Impacts of Anthropogenic Noise on Aquatic Invertebrates In

IMPACTS OF ANTHROPOGENIC NOISE ON AQUATIC INVERTEBRATES IN

WETLAND HABITATS

A dissertation submitted

to Kent State University in partial

fulfillment of the requirements for the

degree of Doctor of Philosophy

Adrienne M. Hopson

August 2019

Except for previously published materials Dissertation written by

Adrienne M. Hopson

B.S., Long Island University: Southampton College, 1999

M.Ed., University of Houston, 2010

Ph.D., Kent State University, 2019

Approved by

Ferenc de Szalay, Ph.D. , Chair, Doctoral Dissertation Committee

Mark Kershner, Ph.D. , Members, Doctoral Dissertation Committee

Sean Veney, Ph.D.

Emariana Widner, Ph.D.

Daniel Holm, Ph.D.

Accepted by

Laura G. Leff, Ph.D. , Chair, Department of Biological Sciences

James L. Blank, Ph.D. , Dean, College of Arts and Sciences

ii TABLE OF CONTENTS TABLE OF CONTENTS ...... iii LIST OF FIGURES ...... v LIST OF TABLES ...... viii ACKNOWLEDGEMENTS ...... ix

I. INTRODUCTION……………...... 1

REFERENCES…………………………………………………………………..10

II. THE IMPACT OF SUBMERSED VEGETATION ON UNDERWATER SOUND TRANSMISSION IN FRESHWATER WETLANDS………………...16

ABSTRACT……...... …...16

INTRODUCTION………………………...... 18

METHODS………...... …..20

RESULTS...... 22

DISCUSSION...... 33

REFERENCES………...... 37

III. COMPARING HOW ABOVE-WATER AND BELOW-WATER SOUNDSCAPES DIFFER BETWEEN RURAL WETLANDS AND SITES AFFECTED BY ROAD NOISE…………………………………………………41

ABSTRACT……...... ………41

INTRODUCTION………………………...... 42

METHODS………...... 44

RESULTS...... 50

DISCUSSION...... 62

REFERENCES………...... 65

iii IV. THE IMPACT OF SHORT-TERM ANTHROPOGENIC NOISE ON SOUND PRODUCTION AND BEHAVIOR OF THE WHITE RIVER CRAYFISH, PROCAMBARUS ACUTUS (DECAPODA: CAMBARIDAE)………………….69

ABSTRACT……...... 69

INTRODUCTION………………………...... 70

METHODS………...... 72

RESULTS...... 77

DISCUSSION...... 83

REFERENCES………...... 86

V. DOES ANTHROPOGENIC NOISE AFFECT BEHAVIOR AND ACOUSTIC COMMUNICATION IN THE WHITE RIVER CRAYFISH, PROCAMBARUS ACUTUS (DECAPODA: CAMBARIDAE)?...... 91

ABSTRACT……...... 91

INTRODUCTION………………………...... 92

METHODS………...... 95

RESULTS...... 100

DISCUSSION...... 106

REFERENCES………...... 108

VI. SUMMARY CHAPTER…...... 113

REFERENCES………...... 117

iv LIST OF FIGURES

Figure 2.1. Spectrograms recorded at 1 m from sound source in wetland ponds arranged from lowest (Pond 1) to highest submersed plant density (Pond 5). ………………………………….25

Figure 2.2. Spectrograms recorded at 7 m from sound source in wetland ponds arranged from lowest (Pond 1) to highest submersed plant density (Pond 5)…………………………………...26

Figure 2.3. Spectrograms recorded at 15 m from sound source in wetland ponds arranged from lowest (Pond 1) to highest submersed plant density (Pond 5). ……………………………...... 27

Figure 2.4. Mean number of frequency bands (+/- 1 standard error) detected in two treatments

(unvegetated, vegetated) at three distances (1 m, 7 m,15 m) from the sound source.…………...28

Figure 2.5. Mean (+/- 1 standard error) intensity (dB) of the Hi-mix sound in two treatments

(unvegetated, vegetated) at three distances (1 m, 7 m, 15 m) from the sound source ...... 31

Figure 2.6. Mean (+/- 1 standard error) intensity (decibel) of Low Base Tones recorded in two treatments (unvegetated and vegetated) at three distances (1,7,15m) from the sound source. ….32

Figure 2.7. Mean (+/- 1 standard error) intensity (dB) of motorboat noise recorded in two treatments (unvegetated and vegetated) at three distances (1,7,15m) from the sound source…...33

Figure 3.1. Locations of wetlands sampled during study ……………………………………….48

Figure 3.2. Number (mean +/- 1 SE) of above-water natural and anthropogenic sounds in

Disturbed and Undisturbed wetlands in May, August and October……………………………..55

Figure 3.3. Number (mean +/- 1 SE) of above-water natural and anthropogenic sounds in

Disturbed and Undisturbed wetlands in May, August and October in low, mid, and high frequency bands …………………………………………………………………………………56

Figure 3.4. Number (mean +/- 1 SE) of bird, frog and insect calls in Disturbed and Undisturbed wetlands at 8:00 in May. ………………………………………………………………………...57

v Figure 3.5 Above water soundscape diversity (mean +/- 1SE) in Disturbed and Undisturbed wetlands in May, August and October. Acoustic diversity is given as Shannon’s diversity

(H’)……………………………………………………………………………………………….58

Figure 3.6. Number (mean +/- 1 SE) of below-water natural and anthropogenic sounds in

Disturbed and Undisturbed wetlands in May, August and October……………………………..60

Figure 3.7. Number (mean +/- 1 SE) of below-water natural and anthropogenic sounds in

Disturbed and Undisturbed wetlands in May, August and October in low, mid and high frequency ban…………………………………………………………………………..………...61

Figure 3.8. Below water soundscape diversity (mean +/- 1 SE) in Disturbed and Undisturbed wetlands in May, August and October. Acoustic diversity is given as Shannon’s diversity

(H’)……………………………………………………………………………………………….62

Figure 4.1. Spectrograms of a representative Procambarus acutus pulse train……………….....78

Figure 4.2. Average count of crayfish behavior in each treatment group (NS = No Sound, S =

Sound) during each Time period (Before, During, After)………………………………..……...80

Figure 4.3. Total activity levels (mean +/- 1 SE) of crayfish in each treatment group (No Sound,

Sound) during each Time period (Before, During, After)…………………………………...... 82

Figure 4.4. Number of clicks produced (mean +/- 1 SE) of crayfish in each treatment group (No

Sound, Sound) during each Time period (Before, During, After)…...…………………………..83

Figure 5.1. Total activity levels (mean +/- 1 SE) of crayfish in each treatment group (No Sound,

Sound) during each Time point (Pre, 2 week, 4 week, Post). Note that values are expressed in log10 ………………………………………………………………………………………….....102

Figure 5.2. Levels of behaviors (mean +/- 1 SE) of crayfish in each treatment group (No Sound,

Sound) during each Time point (Pre, 2 week, 4 week, Post). ………………………………….103

vi Figure 5.3. Number of clicks produced (mean +/- 1 SE) of crayfish in each treatment group (No

Sound, Sound) during each Time period (Pre, 2 week, 4 week, Post). Note that values are expressed in log10. ……………………………………………………………………………...104

vii LIST OF TABLES

Table 2.1. Environmental conditions of wetlands at HAERF in July 2012……………………...23 Table 3.1. Treatment site locations, distance to nearest paved road, and sound levels during Initial visit………………………………………………………………………………………..46 Table 3.2. Baseline characteristics of sites by treatment ………………………………………..52 Table 4.1 Modified crayfish behavior ethogram…………………………………………………76 Table 5.1. Modified crayfish behavior ethogram……………………………………………...... 99 Table 5.2. Results of correlation analyses between the sound production and behavior…….....105

viii ACKNOWLEDGMENTS

I dedicate my dissertation to my parents, Cynthia and Harvey Hopson. Thank you for always making education the most important thing in our lives, even when I didn’t. For not giving up on me even when I had thrown in the towel, and for supporting me through every class and degree until I reached this point. Thank you to my siblings, Alex (big) and Elissa, for reminding me how hard all my math classes were when I was younger, and that I made it through them all, and I would make it through whatever my new challenge was too. Thank you to my nieces, Dany and Alex (little), for giving me two more reasons to keep working and plugging away, I never wanted them to see their aunt quit, so on I went. Thank you to my grandparents, Edna and Spann Watson, for never letting me forget I could do anything, no matter what, and being my example of just how far that mindset can take you. To my husband, Gabe, words are not enough. Thank you for your support through all the changes, sacrifices, lost days and missed moments. You know better than anyone what this momentous task has taken and you have supported, encouraged and helped me complete it.

Thank you to my advisor, Dr. Ferenc de Szalay, for his support, guidance, understanding, help and patience. You sparked an interest in wetlands that has become a true fascination and appreciation for a habitat I knew little about before coming to Kent State. Thank you to my committee, department and Kent State University for years of dedication and resources to help me finish, in the nick of time.:)

ix CHAPTER 1 INTRODUCTION

Soundscapes are defined as the composition of natural and man-made sounds in a habitat, and these are an important but poorly studied ecological component of most ecosystems.

Animals produce sound to attract mates, detect or deter predators, coordinate their movements and defend territory. For example, terrestrial invertebrates (e.g., Orthoptera, Hymenoptera,

Coleoptera) use sound to attract mates or repel competitors. Given the ecological importance of this topic, my dissertation examines soundscape ecology in freshwater wetlands and tests how anthropogenic sounds affect acoustic signals and behavior of freshwater invertebrates. My first project investigated the impacts of vegetation on underwater sound transmission. I found the density of submersed vegetation reduces the decibel level and frequency composition of sound traveling through wetland habitats. For my second project, I sampled ambient soundscapes produced by wetland invertebrates and vertebrates in wetlands affected by noise pollution from nearby roads. This showed that natural sounds (e.g. bird calls) in wetlands affected by anthropogenic noise are very different from those in rural wetlands. For my third and fourth investigations, I examined the impact of anthropogenic noise on sound production and behavior of the White River crayfish, Procambarus acutus, in short term (20 minute) and long-term (4 week) exposure studies. In these experiments, I exposed crayfish to recorded motorboat sounds. I found that short-term exposure reduces overall activity levels but not sound production of crayfish. Long-term exposure had the same effect, and I found that intraspecific aggressive behaviors were reduced in crayfish exposed to anthropogenic noise pollution. Sound transmission is impacted by the properties of the acoustic signal and the media it travels through.

1 In terrestrial habitats, plants reduce airborne sound transmission, especially at high frequencies.

Vegetation attenuates sound by scattering, which is moderated by leaf density. Aiken (1982b) found that aquatic insect sounds travel ~1m in shallow water habitats, and therefore close-range bioacoustics communication may be more important in aquatic than terrestrial environments. In addition, shallow water differs from air and deeper water because it has two boundaries that can cause reflection and absorption, the substrate and the water surface (Forrest, 1994). A study done by Wilson et al. (2013) showed that sea grass meadows can alter bioacoustics transmissions of dolphins and be a refuge for prey animals to help avoid detection through echolocation.

However, little is known about how underwater sound transmission is moderated by aquatic vegetation in wetlands and other habitats.

Soundscapes are defined as the array of natural and anthropogenic sounds that are produced in an ecosystem. An ecosystem’s soundscape includes sounds with a biotic origin

(e.g., animal vocalization, leaf movement) and abiotic origin (e.g., wave action, water flow, wind). In many areas, soundscapes also include anthropogenic sounds (e.g., automobile engines, lawnmowers, chainsaws, human voices). One important component of soundscapes is bioacoustic communication, and many researchers have examined the significance of animal produced sound (Pijanowski et al., 2011a). For example, bird and insect calls are used by conspecific individuals to attract mates. Detection of natural sounds may also be used to detect the presence of danger, such as when animals hear a predator approaching. Furthermore, predators can use sound to locate their prey. Soundscape ecology examines the importance of these sounds and how environmental conditions affect soundscapes (Pijanowski et al., 2011b).

This field also includes the impact of anthropogenic noise (i.e. noise pollution) on natural soundscape.

2 Soundscape ecology is an expanding field, and research is being conducted in both terrestrial and aquatic habitats. For example, soundscapes have been analyzed to investigate the diversity and populations of species in the Amazonian rainforest (Riede 1993) and as a tool to demonstrate the importance of long-term monitoring of avian populations (Farina et al. 2011).

Studies have examined temporal and spatial variability in soundscapes. For example, Krause et al. (2011) recorded at four habitat types (old growth forest, oak savanna, dry savanna and riparian) in Sequoia National Park and discovered that dry savanna had the greatest sound diversity, while riparian habitat had the least. Furthermore, midday had the least biological sounds across all habitats. Morning had the highest levels of biological sounds in the old growth forest, but nighttime had the most biological sounds in oak and dry savannas. However, temporal and spatial variability has not been well studied in most habitats.

Most research in aquatic habitats has been conducted in marine systems. An early study by Painter (1963) reported the soundscape including wind, tidal currents and marine life in a shallow lagoon in Baja, California used as a calving ground by the gray whale. More recently,

Amoser and Ladich (2010) investigated changes in ambient noise levels and sound spectra throughout the year in various freshwater habitats. The study showed that the ambient noise was lower in lentic habitats (lakes and backwaters) than in lotic habitats (rivers and streams). Also, interannual variability was the least in rivers, and greatest in lakes and streams. Wysocki et al.

(2007) found that ambient noise levels in lentic habitats ranged from 77 to 100dB and lotic habitats ranged from 103 to 136 dB. In addition, environmental sound spectra were usually around 500Hz in lentic habitats, but the fast flowing waters had most sound in higher frequencies above 1kHz. These results indicated that communication with bioacoustics would be highly masked in fast moving habitats.

3 A few studies have sampled wetland soundscapes. Wetlands are ecologically and economically important because they absorb floodwaters and take up excess nutrients and toxins, stabilize stream hydrology, protect shorelines, and recharge groundwater (Mitsch et al. 2007).

One study estimated that a 3.25 million acre region of coastal wetlands in Southeast Louisiana generated $145 million from recreational uses alone (Bergstrom et al. 1990). For both ecological and economic reasons, preserving intact, functional wetlands, including acoustic characteristics, is beneficial. Wetlands support a high biodiversity of plants and animals. Common acoustic sources in these soundscapes are calls from frogs, birds, insects, reptiles and even fish. Aquatic invertebrates are important in food webs as abundant herbivores, predators, and detritivores

(Balcombe et al. 2005) and they are important food for wildlife and fish (Batzer & Wissinger,

1996). Giles et al. (2005) found soundscapes in Australian wetlands included many insect calls.

He also found predictable temporal variations and sound levels were greatest from dusk to midnight.

In an aquatic habitat, the physical properties of the water affect sound transmission.

Parameters such as temperature, salinity and depth impact the speed, direction and reflection of sound (Bass & Clark, 2003). For example, airborne sound travels at 343 m/sec, but sound in seawater travel at 1,518 m/sec. (Bass & Clark, 2003). Usually in deep aquatic habitats sounds with high frequencies attenuate first and low frequencies travel greater distances. However, low frequencies in shallow freshwater habitats with a sloping, muddy bottom are lost faster than higher frequencies in shallow (< 2.0 m in depth) freshwater habitats (Forrest et al. 1993).

Vegetation also affects sound transmission. Terrestrial plants reduce acoustic transmission, especially high frequencies, by the scattering of the sound waves. The extent of acoustic scattering is moderated by leaf density (Aylor 1972). Thus, organisms using bioacoustics may be

4 impacted by vegetation changes, including man made changes. For example, African bladder grasshoppers (Orthoptera; Pneumoridae) calls are less distorted when they pass through their native vegetation than non-native vegetation (Couldridge et al, 2004). In situations where sound is used for reproduction or as an alert to danger, these changes could have negative impacts on reproductive success, populations and survival. In aquatic systems, the impact of vegetation is not well understood. Aiken (1985) suggested that since submersed aquatic vegetation have a high water content, they would have little impact on underwater sound transmission. However, I did not find any published studies that tested this idea.

Temporal patterns in soundscapes may be due to seasonal changes in biological activities such as migration, feeding, and reproduction that can be difficult to monitor otherwise. Thus, sampling soundscapes over the year can help determine seasonal peaks in biological activities.

Soundscape ecologists have started to develop holistic concepts about soundscapes. For example, Wrightson (1999) posited that animal species that share a habitat must occupy a unique acoustic “spectral niche” that does not overlap in temporal or frequency characteristics with other organisms. In this way, important bioacoustics communication of a species would not be masked by calls of other species. However, anthropogenic sound introduced into a habitat could mask unique “spectral niches”.

Most studies on the importance of sound to invertebrate behavior have been conducted in terrestrial or marine systems. For example, grasshoppers (Orthoptera), beetles (Coleoptera) and wasps (Hymenoptera) use sound to signal mates and avoid predators (Klappert & Reinhold 2003,

Wilkinson et al. 1967, and Van Den Assem 1970). Some terrestrial insects produce ultrasonic sounds (Gwynne et al. 1988). An example are Arctiidae moths, that produce ultrasonic calls that interrupt the echolocation of bats when they detect the echolocation calls of these predators

5 (Fullard et al. 1979). Snapping shrimp (Decapoda) live in large aggregations in burrows in shallow tropical reefs. In these habitats, they dominate the soundscape by producing a constant crackling noise that is easily audible to human ears. They produce loud high frequency sounds to communicate with others in the colony (Chitre, 2010). California mantis shrimp, Hemisquilla californiensis, (Stomatopoda), produce a low-frequency rumble, but the function of these sounds is unknown (Staaterman et al. 2011).

Far fewer studies have examined freshwater invertebrates, although a review by Aiken

(1985) found some studies more than 100 years old. Aquatic insects such as Haliplidae and

Hydrophilidae beetles and many others produce sound to delineate territorial borders, deter predators, coordinate migration, and communicate with mates. Tropisternus sp. is a widespread aquatic beetle found in the littoral zone of many lentic habitats. They produce a “chirp” like call by stridulation related to stress, calling, courtship, copulation and aggression (Ryker 1976) that is easily audible. The water boatmen (Corixidae) uses stridulation to attract mates (King 1976,

Aiken 1982, Theib 1982, Theiss et al 1983). Sueur et al. (2011) reported that another hemipteran,

Micronecta scholtzi, produce the loudest animal sound recorded when scaled to body length.

Most information on wetland invertebrate bioacoustics is anecdotal and was conducted before the use of modern recording technology (Aiken 1985, Sueur et al. 2011). For example, research sound libraries at Cornell University (Macaulay Library) and The Ohio State University (Borror

Laboratory of Bioacoustics) contain no aquatic wetland insect recordings. This demonstrates a gap in our basic knowledge, understanding and research data as related to aquatic invertebrates.

Modern microphones (i.e., hydrophones) can detect underwater sounds including ultrasonic frequencies, which was not possible in the past. This new technology has allowed studies on characteristics of aquatic invertebrate bioacoustic communication. For example, male

6 and female Procambarus clarkia (Red Swamp crayfish) produce pulsed signals by beating the scaphognathite inside a chamber in the cephalothorax (Favaro et al. 2011). Number of pulses per train was from 2 to 36, pulse train duration was 0.01-0.68 ms and the pulse rate was 4.4-500.0 per ms. These sounds usually occurred in response to humans approaching the tank or handling the crayfish. However, these pulse signals also occurred out of water and in the absence of human operators, and little is known about the ecological importance of the sounds. While previous research has shown that crayfish communicate visually, physically and chemically

(Moore 2007), very little research exist on the use of acoustics by crayfish species. What research does exist is related to the previously mentioned P. clarkii studies. However, understanding how different crayfish species use sound and the impact anthropogenic noise has on both their sound production and behavior is important to fully understand their ecology.

Understanding the possible impact that anthropogenic noise may have on the sounds produced by invertebrates is important, because of the important nature of invertebrate sound use, such as during reproductive cycles and aggressive displays. For example, if the “chirps” of a

Tropisternus sp. or the “clicks” of a P. acutus are masked, this could alter their associated behaviors. Altered behavior may occur after short-term exposure due to a “startle” response, or after long-term exposure due to learned behavioral changes.

The ecological impact of anthropogenic noise pollution is growing with expanding human populations, in both terrestrial and aquatic environments (Barber et al 2011). On land, noise from automobiles, airplanes, trains and construction are all common sources of anthropogenic noise (Barber et al 2011). Hildebrand (2004) found that anthropogenic sounds are also widespread in marine habitats, when noise pollution is usually short, high frequency tones or chronic, low frequency sounds. Hildebrand (2004) estimated that ocean noise levels are

7 over 10 times higher today than in previous decades. Increasingly, boats, air gun arrays used to perform seismic surveys to investigate for natural gas or oil deposits, and military sonar affect marine soundscapes (Whitlow et al., 2012).

There are many impacts of sound pollution on terrestrial animal behavior. For example,

Grey Shrike-thrush, Colluricincla harmonica, and frogs increased the frequency (pitch) of their calls to compensate for traffic noise (Parris and Schneider 2009, Parris et al. 2009b).

Chorthippus biguttulus grasshoppers in noisy roadside habitats called with higher frequencies than in quiet habitats (Lampe et al. 2012). While this may allow their calls to be detected, it also may make them less attractive to mates and reduce their reproductive success. The intense noise from machinery used to drill for oil and natural gas has been shown to decrease songbird densities and pairing rates (Barber et al. 2011).

There have been studies of anthropogenic noise on aquatic animals, but most were conducted on marine habitats. Popper and Hastings (2009) reviewed on studies examining the impact of noise by pile-driving in marine harbors. Some found the sounds increased fish mortality, but others did not detect an impact. Casper et al. (2012) found that sharks often approached low-frequency, erratic noise, but would startle and swim away from sudden, loud noises. Much research has focused on marine mammals. For example, Right Whales changed their vocalization when exposed to shipping noise (Parks et al. 2012). Some individuals increase their call frequency but others decrease calling frequency. They also found that whales had less success perceiving whale calls in the presence of engine noise. Vasconcelos et al. (2007) showed that ship noise masked the mating call of the Lusitanian toadfish, Halobatrachus didactylus. In freshwater systems, ship engine noise may elevate cortisol stress hormone-levels in fish

(Wysocki et al. 2005).

8 Very little research has been done on soundscapes and noise pollution in wetlands.

Understanding the impact of sound pollution is a part of the developing strategies to protect high quality wetland habitats. Furthermore, variability in experimental methods and lack of baseline data, made it difficult to know if there are general patterns of impacts of noise pollution across different habitat types or between invertebrates and vertebrates.

Anthropogenic noise is becoming more prevalent in aquatic habitats and is impacting the organisms that live there. My dissertation research investigated several questions on this topic: 1) what are the temporal and spatial changes in wetland soundscapes, 2) what environmental characteristics affect sound transmission, and potentially bioacoustics communication in wetlands, and 3) are there impacts of sound pollution on wetland invertebrate behavior?

I tested several hypotheses in my dissertation. First, I hypothesize that submersed vegetation will decrease decibel levels and altered frequencies of underwater sound transmission.

This information was important to understand how habitat characteristics and anthropogenic sounds may affect invertebrate communication. Next, I hypothesized that wetland soundscapes differ between sites affected by noise pollution vs. pristine sites. This was also important to test because anthropogenic noise can mask natural sounds, including invertebrate calls.

Anthropogenic noise can also alter behavior through stress and startle response. This may result in wetland taxa altering their behavior and bioacoustics production. Finally, I examined the effects of anthropogenic sounds on the Procambarus acutus crayfish. I hypothesized that sound pollution could mask P. acutus bioacoustic production and alter their behavior, including sound production and intraspecies interactions.

My research will add new information about natural and anthropogenic components of wetland soundscapes and how noise pollution impacts the aquatic ecosystems. Previous research

9 has shown that anthropogenic sound impacts many habitats but far less is known about wetland habitats. My dissertation will provide a better understanding of ecological processes and to develop management strategies to preserve and enhance ecosystem services in these areas.

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15 CHAPTER 2 THE IMPACT OF SUBMERSED VEGETATION ON UNDERWATER SOUND

TRANSMISSION IN FRESHWATER WETLANDS

ABSTRACT

Many animals use auditory signals to communicate, find food and avoid environmental and biotic dangers. Reception and transmission of sounds are very different types of ecosystems, but almost all studies have been conducted in terrestrial or deep marine habitats. Little is known about how submersed vegetation affects sound transmission in shallow freshwater marshes. I compared transmission of underwater sound (pure tones and mixed sounds) in vegetated and unvegetated wetland mesocosms. Sound files were played underwater and recorded at 1 m, 7 m and 15 m from source to test interactions of distance and vegetation. Overall, vegetation density greatly reduced pure tone sound transmission, and these patterns increased with distance. Almost all sounds and frequencies were still audible at 1m even in densely vegetated ponds. Low tones were still detectable at 15 m in open water, but high tones were lost within a few meters. Mixed tone including Hi-mix, Low Bass and motorboat engine sounds showed similar patterns. For example, motorboat sound at 15 m was over 80 dB in open water but was ~10 dB in vegetation.

Furthermore, the higher frequencies of the motorboat sound were lost quickly but the low tones were still detectable at 15 m in open water. We also tested the relationship between plant density and transmission of Hi-mix, Low Bass and motorboat engine sounds. There were significant negative linear correlations between mixed tones and plant density at 7 m and 15 m from sound source. These findings have important implications because many freshwater wetlands are densely vegetated, and thus auditory communication by aquatic organisms may not be useful

16 over great distances. Furthermore, these results show that low frequencies of anthropogenic sounds are transmitted through open water. However, submersed vegetation can reduce the effects of underwater noise pollution much more effectively than terrestrial vegetation.

Therefore, wildlife management practices in freshwater wetlands such as drawdowns may need to incorporate the effects of submersed communities on underwater soundscapes.

17 INTRODUCTION

Transmission of sound is impacted by the properties of the media through which it travels. Therefore, acoustic communication is different in terrestrial and aquatic ecosystems. In terrestrial systems, air turbulence causes fluctuations of sound, which tends to be greatest midday when there is thermal stratification (Richards and Wiley 1980). In contrast, aquatic ecosystems are more thermally stable due of the high density of water. Underwater sounds are also transmitted about 5 times faster and much further than in air because water is a denser media

(Slabekoorn et al. 2010). Another important factor is that sound can reflect between the benthic sediments and the water surface and create intersecting sound waves that cancel each other out and reduce sound intensity and alter sound frequency (Bass and Clark, 2003). This effect is the most pronounced in shallow habitats like wetlands where sound waves can bounce between the water surface and sediments.

Acoustic signals are used to communicate over long distances by many animal taxa, but their effectiveness degrade over distance through absorption and scattering of sound waves.

These changes reduce the ability of organisms to accurately recognize distant acoustic signal.

Plants are one important factor affecting long-distance acoustic transmission. Vegetation attenuates sound by scattering, and this effect is controlled by plant physical structure and density. Most sound attenuation occurs in densely vegetated stands of broad-leafed vegetation.

For example, attenuation of high frequency sounds is strongly correlated with the amount of trees and shrubs between sender and receiver (Aylor 1971, Romer and Lewald 1991).

Alternately, vegetation sometimes amplifies mid-frequency sounds and reduces low frequency noise. For example, planting buffer along roads is sometimes used to reduce low tones from traffic or industrial noise (Martens 1979).

18 There is strong evidence that the acoustic impacts of plant communities can affect animal communities through its impact on sound communication. For example, some terrestrial insects use plant tissue as transducers of acoustic signals during mating and aggressive displays, and fleshy plants transmit acoustic signals further than dense woody ones (Bell, 1980). Also,

Couldridge and van Staaden (2004) found that bladder grasshoppers (Orthoptera; Pneumoridae) transmitted sound better in native plant communities than ones invaded by exotic species.

Changes in animal communities can, in turn, alter the natural array of sounds, the soundscape, of a habitat (Slabbekoorn and Smith 2002, Pijanowski et al. 2011a).

Although much research has been conducted in terrestrial habitats, acoustic communication has been less well studied in aquatic habitats, especially in freshwater ecosystems. However, studies have found that many freshwater invertebrates use acoustic signals to detect approaching predators and mates (Dodson et al. 1994). For example, male pygmy backswimmers, Micronecta scholtzi, create mating calls that reach a remarkable 99 decibels (Sueur et al. 2011). When this sound intensity is scaled against their body size, they are the loudest organisms on the planet. Also, ambient natural sounds reduce predation rates by damselfly larvae (Villalobos‑ Jiminez et al. 2017).

Little is known about factors that alter underwater sound transmission in lakes and wetlands. Aiken proposed that since submersed aquatic plants contain a high amount of water they should have a minor impact on sound transmission (Aiken 1985). Furthermore, sounds generated by many aquatic insects begin to dissipate within ~1 m (Aiken 1982), and most sounds are not detectable past 15 m (Forrest et al. 1993). Therefore, long-range bioacoustic communication may be less important in aquatic than terrestrial environments. Although little is known about noise pollution in freshwater habitats, Slabbekoorn (2012) found that human

19 sounds are common underwater. Therefore, soundscapes in aquatic habitats should be affected by the proximity of human population centers from the habitat. These factors may have a strong impact on organisms that rely on detection of acoustic signals for survival.

In this study, I studied how the transmission of underwater sounds in freshwater wetlands is affected by aquatic vegetation. This is a first step in investigating how natural factors affect bioacoustic communication of aquatic invertebrates. I measured sound transmission of pre- recorded pure and multi-tonal sounds that were played underwater in wetlands with varying amounts of submersed vegetation. I tested the hypothesis that transmission of aquatic sound is negatively correlated with distance and density of submersed plants. I also hypothesized that high frequencies should be filtered out more effectively than low tones.

METHODS

Experimental design

I conducted my experiments at the Kent State University’s Herrick Aquatic Ecology

Research Facility (HAERF). The facility has ten excavated wetland ponds (10 m x 20 m) that receive water from a central stream pool (10 M X 100 m). One wetland was used for a different experiment, so it was excluded from this study. The wetland ponds have a water depth of ~2 m

(total volume = ~2.5 X 105 l). Each pond has separate inlet and outlet water control structures that use PVC boards to adjust the inflow and outflow of water (Drinkard et al. 2011). These wetlands had been flooded over a year, and all had dense beds of submersed vegetation.

In June 2012, four ponds were randomly selected and completely drawn down. The drawdown eliminated most of the submersed aquatic vegetation, and these were termed the unvegetated treatment wetlands. These ponds were reflooded in mid-July. Five other ponds remained flooded throughout this period, and these were termed the vegetated treatment

20 wetlands. Testing occurred on 25 July 2012, which was one week after the Unvegetated ponds were reflooded.

Submersed plant biomass (mostly Elodea canadensis, Potomageton crispus, Chara sp.) was quantified in each pond by clipping and removing all vegetation within a 13-cm diameter quadrat that was lowered to the bottom of the mesocosm. The location of the quadrat was approximately midway between the underwater speaker and the hydrophone. The plant sample was drained, blotted dry, and dried in a drying oven (60 °C for 48 hr). Samples were weighed on a balance to determine plant dry weight. I also measured water depth and temperature in each wetland on the sampling date.

In each pond, I played a 70 second sound file, composed of a mix of pure tones and mixed sounds obtained from various open-source sound file websites, to test how underwater sound transmission was affected by submersed vegetation. The underwater speaker (Model

AQ339, Lubell, Inc., Columbus, OH) was suspended 15 cm below the water surface at one end of each pond. The recordings included a frequency scale ranging from 20 to 20,000 Hz, which span the range of human hearing. The scale included 20 pure tones each with a different frequency: 20 Hz, 30 Hz, 40 Hz, 50 Hz, 60 Hz, 100 Hz, 200 Hz, 500 Hz, 1 kHz, 2 kHz, 5 kHz, 8 kHz, 10 kHz, 12 kHz, 15 kHz, 16 kHz, 17 kHz, 18 kHz, 19 kHz, 20 kHz. The recording also included mixed tones that included a high-pitched trill (“Hi-mix”), low tones (Bass-beats), and the noise from a Motorboat engine. All sounds were played at 85 dB (above-water standard scale: 1 dB is 20µPa @ 1m). This tested a wide range of sounds including anthropogenic sound that could be encountered in wetland habitats.

In each pond, I measured the sound intensity at three distances (1 m, 7 m, 15 m ) from the speaker. I recorded underwater sounds with a hydrophone (Model H2a-XLR, Aquarian Audio,

21 Anacortes, WA) suspended 15 cm below the water surface. The hydrophone connected to a preamp (Rolls MX34c, Aquarian Audio, Anacortes, WA), and a recorder (Tascam DR-07,

Tascam Teac Professional , Tokyo, Japan) that converted sound into Waveform Audio File

(.WAV) format. Recordings were stored on an external hard drive.

Data Analyses

Raven Pro (Cornell University, Cornell, NY) sound analysis programs were used to analyze acoustic properties of the recording. This program provides a visual comparison of sound files, which I used to count the number of pure frequency tones that were detectable at each distance. I also used Raven Pro to measures intensity of all sounds in each recording. The software measures sound intensity by estimating the level relative to an arbitrary reference number. Therefore, all decibel values (dB) are relative readings, and are not scaled to a standardized above-water or below-water scale.

I used t-tests to compare environmental conditions (water depth, temperature) and plant density between treatments. I used mixed model ANOVAs to compare acoustic data because ponds were each repeatedly sampled at each distance. Therefore, mixed model ANOVAs treated Distance (1, 7, 15 m) and Treatments (vegetated, unvegetated) as fixed factors and Pond was a random factor. When there were significant differences among distances, I ran Tukey’s pairwise comparisons to determine which were different. I also used Spearman’s correlation analyses to test if there were correlations between plant density and Hi-mix, Low Bass, and

Motorboat noise levels at each distance. All statistical analyses were done using JMP software

(SAS Institute, Cary, NC).

RESULTS Plant communities responded quickly to the drawdown, and the unvegetated treatment ponds had little submersed plant matter during the experiment (Table 1). Therefore, there was a

22 difference in plant density between treatments (t=2.526, DF=25, p<0.02). However, water depth and temperature were not significantly different between treatments.

Table 2.1. Environmental conditions of wetlands at HAERF in July 2012 (mean +/- 1 SD).

*Indicates means were different between treatments (P<0.05).

Treatment Dry weight g/cm3 Temperature (C) Depth (cm)

Unvegetated 0.14 (1.6)* 27.7 (1.4) 74.9 (4.9)

Vegetated 7.3 (1.5)* 26.4 (1.8) 80.8 (19.0)

23 Examining the visual pattern in the spectrograms (Figs. 1,2,3) showed that most underwater sounds decreased with distance from the sound source. For example, within pond comparisons show that more pure frequency sound bands were detected at 1 m (Fig. 1) than 7 m (Fig. 2) or 15 m (Fig. 3). Also, submersed plant density apparently had a strong impact on sound transmission, and this effect became more pronounced at greater distances. At 1 m (Fig. 1), the number of pure frequency bands was slightly different between the ponds with the highest and lowest plant densities. For example, Pond 5 had a high plant density (19.5 mg/cm3), but 17 out of 20 frequencies were still detected at 1 m. However, at 7 m (Fig. 2), sound loss was much more prominent in Vegetated treatment wetlands (2, 4, 5, 8, 9) than Unvegetated treatment wetlands

(1, 3, 7, 10). At 15m (Fig. 3), only the least vegetated ponds had any sound transmission of pure frequency tones. Another pattern was that vegetation blocked high frequencies more effectively than low frequencies. For example, tones above 12,000 Hz were not detected in ponds with dense submersed vegetation at 7 m from sound source (Fig. 2).

Examining the spectrograms also showed vegetation density and distance had a similar effect on mixed tone sounds. Hi-Mix sounds were greatly reduced by submersed plants at all distances. For example, the Hi-Mix sound at 1 m (Fig. 1) was noticeably fainter in heavily vegetated ponds than less vegetated ponds, and the band largely disappeared in all ponds at 7 m

(Fig. 2). The Low Bass sound was also affected by vegetation, but the band was still present in most ponds at 7 m (Fig. 2). However, by 15 m (Fig. 4) the bass beats also disappeared in all ponds except the least vegetated pond (Pond 1). The Motorboat sound was also reduced by submersed plants and distance. Furthermore, its high frequencies faded at 1 m (Fig 1), but the low tones were still detectable in most ponds at 7 m (Fig. 2), and the three open water ponds at

15 m (Fig. 3).

Pond 1 Pond 3 Pond 10

Pond 7 Pond 4 Pond 2

Pond 8 Pond 9 Pond 5

Figure 2.1. Spectrograms recorded at 1 m from sound source in wetland ponds arranged from lowest (Pond 1) to highest submersed plant density (Pond 5). The y-axis scale shows frequency level (kHz) and the x-axis is time (seconds). The sound recording played the same sequence in all ponds: time: 0-3 s Hi-mix; time: 4-7 s Low Bass; time: 7-55 s pure frequencies; time: 55-75 s

Motorboat. Lighter colors (white, yellow) indicate sounds with high power while darker colors

(red, purple) indicate less power.

Pond 1 Pond 3 Pond 10

Pond 7 Pond 4 Pond 2

Pond 8 Pond 9 Pond 5

Figure 2.2. Spectrograms recorded at 7 m from sound source in wetland ponds arranged from lowest (Pond 1) to highest submersed plant density (Pond 5). The y-axis scale shows frequency level (kHz) and the x-axis is time (seconds). The sound recording played the same sequence in all ponds: time: 0-3 s Hi-mix; time: 4-7 s Low Bass; time: 7-55 s pure frequencies; time: 55-75 s

Motorboat. Lighter colors (white, yellow) indicate sounds with high power while darker colors

(red, purple) indicate less power.

26 Pond 1 Pond 3 Pond 10

Pond 7 Pond 4 Pond 2

Pond 8 Pond 9 Pond 5

Figure 2.3. Spectrograms recorded at 15 m from sound source in wetland ponds arranged from lowest (Pond 1) to highest submersed plant density (Pond 5). The y-axis scale shows frequency level (kHz) and the x-axis is time (seconds). The sound recording played the same sequence in all ponds: time: 0-3 s Hi-mix; time: 4-7 s Low Bass; time: 7-55 s pure frequencies; time: 55-75 s

Motorboat. Lighter colors (white, yellow) indicate sounds with high power while darker colors

(red, purple) indicate less power.

27 The effects of vegetation and distance on the number of frequency bands were both statistically significant (Figure 4). I detected fewer frequency bands in the Vegetated treatment

(DF=1, F=13.295, p<0.002). The number of bands also decreased with distance from sound source (DF=2, F=16.777, p<0.001). Tukey’s tests showed there were more frequency bands at

1m than 7m and 15 m. The Treatment X Distance interaction was not significant (DF=2,

F=0.027, p=0.974).

Figure 2.4. Mean number of frequency bands (+/- 1 standard error) detected in two treatments

(unvegetated, vegetated) at three distances (1 m, 7 m, 15 m) from the sound source. Counts are the number of visible frequency bands out of 20 frequencies ranging from 20 to 20,000 Hz.

28 The effects of vegetation and distance on sound levels of mixed tone sounds were significant. Hi-mix sound levels (Figure 5) were lowest in Vegetated treatments (DF=1,

F=16.020, p<0.006) and different by Distance (DF=2, F=4.271, p<0.05). A Tukey’s test showed that levels were higher at 1 m than 15 m. Unvegetated wetlands had levels between 60-75 dB at all distances. In the Vegetated treatment sound levels were 60db at 1 m, and levels dropped to

25 dB at 7 m and 15 dB at 15 m. There was no Treatment X Distance interaction (DF=2,

F=1.077, p=0.359). There was a negative relationship between plant density and transmitted sound intensity at greater distances. There was a negative correlation between sound level and plant density at 7 m (Spearman’s correlation= -0.655, p<0.05) There was also a negative correlation at 15 m (Spearman’s Correlation= -0.900, p<0.05).

Low Base sound levels were also lower in Vegetated wetlands (DF=1, F=11.576, P<0.02)

(Figure 6). Distance was also significant (DF=2, F= 15.141, p=<0.001), and Tukey’s tests showed that sound levels at 1 m were higher than at 7 m and 15 m. The Treatment X Distance interaction was not significant, (DF=2, F=0.727, p=0.501). There was a negative correlation between sound levels and plant density at 7 m (Spearman’s correlation = -0.700, p<0.01). There was also a negative correlation between sound levels and plant density at 15m (Spearman’s correlation= -0.894, p=0.001).

Motorboat sound levels (Figure 7) were also affected by Treatment (DF= 1, F= 14.836, p<0.001) and Distance (DF=2, F=8.435, P<0.005). Tukey’s Tests found that sound levels were different from 1m to 7 m and 15m. Sound levels again decreased with distance, and vegetation greatly reduced transmitted sound levels. For example, mean levels were 88 dB at 1 m, 65 dB at

7 m and 62 dB at 15 m in unvegetated wetlands. In contrast, motorboat sounds in Vegetated wetlands decreased rapidly from 72 dB at 1 m, to 35dB at 7m and 15 dB at 15m. The Treatment

29 X Distance interaction was not significant, (DF=2, F=2.492, p=0.107). Finally, motor boat noise levels had a negative correlation with plant density at 15m (Spearman’s correlation= -0.862, p<0.05)

Figure 2.5. Mean (+/- 1 standard error) intensity (dB) of the Hi-mix sound in two treatments

(unvegetated, vegetated) at three distances (1 m, 7 m, 15 m) from the sound source. Decibel values are relative to an arbitrary reference level.

Figure 2.6. Mean (+/- 1 standard error) intensity (decibel) of Low Base Tones recorded in two treatments (unvegetated and vegetated) at three distances (1, 7, 15 m) from the sound source.

Decibel values are relative to an arbitrary reference level.

Figure 2.7. Mean (+/- 1 standard error) intensity (dB) of motorboat noise recorded in two treatments (unvegetated and vegetated) at three distances (1, 7, 15 m) from the sound source.

Decibel values are relative to an arbitrary reference level.

DISCUSSION

Although many studies have examined how environmental and biotic factors affect acoustic signals in terrestrial systems (Aylor 1971, Martens 1979, Couldridge and van Staaden

2004) and marine systems (Slabbekoorn et al. 2010, de Soto 2016), almost no information exists about sound transmission in freshwater ecosystems (Gammell and O’Brien 2013). We found that aspects of sound transmission in a freshwater wetland differ from other ecosystems, and this

33 could affect acoustic communication by aquatic animals. We found decibel levels of underwater sounds decrease rapidly with distance from source. For example, at only 15 m from the sound source most frequency bands were lost and sound intensity of Hi-mix had decreased by over 20 decibels in open water wetlands. Although open water typically is an effective transmitter of underwater sound, sound in freshwater wetlands might be quickly reduced as sound waves bounce between the shallow water surface and bottom substrate (Forrest, 1994).

We also found that sound attenuation was greatly increased by the presence of submersed macrophytes (e.g. Elodea, Potamogeton, Chara). Less than 5 of the 20 frequency bands were detected in dense vegetation at a distance of only 7 m from the sound source. This was less than half the number of bands detected in the unvegetated wetlands at the same distance. Also, sound levels of mixed tones (Hi-mix, Low Bass, Motorboat) in vegetated wetlands were less than one third of those in open water at 15 m from sound source. The impact of vegetation on sound attenuation was much greater than reported in terrestrial environments. In terrestrial studies, 30 m of dense tree stands reduced sound intensity only by 5-25 dB. (Aylor 1972, Fan et al. 2010).

Our results were unexpected, because some researchers have proposed that the high water content of submersed macrophytes should make them transparent to sound waves (Aiken 1985).

We also found that submersed vegetation greatly altered the acoustic environment because it filtered out mid to high (2000-20,000 Hz) tones more effectively than low tones <1000

Hz). For example, spectrograms show complete sound loss mid to high tones in vegetated wetlands at 7 m from sound source. These results are more pronounced than reported in terrestrial systems, although high frequencies are also filtered out by vegetation (Aylor 1972).

The effect we found on sound transmission could affect many ecological processes in freshwater communities. In terrestrial systems, invertebrates use acoustic communication to

34 locate mates from over 40 m away (Romer and Lewald 1991), and some aquatic organisms also use acoustic communication. For example, predatory freshwater invertebrates

(Villalobos‑ Jiminez et al. 2017) and fish (Purser and Radford 2011) use sound to locate prey, but our results indicate they probably cannot detect sounds from great distances or in dense underwater vegetation. However, we found that sound transmission at short distances (i.e., 1 m) was similar in vegetated and open water, and therefore animals responding to short-distance cues may not be greatly affected by overall plant density. Furthermore, Aiken (1982) found that most high frequency sounds produced by aquatic insects attenuated within 1 m. Therefore, aquatic invertebrates may be limited to using short-range acoustic communication.

Some unexamined environmental factors might alter the impact of plant communities on sound transmission. Wetlands have diverse plant communities (Cowardin et al. 1979) and different plant species might have different effects on sound transmission. I studied the impact of submersed vegetation, but emergent plants are more fibrous and may be more effective in blocking sound transmission. Also, in temperate wetlands submersed plants usually senesce in fall, and the vegetation is not present through the winter. Therefore, wetland animals may rely on auditory cues more in the fall to winter months when sound attenuation is lowest.

Furthermore, mating calls of terrestrial grasshoppers are transmitted better through stands of native plant species than non-native species (Couldridge and van Staaden 2004). Many wetlands have become invaded by dense stands of exotic plant species (Zedler and Kercher 2004), which might alter sound communication of their fauna. Therefore, unexamined effects of seasonal or spatial variation in plant communities might drive changes in animal’s mating, migration, or feeding behavior (Dodson et al. 1994). Lakes, streams and wetlands contain a disproportionate amount of earth’s biodiversity but these ecosystems are more threatened by human impacts than

35 terrestrial ecosystems (Dudgeon et al. 2006). There has been an increase of underwater anthropogenic noise pollution similar to those studies in terrestrial habitats, but the effects are not well-known (Slabbekoorn et al 2010). Our results show that human-caused factors may strongly impact aquatic ecosystems. A few published studies have shown that anthropogenic noise impacts some aquatic organisms. For example, motorboat noise alters the communication and behavior of fish (Whitfield and Becker 2014) and frogs change their vocalization in locations with high automobile traffic (Cunnington and Fahrig 2010). I tested transmission of engine noise at levels (85 dB) that are produced by recreational motorboats (Haviland-Howell et al. 2007).

Boat engine noise is a common type of anthropogenic noise pollution in lakes and nearshore coastal areas, and I found that motorboat noise was transmitted effectively through the water column. Motorboat sound includes low to high frequency tones, so its transmission pattern was similar to the Hi-mix and Low Bass mixed tones. However, my results also show that dense submersed plants rapidly reduce sound levels of anthropogenic noise. At 15 m from sound source, motorboat sounds were only 15 dB in dense vegetation, but were still 65 dB in open water. Therefore, efforts to promote dense aquatic vegetation might be a useful management strategy to preserve natural soundscapes in areas with high anthropogenic noise pollution.

Further study is needed to understand how freshwater habitat management strategies affect the underwater acoustic environment. Wetland and lake management often target the plant community to promote desirable ecosystem processes. For example, removal of submersed plants in the lake littoral zone is a common method to enhance swimming, boating or fishing activities (Grimm 1989). It is largely unknown how this practice affects the underwater soundscape or behavior of aquatic organisms. While sound travels better through open water, plant removal may have a negative impact on behavior of organisms adapted for densely

36 vegetated habitats. For example, predatory damselfly larvae had higher feeding rates in the absence of natural sounds (Villalobos‑ Jiminez et al. 2017). Therefore, future studies are needed to understand interactions between plant community structure and underwater sound transmission, including a focus on characteristics of anthropogenic noise pollution and how it affects conservation of freshwater ecosystems.

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452 (https://www.cdc.gov/ncbddd/hearingloss/sound.html)

40 CHAPTER 3 COMPARING HOW ABOVE –WATER AND BELOW-WATER

SOUNDSCAPES DIFFER BETWEEN RURAL WETLANDS AND SITES AFFECTED BY

ROAD NOISE

ABSTRACT

Soundscapes are the array of natural and anthropogenic sounds occurring in an area, and they can be impacted by interactions between human and natural components. For example, anthropogenic noise can mask natural environmental sounds, which can alter animal communities. In this study, I compared soundscapes in wetlands disturbed by road traffic noise to wetlands distant from major roads. In each wetland, I recorded above-water and below-water soundscapes in Spring, Summer, and Fall. Wetlands near roads had higher sound intensity, more anthropogenic noise occurrences, and lower acoustic diversity above and below the water line.

In contrast, rural wetlands had more natural sounds above and below the water line, including a higher number of bird calls. When soundscapes were subdivided by frequency, most anthropogenic sounds occurred in low frequencies (0-5 kHz), but natural sounds were found in all frequencies. Soundscapes changed during the year, with more natural sounds occurring in

Spring and the highest acoustic diversity recorded in Spring and Fall. The alteration of soundscapes occurs in many freshwater wetlands, which could alter biotic communities by affecting animal behavior such as intraspecies communication, interaction of predator and their prey, and resource acquisition. These findings indicate that nearby roads change wetland soundscapes by increasing noise pollution, which masks natural sounds such as bird calls and reduces biodiversity.

41 INTRODUCTION

The concept of defining a soundscape as the natural and human-caused acoustic features of an area was developed over five decades ago to study human psychological stress factors in urban environments (Southworth 1969). Because anthropogenic sound is a non-natural component in ecosystems, it is commonly termed “noise pollution” and is regulated under the

United States Clean Air Act by the Environmental Protection Agency, pursuant to the Noise

Control Act of 1972 and the Quiet Communities Act of 1978 (EPA 2017). These Acts govern noise pollution in the United States. The Noise Control Act of 1972 defines thresholds of chronic noise above 55dB to be detrimental to human health. People also study sound in natural habitats, with terrestrial systems being the most widely studied habitat, followed by marine habitats

(Gammell and O’Brien, 2018). However, soundscape analysis has also recently been used to study natural freshwater habitats.

Recently, there have been advances in acoustic equipment that can be used under field conditions and software used to analyze sound recordings that has led to an increase in acoustic studies in ecology. The characteristic of a natural soundscape is affected by the habitat’s physical structure and its biotic community (Joo et al. 2011). Therefore, analyzing soundscapes can be used to determine the presence or absence of individual species (Kasten et al. 2012) and examine effects of environmental changes (Mason et al. 2008). In addition, monitoring species-specific sounds can provide data on their habitat preferences, population numbers, and lifecycle phenology (Riede 1998). For example, recording bird calls has been used to determine the temporal dynamics of seasonal migrations (Blumstein et al. 2011) and calls of Southern Leopard frog (Rana sphenocephala) monitored reproductive timing and changes in frog populations

(Bridges et al. 2000). Monitoring the bioacoustic richness in a soundscape can also provide

42 information on topics such as temporal and spatial changes in animal communities and biodiversity. For example, soundscape analysis showed activity patterns of predators and their prey (Barber et al. 2009).

This method has many implications in conservation of biodiversity, and there is a developing interest in using this method to study the effects of anthropogenic factors on biotic communities. Anthropogenic sound is a component of almost all soundscapes, except in very isolated areas (e.g., mountaintops, polar regions, rainforest interiors). The most prevalent anthropogenic sounds are created by machinery. Their sound levels usually track human activity patterns, and thus they are highest during the daytime until the early evening. Many human- caused sounds have distinct spectral and temporal properties that can easily be distinguished from natural sounds (Pijanowski et al. 2011). For example, automobiles on roadways create a steady sound at low frequencies (0-4 kHz).

Anthropogenic sound can mask natural sound when they occur at the same frequency

(Kuehne et al. 2013). For example, most urban noise is at low frequencies, which masked low frequency bird calls (1.5–4 kHz) but not high-frequency calls (4–7.5 kHz) (Parris and Schneider

2009). The loss of natural acoustic signals can alter animal behavior used for reproduction, feeding, movement, and habitat choice (Kunc et al. 2016). For example, cetacean echolocation is disrupted by ship engine noise, which impairs their navigation and feeding success (Kunc et al.

2016). Prey species that detect predators with sound may also miss cues and have higher mortality rates (Kunc et al. 2016), and predatory invertebrates can have lower feeding success in the presence of noise (Villalobos‑ Jiménez et al. 2017).

Freshwater wetlands cover 5-7% of the global land surface area but they harbor a disproportionate amount of the world’s biodiversity (Reid et al. 2019). Many species of wetland

43 mammals, birds, amphibians and insects use vocalization to communicate. Therefore, their soundscapes are often composed of a diverse array of sounds (Pijanowski et al. 2011a). Wetland soundscapes change throughout the day, with distinct dawn and dusk animal choruses

(Pijanowski et al. 2011a). They also show seasonal fluctuation, with the greatest soundscape diversity in spring to summer (Pijanowski et al. 2011). Because wetlands exist at the land-water interface they are often affected by human degradation, and they harbor a disproportionate amount of endangered species (Mitsch and Gosselink 2015). However, almost no studies have been published examining the effects of anthropogenic sound in freshwater wetland ecosystems

(Gammell and O’Brien 2018)

In this study, I recorded soundscapes in freshwater wetlands in northeastern Ohio throughout one year. I examined the influence of anthropogenic noise pollution by comparing soundscapes in rural wetlands and those located adjacent to active roads. I hypothesized that there would be temporal differences in the amount of sound and type of sound in wetland soundscapes within the year due to changes in biotic communities by season. I also hypothesized that the occurrence of natural sounds would be different in areas that were affected by noise pollution from nearby roads or were relatively undisturbed by anthropogenic sounds.

METHODS

Experimental Design

Soundscapes were recorded in 14 wetlands in Richland, Ashland and Portage Counties in northeast Ohio (Fig. 1). Although some anthropogenic sounds (e.g., airplanes) affected all locations, I assumed that the most important anthropogenic sound was noise from automobiles, motorcycles and trucks. Therefore, I compared locations that varied in their proximity to roadways (Table 1). Linear distance from the nearest paved road was measured with an ArcGIS

44 system using Arc Map. These included major interstate highways and smaller local roads. Sites were selected based on proximity to roads and also road size. We confirmed our selections with an onsite assessment of traffic noise levels measured with a sound meter (Extech Instruments

Model No. 407703, dBA weighted values). We used the EPA’s threshold of annoyance and detrimental sound levels of 55dB to allocate wetlands into treatments. Sites that were more than

250 m from a road consistently had decibel readings below 55dB and were labeled

“Undisturbed”. Most sites that were within 150 m of a road had sound levels above 55dB and were labeled “Disturbed”. The only exception was one site (Cool Springs Preserve) that was within 55 m from a road, but the road was rural and no vehicles passed during the 2-hour assessment period. We labeled this an Undisturbed site.

Table 3.1. Treatment site locations, distance to nearest paved road, and sound levels during initial visit.

Site name and treatment Site location (Decimal degrees, Distance from road Decibel level

latitude, longitude (m) (dB)

Disturbed Wetlands

Apartment Wetlands 40.683617, -82.565867 70 80

Blackfork Ashland University 40.81185, -82.4148 21 65

Daily Monument 41.08705, -81.389933 16 75

Esbaneshade 40.800717, -82.39245 167 68

HAERF, Kent State University 41.137933, -81.3395 119 56

Lex Lanes 40.678417, -82.56975 16 75

Rumpke 40.975267, -82.4853 48 65

Undisturbed Wetlands

Audobon Wetlands 40.861027, -82.264392 372 55

Blackfork Hunting Preserve 40.81185, -82.4148 244 52

46 Cool Springs 40.74333, -82.13025 55 45

Funk Bottom 40.751367, -82.107117 559 47

Gorman 40.699, -82.556583 243 51

Cooke 40.882817, -82, 508933 376 52

Hurdle 41.021383, -82.236 430 55

Figure 3.1. Locations of wetlands sampled during study.

In 2012, I sampled each wetland in Spring (May), Summer (August) and Fall (October) to account for seasonal changes in soundscapes. On each date, all sites were sampled on one day for 5 minutes at four times (8:00, 14:00, 18:00 and 24:00). I recorded below-water and above- water soundscapes at 2 meters from the shoreline. In Disturbed sites, I sampled along the shoreline nearest to the road. Undisturbed sites were sampled nearest to the road that was used to access the wetland. The four daily recordings were combined to characterize the overall soundscape at the site on that date.

All soundscape recordings were taken with a Song Meter SM2/SM2+ programmable audio recorder, and sound files (Waveform Audio File Format , .WAV) were stored on a portable hard drive. I recorded the airborne soundscape with a SMX-II acoustic microphone and below- water sounds with an HTI-96-MIN hydrophone (Wildlife Acoustics, Concord, MA). The hydrophone was suspended at the maximum possible depth without touching the benthic

48 substrate, which was 3 cm to 30 cm below the water’s surface. This ensured recorded sounds were only transmitted through the water column. At each site, water temperature was also measured with a handheld digital thermometer (Fisher Scientific Traceable Thermometer), and water depth was measured with a meter stick. I averaged temperature and depth measurements across all dates to get a mean value at each site.

Data Analyses

Acoustic data were analyzed with an acoustic software program (Raven Pro, Cornell

University, Cornell, NY). Graphical depiction of the soundscape (i.e., spectrogram) allowed me to visually examine power of the combined sounds and power in different frequency bands above and below the water level. The decibel scale used was based on the Raven Pro arbitrary scale and used a reference value of 1. I listened to each recording across the entire frequency range (0-48 kHz) to count occurrences of identifiable anthropogenic and natural sounds. Natural sound occurrences included environmental (e.g., wind, rain, thunder) and biological (e.g., birds, invertebrates, amphibians, mammals) sounds. Anthropogenic sounds included mostly motor vehicles and some human voices and other engines (chainsaws, trains and airplanes). I listened to all above-water and below-water recordings, and counted each natural or anthropogenic sound occurrence. Sounds that could not be identified were not counted for either category. I also quantified the most common types of animal vocalization (bird, insect and frog calls) in the above-water soundscapes at 8:00 on May 2012 to compare their activity during a time of peak seasonal abundance. Most natural sounds occur between 0-15 kHz. Therefore, I used Raven Pro to subdivide the recordings into three frequency bands (low, 0-5 kHz; mid, 5-10 kHz; high, 10-

15 kHz). Next, I listened to each frequency band in all above and below water recordings and counted each occurrence of natural and anthropogenic sounds.

49 Statistical Analyses

Data was analyzed using R packages and JMP software. All statistical comparisons were done separately in above water and below water data.

I treated the acoustic composition of the soundscape as a “community” of sounds, with numbers of occurrences of different sounds equivalent to numbers of individuals in different species. Soundscape diversity was calculated in R using the package Soundecology (Villanueva et al. 2011), which divided the 0-10 kHz frequency range into ten 1 kHz frequency bands and counted the times a sound was detected within each band. Soundecology calculated soundscape diversity with a Shannon’s Index (H’), which is frequently used to estimate biodiversity of biotic communities. I used JMP to run t-tests comparing baseline conditions (distance to road, initial sound levels, mean water depth and mean temperature) between my treatments. The number of bird, frog, and insect calls in May were compared between treatments with a t-test.

I compared natural and anthropogenic sounds and soundscape diversity among Month

(May, August, October) and Treatment (Disturbed, Undisturbed). I used mixed model ANOVAs to compare acoustic data because wetlands were repeatedly sampled across months. The mixed model ANOVAs treated Month and Treatment as fixed factors and Wetland site was a random factor. When there were significant differences among Months, I ran Tukey’s pairwise comparisons to determine which were different.

RESULTS

As expected distance to road (t=4.027, df=12, p<0.005) was different between

Undisturbed and Disturbed sites on the initial site visit. Overall sound levels (t=-5.362, df=12, p<0.005) were also about 20 dB higher in Disturbed wetlands. However, mean water depth

50 (t=0.321, df=39, p>0.05) and water temperature (t=1.074, df=39, p>0.05) did not differ significantly between treatments (Table 2).

51 Table 3.2. Baseline characteristics of sites by treatment (mean +/- 1 SE). Distance to road and Sound level (dBA adjusted scale) were sampled on the initial site visit. Water depth and temperature were collected on each sampling data and averaged across all sampling dates.

Treatment Distance to road Sound level Depth Temp

(m) (dB) (cm) (°C)

Disturbed 62.3+/-46.2 69.1+/-2.4 41.0 +/- 9.3 17.6 +/-1.3

Undisturbed 325.3+/-46.2 51.0+/-2.4 45.1+/- 9.1 19.5 +/-1.3

52 Above-water Soundscapes

Above-water level soundscapes included both natural and anthropogenic sounds, but there were generally more natural sounds than anthropogenic sounds (Fig. 2). Also, the total number of sounds were approximately the same between Undisturbed wetlands and Disturbed wetlands (Fig. 2). However, the types of sounds varied by Treatment. As expected, anthropogenic sounds were more abundant in Disturbed wetlands (F=11.431, df=1, p<0.005).

However, natural sounds were much more abundant in Undisturbed wetlands (F=4.526, df=1, p<

0.05). Month affected natural sound occurrence (F=5.682, df=2, p<0.01), which were more abundant in May and August than October. There was no Month effect for anthropogenic noise.

There was no Treatment X Month interaction for anthropogenic or natural sounds.

When we compared sounds in low, mid and high frequency bands (Figure 3), natural sounds were common across all frequencies, but most anthropogenic sounds occurred in the low frequency band. Anthropogenic sounds were higher in Disturbed treatment wetlands in the 0-5 kHz band (F=6.450, df=1, p<0.05), 5-10 kHz band (F=6.936, df=1, p<0.05), and the 10-15 kHz band (F=4.670, df=1, p<0.05). There was no Month effect or Treatment X Month interaction.

Natural sounds were generally higher in Undisturbed wetlands, but there was no Treatment effect in any frequency band. In the 0-5 kHz band, Month (F=15.901, df=2, p<0.01) and Treatment X

Month interaction (F=4.232, df=2, p<0.05) were both significant for natural sounds. May had more natural sounds than August and October. In the 5-10 kHz band, Month was significant

(F=5.186, df=2, p<0.01), and May and August had more sounds than October. In the 10-15 kHz band Month was again significant (F=55.656, df=2, p<0.001) and August had more natural sounds than May and October. There were differences in Spring in animal vocalizations between treatments (Figure 4). Bird calls were the most common type of animal vocalization. Bird calls

53 were almost three times more abundant in undisturbed wetlands than disturbed wetlands

(t=4.889, df=12, p<0.001). Neither frog (t=-0.349, df=12, p>0.05) nor invertebrate (t=-0.053, df=12, p>0.05) calls were significantly different between treatments.

Soundscape acoustic diversity varied among Treatments (F=6.899, df=1, p<0.05) and

Months (F=28.016, df=2, p<0.001) above water (Figure 5). Shannon’s diversity (H’) was about

1/3 lower in Disturbed wetlands than Undisturbed wetlands. May had the highest acoustic diversity, which was significantly different than August and October. There was not a Treatment

X Month interaction.

Figure 3.2. Number (mean +/- 1 SE) of above-water natural and anthropogenic sounds in

Disturbed and Undisturbed wetlands in May, August and October.

55 0-5 kHz

5-10 kHz

10-15 kHz

Figure 3.3. Number (mean +/- 1 SE) of above-water natural and anthropogenic sounds in

Disturbed and Undisturbed wetlands in May, August and October in low, mid, and high frequency bands

56 Bird Calls

Frog Calls

Insect Calls

Figure 3.4. Number (mean +/- 1 SE) of bird, frog and insect calls in Disturbed and Undisturbed wetlands at 8:00 in May.

Figure 3.5. Above water soundscape diversity (mean +/- 1 SE) in Disturbed and Undisturbed wetlands in May, August and October. Acoustic diversity is given as Shannon’s diversity (H’).

Below-water Soundscapes

The below-water soundscapes looked very different when compared to above-water data

(Figure 2, Figure 6). There were fewer sounds recorded below water than above water. Natural sounds were still more common than anthropogenic sounds, except for Disturbed wetlands had more anthropogenic sounds in August. Also, soundscape acoustic diversity was higher below water than above water. The mixed model ANOVA found that anthropogenic sounds were more abundant in Disturbed wetlands (F=9.679, df=1, p<0.01) and natural sounds were greater in

Undisturbed wetlands (F=5.539, df=1, p<0.05). Anthropogenic sounds did not differ by Month, and there was no Treatment by Month interaction. However, there was a significant Month effect

58 for natural sounds (F=27.368, df=2, p<0.001). May had higher numbers than August and

October. There was also a Treatment X Month interaction for natural sounds (F=3.892, df=2, p<0.05). The interaction was probably an effect of the much higher number of natural sounds in

May in Undisturbed wetlands than Disturbed wetlands

When we compared sounds in low, mid and high frequency bands, anthropogenic sounds were less common than above water and they were almost missing in the mid and high frequency band (Figure 7). In the 0-5 kHz band, anthropogenic sounds were again more abundant in

Disturbed wetlands (F=10.115, df=1, p<0.01). There was also a Treatment X Month interaction

(F=5.727, df=1, p<0.05). There were no significant factors for anthropogenic noise in the 5-10 kHZ band or 10-15 kHz band. Natural sounds were more abundant in Undisturbed wetlands in the 0-5 kHz band (F=5.732, df=1, p<0.05), 5-10 kHz band (F=4.866, df=1, p<0.05), and 10-15 kHz band (F=8.377, df=1, p<0.01). Month was also significant in all three bands: 0-5 kHz

(F=18.255, df=2, p<0.01), 5-10 kHz (F=17.068, df=2, p<0.01), 10-15 kHz (F=6.639, df=2, p<0.01). In all frequency bands, there were more natural sounds in May than August and

October. There were no Treatment X Month interactions in any band.

Soundscape acoustic diversity was different between Treatments (F=4.794, df=1, p<0.05) and among Months (F=5.093, df=2, p=0.01) (Figure 8). Shannon’s diversity (H’) was much lower in Disturbed wetlands than Undisturbed wetlands. August had the highest diversity, which was significantly different than May and October. There was no Treatment X Date interaction.

Figure 3.6. Number (mean +/- 1 SE) of below-water natural and anthropogenic sounds in

Disturbed and Undisturbed wetlands in May, August and October

60 0-5 kHz

5-10 kHz

10-15 kHz

Figure 3.7. Number (mean +/- 1 SE) of below-water natural and anthropogenic sounds in

Disturbed and Undisturbed wetlands in May, August and October in low, mid and high frequency bands

Figure 3.8. Below water soundscape diversity (mean +/- 1 SE) in Disturbed and Undisturbed wetlands in May, August and October. Acoustic diversity is given as Shannon’s diversity (H’).

DISCUSSION

Over 80% of the continental US is within 1.1 km of a road (Ritters and Wickham 2003), and the most common outdoor human-caused sounds are vehicle engines (Barber et al. 2009).

Engine noise is an increasing problem in most terrestrial ecosystems because road use has increased 9% during the past decade to a current estimate of 5.2 trillion vehicle km/year

(USDOT 2019). I found that anthropogenic noise from nearby roads alters the soundscape of wetland habitats. As expected, I found more anthropogenic sounds in disturbed wetlands

62 throughout the study. However, I also found acoustic diversity was lower and overall sound levels were almost 20 dB higher in these wetlands. Furthermore, there were fewer natural sounds in wetlands near roads. These changes were detected in both above-water and below-water recordings, showing that both terrestrial and aquatic organisms are affected. Therefore, my study indicates that noise pollution is pervasive in freshwater wetlands near roads, and most species in these habitats are living under profoundly different environment conditions than in rural areas.

Many studies have found that noise pollution affects terrestrial and marine ecosystems

(Simpson et. al. 2007, Shannon et al. 2016, Reid et al. 2019), but few studies have examined the effects in wetlands. Understanding factors affecting wetland habitat quality is important because they are one of the most valuable habitat types. For example, Costanza et al. (2014) estimated the earth’s wetlands provide $27 trillion/year in ecological services such as protection from storm damage, wildlife and fish habitat, absorbing floodwaters, and purifying surface water. Many of these services are mediated by their biotic communities.

I found that anthropogenic noise from nearby roads alters the soundscape of wetland habitats. As expected, I found more anthropogenic sounds in disturbed wetlands and overall sound levels were almost 20 dB higher in these wetlands. However, I also found the number of natural sounds and soundscape acoustic diversity was lower in wetlands near roads. Although I did not find a difference in frog or insect sounds, I found that bird calls were much less common in disturbed wetlands. My study did not test if birds were being affected directly by anthropogenic sound or other factors that are associated with roads (e.g. air pollution, visual deterrence, vehicle collisions). However, previous studies have found that traffic noise was the single most important factor in reducing bird densities near roads (Reijnen et al. 1995, Peris and

Pescador 2004). Also, road noise will affect bird calling behavior when it masks their calls

63 (Francis et al. 2009). At the elevated sound levels I measured in the disturbed wetlands (~70 dB), acoustic models estimate that most birds could not communicate effectively with their conspecifics at distances of greater than 100 meters (Lohr et al. 2003). Therefore, I expect that it is likely that road noise directly reduced bird numbers in the Disturbed wetlands

Animal calls are masked by sounds occurring at the same frequency (Lohr et al. 2003). I found that most above-water and below-water anthropogenic noise occurred at the low frequency band (0-5 kHz) as reported in other studies (Hu and Cardosa 2010, also see Dissertation Chapter

2). Therefore, road noise may have a greater effect on wetland species that communicate with low frequencies. For example, some bird species that call with low tones, sing in a higher pitch in urban areas than in rural habitats (Hu and Cardosa 2010). Tree cricket, Oecanthus pellucens, also increase the pitch of call when there is more traffic noise (Orci et al. 2016). However, not all species can adapt their calls or behavior, and may become less common in roadside wetlands.

Soundscapes have increasingly been used to asses changes in animal populations, biodiversity, and ecological processes (Sueur and Farina, 2015, Merchan and Diaz-Baltierro

2013). I found that the natural sound component of both above water and below water soundscapes was greatest in May. This probably reflected changes in animal activity levels because this is within the typical breeding season of many wetland species. Long-term collection of soundscapes has been suggested as a method to monitor changes as a result of environmental degradation such as habitat fragmentation and climate change (Blumstein et al.

2011). Therefore, my data will have future value because I have not found any published soundscape studies of freshwater wetlands in the northeastern United States.

In conclusion, my study showed that traffic noise changes above-water and below-water soundscapes. Noise pollution affects the behavior and survival of marine and terrestrial

64 organisms, and therefore it could have a similar effect in wetland communities. Future research should focus on impacts of noise pollution on the interspecies and intraspecies interactions such as communication, aggression and food acquisition among wetland animals.

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68 CHAPTER 4 THE IMPACT OF SHORT-TERM ANTHROPOGENIC NOISE ON SOUND

PRODUCTION AND BEHAVIOR OF THE WHITE RIVER CRAYFISH, PROCAMBARUS

ACUTUS (DECAPODA: CAMBARIDAE)

ABSTRACT

Many animals use sound to communicate and react to their environment, but little is known about bioacoustics of freshwater invertebrates or how anthropogenic sounds affect their behavior. Recent studies have found that Procambarus clarkii crayfish create sound, but this has not been studied in other crayfish species. I recorded sound production of Procambarus acutus

(White River Crayfish) in the laboratory and describe its acoustic characteristics. The crayfish frequently produced sounds, with a mean power of 100 dB. The sounds were 1 s pulse trains made up of 3 to 12 clicks that each lasted 0.008 s. The mean bandwidth of the sound was 20.6 kHz, which reached ultrasonic frequencies. I tested the effect of anthropogenic noise by monitoring crayfish behavior and sound production before, during, and after a 20 minute period of motorboat sound. Most crayfish displayed aggressive behaviors throughout the sound trials.

Crayfish in the sound treatment had lower total activity levels than a control group that was not exposed to anthropogenic sound. However, there was no change in the number of sounds produced between treatment groups. These results indicate that crayfish probably communicate with auditory cues, and anthropogenic sounds can alter their behavior. Therefore, changes in underwater soundscapes may affect crayfish resource acquisition, survival, and reproduction.

Because crayfish are dominant detritivores and predators in freshwater wetlands, anthropogenic noise may affect key ecological processes in these habitats.

69 INTRODUCTION

The impact of ambient sound on human behavior and stress levels has been understood for decades, and levels of anthropogenic sound are regulated as noise pollution under the 1977

Clean Air Act (WHO 2011, EPA 2017). Modern microphones and can detect sounds from the audible through the ultrasonic frequencies, which was not possible in the past. Many animals produce sound to attract mates, detect or deter predators, coordinate their movements and defend territory. However, we have only recently begun to study the impacts of natural and man-made sound in natural ecosystems. Traffic noise alters bird movement and calling behavior and increases their predation risk (Owens et al. 2012, Templeton et al. 2016). Traffic noise also altered prairie dog, Cynomys ludovicianus, activity, reduced foraging and increasing vigilance behaviors (Shannon et. al 2014).

An emerging pattern is that many vertebrate species in marine, terrestrial and freshwater ecosystems use acoustic signals and they are affected by anthropogenic noise (Weilgart 2007,

Barber et al. 2009, Slabbekoorn et al. 2010). Far fewer studies have examined how invertebrates are affected by changes in environmental soundscapes, despite their central importance in ecosystem processes. Of the few studies on invertebrate bioacoustics, most have been conducted in terrestrial or marine systems. For example, terrestrial grasshoppers (Orthoptera), beetles

(Coleoptera) and wasps (Hymenoptera) use audible sound (frequencies up to 20,000 kHz) to signal mates and avoid predators (Klappert & Reinhold 2003, Wilkinson et al. 1967, and Van

Den Assem 1970). Other terrestrial insects can detect and produce ultrasonic sounds (Gwynne et al. 1988). For example, Arctiidae moths produce ultrasonic calls that disrupt echolocation of predatory bats (Fullard et al. 1979). In marine habitats, snapping shrimp (O. Decapoda) are common on shallow tropical reefs, and they dominate the soundscape by producing constant high

70 frequency clicks as they communicate with others in the colony (Chitre, 2010). Mantis shrimp,

Hemisquilla californiensis, (O. Stomatopoda), produce a low-frequency rumble for unknown reasons (Staaterman et al. 2011).

New technology such as underwater microphones (i.e., hydrophones) has allowed studies on bioacoustics in aquatic communities. Yet, almost nothing is known about the use of sound by freshwater invertebrates and the impact of anthropogenic sound on their ecology (Morley et al.

2014). A few studies reported that water beetles (Haliplidae, Hydrophilidae) and water boatmen

(Corixidae) produce sound to delineate territory, deter predators, coordinate migration, and communicate with mates (King 1976, Aiken 1982, Theib 1982, Theiss et al. 1983). Also, pygmy backswimmers (F. Pleidae, Micronecta scholtzi) produce a 99 decibel underwater courtship song by stridulating its penis across ridges on the underside of its abdomen. When scaled to body length, this is the loudest known animal-produced sound (Sueur et al. 2011). However, most information on freshwater invertebrate bioacoustics is anecdotal and most studies were conducted before the use of modern recording technology (Aiken 1985, Sueur et al. 2011). For example, the extensive sound libraries at Cornell University (MaCaulay Library) and The Ohio

State University (Borror Laboratory of Bioacoustics) contain no aquatic insect recordings. This demonstrates a major gap in our basic knowledge about most species of aquatic invertebrates.

Worldwide there are over 650 species of crayfish (O. Decapoda), which are often the dominant invertebrate in freshwater ecosystems (Reynolds et al. 2013). They feed as predators, herbivores, detritivores and omnivores, and they can be very important in food webs (Lodge et al. 1994). While it was long known that freshwater crayfish communicate visually, physically and chemically (Moore 2007), little research exists on crayfish bioacoustic behavior. Recently it was discovered that Procambarus clarkii (Red Swamp Crayfish) produce above-water pulsed

71 signals (i.e. clicking noises) by striking its scathognathite appendage on its mouthparts inside a chamber in the cephalothorax (Favaro et al. 2011). Similar underwater sounds were produced during intraspecific interaction such as mate searching and aggression (Buscaino et al. 2012).

However, some crayfish also produced sound when they were solitary. Little is known about the ecological importance of their sounds and no research has examined other crayfish, in spite of their ecological significance.

There have been almost no published studies on crayfish bioacoustics or the impact of anthropogenic noise on their behavior. In this project, I studied a crayfish species that is native in

Ohio (White River Crayfish, Procambarus acutus (Girard 1852)) and is closely related to the

Red Swamp Crayfish, P. clarkii. I recorded sounds produced by wild-caught Procambarus acutus to determine the basic characteristics of their acoustic signals. I also investigated the impact of anthropogenic noise on their sound production and activity levels. My hypotheses were that: 1) characteristics of P. acutus sounds would be similar to that of the congeneric P. clarkii crayfish, and 2) anthropogenic sounds would reduce the production of P. acutus sounds and alter their behavior.

METHODS

Experimental Design

During September 2014, I used minnow traps to collect approximately 40 Procambarus acutus, from wetlands on the Kent State University campus (Portage Co., Ohio). I obtained about

40 additional P. acutus from a commercial fish hatchery (Calala’s Water Haven, New London,

Ohio) where they were considered a nuisance species in their breeding ponds. Crayfish ranged in size 7.5 cm to 13 cm. In between trials, crayfish were maintained in laboratory aquaria (7.6 l) that were held at room temperature (21 C).

72 Aquaria had gravel substrates and a sheltered hiding place made from half of a 20 cm

PVC pipe. Aquaria were illuminated with dim lighting on an 8:16 light:dark cycle. The water was aerated with bubblers, and it was exchanged every 7 days with dechlorinated tap water. Each aquarium contained 1 crayfish that was fed food pellets (Crayfish and lobster food, Carolina

Biological Supply, Burlington, NC).

Crayfish sound and movement behavior were recorded in recording chambers that were modified 45 l plastic containers (55 cm X 41 cm x 32 cm, L X W X H). Sound reflection was reduced by lining the inside of the tank, bottom and sides with self-adhesive, 3.8cm thick, black acoustic foam. The bottom of the chamber was marked with a 5 cm X 5 cm grid. The chamber was illuminated during trials with overhead fluorescent tube lights and temperature was about 21

C during the trials.

I first characterized sounds produced by P. acutus that were held in the recording chamber in four groups of five crayfish. Each crayfish was used one time in the preliminary sound trials. I recorded each group for five minutes with a hydrophone (HTI-96-MIN , Wildlife

Acoustics, Concord, MA). Audio was converted to a Waveform Audio File (.WAV) file with a

Song Meter SM2/SM2+ programmable audio recorder. This equipment recorded sound from 0 to

48,000 Hz, which spanned the audible to ultrasonic frequencies. All recorded sound files were stored on SD memory cards until the recordings were analyzed.

Second, I tested the effects of anthropogenic noise on intraspecies behavior of crayfish.

The same crayfish were randomly assigned into 28 pairs with a male and a female crayfish. 14 crayfish pairs were randomly assigned into a group tested in the presence of noise (Sound treatment) and the other 14 pairs were assigned into a control group (No sound treatment). All pairs were held in the recording chamber during a 60 minute recording session, and their

73 behavior was monitored with the hydrophone/recorder previously described and also a video camera (Uniden Digital UBW2101 Wireless Video Baby Monitor, Uniden America Corporation,

Irving, Texas). Each 60 minute recording session included three time periods: 1) an initial 20 minute period with no added noise; 2) A second 20 minute period; in the Sound treatment a boat engine recording (SanDisk, Western Digital, San Jose, California, United States) was played below the water line from a speaker in a water tight container (X-Mini II XAM4-PU Portable

Capsule Speaker, Jackson Square, Singapore) and in the No Sound treatment there was no added sound; and 3) a final 20-minute period with no added noise for either treatment.

All trial recordings were done in one of the 3 recording chambers. It is possible that crayfish pairs would release chemical cues that could influence their behavior. To avoid the chance that chemical cues from one trial reinforced behavioral responses in later trials with the same treatment, the tanks were rinsed and water changed between each trial. I also randomized the order of testing to avoid this confounding variable.

Data Analyses

The recorded sound files were analyzed using Raven Pro (Cornell University, Cornell,

NY). Raven Pro produces a spectrogram, which is a visual depiction of a sound recording. The software also can measure sound characteristics such as frequency, temporal patterns, and number of occurrences. It can also count occurrences of specific sounds. In order to detect sounds produced by P. acutus crayfish, I first scanned spectrograms for sounds that were similar to published descriptions of sound produced by Red Swamp crayfish, Procambarus clarkii

(Favaro et al. 2011) and recorded sounds provided by the authors. I also listened to a subset of all recordings to find any other sounds produced by crayfish. Then, I visually characterized the spectral and temporal characteristics of P. acutus produced sounds on the spectrogram.

74 Intraspecific behavior during the anthropogenic noise treatment was assessed by viewing video recordings from each research session. Behaviors were characterized using a modified ethogram from Moore (2007) (Table 1). This method codes crayfish behaviors into 9 levels of intraspecies aggression. I counted the number of times each behavior was observed in the 60- minute session for each male/female crayfish pair. In order to check if crayfish sound production was altered by anthropogenic noise, Raven Pro software searched for crayfish sound parameters

(time, frequency) based on the characteristics identified in the preliminary sound trials. I listened to a subset of recordings to ensure Raven Pro was accurately identifying crayfish sounds.

Afterwards, Raven Pro was used to count sounds produced by P. acutus in all recording sessions.

Statistical analyses

JMP statistical software was used to test if anthropogenic sound altered crayfish behavior and sound production. I compared crayfish activity levels (total number of all observed behaviors excluding “Ignoring Opponent” because this was usually an inactive period) among Treatments and Time. I also compared Sound production among Treatments and Time. Because I gathered data from each crayfish pair in three periods, I used a mixed model ANOVA with Treatments and Time as fixed effects, and Crayfish Pair as a random effect. I followed significant factor results with pairwise comparisons with Tukey’s tests.

75 Table 4.1. Modified crayfish behavior ethogram. Activity code shows a relative level of aggression ranging from low (-3) to neutral (0) to high aggression (5). Values are modified from

Moore (2007).

Activity Description

Code

-3 Mating

-2 Tailflip or fast retreat away from opponent

-1 Slow retreat from opponent

0 Ignoring opponent

1 Approach opponent without threat

2 Approach opponent with threat: antennae whip and/or raised claws

3 Closed claw use: boxing, pushing, touching opponent

4 Open claw use: grabbing opponent

5 Unrestrained fighting

76 RESULTS

When P. acutus crayfish were held in groups in the preliminary sound trials, they often produced audible sounds that were a train of many separate pulses (i.e. “clicks”) (Figure 1).

Most 5-minute recordings had an average of 14 pulse trains with 3-6 clicks in rapid succession per train. An additional one or two pulse trains contained 9-12 clicks per train. Each pulse train lasted approximately 1 s, and the duration of a single click was 0.008s. Each click had a frequency range that started at 7 kHz and the mean bandwidth of the clicks was 20.6 kHz. The upper frequency level was above the software’s upper detection limit of 48 kHz into the ultrasonic range. The average in-band power was 99.8 dB (Raven Pro arbitrary decibel scale).

The pulse train was not audible above the water or while the crayfish were not in water. Pulse trains were spaced throughout each recording session. Sounds of crayfish movement (walking, climbing, sparring) could also be heard in the recording, but these were distinctly different from the pulse trains. Because crayfish produce multiple clicks per pulse train, I counted each pulse train as a separate sound occurrence in our anthropogenic noise experiment.

Figure 4.1. Spectrograms of a representative Procambarus acutus pulse train. Top: Power of clicks, Waveform (abscissa: time in seconds, ordinate: amplitude in Raven Pro arbitrary decibel unit (kU). Bottom: Frequency of clicks, (abscissa: time in seconds, ordinate: frequency in kHz).

78 I categorized 400 individual events of crayfish activity during the Anthropogenic noise experiment (Figure 2). The behaviors observed in the male/female pairs included all 9 levels of aggression from low (Code -3) to high (Code 5) described on Table 1. The most common type of behavior was “Ignoring the opponent” (Code 0), which comprised about half of all activity events. The pairs were males and females, but mating (Code -3) was rarely observed. However, the other low aggression behaviors (Codes -2, -1) were common. Many pairs also exhibited high aggression behavior towards each other. This included behaviors that are very intensive such as opening the claws and grabbing the opponent (Code 4) and unrestrained fighting (Code 5). The total number of all high aggression behaviors observed (Codes 1 to 5) were greater than the number of all low aggression behaviors (Code -3 to -1). There were no clear differences in the types of behaviors observed the No Sound versus Sound treatment. Both had a mix of low to high aggression behaviors. Comparing temporal patterns (Before, During, After anthropogenic sound) of behavior did not show a clear pattern. Generally, the relative amount of low or high aggression behaviors was fairly similar during the experiment. This pattern occurred in both treatments.

Figure 4.2. Average count of crayfish behavior in each treatment group (NS = No Sound, S = Sound) during each Time period

(Before, During, After). Activity Codes are the behavior types listed on Table 1.

80 Anthropogenic noise had an effect on P. acutus behavior. The Treatment factor was significant (F=4.817, df=1, p<0.05) and total activity levels were lower in the Sound treatment than No sound treatment. (Figure 3). The effect of Time (Before, During, After anthropogenic sounds were played in the 60 minute session) was also significant (F=6.849, df=2, p<0.005)

(Figure 2). Pairwise comparisons found that crayfish had more activity Before anthropogenic sound than During and After. The Treatment X Time interaction was not significant.

In the Before time period, Crayfish activity levels were somewhat lower in No Sound than Sound treatment (Figure 3). This was unexpected because this period was before any crayfish were exposed to motorboat sounds. Therefore, pairwise comparisons were examined for

Sound versus No Sound treatments within the Before period. However, there was no difference between treatments (Tukey’s HSD, df=50, t=1.93 p=0.393).

Crayfish pairs frequently produced many sound occurrences (i.e. a pulse train with 3-12 clicks) during the experiment (Figure 4). The mean number of sounds per trial ranged from 65 sounds/trial in the first 20 minutes (Before), to 64 sounds/trial in the second 20 minutes (During), to 75 sounds/trial in the third period (After). However, there were no factors that changed sound production in the experiment. The mixed model ANOVA found that Treatment, Time, and Time

X Treatment interactions were all non-significant.

Figure 4.3. Total activity levels (mean +/- 1 SE) of crayfish in each treatment group (No Sound,

Sound) during each Time period (Before, During, After).

Figure 4.4. Number of clicks produced (mean +/- 1 SE) of crayfish in each treatment group (No

Sound, Sound) during each Time period (Before, During, After).

DISCUSSION

My study found that anthropogenic noise in freshwater wetlands may be affecting the behavior of White River crayfish, Procambarus acutus Crayfish in the Sound treatment had lower overall activity levels than control crayfish that were not exposed to the sound of motorboat engines. However, there was no detectable effect on the crayfish’s production of their own sounds. My results are significant because it is one of the first studies on the impact of

83 anthropogenic sound on freshwater invertebrates. Freshwater habitats account for only 0.01% of the earth’s aquatic habitats, but they support almost 6% of all known terrestrial and aquatic species (Dudgeon et al. 2006). With freshwater habitats becoming increasingly exposed to anthropogenic sounds in the form of road traffic, construction and watercraft (Slaabekoorn et al.

2010), understanding possible impacts of noise pollution will be useful to preserve habitat quality and manage them to provide maximum ecological services.

I did not test any long-term impacts on crayfish survival, but anthropogenic noise affects fish foraging by preventing discrimination between food and non-food items and reducing feeding rates (Kunc et al 2016). Furthermore, Owens et al. (2012) found that the Carolina chickadees, Poecile carolinensis, had higher perceived threat levels in increased traffic noise.

Anthropogenic noise has also been linked to disruptions in fish reproduction (Whitfield and

Becker, 2014). Almost no studies have examined long-term impacts on freshwater invertebrates, but Villalobos-Jiménez et al. (2017) found that noise reduced the prey capture rates by damselfly larvae. If crayfish behavior changes in response to noise pollution like other species, long term chronic effects may reduce their survival and population numbers. My results along with data from previous studies indicate that preserving and managing the acoustic soundscape of an environment could be just as important as preserving other ecological components.

Although the decreased activity levels I found could impact P. acutus foraging, reproduction and predator avoidance, my results did not show a clear impact on any specific behaviors. Most crayfish pairs in both treatments exhibited high aggression behaviors including grabbing their opponents with claws and unrestrained fighting. Furthermore, presence of these behaviors did not change much throughout the experiment. Most crayfish are species are territorial and exhibit aggressive behavior (Kubec et al. 2019), and therefore the male female

84 pairs probably did not react to each other as potential mates. Perhaps a long-term study could allow more time for crayfish to develop changes in some types of behavior in the presence of anthropogenic sound.

It is important to note that crayfish showed an unexpected pattern of declining activity levels during the trials. The time period Before had significantly higher activity levels than either

During or After. This was not an effect of the anthropogenic noise treatment because it occurred in both treatments. This suggest that crayfish may have become habituated to the presence of the other individual in the recording chamber. Crustaceans have relatively simple nervous system, but crayfish are complex enough to learn to alter their behavioral responses (Basil and Sandeman

2000). Therefore, this pattern may be an effect of habituation to stimuli as the 60 minute trial progressed.

My experiment also provided a unique description of acoustic signals produced by P. acutus. Although crayfish have a hair pit organ on the chelae that can detect acoustic vibrations

(Vogt 2002), a recent review on crayfish behavior reported there are almost no published studies on crayfish acoustic signals (Kubec et al. 2019). However, the congeneric Procambarus clarkii

(Red Swamp crayfish) has been shown to produce above water sounds (Favarao et al. 2011) and below water sounds (Busciano et al. 2012). Therefore, I compared the acoustic characteristics of their signals. Procambarus clarkii produced a loud underwater clicking sound that measured 118 dB re 1 µPa (Busciano et al. 2012). Our software does not calibrate the measurements to a reference scale, but P. acutus sounds registered at a similar 98 dB. Both species produce a rapid series of wide-band clicks that reached ultrasonic frequencies. The mean bandwidth of clicks of both species was 20.6 kHz. P. clarkii produces a pulse train comprised of 2 to 36 clicks that

85 lasted 400 ms. P. acutus produced 3-12 clicks per pulse train that lasted over twice as long (~1 s).Therefore, the acoustic signals are relatively similar in both species.

The function of P. acutus sound is unknown, as is the mechanism of its production. I did not test if sounds were associated with their behavior, but Buscaino et al. (2012) found that P. clarkii sound production correlated with intraspecific aggressive behavior. This suggests that P. acutus might use sound to communicate, and perhaps the differences in P. clarkii and P. acutus pulse trains are enough to for them to detect differences in other species’ acoustic signals. If the sounds are used for acoustic communication, it is interesting to note that P. acutus sound production was not affected by anthropogenic sound played into their aquaria. However, anthropogenic sound like engine noise are mostly low frequency tones in the 0-4 kHz range (Hu and Cardosa 2010). Therefore, it is possible that P. acutus can still detect their own acoustic signals, which extended into the ultrasonic frequency range.

In conclusion, my study provides evidence that an aquatic invertebrate produces acoustic signals and it is affected by anthropogenic sounds. However, my experimental design only tested short-term exposure to noise pollution, but many freshwater wetlands in the United States have high levels of anthropogenic sounds in underwater soundscapes (see Chapter 3). Additional research on how aquatic animals are affected by chronic exposure to noise pollution is needed. It is especially important to study invertebrates such as P. acutus because they drive ecological processes in aquatic habitats such as freshwater wetlands.

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90 CHAPTER 5 DOES ANTHROPOGENIC NOISE AFFECT BEHAVIOR AND ACOUSTIC

COMMUNICATION IN THE WHITE RIVER CRAYFISH, PROCAMBARUS ACUTUS

(DECAPODA: CAMBARIDAE)?

ABSTRACT

An organism’s behavior is essential its success and long-term survival, but this can be affected by noise pollution. Important invertebrates in many freshwater ecosystems are crayfish, which act as keystone species and ecosystem engineers in wetlands. In this study, I investigated if chronic exposure to recorded motorboat engine noise affected sound production and behavior of Procambarus acutus crayfish. Crayfish were maintained outdoors in large wading pools. My experiment lasted for 8 weeks and crayfish were monitored in a Pre treatment period, twice during the 4-week Noise treatment period, and once during a Post-treatment period. Sound treatment suppressed total crayfish activity, and several individual behaviors were different in the Sound treatment. Aggressive behaviors were suppressed in the presence of noise pollution.

Furthermore, there were positive correlations between behavior and sound production. However, crayfish sound production was not affected. These results indicated that sound alters the behavior of this native freshwater crayfish, which can impact its ecology and basic survival.

91 INTRODUCTION

Many geologists and biologists consider the current era as ‘The Anthropocene’ because humans are altering most ecosystem processes, changing the atmosphere, and are a leading cause in the decline in a global biodiversity (Crutzen, 2006). A review paper by Dudgeon et al. (2006) identified five leading factors that threaten the biodiversity of the earth’s freshwater habitats.

This seminal paper has been cited over 2300 times, which places it in the top 1% of all biological research papers. One of the main factors they identified was water pollution by chemical inputs, but other types of pollution were not discussed in this paper. However, ongoing ecological research has identified new or previously-overlooked threats, including “noise pollution” caused by anthropogenic sounds (Slaabekoorn et al. 2010). For example, a recent review by Dudgeon and other scientists, now identifies noise pollution as one of the 12 most important emerging threats to freshwater biodiversity (Reid et al. 2019).

The impacts of anthropogenic sound are expected to increase as the human population increases (Barber et al. 2011). For example, Hildebrand (2004) estimated that ocean noise levels have increased ten-fold during the past decade. In terrestrial habitats, the most common sources of noise pollution are engine sound from automobiles, airplanes, and trains (Barber et al. 2011).

In marine systems, boat engines, seismic surveys, and military sonar have been identified as the most important anthropogenic sounds (Whitlow et al., 2012). In freshwater habitats, the origins of anthropogenic sounds are both aquatic (motorboat sounds) and terrestrial (automobile engines) (Reid et al. 2019).

As a result of the increased understanding of this emerging threat, there is a developing interest in studying bioacoustics. The acoustic ecology of terrestrial animals, especially vertebrates, has been the most widely studied. For example, drilling to extract oil and natural gas

92 can drive off nesting songbirds and reduce their reproduction rates (Barber et al. 2011). Traffic noise will reduce the response of dwarf mongoose, Helogale parvula, to conspecific warning calls, which increases predation risk (Kern and Radford, 2016). In marine ecosystems, Popper and Hastings (2009) found that intense noise caused by pile-driving increased fish mortality in harbors, but Casper et al. (2012) found it attracted off shore sharks to the low-frequency, erratic pulses. There are also effects of sound pollution in freshwater systems. For example, anthropogenic noise elevated cortisol stress hormone levels in fish (Wysocki et al. 2005) and altered their communication success (Whitfield and Becker 2014). Some aquatic invertebrates were also affected. For example, predatory damselflies have difficulty detecting the sound of their prey in noisy underwater habitats (Villalobos-Jimenez et al. 2017).

A common impact of anthropogenic noise is that it masks intraspecific acoustic communication, which can affect their survival and reproduction. The mating success of male grey Shrike-thrush, Colluricincla harmonica, and male southern brown tree frog, Litoria ewingii, was reduced when they increased the frequency (pitch) of their mating calls to compensate for low frequency traffic noise (Parris and Schneider 2009, Parris et al. 2009b). Similar to the behavior of terrestrial birds and frogs, endangered northern right whales, Eubalaena glacialis, alter their vocalization in the presence of shipping noise, which affects social communication within their pod (Parks et al. 2012). Fish, like the Lusitanian toadfish, Halobatrachus didactylus, also have difficulty detecting mating calls in the presence of loud ship noise (Vasconcelos et al.

2007). Similar effects have been described in invertebrates. For example, Chorthippus biguttulus grasshoppers called with higher frequencies in noisy roadside habitats, which made them less attractive to mates (Lampe et al. 2012). Although research has tested effects of noise pollution in many ecosystems, almost no studies have been conducted in freshwater wetlands.

93 Wetlands are among the most productive and economically valuable ecosystems in the world (Moreno-Mateos et al. 2012). For example, they reduce flood damage by slowing the rate storm runoff enters rivers, absorbing energy from damaging waves, maintain stream base-level flows as they release water slowly into stream and groundwater, and improve water quality by acting as nutrient sinks (Lane et al. 2018). Many of these ecological services are mediated by the aquatic biota. For example, herbivorous snails alter nutrient cycles with their feeding (Horgan et al. 2014). Therefore, understanding the impact of sound pollution on the wetland fauna is needed to restore ecosystem processes in degraded habitats and preserve environmental conditions in high quality habitats.

Crayfish (O. Decapoda) are dominant invertebrates in many freshwater wetlands where they play a key role in ecosystem processes. They can be used as bioindicators of habitat quality, are sometimes keystone species when they control trophic webs through predation and leaf litter processing, and act as ecological engineers by altering resource levels (Reynolds et al. 2013).

Others found that crayfish control nutrient cycling by feeding and excreting wastes (Vanni

2002). For example, Procambarus clarkii was described as a “nutrient pump”, because they translocate Phosphorous and Nitrogen from the benthic sediments into the water column by their feeding and movement behaviors (Angeler et al. 2001).

The crustacean nervous system is relatively complex compared to other invertebrates

(Brusca and Brusca 2003), and crayfish have been used as a model organism in behavioral studies (Kubec et al. 2019). Research has found that they can learn to alter their behavior in the presence of meaningful stimuli (Basil and Sandeman 2003). However, their memory retention is low after stimuli are removed (Bierbower et al. 2013). Recently, the bioacoustics behavior of an economically important species, Procambarus clarkii, was studied (Busciano et al. 2012). They

94 found this species makes a rapid series of clicking sounds when they were fighting rivals, and these sounds were common at night in large lakes. They proposed that acoustic signals were important at times when visual cues were less effective. However, no published studies have tested how anthropogenic noise affects crayfish behavior or acoustic communication.

In this project, I investigated effects of long-term exposure to motorboat engine noise on the behavior and sound production of White River Crayfish, Procambarus acutus (Girard 1852).

Because little is known about their acoustic ecology, I also examine which types of behavior are associated with sound production in this species. The hypotheses I tested were: 1) P. acutus activity levels and sound production would be suppressed after long exposure to anthropogenic sounds, but the effects would decline after the stimulus was removed, and 3) P. acutus sound production is associated with aggressive behaviors.

METHODS

Experimental Design

In June 2015, I collected Procambarus acutus from three wetlands on the Kent State

University campus (Portage Co., Ohio) with baited minnow traps. In July 2015, additional P. acutus were obtained from an commercial fish hatchery (Calala’s Water Haven, New London,

Ohio). 48 P. acutus were sexed, assigned an ID number, and held outdoors at Kent State

University at the Herrick Aquatic Ecology Research Facility (Drinkard et al. 2011). Crayfish were randomly assigned into two plastic wading pools (91 cm diameter x 20 cm depth) that were filled with water from the adjacent wetlands (24 crayfish per pool). Crayfish were held separately inside a 4 l bucket that was placed inside each plastic wading pool. The water in the wading pools was aerated with electric air pumps, and the bucket exchanged water with the pool water via small holes drilled in the bottom of the bucket. Shade cloth was placed over both

95 wading pools, and their water temperature fluctuated with ambient temperature. Crayfish were fed ad-lib with dry food pellets (Crayfish and lobster food, Carolina Biological Supply,

Burlington, NC).

In July 2015, I randomly assigned crayfish in one wading pool into the Sound treatment and crayfish in the second wading pool were assigned to the No Sound treatment. The experiment ran 8 weeks and included three different periods. The first two weeks were the Pre- treatment period with no sounds added in either wading pool. The next four weeks were the

Treatment Period. Wading pools containing crayfish in the Sound treatment were exposed to a recording of boat engine sound (SanDisk, Western Digital, San Jose, California, United States) using an underwater speaker (Model AQ339, Lubell, Inc., Columbus, OH) placed in the center of the pool. The sound was played at 70 db (re 20 muPa) for 24 hr / day for the entire 4 weeks. The

No Sound control group were not exposed to added sound. The final two weeks was the Post- treatment period. Neither treatment group were exposed to added sound.

After the experiment was started, I retrieved crayfish from the wading pools at 2 week intervals to observe their responses in the laboratory. Therefore, monitoring was conducted at four Time periods: 1) before any sound was played - Pre-treatment, 2) 2 weeks after the Sound

Treatments was started - 2 Week, 3) 4 weeks after the Sound Treatments was started – 4 Week, and 4) after any Sound Treatment was ended – Post-treatment.

During the observation sessions, crayfish in each treatment were randomly paired with an opposite sex member of similar size (12 male/female pairs per treatment). Crayfish were randomly assigned in each session, so different pairs were monitored every time. During the observation sessions, crayfish pairs were held in a 55 cm X 41 cm x 32 cm, (L X W X H) recording chamber filled with dechlorinated tap water. The tank was lined on four sides and the

96 bottom with self-adhesive black acoustic foam (3.8 cm thick) to reduce sound reflection and noise from crayfish movement. The bottom of the tank had gridlines spaced at 5 cm intervals to assist in tracking movement of each crayfish. No sound was played during the observation period for either group.

The observation sessions lasted 20 minutes, and crayfish sounds were recorded with a hydrophone (HTI-96-MIN, Wildlife Acoustics, Concord, MA). Audio was converted to a

Waveform Audio File (.WAV) file with a Song Meter SM2/SM2+ programmable audio recorder.

This equipment recorded sounds from 0-48 kHz, which spans the audible to the ultrasonic frequencies. All WAV sound files were stored on SD memory cards until analyzed. During each observation sessions, crayfish movements were recorded with a video camera (Uniden Digital

UBW2101 Wireless Video Baby Monitor, Uniden America Corporation, Irving, Texas).

Data Analyses

The recorded sound files were analyzed using Raven Pro (Cornell University, Cornell,

NY). Raven Pro can measure sound characteristics such as frequency, temporal patterns, and number of occurrences. It can also count occurrences of specific sounds. In order to quantify sounds produced by P. acutus crayfish in the observation sessions, Raven Pro software scanned the spectrograms for crayfish sound parameters (time, frequency) that I identified in my previous experiment (see Chapter 4). I listened to a subset of recordings to check that Raven Pro was accurately identifying crayfish sounds. Afterwards, Raven Pro was used to count sounds produced by P. acutus in all observation sessions.

Intraspecific behavior during the anthropogenic noise treatment was assessed by viewing video recordings from each research session. Behaviors were characterized using a modified ethogram from Moore (2007) (Table 1). This method codes crayfish behaviors into 9 levels of

97 intraspecies aggression. I counted the number of times each behavior was observed in the 20- minute session for each male/female crayfish pair.

JMP statistical software was used for statistical analysis. Because crayfish were randomly assigned into pairs each observation session, I treated the observations as independent replicates. Examination of the raw data found that it did not meet statistical assumptions, and I used log (x+1) transformed data for all statistical analyses.

I used 2-Way ANOVAs to test if anthropogenic sound altered crayfish behavior and sound production. I compared total activity levels (number of all observed behaviors excluding

“Ignoring Opponent” because this was usually an inactive period) and each type of behavior among Treatments and Time. I also compared sound production among Treatments and Time. I followed significant factor results with pairwise comparisons with Tukey’s tests. The relationship between crayfish sound production and behavior (total activity, each behavior type) was also tested with correlation analyses.

98 Table 5.1. Modified crayfish behavior ethogram. Activity code shows a relative level of aggression ranging from low (-3) to neutral (0) to high aggression (5). Values are modified from

Moore (2007).

Activity Description

Code

-3 Mating

-2 Tailflip or fast retreat away from opponent

-1 Slow retreat from opponent

0 Ignoring opponent

1 Approach opponent without threat

2 Approach opponent with threat: antennae whip and/or raised claws

3 Closed claw use: boxing, pushing, touching opponent

4 Open claw use: grabbing opponent

5 Unrestrained fighting

99 RESULTS

When I tested the effect of anthropogenic sound on crayfish total activity levels, the

Treatment and Time factors were not significant. However, the Time X Treatment interaction was significant (F=8.492, df=3, p<0.001) indicating that the effect of treatment was different among the Time periods. Therefore, I examined the pairwise comparisons within each Time period. There was a difference between Treatments on the 4 week sampling time (Tukeys HSD, adjusted DF = 81, t=4.40, p<0.001). On this date, the activity levels in No Sound treatment was over three times higher than the activity in the Sound treatment (Figure 1).

I also tested the effects of anthropogenic sound on individual behaviors. There was no effect of Time on any behavior, but several behaviors varied between treatments. “Tail Flip

Away” was different among treatment (F=6.06, df=1, p<0.02), and it was more common in

Sound treatment. However, there was also a Treatment X Time interaction (F=4.64, df=3, p<0.005). I examined pairwise comparisons of Treatment within Time periods but there were no significant differences. “Approach opponent without threat” differed among treatments

(F=8.270, df=1, p=0.005), and this behavior was more common in the Sound treatment. There was also a Treatment X Time interaction (F=3.045, df=3, p<0.05), but none of the pairwise comparisons of Treatment within Time were significant. “Approach opponent with threat” did not have a Treatment effect, but there was a significantly Treatment X Time interaction

(F=3.944, df=3, p<0.02). Pairwise comparisons found that on the 4 week Time period this behavior was much higher in No Sound treatment (Tukeys HSD, adjusted DF = 81, t=3.82, p<0.01). “Closed claw use” had a Treatment X Time interaction (F=3.900, df=3, p<0.02).

Pairwise comparisons found that on 4 weeks this behavior was much more common in No Sound treatment (Tukeys HSD, adjusted DF = 81, t=3.56, p<0.025). Finally, “Open claw use” also had

100 a Treatment X Time interaction (F=3.160, df=3, p<0.03). Pairwise comparisons found that this behavior was much higher on 4 weeks in the No Sound treatment than the Sound treatment.

(Tukeys HSD, adjusted DF = 81, t=3.53, p<0.025). There were no other significant factors for any behavior.

Crayfish produced sounds throughout the experiment on all Time periods. The mean number of sounds produced ranged from 46 to 166 per 20 minute observation session. The sounds the crayfish produced are termed a “pulse train”, which is a rapid series of 3 up to 12 clicks (see Chapter 4 for a full description of P. acutus sounds). I counted each pulse train as a single sound event. When I tested the effects of anthropogenic sound on sound production, there was no significant Treatment effect or Treatment X Time interaction. However, there were differences in sound production among Time (F=16.390, df=3, p<0.0001. The Pretreatment, 2 week, and 4 week were all higher than the Post-treatment.

Sound production was compared to crayfish behavior with Correlation analyses (Table

2). Total activity (all behaviors except Ignoring Opponents) was strongly correlated with Sound production. Most of the individual behaviors were also correlated with sound production. . The only exception were “Mating” which was very rarely observed and “Tail flap away”.

101

Figure 5.1.Total activity levels (mean +/- 1 SE) of crayfish in each treatment group (No Sound,

Sound) during each Time point (Pre, 2 week, 4 week, Post). Note that values are expressed in log10.

102

Figure 5.2. Levels of behaviors (mean +/- 1 SE) of crayfish in each treatment group (No Sound,

Sound) during each Time point (Pre, 2 week, 4 week, Post).

103

Figure 5.3. Number of clicks produced (mean +/- 1 SE) of crayfish in each treatment group (No

Sound, Sound) during each Time period (Pre, 2 week, 4 week, Post). Note that values are expressed in log10.

104 Table 5.2. Results of correlation analyses between the sound production and behavior. Total activity excludes “Ignoring opponent”. Significant correlations are in bold

Activity R P

Mating -0.129 0.231 Tail flip away -0.012 0.912

Slow retreat 0.327 0.002

Ignoring opponent 0.346 0.001

Approach without threat 0.213 0.046

Approach with threat 0.213 0.046

Closed claw use 0.318 0.003

Open claw use 0.220 0.040

Unrestrained fighting 0.279 0.009

Total Activity 0.348 0.001

105 DISCUSSION

Anthropogenic sound is increasing in many terrestrial, marine and freshwater ecosystems throughout the world, and there is a growing body of evidence that this alters the behavior of organisms. My data support that Procambarus acutus behavior is altered after long term exposure to noise pollution. Overall activity levels were over 3 times higher in the No Sound treatment than the Sound Treatment after 4 weeks of exposure to motorboat engine noise. It is important to note that the change in behavior occurred during the observation sessions when no sound was added. Therefore, there were residual effects on behavior that lasted for at least a short time after the stimulus was removed. Bierbower et al. (2013) showed that a congeneric crayfish species, Procambarus clarkii, could learn new tasks in as little as 9 days. Results of my experiment support that P. acutus will also learn to modify their behavior in response to novel stimuli.

Examining Figure 1 shows that that the difference in activity levels on Week 4 occurred when activity in the No Sound treatment increased, while they remained constant in the Sound treatment. The reason why the No Sound group became more active at that Time is not known.

However, three behaviors increased in the No Sound treatment on Week 4: Approach with threat,

Closed claw use, Open claw use. These are high aggression behaviors (Moore 2007), which means crayfish pairs were becoming more antagonistic in this treatment. Furthermore, two behaviors were more common in Sound treatment: Tail Flip away, Approach without threat, and these are low aggression behaviors. This could mean that anthropogenic sounds were suppressing aggression between the crayfish. Although few studies have found noise suppresses aggressive behavior in fish (Bruintjes and Radford 2013) and birds (Zwart et al. 2016) I have not found any studies that have tested if similar effects are found in invertebrates. However, any

106 change in behavior for an organism may impact its survival if it affects basic requirements such as resource consumption, reproduction, or predator avoidance. For example, Wale et al. (2013) found that boat noise disrupted the crabs feeding and they were slower to retreat to shelter from an attack, which are all basic survival behaviors. Decreased activity in P. acutus in the presence of anthropogenic noise might have similar negative effects on their survival.

I did not find differences in behavior between Treatments in the Post treatment period.

This could be due to acclimation (the loss of a behavior towards non-important stimuli). The ability of organisms to acclimate in a noisy environments might help them survive changes to their soundscape. For example, reef fish initially respond to motorboat noise with a hiding response, but over time acclimate to the noise (Nedelec et al. 2016, Holmes et al. 2017). This allows them to persist in reefs near human populations. The lack of differences in P. acutus behavior in the Post treatment period mean that they acclimated to noise. Alternately, the crayfish may not have retained the learned behavior because the noise stimulus was removed.

This possibility is also supported because their nervous system is much simpler than vertebrates, and they would be unlikely to have a long retention time (Vogt 2002)

Although the crayfish actively produced sound throughout the experiment, sound production was not affected by the treatment. This is surprising given that other behaviors were affected and the motorboat noise may have masked their sounds. The ecological reason for crayfish sound production is unknown, although Buscaino et al. (2012) found that sound production was correlated with intraspecies aggressive behaviors including unrestrained fighting.

However, it is possible that the sounds are not used as an acoustic signal. I found that sound was not correlated with a single specific behavior, instead the crayfish appeared to be clicking constantly. Sound correlated with low aggression behaviors (e.g., slow retreat) to high

107 aggression behaviors (unrestrained fighting), and even periods of low activity (Ignoring opponents). Certainly, a future area of research would be to decipher the ecological role of these unique sounds.

The World Health Organization lists anthropogenic noise as one of the most pervasive types of pollution. Anthropogenic sounds are becoming just as common in aquatic systems as in terrestrial systems (Kunc et al. 2016), and the amount of noise is likely to continue increasing.

My study shows that this pollution is not just a problem for terrestrial birds, cetaceans or fish but also affects freshwater invertebrates. Furthermore, this project provides important information on acoustic signals of Procambarus acutus. Crayfish are vital component in many aquatic ecosystems, and understanding their ecology will help us preserve their populations in freshwater wetlands affected by anthropogenic noise pollution.

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112 CHAPTER 6 SUMMARY

Today, the tranquil stillness of night or the symphony of an early morning bird chorus are likely to be replaced with the discordant sounds of automobiles, jackhammers, or water boat engines. Common human activities, such as transportation and urban construction, produce outdoor noise pollution that is unhealthy for people but also has a negative impact on nearby fauna (Slabbekoorn, 2018). This understanding has led to new areas of research focus: understanding soundscape ecology and testing the effects of anthropogenic noise pollution.

Many studies have examined these topics using wildlife (Francis et al. 2009, Pater et al. 2009), marine mammals (Weilgart 2007), and fish (Slabbekoorn et al. 2010). However, studies examined the effect of invertebrates on the composition of natural soundscapes and their response to anthropogenic noise pollution has not been well studied (Morley et al. 2014).

Furthermore, early studies focused on the effect of noise pollution on a relatively few species of birds and whales. These studies have provided valuable insight into the bioacoustics ecology of many ecosystems (Slabbekoorn, 2018). However, the increasing threat of anthropogenic noise pollution in freshwater ecosystems has only recently been understood (Reid et al. 2019).

In many parts of the world, traffic is one of the biggest contributors to the increasing levels of anthropogenic noise. Arevalo and Blau (2018) researched the impact of road networks on protected areas. They found that soundscapes in many areas were directly affected by road proximity and that natural sounds were often masked near roads with heavy use or large vehicle traffic. They proposed that this contributes to the degradation of habitats through the loss of natural sounds. Others have recommended management efforts in national parks should not only

113 maintain good quality habitat, but also preserve the natural soundscapes (Herrera-Montes 2018).

Thus, research is needed to identify characteristics of natural soundscapes and also test how they are altered in areas with noise pollution.

In my first study, I used recorded sound to investigate the impact that submersed aquatic vegetation had on sound transmission in shallow wetland habitats. Understanding the movement of sound in shallow vegetated habitats, like freshwater wetlands, is important to understand the bioacoustic environment of those areas. Overall, sound transmission was effectively reduced by submersed vegetation and distance from sound source. There was a predictable negative linear relationship between sound loss and plant density. Furthermore, this effect was greater on high frequency tones. While most of the sounds were still audible at 1 m even in dense vegetation, by

15 meters only the low tones were still audible. Mixed tones, (i.e. those composed of high tones and low tones), showed a similar pattern.

My results indicate that aquatic organisms in densely vegetated wetlands may be restricted to using acoustic signals only at short distance. However, since wetland vegetation senesces in the autumn in temperate climates, it is possible at times of year that form of acoustic communication is more effective. These data also indicate that wetland management practices that reduce dense stands of emergent and submergent vegetation (e.g. herbiciding, mowing, water level drawdowns) may have an unintended impact on the biotic community. While sounds used to communicate information would be enhanced, predators that use acoustic signals to locate their prey might have a higher success rate. Therefore, further research should examine how changes in underwater soundscapes. Furthermore, enhancing submersed vegetation may have a positive impact by reducing the transmission of underwater anthropogenic sounds such as motorboat engine noise. Further research in this area is needed on various types of vegetation

114 and a variety of wetland types. These results would likely vary in a wetland with different characteristics and proper understanding and management will require knowing the acoustic behavior of different ones.

In my second study, I researched how natural soundscapes of freshwater wetlands in northeastern Ohio are affected by road noise. I studied this by recording soundscapes in three seasons in rural wetlands away from roads and wetlands near to roads. My results indicated that wetlands near roads had higher overall sound intensity (70 dB) and more anthropogenic sound components as part of their soundscape. Most anthropogenic noise occurred in the low (0-5 kHz) frequencies while natural sounds occurred across a wider frequency range from 0-15 kHz. The soundscapes also changed throughout the year with Spring having the most natural sounds and the highest acoustic diversity. Unexpectedly, I also found rural wetlands had more natural sounds both above and below the water line and more diverse soundscapes. This may reflect a lower aquatic and terrestrial biodiversity in wetlands near roads or a change in calling behavior of the fauna. My findings indicate that traffic noise altered natural soundscape in freshwater wetlands by masking natural sounds, and that anthropogenic noise impact may play a role in reducing wetland biodiversity if they disrupt vital natural behaviors such as mating, hunting and predator avoidance. Future research focusing on how to analyze soundscape data to identify changes in species assemblages and research testing the impact of human noise on biodiversity and acoustic ecology would add valuable knowledge to this particular area of research.

In my third project, I investigated how a native invertebrate, Procambarus acutus, react to short term (20 minute) exposure to anthropogenic noise (motorboat engine sounds). I also was the first to describe the acoustic characteristics of sounds produced by this species. Crayfish are an important component of aquatic ecosystems, particularly wetlands, because they are important

115 predators, herbivores and can alter resource availability with their burrowing activity. Therefore, factors affecting their behavior and survival could have cascading ecological effects in their habitats. This experiment showed that crayfish exposed to anthropogenic sound reduced their overall activity levels, but their sound production did not change. These results indicate that anthropogenic noise can alter crayfish behavior. Further study in the area of crayfish sound production is needed to gain a better understating of why the sounds are made and how they are used. Understanding how they and other key species react to anthropogenic noise will be an important aspect of developing new wetland management strategies.

My fourth research study investigated how P. acutus were affected by chronic exposure to anthropogenic motorboat noise. Crayfish in the sound treatment were held outdoors in wading pools and exposed to continuous anthropogenic sounds for 4 weeks. Similar to the short-term sound exposure study (Project #3), anthropogenic sounds again suppressed crayfish activity levels but did not affect sound production. However, I also found that aggressive behaviors were the most reduced. This response can have an effect on crayfish reproductive success and defense of their home territories. Furthermore, there was a positive correlation between activity levels and sound production, although no specific behavior was associated with sound production.

Overall, these results indicate that anthropogenic noise can alter the behavior of this crayfish species. Because crayfish are a dominant species in many aquatic habitats, changes to their populations can affect many other aspects of their ecosystem. Future research should investigate how anthropogenic noise affect other freshwater invertebrate species and any impacts on the ecosystem processes.

The primary goals of applied ecology are to understand the effect of anthropogenic factors in natural habitats, to protect biodiversity, and to develop management strategies to

116 enhance valuable ecological services. My research provided useful data on this topic, including characteristics of underwater sound transmission, a comparison of soundscapes in rural and roadside wetlands, and the effects of anthropogenic noise on the ecology of a native aquatic invertebrate species. Future research on soundscape ecology can help provide a better understanding on how mitigate negative impacts of noise pollution in freshwater ecosystems.

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