University of Florida Thesis Or Dissertation

USE OF STRONG HABITAT-ABUNDANCE RELATIONSHIPS TO ASSESS POPULATION STATUS OF CRYPTIC FISHES: AN EXAMPLE USING HARLEQUIN DARTER

KATHRYN M. HARRIGER

A THESIS PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE

UNIVERSITY OF FLORIDA

2018

To the imperiled fishes of the Southeast

ACKNOWLEDGMENTS

I am especially appreciative of the Florida Fish and Wildlife Conservation Commission

(FWC) for providing me with the opportunity to pursue a Master’s degree. My committee members, Dr. Micheal S. Allen, Howard Jelks (U.S. Geological Survey), and Dr. Jeffery Hill, provided support and valuable insights to improve this thesis. I greatly appreciate the time Paul

Schueller (FWC) spent helping me with all aspects of the analysis of this study. This project would not have been completed without the hard work from fellow FWC employees John

Knight, Amanda Mattair, Matt Wegener, and Neil Branson who assisted in the mark-recapture study. I also greatly appreciate the guidance in use of side scan sonar from Adam Kaeser (U.S.

Fish and Wildlife Service) and geoprocessing of side scan sonar images by Cameron Bodine

(FWC). Robert Dorazio (U.S. Geological Survey) provided valuable direction with statistical analyses.

TABLE OF CONTENTS

page

ACKNOWLEDGMENTS ...... 4

LIST OF TABLES ...... 6

LIST OF FIGURES ...... 7

ABSTRACT ...... 8

CHAPTER

1 INTRODUCTION ...... 10

2 METHODS ...... 13

Study Site ...... 13 Mark-Recapture ...... 14 Quantifying In-Stream Wood ...... 15 Analysis ...... 17

3 RESULTS ...... 22

Mark-Recapture ...... 22 Quantifying In-Stream Wood ...... 22 Analysis ...... 23

4 DISCUSSION ...... 28

APPENDIX

A SUPPLEMENTARY TABLES ...... 32

B R CODE FOR THE HIERARCHICAL BAYESIAN MODEL ...... 36

C JAGS CODE FOR THE HIERARCHICAL BAYESIAN MODEL ...... 40

D INPUT FILES FOR THE HIERARCHICAL BAYESIAN MODEL ...... 42

LIST OF REFERENCES ...... 43

BIOGRAPHICAL SKETCH ...... 47

LIST OF TABLES

Figure page

2-1 Wood index used to map wood in sonar images in Big Escambia and Pine Barren creeks ...... 21

3-1 Potential models for the hierarchical Bayesian analysis with AIC scores ...... 26

3-2 Parameter mean, standard deviation and credible intervals for the posterior distribution of the multinomial mixture model ...... 27

A-1 Capture histories for Big Escambia Creek...... 32

A-2 Capture histories for Pine Barren Creek...... 33

A-3 Site abundance estimates for Big Escambia Creek...... 34

A-4 Site abundance estimates for Pine Barren Creek ...... 35

LIST OF FIGURES

Figure page

2-1 Survey reaches in Pine Barren and Big Escambia creeks...... 20

2-2 Three GIS layers used to organize data prior to analysis ...... 21

3-1 Visual depiction of the number of wood pieces and darter abundance at each unsampled site in Big Escambia Creek...... 25

3-2 Visual depiction of the number of wood pieces and darter abundance at each unsampled site in Pine Barren Creek...... 26

3-3 Expected Harlequin Darter abundance as a function of woody debris...... 27

Abstract of Thesis Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Master of Science

USE OF STRONG HABITAT-ABUNDANCE RELATIONSHIPS TO ASSESS POPULATION STATUS OF CRYPTIC FISHES: AN EXAMPLE USING HARLEQUIN DARTER

Kathryn M. Harriger

May 2018

Chair: Micheal S. Allen Major: Fisheries and Aquatic Sciences

Understanding trends in abundance is important to fisheries conservation, but techniques for estimating stream-wide abundance of cryptic fish with strong habitat-abundance relationships are not well established. I developed techniques to address this need using Harlequin Darter

Etheostoma histrio, which is a small, cryptic freshwater fish associated with woody structure in streams where it occurs. Specifically, my objectives were to (1) determine how darter abundance and in-stream wood were related at sampled sites, and (2) to use this relationship to estimate darter abundance at unsampled sites and extrapolate for stream-wide darter abundance estimates and associated uncertainty. I conducted mark-recapture studies using visual surveys to sample

Harlequin Darters from Big Escambia and Pine Barren creeks (Escambia River tributaries in northwest Florida). The amount of in-stream wood in both creeks was quantified and mapped using side scan sonar and geographic information system tools. These darter and wood data were used in a hierarchical Bayesian model (multinomial mixture model) to determine site abundance of Harlequin Darters, the effect of in-stream wood on darter abundance, and to extrapolate darter abundance stream-wide. I found a positive relationship between wood and darter abundance at both creeks, and there were more wood pieces in Pine Barren Creek than Big

Escambia Creek. Therefore, the total site abundance and extrapolated stream-wide abundance of

Harlequin Darters were both greatest in Pine Barren Creek. The extrapolated stream-wide abundance estimates were 7,238 darters (95% credible interval = 5,746–9,220) in Big Escambia

Creek and 8,804 darters (95% credible interval = 7,684–10,116) in Pine Barren Creek. My methods were effective for estimating stream-wide abundance of a small, cryptic fish that uses complex woody habitat, and my findings may assist in the conservation of Harlequin Darters.

CHAPTER 1 INTRODUCTION

Understanding trends in abundance is important for conservation of fishes, especially for imperiled species (a species in decline regardless of their official listing status; Rahel et al.

1999). A common goal of imperiled species management is to determine population status and maintain or improve a species’ status (Rahel et al. 1999; FWC 2013). Since ecological processes vary depending on scale (Lewis et al. 1996; Rabeni and Sowa 1996), choosing the correct scale is important to addressing specific conservation issues. Appropriate sampling and analysis of fish and habitat is important to extrapolate results to extensive reaches of streams.

Estimating abundance of a fish species at a large scale can be logistically difficult and costly. To address these difficulties, many studies follow a general two-step process to first stratify sampling by habitat type within a stream or watershed, estimate abundance of the fish at these sites, and then extrapolate results to the rest of the stream or watershed (similar to the

Basinwide Visual Estimation Technique: Hankin and Reeves 1988; Dolloff et al. 1993).

However, effective and efficient methods for estimating abundance of imperiled fishes, which are often cryptic, elusive, and patchily distributed, may require modifications of this general technique. Toepfer et al. (2000) adjusted methods to account for nonlinear longitudinal distribution of Leopard Darter Percina pantherina, and Poos et al. (2012) used an adaptive sampling strategy that sampled all habitat types, but allowed for the most sampling effort to occur at known high density areas for Redside Dace Clinostomus elongatus, a rare, but locally abundant species.

Techniques for estimating stream-wide abundance of small, cryptic fishes with a strong association to specific habitats are not well established. I developed an improved methodology that fits this situation by mapping in-stream habitat using Humminbird ® side scan sonar and

ESRI geographic information system (GIS), determining habitat-fish abundance relationships, and using those relationships to extrapolate fish abundance stream-wide. These methods are similar to those used by Smit (2014), who successfully estimated abundance of a freshwater mussel (Fat Threeridge Amblema neislerii) for a 700-ha reach of the Apalachicola River. I chose

Harlequin Darter Etheostoma histrio as an example for my methodology because it is a small, cryptic fish that has a strong association with in-stream wood (Hubbs and Pigg 1972; Etnier and

Starnes 1993; Bass et al. 2004; Boschung and Mayden 2004) and is a species of special concern with an unknown population status in Florida (FWC 2013). Specifically, the objectives of this study were to (1) determine how Harlequin Darter abundance and in-stream wood are related at sampled sites, and (2) to use this relationship to estimate Harlequin Darter abundance at unsampled sites and extrapolate abundance stream-wide.

Harlequin Darters occur throughout the Mobile drainage and Gulf Coast streams from the

Neches River, Texas and east to the Escambia River in Florida (Bass 2004; Page and Burr 2011).

It is listed as currently stable throughout its range (Warren et al. 2000), though it is often noted to be uncommon in collections (Page 1983; Etnier and Starnes 1993; Boschung and Mayden 2004).

It is often speculated that infrequent collections may be due to difficulty in sampling a cryptic darter that is associated with woody habitat (Etnier and Starnes 1993; Boschung and Mayden

2004).

Woody habitat is important to Harlequin Darters for cover, food, and probably spawning habitat. Harlequin Darters feed on small aquatic insect larvae (Kuhajda and Warren 1989), which are abundant among instream wood in coastal plain streams (Benke et al. 1985). A laboratory study found that Harlequin Darters attached eggs to moss or algae-covered depressions in rocks in locations nearest to the current produced from the aquaria powerheads,

and the presence of algae or moss on rocks may be more important than current flow in determining a spawning site (Steinberg et al. 2000). In coastal plain streams, wood may provide spawning substrate in the absence of rocky substrate, though this is yet to be confirmed (Bass et al. 2004). Spawning appears to take place in late winter to early spring (Kuhajda and Warren

1989), and it is presumed that Harlequin Darters move into larger rivers with deeper water to spawn since the darter is rarely collected from smaller tributaries in cooler months (Etnier and

Starnes 1993). Females reach sexual maturity by age 1, and the life span is 4+ years (Kuhajda and Warren 1989).

The Harlequin Darter has been listed as either threatened or species of special concern in

Florida since 1977 due to its restricted range (only the Escambia River watershed) and infrequent historical collections in the state (FWC 2013). In 2010, the Florida Fish and Wildlife

Conservation Commission (FWC) reassessed the status of Harlequin Darters and determined that the species should remain under protection until more information about its status in Florida is collected (FWC 2011). In 2013, the FWC released a Species Action Plan for the Harlequin

Darter with the main goal to determine the status of the species in the Escambia watershed (FWC

2013). Results of the present study should be valuable to the conservation of Harlequin Darters range-wide, and should help provide a better understanding of its population status in Florida.

CHAPTER 2 METHODS

Study Site

This study was conducted in the Escambia River watershed, northwest Florida (Figure 2-

1). The Escambia River is classified as a large alluvial river with mostly sand and silt substrate, large woody debris, low water clarity, and it is non-wadeable in most reaches (FWC 2012).

These river conditions were not conducive to quantitatively sampling Harlequin Darters using common fisheries techniques (e.g., boat electrofishing, backpack electrofishing, seining) because turbidity reduces detection of cryptic fish, and deep water with complex woody habitat is difficult to effectively sample with a seine. Therefore, Harlequin Darters were collected from

Escambia River tributaries that are clear and wadeable at baseflow by using small dip nets while conducting snorkel surveys. This technique tends to be more effective than seining for detecting darters in clear, wadeable streams with complex habitat (James 1989; Toepfer et al. 2000; Jordan et al. 2008; Holt et al. 2013). I opted to not use backpack electrofishing in the tributaries in an attempt to limit mortality of Harlequin Darters during sampling.

Big Escambia Creek (BEC) and Pine Barren Creek (PBC) were the two Escambia River tributaries selected for this study. Big Escambia Creek is located in southeast Alabama and northwest Florida. In Florida, it flows approximately 6 km from the Florida-Alabama state line to the Escambia River (Figure 2-1). It is a clear stream at baseflow, substrates are mostly sand with some gravel, and woody habitat is present throughout the creek. Pine Barren Creek flows approximately 35 km from the Alabama-Florida state line to the Escambia River (Figure 2-1). It is a clear stream at baseflow, and the stream has mostly sand and silt substrate with some gravel.

Considerable amounts of woody habitat are present throughout the creek, and cut lumber from a historical shingle mill is present in the lower reach of the creek.

Mark-Recapture

Sampling to estimate site-abundance of Harlequin Darters was conducted in 2014 in BEC and 2015 in PBC. Survey sites for both creeks were randomly selected 25-m stream reaches distributed throughout the survey reaches (Figure 2-1). Sites were restricted to locations that were accessible by boat and where water depth was less than 1.5 m. A small portion of BEC in the lower section of the study area has a braided channel and was not included in the study

(Figure 2-1). Each site was separated from neighboring sites by at least 20 m to ensure movement assumptions were not violated for the abundance estimates. This distance was chosen because my pre-study observations of Harlequin Darters suggested that they are not a highly mobile species during the non-breeding season and have a high site affinity to wood structure on which they are found. Additionally, a large percentage of darters in movement studies are found near (often less than 20 m) their initial capture site, although they are capable of moving longer distances of 100 m or more (Scalet 1973; Skyfield and Grossman 2008; Holt et al. 2013).

Mark-recapture methods and visual surveys using snorkeling gear were used to estimate site-level abundance of Harlequin Darters in both creeks. Each sample site had one marking occasion followed by one recapture occasion 2-4 weeks later. Each site was surveyed by two snorkelers who captured darters using small hand-held nets. During each sampling occasion, each newly captured Harlequin Darter was given a subcutaneous mark using Visible Implant

Elastomer (VIE) paint (Northwest Marine Technology, Inc., Shaw Island, WA). This method of marking has been shown to have high survival and mark retention rates for several darter species

(Roberts and Angermeier 2004, Holt et al. 2013). I conducted a 42-day laboratory study in

September and October 2015 and found VIE mark retention to be 91% and survival to be 81% for Harlequin Darters that did not die immediately due to poor marking technique. Marks were not unique to each individual, but they were unique to the site and sampling occasion by using

different colors (four colors in BEC; six colors in PBC) and marking locations (right or left dorsal in both creeks). All marked fish were returned to their initial capture site when a full recovery was evident.

Quantifying In-Stream Wood

In February and March 2016, a Humminbird 1197c SI side scan sonar system was used to capture images of in-stream wood in BEC and PBC. Scanning was conducted when the creeks were near bank-full river stage. The transducer was bow-mounted to a 14-ft johnboat, and an external Garmin GPSMAP 76CSx GPS unit was connected to the control head (and positioned near the transducer) to collect position information for the images. Ballast (e.g., anchors) was used to balance the boat and reduce vibrations from the boat motor. The control head operating frequency was set to 455 kHz, and the side beam range was set to encompass both river banks.

The boat driver maintained a speed between 6.4 and 8 km/h while keeping the boat positioned mid-channel and moving in the downstream direction. Meanwhile, the passenger took consecutive snapshots (images) on the control head and used an interval timer to ensure a small amount of overlap in the images. These specifications have been shown to improve the quality of side scan images (Kaeser and Litts 2010). Raw sonar images for the creeks were geoprocessed in spring 2016 using ArcGIS (ESRI Redlands, CA), ET Geowizards (copyright:

Ianko Tchoukanski), and Irfanview (copyright: Irfan Skiljan) software to create rectified raster layers (Kaeser and Litts 2011; Kaeser and Litts 2013).

Each rectified sonar image (together covering the entire study area) was ground truthed between August and September 2016 when the creeks were at baseflow conditions. I conducted all habitat work to avoid any bias that could be introduced by additional personnel. The goal of ground truthing was to confirm presence of wood and to determine positional accuracy of objects in the sonar images.

A GIS map layer of all significant pieces of in-stream wood in PBC and BEC (see Figure

2-1 for extent; layer hereafter called Total Wood Layer) was created in ArcMap 10.3 after ground truthing was completed. Digitizing tools in ArcMap were used to place a point on each piece of wood (≥ 1.5 m long and ≥ 0.25 m circumference) that was identifiable in the rectified sonar images (Figure 2-2).

Three steps were taken to improve the accuracy of the Total Wood Layer. First, notes taken during ground truthing and raw sonar images were used to ensure correct placement of wood points and streambank lines in the Total Wood Layer. Second, streambank lines were digitized to represent baseflow water levels in the sonar images. This was an attempt to ensure any wood that was resting on a streambank was not included in the Total Wood Layer. Some of the wood in the creeks was not completely submerged at baseflow, though it was often only a few small branches (not visible in sonar images) or small portions of logs that were exposed.

Since I was not trying to quantify wood area or volume, I felt it was not necessary to account for the unsubmerged wood. Lastly, a wood index was used to give extra weight to larger pieces of wood (> 10 m long: Table 2-1). For example, a log that is 6-m long with a circumference of 1 m received one digitized point, and a 17-m long log with a circumference of 1 m received 3 digitized points. Log jams had a large number of wood pieces of various sizes piled together, and the total number and size of the wood pieces could not always be determined. All log jams had at least 10 pieces of wood and so were given that score in the wood index (Table 2-1).

Three GIS layers were used to organize data prior to analysis: the Total Wood Layer, a layer for site boundaries in BEC and PBC (Sampled Sites Layer), and a layer of consecutive unsampled (no darters sampled) 25-m sites that covered the entire survey reach of each creek

(Unsampled Sites Layer) (Figure 2-2). The number of wood pieces per sampled site in each

creek was determined by overlaying the Total Wood Layer with the Sampled Site Layer. These wood counts were used in the analysis to determine site abundance of the darters and to determine the effect of in-stream wood on site-level darter abundance. The number of wood pieces per unsampled site was determined by overlaying the Total Wood Layer with the

Unsampled Sites Layer. These wood counts were used in the analysis to estimate stream-wide abundance of the darters. The Unsampled Sites Layer was created specifically for use in extrapolating darter abundance stream-wide. Site boundaries of the sampled sites did not lie on perfect 25-m intervals throughout the creeks, so the Unsampled Sites Layer was created to ensure there were consecutive 25-m sites for the extrapolation that covered the entire study reach of each creek (Figure 2-2).

Analysis

I used a hierarchical Bayesian model to determine site abundance of Harlequin Darters, to determine the effect of in-stream wood on darter abundance, and to extrapolate darter abundance stream-wide. Specifically, I used the multinomial mixture model presented in section 8.3.2 of

Royle and Dorazio (2008). I used Akaike Information Criteria (AIC) analysis to determine the most plausible model from a set of five candidate models (Akaike 1973; Burnham and Anderson

2002). The candidate models included various combinations of wood, creek, and their interaction as predictors of abundance; wood, creek, time, and the interaction between time and creek were predictors of capture probability. Model assumptions were: (1) the population is geographically closed (no immigration or emigration) and demographically closed (no mortality or recruitment), (2) marks are not lost or overlooked, and (3) both marked and unmarked fish are equally likely to be captured within each sample (closed capture model assumptions; Williams et al. 2002). Calculating site abundance of darters, and determining the effect of wood on abundance, involved four main groups of equations: likelihood of observations, probability of

observing a capture history, capture probability, and abundance. The likelihood of the observed number of darters with each capture history was used to estimate abundance as follows:

푦 , 푦 , 푦 ~ 푀푢푙푡푖푛표푚푖푎푙(푁 , 휋 , 휋 , 휋 ), 푖1 푖2 푖3 푖 푖1 푖2 푖3 (2-1) 푁 ~ 푃표푖푠푠표푛(휆 ), 푖 푖 (2-2) where 푦푖1 is the number of individuals observed only on the first sampling occasion at site i, 푦푖2 is the number of individuals observed only on the second sampling occasion at site i, and 푦푖3 is the number of individuals observed in both sampling occasions at site i. However, I used the following equations to replace the multinomial distribution for all y’s (and the equation for Ni is no longer needed) above since they are mathematically equivalent (Royle and Dorazio 2008):

푦푖1, ~ 푃표푖푠푠표푛(휆푖 ∗ 휋푖1), (2-3)

푦푖2, ~ 푃표푖푠푠표푛(휆푖 ∗ 휋푖2), (2-4)

푦푖3, ~ 푃표푖푠푠표푛(휆푖 ∗ 휋푖3), (2-5)

The probability of observing a capture history (πi) was modeled with the following equations:

휋푖1 = 푝푖1 ∗ (1 − 푝푖2), (2-6)

휋푖2 = (1 − 푝푖1) ∗ 푝푖2, (2-7)

휋푖3 = 푝푖1 ∗ 푝푖2, (2-8) where pi is capture probability at site i on either the first or second sampling occasion. Capture probability (pit) was estimated for each sampling occasion in each creek as:

푙표푔푖푡(푝푖1) = 훼푡1 + 훼푐푟푒푒푘,푡1 ∗ 푐푟푒푒푘푖, (2-9)

푙표푔푖푡(푝푖2) = 훼푡2 + 훼푐푟푒푒푘,푡2 ∗ 푐푟푒푒푘푖. (2-10)

Four capture probability parameters (α) were estimated from these two equations. Expected darter abundance (λi) for each sampled site was modeled using a Poisson regression that accounted for the effects of wood and creek:

log(휆푖) = 훽0 + 훽1 ∗ 푤표표푑푖 + 훽2 ∗ 푐푟푒푒푘푖 + 훽3 ∗ 푤표표푑푖 ∗ 푐푟푒푒푘푖, (2-11) where woodi is the wood count per site (standardized to a mean of zero and standard deviation of one), and creeki is a binary indicator for creek (i.e., zero for BEC, one for PBC). Four parameters (β) were estimated from the Poisson regression. Darter abundance was extrapolated to unsampled sites (from the Unsampled Sites Layer) in both BEC and PBC using the estimated parameters of the Poisson regression model above and wood counts at the unsampled sites:

log(푝푟푒푑푁푘) = 훽0 + 훽1 ∗ 푝푟푒푑푊표표푑푘 + 훽2 ∗ 푝푟푒푑퐶푟푒푒푘푘 + 훽3 ∗ (2-12) 푝푟푒푑푊표표푑푘 ∗ 푝푟푒푑퐶푟푒푒푘푘, where predWoodk is the wood count per unsampled site k, predCreekk is a binary indicator for creek (i.e., BEC = 0, PBC = 1) at unsampled site k, and predNk is the predicted darter abundance at unsampled site k. Total stream-wide abundance of Harlequin Darters for each creek was calculated as the sum of estimated abundances at all unsampled sites in a creek.

Estimation of this model was completed in program R (R Core Team 2014) and program

JAGS (Plummer 2003) with modified code provided as online supplemental material to Royle and Dorazio (2008; https://www.mbr-pwrc.usgs.gov/pubanalysis/roylebook/ manateeCounts.Bayes.R). All regression parameters (all β and α) had normal prior distributions with a mean of zero and a standard deviation of 100. Markov Chain Monte Carlo (MCMC) sampling began with an adaptive phase of 2,500 iterations, followed by burn-in of 5,000 iterations, and finally 20,000 sampling iterations. These iterations were completed for each of three MCMC chains. After sampling iterations, I confirmed that all parameters had a 푅̂ of <1.1, which indicates model convergence (Gelman and Rubin 1992). By calculating the derived parameters (i.e., predicted abundance at unsampled sites) within the MCMC sampling of

program JAGS, an estimate was calculated for every iteration. Therefore, all uncertainty in the model parameters was incorporated into the posterior distribution of the derived parameters.

A B Figure 2-1. Study survey reaches. A) Pine Barren and Big Escambia creeks are located in the Escambia River watershed in northwest Florida. The braided section of Big Escambia Creek was not included in the study. B) A map of the general location of the survey reaches in relation to the state of Florida.

Figure 2-2. Three GIS layers were used to organize data prior to analysis: the Total Wood Layer, Sampled Sites Layer, and Unsampled Sites Layer. This figure shows a small portion of Pine Barren Creek with the three layers displayed together and the rectified sonar image layer for reference.

Table 2-1. Wood index used to map wood in sonar images in Big Escambia and Pine Barren creeks.

Type Definition Points

1.5 m long to 10 m long Wood 1 > 0.25 m circumference

10 m long and greater Large log 2+ (Each 5-m increment = 1 point) > 0.25 m circumference

Log jam > 10 pieces of wood 10

CHAPTER 3 RESULTS

Mark-Recapture

Twenty-four sites were sampled in BEC from August 28–October 29, 2014. A total of

341 Harlequin Darters were captured in BEC, and 27% of those darters were recaptured (see

Table A-1 for capture histories). Eighteen sites were sampled in PBC from September 16–

October 23, 2015. A total of 655 Harlequin Darters were captured in PBC, and 40% of those darters were recaptured (see Table A-2 for capture histories). Two Harlequin Darters moved out of their initial capture sites (less than 120 m in both cases) during the study in BEC, and no individuals moved out of their site during the study in PBC. These migrating darters were not included in the abundance estimates since they could not be assigned to a single site.

Quantifying In-Stream Wood

Ground truthing of sonar images occurred from August 25–September 16, 2016, during baseflow river stage. Wood length measurements in sonar images for PBC were on average smaller than ground truthed measurements (mean difference = -2.04 m, SD = 1.37), and positional accuracy of wood in the sonar images were displaced by 32.29 m (SD = 17.71) on average. Positional accuracy was low in PBC due to a high amount of canopy cover over the stream. Wood length measurements in sonar images for BEC were slightly larger than ground truthed measurements on average (mean difference = 0.50 m, SD = 2.06), and positional accuracy of wood in the sonar images were displaced by 8.5 m (SD = 6.41) on average. I felt that the wood length accuracy was acceptable for this study. Despite the poor positional accuracy, I was able to use landmarks (e.g., unique wood structures and stream bank features) to ensure wood in sonar images were correctly matched with wood in the creeks. Overall, there was more wood in PBC than BEC (Figure 3-1 and Figure 3-2). A total of 1,147 pieces of wood

were mapped in BEC, and 2,200 pieces were mapped in PBC. The mean number of pieces of wood per unsampled site in PBC (mean = 16.84, SD = 9.08) was greater than the mean number of wood pieces per site in BEC (mean = 5.83, SD = 5.73).

Analysis

There was some uncertainty in determining the most parsimonious model for the analysis in this study. The four best fitting models only differed in how capture probability was modeled, and their AIC scores were similar. There was no evidence available to identify the most parsimonious model. Although it is often standard to choose the model with the smallest number of parameters in this situation, I selected the model that examined the interaction of wood and creek on abundance and the interaction of time and creek on capture probability [λ(wood*creek), p(time*creek)] because AIC supported the inclusion of the creek-time interaction for capture probability (Table 3-1). Since this model’s AIC score (903) is one unit less than the score for the second best model (904), this provides support that addition of the creek-time interaction for capture probability improved the model fit (section 3.5.3 in Burnham and Anderson 2002). If the addition of two parameters in the selected model had not improved the fit of that model, its AIC score would have been four units greater than that of the second best model.

Site abundance estimates of Harlequin Darters ranged from 17 darters (95% credible interval = 11–25) to 49 darters (95% credible interval = 42–58) in BEC and ranged from 34

(95% credible interval = 28-41) to 113 darters (95% credible interval = 97–133) (see all site abundance estimates in Table A-3 and Table A-4). Combined site abundance estimates (before extrapolation) were 846 darters (95% credible interval = 685–1,064) for all 24 sampled sites at

BEC and 1,224 darters (95% credible interval = 1,091–1,386) for all 18 sampled sites at PBC.

Capture probability was not significantly different between the creeks during the first sampling occasion, but was significantly greater in PBC during the second sampling occasion (Table 3-2).

There was a significant positive relationship between woody debris and observed site abundance

(mean and credible intervals were positive for β1 and the credible intervals do not contain zero:

Table 3-2; Figure 3-3). Additionally, there was a significant interaction between in-stream wood and creek, which indicates the relationship between wood and abundance differed between the creeks (credible intervals for β3 do not contain zero: Table 3-2; Figure 3-3). Stream-wide abundance (total abundance of unsampled sites in a creek) for BEC was 7,238 adult darters (95% credible interval = 5,746–9,220) over a 4.90 km survey reach with a mean stream width of approximately 25.04 m. Stream-wide abundance for PBC was 8,804 adult darters (95% credible interval = 7,684–10,116) over a 3.25 km survey reach with a mean stream width of 13.65 m.

Using the stream-wide darter abundance estimates, stream lengths, and mean stream widths, the density of darters in BEC is roughly 0.06 adult darters/m2 and 0.20 adult darters/m2 in PBC.

Figure 3-1. Visual depiction of the number of wood pieces (Wood) and Harlequin Darter abundance (Darters) at each unsampled site in Big Escambia Creek. Wood in the braided section of the creek could not be mapped effectively using side scan sonar, so this reach was not included in the study.

Figure 3-2. Visual depiction of the number of wood pieces (Wood) and Harlequin Darter abundance (Darters) at each unsampled site in Pine Barren Creek.

Table 3-1. Potential models for the hierarchical Bayesian analysis with Akakie Information Criterion (AIC) scores. The best fitting model has the lowest AIC score. Lambda (λ) = abundance, p = capture probability, wood = number of wood pieces, creek = Pine Barren Creek or Big Escambia Creek, time = first or second sampling occasion. Asterisks indicate interaction (relationship is allowed to be different), and addition symbols indicate additive effects (relationships are held the same). K = number of parameters in model. Model K AIC λ(wood*creek), p(time*creek) 8 903 λ(wood*creek), p(time) 6 904 λ(wood*creek), p(time+wood) 7 905 λ(wood+creek), p(time) 5 906 λ(wood), p(time) 4 962

Table 3-2. Parameter mean, standard deviation, and lower and upper 95% credible intervals from the posterior distributions of the multinomial mixture model.

Parameter Mean SD Lower Upper Abundance Parameters β0 - intercept 3.75 0.12 3.52 4.00 β1 - wood 0.42 0.08 0.27 0.56 β2 - creek (Pine Barren) 0.26 0.15 -0.04 0.54 β3 - creek, wood interaction -0.18 0.08 -0.34 -0.01 Capture Probability Values p1 - Big Escambia, time 1 0.20 0.03 0.15 0.26 p1 - Pine Barren, time 1 0.23 0.02 0.19 0.27 p2 - Big Escambia, time 2 0.27 0.03 0.20 0.33 p2 - Pine Barren, time 2 0.40 0.03 0.34 0.46 Capture Probability Parameters αt1 - Big Escambia, time 1 -1.38 0.17 -1.71 -1.06 αcreek, t1 - Pine Barren, time 1 0.17 0.20 -0.22 0.56 αt2 - Big Escambia, time 2 -1.02 0.17 -1.37 -0.69 αcreek, t2 - Pine Barren, time 2 0.61 0.22 0.20 1.03

Figure 3-3. Expected Harlequin Darter abundance as a function of woody debris. Points represent estimated site abundances, while the line represents the expected relationship between abundance and wood based on the mean parameter estimates.

CHAPTER 4 DISCUSSION

A strong wood-darter abundance relationship, and use of side scan sonar technology to detect wood, allowed me to effectively estimate the abundance of Harlequin Darters throughout extensive sections of two streams. These abundance estimates appear to be the first published for this species, so a direct comparison to other abundance estimates was not possible. Density estimates for darters vary widely. The mean density estimate for Leopard Darter Percina pantherina was 0.07 darters/m2 (Toepfer et al. 2000) and the mean density was 18.9 darters/m2 for Watercress Darter Etheostoma nuchale (Duncan et al. 2016). More intermediate densities were reported for Brook Darter Etheostoma burri (2.18 adult darters/m2 and 1.58 adult darters/m2 in two tributaries; Martin et al. 1999) and Orangebelly Darter Etheostoma radiosum

(2.66 adult darters/m2; Scalet 1973). Harlequin Darter densities in this study were closer to the lower end of reported darter densities, where darter density was roughly 0.06 adult darters/m2 in

BEC and 0.20 adult darters/m2 in PBC.

My methods should be applicable for other cryptic fishes with strong habitat-abundance relationships, especially when habitat can be mapped with side scan sonar and differentiated from other habitats in the sonar images (e.g., in-stream wood, gravel in a sand-dominated stream). Estimating stream-wide abundance of madtoms may be one of the best applications for my methods. Many madtoms, such as the Brown Madtom Noturus phaeus and Freckled

Madtom Noturus nocturnus, use woody habtiat as cover during the day (Etnier and Starnes 1993;

Monzyk et al. 1997; Dolloff and Warren 2003). Dolloff and Warren (2003) listed many other

Southeastern United States stream fishes that use wood (logs, snags, debris) as cover (mostly darters, centrarchids, and bullheads), though the level of association with wood, and the size of wood structure used, is not well known for many of these species. My methods will be most

effective for fishes that use wood of larger sizes (≥ 1.5 m long and ≥ 0.25 m circumference) since smaller, twiggy wood was not visible in my sonar images. Estimating abundance of fishes that use destinctive substrate types in river systems may be another application for my methods. Side scan sonar has been used successfully to map multiple substrates; substrate types can be differentiated by examining unique signatures for each habitat in sonar images (Kaeser et al.

2012). Estimating abundance of endangered Boulder Darter Etheostoma wapiti, which inhabits deep, flowing pools with boulders, may be one application (Etnier and Starnes 1993).

Using a Bayesian approach to hierarchical modeling has several advantages over use of traditional methods for estimating and extrapolating darter abundance. Hierarchical Bayesian models tend to provide more precise estimates of abundance compared to traditional analyses.

Dorazio et al. (2005) found that abundance estimates for a rare darter, the Okaloosa Darter

Etheostoma okaloosae, were similar or more precise using a hierarchical Bayesian model compared to a conventional removal model (Zippin 1956). Another advantage is the ability to share data between neighboring sites to estimate abundance when a dataset is lacking (Rivot et al. 2008). Finally, hierarchical Bayesian models have the ability to incorporate covariate (e.g., habitat variable) data directly into the model. This is ideal when using the relationship between a covariate(s) and fish abundance/density to extrapolate fish abundance/density to unsampled sites, as I and others (Wyatt 2003; Rivot et al. 2008) have done to achieve stream-wide abundance/density. By incorporating the relationship and its uncertainty into the model, the uncertainty around the extrapolated abundace can be modeled easily and explicitly. Use of hierarchical Bayesian models tend to be more computationally complex than traditional likelihood based analyses; therefore I provided the code for my analysis in the appendix to serve as an example (Appendix B-D).

Capture probability was similar between BEC and PBC during the first sampling occasions, but was much greater in PBC than BEC during the second sampling occasion. This was most likely due to surveyors’ improved ability to capture darters over time. Capture probability improved with each successive sampling occasion during the study. Differences in water clarity may have also influenced capture probability. Turbidity could have been incorporated into my hierarchical Baysian model as a covariate, but measurements were not recorded at every site during each sampling occasion and so could not be used.

Higher abundances of Harlequin Darters at sites with more wood suggests that removal of wood from streams could be detrimental to Harlequin Darter populations. Monitoring wood removal from streams over time, and maintaining healthy riparian zones to ensure a constant supply of in-stream wood, will be important to Harlequin Darter conservation in Florida and througout its range. Bass et al. (2004) also considered preservation of in-stream wood to be critical to conservation of Harlequin Darters in Florida. The Yazoo Darter Etheostoma raneyi has been given a similar prescription for its conservation, as stable and complex in-stream wood is considered important habitat for this species (Sterling and Warren 2017). Protecting in-stream wood will not only be important to Harlequin Darters, but also to many other stream fishes that are known to use wood for feeding, cover, and spawning habitat (Dolloff and Warren, 2003).

Further research examining the specifics of the wood-Harlequin Darter abundance relationship could lead to better identification of specific threats and appropriate conservation actions for this species. I suggest research on the importance of wood size, volume, embededness, and complexity (degree and extent of overlapping wood) to Harlequin Darters. I feel that large (> 10 m long, > 1 m circumference), embeded wood provides stable habitat that is not likely to be displaced by normal high flood pulses. Extensive overlapping wood may provide

darters with safe highways to additional resources and habitat, whereas isolated logs may act like islands to Harlequin Darters if leaving the safety of wood increases chances of predation.

I will continue honing the analysis in this study to achieve the most precise and accurate abundance estimates for Harlequin Darters, and the final improvments will be available in a future manuscript. Improvements will involve a more thorough model selection using maximum likelhood (possibly via the program UNMARKED) and use of a negative binomial distribution in place of the Poisson distribution in the current model. The negative binomial distribution is often a better fit than the Poisson distribution because it is able to account for overdispersed abundance distributions, which are often observed in animal populations that tend to aggregate

(Dorazio et al. 2005). Finally, I will use goodness of fit tests to determine whether the current or future model is most appropriate for this study.

APPENDIX A SUPPLEMENTARY TABLES

Table A-1. Frequency (Freq) of each capture history (CapHist) for Harlequin Darters in Big Escambia Creek (BEC), where y1=darters captured only during the first sampling occasion, y2=darters captured only during the second occasion, y12=darters captured during the first and second sampling occasions. Creek Site CapHist Freq Creek Site CapHist Freq BEC 2 y1 10 BEC 14 y1 5 BEC 2 y2 16 BEC 14 y2 3 BEC 2 y12 3 BEC 14 y12 0 BEC 3 y1 1 BEC 15 y1 6 BEC 3 y2 4 BEC 15 y2 7 BEC 3 y12 1 BEC 15 y12 3 BEC 4 y1 4 BEC 16 y1 2 BEC 4 y2 5 BEC 16 y2 4 BEC 4 y12 2 BEC 16 y12 0 BEC 5 y1 4 BEC 17 y1 0 BEC 5 y2 3 BEC 17 y2 4 BEC 5 y12 4 BEC 17 y12 1 BEC 6 y1 24 BEC 18 y1 8 BEC 6 y2 9 BEC 18 y2 15 BEC 6 y12 4 BEC 18 y12 2 BEC 7 y1 0 BEC 19 y1 8 BEC 7 y2 0 BEC 19 y2 10 BEC 7 y12 1 BEC 19 y12 3 BEC 8 y1 13 BEC 20 y1 1 BEC 8 y2 20 BEC 20 y2 1 BEC 8 y12 2 BEC 20 y12 1 BEC 9 y1 3 BEC 21 y1 9 BEC 9 y2 2 BEC 21 y2 18 BEC 9 y12 1 BEC 21 y12 2 BEC 10 y1 2 BEC 22 y1 0 BEC 10 y2 13 BEC 22 y2 0 BEC 10 y12 3 BEC 22 y12 0 BEC 11 y1 3 BEC 23 y1 0 BEC 11 y2 17 BEC 23 y2 2 BEC 11 y12 3 BEC 23 y12 2 BEC 12 y1 0 BEC 24 y1 7 BEC 12 y2 0 BEC 24 y2 7 BEC 12 y12 0 BEC 24 y12 4 BEC 13 y1 7 BEC 25 y1 6 BEC 13 y2 8 BEC 25 y2 7 BEC 13 y12 1 BEC 25 y12 1

Table A-2. Frequency (Freq) of each capture history (CapHist) for Harlequin Darters in Pine Barren Creek (PBC), where y1=darters captured only during the first sampling occasion, y2=darters captured only during the second occasion, y12=darters captured during the first and second sampling occasions (recaptures). Creek Site CapHist Freq Creek Site CapHist Freq PBC 1 y1 2 PBC 10 y1 14 PBC 1 y2 6 PBC 10 y2 44 PBC 1 y12 1 PBC 10 y12 13 PBC 2 y1 15 PBC 11 y1 8 PBC 2 y2 17 PBC 11 y2 9 PBC 2 y12 3 PBC 11 y12 2 PBC 3 y1 12 PBC 12 y1 12 PBC 3 y2 15 PBC 12 y2 18 PBC 3 y12 8 PBC 12 y12 10 PBC 4 y1 3 PBC 13 y1 19 PBC 4 y2 20 PBC 13 y2 19 PBC 4 y12 4 PBC 13 y12 18 PBC 5 y1 3 PBC 14 y1 11 PBC 5 y2 22 PBC 14 y2 20 PBC 5 y12 2 PBC 14 y12 9 PBC 6 y1 18 PBC 15 y1 1 PBC 6 y2 28 PBC 15 y2 6 PBC 6 y12 10 PBC 15 y12 3 PBC 7 y1 5 PBC 16 y1 8 PBC 7 y2 32 PBC 16 y2 29 PBC 7 y12 4 PBC 16 y12 7 PBC 8 y1 8 PBC 17 y1 3 PBC 8 y2 37 PBC 17 y2 29 PBC 8 y12 6 PBC 17 y12 6 PBC 9 y1 9 PBC 18 y1 18 PBC 9 y2 12 PBC 18 y2 11 PBC 9 y12 2 PBC 18 y12 4

Table A-3. Site abundances (mean) for Big Escambia Creek (BEC) with standard deviation (SD), lower and upper 95% credible intervals, and the number of wood pieces (Wood) per site. Creek Site Mean SD Lower Upper Wood BEC 2 49.36 3.95 42.79 58.18 6 BEC 3 27.81 4.22 20.78 37.25 8 BEC 4 27.57 3.32 22.1 35 0 BEC 5 30.01 3.71 23.85 38.3 4 BEC 6 58.81 4.22 51.78 68.25 8 BEC 7 17.57 3.32 12.1 25 0 BEC 8 52.75 3.5 46.95 60.57 2 BEC 9 22.57 3.32 17.1 30 0 BEC 10 35.75 3.5 29.95 43.57 2 BEC 11 52.8 6.02 42.78 66.26 17 BEC 12 19.01 3.71 12.85 27.3 4 BEC 13 35.67 3.83 29.31 44.22 5 BEC 14 24.57 3.32 19.1 32 0 BEC 15 34.36 3.6 28.39 42.42 3 BEC 16 27.07 4.08 20.28 36.19 7 BEC 17 21.57 3.32 16.1 29 0 BEC 18 44.01 3.71 37.85 52.3 4 BEC 19 40.01 3.71 33.85 48.3 4 BEC 20 23.36 3.95 16.79 32.18 6 BEC 21 77.72 11.83 58.52 104.66 31 BEC 22 17.75 3.5 11.95 25.57 2 BEC 23 23.67 3.83 17.31 32.22 5 BEC 24 38.36 3.95 31.79 47.18 6 BEC 25 38.19 4.71 30.34 48.7 11

Table A-4. Site abundances (mean) for Pine Barren Creek (PBC) with standard deviation (SD), lower and upper 95% credible intervals, and the number of wood pieces (Wood) per site. Creek Site Mean SD Lower Upper Wood PBC 1 32.5 3.36 26.63 39.77 8 PBC 2 66.45 4.2 59.08 75.5 23 PBC 3 64.09 3.91 57.21 72.53 19 PBC 4 51.43 3.44 45.4 58.86 10 PBC 5 57.25 4.05 50.13 65.98 21 PBC 6 113.78 9.24 97.77 133.88 54 PBC 7 85.72 6.35 74.63 99.43 41 PBC 8 77.92 3.68 71.45 85.85 15 PBC 9 46.05 3.32 40.25 53.24 7 PBC 10 105.67 4.63 97.54 115.65 28 PBC 11 53 4.54 45.03 62.78 27 PBC 12 69.66 3.98 62.67 78.25 20 PBC 13 92.76 4.95 84.09 103.43 31 PBC 14 66.92 3.68 60.45 74.85 15 PBC 15 34.43 3.44 28.4 41.86 10 PBC 16 74.84 4.121 67.604 83.728 22 PBC 17 69.447 4.197 62.078 78.495 23 PBC 18 62.662 3.98 55.668 71.246 20

APPENDIX B R CODE FOR THE HIERARCHICAL BAYESIAN MODEL library(tidyverse) library(jagsUI)

#Read in the data catch <- read.csv("HADACombined.csv") #capture histories for each sampled site wood <- read.csv("Wood_per_SampledSite.csv") #wood counts at sampled sites in both creeks extrapWood <- read.csv("Allwood_PBC_BEC_ForExtrap.csv") #wood counts for unsampled sites in both creeks (for extrapolation)

#The files above, and the JAGS file for the hirarchical Bayesisan model, should be located in the working directory.

#------#Preparing the data #------

#Standardize wood data and add binary coding for the creeks (BEC = 0, PBC = 1) extrapWood$wood <- (extrapWood$wood-mean(extrapWood$wood))/sd(extrapWood$wood) #Standardize wood counts: wood value/(mean wood value/sd wood value) extrapWood$creek <- 0 #binary coding extrapWood$creek[extrapWood$stream=="PBC"] <- 1 #BEC = 0; PBC = 1

#Adjust capture history data and add in "wood" data (add column for wood per sampled site) catch <- catch %>% spread(capHist, freq) %>% left_join(wood, by = c("stream", "site"))

#Standardize wood counts (like above) and add binary coding for creek catch$site[catch$stream=="BEC"] <- catch$site[catch$stream=="BEC"]-1 catch$wood <- (catch$wood-mean(catch$wood))/sd(catch$wood) catch$creek <- 0 #binary coding - BEC = 0 catch$creek[catch$stream=="PBC"] <- 1 #PBC = 1

#Give data names for use in the JAGS file for the hierarchical Bayesian model d <- within( data = list(), expr = { catchDATA = as.matrix(catch[,c("y1", "y2", "y12")]) #capture histories wood = catch$wood #wood per sampled site creek = catch$creek #binary coding for creek associated with wood value (BEC = 0, PBC = 1) M = nrow(catch) #number of rows in “catch” yiDot = rowSums(catchDATA) predWood = extrapWood$wood #wood per unsampled site predCreek = extrapWood$creek #binary code for creek associated with predWood value

nPred = length(predWood) #total number of predWood })

#------#Run the model #------

# MCMC settings nAdapt <- 2500 nBurn <- 5000 nIter <- 20000 nThin <- 1 nChains <- 3 modelName <- 'HADA_JAGS.txt' #the hierarchical Bayesian model inits <- function(){ within( data = list(), expr = { beta = rnorm(4, 0, 10) pBeta = rnorm(4, 0, 10) }) }

#Parameters of interest from the hierarchical Bayesian model varsToMonitor<-c( "N", "beta", "p1[1]", "p1[25]", "p2[1]", "p2[25]", "pBeta", "predN"#, ) cat('Model started on ',format(Sys.time(),'%c')) system.time({ out <- jags(d, inits=inits,varsToMonitor, model.file=modelName, n.chains=nChains,n.adapt=nAdapt, n.burnin=nBurn, n.iter=nIter, parallel=TRUE) })

# out = output from the hierarchical Bayesian model

# ------#Model output #------

#N estimates (site abundance from mark-recap) and parameter estimates at bottom of table “out”.

#Text associated with the “out” table shows that the model converged because all Rhat values were <1.1. sampledN_BEC <- rowSums(out$sims.list$N[,1:24]) #total abundance for sampled sites in BEC sampledN_PBC <- rowSums(out$sims.list$N[,25:42]) #total abundance for sampled sites in PBC unsampN_BEC <- rowSums(out$sims.list$predN[,131:326]) #total unsampled abundance BEC unsampN_PBC <- rowSums(out$sims.list$predN[,1:130]) #total unsampled abundance PBC

#Summary data to report for site abundance mean(sampledN_BEC) #BEC sd(sampledN_BEC) quantile(sampledN_BEC,c(0.025,0.975)) mean(sampledN_PBC) #PBC sd(sampledN_PBC) quantile(sampledN_PBC,c(0.025,0.975))

#Summary data to report for stream-wide abundance mean(unsampN_BEC) #BEC sd(unsampN_BEC) quantile(unsampN_BEC,c(0.025,0.975)) mean(unsampN_PBC) #PBC sd(unsampN_PBC) quantile(unsampN_PBC,c(0.025,0.975))

#List of all site abundances for sampled sites allsites<- data.frame(out$mean$N) #rows 1:24 are BEC; rows 25:42 are PBC write.csv(allsites, file = 'AllSampSites.csv')

#List of all extrapolated darter abundances per 25 m extrapN <- data.frame(out$mean$predN) #rows 1:130 are PBC; rows 131:326 are BEC write.csv(extrapN, file = 'ExtrapN_HBM.csv')

#Graph the relationship between wood and abundance in each creek #estN = table with n, wood, creek

#expectedN= wood for extrapolation, creek, mean abundance (from mean parameter estimates of the Poisson regression for extrapolated abundance) estN <- data.frame(n = out$mean$N, wood = catch$wood, creek = c(rep("BEC",24), rep("PBC", 18))) expectedN <- expand.grid(wood = seq(min(catch$wood), max(catch$wood), length.out = 50), creek = c(0,1)) expectedN$mean <- exp(out$mean$beta[1] + out$mean$beta[2]*expectedN$wood + out$mean$beta[3]*expectedN$creek + out$mean$beta[4]*expectedN$wood*expectedN$creek) expectedN$creek[expectedN$creek==0] <- "BEC" expectedN$creek[expectedN$creek==1] <- "PBC" expectedN$creek <- as.factor(expectedN$creek) xLabels <- c(0, 15, 30, 45, 60) xBreaks <- (xLabels-mean(wood$wood))/sd(wood$wood)

#Figure specifications #Plot estimated abundances as points; plot expected relationship between wood and darter abundance as a line. nWoodPlot <- ggplot(expectedN, aes(x = wood, y = mean)) + theme(panel.border=element_blank(),axis.line=element_line(),axis.text = element_text(color='black', size=16), text = element_text(size=20),panel.background=element_blank(),panel.grid.major=element_blank(), panel.grid.minor=element_blank(), legend.position = c(0.2,0.9),legend.text=element_text(size=20, family="serif"), legend.key.width = unit(4, "line"), legend.key = element_rect(fill = "white"), legend.title.align = 0.5, legend.title = element_blank())+ geom_line(size=1, aes(linetype = creek)) + geom_point(data = estN, aes(x = wood, y = n, shape = creek), size = 2)+ scale_linetype_manual(values = c("solid", "dotted")) + scale_x_continuous("Number of wood pieces", labels = xLabels, breaks = xBreaks) + scale_y_continuous("Expected abundance") + theme(text = element_text(family="serif",size = 20)) ggsave('nWoodPlot_test6.png', plot=nWoodPlot, dpi=200, width=12, height=8, units='in') #This plot can be found in the working directory.

APPENDIX C JAGS FILE FOR THE HIERARCHICAL BAYESIAN MODEL

{

#Define the capture probability prior distribution for(i in 1:4){ pBeta[i] ~ dnorm(0, 0.001) }

#Define the Poisson regression prior distribution for(i in 1:4){ beta[i] ~ dnorm(0, 0.001) } for(i in 1:M){

#Poisson distributions for the capture histories catchDATA[i,1] ~dpois(mu1[i]) catchDATA[i,2] ~dpois(mu2[i]) catchDATA[i,3] ~dpois(mu3[i])

mu1[i] <- lambda[i]*pi[1,i] mu2[i] <- lambda[i]*pi[2,i] mu3[i] <- lambda[i]*pi[3,i]

#Regression for site abundance of darters log(lambda[i]) <- beta[1] + beta[2]*wood[i] + beta[3]*creek[i] + beta[4]*wood[i]*creek[i]

#Logistic regression for capture probability logit(p1[i]) <- pBeta[1] + pBeta[2]*creek[i] logit(p2[i]) <- pBeta[3] + pBeta[4]*creek[i]

#Probability of observing each capture history pi[1,i] <- p1[i]*(1-p2[i]) pi[2,i] <- (1-p1[i])*p2[i] pi[3,i] <- p1[i]*p2[i]

}

###Derived

#Site abundance of darters for(i in 1:M){ piDot[i] <- sum(pi[1,i], pi[2,i], pi[3,i]) N[i] <- yiDot[i] + lambda[i]*(1-piDot[i]) }

#Stream-wide darter abundance for(i in 1:nPred){ log(predN[i]) <- beta[1] + beta[2]*predWood[i] + beta[3]*predCreek[i] + beta[4]*predWood[i]*predCreek[i] } }

APPENDIX D INPUT FILES FOR THE HIERARCHICAL BAYESIAN MODEL

#Rows 20-30 of the “catch” input file >catch[20:30,] stream site capHist freq 20 BEC 8 y2 20 21 BEC 8 y12 2 22 BEC 9 y1 3 23 BEC 9 y2 2 24 BEC 9 y12 1 25 BEC 10 y1 2 26 BEC 10 y2 13 27 BEC 10 y12 3 28 BEC 11 y1 3 29 BEC 11 y2 17 30 BEC 11 y12 3

#Rows 20-30 of the “wood” input file > wood[20:30,] stream site wood 20 BEC 21 31 21 BEC 22 2 22 BEC 23 5 23 BEC 24 6 24 BEC 25 11 25 PBC 1 8 26 PBC 2 23 27 PBC 3 19 28 PBC 4 10 29 PBC 5 21 30 PBC 6 54

#Rows 125-135 of the “extrapWood” input file stream wood 125 PBC 18 126 PBC 5 127 PBC 5 128 PBC 8 129 PBC 4 130 PBC 9 131 BEC 17 132 BEC 14 133 BEC 13 134 BEC 18 135 BEC 17

LIST OF REFERENCES Akaike, H. 1973. Information theory as an extension of the maximum likelihood principle. Pages 267–281 in B. M., Petrov and F. Csaki, editors. Second international symposium on information theory. Akademiai Kiado, Budapest.

Bass, G., T. Hoehn, J. Couch, and K. McDonald. 2004. Florida imperiled fish species investigation. Final Report to the U. S. Fish and Wildlife Service. Federal Grant R-3. FWC, Tallahassee, Florida.

Benke, A. C., R. L. Henry, III, D. M. Gillespie, and R. J. Hunter. 1985. Importance of snag habitat for animal production in Southeastern streams. Fisheries 10(5):8–13.

Boschung, H. T., and R. L. Mayden. 2004. Fishes of Alabama. Smithsonian Institution Press, Washington, D.C.

Burnham, K. P. and D. R. Anderson. 2002. Model selection and multimodel inference. A practical information-theoretic approach, 2nd edition. Springer, New York.

Dolloff, C. A., D. G. Hankin, and G. H. Reeves. 1993. Basinwide estimation of habitat and fish populations in streams. U.S. Forest Service General Technical Report SE-83.

Dolloff, C. A. and M. L. Warren, Jr. 2003. Fish relationships with large wood in small streams. American Fisheries Society Symposium 37:179–193.

Dorazio, R. M., H. L. Jelks, and F. Jordan. 2005. Improving removal-based estimates of abundance by sampling a population of spatially distinct subpopulations. Biometrics 61:1093–1101.

Duncan, R. S., B. R. Kuhajda, C. A. Hodges, A. L. Messenger. 2016. Population structure and habitat use of the endangered Watercress Darter. Journal of Fish and Wildlife Management 7:499–508.

Etnier, D. A., and W. C. Starnes. 1993. The fishes of Tennessee. University of Tennessee Press, Knoxville.

FWC (Florida Fish and Wildlife Conservation Commission). 2011. Biological status review report for the Harlequin Darter (Etheostoma histrio). FWC, Tallahassee, Florida.

FWC (Florida Fish and Wildlife Conservation Commission). 2012. Florida’s wildlife legacy initiative: Florida’s state wildlife action plan. FWC, Tallahassee, Florida.

FWC (Florida Fish and Wildlife Conservation Commission). 2013. A species action plan for the Harlequin Darter (Etheostoma histrio). FWC, Tallahassee, Florida.

Gelman, A, and D. B. Rubin. 1992. Inference from iterative simulation using multiple sequences. Statistical science 7:457–472.

Hankin, D. G. and G. H. Reeves. 1988. Estimating total fish abundance and total habitat area in small streams based on visual estimation methods. Canadian Journal of Fisheries and Aquatic Sciences 45:834–844.

Holt, D. E., H. L. Jelks, and F. Jordan. 2013. Movement and longevity of imperiled Okaloosa Darters (Etheostoma okaloosae). Copeia 4:653–659.

Hubbs, C. and J. Pigg. 1972. Habitat preferences of the Harlequin Darter, Etheostoma histrio, in Texas and Oklahoma. Copeia 1:193–194.

James, P. W. 1989. Reproductive ecology and habitat preference of the Leopard Darter, Percina pantherina. Doctoral dissertation. Oklahoma State University, Stillwater.

Jordan, F., H. L. Jelks, S. A. Bortone, and R. M. Dorazio. 2008. Comparison of visual survey and seining methods for estimating abundance of an endangered, benthic stream fish. Environmental Biology of Fishes 81:313–319.

Kaeser, A. J. and T. L. Litts. 2010. A novel technique for mapping habitat in navigable streams using low-cost side scan sonar. Fisheries 35:163–174.

Kaeser, A. J., and T. L. Litts. 2011. Sonar imagery geoprocessing workbook. Georgia Department of Natural Resources, Social Circle, Georgia.

Kaeser, A. J., and T. L. Litts. 2013. An illustrated guide to low-cost, side scan sonar habitat mapping. U.S. Fish and Wildlife Service, Panama City, Florida.

Kaeser, A. J., T. L. Litts, and T. W. Tracy. 2012. Using low-cost side-scan sonar for benthic mapping throughout the lower Flint River, Georgia, USA. River Research and Applications 29:634–644.

Lewis, C. A., N. P. Lester, A. D. Bradshaw, J. E. Fitzgibbon, K. Fuller, L. Hakanson, and C. Richards. 1996. Considerations of scale in habitat conservation and restoration. Canadian Journal of Fisheries and Aquatic Science 53:440–445.

Martin, A. D., G. A. Zapfe, and H. T. Mattingly. 1999. Abundance of the Brook Darter, Etheostoma burri, in selected Black River tributaries. Journal of Freshwater Ecology 14:141–148.

Monzyk, F. R., W. E. Kelso, and D. A. Rutherford. 1997. Characteristics of woody cover used by Brown Madtoms and Pirate Perch in Coastal Plain streams. Transactions of the American Fisheries Society 126:665–675.

Page, L. M. 1983. Handbook of darters. TFH Publications, Neptune City, New Jersey.

Page, L. M., and B. M. Burr. 2011. Peterson field guide to freshwater fishes of North America, north of Mexico. Houghton Mifflin Harcourt Publishing Company, New York.

Plummer, M. 2003. JAGS: A program for analysis of Bayesian graphical models using Gibbs sampling. Proceedings of the 3rd international workshop on distributed statistical computing.

Poos, M., D. Lawrie, C. Tu, D. A. Jackson, and N. E. Mandrak. 2012. Estimating local and regional population sizes for an endangered minnow, Redside Dace (Clinostomus elongatus), in Canada. Aquatic Conservation: Marine and Freshwater Ecosystems 22:47– 57.

R Core Team (2014). R: a language and environment for statistical computing. R Foundation for Statistical Compusing, Vienna, Austria.

Rabeni, C. F. and S. P. Sowa. 1996. Integrating biological realism into habitat restoration and conservation strategies for small streams. Canadian Journal of Aquatic Sciences 53:252– 259.

Rahel, F. J., R. T. Muth, and C. A. Carlson. 1999. Endangered species management. Pages 431- 454 in C.C. Kohler and W. A. Hubert, editors. Inland fisheries management in North America, 2nd edition. American Fisheries Soecity, Bethesda, Maryland.

Rivot, E., E. Prevost, A. Cuzol, J. L. Bagliniere, and E. Parent. 2008. Hierarchical Bayesian modeling with habitat and time covariates for estimating riverine fish population by successive removal methods. Canadian Journal of Fisheries and Aquatic Sciences 65:117–133.

Roberts, J. H. and P. L. Angermeier. 2004. A comparison of injectable fluorescent marks in two genera of darters: effects on survival and retention rates. North American Journal of Fisheries Management 24:1017–1024.

Royle, J. A., and R. M. Dorazio. 2008. Metapopulation models of abundance. Pages 267- 295 in Hierarchical modeling and inference in ecology: the analysis of data from populations, metapopulations and communities. Academic Press.

Scalet, C. G. 1973. Stream movements and population density of the Orangebelly Darter, Etheostoma radiosum cyanorum (Osteichthyes: Percidae). The Southeastern Naturalist 17:381–387.

Skyfield, J. P. and G. D. Grossman. 2008. Microhabitat use, movement and abundance of Gilt Darters (Percina evides) in southern Appalachian (USA) streams. Ecology of Freshwater Fish 17:219–230.

Smit, R. B. 2014. Using sonar habitat mapping and GIS analyses to identify freshwater mussel habitat and estimate population size of a federally endangered freshwater mussel species, Amblema neislerii, in the Apalachicola River, FL. Master’s Thesis, Auburn University, Auburn, Alabama.

Steinberg, R., L. M. Page, and J. C. Porterfield. 2000. The spawning behavior of the Harlequin Darter Etheostoma histrio (Osteichthyes: Percidae). Ichthyological Exploration of Freshwaters 11(2): 141–148.

Sterling, K. A. and M. L. Warren, Jr. 2017. Microhabitat estimation of an imperiled headwater fish, the Yazoo Darter (Etheostoma raneyi), in Coastal Plain streams. Environmental Biology of Fishes 100:1223–1235.

Toepfer, C. S., W. L. Fisher, and W. D. Warde. 2000. A multistage approach to estimate fish abundance in streams using geographic information systems. North American Journal of Fisheries Management 20:634–645.

Warren, M. L. Jr., B. M. Burr, S. J. Walsh, H. L. Bart, Jr., R. C. Cashner, D. A. Etnier, B. J. Freeman, B. R. Kuhajda, R. L. Mayden, H. W. Robison, S. T. Ross, and W. C. Starnes. 2000. Diversity, distribution, and conservation status of the native freshwater fishes of the southern United States. Fisheries 25(10):7–31.

Williams, B. K., J. D. Nichols, and M. J. Conroy. 2002. Estimating abundance for closed populations with mark-recapture methods. Pages 289-332 in Analysis and management of animal populations. Academic press.

Wyatt, R. J. 2003. Mapping the abundance of riverine fish populations: integrating hierarchical Bayesian models with a geographic information system (GIS). Canadian Journal of Fisheries and Aquatic Sciences 60:997–1006.

Zippin, C. 1956. An evaluation of the removal method of estimating animal populations. Biometrics 12:163–189.

BIOGRAPHICAL SKETCH

Kathryn M. Harriger grew up in Gaylord in the northern part of lower Michigan. She enjoyed spending time outside exploring the extensive forests, marshes, dunes, lakes, and streams in the area, and those interests led her to pursue a career in the natural resource field.

She completed a Bachelor of Science degree in fish and wildlife management at Lake Superior

State University (LSSU) in 2009. While at LSSU she conducted surveys of freshwater mussels in the Upper Peninsula of Michigan and was invloved in a project assessing the impact of non- native salmonids on aquatic communities in Great Lakes tributaries. After graduation, she enjoyed conducting salmon surveys for a timber company that managed redwood forests in northern California. In 2010, she began working for the Freshwater Fisheries Research Division of the Florida Fish and Wildlife Conservation Commission (FWC) in Holt, Florida. Over the last eight years, she has been involved with creel surveys on the Escambia River, long-term monitoring of fish communities in Florida Panhandle rivers, host-fish identification for freshwater mussels, research on Alligator Gar movement and habitat use, and determining population status of several imperiled non-game fishes. In fall 2014, she had the opportunity to pursue an online Master of Science at the University of Florida under direction of Dr. Micheal S.

Allen while continuing to work for the FWC. After graduation, she plans to continue conducting research to address needs for imperiled species conservation.