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Using fish hard‐part microchemistry and genetics to quantify population impacts of low‐use lock‐and‐dam structures on the Alabama River

June 2023
Transactions of the American Fisheries Society 152(4)

June 2023
152(4)

DOI:10.1002/tafs.10419

Authors:

Dennis R. Devries

Auburn University

Russell Alan Wright

Auburn University

Eric Peatman

Auburn University

Show all 6 authorsHide

Objective We used two approaches, fish hard‐part microchemistry and genetics, to quantify effects of low‐use lock‐and‐dam structures on riverine fish movement. Each approach varied in temporal scope, with microchemistry addressing effects within a lifetime and genetics addressing effects across generations. Methods Water samples and individuals of two species (Paddlefish Polyodon spathula and Smallmouth Buffalo Ictiobus bubalus ) were collected from four river sections that were separated by three low‐use lock‐and‐dam structures on the Alabama River. Quarterly water samples were collected from 15 sites during 2017–2018, and concentrations of Sr, Ba, Mn, Mg, and Ca were quantified using mass spectrometry. Result Water elemental signatures were spatially variable but temporally consistent. The Sr:Ca ratios in fish hard parts differed significantly among river sections for both species. Additionally, discriminant function analyses classified fish to their river capture section with accuracy between 55% and 74% for Paddlefish (errors nearly always assigned individuals to adjacent river sections) and 37–47% for Smallmouth Buffalo. Population genetic analyses included fish from each river section, as well as from Alabama River tributaries and a neighboring watershed. Genotyping‐by‐sequence techniques identified 1,889 and 3,737 single nucleotide polymorphisms postfiltering in Paddlefish and Smallmouth Buffalo, respectively, which we used to estimate population diversity indices and conduct differentiation analyses. Analysis of molecular variance, discriminant analysis of principal components, Bayesian clustering, and pairwise comparisons of F ST values indicated no strong evidence for genetic divergence in either species among river sections. Conclusion Within‐lifespan results based on hard‐part microchemistry suggested a potential for population isolation. However, longer‐term genetic effects were not apparent, possibly because the life span of these large and relatively long‐lived species means that few generations have passed since dam construction, and there could be sufficient mixing or population connectivity to prevent genetic divergence across river sections, particularly at the most downstream structure.

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490

wileyonlinelibrary.com/journal/tafs Transactions of the American Fisheries Society. 2023;152:490–512.

Received: 21 June 2022

Revised: 22 March 2023

Accepted: 29 March 2023

DOI: 10.1002/tafs.10419

ARTICLE

Using fish hard- part microchemistry and genetics to

quantify population impacts of low- use lock- and- dam

structures on the Alabama River

Garret J.Kratina1

Dennis R.DeVries1

Russell A.Wright1

EricPeatman1

Steven J.Rider2

HonggangZhao1

1School of Fisheries, Aquaculture and

Aquatic Sciences, Auburn University,

Auburn, Alabama, USA

2Alabama Division of Wildlife and

Freshwater Fisheries, River and Stream

Fisheries Program, Montgomery,

Alabama, USA

Correspondence

Garret J. Kratina

Email: gkratina@pa.gov

Present address

Garret J. Kratina, Pennsylvania Fish

and Boat Commission, Division of

Environmental Services, Bellefonte,

Pennsylvania, USA

Honggang Zhao, Department of Natural

Resources and the Environment,

Cornell University, Ithaca, New York,

USA

Abstract

Objective: We used two approaches, fish hard- part microchemistry and genetics, to

quantify effects of low- use lock- and- dam structures on riverine fish movement. Each

approach varied in temporal scope, with microchemistry addressing effects within a

lifetime and genetics addressing effects across generations.

Methods: Water samples and individuals of two species (Paddlefish Polyodon

spathula and Smallmouth Buffalo Ictiobus bubalus) were collected from four river sec-

tions that were separated by three low- use lock- and- dam structures on the Alabama

River. Quarterly water samples were collected from 15 sites during 2017– 2018, and

concentrations of Sr, Ba, Mn, Mg, and Ca were quantified using mass spectrometry.

Result: Water elemental signatures were spatially variable but temporally consistent.

The Sr:Ca ratios in fish hard parts differed significantly among river sections for both

species. Additionally, discriminant function analyses classified fish to their river cap-

ture section with accuracy between 55% and 74% for Paddlefish (errors nearly always

assigned individuals to adjacent river sections) and 37– 47% for Smallmouth Buffalo.

Population genetic analyses included fish from each river section, as well as from

Alabama River tributaries and a neighboring watershed. Genotyping- by- sequence

techniques identified 1,889 and 3,737 single nucleotide polymorphisms postfiltering

in Paddlefish and Smallmouth Buffalo, respectively, which we used to estimate pop-

ulation diversity indices and conduct differentiation analyses. Analysis of molecular

variance, discriminant analysis of principal components, Bayesian clustering, and

pairwise comparisons of FST values indicated no strong evidence for genetic diver-

gence in either species among river sections.

Conclusion: Within- lifespan results based on hard- part microchemistry suggested

a potential for population isolation. However, longer- term genetic effects were not

apparent, possibly because the life span of these large and relatively long- lived spe-

cies means that few generations have passed since dam construction, and there could

be sufficient mixing or population connectivity to prevent genetic divergence across

river sections, particularly at the most downstream structure.

KEYWORDS

dentary bone microchemistry, genetics, otolith microchemistry, Paddlefish, Smallmouth Buffalo

491

LOW USE LOCK- AND- DAM POPULATION IMPACTS

INTRODUCTION

Construction of dams in the United States proliferated

during the 1930s, and while the rate of dam construction

has decreased, effects due to substantial alteration and

fragmentation of aquatic habitats have become apparent

(Neves et al.1997; Nilsson et al.2005; Dudgeon et al.2006).

The fragmentation of lotic habitats by dams can obstruct

migration and dispersal of resident and migratory species

with the potential to reduce population persistence and

recolonization (Fagan2002; Geist2011). This can also im-

pact completion of species life cycles (Auer1996; Joy and

Death2001; Cumming2004), particularly those relying on

natural flow regimes and extensive migration routes. Such

impacts can potentially result in reduced genetic diversity

and even species extirpation (Freeman et al.2003; Faulks

et al.2011; Braulik et al.2014).

To mitigate some effects of dams on migratory fish

populations, fish passage operations have been imple-

mented throughout the United States to help facilitate

movements past dams and restore connectivity in river-

ine systems (Clay1994; Jackson et al.2001; Roscoe and

Hinch2010). Fish passage efforts in the USA have been

concentrated on the Atlantic and Pacific coasts for anad-

romous species such as salmonids, Striped Bass Morone

saxatilis, and clupeids (Jackson et al. 2001; Bernhardt

et al. 2005). The southeastern USA has become a focus

for studying the impacts of dams on fish populations in

recent years due to this region's high density of dams

(Graf1999), combined with a number of endemic fishes

and a large portion of its fauna being threatened (Warren

et al.2000). Specialized retrofits of existing dams to in-

clude fish passage facilities require substantial funding

for planning, construction, and operation (Clay 1994).

Therefore, it is critical to quantify the effects that dams

have on fishes to determine the costs and benefits before

implementing fish passage facilities.

The Alabama River watershed is an example of a

southeastern U.S. ecosystem that has been influenced

by the presence of dams. Dams on the Alabama River

have modified flow and have led to fish assemblage sim-

plification and species losses, particularly those that are

diadromous and highly migratory (Freeman et al.2005).

As a result of these declines, understanding the degree

to which fish passage is occurring and identifying oppor-

tunities for improving fish passage at dam structures on

the Alabama River is important (Mettee et al.2009, 2015;

Simcox et al. 2015). For example, during normal flow

conditions, the hydraulic turbulence through spill gates

at each of the Alabama River dams restricts upstream

movement of most fishes (Mettee et al. 2009, 2015).

However, successful fish passage has been documented

at one of the Alabama River dams during periods when

flooding inundates an existing crested spillway (Mettee

et al.2009; Simcox et al.2015). Specialized lock opera-

tions (termed “conservation lockages”) during migration

periods may help facilitate fish passage in some river sys-

tems or with some species but may not be effective in all

situations (Mettee et al.2009; Smith and Hightower2012;

Young et al.2012). For example, while conservation lock-

ages did increase the opportunity for passage and facili-

tated some passage events at two Alabama River dams,

relatively few fish successfully passed upstream (Simcox

et al.2015).

Given the limited opportunities for fish passage in the

Alabama River, here we quantify the influence of these

dam structures on fish at a population level. Towards this

end, we used two approaches, (1) hard- part microchem-

istry and (2) genetics, and two fish species that exhibit

varying life history and migratory patterns, Paddlefish

Polyodon spathula and Smallmouth Buffalo Ictiobus bub-

alus. These approaches were selected to evaluate potential

population impacts at differing time scales, with hard-

part microchemistry reflecting shorter- term impacts and

genetic responses reflecting longer- term impacts. For ex-

ample, while genetics can be useful for quantifying natal

homing and stock structure or determining individual

metrics such as paternity or genotype, it provides limited

insight into short- term (within lifetime) individual move-

ments, which is insight that can be gained through analy-

sis of hard- part microchemistry. Investigating populations

using a combination of techniques can help to increase

understanding and inferential power (Pracheil et al.2014).

Our objectives were to (1) quantify spatial and tem-

poral variation in trace element signatures across the

Alabama River and (2) use hard- part microchemistry and

genetic analyses (SNP technology) to quantify potential

fish population differences and the extent of mixing and

connectivity of two fish species across distinct sections of

the Alabama River that are separated by lock- and- dam

structures as well as two upper tributaries.

Impact Statement

Paddlefish and Smallmouth Buffalo showed evi-

dence of population separation by Alabama River

lock- and- dams via a within- generation measure

(chemical composition of fish jaw bones and ear

stones) but not with a longer- term metric (genetic

isolation). Both species are long lived relative to

the age of the dams; effects may be more apparent

in shorter- lived species.

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492

KRATINA et al.

METHODS

Study site

The Alabama River is formed by the confluence of the

Tallapoosa and Coosa rivers and flows for approximately

500 km across the coastal plain through central and

southern Alabama before it joins the Tombigbee River,

forming the Mobile River, which then flows into Mobile

Bay (Figure1). The headwaters of the Alabama River, the

Coosa and Tallapoosa rivers, have wide ranging physio-

graphic diversity, originating in the Blue Ridge, Valley and

Ridge, and Upland Piedmont physiographic regions along

the southern bend of Appalachia (Freeman et al.2005).

FIGURE  Map indicating the location of the Alabama River, its major tributaries, the three Alabama River lock- and- dam (L&D)

structures, 15 water sampling locations, and four river sections, two tributaries, and a neighboring watershed where fish were collected (all

indicated by unique colors). River sections are defined as follows: TOM = Tombigbee River (river kilometer [rkm] 0– 188 on the Tombigbee

River measuring from its confluence with the Alabama River), LAR = lower Alabama River (rkm 0– 117 on the Alabama River measuring

from its confluence with the Tombigbee River), CL = Claiborne Lake (rkm 117– 214 on the Alabama River), MFR = Millers Ferry Reservoir

(rkm 214– 380 on the Alabama River), JBR = Jones Bluff Reservoir (rkm 380– 491 on the Alabama River), CSA = Coosa River (rkm 0– 30 on

the Coosa River measuring from its confluence with the Tallapoosa River), and TAL = Tallapoosa River (rkm 0– 80 on the Tallapoosa River

measuring from its confluence with the Coosa River).

493

LOW USE LOCK- AND- DAM POPULATION IMPACTS

The distinctive characteristics of each of these rivers are

attributed to the varied lithography and soil horizons that

occur in their watersheds (Freeman et al.2005).

The United States Army Corps of Engineers Mobile

District operates three low- use lock- and- dam structures

on the Alabama River (Figure1). From downstream to

upstream these structures are Claiborne Lock- and- Dam

(river kilometer [rkm] 117, measuring from the conflu-

ence of the Alabama River with Tombigbee River), Millers

Ferry Lock- and- Dam (rkm 214), and Robert F. Henry

Lock- and- Dam (rkm 380). These dams were completed

between 1968 and 1971, and each contains gated spill-

ways. Claiborne Lock- and- Dam differs from the others

in that it does not have a hydropower structure and has

both an ungated crested spillway that allows water flow at

low to moderate flow conditions as well as gated spillways

that are opened during high- water events. Laminar flow

over the ungated spillway occurs when water levels reach

an elevation of 10 m (U.S. Army Corps of Engineers2013;

Mettee et al.2015).

Water sample collection

Water samples were collected quarterly (once during each

season) between spring 2017 through summer 2018 from

15 sites (Figure1). Sampling locations were selected to in-

clude a full spatial distribution throughout the Alabama

River and its upper tributaries. A Van Dorn sampler was

held open at a 1- m depth for 30 s to flush any residual

water prior to sample collection (Farmer et al.2013). The

water sample was collected with a 50- mL sterile syringe

from the Van Dorn sampler and filtered through a dispos-

able 0.45- μm PTFE glass- fiber filter (Whatman GD/XP)

directly into a 125- mL pre- acid- washed high- density poly-

ethylene (HDPE) bottle containing 0.5 mL of 1:1 (≈34%)

nitric acid solution for field preservation and storage.

This process was repeated three times to fill each 125-

mL HDPE bottle. Samples were stored on ice in the field,

returned to the lab, and refrigerated until analysis (U.S.

Environmental Protection Agency1996; Lowe et al.2011;

Farmer et al.2013).

Fish collection

Paddlefish were collected during 2012– 2015 and 2017–

2019, and Smallmouth Buffalo were collected during

2017– 2018. Fish were captured using boat- mounted

pulsed- DC electrofishing (DC GPP 7.5 electrofisher;

Smith- Root) and gill nets (45.72 m in length, 152.4- mm bar

measure) throughout the Alabama River. Additionally,

Paddlefish samples were collected during the 2017

regulated commercial Paddlefish season occurring on the

Alabama River, where commercial fishers used gill nets

in designated stretches of the river. Harvested Paddlefish

were checked by Alabama Division of Wildlife and

Freshwater Fisheries biologists, who then collected bio-

logical data, fin clips, and dentary bones. Commercially

harvested Paddlefish were required to be egg- bearing fe-

males with an eye- to- fork length >863.6 mm when meas-

ured with a flexible tape measure along the curvature of

the body (Alabama Division of Wildlife and Freshwater

Fisheries2013). Location of capture assigned each fish to

a population group, defined to be specific river sections

separated by the three low- use lock- and- dam structures

on the Alabama River, as well as its major tributaries, and

a neighboring watershed (Figure1). All fish were weighed

(nearest 0.1 kg) and measured (nearest millimeter eye-

to- fork length for Paddlefish, nearest millimeter TL for

Smallmouth Buffalo).

Hard- part and tissue sample collection

Dentary bones were removed from each collected Paddlefish.

Any flesh adhering to the extracted piece of dentary was re-

moved, and dentary bones were allowed to air dry prior to

sectioning (Scarnecchia et al.2006; Bock et al.2016). Both

sagittal otoliths were removed from Smallmouth Buffalo

with Teflon- coated forceps and cleaned for 30 s in 30% H2O2.

Following cleaning, otoliths were rinsed in ultrapure water,

dried, and stored in individual sterile polyethylene vials.

Final sample sizes for hard- part microchemistry analysis

included 186 Paddlefish dentary bones (30– 56 per Alabama

River section) and 209 Smallmouth Buffalo otoliths (42– 53

per Alabama River section). Fin clips were collected from

Paddlefish (left pectoral) and Smallmouth Buffalo (right pel-

vic) and preserved in 95% ethanol. Clips from 166 Paddlefish

and 121 Smallmouth Buffalo were used for genotyping- by-

sequencing (GBS) library construction and sequencing and

those from 159 Paddlefish and 119 Smallmouth Buffalo

were used for population genetic analyses.

Hard- part sample preparation

For Smallmouth Buffalo, one otolith from each fish was

selected at random and set in an epoxy resin, and an ap-

proximately 0.5- mm transverse section through the core

was removed using a low- speed diamond blade saw.

Similarly, for Paddlefish a 0.50– 0.65- mm transverse sec-

tion was cut from the right- side dentary bone approxi-

mately 10 mm posterior to the point of greatest curvature

(Scarnecchia et al.1996; Bock et al.2016). All sections

were then polished sequentially with 300– 14,000- grit

494

KRATINA et al.

lapping film until sections were smooth, after which

they were rinsed with ultrapure water. Individual oto-

lith sections were sonicated for 10 min, washed thor-

oughly once more with ultrapure water, and allowed to

dry. Following drying, each section was mounted onto

standard glass slides with cyanoacrylate glue (Ludsin

et al. 2006; Farmer et al. 2013; Nelson et al. 2015;

Daugherty et al.2017). Slides were stored in sealed pre-

cleaned HDPE containers until microchemistry analysis

and age estimation.

Hard- part microchemistry analysis

Hard- part microchemical analyses were conducted at

Dauphin Island Sea Laboratory, Dauphin Island, Alabama,

using laser ablation inductively coupled plasma mass

spectrometry (LA- ICPMS) on a Agilent Technologies 7700

series ICPMS coupled to a 213- nm New Wave NWR- 213

internally homogenized, optically attenuated laser system

(Zeigler and Whitledge2011; Bock et al.2016; Nelson and

Powers2019).

Each dentary bone section had a linear transect ab-

lated from inside its first annulus to the edge of its me-

sial arm, and each otolith section had a linear transect

ablated from the focus of its core to its proximal edge.

For all transects, a preablation pass was performed with

20% energy, 5 repetition rate (Hz), 40 spot size (μm), and

100 scan speed (μm/s). Laser parameters for each abla-

tion were 25– 28% energy, 10 repetition rate (Hz), 25 spot

size (μm), and 5 scan speed (μm/s), quantifying the con-

centration (counts per second) of Ca43, Sr88, Ba137, Mg24,

and Mn55.

Before each ablation, 60 s of background signal were

collected and duplicate runs of certified reference ma-

terials (CRMs) NIST- 612, MACS- 3, and MAPS- 4 were

performed before each day's analysis and after each

subsequent hour of analysis. Limits of detection (3 SDs

above background), background signal removal, instru-

ment drift correction, and raw count transformation to

concentration (ppm) were conducted using the Trace

Elements IS Data Reduction scheme in Iolite version

3.63 built under IGOR Pro 7 software from WaveMetrics

(2017), with Ca43 used as an internal standard held con-

stant at 37.69% for otoliths with MACS- 3 as the CRM

and 27.00% for dentary bones with MAPS- 4 as the

CRM (Longerich et al.1996; Ludsin et al. 2006; Paton

et al. 2011; Nelson and Powers 2019). Concentrations

(ppm) of Sr88, Ba137, Mg24, and Mn55 obtained from the

Iolite software were converted to molar ratios relative to

Ca (μmol/mol).

All elements examined with LA- ICPMS (Ca, Sr, Ba,

Mg, Mn) met the criteria (> limit of detection in 50% of

samples) to be used in dentary bone (Paddlefish) and oto-

lith (Smallmouth Buffalo) comparisons across all river

sections in this study.

Water analysis

Dissolved elemental concentrations (Ca43, Sr88, Ba137,

Mg24, and Mn55) in water were quantified on the same

Agilent ICPMS in solution mode at Dauphin Island Sea

Laboratory. Water samples underwent a 10- fold dilution

with a solution of 2% HNO3. Additionally, each sample

was spiked with internal standards Be9 and In115 at con-

centrations of 10 and 1 ppm, respectively, to correct for in-

strument mass bias, drift, and matrix effects. An external

five- point calibration curve with all elements of interest,

and the same internal standards, was run before analysis

began and used to convert raw count data into elemental

concentrations (ppb), using Agilent Masshunter software.

The NIST 1643f CRM also underwent a 10- fold dilution

that was run every hour during analysis to assess instru-

ment accuracy. The fourth point on the calibration curve

and 2% HNO3 blanks were also run every hour to further

check for instrument drift and verify blank concentrations

throughout analyses. All elemental concentrations output

were converted to molar ratios relative to Ca (μmol/mol).

All five elements (Ca, Sr, Ba, Mg, and Mn) analyzed met

the inclusion criteria (i.e., being above limit of detection in

>50% of samples) in the otolith analysis and were there-

fore quantified in our water samples.

Genetics

Genomic DNA from Paddlefish and Smallmouth Buffalo

was extracted from fin clips using the DNeasy Blood &

Tissue kit (Qiagen) according to the manufacturer's pro-

tocol. The DNA quality was assessed by running 100 ng

of each DNA sample on 1% agarose gels. The DNA con-

centration was determined using the Quant- iT PicoGreen

dsDNA Assay Kit (Invitrogen). All DNA samples were

sent to the University of Minnesota Genomics Center

for GBS library construction and sequencing. Briefly,

100 ng of DNA was digested with 10 units BamHI- HF and

NsiI- HF (New England Biolabs) enzyme combination

and incubated at 37°C for 2 h. Following digestion, sam-

ples were then ligated with 200 units of T4 ligase (New

England Biolabs) and phased adaptors at 22°C for 2 h to

inactivate the T4 ligase. The ligated samples were purified

with SPRI (solid- phase reversible immobilization) beads

and then amplified for 18 cycles with 2× HiFi Master

Mix (New England Biolabs) to add barcodes. Libraries

of Paddlefish and Smallmouth Buffalo samples were

495

LOW USE LOCK- AND- DAM POPULATION IMPACTS

purified, quantified, pooled, and size selected for the 300-

to 744- bp library region and diluted to 1.7 pm for sequenc-

ing. The pooled libraries were loaded across four lanes of

a 150- bp single- read sequencing run on the NextSeq 550

instrument.

To perform reference- based SNP calling, we assembled

the rough draft genomes for Paddlefish and Smallmouth

Buffalo. One DNA sample from each species (LAR_19

and SBF_20_8) was selected and sent to the University

of Minnesota Genomics Center for library construction

and sequencing. During library creation, 100 ng of DNA

was fragmented to target a 350- bp insert length using

Covaris ultrasonic shearing. The sheared DNA was then

end- repaired and subjected to a bead- based size selection.

After adaptor and index ligation, the library was ampli-

fied using eight cycles of PCR. The amplified libraries

were sequenced across two lanes of HiSeq 2500 125- bp

paired- end run. A total of 98.7 gigabases (Gb) were gen-

erated from library sequencing and assembled into a 1.34

Gb (N50 = 3.85 kb, 562,901 scaffolds) Paddlefish draft ge-

nome (~73.7× genome coverage) using MaSuRCA version

3.2.4 with default settings (Zimin et al.2013). Similarly,

a total of 73.9 Gb data were generated and assembled

into a 2.27 Gb Smallmouth Buffalo draft genome (~32.6

× coverage) with an N50 of 1.63 kb and 1,731,047 scaf-

folds. Genomewide SNPs were called using TASSEL (Trait

Analysis by aSSociation, Evolution and Linkage) 5.0 GBS

pipeline V2 (Glaubitz et al.2014) with default parameter

settings. During initial SNP filtering, the minimum minor

allele frequency was set to 0.05 (overall), the minimum

minor allele count was set to 10 (overall), and minimum

locus coverage was set to 0.1 (overall). Only biallelic SNP

were retained after initial filtering.

To ensure that the SNPs were polymorphic and infor-

mative for Paddlefish and Smallmouth Buffalo population

genetics analyses, VCFtools (Danecek et al.2011), TASSEL

(Glaubitz et al.2014), and GENEPOP (Rousset2008) were

used for stringent filtration of SNPs based on the follow-

ing criteria: (1) individuals with more than 30% missing

genotypes were removed, (2) the SNPs were called in at

least 95% of individuals, (3) adjacent SNPs within 64 bp

were removed to avoid loci that generated from ambigu-

ous alignments, (4) SNPs deviating from Hardy– Weinberg

equilibrium with p- value < 0.01 were removed, and (5)

SNPs that showed significant linkage disequilibrium

(r2 > 0.3) and false discovery rate (p- value < 0.01) were

removed.

Paralogous loci that arise during ancestry whole-

genome duplication, autopolyploidy or allopolyploidy, or

chromosomal duplication have been widely reported in

species of plants, fish, and amphibians (Devos et al.2005;

Lien et al.2016; Knytl et al.2017). Such duplication events

can facilitate species evolution and adaption but may lead

to complicated genetic patterns where different ploidy lev-

els exist in the genome (McKinney et al.2018). Excluding

paralogous loci is commonplace for GBS studies, regard-

less of their important role in species adaption and evo-

lution (McKinney et al.2017). Both species in this study

were previously documented to have undergone whole

genome duplication followed by genomic and chromo-

somal reorganization (Uyeno and Smith 1972; Ferris

and Whitt1977; Clements et al. 2004; Crow et al. 2012;

Symonová et al.2017). In order to keep only biallelic SNPs

for downstream population genetics analyses, methods

proposed by McKinney et al.(2017) were followed to iden-

tify paralogs from GBS data using two characteristics: the

relative proportion of heterozygotes in a population (H)

and the deviation of allele- specific reads of each locus

from a 1:1 ratio (D). The SNP data sets after stringent

filtering were used as input. The H and D for each locus

were plotted to visualize the distribution of these variables

across all loci using HDplot (with minor modification;

https://github.com/hzz00 24/paral og- finder). Based on

the distribution plots, loci with H > 0.5 and |D| > 4 were

classified as paralogs and excluded. The retained loci were

used for population genetic analyses.

Genotyping and SNP discovery

For Paddlefish, a total of 152,964,388 high- quality reads

were generated from Illumina NextSeq sequencing, with

an average of 921,473 reads for each sequenced sample. A

total of 99,086,273 high- quality reads were generated for

Smallmouth Buffalo, with an average of 839,714 reads for

each sequenced sample. During GBS analyses, TASSEL

generated 18,137 and 56,744 prefiltered SNP loci for

Paddlefish and Smallmouth Buffalo, respectively. Further

individual filtering using 30% missing loci as a parameter

reduced the sample size from 166 to 159 for Paddlefish

and from 121 to 118 for Smallmouth Buffalo. To ensure

that SNP data sets were polymorphic and informative for

population genetics analyses, further stringent filtering

steps using VCFtools, TASSEL, and GENEPOP software

resulted in the identification of 2,770 and 4,248 SNPs for

Paddlefish and Smallmouth Buffalo, respectively.

Paralog identification and marker

characteristics

To keep only biallelic SNPs for downstream popula-

tion genetics analyses, paralogs were identified from

GBS data sets. Diploid SNP identification based on the

relative proportion of heterozygotes (H) and the read

deviation (D) found 1,889 (68.19%) of 2,770 markers

496

KRATINA et al.

to be diploid loci in Paddlefish and 3,737 (87.97%) of

4,248 SNPs to be diploid SNPs in Smallmouth Buffalo.

Plotting H and D revealed two distinct clouds of loci

in Paddlefish (Kratina 2019). The SNP loci within the

inner dense cloud concentrated at 0.09– 0.5 for H and

0.0 for D, suggesting the distribution of singleton loci.

This cloud was ringed by a second cloud representing

31.81% of loci with clear deviation from equal ratios of

sequence reads, consistent with expectations for dupli-

cate loci. The Smallmouth Buffalo loci clustered into a

cloud at 0.06– 0.5 for H and 0.0 for D, corresponding to

singleton loci distribution, with 12.03% loci outside this

central range (Kratina2019).

Data processing and statistical analyses

R Statistical Software (version 4.1.1; R Core Team2019)

was used for all statistical analyses.

Water chemistry

Akaike information criterion (AIC) was used to com-

pare linear models that included unique combinations

of the explanatory variables (site, season, and Claiborne

gauge height). Site × season and site × gauge height in-

teractions were included in the models compared by

AIC. The model with the lowest AIC score for each ele-

ment best explained the data. The explanatory variables

identified from the best- fit linear model were then in-

corporated into an analysis of variance (ANOVA) model

for each element to investigate differences among sites

and identify patterns throughout the study area. A site

× gauge height interaction was identified in the best-

fit linear model for Mn; however, this interaction was

not significant in the Mn ANOVA model. The ANOVA

models were tested for normality of the residuals using

the Shapiro– Wilks test and for homogeneity of variance

using Levene's test. Although some ANOVAs passed

these tests, some did fail. After testing appropriate data

transformations, it was clear that no single transforma-

tion would correct for these violations of assumptions.

Given that ANOVA is robust to violations of normality

assumptions and has greater power than its nonpara-

metric counterparts (Kruskal– Wallace or rank- sum ap-

proaches), it was still used (Brownie and Boos 1994;

Underwood 1997; Khan and Rayner 2003; Nelson

et al.2014, 2015, 2017), and all ANOVA tests were con-

ducted with untransformed data. Lastly, to determine

consistency in element water chemistry between sam-

pling years, paired t- tests (paired by site) for each ele-

ment were performed to facilitate a comparison of the

samples collected in spring of 2017 versus 2018 and the

summer of 2017 versus 2018.

Hard- part edge to water comparison

Least squares linear regression was used to relate mean

river section fish hard- part edge element concentra-

tion to mean water concentrations collected in prox-

imity and time to fish collection. Hard- part “edge” was

defined as the mean concentration ratios of the outer

20 μm of a fish's hard part (Farmer et al. 2013; Bock

et al. 2016). Fish hard parts used for these compari-

sons were from Paddlefish collected during the spring

2017 and Smallmouth Buffalo collected during spring

and fall 2017. A mean water element concentration for

each river section was generated as the average across

all water samples collected within the same river section

during the season of interest.

Hard- part microchemistry

An ANOVA was used to determine if elemental concen-

trations averaged across hard- part transect profile regions

differed among river sections. Mean core (first 20 μm),

edge (last 20 μm), and whole otolith elemental concen-

trations were analyzed to test for any differences across

the four sections of the Alabama River and its tributaries

(when applicable).

For those elements with significant (α = 0.05) dif-

ferences among sites for a given hard- part segment,

Tukey's honestly significant difference pairwise tests

were used to compare mean hard- part element con-

centrations among river sections to determine site dif-

ferences. Although data were not always distributed

normally or did not always have homogeneous vari-

ance, ANOVAs using untransformed data were still

used, given that the ANOVA test is robust to violations

in normality assumptions and has greater power than

their nonparametric counterparts (Kruskal– Wallace or

rank– sum approaches). This is particularly true when

sample sizes are large, as is the case here (Brownie and

Boos 1994; Underwood 1997; Khan and Rayner 2003;

Nelson et al.2014, 2015, 2017).

Additionally, discriminant function analyses (DFAs)

were performed to see how accurately fish could be clas-

sified to their capture locations based on the multivari-

ate signatures observed in their hard- part laser ablation

transects. The DFAs were performed for each region of

the hard- part transect profile and included each element

analyzed. An additional DFA performed combined lower

Alabama River and Claiborne Lake Paddlefish into a single

497

LOW USE LOCK- AND- DAM POPULATION IMPACTS

group (due to Claiborne Lock- and- Dam likely experienc-

ing some degree of fish passage over its crested spillway).

This resulted in three river sections for this second DFA in

order to determine if classification accuracy for Paddlefish

could be further improved by treating the lower two river

sections as one. Fish collected in the Tallapoosa and Coosa

rivers were not included in the DFAs as they did not help

improve classification accuracy.

Finally, all box plots throughout this manuscript used the

default box plot code in the statistical analysis software R,

whereby the interquartile range (IQR; boxplot length) = Q_3

(quartile 3 [75th percentile]) − Q_1 (quartile 1 [25th per-

centile]), and the band within the box is quartile 2 (median

[50th quartile]). The upper whisker was = Q_3 + 1.5 × IQR,

and the lower whisker = Q_1 – 1.5 × IQR.

Population genetic analyses

Population genetic diversity indices were evaluated by

computing the observed (Ho) and expected heterozygosity

(He) using the R package Arlequin version 3.5 (Excoffier

and Lischer2010). The inbreeding coefficient (Fis) for each

river section was calculated as well. Effective population

size (Ne) was estimated using the linkage disequilibrium

method implemented in NeEstimator version 2.1 (Do

et al.2014).

Population connection(s) were visualized with a dis-

criminant analysis of principal components (DAPC) using

the Adegenet package in R (Jombart and Ahmed2011). The

DAPC was run on individuals grouped by major river sec-

tions. In each case, the optimal number of principal com-

ponents was determined by alpha- score procedure with 20

repeated runs. Another program based on Bayesian clus-

tering algorithms, STRUCTURE version 2.3.4 (Pritchard

et al. 2000), was used for population structure analysis.

The admixture model with correlated allele frequencies

was applied for data analysis with a burn- in of 20,000 it-

erations followed by 200,000 Markov chain– Monte Carlo

repetitions. Different numbers of assumed population ge-

netic clusters (K = 1– 10) were used to determine the best

K value using the Web server STRUCTURE HARVESTER

(Earl and VonHoldt2012) and were repeated 10 times for

each K value.

Population differentiation analyses were performed

for all pairs of major river sections using analysis of mo-

lecular variance (AMOVA) and the Weir and Cockerham

estimator of FST (Weir and Cockerham1984) in Arlequin

version 3.5 (Excoffier and Lischer 2010). Significance

level for each FST comparison was estimated using 10,000

permutations with false discovery rate correction (initial

P = 0.05). The significance level of the AMOVA test was

also estimated using 10,000 permutations.

RESULTS

Water chemistry

All four element ratios exhibited significant variation

across sites (Table1; Figure2). In addition, all but one

comparison between years for spring or summer ratios of

Sr:Ca, Ba:Ca, Mg:Ca, and Mn:Ca were not significant (t-

tests: all p- values > 0.21; spring Mg:Ca ratios p = 0.02). See

Supplemental Tables S1– S4 (available in the online ver-

sion of this article) for the results of the AIC scores.

Correlation of water to hard- part edge

Paddlefish

Mean Paddlefish dentary bone edge element concentra-

tion was significantly related to mean water element con-

centrations across river sections for both Sr and Mn in

spring 2017. Simple linear regressions between water and

dentary edge chemistry were positive for Sr:Ca (R2 = 0.98,

p = 0.008) and Mn:Ca (R2 = 0.91, p = 0.044). Linear regres-

sions between water and dentary bone chemistry were

not significant for Ba:Ca (R2 = 0.01, p = 0.891) or Mg:Ca

(R2 = 0.72, p = 0.154). See Supplemental Figure S7 (avail-

able in the online version of this article) for these graphs.

Smallmouth Buffalo

Mean Smallmouth Buffalo otolith edge Sr:Ca ratio was

significantly related to mean water Sr:Ca ratio across

river sections in fall 2017 (R2 = 0.92, p = 0.043) and mar-

ginally significantly (0.05 < p < 0.10) related in spring

TABLE  Results of one- way ANOVA models comparing water

Sr:Ca, Ba:Ca, Mg:Ca, and Mn:Ca among water sampling sites,

along with other explanatory variables (season and gauge height)

identified via Akaike information criterion from the best- fit linear

model for each element.

Signature Comparison N F df p

Sr:Ca Site 89 88.716 14 <0.01

Season 4 2.954 3 0.04

Gauge height 89 12.293 1 <0.01

Ba:Ca Site 89 20.640 14 <0.01

Gauge height 89 16.810 1 <0.01

Mg:Ca Site 89 35.376 14 <0.01

Gauge height 89 2.608 1 0.11

Mn:Ca Site 89 3.358 14 <0.01

Site : gauge height 0.308 14 0.99

498

KRATINA et al.

2017 samples (R2 = 0.99, p = 0.077). Linear regressions

between water and otolith chemistry in spring 2017 were

not significant for Ba:Ca (R2 = 0.66, p = 0.399), Mg:Ca

(R2 = 0.77, p = 0.318), or Mn:Ca (R2 = 0.01, p = 0.952).

Simple linear regressions between water and otolith

chemistry in fall 2017 were positive for Mn:Ca (R2 = 0.87,

p = 0.066) but not for Ba:Ca (R2 = 0.12, p = 0.653) or

Mg:Ca (R2 = 0.50, p = 0.294). See Supplemental Figure

S8 for these graphs.

Hard- part microchemistry

Paddlefish comparison among river sections

Paddlefish dentary bone mean whole transect Sr:Ca ra-

tios differed significantly among river sections (ANOVA:

F4, 181 = 69.31, p < 0.001). Dentary bone mean edge Sr:Ca

ratios also differed significantly among river sections

(ANOVA: F4, 181 = 66.05, p < 0.001) as did dentary bone

mean first 20 μm Sr:Ca ratios (ANOVA: F4, 178 = 41.16,

p < 0.001). The Sr:Ca ratios were all highest in LAR

(Figure3; Kratina2019). Paddlefish dentary bone Ba:Ca

ratios also differed across sites for whole transect, first

20 μm, and edge (all p ≤ 0.001), being highest at the

Tallapoosa River site and slightly decreasing with pro-

gressive distance downstream (Supplemental Figure S1;

Kratina2019). And both Mg:Ca and Mn:Ca ratios differed

across river locations for whole transect, first 20 μm, and

edge dentary bone measures (all p ≤ 0.004; Supplemental

Figures S2 and S3; Kratina2019).

Paddlefish discrimination among river sections

A first set of discriminant function analyses assigned

fish to the correct river collection section with a moder-

ate degree of accuracy (55– 74%) across dentary regions

(Table2). Fish collected in MFR consistently had the

highest classification percentages for each region of the

dentary. Elements that contributed to the discriminat-

ing axes were Ba137, Sr88, and Mn55 (in decreasing order

of importance). The relative similarity in concentration

of these elements in adjacent river sections limited the

ability to discriminate between these areas and misclas-

sifications almost always occurred to an adjacent river

section.

Smallmouth Buffalo comparison among

river sections

Smallmouth Buffalo otolith mean whole transect Sr:Ca ra-

tios differed significantly among river sections (ANOVA:

FIGURE  Trace element water sample concentration (Sr:Ca, Ba:Ca, Mg:Ca, and Mn:Ca) across sites ranging from upstream (to the left

of the x- axis) to downstream (to the right of the x- axis). Each site is represented by a box plot, where the horizontal line in each box indicates

the median, the box dimensions represent the 25th to 75th percentile ranges, and whiskers show the 10th to 90th percentile ranges. The

numbers by Alabama River (ALR) site names indicate the river mile of that site.

499

LOW USE LOCK- AND- DAM POPULATION IMPACTS

F5, 203 = 3.142, p = 0.009), being lowest in JBR (Figure4A).

Otolith mean edge Sr:Ca ratios differed significantly

among river sections (ANOVA: F5, 203 = 4.714, p < 0.001),

being highest in TAL (Figure4B). Otolith first mean 20 μm

Sr:Ca ratios did not differ among river sections (ANOVA:

F5, 203 = 1.941, p = 0.089) (Figure4C). Smallmouth Buffalo

whole transect, edge, and first 20 μm Ba:Ca ratios did not

differ across river sections (all p > 0.67; Supplemental

Figure S4). The Mg:Ca ratios differed across river loca-

tions for whole transect, first 20 μm, and edge measures

(with most measures showing a slight increasing pat-

tern with progressive distance downstream; all p ≤ 0.017;

Supplemental Figure S5; Kratina2019), and Mn:Ca ratios

for whole transect and edge differed across river sections

(higher upstream; both p < 0.001), while that for first mean

20 μm did not (p = 0.28; Supplemental Figure S6).

Smallmouth Buffalo discrimination among

river sections

Discriminant function analyses assigned fish to the cor-

rect river collection section with a low degree of accuracy

(37– 47%) in all otolith regions (Table2). The river section

of highest classification accuracy varied among otolith

regions, with JBR being most accurate for the transect

mean, MFR most accurate for the mean edge, and LAR

being most accurate for the first 20 μm mean. Elements

that contributed to the discriminating axes were Ba137,

Sr88, and Mn55 (in decreasing order of importance). The

relative similarity in concentration of these elements in

adjacent river sections limited the ability to discriminate

between these areas.

Genetics

Marker characteristics

Observed (Ho) and expected heterozygosity (He) were

calculated for fish from each river section (Table3).

Heterozygosity rate gradually decreased downstream

from JBR to LAR. Inbreeding coefficients (Fis) were calcu-

lated for fish collected from each river section, with high-

est levels of inbreeding observed in LAR for both species

(Table3).

Population structure and differentiation

The AMOVA for Paddlefish and Smallmouth Buffalo

data sets did not detect genetic differentiation among

river sections, and most genetic variation was generated

within individuals (Table4). In Paddlefish, 96.16% of the

total variation was explained by differences within indi-

viduals, 3.84% was due to variation among individuals

within populations, and variation among populations

did not contribute to the total variation. In Smallmouth

Buffalo, 93.68% of the total variation was explained by

differences within individuals, 6.28% was due to the

FIGURE  Concentrations of Sr:Ca in Paddlefish dentary bones for each region of the ablation transect analyzed, showing (A) mean

transect, (B) mean edge, and (C) mean first 20 μm. Each river section has a box plot that represents the observed values, and different

letters above the box plots indicate significant differences among river sections based on Tukey pairwise comparisons. For the box plots, the

horizontal line in each box indicates the median, the box dimensions represent the 25th to 75th percentile ranges, and whiskers show the

10th to 90th percentile ranges. See Figure1 for river section definitions.

500

KRATINA et al.

variation among individuals within populations, and

variation among populations only accounted 0.04% of

the total variation.

To examine genetic differentiation among populations

and their degree of variance in allele frequencies, pairwise

FST values were measured (Table5). The FST statistics in

this study were, in general, exceptionally low, and only

one pairwise comparison was significant (between JBR

and MFR in Smallmouth Buffalo). This suggests that JBR

and MFR are the most genetically divergent river sections

for Smallmouth Buffalo. Meanwhile, no significant FST

values were detected for Paddlefish between river sec-

tions. While FST values between CSA Paddlefish and other

river sections were relatively higher than the other com-

parisons, this was most likely due to the small sample size

(n = 4) from the CSA.

We used STRUCTURE software to examine popula-

tion structure for both species, examining the optimal

number of upper genetic clusters (K) for the GBS data

sets using the 1,889 SNPs for Paddlefish and 3,737 SNPs

for Smallmouth Buffalo. For both species, the optimal

numbers of uppermost genetic clusters (K) were 1 (K = 1)

according to the calculated probabilities of each K. This

suggested a lack of spatial genetic clustering between fish

in each river section.

Lastly, DAPC also showed a general pattern of low ge-

netic differentiation, similar to that of the previous anal-

yses performed. The DAPC plot of Paddlefish, including

all river sections and SNPs, showed no clear separation or

clusters among river sections (Figure5), with only CSA

samples slightly isolating from all other river sections. This

pattern was consistent with the FST results, potentially due

to the small sample size from the CSA. The DAPC analy-

sis of Smallmouth Buffalo with all SNPs also showed no

clear genetic clusters or subdivision among river sections

(Figure6).

TABLE  Results of the discriminant function analysis for Paddlefish (PAD) and Smallmouth Buffalo (SBF) using river section as each

group in the analysis. Discriminant function analyses were performed on each part of the ablation transect. See Figure1 for river section

definitions.

Transect analysis Species

Collection

location

Assigned collection location

% Correct

Total

classification

accuracy (%)JBR MFR CL LAR

Mean transect PAD JBR 20 10 0 0 67 68

MFR 1 47 1 1 94

CL 1 14 16 12 37

LAR 1 8 8 39 70

SBF JBR 26 8 1 7 62 43

MFR 3 26 9 10 54

CL 5 16 23 9 43

LAR 6 16 21 9 17

Mean edge PAD JBR 24 6 0 0 80 74

MFR 0 47 3 0 94

CL 1 16 14 12 33

LAR 1 3 5 47 84

SBF JBR 23 12 0 7 55 47

MFR 3 29 5 11 60

CL 9 15 14 15 26

LAR 2 18 7 25 48

Mean first 20 μm PAD JBR 8 22 0 0 27 55

MFR 2 39 5 4 78

CL 0 19 12 11 29

LAR 0 7 10 37 69

SBF JBR 12 15 7 8 29 37

MFR 5 22 14 7 46

CL 4 16 12 21 23

LAR 4 13 8 27 52

501

LOW USE LOCK- AND- DAM POPULATION IMPACTS

DISCUSSION

We quantified short- term (hard- part microchemistry)

and long- term (genotype by sequencing) effects of three

Alabama River lock- and- dam structures on the popula-

tion structure of two fish species. Previous work has used

tagging and tracking of individuals to show that individ-

ual fish movement was restricted to varying degrees by

the dams (Simcox et al. 2015; Hershey et al. 2022), and

here we wanted to quantify potential effects at longer

time scales (hard- part microchemistry = within the life

span of an organism, genetics = across generations); how-

ever, it appears that longer- term genetic divergence was

not manifesting in these long- lived species, perhaps in

part because the dams have been in place for a relatively

short time relative to the lifespans and potential genera-

tion times of these species. Below we further consider and

discuss these findings.

FIGURE  Concentrations of Sr:Ca in Smallmouth Buffalo otoliths for each region of the ablation transect analyzed, showing (A)

meant transect, (B) mean edge, and (C) mean first 20 μm. Each river section has a box plot that represents the observed values, and different

letters above the box plots indicate significant differences among river sections based on Tukey pairwise comparisons. For the box plots, the

horizontal line in each box indicates the median, the box dimensions represent the 25th to 75th percentile ranges, and whiskers show the

10th to 90th percentile ranges. See Figure1 for river section definitions.

TABLE  Genetic diversity parameters of Paddlefish and Smallmouth Buffalo from each river section. Abbreviations are as follows:

Ho indicates average observed heterozygosity, He indicates average expected heterozygosity, Fis indicates inbreeding coefficient, and Ne

indicates the estimate of contemporary effective population size (with 95% confidence intervals in parentheses). See Figure1 for river

section definitions.

Species River section Sample size HoHeFis Ne

Paddlefish CSA 4 0.389 0.403 0.034

JBR 27 0.291 0.304 0.041 844 (670– 1,138)

MFR 33 0.289 0.302 0.040 1,516 (1,124– 2,324)

CL 35 0.290 0.302 0.037 1,198 (954– 1,607)

LAR 31 0.286 0.301 0.045 1,960 (1,317– 3,812)

TOM 29 0.294 0.304 0.032 1,673 (1,144– 3,099)

Mean 0.306 0.319 0.038 1,438

Smallmouth

Buffalo

JBR 29 0.238 0.250 0.043 1,739 (1,398– 2,299)

MFR 30 0.232 0.249 0.060 1,619 (1,303– 2,134)

CL 30 0.232 0.250 0.070 Infinite

LAR 29 0.228 0.249 0.078 Infinite

Mean 0.232 0.249 0.063

502

KRATINA et al.

Water chemistry

Element : Ca ratios in water (particularly Sr:Ca) were

spatially variable (and consistent with watershed

geology; Newton et al.1987; Wells et al.2003) but tempo-

rally consistent, as needed for hard- part microchemistry

studies (Campana1999; Elsdon et al.2008; Walther and

Limburg2012; Pracheil et al.2014). In addition, strontium

TABLE  Analysis of molecular variance for the SNP genotypes of Paddlefish and Smallmouth Buffalo. An asterisk indicates p < 0.001.

Species Source of variation df Sum of squares % Variation

Paddlefish Among populations 4 1,669.19 0.02

Among individuals within

populations

150 61,687.62 7.22*

Within individuals 155 55,329 92.76*

Total 309 118,873.81

Smallmouth Buffalo Among populations 3 1,489.05 0.04

Among individuals within

populations

114 55,403.58 6.28*

Within individuals 118 50,567.00 93.68*

Total 235 107,459.63

TABLE  Pairwise FST values between river sections for Paddlefish (below the diagonal) and Smallmouth Buffalo (above the diagonal).

Bold italics indicates significance after false discovery rate correction set at p < 0.05. See Figure1 for river section definitions.

River section CSA JBR MFR CL LAR TOM

CSA

JBR 0.00322 0.00232 0.00051 0.00149

MFR 0.00013 0.00056 0.00203 0.00206

CL 0.00279 0.00127 0.00064 0.00045

LAR 0.00397 −0.00066 0.00057 0.00109

TOM −0.00017 0.0005 −0.00043 0.00125 −0.00007

FIGURE  Scatterplot output from discriminant analysis of principal components for genetic signatures from SNPs of Paddlefish

individuals (based on alpha score of 49). Colors indicate river sections as in Figure1.

503

LOW USE LOCK- AND- DAM POPULATION IMPACTS

concentration increased downstream in proximity to the

coastal and estuarine environment (Elsdon et al. 2008;

Walther and Limburg 2012; Farmer et al. 2013), with

Sr:Ca ratio ranges consistent with previous freshwa-

ter studies (Kraus and Secor 2004; Farmer et al.2013;

Daugherty et al.2017). While element : Ca ratios in large

river systems, particularly those not connected to down-

stream marine habitats, can vary temporally (e.g., Phelps

et al.2012), our data over 2 years of sampling were tem-

porally consistent (for the spring and summer compari-

sons) as has been found in some previous riverine work

(Morissette and Sirois2021). Given that our target spe-

cies have life spans that exceed this 2- year period, their

accumulated hard- part microchemistry likely includes

periods outside our two water chemistry sample years,

making the temporal stability in the water an important

observation.

Among sites, the Tallapoosa River was unique with

extremely high element : Ca ratios. This uniqueness

derived from the geology of this system (Freeman

et al.2005) due to lower calcium concentrations, lead-

ing to increased trace element : Ca ratios. When ab-

solute element concentrations (other than calcium)

for this tributary were considered, they mimicked the

north- to- south patterns observed for the rest of the

watershed. The elevated element : Ca ratios in the

Tallapoosa River proved to be particularly important

when analyzing fish hard parts collected from the

Tallapoosa River.

Hard- part microchemistry

The Sr:Ca ratios in ambient water were correlated with

those in Paddlefish dentary bone and Smallmouth Buffalo

otolith edge material, which is required for microchem-

istry studies to be effective in reconstructing environ-

mental histories (Campana 1999; Pracheil et al. 2014).

Dentary bone microchemistry proved particularly valu-

able for quantifying riverine Paddlefish population con-

nectivity and natal origin as has been suggested previously

(Gillanders and Kingsford1996; Campana1999; Campana

et al.2000; Thorrold et al.2001; Gillanders 2002). Trace

element concentrations compared across river sections

and DFA classifications supported that some population

groups are mixing, while others remain isolated, and

strontium, barium, and manganese (based on their con-

tribution to the discriminating axes) contributed to deter-

mining Paddlefish population connectivity, movement,

and natal origin. Based on these results, fish in Millers

Ferry Reservoir and Jones Bluff Reservoir appear func-

tionally isolated from Paddlefish populations in Claiborne

Lake and the lower Alabama River, with movement

within these systems essentially blocked by Millers Ferry

and Robert F. Henry dams (Thomas2020).

We expected to see stronger differences in Smallmouth

Buffalo otolith microchemistry across river sections given

their reduced migratory nature. As with Paddlefish, stron-

tium best described population connectivity or separation

patterns for Smallmouth Buffalo, but patterns were not

FIGURE  Scatterplot output from discriminant analysis of principal components for genetic signatures from SNPs of Smallmouth

Buffalo individuals (based on alpha score of 30). Colors indicate river sections as in Figure1.

504

KRATINA et al.

nearly as strong or distinct as in Paddlefish. The Sr:Ca

concentration in otoliths increased with distance down-

stream, but only samples from river sections furthest apart

differed significantly. The DFA classification accuracy

for Smallmouth Buffalo was much lower versus that for

Paddlefish.

The crested spillway that is periodically inundated

during high- flow events at Claiborne Lock- and- Dam pro-

vides an opportunity for fish populations to mix across

this barrier (as has been seen in this and other low-

head dam systems; Nichols and Louder1970; Smith and

Hightower2012; Simcox et al.2015). Fish may also pass

via lock chambers (Zigler et al.2004; Tripp et al.2014),

although this behavior has been limited on the Alabama

River (Simcox et al. 2015). Significant differences ob-

served in mean Sr:Ca over multiple parts of dentary bone

ablation transects, reflecting different times during a

Paddlefish's life, suggest that the Alabama River sections

are relatively isolated. However, when examining the DFA

classifications for these lower sections, the results suggest

the potential for some limited mixing as indicated by mis-

classifications among river sections as has been observed

previously (Rooker et al.2008; Geffen et al.2011).

Relative to natal origins (as in Thorrold et al. 1998;

Whitledge et al. 2007; Walther et al. 2008; Gahagan

et al.2012), we found element : Ca ratios in the first 20 μm

to be clearly distinguishable across river sections, simi-

lar to results from the edge and whole transect, suggest-

ing that Paddlefish reproduction is occurring within all

river sections of the Alabama River. Paddlefish spawning

has been observed to occur below dams and in sections

of rivers separated by dams (Ruelle and Hudson 1977;

Pasch et al.1980; Wallus1986), suggesting that the pat-

tern being observed here occurs more broadly geograph-

ically. Interestingly, similar patterns were not seen in

Smallmouth Buffalo otoliths.

Patterns in Sr:Ca across river sections observed in

Smallmouth Buffalo versus Paddlefish may be due to

several factors. First, these species exhibit different hab-

itat use patterns. Paddlefish are pelagic (Clark- Kolaks

et al. 2009) and occupy a variety of habitats, including

backwaters, oxbows, and main- channel areas (Boschung

and Mayden2003). In contrast, Smallmouth Buffalo are

more benthic (Gido2002), although they also occupy a di-

versity of habitats, including main channels, backwaters,

and other low- water- velocity habitats (Kallemeyn and

Novotny1977; Edwards and Twomey1982). Backwaters

and side- channel areas may have different trace element

signatures compared with that of the main channel, which

would not have been detected through our main- channel

and tributary water sampling. Additionally, different

hard- part structures were used for Smallmouth Buffalo

versus Paddlefish, and different species may incorporate

elements into their otoliths differently (Gillanders and

Kingsford2003; Rooker et al.2004; Pracheil et al.2014).

Therefore, our results for Paddlefish versus Smallmouth

Buffalo may have been influenced by a combination

of being different species and use of different hard- part

structures (dentary bones versus otoliths).

Relative to the upper tributaries, Paddlefish in the

Tallapoosa and Coosa rivers (tributaries that form the

Alabama River) are believed to move between Jones Bluff

Reservoir and these tributaries (Lein and DeVries1998;

DeVries et al. 2009, S. J. Rider, Alabama Division of

Wildlife and Freshwater Fisheries, personal observation).

Water trace element : Ca ratios were significantly higher

in the Tallapoosa River, allowing us to test this hypothesis.

Significant differences in fish collected from TAL versus

JBR were identified for Sr:Ca in the first 20 μm portions

of the ablation transect. Higher Sr:Ca concentrations were

observed for the whole transect and edge in TAL fish as

well, but these were not significantly different from fish

collected from JBR. This may indicate that Paddlefish col-

lected in the Tallapoosa River move freely between Jones

Bluff Reservoir and the Tallapoosa River, most likely for

reproduction purposes. Additionally, significant differ-

ences observed in the natal (first 20 μm) region of abla-

tion transects indicates spawning site fidelity as observed

in other studies of Paddlefish (Lein and DeVries 1998;

Stancill et al. 2002; Firehammer and Scarnecchia2006;

Jennings and Zigler2009). In other words, Paddlefish col-

lected in the Tallapoosa River appear to return to and re-

produce in this system, whereas fish collected from Jones

Bluff Reservoir do not move upstream into the Tallapoosa

River to spawn (similar to individual variation in behavior

seen in salmonids; Birnie- Gauvin et al.2021).

Genetics

Genotyping- by- sequencing techniques proved particu-

larly valuable here, identifying thousands of SNPs for each

species for population genetic analyses. Acipenseriform

fishes share a tetraploid ancestor followed by genomic and

chromosomal reorganization, as do Catostomidae species

(Uyeno and Smith1972; Schwemm et al.2014). Therefore,

it was critical to identify diploid markers rather than poly-

poid markers because characteristics such as ambiguous

homology and dosage uncertainty typically prevent the

use of polyploid markers for population genetic analyses.

When identifying biallelic SNPs via HDplot, Paddlefish

showed a similar distribution of z- score (D) and pro-

portion of heterozygotes (H) as did mountain barberry

Berberis alpine (McKinney et al.2017), a shrub also known

to have a large proportion of duplicate loci (35%), which

was confirmed by the same paralog identification method

505

LOW USE LOCK- AND- DAM POPULATION IMPACTS

(Mastretta- Yanes et al. 2014; McKinney et al. 2017).

Additionally, previous efforts in Paddlefish microsatellite

development have shown varied proportions of single-

ton loci in marker discovery, ranging from 52% (13 of 25;

Schwemm et al.2014) to 75% (6 of 8; Heist et al. 2002),

which overlapped the singleton loci rate (68.19%) in this

study. Smallmouth Buffalo loci were primarily clustered

into one cloud, corresponding to singleton loci distribu-

tion, with only a small percentage outside its central range.

This suggests that duplicate loci were present at low fre-

quency in the Smallmouth Buffalo genome, potentially

due to extensive rediploidization after whole- genome

duplication (Uyeno and Smith 1972). As a result of the

effectiveness of this approach in selecting representative

singleton SNPs, our optimized parameters for multiplex

panel design and validation can benefit future studies.

This study also represents the first population genet-

ics assessment of Paddlefish using SNP markers. Other

Paddlefish population studies have been conducted using

allozymes, mitochondrial DNA, or microsatellite mark-

ers (Carlson et al. 1982; Epifanio et al. 1996; Heist and

Mustapha2008; Sloss et al.2009; Zheng et al. 2014). For

example, a previous genetic survey using seven microsat-

ellite loci observed higher genetic heterozygosity values in

Paddlefish populations from Mississippi (0.68) and Poland

stocks (0.59– 0.60, imported from USA) (Kaczmarczyk

et al.2012) compared with this study with SNPs. Given

that microsatellites typically have more alleles and higher

heterozygosity than SNPs (Tokarska et al.2009) and that

evidence from other animal species have shown only weak

or no congruence between the estimates of heterozygosity

derived from the two marker types (Vignal et al.2002),

we did not directly compare heterozygosity. Additionally,

our Paddlefish inbreeding levels were generally consis-

tent with a previous study in four geographically sepa-

rate Paddlefish populations, with Fis ranging from 0.013

(Tallapoosa River) to 0.099 (Yellowstone– Missouri River)

(Zheng et al. 2014). Overall, heterozygosity values were

neither very low nor high, suggesting that fish in each

river section have an intermediate level of genetic variabil-

ity. Observed heterozygosities were slightly lower than ex-

pected, which may be attributed to inbreeding. However,

when looking at inbreeding coefficients (Fis) for each river

section, they were low, indicating that inbreeding does not

currently appear to be an issue for these species in any

river section.

Also, estimates of effective population sizes of these

species for each river section, particularly for Paddlefish,

can provide managers with critical information for best

management of these species. The metric Ne is a com-

monly used population estimation measurement as it

only requires a single population for calculation (see

equation in Cervantes et al.2011). Some factors such as

the variance in reproductive success of individuals (Heist

and Mustapha 2008), sample size (normally <1% of the

census population), and overlapping generations may

impact the accuracy of Ne and generally lead to underes-

timate of population size (Marandel et al. 2019). For ex-

ample, a simulation study on Thornback Ray Raja clavata

found NeEstimator underestimated Ne by 31% (Marandel

et al.2019), while Waples et al.(2014) found a 10% bias

for Atlantic Cod Gadus morhua. Therefore, particular at-

tention should be paid to the interpretation of Ne results.

Infinite values of Ne for Smallmouth Buffalo were poten-

tially due to the limited sample size leading to overestima-

tion (Marandel et al.2019). Small sample sizes may also

affect the accuracy of our Paddlefish Ne estimate, and they

also may be biased due to overlapping generations and/or

Paddlefish life history traits (i.e., iteroparity, delayed ma-

turity, and high fecundity; Epifanio et al.1996; Heist and

Mustapha2008).

Population structure and differentiation analyses

all converged on a similar conclusion that there was no

strong evidence for population divergence among river

sections for either species. When initially designing this

study, it was believed that if there would be genetic differ-

entiation among river sections, it would be observed be-

tween those that were separated by the greatest distance,

and that it would be more prevalent in a species with less

migratory capabilities. Almost no genetic differentiation

was observed between any river sections for either species.

The only differentiation observed did occur for the less

migratory of the species, Smallmouth Buffalo, although

this differentiation was found to be in adjacent river sec-

tions, rather than those separated by the greatest distance.

Genetic variation among Paddlefish and Smallmouth

Buffalo populations in different river sections was not

detected using AMOVA, and most genetic variation

was attributed to within individuals. The FST statistics

in general were very low for both species in this study,

suggesting that there is sufficient gene flow to maintain

similar allele frequencies and to avoid harmful effects of

local inbreeding (Lowe and Allendorf2010). Only one

pairwise comparison was significant for Smallmouth

Buffalo (between MFR and JBR), providing evidence

that Smallmouth Buffalo may be experiencing some

genetic differentiation between these regions; as such,

these dams may be impacting Smallmouth Buffalo pop-

ulation genetics in this system slightly more than that

of Paddlefish. Population structure analysis confirmed

similar results to differentiation, in that one genetic

cluster for each species was identified from the multi-

ple river sections. Finally, DAPC analysis also showed

a general pattern of low genetic differentiation, similar

to that observed in the other analyses showing no clear

separation or clusters among river sections for either

506

KRATINA et al.

species. A lack of genetic structure differentiation be-

tween isolated populations of fish has also been docu-

mented for Red Lionfish Pterois volitans collected in the

northwestern Atlantic Ocean and the Gulf of Mexico

(Pérez- Portela et al. 2018). Additionally, Raeymaekers

et al.(2009) suggested that even if genetics did not reveal

differentiation, it does not mean that processes leading

to loss of diversity are not occurring as populations may

not yet have reached equilibrium since being separated

by barriers.

While it is clear that dams can affect genetic diversity

in fish populations (Yamamoto et al.2004; Heggenes and

Røed2006), it has been suggested that for most fish taxa,

50– 100 years is sufficient time for barrier- induced diver-

gence to occur. For example, Brown Trout Salmo trutta

has a generation time of 3.5 years, and previous microsat-

ellite analyses revealed substantial population divergence

when comparing contemporary Brown Trout samples

after 94 years of isolation due to dam construction (~27

generations; Heggenes and Røed 2006). The authors at-

tribute the observed population differentiation to genetic

drift because of low population densities among examined

Brown Trout populations. The large values of Ne in both

Paddlefish and Smallmouth Buffalo suggest that these

populations are less prone to genetic drift, and therefore

it is much easier to maintain genetic homogeneity across

years of isolation. For long- lived species, such as stur-

geons (family Acipenseridae) and Paddlefish, the current

genetic structure is more likely to reflect historical pro-

cesses and natural impediments to gene flow, especially

when sampled individuals are adults (Lloyd et al.2013;

McDougall et al. 2017). Population differentiation in

lock- and- dam systems may be caused by local adaptation

through natural selection, genetic drift, gene flow, anthro-

pogenic interference, or a combination of these factors.

Extensive simulation and population genetic modeling is

needed to place time points on genetic divergence. Finally,

it is believed that the minimum effective population size

necessary for viable isolated populations may be approx-

imately 100 individuals to avoid inbreeding depression

and approximately 1,000 to maintain adaptive potential

into the long term (Frankham et al.2014). As such, ef-

fective population sizes in these four river sections of the

Alabama River should be monitored going forward.

Management implications

Our findings support that Paddlefish reproduction is oc-

curring in each river section of the Alabama River, as

well as in at least one of its upper tributaries. In addition,

Paddlefish populations appear to be functionally isolated

in JBR and MFR but less so between CL and LAR, where

the crested spillway represents a partial barrier to mixing.

Isolation of Paddlefish among river sections does not ap-

pear to have occurred over a long enough time to allow

for genetic differences to develop in this long- lived spe-

cies, or perhaps sufficient downstream drift of juveniles

occurs to reduce this potential, which warrants future

study. However, there remains a potential for genetic di-

vergence to occur over a longer time frame, the implica-

tions of which are unclear. Similar patterns were seen for

Smallmouth Buffalo, although variable results reduced

our ability to detect significant differences. In addition,

similar isolation and genetic divergence could be occur-

ring in shorter- lived species, although on a shorter time

scale. When considering the broader aquatic community,

interruption of movement also has implications for the

movement of mussel glochidia by fish hosts, potentially

leading to broader impacts on the aquatic community.

Similarly, altered habitats can become less suitable for

the native fish community, often resulting in their decline

and a reduction in the critical ecosystem functions they

provide. Finally, some species, including Paddlefish, can

be important economically, providing further rationale

for the continued desire to maintain robust sustainable

populations. Given the breadth of ecological, genetic, and

economic concerns, longer- term assessment of the genetic

consequences of the Alabama River dams should be quan-

tified (for both shorter- lived and longer- lived species), and

the facilitation of fish movement past these dams, particu-

larly at Millers Ferry Lock and Dam and Robert F. Henry

Lock and Dam, should be explored to strategically provide

increases in upstream passage ability sequentially over

time.

ACKNOWLEDGMENTS

Thanks to the many technicians, graduate students,

and agency staff (Alabama Department of Conservation

and Natural Resources) who assisted with field and lab

work and statistical analysis, including Henry Hershey,

Byron Daniel Thomas, Dustin Mckee, Ben Staton,

Tammy DeVries, Sarah Johnson, Megan Roberts,

Tyler Coleman, Lindsay Horne, Mae Aida, Caroline

Cox, Collin Chittam, Tom Hess, Davis Walley, Keith

Henderson, Kyle Bolton, Travis Powell, and Greg Miles.

In addition, we thank David Smith and Christa Woodley

(U.S. Army Corps of Engineers, Engineer Research and

Development Center) for their collaboration and in-

valuable advice, the staff at the University of Minnesota

Genomics Center for performing genetic sequencing,

Reid Nelson for his expertise and willingness to assist

throughout the hard- part microchemistry process, and

Laura Linn (Dauphin Island Sea Lab) for access to the

ICPMS lab equipment. We also thank three anonymous

reviewers for helpful comments provided on a previous

507

LOW USE LOCK- AND- DAM POPULATION IMPACTS

draft of this manuscript. Support for this work was pro-

vided in part by the U.S. Army Corps of Engineers, the

Alabama Experiment Station, and the Hatch Program of

the U.S. Department of Agriculture's National Institute

of Food and Agriculture.

CONFLICT OF INTEREST STATEMENT

There is no conflict of interest declared in this document.

DATA AVAILABILITY STATEMENT

Data are available upon request from the corresponding

author.

ETHICS STATEMENT

Handling of fish was conducted in accordance with

American Fisheries Society's Guidelines for the Use of

Fishes in Research (Use of Fishes in Research Committee

2014), and under Auburn University’s Institutional

Animal Care and Use Committee Protocol 2016- 2939.

ORCID

Garret J. Kratina https://orcid.

org/0000-0003-2521-4796

Dennis R. DeVries https://orcid.

org/0000-0002-3831-0755

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SUPPORTING INFORMATION

Additional supplemental information can be found online

in the Supporting Information section at the end of this

article.

Pre‐impoundment fish migrations in the Mobile Basin, Alabama

Article

Feb 2024

Assessing the status of several migratory fishes in the Mobile River Basin, Alabama, has been complicated due to a general lack of historical data on their life history, habitat requirements, and distributions. Whether distributions were restricted by natural or man‐made barriers to migration is difficult to answer because few scientific collections were made before dams were built, and the earliest dams were built at the largest biogeographic barrier in the basin: the geological fall line. Therefore, we used what information was available, including anecdotal information, primarily records from archived newspapers and government reports, to describe the ranges of six migratory species prior to the construction of dams in the Mobile Basin. We describe the complicated history of Alabama Shad Alosa alabamae and show that range declines may have been masked by the stocking of American Shad Alosa sapidissima in the late 19th century. We show that Gulf Sturgeon Acipenser oxyrinchus desotoi probably migrated well above the fall line in the Coosa River, and may have been sympatric with Lake Sturgeon Acipenser fulvescens . We found no records of Alabama Sturgeon Scaphirhynchus suttkusi above the fall line. American Eel Anguilla rostrata migrated above the fall line in every Mobile Basin river before dams were built. Finally, Paddlefish Polyodon spathula may have once occurred above the fall line in at least two rivers, but they persist today in impounded reaches in the coastal plain, unlike some other species. We hope that future work will continue to consider archival sources of information to re‐trace the histories of imperilled species.

Flowing down the river: Influence of hydrology on scale and accuracy of elemental composition classification in a large fluvial ecosystem

Article

Full-text available

Nov 2020

Trace metals found in the calcified structures of fish (i.e. otolith, scales and vertebrae) serve as proxies for the ambient water composition at the time of mineralization, and these trace metals are increasingly used as a tool for assessing population structure and the migratory patterns of fish. However, the appropriate scale (e.g. resolution) for such applications can be uncertain because of a poor understanding of the spatiotemporal variations of metal-to-calcium ratios (Me:Ca) in the studied watersheds. This study aims to assess Me:Ca spatiotemporal variability within the St. Lawrence River and nine major tributaries and evaluate the ability of random-forest models to correctly identify rivers on the basis of their elemental composition. We tested the influence of daily discharge on four measured ratios (Sr:Ca, Ba:Ca, Mg:Ca and Mn:Ca) to document local and regional trace element sources and dynamics. The four element ratios displayed a low spatiotemporal variation, reflecting a marked stability over time. We observed that most element- and tributary-specific concentration–discharge relationships were either not significant or showed a weak influence, thereby confirming a stable point source dynamic. The classification performance based on a four-element model (Sr:Ca, Ba:Ca, Mg:Ca and Mn:Ca) produced a classification accuracy of 92.5%, which correspond to a small decrease of accuracy compared to the full model (25 elements, 96.6% of correct classification). A classification based on two elements (Sr:Ca and Ba:Ca) produced a lower classification accuracy (72.6%). Classification errors related mainly to tributaries in close proximity, a problem tempered by grouping these geochemically similar watersheds. Our results show that surveys of the elemental fingerprint of regional tributaries within a given region can provide critical information to determine the appropriate scale (tributary or watershed) for trace metal analysis of the hard-calcified parts of fish.

Validation of species specific otolith chemistry and salinity relationships

Article

Full-text available

May 2019
ENVIRON BIOL FISH

Strontium (Sr), barium (Ba), and their ratios to calcium (Ca) may be used as indirect proxies of salinity in otoliths, given that generally Sr:Ca is positively and Ba:Ca is negatively related to salinity. However, these relationships are non-linear, dependent on water chemistry, and vary among species. To determine if salinity reconstructions in northern Gulf of Mexico estuaries (nGOM) are possible for Red Drum (Sciaenops ocellatus) and Gulf Killifish (Fundulus grandis), and if ratios were similar between species, the relationships of Sr:Ca and Ba:Ca with salinity in water and otoliths were examined. Fishes were held at low (<1 psu), mid (10 psu), and high (30 psu) salinities established and maintained with water collected from local estuaries. Sr:Ca differed among all treatments for water and Gulf Killifish otoliths, increasing with salinity increases. Sr:Ca was lowest at the <1 psu treatment in Red Drum otoliths, remaining treatments did not differ, and was lower than Gulf Killifish at all treatments. Red Drum otolith and water Ba:Ca differed among all treatments, decreasing with increasing salinity, while Gulf Killifish otolith Ba:Ca was higher at the <1 psu than other treatments. Partition coefficients also differed among species and salinities. These results demonstrate that it should be possible to reconstruct salinities of Red Drum using otolith chemistry in nGOM estuaries. However, Gulf Killifish is not a good proxy species and species-specific validation, across a wide range of salinities, in the estuary of interest, should be conducted before salinity exposure is inferred from otolith elemental ratios.

Genetic homogeneity of the invasive lionfish across the Northwestern Atlantic and the Gulf of Mexico based on Single Nucleotide Polymorphisms OPEN

Article

Full-text available

Mar 2018

Despite the devastating impact of the lionfish (Pterois volitans) invasion on NW Atlantic ecosystems, little genetic information about the invasion process is available. We applied Genotyping by Sequencing techniques to identify 1,220 single nucleotide polymorphic sites (SNPs) from 162 lionfish samples collected between 2013 and 2015 from two areas chronologically identified as the first and last invaded areas in US waters: the east coast of Florida and the Gulf of Mexico. We used population genomic analyses, including phylogenetic reconstruction, Bayesian clustering, genetic distances, Discriminant Analyses of Principal Components, and coalescence simulations for detection of outlier SNPs, to understand genetic trends relevant to the lionfish's long-term persistence. We found no significant differences in genetic structure or diversity between the two areas (F ST p-values > 0.01, and t-test p-values > 0.05). In fact, our genomic analyses showed genetic homogeneity, with enough gene flow between the east coast of Florida and Gulf of Mexico to erase previous signals of genetic divergence detected between these areas, secondary spreading, and bottlenecks in the Gulf of Mexico. These findings suggest rapid genetic changes over space and time during the invasion, resulting in one panmictic population with no signs of divergence between areas due to local adaptation.

Evaluating Fish Passage and Tailrace Space Use at a Low‐Use Low‐Head Lock and Dam

Article

Aug 2021

Impacts of low‐head run‐of‐the‐river dams on migratory fish movements depend on the structure of the dam, river hydrology, and the ability of fish to navigate the tailrace environment. Here, we present results from a three‐year movement study where telemetered Paddlefish Polyodon spathula and Smallmouth Buffalo Ictiobus bubalus were tracked as they approached and sometimes migrated past Claiborne Lock and Dam (CLD), a low‐head, low‐use lock‐and‐dam in the Alabama River. A spillway portion of the dam is periodically inundated during early spring, dependent on precipitation and releases from the next upstream dam. Our goals were to 1) quantify dam‐passage rates for both species, 2) assess the importance of factors affecting passage success, and 3) quantify space use patterns in the tailrace. Both species exhibited annual upstream migrations during the study period. Correlation of daily average river position versus the CLD hydrograph showed that movements by both species appeared to be related to flow variation. Passage efficiency (range: 10.7% ‐ 30.2%) varied between species and among years with tailrace gage height being the most important factor affecting passage success for both species. Using an acoustic positioning system in the tailrace, we quantified space use by individuals of both species. Time spent in the tailrace (mean: 3.9 continuous days; range: 0 – 94 continuous days) did not differ between individuals that passed versus those that did not pass. Fine scale position estimates showed that space use differed between species, across gage heights, and between individuals that passed versus those that did not pass. Differential space use may be due to species habitat preferences or swimming abilities. Our findings (1) provide information to inform potential design of mitigation structures, and (2) identify the need for additional work required to more fully understand the mechanisms of passage success versus failure for these, and other native potamodromous species.

Life‐history strategies in salmonids: the role of physiology and its consequences

Article

May 2021

Salmonids are some of the most widely studied species of fish worldwide. They span freshwater rivers and lakes to fjords and oceans; they include short‐ and long‐distance anadromous migrants, as well as partially migratory and non‐migratory populations; and exhibit both semelparous and iteroparous reproduction. Salmonid life‐history strategies represent some of the most diverse on the planet. For this reason, salmonids provide an especially interesting model to study the drivers of these different life‐history pathways. Over the past few decades, numerous studies and reviews have been published, although most have focused on ultimate considerations where expected reproductive success of different developmental or life‐history strategies are compared. Those that considered proximate causes generally focused on genetics or the environment, with less consideration of physiology. Our objective was therefore to review the existing literature on the role of physiology as a proximate driver for life‐history strategies in salmonids. This link is necessary to explore since physiology is at the core of biological processes influencing energy acquisition and allocation. Energy acquisition and allocation processes, in turn, can affect life histories. We find that life‐history strategies are driven by a range of physiological processes, ranging from metabolism and nutritional status to endocrinology. Our review revealed that the role of these physiological processes can vary across species and individuals depending on the life‐history decision(s) to be made. In addition, while findings sometimes vary by species, results appear to be consistent in species with similar life cycles. We conclude that despite much work having been conducted on the topic, the study of physiology and its role in determining life‐history strategies in salmonids remains somewhat unexplored, particularly for char and trout (excluding brown trout) species. Understanding these mechanistic links is necessary if we are to understand adequately how changing environments will impact salmonid populations.

CONNECTIVITY, FRAGMENTATION, AND EXTINCTION RISK IN DENDRITIC METAPOPULATIONS

Article

Dec 2002

William F. Fagan

Estimating effective population size of large marine populations, is it feasible?

Article

Oct 2018

Sustainable exploitation of marine populations is a challenging task relying on information about their current and past abundance. Fisheries related data can be scarce and unreliable making them unsuitable for quantitative modeling. One fishery independent method that has attracted attention in this context consists in estimating the effective population size (Ne), a concept founded in population genetics. We reviewed recent empirical studies on Ne and carried out a simulation study to evaluate the feasibility of estimating Ne in large fish populations with the currently available methods. The detailed review of 26 studies found that published empirical Ne values were very similar despite differences in species and total population sizes (N). Genetic simulations for an age structured fish population were carried out for a range of population and samples sizes and Ne was estimated using the Linkage Disequilibrium method. The results showed that already for medium sized populations (1 million individuals) and common sample sizes (50 individuals), negative estimates were likely to occur which for real applications is commonly interpreted as indicating very large (infinite) Ne. Moreover, on average N_e estimates were negatively biased. The simulations further indicated that around 1% of the total number of individuals might have to be sampled to ensure sufficiently precise estimates of Ne. For large marine populations this implies rather large samples (several thousands to millions of individuals). If however such large samples were to be collected, many more population parameters than only Ne could be estimated.

Resolving allele dosage in duplicated loci using genotyping-by-sequencing data: A path forward for population genetic analysis

Article

Feb 2018
MOL ECOL RESOUR

Whole genome duplications have occurred in the recent ancestors of many plants, fish, and amphibians. Signals of these whole genome duplications still exist in the form of paralogous loci. Recent advances have allowed reliable identification of paralogs in genotyping by sequencing (GBS) data such as that generated from restriction-site associated DNA sequencing (RADSeq); however, excluding paralogs from analyses is still routine due to difficulties in genotyping. This exclusion of paralogs may filter a large fraction of loci, including loci that may be adaptively important or informative for population genetic analyses. We present a maximum-likelihood method for inferring allele dosage in paralogs and assess its accuracy using simulated GBS, empirical RADSeq, and amplicon sequencing data from Chinook salmon. We accurately infer allele dosage for some paralogs from a RADSeq dataset and show how accuracy is dependent upon both read depth and allele frequency. The amplicon sequencing dataset, using RADSeq-derived markers, achieved sufficient depth to infer allele dosage for all paralogs. This study demonstrates that RADSeq locus discovery combined with amplicon sequencing of targeted loci is an effective method for incorporating paralogs into population genetic analyses. This article is protected by copyright. All rights reserved.

Salinity and Temperature Effects on Element Incorporation of Gulf Killifish Fundulus grandis Otoliths

Article

Oct 2017

Many applications of otolith chemistry use the ratios of strontium (Sr) and barium (Ba) to calcium (Ca) as indicators of salinity exposure, because typically, as salinity increases, Sr concentration increases and Ba concentration decreases. However, these relationships are nonlinear, can be confounded by temperature, and investigations of salinity and temperature effects on otolith chemistry produce varied results. To determine the relationships of temperature and salinity on Sr:Ca and Ba:Ca in otoliths, we used free ranging Gulf Killifish (Fundulus grandis) in the northern Gulf of Mexico. This species is ideal because it is euryhaline and exhibits limited movements. Otolith edge Sr:Ca and Ba:Ca ratios were related to the previous 30-day mean salinity and temperature experienced by fish. The best model to describe otolith Sr:Ca was one that included a positive asymptotic relationship for both salinity and temperature. However, the salinity asymptotic maximum was reached at 10 psu and changes in otolith Sr:Ca above 10 psu were indicative of temperature changes. Otolith Ba:Ca exhibited an exponential decreasing relationship with salinity, and an exponential increasing relationship with temperature, and these two models combined best explained otolith Ba:Ca. Above 10 psu, the modeled Ba:Ca ratio continued to decrease demonstrating that this ratio may be indicative of salinity changes beyond this value. Therefore, using both Sr:Ca and Ba:Ca could be beneficial in reconstructing fish environmental histories. Temperature effects on otolith element ratios could confound past salinity reconstructions as well and must be a result of endogenous processes, given that no relationship between temperature and water chemistry existed.

R: A Language and Environment for Statistical Computing

Book

Jan 2015

Core R Team

Using fish hard‐part microchemistry and genetics to quantify population impacts of low‐use lock‐and‐dam structures on the Alabama River

Abstract

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