Acoustic monitoring indicates a correlation between calling and spawning in captive spotted seatrout (Cynoscion nebulosus)

Introduction

Many fish families contain species that produce sounds for communication purposes. Some examples include African freshwater fishes (Mormyridae), damselfishes (Pomacentridae), grouper (Serranidae), catfishes (Ictaluridae, Pimelodidae, Doradidae, and Mochokidae), rockfishes (Sebastidae), haddock and cod (Gadidae), triglids (Triglidae), cichlids (Cichlidae), toadfishes (Batrachoididae), and drums (Sciaenidae) (e.g.,  Tavolga, 1958; Holt, Holt & Arnold, 1985; Fine, Burns & Harris, 1990; Mann & Lobel, 1995; Amorim, 2006; Rowe & Hutchings, 2006; Luczkovich et al., 2008; Mann & Grothues, 2009; Parmentier et al., 2009; Walters et al., 2009; Širović et al., 2009; Mann et al., 2010; Locascio & Mann, 2011). One of the primary functions of sound production in fishes is for courtship during the reproductive season.

The Family Sciaenidae contains fish renowned for producing sound. Some representative examples include the Atlantic croaker (Micropogonias undulatus), red drum (Sciaenops ocellatus), weakfish (Cynoscion regalis), American star drum (Stellifer lanceolatus), black drum (Pogonias cromis), silver perch (Bairdiella chrysoura), and spotted seatrout (Cynoscion nebulosus) (e.g.,  Hill, Michael & Musick, 1987; Nieland & Wilson, 1993; Sprague et al., 2000; Collins, Callahan & Post, 2001; Montie, Vega & Powell, 2015). Sciaenids most likely evolved the ability to produce acoustic signals in order to communicate in turbid estuaries where the water visibility is minimal (Holt, Godbout & Arnold, 1981; Holt, 2008). In this family, fish produce sound by contracting a sonic muscle against an inflated swim bladder (reviewed in Fine & Parmentier, 2015). In most Sciaenids, males are the prominent sound producers; however, in the Atlantic croaker and black drum, both male and female have sonic muscles and call (Hill, Michael & Musick, 1987; Tellechea et al., 2010). In most cases, sound production is associated with defending territory and reproduction. However, there are still some questions on how exactly calling and spawning are correlated in many sound-producing species (e.g., Luczkovich et al., 1999a; Locascio, Burghart & Mann, 2012).

Research studies have illustrated that patterns of fish calling overlap with patterns of reproductive senescence in the wild (Connaughton & Taylor, 1995). By performing acoustic recordings and plankton tows simultaneously, studies have also shown an association between calling and spawning (Mok & Gilmore, 1983; Saucier & Baltz, 1993; Luczkovich et al., 1999a; Aalbers & Drawbridge, 2008). These comparisons are necessary if acoustic metrics are to be used as a means to monitor fish spawning patterns. These data are difficult to acquire in the wild because of the uncertainties associated with ensuring that the collected eggs are from the same fish that are calling (Locascio, Burghart & Mann, 2012). In the field, it is likely that egg counts are affected by water currents, predator activity, and plankton tow efficiency. Captive studies can remove these variables. A few studies have mimicked wild environmental conditions and examined the relationship between calling and spawning in the laboratory (Guest & Lasswell, 1978; Connaughton & Taylor, 1996; Montie et al., 2016). Recently, Montie et al. (2016) collected quantitative data to better understand the association between sound production, call structure, and eggs released in a captive brood stock of red drum. In this study, red drum spawned only on evenings when males called. In addition, more eggs were collected on evenings when red drum calls were longer in duration and contained more pulses.

In the present study, our main objective was to examine the relationship between acoustic metrics (i.e., the amount of calling and sound pressure levels) and the number of eggs collected in laboratory tanks containing spotted seatrout. Spotted seatrout are found in estuaries from Cape Cod, Massachusetts to Key West, Florida and from southwest Florida to southern Mexico in the Gulf of Campeche (Welsh & Breder jr, 1924; Mather, 1952; Tabb, 1966). Spotted seatrout are group-synchronous spawners with a long spawning season from April to September along the South Atlantic and the Gulf of Mexico coasts (Overstreet, 1983; Brown-Peterson, Thomas & Arnold, 1988; McMichael & Peters, 1989; Saucier & Baltz, 1993; Brown-Peterson et al., 2001; Brown-Peterson & Warren, 2002; Nieland, Thomas & Wilson, 2002; Brown-Peterson, 2003; Roumillat & Brouwer, 2004). It was estimated that spotted seatrout in age classes 1–3 spawn every 4.7, 4.2, and 4 days, respectively, in Charleston Harbor, South Carolina (Roumillat & Brouwer, 2004). Tabb (1966) was the first to report that male spotted seatrout use acoustic signals that are associated with reproductive activity. Mok & Gilmore (1983) discovered that male spotted seatrout produce four major sound types, which were all recorded at spawning sites in Indian River Lagoon, Florida. These calls were designated as (i) a “grunt” followed by a series of “knocks”, (ii) “aggregated grunts,” (iii) “long grunt”; and (iv) a rapid series of short pulses known as the “staccato.” Females do not have a sonic muscle and do not produce sound (Mok & Gilmore, 1983). It is well known that spotted seatrout form large mating aggregations (i.e., leks). These aggregations produce higher sound pressure levels (>50 dB–90 dB re 1 µPa at 3 m) as compared to the levels of individual callers (<30 dB re 1 µPa at 3 m) (Gilmore, 2003). It is speculated that this combined acoustic energy derived from a male aggregation increases the number of receptive spawning females attracted to the lek (Gilmore, 2003). In the present study, our specific objectives were to: (i) briefly describe the different call types of captive spotted seatrout; (ii) investigate if captive fish showed daily rhythms in calling; (iii) determine if a quantitative relationship exists between the amount of calling and the quantity of eggs released; and (iv) examine if certain call types or changes in call structure were associated with spawning productivity. These data provide supportive evidence that acoustic metrics can be used to better understand reproductive rhythms over various time scales (i.e., daily, tidal, lunar, seasonal, yearly, and decadal) and that these metrics can be used to investigate the impacts of natural and anthropogenic stressors on these reproductive patterns.

Materials and Methods

For this study, sexually mature spotted seatrout were placed into three separate, 3.67 m diameter, 1.7 m deep, circular fiberglass tanks (i.e., Tank 1, Tank 2, and Tank 3) (Fig. 1A). Each tank was filled with settled, sterilized Charleston Harbor seawater and contained individual re-circulating aquaculture systems outfitted with UV sterilizers, protein fractionators, and bead filters. Fish numbers, sexes, and sizes for each tank are provided in Table 1. Tank 1 housed seven males and seven females. Tank 2 contained eight males and seven females. Tank 3 had three males and thirteen females. Three times a week, fish were fed equal parts of shrimp, squid, and Boston mackerel (Scomber scombrus). Tank temperatures were individually controlled. Water temperature and photoperiod were held at a cycle that encouraged spawning and followed a natural reproductive season for spotted seatrout in South Carolina (Roumillat & Brouwer, 2004; Montie, Vega & Powell, 2015). However, in Tank 1, the temperature was maintained at 27.8 °C and the photoperiod was left at 14.5 h light until late November to extend the spawning season and provide more data points to test the association between calling and spawning. Acoustic monitoring occurred from April 13 to December 19, 2012 for Tank 1 and from April 13 to November 21, 2012 for Tanks 2 and 3. Different fish compositions, temperature cycles, and photoperiods among the tanks were set by SCDNR to investigate how these variables affected reproductive success.

In order to collect floating eggs from spawns, the large tanks contained a surface, skimming port that emptied into an egg collection tank following methods previously described (Montie et al., 2016). Each morning, the nets were checked for eggs. If present, the eggs were harvested and transferred to a 15 L Artemia hatching cone, which allowed separation into fertile (i.e., floating) and non-fertile (i.e., sinking) eggs. Once separated, fertile and non-fertile eggs were enumerated volumetrically using graduated cylinders. In all results reported in this study, the quantity of eggs collected refers to the sum of the fertile and non-fertile eggs. This work was part of the SCDNR restocking program for spotted seatrout and did not require an IACUC. No fish were harmed or sacrificed in this study.

We deployed acoustic loggers (DSG-Oceans, Loggerhead Instruments, Sarasota, FL, USA, www.loggerhead.com) into each tank following methods previously described (Montie et al., 2016). The DSG-Ocean was positioned vertically in the tank center (i.e., 3.67 diameter tanks) inside a cement mold and attached to a piece of vertical rebar, which was embedded in the cement mold (Fig. 1B). The DSG-Ocean was approximately 1.83 m from the side of the tank and 1.05 m from the water surface. Observations of fish in tanks indicated that the average swimming patterns occurred counterclockwise at mid-depth (i.e., 0.85 m) approximately half way between the hydrophone and the tank side (Fig. 1C).

When recording fish sounds in tanks, there is always the possibility that reverberation, resonance, and tank size can affect the sounds recorded. For example, Parmentier et al. (2014) provided some evidence that fiberglass tanks (i.e., 6 m³ and 13 m³) can affect sound duration, pulse duration, pulse period, and dominant frequency. Akamatsu et al. (2002) illustrated that dominant frequency, sound pressure level, and power spectrum recorded in very small, 170 L (i.e., 0.17 m³) rectangular glass tanks were significantly distorted compared to the original acoustic signal. Nonetheless, most captive studies that focus on sound production live with resonance and distortion effects present in tanks (e.g., Connaughton & Taylor, 1996; Montie et al., 2016). Akamatsu et al. (2002) do provide a practical procedure for correcting the measurements of fish sounds in small tanks. These guidelines indicate that the targeted sound frequency (i.e., the frequency of the fish calls) should be different than the minimum resonant frequency (f_min) of the tank. In addition, the hydrophone should be positioned within the attenuation distance (D) of the sound source to minimize possible distortion.

Even though Akamatsu et al. (2002) used tanks that were much smaller than the tanks used in the present study (i.e., 0.17 compared to 17.88 m³), we calculated F_min and D using the guidelines and equations provided by Akamatsu et al. (2002). We determined that F_min = 541 Hz, which was different than the expected peak frequency of spotted seatrout chorusing in the wild (i.e., ∼239 Hz; E Montie, 2016, unpublished data); thus possible resonance was minimal. We also determined that D = 1.48 m, which was smaller than the radius of the tank and the maximum possible distance of a calling fish from the hydrophone (i.e., 1.83 m); thus some distortion was possible. We minimized this distortion effect by placing the recorders in the center of the tank. Therefore, calling fish ranged anywhere from 0 m (i.e., when a fish was immediately next to recorder) to 1.83 m (i.e., when a fish was located next to the side of the tank). As stated previously, observations of fish in these tanks indicated that the average swimming patterns occurred counterclockwise half way between the hydrophone and the tank side (i.e., ∼1 m from the hydrophone). Therefore, generally speaking, calling fish were less than 1 m from the hydrophone, which was within the attenuation distance of the sound source and distortion was minimized.

The DSG-Ocean contains a High Tech Inc. hydrophone (i.e., −185 dBV µPa⁻¹ sensitivity) attached to a microcomputer circuit board with a gain of 20 dB that is powered by 24 D-cell alkaline batteries. This equipment is housed in a cylindrical PVC housing (i.e., 0.65 cm length, 11.5 cm diameter). The DSG board is calibrated with a 0.1 V (peak) frequency sweep from 2–100 kHz. For this experiment, DSG-Oceans were set to a sampling rate of 50 kHz and were scheduled to record sound for 2 min every 20 min. Files were saved as ‘DSG files’ on a 128 GB SD-card. Recorders were retrieved nine times during the experiment, once on May 5th, May 7th, May 25th, June 25th, July 23rd, August 24th, September 21st, and October 26th to download data and change batteries and again on November 21st and December 19th to download final files. In Tank 1, acoustic recordings were not collected between 16:40, May 18th and 19:40, May 22nd, 2012 due to recorder malfunction. After each data retrieval, the ‘DSG files’ were downloaded to a network drive and batch converted into ‘wav files’ using DSG2wav©software (Loggerhead Instruments, Sarasota, FL, USA).

We manually counted the quantity of calls within each ‘wav file’ by viewing the files in Adobe Audition (Adobe Systems Incorporated, San Jose, CA, USA, http://www.adobe.com). In our captive study, we grouped the “grunt followed by knocks” and “aggregated grunts” observed by Mok & Gilmore (1983) together because of their similarity in call structure and described these calls as “drums” to avoid confusion with the “grunt.” We classified the “long grunt” as the “grunt,” while the “staccato” strictly followed the description provided by Mok & Gilmore (1983). For each tank, we determined the number of “grunts,” “drums,” and “staccatos” per day by adding up the calls that occurred between 18:00 and 06:00, which was the time range in which most calling occurred. The ‘wav’ file with the most abundant calls was used to determine the mean number of pulses in a “staccato” call as well as the mean duration for that specific day. The pulse number was evaluated by manually counting each individual pulse in a “staccato.” The “staccato” duration was determined by manually subtracting the time of call termination from the time of call initiation.

We determined the received root mean square (rms) sound pressure level (SPL; dB re 1 uPa; between 50 and 2,000 Hz) of each ‘wav file’ using MATLAB (The MathWorks, Inc., Natick, MA, USA, http://www.mathworks.com). Noise due to aeration, pumps, filters, and electrical systems between 50 and 2,000 Hz was minimal compared to the SPL of calling spotted seatrout (see Figs. 2 and 3). The equations in our MATLAB script for received SPL determination followed PAMGuide scripts as described by Merchant et al. (2015): ${\displaystyle \begin{array}{cccc}\mathrm{S}=\mathrm{h}+\mathrm{g}+20log10\left(1∕{\mathrm{V}}_{\mathrm{adc}}\right);\hfill & \hfill & \hfill & \hfill \\ \mathrm{b}=20log10\left(\mathrm{sqrt}\left(\mathrm{mean}\left({\mathrm{y}}^{\wedge }2\right)\right)\right);\hfill & \hfill & \hfill & \hfill \\ \mathrm{a}=\mathrm{b}-\mathrm{S};\hfill & \hfill & \hfill & \hfill \end{array}}$ where a = calibrated sound level in dB re 1 µPa; b = uncorrected signal; S = correction factor; h = hydrophone sensitivity (i.e., −185 dBV µPa⁻¹) ; g = DSG gain (i.e., 20); V_adc = analog-to-digital conversion (i.e., 1 volt); y = signal. Wav files that had noise artifacts due to fish hitting the tank or recorder and tank maintenance were not included in SPL analysis. From these data, we then calculated the mean rms SPL per day for each tank by calculating the mean of all the SPLs between 18:00 and 06:00. For Tanks 1 through 3, the average background noise levels were 114, 113, and 112 dB re 1 µPa, while the highest received rms SPLs when spotted seatrout were calling were 148 (i.e., 139 calls detected), 146 (i.e., 76 calls detected), and 138 (i.e., 67 calls detected) dB re 1 µPa, respectively.

We used Microsoft Excel (Microsoft, Redmond, WA, USA; http://www.microsoft.com/en-us), MATLAB, and SYSTAT 13 (Systat Software, Inc., San Jose, CA, USA; http://www.systat.com) for data and statistical analysis. First, the time domain and power spectral density (PSD) were illustrated for “drums,” “grunts,” and “staccatos.” Then, the number of calls and received SPLs, spawning productivity, and call characteristics for each tank were summarized. Using Pearson correlation analysis, we investigated the relationship between all calls (i.e., sum of “grunts,” “drums,” and “staccatos”) and received SPLs for each tank. To examine the relationship between simulated seasonal parameters and calling, we plotted water temperature, photoperiod, and the number of calls versus date. To investigate the daily rhythms of calling, we determined the mean number of “drums” for each time interval during the 14.5 h of light photoperiod for each tank.

We performed a quantitative, stepwise investigation to determine the relationship between calling and spawning. In order to examine the overlap of sound production and reproductive events, our first approach was to plot the total number of calls per day and the quantity of eggs collected (i.e., the next morning) versus the date for each tank. The next step was to perform autocorrelation analysis (i.e., at −5 to +5 day lags) to examine the relationship between calling variables (i.e., number of calls and received SPL) and eggs collected. This analysis determined if the correlation between calling and spawning was the highest when these two variables were aligned within the same timeframe as compared to a negative or positive lag. Then, we used logistic regression to test whether or not the likelihood that spotted seatrout tended to spawn increased as calling (i.e., the total quantity of calls between 15:40 and 00:00) and received SPLs (i.e., the mean SPL between 15:40 and 00:00) increased. The categorical dependent variable was spawning (0 = no; 1 = yes) and the predictor variables were calling or received SPL. Next, we used linear regression analysis to investigate if more productive spawns were associated with more calling or higher sound levels. We performed two separate linear regressions. One regression analysis included all the data, and the other analysis excluded the days when there were no eggs. We also performed logistic regression to test whether or not the likelihood that spotted seatrout tended to spawn increased as “staccato” calls increased in duration and pulse number. To test the relationship between call structure and spawning productivity, we performed linear regression analysis with call duration or pulse number as the independent variable and the quantity of eggs as the dependent variable.

Results

Seasonal and daily patterns in calling

Three general patterns in calling were observed over the simulated reproductive season. First, fish calling occurred in all tanks (Table 2; Fig. 4). Second, the amount of calling differed among tanks (Tank 1 > Tank 2 > Tank 3; Table 2; Fig. 4). Third, sound production changed with photoperiod and water temperature adjustments. Maximal calling occurred when the photoperiod shifted to 14.5 h of light, and the temperature increased to 27.7 °C. Sound production began to decrease as the temperature dropped below 27.7 °C, and the light cycle changed to 12 h light per day. In Tank 3, spotted seatrout calling was more sporadic (Fig. 4C). The general pattern was that calling increased as the light cycle shifted from 12.5 to 14.5 h light per day and as the temperature increased to 27.8 °C. Between 9/21/2012 and 11/8/2012, calling was more prevalent than other time periods. Sound production began to diminish as the temperature dropped below 24.0 °C on 11/14/2012. In Tanks 1–3, rapid fluctuations in water temperature affected sound production; rapid elevations in temperature increased calling rates, while rapid declines in temperature decreased calling (Fig. 4).

In all tanks, spotted seatrout showed daily rhythms in sound production (Fig. 5). Generally, calling began once the lights turned off (i.e., 17:45). The highest number of drums occurred at 21:20 in Tank 1; 21:00 in Tank 2; and 20:40 in Tank 3 (Fig. 5). The number of grunts, staccatos, and received SPLs followed similar patterns.

Table 3:

Results of logistic regression analysis that tested whether or not the amount of calling and sound pressure level (SPL) were significant predictors of spotted seatrout (Cynoscion nebulosus) spawning.

In all cases, the dependent variable was spawning (0 = no; 1 = yes). No. of calls and mean SPL are measurements per evening from 15:40 to 0:00. The overall model evaluation used the log-likelihood statistical test.

Tank	Predictor variable	Logistic model	z	P	Odds ratio (eβ)	Overall model evaluation
						X2	df	P
Tank 1	No. of drums	Logit(y) = −2.926 + 0.002x	7.073	<0.001	1.002	70.216	1	<0.001
Tank 1	No. of grunts	Logit(y) = −2.154 + 0.026x	5.873	<0.001	1.026	46.545	1	<0.001
Tank 1	No. of staccatos	Logit(y) = −2.555 + 0.132x	7.495	<0.001	1.141	94.895	1	<0.001
Tank 1	Total calls	Logit(y) = −2.983 + 0.002x	7.123	<0.001	1.002	72.215	1	<0.001
Tank 1	Mean SPL	Logit(y) = −42.521 + 0.344x	6.793	<0.001	1.411	66.415	1	<0.001
Tank 2	No. of drums	Logit(y) = −12.522 + 0.008x	4.172	<0.001	1.008	64.594	1	<0.001
Tank 2	No. of grunts	Logit(y) = −5.902 + 0.066x	4.130	<0.001	1.068	31.143	1	<0.001
Tank 2	No. of staccatos	Logit(y) = −4.033 + 0.090x	4.573	<0.001	1.094	26.282	1	<0.001
Tank 2	Total calls	Logit(y) = −13.197 + 0.008x	4.131	<0.001	1.008	66.084	1	<0.001
Tank 2	Mean SPL	Logit(y) = −103.059 + 0.841x	4.381	<0.001	2.318	39.716	1	<0.001
Tank 3	No. of drums	Logit(y) = −7.385 + 0.010x	2.784	0.005	1.010	20.540	1	<0.001
Tank 3	No. of grunts	Logit(y) = −5.653 + 0.055x	3.257	0.001	1.057	11.028	1	0.001
Tank 3	No. of staccatos	Logit(y) = −5.236 + 1.725x	2.501	0.012	5.612	13.344	1	<0.001
Tank 3	Total calls	Logit(y) = −7.226 + 0.009x	2.797	0.005	1.009	20.060	1	<0.001
Tank 3	Mean SPL	Logit(y) = −87.532 + 0.722x	2.897	0.004	2.058	10.877	1	0.001

DOI: 10.7717/peerj.2944/table-3

Notes:

SPL, received sound pressure level in dB re 1 µPa.

The Association between Calling and Spawning

In this captive experiment, our data indicated that sound production served an important function in courtship. First, we discovered that successful reproductive events happened only on evenings in which spotted seatrout produced sound (Fig. 6). On many evenings, male spotted seatrout did call and no spawning was observed, but spawning never happened without a significant increase in sound production. Second, autocorrelation analysis showed that the greatest correlation between sound production and eggs released happened on the same evening when the lag = 0 (Fig. 7). Third, logistic regression results indicated that the likelihood that spotted seatrout reproduced was significantly related to calling variables (i.e., number of calls and received SPL) (Table 3; Fig. 8). Fourth, there was some evidence that spawning was more productive when spotted seatrout called more frequently. When all data were included in regression analysis, more productive spawns (i.e., larger egg numbers) were associated with more calling and higher received SPLs (Table 4). If the days when no eggs were collected were removed from statistical tests, then the amount of calling in relation to egg deposition was not significant (Table 4). However, in Tanks 1 and 3, the relationships between SPL and the number of eggs collected were significant, even when the non-spawning events were removed from linear regressions (Table 4). Fifth, tanks with more sound production had more spawns (Tank 1 > Tank 2 > Tank 3; Table 1). Tanks with more calling and higher mean SPLs resulted in larger total egg yields per gram female biomass (Fig. 9). We did test whether or not spotted seatrout were more likely to spawn when staccato calls contained more pulses and calls were longer in duration, but we found no significant difference.

Table 4:

Results of linear regression analysis that tested the significance of the amount of calling and sound pressure level in relation to spawning success of spotted seatrout (Cynoscion nebulosus) held in captivity.

In all cases, the dependent variable is the number of eggs collected. P-values were statistically significant when P <0.050.

Tank	Independent variable	Fitted equation	r2	P	df
Tank 1	Total no. of calls	y = 402x − 104,522	0.208	<0.001	243
Tank 1a	Total no. of callsa	y = 246x + 530,897a	0.043a	0.062a	79a
Tank 1	Mean SPL	y = 74,752x − 8,745,163	0.245	<0.001	243
Tank 1a	Mean SPLa	y = 74,494x − 8,315,774a	0.136a	0.001a	79a
Tank 2	Total no. of calls	y = 235x − 77,917	0.065	<0.001	220
Tank 2a	Total no. of callsa	y = 714x − 180,228a	0.015a	0.687a	11a
Tank 2	Mean SPL	y = 36,452x − 4,193,465	0.046	0.001	220
Tank 2a	Mean SPLa	y = 230,218x − 26,911,981a	0.048a	0.470a	11a
Tank 3	Total no. of calls	y = 689x + 35,106	0.398	0.003	220
Tank 3a	Total no. of callsa	y = 2,052x − 506,339a	0.964a	0.122a	1a
Tank 3	Mean SPL	y = 31,294x − 3,533,082	0.112	<0.001	220
Tank 3a	Mean SPLa	y = 250,379x − 28,201,967a	0.997a	0.036a	1a

DOI: 10.7717/peerj.2944/table-4

Notes:

SPL, received sound pressure level in dB re 1 µPa.

aSeparate linear regression analysis was performed and did not include non-spawning events (i.e., when eggs collected were equal to 0).

Discussion

Conclusions

Spotted seatrout were more likely to spawn when male fish called more frequently. Yet, there were several times during the recording period when there was calling but no spawning occurred. Some aspects of spotted seatrout reproductive biology may explain this observation. Most estimates of spawning frequency suggest that female spotted seatrout spawn once every 4–5 days, as reviewed by Brown-Peterson (2003). In addition, there is some evidence that spotted seatrout calling and spawning are associated with the lunar cycle, which was not controlled for in the present study.

Nonetheless, our findings indicate that we can use acoustic metrics, with confidence, to predict spawning potential. Passive acoustics can be used to find the location of spawning aggregations, determine spawning start and end dates, and possibly estimate maximal values of spawning frequency within a reproductive season. These findings are significant because plankton tows may not accurately reflect spawning locations since egg capture is likely affected by predator activity, water currents, and tow efficiency. Instead, passive acoustics could be used to monitor spotted seatrout reproduction. Future studies can use this captive study as a model to record the estuarine soundscape precisely over long time periods to better understand how human-made stressors (e.g., climate change, noise pollution, and chemical pollutants) may affect spawning patterns.

Supplemental Information