habitats in natural connected river-lake 

systems (Webster 1954; Gerber and Haynes 1988; Barthel et al. 2008). The movements we 

observed by the Neosho subspecies did reflect relationships with environmental drivers and were 

often stream-specific with noticeable differences between the large river and smaller stream 

populations.
35

We found movement rates by tagged Smallmouth Bass reflected both season- and streamspecific environmental cues. As expected, increasing movement during the spring spawning 

season was positively related to discharge (Cleary 1956; Lyons and Kanehl 2002). Slightly 

higher movement rates by larger individuals may correspond with greater movement among 

older individual Smallmouth Bass (Dauwalter and Fisher 2008) with more experience locating 

suitable habitat patches (Orth and Newcomb 2002). Discharge was less variable and unrelated to 

fish movement outside the spring season. In general, we found lower movement rates and 

dampened relationships between environmental in the two smaller streams (Buffalo Creek and 

Sycamore Creek). The movement patterns associated with the smaller streams may relate to 

restricted movement potential due to seasonal drying (i.e., loss of habitat connectivity). The 

slight but positive effect of flow variability on movement rate may be due to small flow pulses 

that increase habitat connectivity when flows are otherwise low (Bradford and Heinonen 2008). 

Similar to discharge patterns, we found the relationship between water temperature and fish 

movement was positive in the Elk River but appeared less important in the smaller streams. The 

effects of temperature on freshwater fish movements are variable but increasing spring 

temperatures are a common movement cue for Smallmouth Bass (Lyons and Kanehl 2002; 

Barthel et al. 2008). Reduced movements across all streams in winter may reflect use of thermal 

refugia or sedentary behavior to reduce loss of energy stores (Westhoff et al. 2016). The slightly 

greater movement rates in all systems at lower temperatures during winter may reflect 

movements to refuge habitats in response to harsher temperatures (Lyons and Kanehl 2002). 

Buffalo Creek has a higher contribution of groundwater (Zhou et al. 2018); thus, local 

groundwater inputs may offer refuge locations that minimize the need for winter movements. 

Although seasonal and stream-specific patterns emerged, high individual variability in 

movements was observed.

Individual variation and unexplained residual variability in movement rates among 

tagged Smallmouth Bass were apparent in our results. This variation may reflect individual 

differences (e.g., condition, inherent movement propensity, reproductive status) and their 

interactions with measured or unmeasured extrinsic ecological factors (e.g., habitat, predation 

risk, presence of mates, Rasmussen and Belk 2017). A mix of mobile and non-mobile individuals 

within a population has been observed in many studies of Smallmouth Bass in riverine and 

connected environments (e.g., Gerber and Haynes 1988; Gunderson VanArnum et al. 2004; 
36

Barthel et al. 2008). Additional individuality may reflect fidelity to specific spawning or 

overwinter habitats, which we observed via a few tagged individuals. This behavior occurs in 

some Smallmouth Bass populations (Ridgway et al. 1991; Barthel et al. 2008) but not others 

(Gerber and Haynes 1988; Lyons and Kanehl 2002). 

We observed seasonal and stream-specific patterns in habitat use by Smallmouth Bass. 

Use of deeper-water habitats during winter, regardless of stream size, is consistently reported for 

Smallmouth Bass populations (Munther 1970; Lyons and Kanehl 2002; Ettinger-Dietzel et al. 

2016). Interestingly, tagged fish in Buffalo Creek also used deeper habitats compared to tagged 

fish in the other study streams. This may relate to several factors: 1) the availability of deeper 

water, 2) the association of pools with thermal refugia at a patch scale that we did not measure, 

and 3) the regular disconnection of surface flows during the summer and autumn seasons in 

Buffalo Creek (see Hafs et al. 2010). Modeled velocity use by tagged Smallmouth Bass was 

greatest in the largest river we examined (Elk River) and generally the lowest across all streams 

during autumn. This result may reflect that Smallmouth Bass were more often associated with 

run habitats in the Elk River than in the other streams. The Elk River is much wider and faster 

when compared to the other two streams in this study. Use of low-velocity habitats in autumn is 

likely important for conserving energy prior to the overwinter period when fish subsist primarily 

on accumulated energy stores (Coble 1975). Considerable individual variation remained in 

explaining patterns of depth and velocity use across the three streams we examined. Use of 

gravel substrates increased in spring, consistent with the reproductive period and known 

spawning habitat preferences (Dauwalter and Fisher 2007). Many tagged fish were not 

associated with cover, which seems common among adult Smallmouth Bass (Todd and Rabeni 

1989; Lyons and Kanehl 2002). Cover used by tagged fish in this study did not differ among 

seasons, in contrast to previous studies (Todd and Rabeni 1989; Ettinger-Dietzel et al. 2016).

One potential factor complicating this study was the loss of transmitters by tagged fish. 

Transmitter loss is often unavoidable in telemetry studies, but may arise due to mortality of 

tagged individuals, stress related expulsion, or poor tagging practices (Jepsen et al. 2002). 

Natural mortality may explain some of the lost transmitters, as tag loss peaked when natural 

mortality was expected to be greatest (i.e., summer, winter, Hurst 2007; Dauwalter and Fisher 

2008) and several recovered tags showed signs of predation or perhaps scavenging (e.g., bite and 

tooth marks). Harvest can greatly reduce numbers of tagged fish (Westhoff et al. 2016) and 
37

several of our tagged fish were caught by anglers. We know of one tagged fish that was 

harvested by anglers because the transmitter was returned. Several catch-and-release anglers we 

encountered while tracking mentioned catching and releasing tagged bass suggesting post-release 

mortality or tag expulsion may have occurred throughout the study. On two occasions, we 

tracked fish to anglers’ stringers and convinced the angler to release the fish; in both instances, 

the transmitter was found by itself during the following tracking period (i.e., shed). Surgically, 

we followed best practices by moving the inserted transmitter away from the incision site and by 

trailing the antenna out a separate incision to reduce pressure on the sutures (Ross and Kleiner 

1982; Jepsen et al. 2002). As most tag loss occurred several months after tagging surgeries and 

multiple relocations and (or) visual confirmations of fish survival, we are confident that surgical 

technique or post-release mortality was not the underlying cause of transmitter loss. Transmitter 

loss had little effect on the analysis of movement rate and habitat use because each observation 

was treated as a unique response (i.e., with individual fish treated as a random effect).

Nest habitat

Nest clustering by Smallmouth Bass appears to be unique to the Neosho subspecies and may 

relate to high nesting densities even though microhabitat use was comparable to the nominal 

species. Smallmouth Bass are territorial nest guarders in lakes and rivers throughout most of 

their range, resulting in considerable spacing between nests (i.e., > 17–77 m, Winemiller and 

Taylor 1982; Scott 1996). This may reflect territorial males interfering with courtship by 

neighboring males (Beeman 1924; Wiegmann et al. 1992). Clusters, where neighboring nests 

were within 2 m of one another, accounted for 25% of the observed Neosho Smallmouth Bass 

nests in 2017–2018. Clustering behavior may explain why our greatest observed nest densities 

(i.e., 140.1 km-1) and those from another study of the Neosho subspecies (i.e., 100–147 km-1, 

Dauwalter and Fisher 2007) are much greater than nest densities reported in the literature for the 

nominal species (maximum = 75.3 km-1, Reynolds and O’Bara 1991). We observed nests built 

across a range of water depths (i.e., 0.26–1.85 m), as has been documented for both the Neosho 

and northern subspecies (Reynolds and O’Bara 1991; Knotek and Orth 1998; Orth and 

Newcomb 2002; Dauwalter and Fisher 2007). The use of low-velocity nesting habitats and of 

gravel and occasional bedrock nesting substrate is consistent with previous work on the Neosho 
38

(Dauwalter and Fisher 2007) and northern subspecies (Pflieger 1966; Winemiller and Taylor 

1982; Sabo and Orth 1994). 

Several physicochemical factors correlated with the occurrence of Smallmouth Bass nest 

clusters. Nest clusters and individual nests were more likely to occur in warmer streams (> 

19.5°C), possibly reflecting favorable development and growing conditions for offspring (27–

28°C, Peek 1965; Coutant and DeAngelis 1983). The greater abundance of nests in warm 

streams may relate to the positive relationship between cluster presence and warm water 

temperature. The presence of nest clusters was less likely in gaining reaches, though 

groundwater flow did not affect individual nest abundance. The magnitude of groundwater flow 

varied considerably among reaches, and there was no consistent relationship between 

groundwater flow and water temperature class. In summer, YOY Smallmouth Bass are typically 

more abundant but may grow slower in stream segments with high spring-flow influence 

(Brewer 2013a). Complex patterns of groundwater exchange, wherein upwelling and 

downwelling occur at different spatial scales, may further complicate nest-groundwater 

relationships (e.g., Baxter and Hauer 2000). Alternatively, lower abundances of a potential nest 

predator, Longear Sunfish Lepomis megalotis, associated with gaining stream reaches (R. 

Mollenhauer, unpubl. data) may help explain the lower incidence of nest clusters despite normal 

nest abundances in these reaches. We found relatively wide, shallow stream reaches were more 

likely to contain nest clusters. These reaches tend to have more open-water habitat and high 

abundances of Longear Sunfish (R. Mollenhauer and S. Brewer, unpubl. data), thereby 

increasing the risk of nest predation. Aggregations of guarding male fish help to deter potential 

predators (Dominey 1981, 1983) and may have been beneficial for nest success in these systems. 

Future efforts focused on examining survival by clustering versus individual nests would provide 

insight into the possible mechanisms. 

We found nest clusters were more common earlier in the spawning season. This may 

reflect coincident nesting efforts following delays in spawning due to high flows or cool 

temperatures (e.g., Sabo and Orth 1994), as we observed in 2016–2017 and 2018, respectively. 

Furthermore, although many bass build new nests if earlier broods are destroyed (e.g., due to 

flooding or predation, Cleary 1956; Knotek and Orth 1998) or successfully disperse (Pflieger 

1966), not all bass make multiple nesting attempts (Lukas and Orth 1995; Orth and Newcomb 

2002), potentially alleviating habitat limitation that leads to nest clustering. The threat of 
39

predation may also influence this relationship, as Longear Sunfish are potential nest predators 

early in the Smallmouth Bass nesting season but begin their own spawning activities in late May 

(Warren 2009). It seems possible clustering behavior by Neosho Smallmouth Bass reflects both 

habitat conditions conducive to greater nest densities and a response to the threat of highly 

abundant nest predations, primarily Longear Sunfish.

Multiple reach-scale habitat factors influenced the presence and abundance of Neosho 

Smallmouth Bass nests in Ozark streams. Absences of nests from stream reaches were expected 

for 13% of stream reaches, though the specific factors underpinning these absences remains 

unclear. The absence of nests at these sites could reflect habitat features at various scales that 

were not measured. Nest abundance increased with residual pool depth in both warm and cool 

streams, though nest abundances, in general, were much higher in warm streams. The positive 

relationship between nest abundance and increasing pool depths may reflect better protection 

from terrestrial predators (Orth and Newcomb 2002) or from scour during high flows (Lukas and 

Orth 1995). Nest abundance also reflected the availability of low-velocity pool habitat and wide, 

shallow channels. Based on our observations of nest microhabitat and on existing literature, there 

is a clear preference by nesting Smallmouth Bass for low-velocity areas such as those common 

in pools (Pflieger 1966; Winemiller and Taylor 1982; Dauwalter and Fisher 2007). Wide, 

shallow stream reaches receive more solar radiation due to relatively less riparian overhang, 

which may benefit adults and developing offspring via increased water temperatures and primary 

productivity (Vannote et al. 1980; Whitledge et al. 2006). One potential mechanism underlying 

the interactive relationship between pool habitat and substrate size is a lack of suitable velocity 

refugia available for nesting, regardless of pool habitat amount, under increased shear stress 

conditions (Knighton 1998; Schwendel et al. 2010). 

Young of year 

We found several relationships between YOY Smallmouth Bass abundance and abiotic 

conditions were modified by stream network position and stream size, indicating large and small 

streams serve as complementary refuge habitats under different abiotic scenarios. The relative 

influences of water temperature, July flow magnitude, and pool depth on YOY abundance were 

variable across the riverscape. These findings highlight the importance of movement corridors 
40

that allow fish to seek refuge from natural disturbances and environmental variability. Although 

YOY Smallmouth Bass use a diverse range of habitats throughout their distribution, often in 

response to coarse-scale conditions (Orth and Newcomb 2002; Pert et al. 2002; Dauwalter et al. 

2007; Brewer 2013b), current conservation and management efforts often do not consider this 

variability. Efforts to preserve and rehabilitate complementary habitats and areas that provide 

natural habitat diversity (e.g., tributary junctions) across riverscapes would be beneficial for the 

persistence of the Neosho subspecies under varying environmental conditions. 

Occurrence and abundance of YOY Smallmouth Bass were lower in colder (i.e., ≤ 19.5 

°C) stream reaches, though the latter relationship varied with stream network position. Optimum 

growth of juvenile Smallmouth Bass occurs between 25 and 28 °C (Peek 1965; Coutant and 

DeAngelis 1983), though development and growth are possible between 20 and 32 °C (Wrenn 

1980). As these warmer stream temperatures accelerate larval development, they confer added 

resistance against predation and environmental disturbance (e.g., displacement during floods; 

Harvey 1987; Armour 1993). Growth of YOY Smallmouth Bass also creates an important buffer 

against starvation during the overwinter period, which often serves as a recruitment bottleneck 

(Oliver et al. 1979; Shuter et al. 1980). Thus, it was not surprising that model-predicted absence 

was nearly four times more likely in cold streams. Despite this, we observed YOY Smallmouth 

Bass in 58% of cold stream reaches, suggesting that these habitats are important for reasons other 

than growth or that thermal refugia exist at finer scales. Groundwater contributions via springs 

and seeps are common in the Ozark Highlands and confer fine-scale thermal heterogeneity 

(Peterson and Rabeni 1996; Brewer 2013a). This variation may confer a favorable mix of 

habitats for feeding and growth (e.g., colder stream reaches often lacked larger piscivores; 

personal observations), which can be exploited by juvenile stream fishes across considerable 

distances (Armstrong et al. 2013). YOY Smallmouth Bass are capable of fine-scale movements 

between habitats in search of refuge (Brewer et al. 2019; Miller et al. 2019), and larger 

migrations of YOY among tributaries occur elsewhere in their range (Humston et al. 2010; 

Humston et al. 2017). Short-term movements to exploit favorable foraging conditions in warmer, 

more productive habitats may explain how network position mitigates the influence of colder 

streams on YOY Smallmouth Bass abundance. Access to larger, more productive streams may 

benefit growth (Vannote et al. 1980; Gorman 1986) and, in turn confer resistance to sizedependent displacement and mortality during floods (Larimore 1975; Jager et al. 1993).
41

Flood magnitude and timing during spawning and post-nesting periods are important 

predictors of YOY Smallmouth Bass abundance at the end of the first summer, although we 

found this relationship was influenced by stream network position. Late-summer abundance of 

YOY Smallmouth Bass was greater in years where July flow EP was greater (i.e., July flows 

were more benign). Considerable inter-annual variability in YOY stream fish abundance and 

recruitment is common and often reflects the timing of high flows, with floods that occur during 

nesting and larval development being particularly detrimental (Pearsons et al. 1992; Smith et al. 

2005; Kanno et al. 2016; Blum et al. 2018). For example, autumn abundance of YOY

Smallmouth Bass in large Virginia rivers is inversely related to the magnitude of June 

streamflow (Smith et al. 2005). In streams throughout their range, floods destroy and displace 

Smallmouth Bass eggs and larvae (Larimore 1975; Winemiller and Taylor 1982; Lukas and Orth 

1995). Flood timing was important in this study, which likely reflects the protracted nature of 

Smallmouth Bass spawning. Early spring flooding, as was observed in 2017, destroys nests but 

allows for ample re-nesting opportunities over the following months (Lukas and Orth 1995). 

Conversely, large floods later in this period, as we observed in 2015, precluded re-nesting 

attempts as stream temperatures become too warm (i.e., > 25 °C, Robbins and MacCrimmon 

1977; Graham and Orth 1986). The observed abundance-flow relationship was modified by 

proximity of the study reach to larger streams, with the relationship less pronounced in reaches 

adjacent to larger streams. This suggests that nearby large streams serve as a buffer against flowrelated mortality, possibly by providing refuge habitats (e.g., logs, boulders, eddy pools; Todd 

and Rabeni 1989). Indeed, use of low-velocity refuge habitats (e.g., forewaters and backwaters) 

by YOY Smallmouth Bass increases under elevated flow conditions (Brewer et al. 2019). The 

abundance-flow relationship was more pronounced in more isolated streams (i.e., those located 

upstream), suggesting that the importance of these streams for YOY Smallmouth Bass is greater 

when flow conditions are moderate. Regardless of relative network position, smaller streams 

may serve as important refugia from predators when flow conditions allow (Meyer et al. 2007; 

Richardson and Danehy 2007). Although our findings are informative, the reliance on limited 

hydrology data (i.e., gaged streams only) only allowed the use of coarse metrics that could 

overlook variability that drives additional landscape and local habitat relationships. Similarly, 

our choice of 10 m3/s for calculating high-flow frequency and duration metrics likely overlooks 

stream-size dependent variability in this threshold. We chose this value from observations of 
42

Smallmouth Bass nest failure in similarly sized streams (Lukas and Orth 1995), though many of 

our study streams were smaller and were likely vulnerable to substrate mobility and nest failure 

at lower flows (Pflieger 1975). Our use of annual averages across all gaged streams was a 

conservative approach that we felt best represented the range of study streams given the available 

data. Future work focusing on finer-scale modeling of streamflow dynamics and developing 

mechanistic linkages between hydrology and habitat on YOY abundance would be beneficial.

An interactive effect of residual pool depth and drainage area also explained variation in 

YOY Smallmouth Bass abundance. YOY Smallmouth Bass are often associated with 

intermediate depths, but deep habitats can also contain high densities (Aadland 1993; Sabo and 

Orth 1994; Dauwalter et al. 2007; Fore et al. 2007) and considerable plasticity in depth use has 

been observed (Pert et al. 2002; Brewer 2011). Our modeled relationship between depth and 

abundance was positive in smaller streams and negative in larger streams and may reflect habitat 

limitations and some of the discrepancy observed among streams (see overview by Pert et al. 

2002). Greater habitat area, whether due to deeper pools or larger streams, provides habitat 

heterogeneity and associated thermal variability (Arrigoni et al. 2008; Westhoff and Paukert 

2014), diverse foraging opportunities (Aadland et al. 1991; Sabo et al. 1996), and refuge from 

disturbance, predation, and density-dependent effects (Lukas and Orth 1995; Nislow et al. 2004). 

YOY Smallmouth Bass use a range of prey types during their first growing season (Dauwalter 

and Fisher 2008b), and foraging rates increase in faster habitats (Simonson and Swenson 1990; 

Sabo et al. 1996) where their prey is typically more common (Aadland et al. 1991). Larger 

streams, independent of pool depth, provide greater total riffle habitat areas that 

disproportionately contribute to prey production (Rabeni 1992; Zweifel et al. 1999) and 

Smallmouth Bass abundance (Sowa and Rabeni 1995; Brewer 2013b). Heterogeneous 

microhabitat conditions provide greater refuge from abiotic disturbance and can reduce nest 

failure and brood loss (Lukas and Orth 1995). Predator avoidance is important for small-bodied

fishes such as YOY Smallmouth Bass, and risks are often greatest at depth extremes (Power 

1987; Orth and Newcomb 2002). Deeper habitats may be favorable in small streams where avian 

predators predominate (Power et al. 1989; Harvey and Stewart 1991), whereas deeper habitats 

are less favorable in larger streams due to larger fish predators (Harvey 1991; Steinmetz et al. 

2008). Density-dependent effects on YOY Smallmouth Bass survival and growth have been 

documented in simulation studies (DeAngelis et al. 1991, 1993; Dong and DeAngelis 1998), 
43

though evidence for such dynamics in natural systems is lacking (Serns 1982; Orth and 

Newcomb 2002). However, loss of habitat area due to drying in late summer and autumn, a 

common feature of Ozark streams, may drive temporal variation in abundance and densitydependence for YOY and adult Smallmouth Bass (Dauwalter and Fisher 2008a; Hafs et al. 

2010).

MANAGEMENT RECOMMENDATIONS

Accounting for fish movements is essential for defining the appropriate scale for effective 

conservation and management actions. Movement data provide insight on the diversity of 

habitats needed for the completion of the life cycle and the corridors that connect these habitats 

(i.e., the functional habitat unit, Schlosser 1991; Cooke et al. 2016; Thurow 2016). Because 

tagged fish in smaller streams moved primarily during the spawning season and in response to 

high discharge, efforts to limit flow peaks (i.e., removal to offsite storage) are likely not 

beneficial to these populations. Because local populations appear to exhibit some variation in 

their movement tendencies, tailoring management actions to specific population units may also 

be a useful approach. Limiting the introgression of nonnative Smallmouth Bass genetics into 

these populations is also important, as this poses a conservation threat to the native form by 

compromising locally adapted genotypes (Koppelman 2015). There is evidence of some 

introgressive hybridization with nonnative forms in both the Elk River and Buffalo Creek 

populations (Taylor et al. 2018); curiously, introgression appears to be rare in the smallest stream 

where we examined fish movement despite connection to the same reservoir. Educating 

stakeholders about the value of endemic Neosho Smallmouth Bass may help discourage future 

unauthorized introductions (e.g., Cambray 2003). Although the Sycamore Creek population of 

Neosho Smallmouth Bass appears to be effectively isolated by Grand Lake from other 

populations, connectivity between the Elk River and Buffalo Creek populations could be 

increased by keeping Grand Lake pool levels low during winter and spring when possible (i.e., 

not a human safety concern). This would improve access to warmer mainstem winter habitats 

and to a wider range of spawning habitats between the two systems. Efforts to protect this 

narrow-rage endemic black bass could be valuable to scientists and managers dealing with 

similar issues (e.g., endemism, human alteration of the environment, scale of management) and 
44

strategies may be transferrable to other endemic black basses in the face of environmental 

change. Movement of individuals represents an important mechanism for coping with 

environmental changes and reduces vulnerability to the myriad effects of climate change (Pörtner 

and Peck 2010). Therefore, protecting or enhancing the physical conditions that allow stream 

fish movement in response to changing environmental conditions and biological needs may 

prove beneficial to population persistence.

We suspect angler effort may be substantially higher on the Elk River where we lost 

more tagged fish when compared to other sample reaches (i.e., Buffalo Creek, Sycamore Creek). 

We tracked tagged fish to an angler’s stringer on one occasion, and we received multiple,

anecdotal angler reports of tagged fish being captured and released (from conversations during 

our tracking efforts). We also received two reports of tagged fish capture by anglers on Buffalo 

Creek (one fish was harvested, and the tag returned). Exploitation, as suggested by tag loss due 

to angling, may be an issue of interest to the management agency. 

The unique nest clustering behavior and influence of multi-scale habitat features on 

Neosho Smallmouth Bass nest abundances create conflicts for the management of an endemic 

sportfish. Fish management and biodiversity conservation typically represent conflicting interests 

in freshwater systems, but combined approaches (e.g., protected areas) may provide 

disproportionate conservation benefits when focused on spawning aggregations (Erisman et al. 

2015). In contrast to the northern subspecies, a proportion of Neosho Smallmouth Bass often 

construct nests in close proximity to neighboring nests. These aggregations may be more 

susceptible to anglers targeting nesting males given the vulnerability of nest-guarding males to 

anglers (Philipp et al. 1997; Steinhart et al. 2004). Males with greater quantities of eggs tend to 

be more aggressive toward potential predators and more vulnerable to angling, suggesting that 

angling disproportionately affects individuals with the greatest potential to contribute to annual 

recruitment (Suski and Philipp 2004). Angling also frequently results in nest predation and nest 

failure even with catch and release angling (Philipp et al. 1997; Suski et al. 2003). Regulations 

vary throughout the range of Neosho Smallmouth Bass (i.e., closed season March–late May in 

Missouri, no closed season in Oklahoma or Arkansas), and could be modified to cover the early 

part of the nesting season in all streams or specifically applied to areas that support nesting 

aggregations to alleviate angling vulnerability (Kubacki et al. 2002; Noble 2002). 
45

Disentangling the multi-scale influences on spawning habitat is important for conserving 

existing habitat and for guiding stream restoration efforts. Although cooler water temperatures 

broadly restrict nesting habitat potential of Neosho Smallmouth Bass, cooler streams, especially 

those with deeper pools, provide additional spawning habitat. As deeper pools are beneficial to 

nest abundance regardless of stream temperature, efforts to limit landscape and channel 

modification that alter natural geomorphology (e.g., bedform, sedimentation, channel 

dimensions) would be beneficial (Rabeni and Jacobson 1993; Leasure et al. 2016). The ability of 

nesting Neosho Smallmouth Bass to use various low-velocity refugia helps explain their 

presence across streams with varying hydraulic conditions and availabilities of low-velocity 

mesohabitat. Habitat plasticity, in general, provides resistance to environmental changes (Goniea 

et al. 2006; Beever et al. 2017) and has been observed for YOY Smallmouth Bass (Pert et al. 

2002; Fore et al. 2007). With several subpopulations of Neosho Smallmouth Bass that appear 

isolated due to limited gene mixing (Taylor et al. 2018), observed habitat plasticity may reflect 

adaptation to specific habitat types among drainages. Our results highlight the unique nesting 

behavior of Neosho Smallmouth Bass and the myriad of habitat features that influence 

clustering, presence, and nest abundance during this life-history period. 

YOY Smallmouth Bass survival is influenced by habitat characteristics that operate at 

different spatiotemporal scales and is important for the conservation and management of these 

populations. Despite the inherent stochasticity in YOY Smallmouth Bass survival due to flashy 

hydrology (Lukas and Orth 1995; Smith et al. 2005), Smallmouth Bass populations are 

behaviorally adapted to such dynamics (e.g., protracted spawning season, building nests in 

current refugia; Lukas and Orth 1995; Orth and Newcomb 2002). Nevertheless, juvenile rearing 

conditions would be improved by limiting further flow alteration and land-use practices that 

exacerbate extreme flows and result in wider and shallower streams (e.g., deforestation, 

urbanization, groundwater pumping; Poff et al. 1997; Paul and Meyer 2001). Gravel-bed streams 

emblematic of the Ozark Highlands are frequently reorganized during high, flashy flows (Rabeni 

and Jacobson 1993; Leasure et al. 2016), and these geomorphic changes (e.g., bedform change, 

sedimentation, channel shallowing and widening) can lead to decreases in YOY stream fish 

survival and abundance (Nislow et al. 2002; Harvey et al. 2009). The removal of riparian buffers 

and gravel mining exacerbate the geomorphic consequences of high flows (Rabeni and Jacobson 

1993; Rabeni and Smale 1995; Naiman and Décamps 1997) and would be expected to negatively 
46

affect rearing habitat. Maintaining existing stream connectivity patterns is important for 

permitting fish movement to refuge and foraging habitats and may offset negative influences on 

survival in different portions of basins depending on the threat (Peterson and Rabeni 1996; 

Labbe and Fausch 2000). For example, many warmwater fishes use thermal refuge in the winter, 

as these environments provide relatively warmer conditions that benefit fish growth and survival 

(Langhurst and Schoenike 1990; Peterson and Rabeni 1996). These efforts will be especially 

important in light of a changing climate anticipated to increase extreme weather and hydrology 

patterns (Mulholland et al. 1997; Döll and Zhang 2010; Singh et al. 2013). Our results show 

network position would be useful when examining landscape-scale approaches to stream 

management (Rabeni and Sowa 2002) or restoration (Roni et al. 2002; Bond and Lake 2003) 

although considerable efforts to acquire available resources and avoid conflicts of interest in 

smaller watersheds may be necessary (i.e., Lake et al. 2007).

The results of both our nesting and juvenile habitat analyses suggest small streams, 

typically not considered important to many fisheries, are responsible for a proportion of 

Smallmouth Bass end-of-summer abundance. This is especially interesting to the Neosho 

subspecies as these populations appear to be somewhat insulated from hybridization resulting 

from historic or unknown stockings (Taylor et al. 2018). We found that nest abundance was 

positively related to deeper pools of smaller streams and low-velocity habitat was more 

important in streams with smaller than average substrates. Both findings have implications for 

both habitat protection and restoration. For example, protecting deeper pool morphology (i.e., 

fencing cattle from smaller streams or other activities that result in widening and shallowing of 

stream channels) is more important in smaller streams compared to larger stream systems. We 

also highlight the importance of smaller, cool-water tributaries located close in proximity to 

larger streams. These streams are important juvenile rearing habitat and their stream morphology 

is also an important element to quality habitat for Neosho Smallmouth Bass. 
47

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). Funding was provided by the 

Oklahoma Department of Wildlife Conservation (F15AF00102). Our scientific collector’s permit 

numbers were 6852 (2015-2017, Oklahoma permits) and 16738 and 17124 (2016 and 2017, 

Missouri permits). This study was performed under the auspices of Oklahoma State University 

Animal Care and Use protocols AG-16-8 and AG-15-25. The authors have no conflicts of 

interest to report. We thank Kelsey Anderson, Jim Burroughs, Jason Glover, Josh Hendricks, 

Kelly Hoyer, Becky Long, Frances Marshall, Bob Mollenhauer, Josh Mouser, Robert Sheffer, 

and Maeghen Wedgeworth for technical assistance. We thank Charlie Rabeni for providing 

comments that improved the overall project. Mike Quist and Dan Daugherty provided helpful 

comments on an earlier draft. Special thanks to the many private landowners that allowed us 

stream access to complete the project. Any use of trade, firm, or product names is for descriptive 

purposes only and does not imply endorsement by the U.S. Government.
48

Table 1. Summary statistics and data sources for ancillary and habitat predictor variables used to 

model Neosho Smallmouth Bass nest detection efficiency and nest density-habitat relationships. 

We measured reach-scale (~20 times channel width) variables in the field and calculated 

segment-scale (tributary confluence to tributary confluence) variables in ArcMap 10.3.1 (ESRI, 

Redlands, California). We retained 43 terms following variable reduction: 12 univariate terms 

(bolded) and 31 two-way interaction terms. 

Variable Mean ± SD Range Data source

Detection efficiency

 Water clarity (m) 3.1 ± 1.2 1.3 – 7.6 Field-collected

 Mean thalweg depth (m) 0.72 ± 0.27 0.22 – 1.70 Field-collected

 Wetted width/observer (m) 5.4 ± 2.0 2.4 – 11.6 Field-collected

Reach scale

 Sampling date (day of 

year)ab

155.5 ± 19.2 122 – 190 Field-collected

 Stream temperature (°C)a19.7 ± 2.4 15.1 – 24.6 Field-collected

 Residual pool depth (m)a1.03 ± 0.53 0.35 – 3.14 Field-collected

 Pool habitat (%)a63.6 ± 17.0 3.0 – 92.0 Field-collected

 Off-channel habitat (%) 5.7 ± 6.3 0.0 – 30.0 Field-collected

 Riffle habitat (%) 12.5 ± 8.5 0.0 – 41.0 Field-collected

 Median substrate size 

(mm)ab

25.1 ± 5.3 10.0 – 40.0 Field-collected

 Bankfull width-depth 

ratioa

42.0 ± 21.8 7.9 – 122.7 Field-collected

 Groundwater flow (m3/s)a-0.01 ± 0.24 -0.70 – 1.13 Field-collected

 Area sampled (m2) 4771.9 ± 

3887.6

565.9 –

20127.3

Field-collected

Segment scale

 Link magnitude 23.5 ± 37.9 1 – 264 USGS NHDc

 Downstream linka69.2 ± 132.5 3 – 874 USGS NHDc
49

 Stream gradient (%)a0.34 ± 0.16 0.03 – 0.73 NHD Plus Version 

2

d

 Drainage area (km2)

ab 172.2 ± 176.0 18.0 – 886.6 USGS NEDef

 Catchment slope (%) 7.2 ± 3.3 1.4 – 15.4 USGS NEDef

 Disturbance index 2.3 ± 0.4 1.5 – 3.9 NLCD 2011eg

 Hydro soil group D (%) 33.5 ± 14.4 5.9 – 70.3 USDA NRCS 

SSURGO 2.2eh

 Carbonate geology (%)a96.8 ± 8.9 17.3 – 100.0 USGS, USDA 

NRCSei

 Base flow/total flow (%)a46.0 ± 4.1 34.4 – 53.3 USEPA 

StreamCatej

 Runoff (mm)a350.4 ± 17.8 316.0 – 381.6 USEPA 

StreamCatej

a: Variables (n = 16) used to evaluate model form.

b: Variables for which quadratic effects were used to evaluate model form.

c: http://nhd.usgs.gov/

d: http://www.horizon-systems.com/nhdplus/nhdplusv2_home.php

e: These variables were summarized for the entire catchment draining to the downstream end of 

each stream segment.

f: http://ned.usgs.gov/

g: Homer et al. 2015, https://www.mrlc.gov/nlcd11_data.php

h: NRCS 2017, https://websoilsurvey.sc.egov.usda.gov

i: Stoeser et al. 2005, https://datagateway.nrcs.usda.gov/

j: Hill et al. 2016, https://www.epa.gov/national-aquatic-resource-surveys/streamcat 
50

Table 2. Disturbance coefficients used in relation to 2011 National Land Cover Dataset (Homer 

et al. 2015, https://www.mrlc.gov/nlcd2011.php) land cover categories for calculating 

disturbance index, where greater values equate to a greater degree of disturbance. The large 

coefficient used for barren land reflects the association of such lands with mining activities in 

this region (Smart et al. 1981), although overall this cover class was uncommon.

Land cover type NLCD Class Coefficient

Open water 11 1.00

Perennial ice/snow 12 1.00

Developed, open space 21 1.83

Developed, low intensity 22 6.90

Developed, medium intensity 23 7.64

Developed, high intensity 24 7.92

Barren land (rock/sand/clay) 31 8.32

Deciduous forest 41 1.00

Evergreen forest 42 1.00

Mixed forest 43 1.00

Shrub/scrub 52 1.00

Grassland/herbaceous 71 1.00

Pasture/hay 81 3.09

Cultivated crops 82 4.54

Woody wetlands 90 1.00

Emergent herbaceous wetlands 95 1.00
51

Table 3. Coefficients, standard errors (SE), and 95% confidence intervals (CI) for mixed-effects 

logistic regression describing presence of Smallmouth Bass nest clusters in Ozark streams. 

Hypothesized explanations for including each predictor variable are provided. Model coefficients 

are presented on a logit scale, and predictors were standardized to mean = 0 and standard 

deviation = 1 prior to model specification. The reference condition for stream temperature was

warm (> 19.5°C) and the reference condition for groundwater flow was losing (i.e., longitudinal 

decrease in stream discharge). Area sampled was included to account for differences in sampling 

effort among stream reaches. 

Predictor variable Coefficient ± SE 95% CI Hypotheses

Intercept -1.35 ± 0.58 -2.49, -0.21

Stream temperature -2.32 ± 1.04 -4.36, -0.27 Habitat limitation

Pool habitat 0.54 ± 0.32 -0.09, 1.17 Habitat limitation

Off-channel habitat 0.59 ± 0.40 -0.20, 1.38 Habitat limitation

Stream gradient -0.50 ± 0.34 -1.17, 0.17 Habitat limitation

Bankfull width-depth ratio 0.79 ± 0.39 0.04, 1.55 Habitat limitation, Disturbance

Disturbance index 0.26 ± 0.33 -0.39, 0.92 Disturbance

Neutral groundwater flow 0.11 ± 0.73 -1.32, 1.55 Groundwater

Gaining groundwater flow -1.55 ± 0.76 -3.04, -0.06 Groundwater

Sampling date -1.04 ± 0.41 -1.84, -0.24 Progression of spawning season

Young-of-year abundance 1.39 ± 1.58 -1.71, 4.49 Increased offspring survival

Area sampled 0.75 ± 0.44 -0.10, 1.60 Sampling effort
52

Table 4. Coefficient estimates, standard errors (SE) and 95% confidence intervals (CI) for the 

model of Smallmouth Bass nest detection efficiency in Ozark streams. We adjusted SE and CI to 

account for model overdispersion (ĉ= 2.18). Detection model coefficients are on a logit scale, 

and abundance model coefficients are on a natural-log scale. Continuous predictors were 

standardized to mean = 0 and standard deviation = 1. Warm streams (> 19.5°C) were the 

reference condition for stream temperature. Model-estimated zero inflation was -1.40 ± 0.45 

(logit scale).

Predictor variable Coefficient ± SE 95% CI

Detection model

 Intercept 0.94 ± 0.39 0.17, 1.72

 Width × Clarity × Depth -0.09 ± 0.13 -0.35, 0.17

 Width × Clarity -0.18 ± 0.21 -0.60, 0.23

 Width × Depth -0.19 ± 0.12 -0.43, 0.05

 Clarity × Depth -0.03 ± 0.20 -0.43, 0.36

 Width -0.46 ± 0.19 -0.83, -0.09

 Clarity 0.84 ± 0.23 0.40, 1.28

 Depth 0.53 ± 0.20 0.14, 0.92

Abundance model

 Intercept 2.28 ± 0.12 2.04, 2.52

 Area sampled (effort) 0.74 ± 0.10 0.55, 0.93

 Drainage area 0.03 ± 0.10 -0.17, 0.23

 Stream temperature -1.73 ± 0.32 -2.36, -1.09

 Pool habitat 0.20 ± 0.07 0.06, 0.34
53

Table 5. Predictor variables used for modeling YOY Smallmouth Bass abundance-habitat 

relationships in Ozark Highland streams, along with summary statistics for n = 120 stream 

reaches and data sources. Reach-scale (~20 times wetted channel width) variables were measured 

in the field, segment-scale (tributary confluence to tributary confluence) variables were 

calculated using ArcMap 10.3.1 (ESRI, Redlands, California). We also calculated several coarseresolution hydrologic variables to describe annual trends in streamflow patterns across the study 

area. The 25 variables were considered in an initial screening process to determine which 

variables to retain in the modeling process. We retained 10 univariate terms (bolded) and 11 twoway interactions (all combinations of Drainage area, July flow EP, Downstream link, and Stream 

temperature; Drainage area × Residual pool depth, Drainage area × April-July precip, Drainage 

area × Pool habitat, July flow EP × Soil permeability, and Stream temperature × April-July 

precip) following variable reduction. We scaled abundance by wetted area sampled by including 

the latter as a model offset. 

Variable Mean ± SD Range Data source

Reach scale

 Sampling date (day of year)a258.4 ± 19.1 215 – 312 Field-collected

 Water clarity (m)b4.0 ± 1.6 1.5 – 8.7 Field-collected

 Discharge (m3/s)b0.46 ± 0.61 0.01 – 3.68 Field-collected

 Stream temperature (°C)a21.6 ± 2.7 15.1 – 27.5 Field-collected

 Wetted width (m)a9.9 ± 4.0 3.8 – 21.8 Field-collected

 Residual pool depth (m)a0.92 ± 0.40 0.20 – 2.37 Field-collected

 Thalweg depth (m)b0.54 ± 0.21 0.16 – 1.08 Field-collected

 Pool habitat (%)a73.7 ± 15.8 9.9 – 100 Field-collected

 Riffle habitat (%) 9.4 ± 7.8 0.0 – 35.3 Field-collected

 Wetted area sampled (m2) 4046.5 ± 4195.7 295.2 – 28415.1 Field-collected

Segment scale

 Drainage area (km2)

a

171.8 ± 165.9 16.9 – 772.7 USGS NEDc

 Link magnitude 26.2 ± 34.3 1 – 215 USGS NHDd

 Downstream linka73.3 ± 127.0 4 – 694 USGS NHDd

 Disturbance index 2.21 ± 0.32 1.48 – 2.90 NLCD 2011e
54

 Impervious cover (%) 0.93 ± 0.64 0.17 – 3.64 NLCD 2011f

 Catchment slope (%) 7.33 ± 3.31 2.59 – 18.02 USGS NEDc

 Hydro soil group D (%) 33.04 ± 14.60 5.93 – 72.49 SSURGO 2.2g

 Carbonate geology (%) 94.45 ± 11.07 43.76 – 100 USGS, USDA NRCSh

 Baseflow/total flow (%) 44.58 ± 5.13 31.43 – 53.26 USEPA StreamCati

 Runoff (mm) 345.8 ± 21.3 316.0 – 383.7 USEPA StreamCati

 Soil permeability (cm/hour)a4.08 ± 0.31 3.08 – 4.90 USEPA StreamCati

Precipitation and streamflow 

metrics

 April-July precip (mm)a646.4 ± 179.2 417.6 – 1069.6 Oklahoma Mesonetj

 Mean April-July flow (m3/s)a8.5 ± 6.2 3.0 – 11.7 USGS NWISk

 Mean April flow EP (%) 3.76 ± 3.06 0.04 – 6.43 USGS NWISk

 Mean May flow EP (%) 2.90 ± 2.93 0.32 – 6.23 USGS NWISk

 Mean June flow EP (%) 14.13 ± 9.00 0.84 – 23.86 USGS NWISk

 Mean July flow EP (%)a13.43 ± 7.75 0.34 – 21.79 USGS NWISk

 Mean flood frequency 3.32 ± 1.33 1.82 – 4.73 USGS NWISk

 Mean flood duration (d) 17.13 ± 10.93 5.27 – 33.00 USGS NWISk

a: Predictors (n = 11) used in evaluation of model distribution.

b: Ancillary variables (n = 3) measured only to evaluate sampling efficiency. Stream temperature 

and wetted width were considered for modeling both sampling efficiency and fish abundance.

c: http://ned.usgs.gov/

d: http://nhd.usgs.gov/

e: Homer et al. 2015, https://www.mrlc.gov/nlcd11_data.php

f: Xian et al. 2011, https://www.mrlc.gov/nlcd11_data.php

g: https://nrcs.app.box.com/v/soils

h: Stoeser et al. 2005, https://datagateway.nrcs.usda.gov/

i: Hill et al. 2016, https://www.epa.gov/national-aquatic-resource-surveys/streamcat

j: https://www.mesonet.org/

k: https://nwis.waterdata.usgs.gov/nwis/sw
55

Table 6. Rankings of models within 2 AICc of the top model from candidate set of linear mixed 

models of movement rate (natural-log transformed) by adult Smallmouth Bass in Ozark streams 

(n = 563 models). Yij is daily movement rate at relocation i for fish j, β0 is the grand intercept, γ 

is the random fish intercept, X1 is season, X2 is stream, X3 is the mean discharge (m3/s) between 

relocations, X4 is the coefficient of variation (CV) for discharge between relocations, X5 is the 

mean water temperature (°C) between relocations, X6 is the CV of water temperature between 

relocations, X7 is the change in mean daily water temperature (°C) between relocations, X8 is 

fish TL (mm). Main effects involved in interactions were included in respective models but are 

not shown below for each model. K is the number of model parameters, LL is log-likelihood, 

AICc is Akaike’s information criterion corrected for small sample size, and ∆AICc is the 

difference in AICc score between a given model and the top model. Akaike weight (wi) indicates 

the relative support for each model. Marginal R2(R2

m) describes variance explained by fixed 

effects and conditional R2(R2

c) describes variance explained by both fixed and random effects.

Model K LL AICc ∆AICc wi R

2

m R

2

c

 Yij = β0 + γj + β1X1i∙X3i + β2X1i∙X5i +

 β3X2i∙X5i + β4X1i∙X2i + β5X4i + 

 β6X8j

26 -3671.84 7396.31 0.00 0.27 0.16 0.29

 Yij = β0 + γj + β1X1i∙X3i + β2X1i∙X5i +

 β3X2i∙X5i + β4X1i∙X2i + β5X4i + 

 β6X8j + β7X2i∙X3i

28 -3670.44 7397.62 1.31 0.14 0.16 0.29

 Yij = β0 + γj + β1X1i∙X3i + β2X1i∙X5i +

 β3X2i∙X5i + β4X1i∙X2i + β5X4i + 

 β6X8j + β8X7i

27 -3671.78 7398.24 1.93 0.10 0.16 0.29
56

Table 7. Models within 2 AICc of the top model from candidate sets of linear mixed models of 

depth and velocity use (natural-log transformed) by adult Smallmouth Bass in Ozark Highland 

streams (n = 16 models each). Yij is the depth (or velocity) at location i for fish j, β0 is the grand 

intercept, γ is the random fish intercept, X1 is season, X2 is stream, X3 is fish sex, and X4 is fish 

TL (mm). K is the number of model parameters, LL is log-likelihood, AICc is Akaike’s 

information criterion adjusted for small sample size, and ∆AICc is the difference in AICc score 

between each model and the top model. Relative support for each model is given by Akaike 

weight (wi). Variance explained by fixed effects is given by marginal R2(R2

m) while the variance 

explained by both fixed and random effects is described by conditional R2(R2

c).

Model K LL AICc ∆AICc wi R

2

m R

2

c

Depth

 Yij = β0 + γj + β1X1i + β2X2i 8 -501.84 1019.75 0.00 0.41 0.10 0.41

 Yij = β0 + γj + β1X1i + β2X2i + β3X3j 10 -500.31 1020.71 0.96 0.26 0.11 0.41

 Yij = β0 + γj + β1X1i + β2X2i + β4X4j

Velocity

9 -501.48 1021.03 1.29 0.22 0.10 0.41

 Yij = β0 + γj + β1X1i + β2X2i 8 -5227.77 10471.59 0.00 0.48 0.07 0.15

 Yij = β0 + γj + β1X1i + β2X2i + β3X3j 10 -5226.43 10472.95 1.36 0.24 0.07 0.15

 Yij = β0 + γj + β1X1i + β2X2i + β4X4j 9 -5227.73 10473.53 1.94 0.18 0.07 0.15
57

Table 8. Coefficient estimates, standard errors (SE) and 95% confidence intervals (CI) for the 

model of Smallmouth Bass nest detection efficiency in Ozark Highland streams. We adjusted SE 

and CI to account for model overdispersion (ĉ= 2.18). Detection model coefficients are on a 

logit scale, and abundance model coefficients are on a natural-log scale. Continuous predictors 

were standardized to mean = 0 and standard deviation = 1. Warm streams (> 19.5°C) are the 

reference condition for stream temperature. Model-estimated zero inflation was -1.40 ± 0.45 

(logit scale).

Predictor variable Coefficient ± SE 95% CI

Detection model

 Intercept 0.94 ± 0.39 0.17, 1.72

 Width × Clarity × Depth -0.09 ± 0.13 -0.35, 0.17

 Width × Clarity -0.18 ± 0.21 -0.60, 0.23

 Width × Depth -0.19 ± 0.12 -0.43, 0.05

 Clarity × Depth -0.03 ± 0.20 -0.43, 0.36

 Width -0.46 ± 0.19 -0.83, -0.09

 Clarity 0.84 ± 0.23 0.40, 1.28

 Depth 0.53 ± 0.20 0.14, 0.92

Abundance model

 Intercept 2.28 ± 0.12 2.04, 2.52

 Area sampled (effort) 0.74 ± 0.10 0.55, 0.93

 Drainage area 0.03 ± 0.10 -0.17, 0.23

 Stream temperature -1.73 ± 0.32 -2.36, -1.09

 Pool habitat 0.20 ± 0.07 0.06, 0.34
58

Table 9. Rankings of top (i.e., within 2 AICc) candidate linear mixed models of Smallmouth 

Bass nest abundance in Ozark Highland streams. Yij is nest abundance of stream reach i in 

stream segment j, β0 is the grand intercept, and γj is the random stream segment intercept. X1 is 

water temperature class, where warm (> 19.5°C) is the reference condition. X2 is residual pool 

depth (m), X3 is percent pool habitat, X4 is bankfull width-depth ratio, and X5 is median substrate 

size (mm). K is the number of model parameters, LL is log-likelihood, AICc is Akaike’s 

information criterion adjusted for small sample size, and ∆AICc is the difference in AICc score 

between the given model and the top model. Akaike weight (wi) indicates the relative support for 

the given model. Marginal R2(R2

m) and conditional R2

(R2

c) values represent the amount of 

variance explained by fixed effects only and by both fixed and random effects, respectively. All 

models containing interaction terms also include main effects for predictors involved in 

interactions.

Model K LL AICc ∆AICc wi R

2

m R

2

c

Yij = β0 + γj + β1X1i∙X2i + β2X3i + 

 β3X4i

9 -

289.5

598.6 0.0 0.1

2

0.8

0

0.8

0

Yij = β0 + γj + β1X1i∙X2i + β2X3i 8 -

291.0

599.3 0.7 0.0

8

0.8

0

0.8

1

Yij = β0 + γj + β1X1i∙X2i + 

β4X3i∙X5i

10 -

288.7

599.4 0.8 0.0

8

0.8

1

0.8

3

Yij = β0 + γj + β1X1i∙X2i + β3X4i 8 -

291.6

600.5 1.9 0.0

5

0.7

9

0.7

9
59

Table 10. Coefficient estimates and 95% confidence intervals (CI) from the top-ranked 

candidate model of YOY Neosho Smallmouth Bass abundance in Ozark streams. Conditional 

(abundance) model coefficients are on the natural-log scale and reflect unit increments of one 

standard deviation and mean levels of all other continuous predictors. Zero-inflation coefficients 

are on a logit scale. Warm streams (> 19.5 °C) are the reference condition for water temperature. 

EP is exceedance probability, D-link is downstream link, and pool depth is residual pool depth. 

Coefficient ± SE 95% CI

Conditional model

 Intercept -4.03 ± 0.09 -4.21, -3.85

 July flow EP x D-link -0.22 ± 0.08 -0.38, -0.06

 Drainage area x Pool depth -0.41 ± 0.10 -0.59, -0.22

 Stream temperature x D-link 0.56 ± 0.30 -0.03, 1.16

 July flow EP 0.54 ± 0.08 0.38, 0.70

 Drainage area 0.24 ± 0.12 0.01, 0.47

 Stream temperature -1.18 ± 0.41 -1.98, -0.38

 Downstream link 0.04 ± 0.10 -0.15, 0.24

 Pool depth -0.01 ± 0.09 -0.20, 0.17

Zero-inflation model

 Intercept -2.45 ± 0.44 -3.31, -1.58

 Stream temperature 1.57 ± 0.90 -0.19, 3.34
60

Figure 1. Tagging locations and tracking extents (bolded) for three Ozark streams where we 

evaluated movement and habitat use by adult Smallmouth Bass. 
61

Figure 2. Map of stream reaches (n = 120) surveyed for Neosho Smallmouth Bass nests. 

Number of years (from 2016-2018) each reach was sampled is indicated by dot color. Reaches 

were sampled only once within a given year, and thus were treated as unique sites across years.
62

Figure 3. Location of stream reaches surveyed for YOY Smallmouth Bass. Dot color represents 

the number of years a reach was sampled (from 2015-2017). We did not sample reaches twice 

within a given year and reaches sampled in different years were treated as unique sites.
63

Figure 4. Daily mean water temperature (top) and discharge (bottom) conditions for Elk River 

(solid line), Buffalo Creek (dashed) and Sycamore Creek (dotted) during the June 2016–

February 2018 study of adult Smallmouth Bass movement and habitat use. For reference, 1.0 

m3/s equals 35.3 ft3/s. Note that discharge is plotted using a log10 scale and that the y-axis ranges 

from 0.01 m3/s to the maximum discharge during the study period (1,557 m3/s on 30 April 2017 

in the Elk River).
64

Figure 5. Predicted relationship between Smallmouth Bass movement rate (m/d) and mean 

discharge (m3/s) in spring and summer. Predicted movement rates are not pictured for autumn 

(mean: 3.4 m/d) or winter (mean: < 0.1 m/d) as these predictions do not vary over the small 

range of discharge values (0.29–0.92 m3/s) observed in the field. Predicted movement rates 

represent back-transformed mean discharge values in Buffalo Creek with all other continuous 

predictors held constant at mean levels.
65

Figure 6. Modeled relationship between movement rate (m/d) of Smallmouth Bass and mean 

water temperature (°C) across three study streams and four seasons. The study streams are Elk 

River (solid line), Buffalo Creek (dashed), and Sycamore Creek (dotted). Predictions represent 

changes in water temperature with all other continuous model predictors held constant at their 

mean values.
66

Figure 7. Relationship between movement rate (m/d) and fish size (TL, mm) from the topranked model of movement rate. Dashed lines indicate 95% confidence intervals. Predictions are 

made with all other continuous predictors at mean levels and with Elk River as the reference 

stream and summer as the reference season.
67

Figure 8. Summaries of proportional substrate and cover use across seasons and streams based 

on n = 2,362 observations of radio-tagged Smallmouth Bass. Column widths are proportional to 

sample size. Substrate types include silt (< 0.06 mm), sand (> 0.06–2 mm), gravel (> 2–16 mm; 

GRAV), pebble (> 16–64 mm; PEBB), cobble (> 64–256 mm; COBB), boulder (> 256 mm; 

BLDR) and bedrock (BR). Cover types are open water/no cover (OW), woody debris (WOOD), 

boulder (BLDR), rootwad (RW), undercut bank (BANK) or bedrock (UCBR), vegetation (VEG), 

man-made (MISC) and combinations of these types (COMBO). Man-made cover includes 

miscellaneous structures such as bridge pilings and broken concrete.
68

Figure 9. Relationship between residual pool depth (m) and Smallmouth Bass nest density in 

streams of two temperature classes: warm (> 19.5°C midday) and cool (≤ 19.5°C). Cool streams 

include those with relatively higher groundwater influence and those sampled earlier in the May–

July study period.
69

Figure 10. Model-predicted relationship between July flow exceedance probability and YOY

Smallmouth Bass abundance in stream reaches with varying downstream link magnitudes. 

Modeled relationships are at mean values of other continuous predictors and at the reference 

stream temperature (i.e., warm; > 19.5 °C).
70

Figure 11. Model-predicted effect of residual pool depth (m) on YOY Smallmouth Bass 

abundance across three representative stream sizes (drainage areas [km2]). Predictions are only 

included for combinations of residual pool depth and drainage area that co-occurred in the field. 

Relationships are modeled with other continuous predictors held at mean levels and for the 

reference stream temperature (i.e., warm; > 19.5 °C).
71

Figure 12. Relationship between downstream link magnitude and YOY Smallmouth Bass 

abundance in relation to stream temperature based on our top model. Stream temperature consists 

of two levels, cold (≤ 19.5 °C) and warm (> 19.5 °C), based on a natural break in our data.
72

REFERENCES

Aadland, L. P. 1993. Stream habitat types: their fish assemblages and relationship to flow. North 

American Journal of Fisheries Management 13:790-806. 

Allan, J. D. 2004. Landscapes and riverscapes: the influence of land use on stream ecosystems. 

Annual Review of Ecology, Evolution, and Systematics 35:257-284.

Allan, J. D., and M. M. Castillo. 2007. Stream ecology: structure and function of running waters. 

Springer, Dordrecht, Netherlands. 

ander Zanden, M. J., J. M. Casselman, and J. B. Rasmussen. 1999. Stable isotope evidence for 

the food web consequences of species invasions in lakes. Nature 401:464-467.

Angermeier, P. L. 1995. Ecological attributes of extinction-prone species: loss of freshwater 

fishes of Virginia. Conservation Biology 9:143-158.

Armour, C. L. 1993. Evaluating temperature regimes for protection of Smallmouth Bass. United 

States Department of the Interior, Resource Publication 191, Washington, D.C.

Armstrong, J. B., D. E. Schindler, C. P. Ruff, G. T. Brooks, K. E. Bentley, and C. E. Torgersen. 

2013. Diel horizontal migration in streams: juvenile fish exploit spatial heterogeneity in 

thermal and trophic resources. Ecology 94:2066-2075. 

Arnold, T. W. 2010. Uninformative parameters and model selection using Akaike’s information 

criterion. Journal of Wildlife Management 74:1175-1178. 

Barthel, B. L., S. J. Cooke, J. H. Svec, C. D. Suski, C. M. Bunt, F. J. S. Phelan, and D. P. 

Philipp. 2008. Divergent life histories among Smallmouth Bass Micropterus dolomieu 

inhabiting a connected river-lake system. Journal of Fish Biology 73:829-852.

Bartoń, K. 2018. MuMIn: multi-model inference. R package version 1.40.4. https://cran.rproject.org/package=MuMIn.

Bates, D., M. Maechler, B. Bolker, and S. Walker. 2015. Fitting linear mixed-effects models 

using lme4. Journal of Statistical Software 67:1-48.

Baxter, C. V., and F. R. Hauer. 2000. Geomorphology, hyporheic exchange, and selection of 

spawning habitat by bull trout (Salvelinus confluentus). Canadian Journal of Fisheries and 

Aquatic Sciences 57:1470-1481. 

Beeman, H. W. 1924. Habits and propagation of the small-mouthed black bass. Transactions of 

the American Fisheries Society 54:92-107. 
73

Beever, E. A., L. E. Hall, J. Varner, A. E. Loosen, J. B. Dunham, M. K. Gahl, F. A. Smith, and J. 

J. Lawler. 2017. Behavioral flexibility as a mechanism for coping with climate change. 

Frontiers in Ecology and the Environment 15:299-308.

Betz, R., N. P. Hitt, R. L. Dymond, and C. D. Heatwole. 2010. A method for quantifying stream 

network topology over large geographic extents. Journal of Spatial Hydrology 10:15-29.

Blum, A. G., Y. Kanno, and B. H. Letcher. 2018. Seasonal streamflow extremes are key drivers 

of Brook Trout young-of-the-year abundance. Ecosphere 9:e02356. doi: 10.1002/ecs2.2356. 

Bolker, B. 2017. bbmle: tools for general maximum likelihood estimation. R package version 

1.0.20. https://cran.r-project.org/package=bbmle.

Bond, N. R., and P. S. Lake. 2003. Local habitat restoration in streams: constraints on the 

effectiveness of restoration for stream biota. Ecological Management and Restoration 4:193-

198.

Bradford, M. J., and J. S. Heinonen. 2008. Low flows, instream flow needs and fish ecology in 

small streams. Canadian Water Resources Journal 33:165-180. 

Brewer, S. K. 2011. Patterns in young-of-year Smallmouth Bass microhabitat use in multiple 

stream segments with contrasting land uses. Fisheries Management and Ecology 18:506-512.

Brewer, S. K. 2013a. Groundwater influences on the distribution and abundance of riverine 

Smallmouth Bass, Micropterus dolomieu, in pasture landscapes of the midwestern USA. 

River Research and Applications 29(3):269–278.

Brewer, S. K. 2013b. Channel unit use by smallmouth bass: Do land-use constraints or quantity 

of habitat matter? North American Journal of Fisheries Management 33:351-358.

Brewer, S. K., and C. F. Rabeni. 2011. Interactions between natural-occurring landscape 

conditions and land use influencing the abundance of riverine smallmouth bass, Micropterus 

dolomieu. Canadian Journal of Fisheries and Aquatic Sciences 68:1922-1933.

Brewer, S. K., and D. J. Orth. 2015. Smallmouth Bass Micropterus dolomieu Lacepède, 1802. 

Pages 9-26 in Tringali, M. D., M. S. Allen, T. Birdsong, and J. M. Long, editors. Black Bass 

Diversity: Multidisciplinary Science for Conservation. American Fisheries Society, 

Bethesda, Maryland.

Brewer, S. K., and J. M. Long. 2015. Biology and ecology of genetically-distinct Neosho and 

Ouachita Smallmouth Bass. Pages 281-296 in Tringali, M. D., M. S. Allen, T. Birdsong, and 
74

J. M. Long, editors. Black Bass Diversity: Multidisciplinary Science for Conservation. 

American Fisheries Society, Bethesda, Maryland.

Brewer, S. K., and M. R. Ellersieck. 2011. Evaluating two observational sampling techniques for 

determining the distribution and detection probability of YOY Smallmouth Bass in clear, 

warmwater streams. North American Journal of Fisheries Management 31(5):894–904.

Brewer, S. K., B. Brown, T. A. Worthington, R. Mollenhauer, A. Rodger, M. Skoog, and J. 

Burroughs. 2019. First summer survival and channel unit habitat use by the Neosho 

subspecies of Smallmouth Bass (Micropterus dolomieu velox). Pages 21-37 in Managing 

Centrarchid Fisheries. American Fisheries Society Symposium, Bethesda.

Brewer, S. K., C. F. Rabeni, S. P. Sowa, and G. Annis. 2007. Natural landscape and stream 

segment attributes influencing the distribution and relative abundance of riverine smallmouth 

bass in Missouri. North American Journal of Fisheries Management 27:326-341.

Brown, M. T., and M. B. Vivas. 2005. Landscape development intensity index. Environmental 

Monitoring and Assessment 101:289-309.

Bunn, S. E., and A. H. Arthington. 2002. Basic principles and ecological consequences of altered 

flow regimes for aquatic biodiversity. Environmental Management 30:492-507.

Bunnell, D. B., and J. J. Isely. 1999. Influence of temperature on mortality and retention of 

simulated transmitters in Rainbow Trout. North American Journal of Fisheries Management 

19:152-154. 

Burnham, K. P., and D. R. Anderson. 2002. Model selection and multimodel inference: a 

practical information-theoretical approach. 2nd ed. Springer-Verlag, New York.

Caissie, D. 2006. The thermal regime of rivers: a review. Freshwater Biology 51:1389-1406.

Cambray, J. A. 2003. Impact on indigenous species biodiversity caused by the globilisation of 

alien recreational freshwater fisheries. Hydrobiologia 500:217-230. 

Carey, M. P., B. L. Sanderson, T. A. Friesen, K. A. Barnas, and J. D. Olden. 2011. Smallmouth 

Bass in the Pacific Northwest: a threat to native species; a benefit for anglers. Reviews in 

Fisheries Science 19:305-315.

Carlin, B. P., and T. A. Louis. 1996. Bayes and empirical Bayes methods for data analysis. 

Chapman and Hall, London. 

Cleary, R. E. 1956. Observations on factors affecting Smallmouth Bass production in Iowa. 

Journal of Fish and Wildlife Management 20:353-359.
75

Clifton, C. 1989. Effects of vegetation and land use on channel morphology. Pages 121-129 in R. 

E. Gresswell, B. A. Barton, and J. L. Kershner, eds. Practical approaches to riparian resource 

management: an educational workshop, May 8-11, 1989, Billings, Montana. USDI/BLM: 

Billings, MT. 

Coble, D. W. 1975. Smallmouth Bass. Pages 21-33 in H. Clepper, editor. Black bass biology and 

management. Sport Fishing Institute, Washington, D.C.

Cooke, S. J., and C. M. Bunt. 2001. Assessment of internal and external antenna configurations 

of radio transmitters implanted in Smallmouth Bass. North American Journal of Fisheries 

Management 21(1):236–241.

Cooke, S. J., C. Paukert, and Z. Hogan. 2012. Endangered river fish: factors hindering 

conservation and restoration. Endangered Species Research 17:179-191.

Cooke, S. J., D. P. Philipp, D. H. Wahl, and P. J. Weatherhead. 2006. Energetics of parental care 

in six syntopic centrarchid fishes. Oecologia 148:235-249.

Cooke, S. J., E. G. Martins, D. P. Struthers, L. F. G. Gutowsky, M. Power, S. E. Doka, J. M. 

Dettmers, D. A. Crook, M. C. Lucas, C. M. Holbrook, and C. C. Krueger. 2016. A moving 

target—incorporating knowledge of the spatial ecology of fish into the assessment and 

management of freshwater fish populations. Environmental Monitoring and Assessment 

188:239. 

Coutant, C. C., and D. L. DeAngelis. 1983. Comparative temperature-dependent growth rates of 

Largemouth and Smallmouth Bass fry. Transactions of the American Fisheries Society 

112:416-423. 

Dauwalter, D. C., and W. L. Fisher. 2007. Spawning chronology, nest site selection and nest 

success of Smallmouth Bass during benign streamflow conditions. American Midland 

Naturalist 158:60-78.

Dauwalter, D. C., and W. L. Fisher. 2008. Spatial and temporal patterns in stream habitat and 

Smallmouth Bass populations in eastern Oklahoma. Transactions of the American Fisheries 

Society 137:1072-1088. 

DeAngelis, D. L., B. J. Shuter, M. S. Ridgway, and M. Scheffer. 1993. Modeling growth and 

survival in an YOY fish cohort. Transactions of the American Fisheries Society 122:927-941. 
76

DeAngelis, D. L., L. Godbout, and B. J. Shuter. 1991. An individual-based approach to 

predicting density-dependent dynamics in Smallmouth Bass populations. Ecological 

Modelling 57:91-115.

Dochtermann, N. A., and S. H. Jenkins. 2011. Multivariate methods and small sample sizes. 

Ethology 117:95-101.

Döll, P., and J. Zhang. 2010. Impact of climate change on freshwater ecosystems: a global-scale 

analysis of ecologically relevant river flow alterations. Hydrology and Earth System Sciences 

14:783-799.

Dominey, W. J. 1981. Anti-predator function of Bluegill sunfish nesting colonies. Nature 

290:586-588.

Dominey, W. J. 1983. Mobbing in colonially nesting fishes, especially the Bluegill, Lepomis 

macrochirus. Copeia 1983:1086-1088. 

Dong, Q., and D. L. DeAngelis. 1998. Consequences of cannibalism and competition for food in 

a Smallmouth Bass population: an individual-based modeling study. Transactions of the 

American Fisheries Society 127:174-191. 

Dormann, C. F., J. Elith, S. Bacher, and 15 others. 2013. Collinearity: a review of methods to 

deal with it and a simulation study evaluating their performance. Ecography 36:27-46.

Dudgeon, D., A. H. Arthington, M. O. Gessner, Z.-I. Kawabata, D. J. Knowler, C. Lévêque, R. J. 

Naiman, A.-H. Prieur-Richard, D. Soto, M. L. J. Stiassny, and C. A. Sullivan. 2006. 

Freshwater biodiversity: importance, threats, status and conservation challenges. Biological 

Reviews 81:163-82.

Dunham, J. B., A. E. Rosenberger, R. F. Thurow, C. A. Dolloff, and P. J. Howell. 2009. 

Coldwater fish in wadeable streams. Pages 119-138 in S. A. Bonar, W. A. Hubert, and D. W. 

Willis, editors. Standard methods for sampling North American freshwater fishes. American 

Fisheries Society, Bethesda, Maryland.

Erisman, B. E., W. Heyman, S. Kobara, T. Ezer, S. Pittman, O. Aburto-Oropeza, and R. S. 

Nemeth. 2015. Fish spawning aggregations: where well‐placed management actions can 

yield big benefits for fisheries and conservation. Fish and Fisheries 18:128-144. 

Ettinger-Dietzel, S. A., H. R. Dodd, J. T. Westhoff, and M. J. Siepker. 2016. Movement and 

habitat selection patterns of Smallmouth Bass Micropterus dolomieu in an Ozark river. 

Journal of Freshwater Ecology 31:61-75. 
77

Fisher, W. L., D. F. Schreiner, C. D. Martin, Y. A. Negash, and E. Kessler. 2002. Recreational 

fishing and socioeconomic characteristics of eastern Oklahoma stream anglers. Proceedings 

of the Oklahoma Academy of Science 82:79-87.

Fiske, I. J., and R. B. Chandler. 2011. Unmarked: An R package for fitting hierarchical models 

of wildlife occurrence and abundance. Journal of Statistical Software 43:1-23.

Fiske, I., R. Chandler, D. Miller, A. Royle, M. Kery, J. Hostetler, and R. Hutchinson. 2017. 

unmarked: models for data from unmarked animals. R package version 0.12-2. https://cran.rproject.org/package=unmarked.

Fore, J. D., D. C. Dauwalter, and W. L. Fisher. 2007. Microhabitat use by Smallmouth Bass in an 

Ozark stream. Journal of Freshwater Ecology 22:189-199. 

Frissell, C. A., W. J. Liss, C. E. Warren, and M. D. Hurley. 1986. A hierarchical framework for 

stream habitat classification: viewing streams in a watershed context. Environmental 

Management 10:199-214. 

Gelman, A., and J. Hill. 2007. Data analysis using regression and multilevel/hierarchical models. 

Cambridge University Press.

Gerber, G. P., and J. M. Haynes. 1988. Movements and behavior of Smallmouth Bass, 

Micropterus dolomieui, and Rock Bass, Ambloplites rupestris, in southcentral Lake Ontario 

and two tributaries. Journal of Freshwater Ecology 4:425-440.

Goldstein, R. M. 1978. Quantitative comparison of seining and underwater observation of stream 

fishery surveys. The Progressive Fish-Culturist 40:108-111.

Goniea, T. M., M. L. Keefer, T. C. Bjornn, C. A. Peery, D. H. Bennett, and L. C. Stuehrenberg. 

2006. Behavioral thermoregulation and slowed migration by adult fall Chinook salmon in 

response to high Columbia River water temperatures. Transactions of the American Fisheries 

Society 135:408-419.

Gordon, N. D., T. A. McMahon, B. L. Finlayson, C. J. Gippel, and R. J. Nathan. 2004. Stream 

Hydrology: An Introduction for Ecologists. 2nd edition. Wiley, New York.

Graham, R. J., and D. J. Orth. 1986. Effects of temperature and streamflow on time and duration 

of spawning by Smallmouth Bass. Transactions of the American Fisheries Society 115:693-

702.
78

Grueber, C. E., S. Nakagawa, R. J. Laws, and I. G. Jamieson. 2011. Multimodel inference in 

ecology and evolution: challenges and solutions. Journal of Evolutionary Biology 24:699-

711.

Gunderson VanArnum, C. J., G. L. Buynak, and J. R. Ross. 2004. Movement of Smallmouth 

Bass in Elkhorn Creek, Kentucky. North American Journal of Fisheries Management 24:311-

315. 

Hafs, A. W., C. J. Gagen, and J. K. Whalen. 2010. Smallmouth Bass summer habitat use, 

movement, and survival in response to low flow in the Illinois Bayou, Arkansas. North 

American Journal of Fisheries Management 30:604–612.

Harrell, F. E., Jr. 2001. Regression modeling strategies. Springer-Verlag, New York.

Harvey, B. C. 1987. Susceptibility of young-of-the-year fishes to downstream displacement by 

flooding. Transactions of the American Fisheries Society 116:851-855.

Harvey, B. C., and A. J. Stewart. 1991. Fish size and habitat depth relationships in headwater 

streams. Oecologia 87:336-342. 

Harvey, B. C., J. L. White, and R. J. Nakamoto. 2009. The effect of deposited fine sediment on 

summer survival and growth of Rainbow Trout in riffles of a small stream. North American 

Journal of Fisheries Management 29:434-440. 

Harvey, J. W., and B. J. Wagner. 2000. Quantifying hydrologic interactions between streams and 

their subsurface hyporheic zones. Pages 3-44 in J. B. Jones and P. J. Mulholland, editors. 

Streams and ground waters. Academic Press, San Diego. 

Henshall, J. A. 1881. Book of the black bass, comprising its complete scientific and life history: 

together with a practical treatise on angling and fly fishing and a full description of tools, 

tackle and implements. R. Clarke and Company, Cincinnati, Ohio.

Herbert, M. E., and F. P. Gelwick. 2003. Spatial variation of headwater fish assemblages 

explained by hydrologic variability and upstream effects of impoundment. Copeia 2003:273-

284. 

Hicks, B. J., and N. R. N. Watson. 1985. Seasonal changes in abundance of Brown Trout (Salmo 

trutta) and Rainbow Trout (S. gairdnerii) assessed by drift diving in the Rangitikei River, 

New Zealand. New Zealand Journal of Marine and Freshwater Research 19:1-10.

Hill, J., and G. D. Grossman. 1987. Home range estimates for three North American stream 

fishes. Copeia 1987:376-380. 
79

Hill, R. A., M. H. Weber, S. G. Leibowitz, A. R. Olsen, and D. J. Thornbrugh. 2016. The streamcatchment (StreamCat) dataset: a database of watershed metrics for the coterminous United 

States. Journal of the American Water Resources Association 52:120-128.

Hillman, T. W., J. W. Mullan, and J. S. Griffith. 1992. Accuracy of underwater counts of 

juvenile Chinook Salmon, Coho Salmon, and Steelhead. North American Journal of Fisheries 

Management 12:598-603.

Homer, C. G., J. A. Dewitz, L. Yang, S. Jin, P. Danielson, G. Xian, J. Coulston, N. D. Herold, J. 

D. Wickham, and K. Megown. 2015. Completion of the 2011 National Land Cover Database 

for the coterminous United States – representing a decade of land cover change information. 

Photogrammetric Engineering and Remote Sensing 81:345-354.

Hubbs, C. L., and R. M. Bailey. 1940. A revision of the black basses (Micropterus and Huro) 

with description of four new forms. Miscellaneous Publication 48, Museum of Zoology, 

University of Michigan.

Humston, R., B. M. Priest, W. C. Hamilton, and P. E. Bugas Jr. 2010. Dispersal between 

tributary and main-stem rivers by juvenile smallmouth bass evaluated using otolith 

microchemistry. Transactions of the American Fisheries Society 139:171-184.

Hurst, T. P. 2007. Causes and consequences of winter mortality in fishes. Journal of Fish 

Biology 71:315-345. 

Hurvich, C.M, and C.-L. Tsai. 1989. Regression and time series model selection in small 

samples. Biometrika 76:297-307.

Hynes, H. B. N. 1975. The stream and its valley. Verhandlungen des Internationalen Verein 

Limnologie 19:1–15.

Jager, H. I., D. L. DeAngelis, M. J. Sale, W. Van Winkle, D. D. Schmoyer, M. J. Sabo, D. J. 

Orth, and J. A. Lukas. 1993. An individual-based model for Smallmouth Bass reproduction 

and young-of-year dynamics in streams. Rivers 4:91-113.

Jelks, H. L., S. J. Walsh, N. M. Burkhead, S. Contreras-Balderas, E. Diaz-Pardo, D. A. 

Hendrickson, J. Lyons, N. E. Mandrak, F. McCormick, J. S. Nelson, S. P. Platania, B. A. 

Porter, C. B. Renaud, J. J. Schmitter-Soto, E. B. Taylor, and M. L. Warren Jr. 2008. 

Conservation status of imperiled North American freshwater and diadromous fishes. 

Fisheries 33:372-407.
80

Jenson, S. K., and J. O. Domingue. 1988. Extracting topographic structure from digital elevation 

data for Geographic Information System analysis. Photogrammetric Engineering and Remote 

Sensing 54:1593-1600.

Jepsen, N., A. Koed, E. B. Thorstad, and E. Baras. 2002. Surgical implantation of telemetry 

transmitters in fish: how much have we learned? Hydrobiologia 483:239-248.

Jowett, I. G., J. Richardson, and R. M. McDowall. 1996. Relative effects of in‐stream habitat and 

land use on fish distribution and abundance in tributaries of the Grey River, New Zealand. 

New Zealand Journal of Marine and Freshwater Research 30:463-475.

Kanno, Y., K. C. Pregler, N. P. Hitt, B. H. Letcher, D. J. Hocking, and J. E. B. Wofford. 2016. 

Seasonal temperature and precipitation regulate Brook Trout young-of-the-year abundance 

and population dynamics. Freshwater Biology 61:88-99.

Kéry, M., and J. A. Royle. 2015. Applied hierarchical modeling in ecology. Academic Press, 

London.

King, R. S., M. E. Baker, P. F. Kazyak, and D. E. Weller. 2011. How novel is too novel? Stream 

community thresholds at exceptionally low levels of catchment urbanization. Ecological

Applications 21:1659-1678.

Knighton, D. 1998. Fluvial forms and processes. Hodder Education, London, England.

Knotek, W. L., and D. J. Orth. 1998. Survival for specific life intervals of smallmouth bass, 

Micropterus dolomieu, during parental care. Environmental Biology of Fishes 51:285-296.

Koehn, J. D., J. H. Eiler, J. A. McKenzie, and W. G. O’Connor. 2012. An improved method for 

obtaining fine-scale location of radio tags when tracking by boat. Pages 379-384 in J. 

McKenzie, B. Parsons, A. Seitz, R. K. Kopf, M. Mesa, and Q. Phelps, editors. Advances in 

fish tagging and marking technology. American Fisheries Society, Symposium 76, Bethesda, 

Maryland.

Koppelman, J. B. 2015. Black bass hybrids: a natural phenomenon in an unnatural world. Pages 

467-479 in M. D. Tringali, J. M. Long, T. W. Birdsong, and M. S. Allen, editors. Black bass 

diversity: multidisciplinary science for conservation. American Fisheries Society Symposium 

82, Bethesda, Maryland.

Kubacki, M. F., F. J. S. Phelan, J. E. Claussen, and D. P. Philipp. 2002. How well does a closed 

season protect spawning bass in Ontario? Pages 379-386 in D. P. Philipp and M. S. Ridgway, 
81

editors. Black bass: ecology, conservation, and management. American Fisheries Society, 

Symposium 31, Bethesda, Maryland.

Labbe, T. R., and K. D. Fausch. 2000. Dynamics of intermittent stream habitat regulate 

persistence of a threatened fish at multiple scales. Ecological Applications 10:1774-1791.

Lake, P. S., N. Bond, and P. Reich. 2007. Linking ecological theory with stream restoration. 

Freshwater Biology 52:597-615.

Langhurst, R. W., and D. L. Schoenike. 1990. Seasonal migration of Smallmouth Bass in the 

Embarrass and Wolf Rivers, Wisconsin. North American Journal of Fisheries Management 

10:223-227.

Langhurst, R. W., and D. L. Schoenike. 1990. Seasonal migration of Smallmouth Bass in the 

Embarrass and Wolf Rivers, Wisconsin. North American Journal of Fisheries Management 

10:224-227.

Larimore, R. W. 1975. Visual and tactile orientation of Smallmouth Bass fry under floodwater 

conditions. Pages 323-332 in H. Clepper, editor. Black bass biology and management. Sport 

Fishing Institute, Washington, D.C.

Larimore, R. W. 2002. Temperature acclimation and survival of smallmouth bass fry in flooded 

warmwater streams. Pages 115-122 in D. P. Philipp, and M. S. Ridgeway, editors. Black 

bass: ecology, conservation, and management. American Fisheries Society, Bethesda, 

Maryland.

Leasure, D. R., D. D. Magoulick, and S. D. Longing. 2016. Natural flow regimes of the OzarkOuachita Interior Highlands region. River Research and Applications 32:18-35.

Leopold, L. B. 1970. An improved method for size distribution of stream-bed gravel. Water 

Resources Research 6:1357-1366.

Ligon, F. K., W. E. Dietrich, and W. J. Trush. 1995. Downstream ecological effects of dams. 

BioScience 45:183-192.

Lisle, T. E. 1987. Using “residual depths” to monitor pool depths independently of discharge. 

Res. Note PSW-RN-394. Berkeley, CA: U.S. Department of Agriculture, Forest Service, 

Pacific Southwest Forest and Range Experiment Station. 4 p.

Long, J. M., M. S. Allen, W. F. Porak, and C. D. Suski. 2015. A historical perspective of black 

bass management in the United States. Pages 99–122 in M. D. Tringali, J. M. Long, T. W. 
82

Birdsong, and M. S. Allen, editors. Black bass diversity: multidisciplinary science for 

conservation. American Fisheries Society Symposium 82, Bethesda, Maryland.

Lüdecke, D. 2018. sjstats: collection of convenient functions for common statistical 

computations. R package version 0.17.2. https://cran.r-project.org/package=sjstats.

Lukas, J. A., and D. J. Orth. 1995. Factors affecting nesting success of smallmouth bass in a 

regulated Virginia stream. Transactions of the American Fisheries Society 124:726-735.

Lundberg, G., M. Kottelat, G. R. Smith, M. L. J. Stiassny, and A. C. Gill. 2000. So many fishes, 

so little time: an overview of recent ichthyological discovery in continental waters. Annals of 

the Missouri Botanical Gardens 87:26-62.

Lyons, J., and P. Kanehl. 2002. Seasonal movements of smallmouth bass in streams. Pages 149-

160 in D. P. Philipp and M. S. Ridgway, editors. Black bass: ecology, conservation, and 

management. American Fisheries Society, Symposium 31, Bethesda, Maryland.

MacKenzie, D. I., and L. L. Bailey. 2004. Assessing the fit of site-occupancy models. Journal of 

Agricultural, Biological, and Environmental Statistics 9:300-318.

MacRae, P. S. D., and D. A. Jackson. 2001. The influence of Smallmouth Bass (Micropterus 

dolomieu) predation and habitat complexity on the structure of littoral zone fish assemblages. 

Canadian Journal of Fisheries and Aquatic Sciences 58:342-351.

Magnusson, A., H. Skaug, A. Nielsen, C. Berg, K. Kristensen, M. Maechler, K. van Bentham, B. 

Bolker, and M. Brooks. 2018. glmmTMB: generalized linear mixed models using template 

model builder. R package version 0.2.2.0. https://cran.r-project.org/package=glmmTMB.

Mallen-Cooper, M., and I. G. Stuart. 2003. Age, growth and non-flood recruitment of two 

potamodromous fishes in a large semi-arid/temperate river system. River Research and 

Applications 19:697-719.

Martin, C. D., and W. L. Fisher. 2008. Recreational fishing for black bass in Eastern Oklahoma 

streams. Proceeding of the Annual Conference of Southeast Fish and Wildlife Agencies 

62:168-176.

Mazerolle, M. J. 2017. AICcmodavg: model selection and multimodel inference based on 

(Q)AIC(c). R package version 2.1-1. https://cran.r-project.org/package=AICcmodavg.

McCune, B., and J. B. Grace. 2002. Analysis of ecological communities. MjM Software Design, 

Gleneden Beach, Oregon.
83

Meyer, J. L., D. L. Strayer, J. B. Wallace, S. L. Eggert, G. S. Helfman, and N. E. Leonard. 2007. 

The contribution of headwater streams to biodiversity in river networks. Journal of the 

American Water Resources Association 43:86-103.

Miller, A. D., R. Mollenhauer, and S. K. Brewer. 2019. Movement and diel habitat use of 

juvenile Neosho Smallmouth Bass in an Ozark stream. North American Journal of Fisheries 

Management 39:240-253.

Mouser, J. B., R. Mollenhauer, and S. K. Brewer. 2019. Relationships between landscape 

constraints and a crayfish assemblage with consideration of competitor presence. Diversity 

and Distributions 25:61-73.

Moyle, P. B., and R. A. Leidy. 1992. Loss of biodiversity in aquatic ecosystems: evidence from 

fish faunas. Pages 127–169 in P. L. Fiedler and S. K. Jain, editors. Conservation biology. 

Springer, Dordrecht, Netherlands.

Mraz, D. 1964. Observations on Large and Smallmouth Bass nesting and early life history. 

Wisconsin Conservation Department, Fisheries Research Report no. 11.

Mulholland, P. J., G. R. Best, C. C. Coutant, G. M. Hornberger, J. L. Meyer, P. J. Robinson, J. R. 

Stenberg, R. E. Turner, F. Vera-Herrera, and R. G. Wetzel. 1997. Effects of climate change 

on freshwater ecosystems of the South-eastern United States and the Gulf Coast of Mexico. 

Hydrological Processes 11:949-970.

Munther, G. L. 1970. Movement and distribution of Smallmouth Bass in the Middle Snake 

River. Transactions of the American Fisheries Society 99:44-53. 

Naiman, R. J., and H. Décamps. 1997. The ecology of interfaces: riparian zones. Annual Review 

of Ecology and Systematics 28:621-658.

Nakagawa, S., and H. Schielzeth. 2013. A general and simple method for obtaining R

2

from 

generalized linear mixed-effects models. Methods in Ecology and Evolution 4:133-142.

Nislow, K. H., F. J. Magilligan, C. L. Folt, and B. P. Kennedy. 2002. Within-basin variation in 

the short-term effects of a major flood on stream fishes and invertebrates. Journal of 

Freshwater Ecology 17:305-318.

Noble, R. L. 2002. Reflections on 25 years of progress in black bass management. Pages 419-

432 in D. P. Philipp and M. S. Ridgway, editors. Black bass: ecology, conservation, and 

management. American Fisheries Society, Symposium 31, Bethesda, Maryland.
84

NRCS 2017. Soil Survey Staff, Natural Resources Conservation Service, U.S. Department of 

Agriculture. Soil Survey Geographic (SSURGO) Database for Lake O’ the Cherokees 

Subbasin. Available online at https://websoilsurvey.sc.egov.usda.gov [accessed 31 March 

2018].

O’Callaghan, J. F., and D. M. Mark. 1984. The extraction of drainage networks from digital 

elevation data. Computer Vision, Graphics, and Image Processing 28:323-344.

O’Neal, J. S. 2007. Snorkel surveys. Pages 325-340 in D. H. Johnson, B. M. Shrier, J. S. O’Neal, 

J. A. Knutzen, X. Augerot, T. A. O’Neil, and T. N. Pearsons, editors. Salmonid filed 

protocols handbook: techniques for assessing status and trends in salmon and trout 

populations. American Fisheries Society, Bethesda, Maryland.

Oliver, J. D., G. F. Holeton, and K. E. Chua. 1979. Overwinter mortality of fingerling 

Smallmouth Bass in relation to size, relative energy stores, and environmental temperature. 

Transactions of the American Fisheries Society 108:130-136.

Orth, D. J., and T. J. Newcomb. 2002. Certainties and uncertainties in defining essential habitats 

for riverine smallmouth bass. Pages 251-264 in D. P. Philipp, and M. S. Ridgway, editors. 

Black bass: ecology, conservation and management. American Fisheries Society, Bethesda, 

Maryland.

Osborne, L. L., and M. J. Wiley. 1992. Influence of tributary spatial position on the structure of 

warmwater fish communities. Canadian Journal of Fisheries and Aquatic Sciences 49:671-

681.

Otis, D. L., and G. C. White. 1999. Autocorrelation of location estimates and the analysis of 

radiotracking data. Journal of Wildlife Management 63:1039-1044.

Paul, M. J., and J. L. Meyer. 2001. Streams in the urban landscape. Annual Review of Ecology 

and Systematics 32:333-365.

Pearsons, T. N., H. W. Li, and G. A. Lamberti. 1992. Influence of habitat complexity on 

resistance to flooding and resilience of stream fish assemblages. Transactions of the 

American Fisheries Society 121:427-436.

Peduzzi, P., J. Concato, A. R. Feinstein, and T. R. Holford. 1995. Importance of events per 

independent variable in proportional hazards regression analysis II. Accuracy and precision

of regression estimates. Journal of Clinical Epidemiology 48:1503-1510.
85

Peek, F. 1965. Age and growth of the Smallmouth Bass Micropterus dolomieui Lacepede in 

Arkansas. Proceedings of the Nineteenth Annual Conference of the Southeastern Association 

of Game and Fish Commissioners 19:422-431.

Pelicice, F. M., P. S. Pompeu, and A. A. Agostinho. 2015. Large reservoirs as ecological barriers 

to downstream movements of Neotropical migratory fish. Fish and Fisheries 16:697-715.

Peterson, J. T., and C. F. Rabeni. 1996. Natural thermal refugia for temperate warmwater stream 

fishes. North American Journal of Fisheries Management 16:738-756.

Peterson, J. T., and C. P. Paukert. 2009. Converting nonstandard fish sampling data to 

standardized data. Pages 195-215 in S. A. Bonar, W. A. Hubert, and D. W. Willis, eds. 

Standard methods for sampling North American Freshwater Fishes. American Fisheries 

Society, Bethesda, Maryland.

Peterson, J. T., and T. J. Kwak. 1999. Modeling the effects of land use and climate change on 

riverine Smallmouth Bass. Ecological Applications 9:1391-1404.

Pflieger, W. L. 1966. Reproduction of the smallmouth bass (Micropterus dolomieui) in a small 

Ozark stream. American Midland Naturalist 76:410-418. 

Pflieger, W. L. 1975. Reproduction and survival of the smallmouth bass in Courtois Creek. 

Pages 231-239 in H. Clepper, editor. Black bass biology and management, Sport Fishing 

Institute, Washington, D.C.

Philipp, D. P., C. A. Toline, M. F. Kubacki, D. B. F. Philipp, and F. J. S. Phelan. 1997. The 

impact of catch-and-release angling on the reproductive success of Smallmouth Bass and 

Largemouth Bass. North American Journal of Fisheries Management 17:557-567.

Poff, N. L., and J. K. H. Zimmerman. 2010. Ecological responses to altered flow regimes: a 

literature review to inform the science and management of environmental flows. Freshwater 

Biology 55:194-205.

Poff, N. L., J. D. Allan, M. B. Bain, J. R. Karr, K. L. Prestegaard, B. D. Richter, R. E. Sparks, 

and J. C. Stromberg. 1997. The natural flow regime. BioScience 47:769-784.

Pörtner, H. O., and M. A. Peck. 2010. Climate change effects on fishes and fisheries: towards a 

cause-and-effect understanding. Journal of Fish Biology 77:1745-1779.

Power, M. E. 1987. Predator avoidance by grazing fishes in temperate and tropical streams: 

importance of stream depth and prey size. Pages 333-351 in W. C. Kerfoot and A. Sih, 
86

editors. Predation: direct and indirect impacts on aquatic communities. University Press of 

New England, Hanover, New Hampshire.

Power, M. E., T. L. Dudley, and S. D. Cooper. 1989. Grazing catfish, fishing birds, and attached 

algae in a Panamanian stream. Environmental Biology of Fishes 26:285-294.

Power, M. E., W. J. Matthews, and A. J. Stewart. 1985. Grazing minnows, piscivorous bass, and 

stream algae: dynamics of a strong interaction. Ecology 66:1448-1456. 

Probst, W. E., C. F. Rabeni, W. G. Covington, and R. E. Marteney. 1984. Resource use by 

stream-dwelling Rock Bass and Smallmouth Bass. Transactions of the American Fisheries 

Society 113:283-294.

R Core Team. 2018. R: A language and environment for statistical computing. R Foundation for 

Statistical Computing, Vienna, Austria. URL http://www.R-project.org/.

Rabeni, C. F. 1992. Trophic linkage between stream centrarchids and their crayfish prey. 

Canadian Journal of Fisheries and Aquatic Sciences 49:1714-1721.

Rabeni, C. F., and M. A. Smale. 1995. Effects of siltation on stream fishes and the potential 

mitigating role of the buffering riparian zone. Hydrobiologia 303:211-219.

Rabeni, C. F., and R. B. Jacobson. 1993. The importance of fluvial hydraulics to fish-habitat 

restoration in low-gradient alluvial streams. Freshwater Biology 29:211-220.

Rabeni, C. F., and S. P. Sowa. 2002. A landscape approach to managing the biota of streams. 

Pages 114-143 in J. Liu and W. W. Taylor, editors. Integrating landscape ecology into 

natural resources management. Cambridge University Press, Cambridge, UK.

Rasmussen, J. E., and M. C. Belk. 2017. Individual movement of stream fishes: linking 

ecological drivers with evolutionary processes. Reviews in Fisheries Science & Aquaculture 

25:70-83.

Revelle, W. 2018. psych: procedures for psychological, psychometric, and personality research. 

R package version 1.8.10. https://cran.r-project.org/package=psych.

Reynolds, C. R., and C. J. O’Bara. 1991. Reproductive ecology and spawning habitat of 

smallmouth bass in two small streams of the Tennessee River system. Pages 61-65 in D. C. 

Jackson, editor. The first international smallmouth bass symposium. Mississippi Agricultural 

and Forestry Experiment Station, Mississippi State University.

Richardson, J. S., and R. J. Danehy. 2007. A synthesis of the ecology of headwater streams and 

their riparian zones in temperate forests. Forest Science 53:131-147.
87

Richter, B. D., J. V. Baumgartner, R. Wigington, and D. P. Braun. 1997. How much water does a 

river need? Freshwater Biology 37:231-249.

Ridgway, M. S., and T. G. Friesen. 1992. Annual variation in parental care in smallmouth bass, 

Micropterus dolomieu. Environmental Biology of Fishes 35:243-255.

Ridgway, M. S., J. A. MacLean, and J. C. MacLeod. 1991. Nest-site fidelity in a centrarchid fish, 

the smallmouth bass (Micropterus dolomieui). Canadian Journal of Zoology 69:3103-3105. 

Robbins, W. H., and H. R. MacCrimmon. 1977. Vital statistics and migratory patterns of a 

potamodromous stock of Smallmouth Bass, Micropterus dolomieui. Journal of the Fisheries 

Research Board of Canada 34:142-147.

Roni, P., T. J. Beechie, R. E. Bilby, F. E. Leonetti, M. M. Pollock, and G. R. Pess. 2002. A 

review of stream restoration techniques and a hierarchical strategy for prioritizing restoration 

in Pacific Northwest watersheds. North American Journal of Fisheries Management 22:1-20.

Rosenfeld, J., M. Porter, and E. Parkinson. 2000. Habitat factors affecting the abundance and 

distribution of juvenile Cutthroat Trout (Oncorhynchus clarki) and Coho Salmon 

(Oncorhynchus kisutch). Canadian Journal of Fisheries and Aquatic Sciences 57:766-774.

Ross, M. J., and C. F. Kleiner. 1982. Shielded-needle technique for surgically implanting radiofrequency transmitters in fish. The Progressive Fish-Culturist 44:41-43.

Royle, J. A. 2004. N-mixture models for estimating population size from spatially replicated 

counts. Biometrics 60:108-115. 

Royle, J. A., and R. M. Dorazio. 2008. Hierarchical modeling and inference in ecology. 

Academic Press, San Diego. 

Sabo, M. J., and D. J. Orth. 1994. Temporal variation in microhabitat use by YOY Smallmouth 

Bass in the North Anna River, Virginia. Transactions of the American Fisheries Society 

123:733-746.

Sabo, M. J., D. J. Orth, and E. J. Pert. 1996. Effect of stream microhabitat characteristics on rate 

of net energy gain by juvenile Smallmouth Bass, Micropterus dolomieu. Environmental 

Biology of Fishes 46:393-403.

Sala, O. E., F. S. Chapin III, J. J. Armesto, E. Berlow, J. Bloomfield, R. Dirzo, E. HuberSanwald, L. F. Huenneke, R. B. Jackson, A. Kinzig, R. Leemans, D. M. Lodge, H. A. 

Mooney, M. Oesterheld, N. L. Poff, M. T. Sykes, B. H. Walker, M. Walker, and D. H. Wall. 

2000. Global biodiversity scenarios for the year 2100. Science 287:1770-1774.
88

Sammons, S. M. 2015. First evidence of potadromy and partial migration in black basses: Shoal 

Bass Micropterus cataractae (Actinopterygii, Centrarchidae) in the Upper Flint River, USA. 

Hydrobiologia 751:135-146.

Schill, D. J., and J. S. Griffith. 1984. Use of underwater observations to estimate Cutthroat Trout 

abundance in the Yellowstone River. North American Journal of Fisheries Management 

4:479-487.

Schindler, D. E., R. Hilborn, B. Chasco, C. P. Boatright, T. P. Quinn, L. A. Rogers, and M. S. 

Webster. 2010. Population diversity and the portfolio effect in an exploited species. Nature 

465:609-612.

Schlosser, I. J. 1991. Stream fish ecology: a landscape perspective. BioScience 41:704-712.

Schwarz, C. J., and A. N. Arnason. 1990. Use of tag-recovery information in migration and 

movement studies. Pages 588-603 in N. C. Parker, A. E. Giorgi, R. C. Heidinger, D. B. 

Jester, Jr., E. D. Prince, and G. A. Winans, editors. Fish-marking techniques. American 

Fisheries Society, Bethesda, Maryland.

Schwendel, A. C., R. G. Death, and I. C. Fuller. 2010. The assessment of shear stress and bed 

stability in stream ecology. Freshwater Biology 55:261-281.

Scott, R. J. 1996. The influence of parental care behaviour on localized nest spacing in 

Smallmouth Bass, Micropterus dolomieu. Environmental Biology of Fishes 46:103-107.

Serns, S. L. 1982. Relation of temperature and population density to first-year recruitment and 

growth of Smallmouth Bass in a Wisconsin lake. Transactions of the American Fisheries 

Society 111:570-574.

Shields, A. 1936. Anwendung der Aehnlichkeitsmechanik und der Turbulenzforschung auf die 

Geschiebebewegung. Mitteilungen der Preußischen Versuchsanstalt für Wasserbau 26, 

Berlin.

Shreve, R. L. 1966. Statistical law of stream numbers. Journal of Geology 74:17-38.

Shuter, B. J., J. A. MacLean, F. E. J. Fry, and H. A. Regier. 1980. Stochastic simulation of 

temperature effects on first-year survival of smallmouth bass. Transactions of the American 

Fisheries Society 109:1-34.

Simonson, T. D., and W. A. Swenson. 1990. Critical stream velocities for young-of-the-year 

Smallmouth Bass in relation to habitat use. Transactions of the American Fisheries Society 

119:902–909.
89

Singh, D., M. Tsiang, B. Rajaratnam, N. S. Diffenbaugh. 2013. Precipitation extremes over the 

continental United States in a transient, high-resolution, ensemble climate model experiment. 

Journal of Geophysical Research: Atmospheres 118:7063-7086.

Smith, S. M., J. S. Odenkirk, and S. J. Reeser. 2005. Smallmouth Bass recruitment variability 

and its relation to stream discharge in three Virginia rivers. North American Journal of 

Fisheries Management 25:1112-1121.

Stark, W. J., and A. A. Echelle. 1998. Genetic structure and systematics of Smallmouth Bass, 

with emphasis on Interior Highlands populations. Transactions of the American Fisheries 

Society 127:393-416.

Steinhart, G. B., R. A. Stein, and E. A. Marschall. 2004. High growth rate of young-of-the-year 

Smallmouth Bass in Lake Erie: a result of the Round Goby invasion? Journal of Great Lakes 

Research 30:381-389.

Steinmetz, J., D. A. Soluk, and S. L. Kohler. 2008. Facilitation between herons and Smallmouth 

Bass foraging on common prey. Environmental Biology of Fishes 81:51-61.

Stoeser, D. B., G. N. Green, L. C. Morath, W. D. Heran, A. B. Wilson, D. W. Moore, and B. S. 

Van Gosen. 2005. Preliminary integrated geologic map databases for the united states: 

central states: Montana, Wyoming, Colorado, New Mexico, North Dakota, South Dakota, 

Nebraska, Kansas, Oklahoma, Texas, Iowa, Missouri, Arkansas, and Louisiana. Version 1.2, 

updated 2007. https://pubs.usgs.gov/of/2005/1351//.

Strahler, A. N. 1952. Hypsometric (area–altitude) analysis of erosional topography. Bulletin of 

the Geological Society of America 63:1117-1142.

Sugiura, N. 1978. Further analysis of the data by Akaike’s information criterion and the finite 

corrections. Communications in Statistics – Theory and Methods 7:13-26.

Suski, C. D., and D. P. Philipp. 2004. Factors affecting the vulnerability to angling of nesting 

male Largemouth and Smallmouth Bass. Transactions of the American Fisheries Society 

133:1100-1106.

Suski, C. D., J. H. Svec, J. B. Ludden, F. J. S. Phelan, and D. P. Philipp. 2003. The effect of 

catch-and-release angling on the parental care behavior of male Smallmouth Bass. 

Transactions of the American Fisheries Society 132:210-218.

Taylor, A. T., J. M. Long, M. D. Tringali, and B. L. Barthel. 2019. Conservation of black bass 

diversity: an emerging management paradigm. Fisheries 44:20-36.
90

Taylor, A. T., J. M. Long, M. R. Schwemm, and S. K. Brewer. 2018. Hybridization and genetic 

structure of Neosho Smallmouth Bass in the Ozark Highlands. North American Journal of 

Fisheries Management. DOI: 10.1002/nafm.10225.

Thorstad, E. B., F. Økland, and T. G. Heggberget. 2001. Are long term negative effects from 

external tags underestimated? – Fouling of an externally attached telemetry transmitter. 

Journal of Fish Biology 59:1092-1094.

Thurow, R. F. 1994. Underwater methods for study of salmonids in the Intermountain West. U.S. 

Forest Service, Intermountain Research Station, General Technical Report INT-GTR-307, 

Odgen, Utah.

Thurow, R. F. 2016. Life history of potamodromous fishes. Pages 29–54 in P. Morais and F. 

Daverat, editors. An introduction to fish migration. CRC, Boca Raton, Florida.

Thurow, R. F., C. A. Dolloff, and J. E. Marsden. 2012. Visual observation of fishes and aquatic 

habitat. Pages 781-817 in A. V. Zale, D. L. Parrish, and T. M. Sutton, editors. Fisheries 

techniques, 3rd edition. American Fisheries Society, Bethesda, Maryland.

Todd, B. L., and C. F. Rabeni. 1989. Movement and habitat use by stream-dwelling Smallmouth 

Bass. Transactions of the American Fisheries Society 118:229–242.

Vannote, R. L., G. W. Minshall, K. W. Cummins, J. R. Sedell, and C. E. Cushing. 1980. The 

river continuum concept. Canadian Journal of Fisheries and Aquatic Sciences 37:130-137.

Vonesh, E. F., V. P. Chinchilli, and K. W. Pu. 1996. Goodness-of-fit in generalized nonlinear 

mixed-effects models. Biometrics 52:572-587.

Vörösmarty, C. J., P. B. McIntyre, M. O. Gessner, D. Dudgeon, A. Prusevich, P. Green, S. 

Glidden, S. E. Bunn, C. A. Sullivan, C. Reidy Liermann, and P. M. Davies. 2010. Global 

threats to human water security and river biodiversity. Nature 467:555-561.

Wagner, T., D. B. Hayes, and M. T. Bremigan. 2006. Accounting for multilevel data structures 

in fisheries data using mixed models. Fisheries 31:180-187.

Walsh, M. G., K. A. Bjorgo, and J. J. Isely. 2000. Effects of implantation method and 

temperature on mortality and loss of simulated transmitters in Hybrid Striped Bass. 

Transactions of the American Fisheries Society 129:539-544.

Wang, L., J. Lyons, and P. Kanehl. 2001. Impacts of urbanization on stream habitat and fish 

across multiple spatial scales. Environmental Management 28:255-266.
91

Ward, J. V., and J. A. Stanford. 1983. The serial discontinuity concept of lotic ecosystems. Pages 

29–42 in T. D. I. Fontaine III and S. M. Bartell, editors. Dynamics of lotic ecosystems. Ann 

Arbor Science Publishers, Ann Arbor, Michigan. 

Warren, M. L., Jr. 2009. Centrarchid identification and natural history. Pages 375-534 in S. J. 

Cooke and D. P. Philipp, editors. Centrarchid fishes: diversity, biology, and conservation. 

Wiley, New York.

Warton, D. I., and F. K. C. Hui. 2011. The arcsine is asinine: the analysis of proportions in 

ecology. Ecology 92:3-10. 

Webster, D. A. 1954. Smallmouth Bass, Micropterus dolomieui, in Cayuga Lake: Part I. Life 

history and environment. Cornell University Agricultural Experiment Station, Memoir 327, 

Ithaca, New York.

Westhoff, J. T., and C. P. Paukert. 2014. Climate change simulations predict altered biotic 

response in a thermally heterogeneous stream system. PLoS One 9:e111438. doi: 

10.1371/journal.pone.0111438.

Westhoff, J. T., C. Paukert, S. Ettinger-Dietzel, H. Dodd, and M. Siepker. 2016. Behavioural 

thermoregulation and bioenergetics of riverine Smallmouth Bass associated with ambient 

cold-period thermal refuge. Ecology of Freshwater Fish 25(1):72–85.

Whitledge, G. W., C. F. Rabeni, G. Annis, and S. P. Sowa. 2006. Riparian shading and 

groundwater enhance growth potential for Smallmouth Bass in Ozark streams. Ecological 

Applications 16:1461-1473.

Whittier, T. R., and T. M. Kincaid. 1999. Introduced fish in Northeastern USA lakes: regional 

extent, dominance, and effect on native species richness. Transactions of the American 

Fisheries Society 128:769-783.

Wiegmann, D. D., J. R. Baylis, and M. H. Hoff. 1992. Sexual selection and fitness variation in a 

population of Smallmouth Bass, Micropterus dolomieui (Pisces: Centrarchidae). Evolution 

46:1740-1753.

Winemiller, K. O., and D. H. Taylor. 1982. Smallmouth Bass nesting behavior and nest site 

selection in a small Ohio Stream. Ohio Journal of Science 82:266-273.

Wolman, M. G. 1954. A method of sampling coarse river-bed material. EOS, Transactions 

American Geophysical Union 35:951-956.
92

Wrenn, W. B. 1980. Effects of elevated temperature on growth and survival of Smallmouth Bass. 

Transactions of the American Fisheries Society 109:617-625.

Xian, G., C. Homer, J. Dewitz, J. Fry, N. Hossain, and J. Wickham. 2011. Change in impervious 

surface area between 2001 and 2006 in the coterminous United States. Photogrammetric 

Engineering and Remote Sensing 77:758-762.

Young, M. K. 1994. Mobility of Brown Trout in south-central Wyoming streams. Canadian 

Journal of Zoology 72:2078-2083.

Zhou, Y., G. A. Fox, R. B. Miller, R. Mollenhauer, and S. Brewer. 2018. Groundwater flux 

estimation in streams: a thermal equilibrium approach. Journal of Hydrology 561:822-832.

Zweifel, R. D., R. S. Hayward, and C. F. Rabeni. 1999. Bioenergetics insight into Black Bass 

distribution shifts in Ozark border region streams. North American Journal of Fisheries 

Management 19:192-197.