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Exploring the soundscape of small freshwater lakes
Journal Pre-proof Exploring the soundscape of small freshwater lakes
Putland, Mensinger PII:
S1574-9541(19)30329-2
DOI:
https://doi.org/10.1016/j.ecoinf.2019.101018
Reference:
ECOINF 101018
To appear in:
Ecological Informatics
Received date:
22 April 2019
Revised date:
9 September 2019
Accepted date:
30 September 2019
Please cite this article as: Putland, and Mensinger, Exploring the soundscape of small freshwater lakes, Ecological Informatics(2019), https://doi.org/10.1016/ j.ecoinf.2019.101018
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© 2019 Published by Elsevier.
Journal Pre-proof Exploring the soundscape of small freshwater lakes Putland, R.L.* [email protected], Mensinger, A.F. Department of Biology, Swenson Science Building, University of Minnesota Duluth, MN, USA, 55812
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*Corresponding author.
Journal Pre-proof Abstract Monitoring freshwater ecosystems using passive acoustics is a largely unexplored approach, despite having the potential to yield information about the biological, geological and anthropogenic activity of a lake or river system. The state of Minnesota, located in the upper Midwest of the USA and nicknamed ‘land of 10,000 lakes’, provides an interesting case study for soundscape research, because lakes offer ecological, recreational and economic value throughout the area. The underwater soundscape was monitored at fifteen small lakes < 10 km2 on representative days in winter (during 100 % ice cover) and summer 2018 using a
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hydrophone suspended 2 m below the water’s surface. Median broadband sound pressure level (100 – 12,000 Hz) was significantly lower in winter (57.2 dB re 1μPa) compared to summer
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(66.7 dB re 1μPa), possibly because low frequency wind sounds were reduced in winter. Recordings suggest small freshwater lakes in Minnesota have a relatively pristine soundscape,
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where vocalizing aquatic animals may hold acoustic niches. However, sound from anthropogenic activity was also present in the study lakes. Ice auger and motorboat sound
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increased the intensity of the soundscape by 10 dB and overlapped the frequency range (300 –
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1,000 Hz) of biological sounds in the environment, that may be important to aquatic life. Understanding current baseline sound levels in ecologically significant freshwater lakes, like those in this study, is the first step in determining any potential consequences of anthropogenic
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sound. Moving forward, baseline sound levels provide vital evidence for scientists and governing bodies to make proactive decisions for soundscape conservation.
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Keywords: Underwater sound, freshwater, lakes, ambient sound, anthropogenic noise
Journal Pre-proof 1. Introduction Passive acoustic monitoring technology is a valuable and flexible tool in ecology, providing the unique opportunity to rapidly quantify and compare sound across habitats, space and time (Browning and Gibb, 2018). Soundscape ecology encompasses all sounds in an environment: including geological (weather and geophysical activity), biological (vocalizations and mechanical sounds produced by animals) and anthropogenic (sounds produced by human activity) (Pijanowski et al., 2011). Ecologists and conservation managers recognize that soundscapes provide both ecological and social value (Dumyahn and Pijanowski, 2011). However, little is
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known about the soundscape of freshwater environments, despite lakes and rivers being home to a diverse array of wildlife and a place for both recreational and commercial activities.
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Freshwater acoustic research has focused principally on characterizing the bioacoustics of
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individual species, behavior or sound production mechanisms. Many freshwater animals produce sound, such as turtles (Giles et al., 2009), amphibians (Narins and Feng, 2006; Bee,
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2012), arthropods (Alexander, 1967; Popov, 1990), crustaceans (Buscaino et al., 2012; Silva et al., 2019) and fish (Mann et al., 2007; Cott et al., 2013). Sound is either produced passively
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during feeding or movement (Rice and Lobel, 2003; Rountree et al., 2018; Silva et al., 2019), or actively to communicate with conspecifics, attract mates or defend territory (Desjonquères et al.,
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2019). However, the diversity and dynamics of freshwater soundscapes is mostly inferred from marine or terrestrial recordings of abiotic sound. One study conducted in Austria suggested sound levels were higher in rivers with fast flowing water (110 dB re 1μPa) than stagnant water
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bodies such as lakes (100 dB re 1μPa) (Wysocki et al., 2007). However, ten of the twelve
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deployment sites were < 2 m water depth meaning low frequency sound could not be analyzed because the frequency cut-off would be ~2,000 Hz (Weston, 1971; Urick, 1983) and recordings only lasted up to 3 minutes. Additionally, the habitats differed in size, bathymetry, sediment composition and water depth, which all influence sound propagation (Farcas et al., 2016). Sound levels in freshwater habitats depend primarily on hydrology, most notably the volume and speed of water flow. For example, higher spectral levels (80 dB re 1μPa) were recorded in Lake Traunsee, Austria in the lower frequencies (< 1,000 Hz) because of water flowing into the lake from a stream (Amoser et al., 2004). Sound levels are also influenced by meteorology; in Lake Clinton, IL, USA spectral levels ranged from 40 – 60 dB re 1μPa (1 Hz bands) during rainfall compared to 30 – 50 dB re 1μPa when no rain was falling (Nystuen, 1986). These recordings were taken in very shallow water environments, < 2 m deep, therefore sounds caused by wind generated waves may have become distorted by surface and bottom reflections however, when
Journal Pre-proof rain hits the water’s surface it produces a cavitation bubble that increases the sound levels at frequencies > 200 Hz (Ma et al., 2005). It is also imperative to understand the role of ice formation in lake soundscape ecology, because of the world’s 117 million lakes (Verpoorter et al., 2014), almost half periodically freeze (Weyhenmeyer et al., 2011). Yet, comparatively few ecological studies have been conducted during winter because limnology fieldwork during ice cover is logistically difficult (Block et al., 2019). Winter is an important period for limnological processes ranging from biogeochemistry to fish ecology, therefore understanding seasonal variation in soundscape ecology is important.
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The first study to describe the under ice acoustic soundscape of a freshwater lake was conducted in the Northwest Territories, Canada. Ambient sound was influenced by ice cracking
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(160 dB re 1μPa) as well as anthropogenic activity (150 dB re 1μPa) (Martin and Cott, 2016). In winter, sounds are associated with ice augers to open fishing holes, pedestrian and vehicle
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traffic, whereas, in summer recreational and commercial vessel activity is prevalent. To date, the variation of ambient sound in lakes during the year has received little attention. However,
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passive acoustic monitoring allows remote areas to be surveyed over extended timescales.
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The growing understanding that sound plays a critical role in the life history of many aquatic animals has led to the recognition of anthropogenic sound as a major pollutant of international
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concern (Buxton et al., 2017). Acute impacts of exposure of anthropogenic sound have include physical injury and hearing damage whereas chronic impacts of increased sound levels have
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been indirectly related to ecology, behavior and fitness (Shannon et al., 2016; Gurule-Small and Tinghitella, 2019). For example, rudd (Scardinius erythrophthalmus) and roach (Rutilus rutilus)
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actively avoided high speed boating traffic in the Meuse River, Belgium (Boussard, 1981) and exposure to boat sound resulted in an increase in cortisol secretion in European freshwater fishes such as common carp (Cyprinus carpio), gudgeon (Gobio gobio) and perch (Perca fluviatilis) (Wysocki et al., 2006). Anthropogenic sound generally lies between 10 – 1,000 Hz, which directly overlaps the frequency range used by many species of aquatic animals for communication and if sufficiently intense, may result in acoustic masking, which impedes an individual’s ability to effectively perceive, recognize or decode sounds of interest (Clark et al., 2009). Animals may adapt to overlapping sound signals by using different frequencies, increasing the amplitude of their vocalizations [otherwise known as the Lombard effect (Lombard, 1911)] or producing sound during quieter time (Naguib, 2013; Holt and Johnston, 2014). Furthermore, there may be a trade-off between the detection and recognition of acoustic signals and hearing sensitivity, with animals developing lower sensitivity in ‘noisy’ environments
Journal Pre-proof (Lugli, 2019). A recent review on the effects of anthropogenic sound on freshwater fish provided an in-depth analysis on changes in behavioral and physiological outputs (Mickle and Higgs, 2017). Additionally, an investigation on freshwater soundscapes (near Puget Sound, Washington, USA) found sound levels correlated to urbanization within a 10 km radius (Kuehne et al., 2013). Lakes categorized as low, medium or high urbanization (percent land cover within 10k m radius with impervious surface) had 0 %, 17 % and 67 % of hourly measurements respectively, over the noise threshold of 55 dB (Kuehne et al., 2013). Scientists and land managers have emphasized the importance of small inland lakes in terms of aesthetic, cultural, educational, economic and environmental interests (Dudgeon et al., 2007). This study was
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designed to establish a baseline for ambient sound in 15 small freshwater lakes in Minnesota
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and compare sound spectra between summer and winter. Data collected will provide information for management on the potential effect of anthropogenic sound and how to prioritize
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soundscape conservation of freshwater ecosystems in the future.
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2. Materials and Methods 2.1 Data collection
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Sound was recorded at 15 lakes in St Louis and Carlton counties, Minnesota, USA (Figure 1). The lakes were chosen to reflect different urbanization patterns (number of houses, boat ramps
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and proximity to arterial roads (defined as single lane highways), while being < 7 km2 in surface area (Table 1) to facilitate access to the deployment sites without motorized vehicles.
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Additionally, all lakes contain a diverse range of fish species (Supplementary Information Table 1) and are regularly used for recreational fishing and motorboat activities (per obs. Putland
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2019).
Acoustic recordings were taken at each lake using an omni-directional hydrophone (ST300, Ocean Instruments, NZ); for one hour between 13:00 and 17:00 on a single day in winter (13th February – 9th March 2018) and summer (20th August – 20th September 2018). Recordings were conducted on weekdays to minimize the influence of recreational activity and allow an accurate background sound baseline (ambient sound minus anthropogenic activity). For all deployments, recording apparatus (hydrophone, battery, recorder and timer) was attached to a weighted rope two meters below the water surface (or base of ice in winter), with deployment location chosen to be the deepest point on each lake (Supplementary Information Figures 1 – 15). In winter, all lakes were completely covered with ice and site access was on foot, therefore the weighted rope was lowered through an 0.25 m diameter hole drilled using an electric auger, and measurements were taken of snow and ice thickness (m) (Supplementary Figure 16). In
Journal Pre-proof summer, deployments at the same GPS location were conducted from a Kevlar canoe to avoid introducing sound into the recordings (Supplementary Figures 2 - 15), except at Grand Lake where owing to the presence of recreational canoeists a different position was used for the summer recording (Supplementary Figure 1). The hydrophone was programmed to sample continuously at 24,000 Hz for the duration of each deployment. The hydrophone had a flat -3dB frequency response between 10 – 72,000 Hz and was calibrated prior and post deployment using a pistonphone with a 1,000 Hz tone (GRAS 42AA). Self- noise of the hydrophone was < 32 dB / Hz re 1µPa above 2,000 Hz (Ocean Instruments, NZ). The low frequency cut-off for underwater sound is inversely proportional to water depth, each lake was > 6 m water depth
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therefore the minimum low frequency cut-off was 100 Hz (Tindle et al., 1978; Urick, 1983). Sound speed profiles of the water column were calculated using salinity and temperature data
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collected with a YSI meter (± 0.1 ˚C), with readings taken at meter intervals from the surface (0
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m) to lake bottom (6 – 19 m) (Supplementary Information Table 2, Figures 17 - 18). No specific permits were required for the described field sampling. All lakes were publicly accessible via
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boat launches maintained by the Minnesota Department for Natural Resources. No biological
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samples were collected and therefore state scientific collection permits were not needed.
2.2 Data analysis
All acoustic data were reviewed aurally and visually using a scrolling spectrographic display of
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10 seconds (Hanning window, FFT length = 512 with 50% overlap, providing a frequency resolution of 46.8 Hz and a time resolution of 0.4 ms) in Raven Pro (version 1.5.0) to assess the
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proportion of recordings where anthropogenic and biological sounds were present. Spectrograms were also produced to show qualitatively the variability in sound within each 1-
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hour recording. All sound recordings (60 x 1-minute files from each lake) were then analyzed using MATLAB software (version 2015a) to determine acoustic metrics. Each sound recording were high-pass filtered at 100 Hz owing to the low-frequency cut-off and analyzed up to the Nyquist frequency of 12,000 Hz. A Friedman’s two-way ANOVA (p < 0.05) was performed to determine whether sound [broadband (100 – 12,000 Hz) root-mean-squared (RMS) sound pressure level (SPL)] varied between season and lake. Following this, a multiple comparison Holm-Šídák post hoc test (p < 0.05) was conducted to determine individual effects. To determine whether the sound pressure level varied with frequency, power spectral density (PSD) (dB re 1μPa2/Hz) was calculated over sixty, 1-minute integration periods for the entire 1 hour recording between 10 – 12,000 Hz using a fast Fourier transformation of 30 second samples, creating 1 Hz frequency resolution and applying a Hanning window with 50% overlap.
Journal Pre-proof Octave band sound pressure levels (16, 31.5, 63, 125, 250, 500, 1000, 2000, 4000, 8000 Hz) (dB re 1μPa) were subsequently calculated for each recording using PSDs. Median octave band levels were then compared between summer and winter using a Mann-Whitney U test. Abiotic sounds including weather or water movement can alter the soundscape (Wenz, 1962; Ma et al., 2005). Therefore, to assess how weather may have altered the soundscape of the freshwater lakes, wind speed and direction was compared to broadband SPL using a Pearson’s product correlation. Data was collected from the NOAA weather station in Duluth, MN (46.837, 92.211) , which despite being 6 km away from the nearest lake provides a proxy of weather in
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the area for the duration of each deployment. Ice and snow thickness measurements taken at using a Pearson’s product correlation (p < 0.05).
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each deployment site in winter also were compared to broadband SPL of winter recordings
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All recordings were inspected aurally and visually using scrolling spectrograms (fast Fourier transformation (FFT) length = 4096, Hanning window, 50% overlap) and sounds matched to
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fieldnotes of anthropogenic activity at the time. Average PSD was calculated for each type of sound identified and used to compare different sounds. Furthermore, to determine whether
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sound recorded was affected by the number of houses, boat ramps on the lake or proximity to the nearest arterial road, defined as single lane highway, broadband SPL was compared to
3. Results
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these factors using a Pearson’s product correlation (p < 0.05).
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There was a significant difference in broadband SPL according to season and lake (two-way ANOVA F1 = 2880.673, p < 0.001 and F14 = 73.726, p < 0.001) as well as the interaction
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between lake and season (two-way ANOVA F14 = 113.969, p < 0.001) (Figure 2). During summer, the median broadband SPL across all lakes was 66.7 re 1μPa, with highest sound levels recorded at Barrs, Big, Nichols, Grand and Leora (71.5 – 78.1 dB re 1μPa) and the lowest recorded at Pike, Island and Spring II (59.8 – 60.9 dB re 1μPa) (Table 2). In comparison, during the winter the median broadband SPL across all the lakes was 57.2 dB re 1μ, with higher sound levels recorded at Briar, Mirror and Pike Lake (60. 0 – 65.2 dB re 1μPa) compared to all the other lakes (50.7 – 58.0 dB re 1μPa) (Table 2). To isolate the effect of season from lake, a multiple comparison Holm-Sidak post-hoc test was performed, with all lakes, except Pike Lake, found to have a significant difference between summer and winter recordings (Supplementary Table 1). Median broadband SPL at Pike Lake was 60.9 dB re 1μPa in summer and in winter was 60.0 dB re 1μPa (Table 2).
Journal Pre-proof Frequency (octave band level) was found to be a significant factor in sound level recorded between seasons (Mann-Whitney U > 5.00, p < 0.001) with the median octave band level at least 4.8 dB re 1μPa higher in summer compared to winter (Figure 3, Table 3). The greatest difference (up to 13.9 dB) between seasons was seen in the lower frequencies (16.0, 31.5, 63.0 Hz). Furthermore, there was a steady decrease in sound level as the octave band frequency increased. For example, the median octave band level for 125 Hz was 59.7 and 52.1 dB re 1μPa for summer and winter respectively, compared to 38.3 and 32.7 dB re 1μPa at 8000 Hz, which was very close to the noise floor of the instrument (32 dB > 2000 Hz) (Table 4).
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Broadband SPL was compared for all lakes to weather metrics using a Pearson’s correlation coefficient. There was no significant correlation between sound recorded and average wind
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speed (r = -0.246 p = 0.225), maximum wind speed (r = -0.339 p = 0.091), ice thickness (r = 0.277 p = 0.337) or snow thickness (r = -0.233 p = 0.423) (Figure 4). However, at similar wind
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speeds, broadband SPL was noted as lower in winter compared to summer (Figure 4). For example, when the average wind speed was 4.47 m/s, SPL was 66.0 dB re 1μPa on Spring
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Lake in summer, compared to 55.0 dB re 1μPa on Beauty Lake in winter.
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Broadband SPL also was compared to anthropogenic metrics of land use each lake: there was no significant correlation between broadband SPL recorded and the number of houses/shoreline
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length (r = 0.050, p = 0.773), number of boat ramps/shoreline length (r = 0.020, p = 0.898) and nearest arterial road (r = 0.075, p = 0.693) (Figure 5). However, the PSD recorded during
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example anthropogenic activities (auger or motorboat) was higher across all frequencies (10 – 12,000 Hz) when compared to the median ambient soundscape recorded at all lakes (Figure 6,
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Supplementary Figure 19). Motorboat sound PSD was also > 10 dB higher than summer ambient sound between 300 – 1,000 Hz and auger sound was > 10 dB higher than winter ambient sound across the entire frequency spectrum (Figure 6). Motorboat sound was detected for the following percentages of summer recording at Pike (10 %), Spring (13 %), Briar (22 %) and Bob (7 %) lake and auger sound was present at Grand (3 %), Big (8 %), Pike (22 %), Nichols (10 %), Briar (7 %) and Bob (13 %) lake during the winter recordings (Supplementary Table 3). 26 sources of biological sound were also detected from visual and aural analysis ranging between 81 – 1406 Hz and 0.1 – 1.5 seconds in duration, although sources were unknown. In winter, 21 of the sounds were detected, and in summer, 5 sounds were detected (Supplementary Table 4). Furthermore, potential biological sound was present in 12 of the 15 lakes during the winter: Grand (3 %), Caribou (7 %), Pike (2 %), Nichols (3 %), Island (2 %), Beauty (2 %), Sandy (2 %), Barrs (3 %), Spring (2 %), Briar (3 %), Spring II (2 %), Mirror (5%)
Journal Pre-proof during the summer. Whereas, in summer potential biological sound was present in 2 of the 15 lakes: Pike (2 %) and Mirror (7 %). Overall, visual and aural inspection of spectrograms highlighted the limited variability of SPL within the 1-hour sound recordings (Supplementary Information Figures 20 – 34) despite the present of anthropogenic and biological sounds.
4. Discussion This study provides insight into the baseline ambient sound level of freshwater lakes and has application to the development of policy initiatives to monitor and regulate the soundscape. There was a significant difference in broadband SPL according to season, with the median SPL
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recorded in summer 9.5 dB louder than in winter, equating to a tenfold increase in power (Urick,
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1983). Additionally, at all lakes recorded, the highest sound levels (55 - 90 dB re 1μPa) were observed in the low frequency region (< 125 Hz), with a steady decline in the sound level up to
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12,000 Hz (35 - 45 dB re 1μPa) (Figure 3), suggesting geophony (weather and open water movements) may have played a large role in the soundscape (Wenz, 1962; Urick, 1983). It
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would be expected that other temperate lakes (< 7 km2) will have a similar range in sound level recorded during the summer and winter months, especially if they are subject to 100% ice cover
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because ice and snow provides an additional layer between the underwater and terrestrial environment for sound to reflect and refract from.
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Recordings were conducted during minimal human activity and in the absence of atmospheric disturbances other than wind to achieve the goal of measuring baseline sound levels. The major
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abiotic variable was wind speed which varied from 5.3 to 17.8 m/s (Table 2) however, in this study there was no signification correlation between wind speed and broadband sound level.
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Wind speed is a crude measurement of surface agitation which also depends on factors such as duration and constancy of the wind and its direction in relation to near shore topography (Wenz, 1972). Furthermore, a limitation of recording the soundscape of small shallow lakes is that sound propagation is strongly influenced by variation in sound speed profile within the water column. In this study, maximum water depth ranged between 6 – 18 m (Briar Lake – Island Lake, Table 1) resulting in a frequency cut-off of approximately 100 Hz (Urick, 1983), therefore sounds caused by wind generated waves may have become distorted by surface and bottom reflections. In winter, the presence, thickness and movement of ice can also contribute significantly to ambient sound levels, while ice and snow on the lake surface may function to dampen or eliminate sound created by wind generated waves (Martin and Cott, 2016). For
Journal Pre-proof example, when the same wind speed (4.47 m/s) was recorded in summer and winter, broadband SPL was 66.0 and 55.0 dB re 1μPa (100 – 12,000 Hz) respectively (Figure 4). In this study, the focus was to measure the baseline sound level across as many small lakes within approximately the same seasonal window (i.e. short time frame), maximize equipment security and optimize deployment logistics. Comparing the fifteen freshwater lakes, the broadband SPL of ambient sound ( 100 – 12,000 Hz) was similar, ranging from 58 – 74 dB re 1μPa in summer and 50 – 65 dB re 1μPa in winter, despite differences in lake area, water depth and bottom type (Figure 2). Summer recordings taken in Lake Sarzana, Italy, 40 - 50 dB re
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1μPa (Bom, 1969), Lake Clinton, IL, USA, 40 - 60 dB re 1μPa (Nystuen, 1986) and the Niger River, Mali, 40 – 70 dB re 1μPa (Crawford et al., 1997) showed very similar sound levels with
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high amounts of energy in the low frequencies, followed by a rapid decline to higher frequencies and a more gradual decline above 10 kHz. However, in the literature there are limited winter
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recordings available to compare to this study. In temperate marine environments, ambient sound levels are often higher during summer than winter, according to the behavior of both
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resident and transient soniferous animals (Radford et al., 2008; Haxel et al., 2013). In the
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freshwater lakes recorded in this study, sound levels were significantly lower in the winter which may provide an acoustic refuge for soniferous fish species that spawn in the winter months, such as burbot (Lota lota) (Bergersen et al., 1993; Cott et al., 2014; Cott et al., 2015) and allow
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individual fish to communicate over a wider area (Putland et al., 2017). Lower sound levels may be the result of soniferous animals being less active or unable to generate sound in cold water
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conditions. However, more potential biological sounds were detected during winter month therefore a reduction in the contribution of geophony to the soundscape may be more plausible.
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Moving forward, it is understood that long term acoustic monitoring is needed to record day and night, bolster conclusions, as well during a greater variety of meteorological events, such as storms and ice formation. For example, ice cracking produced 0.05 – 1 second duration pulses between 800 – 8,000 Hz, in recordings conducted in Great Slave Lake, Canada (Martin and Cott, 2016) and sound pressure levels of ice cracking sounds exceeded 160 dB re 1μPa in recordings taken in Kennady Lake, Canada (Mann et al., 2009). Although, in the winter recordings taken for this study ice sounds were not recorded probably because thick full ice coverage on the lakes minimized cracking. Additionally, sounds produced as ice forms during the transition from open (summer) to closed (winter) water conditions have not previously been recorded in a lake environment. With technological improvements in remote acoustic monitoring and a greater interest in winter limnology, it is expected that winter sampling will increase
Journal Pre-proof (Magnuson et al., 2000; Jensen et al., 2007; Hewitt et al., 2018; Block et al., 2019) allowing questions about temporal and spectral characteristics of ice formation to be answered. Freshwater lakes and ponds are home to a diverse array of wildlife with 162 species of fishes, 78 mammals, 22 amphibians, 31 reptiles and 428 bird species resident to Minnesota waters (Minnesota DNR, 2019). Many of these animals reliant on sound for interspecific communication or to mediate predator/prey interactions. Potential fish sounds may have been produced from catostomidae, centrarchidae, ictaluridae and lottidae, which inhabited some of all the lakes sampled. White sucker (Catastomus commersonnii) produce sound (~1500 Hz, duration 0.014
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s) via air movement with air bubbles released from the mouth and gills as they descend to depths (Rountree et al., 2018). Six different species of sunfish including green sunfish (Lepomis
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cyanellus) were found to produce grunts (<1000 Hz and <0.3 s) during active courtship by nesting males. Channel catfish (Ictalurus punctatus) produce stridulatory sounds (30 – 300 Hz)
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by rubbing the base of their pectoral spine against their pectoral gridle (Fine et al., 1997; Vance, 2000). Burbot (Lota lota) have a wide repertoire of vocalizations, from slow knocks (< 500 Hz, <
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1 s) to a complex hum (< 1000 Hz, < 10 s) (Cott et al., 2014). 87 freshwater species have been
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reported to produce sounds in North America and Europe over the last 200 years, accounting for 5% of known freshwater fish species (Rountree et al., 2019). In this study, 26 unique sounds were recorded between 100 – 2,000 Hz that maybe biological in origin, based on lower
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frequencies as all were < 1500 Hz and had a limited durations < 1 s, although sources were unknown. The challenge to deciphering species specific recordings is greater when you
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consider that other freshwater organisms such as turtles, birds amphibians, aquatic insects, crustaceans and some mammals also produce underwater sounds which are poorly
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documented (Rountree et al., 2019). However, using acoustic recorders to monitor soniferous species could give an indication of biodiversity in each lake recorded especially during the winter months when it is difficult to use other sampling methods, and the ice cover greatly reduces background sound. For example, burbot are a freshwater gadoid fish that spawns in the winter months (Cott et al., 2014). Their relatively sensitive hearing (Cott et al., 2013) and the presence of drumming muscles suggest that sound production and reception plays a primary role for burbot in communication (Cott et al., 2014). Therefore, passive acoustic monitoring has the potential to document mating vocalizations to aid in modelling the reproductive success of fishes in a particular lake system (Putland et al., 2018). In future studies, by combining video footage with acoustic recordings, scientists could unravel sources of unknown sounds as well as associated behavior, to create a catalog of freshwater fish sounds. In the present study, motorboat and ice auger sound increased the ambient sound level by >10 dB between 300 –
Journal Pre-proof 1,000 Hz and 100 – 12,000 Hz respectively (Figure 6) within 100 – 200 m from the source. Within each study lake, aquatic life has limited area to move from a sound source, which could result in chronic (sustained) sound exposure and subsequent ecological consequences. Some of the most popular recreational fish species in Minnesota, such as walleye (Sander vitreus) and northern pike (Esox lucuis) (Minnesota DNR, 2019), inhabit the study lakes (Supplementary Information Table 2). Walleye and northern pike have auditory sensitivity within 10 – 300 Hz (Mann et al., 2007) and 100 – 1,000 Hz (Popper et al., 2005; Mann et al., 2007) respectively, therefore have the potential to be impacted by sound produced by motorboats and augers (Figure 6). However, behavioral disturbance and physiological stress caused by anthropogenic
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sound is unpredictable. By combining information on the auditory sensitivity of lake inhabitants
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with long-term recordings of the soundscape, future studies could evaluate how individuals and/or populations may perceive and respond to sound.
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Multi-day sampling would offer the opportunity to investigate the frequency, timing and intensity of biological and anthropogenic activity on each lake (Joo et al., 2011). The short-term data
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reported may also not reflect differences across entire seasons, therefore longer-term
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monitoring is needed to confirm that the differences described in the present study reflect typical seasonal differences. One of the biggest challenges faced by those charged with monitoring ecosystems is decision making in the absence of essential information. This study focused on
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establishing the baseline ambient sound level for small lakes in summer and winter, providing managers with a starting point for understanding how geological (wind, rain and ice formation),
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biological (comprised of mostly unknown sources for the freshwater environment) and anthropogenic sounds (ice auger, fishing and boat sound) make up the ambient soundscape. It
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is expected that the sound levels of other small (< 7km2) lakes in temperate regions will also range between 55 – 90 dB re 1µPa with a significant difference between winter and summer recordings owing to the ice/snow interface between the underwater and terrestrial environments. Baseline sound levels also offer the potential to track changes in soundscape ecology in response to other large-scale environmental processes such as climate change or urban development.
Journal Pre-proof Acknowledgements Special thanks to Donn Branstrator for advice on winter limnology and Samuel Seddon for producing maps of the different lakes. We would also like to thank Emily Fleissner, Noland Michels, Alayna Mackiewicz and Loranzie Rogers for helping with fieldwork. Funding was
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provided by a Mistletoe Research Fellowship awarded to RLP.
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Alexander, R. D. (1967). Acoustical communication in arthropods. Annual Review of Entomology 12, 495526. Amoser, S., Wysocki, L. E. & Ladich, F. (2004). Noise emission during the first powerboat race in an Alpine lake and potential impact on fish communities. The Journal of the Acoustical Society of America 116, 3789-3797. Bee, M. A. (2012). Sound source perception in anuran amphibians. Current Opinion in Neurobiology 22, 301-310. Bergersen, E. P., Cook, M. F. & Baldes, R. J. (1993). Winter movements of burbot (Lota lota) during an extreme drawdown in Bull Lake, Wyoming, USA. Ecology of Freshwater Fish 2, 141-145. Block, B. D., Denfeld, B. A., Stockwell, J. D., Flaim, G., Grossart, H. F., Knoll, L. B., Maier, D. B., North, R. L., Rautio, M., Rusak, J. A., Sadro, S., Weyhenmeyer, G. A., Bramburger, A. J., Branstrator, D. K., Salonen, K. & Hampton, S. E. (2019). The unique methodological challenges of winter limnology. Limnology and Oceanography: Methods 17, 42-57. Bom, N. (1969). Effect of rain on underwater noise level. Journal of the Acoustical Society of America 45, 150-156. Boussard, A. (1981). The reactions of roach (Rutilus rutilus) and rudd (Scardinius erythrophtalmus) to noises produced by high speed boating. In Proceedings of 2nd British Freshwater Fish Conference, pp. 188-200. Browning, E. & Gibb, R. (2018). Monitoring ecosystems through sound: the present and future of passive acoustics. Methods Blog. Buscaino, G., Filiciotto, F., Buffa, G., Di Stefano, V., Maccarrone, V., Buscaino, C., Mazzola, S., Alonge, G., D’Angelo, S. & Maccarrone, V. (2012). The underwater acoustic activities of the red swamp crayfish Procambarus clarkii. The Journal of the Acoustical Society of America 132, 1792-1798. Buxton, R. T., McKenna, M. F., Mennitt, D., Fristrup, K. M., Crooks, K. R., Angeloni, L. & Wittemyer, G. (2017). Noise pollution is pervasive in U.S. protected areas. Science 356, 531. Clark, C., Ellison, W., Southall, B., Hatch, L., Parijs, S., Frankel, A. & Ponirakis, D. (2009). Acoustic masking in marine ecosystems: intuitions, analysis, and implication. Mar Ecol Prog Ser 395. Cott, P. A., Guzzo, M. M., Chapelsky, A. J., Milne, S. W. & Blanchfield, P. J. (2015). Diel bank migration of Burbot (Lota lota). Hydrobiologia 757, 3-20. Cott, P. A., Hawkins, A. D., Zeddies, D. G., Martin, B., Johnston, T. A., Reist, J. D., Gunn, J. M. & Higgs, D. M. (2014). Song of the burbot: Under-ice acoustic signaling by a freshwater gadoid fish. Journal of Great Lakes Research 40, 435-440. Cott, P. A., Johnston, T. A., Gunn, J. M. & Higgs, D. M. (2013). Hearing sensitivity of the burbot. Transactions of the American Fisheries Society 142, 1699-1704. Crawford, J. D., Philippe, J. & Vincent, B. (1997). Sound production and reproductive ecology of strongly acoustic fish in Africa: Pollimyrus isidori, mormyridae. Behaviour 134, 677-725. Desjonquères, C., Gifford, T. & Linke, S. (2019). Passive acoustic monitoring as a potential tool to survey animal and ecosystem processes in freshwater environments. Freshwater Biology 0. Dudgeon, D., Arthington, A. H., Gessner, M. O., Kawabata, Z., Knowler, D. J., Lévêque, C., Naiman, R. J., Prieur-Richard, A., Soto, D., Stiassny, M. L. J. & Sullivan, C. A. (2007). Freshwater biodiversity: importance, threats, status and conservation challenges. Biological Reviews 81, 163-182. Dumyahn, S. L. & Pijanowski, B. C. (2011). Soundscape conservation. Landscape Ecology 26, 1327. Farcas, A., Thompson, P. M. & Merchant, N. D. (2016). Underwater noise modelling for environmental impact assessment. Environmental Impact Assessment Review 57, 114-122. Fine, M. L., Friel, J. P., McElroy, D., King, C. B., Loesser, K. E. & Newton, S. (1997). Pectoral spine locking and sound production in the channel catfish Ictalurus punctatus. Copeia 1997, 777-790.
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Giles, J. C., Davis, J. A., McCauley, R. D. & Kuchling, G. (2009). Voice of the turtle: The underwater acoustic repertoire of the long-necked freshwater turtle, Chelodina oblonga. The Journal of the Acoustical Society of America 126, 434-443. Gurule-Small, G. A. & Tinghitella, R. M. (2019). Life history consequences of developing in anthropogenic noise. Global Change Biology 25, 1957-1966. Haxel, J. H., Dziak, R. P. & Matsumoto, H. (2013). Observations of shallow water marine ambient sound: the low frequency underwater soundscape of the central Oregon coast. The Journal of the Acoustical Society of America 133, 2586-2596. Hewitt, B. A., Lopez, L. S., Gaibisels, K. M., Murdoch, A., Higgins, S. N., Magnuson, J. J., Paterson, A. M., Rusak, J. A., Yao, H. & Sharma, S. (2018). Historical trends, drivers, and future projections of ice phenology in small north temperate lakes in the Laurentian Great Lakes region. Water 10. Holt, D. E. & Johnston, C. E. (2014). Evidence of the Lombard effect in fishes. Behavioral Ecology 25, 819826. Jensen, O. P., Benson, B. J., Magnuson, J. J., Card, V. M., Futter, M. N., Soranno, P. A. & Stewart, K. M. (2007). Spatial analysis of ice phenology trends across the Laurentian Great Lakes region during a recent warming period. Limnology and Oceanography 52, 2013-2026. Joo, W., Gage, S. H. & Kasten, E. P. (2011). Analysis and interpretation of variability in soundscapes along an urban–rural gradient. Landscape and Urban Planning 103, 259-276. Kuehne, L. M., Padgham, B. L. & Olden, J. D. (2013). The soundscapes of lakes across an urbanization gradient. PLOS ONE 8, e55661. Lombard, E. (1911). Le signe de l'élévation de la voix. Annales des Maladies de L'Oreille et du Larynx 37, 101-119. Lugli, M. (2019). How ambient noise may shape peripheral auditory sensitivity: a theoretical model on the trade-off between signal detection and recognition. Evolutionary Ecology 33, 173-194. Ma, B. B., Nystuen, J. A. & Lien, R. (2005). Prediction of underwater sound levels from rain and wind. The Journal of the Acoustical Society of America 117, 3555-3565. Magnuson, J. J., Robertson, D. M., Benson, B. J., Wynne, R. H., Livingstone, D. M., Arai, T., Assel, R. A., Barry, R. G., Card, V. M., Kuusisto, E., Granin, N. G., Prowse, T. D., Stewart, K. M. & Vuglinski, V. S. (2000). Historical trends in lake and river ice cover in the Northern Hemisphere. Science 289, 1743. Mann, D. A., Cott, P. A., Hanna, B. W. & Popper, A. N. (2007). Hearing in eight species of northern Canadian freshwater fishes. Journal of Fish Biology 70, 109-120. Mann, D. A., Cott, P. A. & Horne, B. (2009). Under-ice noise generated from diamond exploration in a Canadian sub-arctic lake and potential impacts on fishes. The Journal of the Acoustical Society of America 126, 2215-2222. Martin, B. S. & Cott, P. A. (2016). The under-ice soundscape in Great Slave Lake near the city of Yellowknife, Northwest Territories, Canada. Journal of Great Lakes Research 42, 248-255. Mickle, M. F. & Higgs, D. M. (2017). Integrating techniques: a review of the effects of anthropogenic noise on freshwater fish. Canadian Journal of Fisheries and Aquatic Sciences. Minnesota DNR (2019). Species found in Minnesota https://www.dnr.state.mn.us/animals/index.html. Naguib, M. (2013). Living in a noisy world: indirect effects of noise on animal communication. Behaviour 150, 1069-1084. Narins, P. M. & Feng, A. S. (2006). Hearing and sound communication in amphibians: prologue and prognostication. In Hearing and Sound Communication in Amphibians (Narins, P. M., Feng, A. S., Fay, R. R. & Popper, A. N., eds.), pp. 1-11. New York, NY: Springer New York. Nystuen, J. A. (1986). Rainfall measurements using underwater ambient noise. The Journal of the Acoustical Society of America 79, 972-982. Pijanowski, B. C., Farina, A., Gage, S. H., Dumyahn, S. L. & Krause, B. L. (2011). What is soundscape ecology? An introduction and overview of an emerging new science. Landscape Ecology 26, 1213-1232.
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Popov, A. V. (1990). Co-Evolution of sound-production and hearing in insects. In Sensory Systems and Communication in Arthropods: Including the First Comprehensive Collection of Contributions by Soviet Scientists (Gribakin, F. G., Wiese, K. & Popov, A. V., eds.), pp. 301-304. Basel: Birkhäuser Basel. Popper, A. N., Smith, M. E., Cott, P. A., Hanna, B. W., MacGillivray, A. O., Austin, M. E. & Mann, D. A. (2005). Effects of exposure to seismic airgun use on hearing of three fish species. Journal of the Acoustical Society of America 117, 3958-3971. Putland, R. L., Mackiewicz, A. G. & Mensinger, A. F. (2018). Localizing individual soniferous fish using passive acoustic monitoring. Ecological Informatics. Putland, R. L., Merchant, N. D., Farcas, A. & Radford, C. A. (2017). Vessel noise cuts down communication space for vocalizing fish and marine mammals. Global Change Biology. Radford, C. A., Jeffs, A. G., Tindle, C. T. & Montgomery, J. C. (2008). Temporal patterns in ambient noise of biological origin from a shallow water temperate reef. Oecologia 156, 921-929. Rice, A. N. & Lobel, P. S. (2003). The pharyngeal jaw apparatus of the Cichlidae and Pomacentridae: function in feeding and sound production. Reviews in Fish Biology and Fisheries 13, 433-444. Rountree, R. A., Bolgan, M. & Juanes, F. (2019). How Can We Understand Freshwater Soundscapes Without Fish Sound Descriptions? Fisheries 44, 137-143. Rountree, R. A., Juanes, F. & Bolgan, M. (2018). Air movement sound production by alewife, white sucker, and four salmonid fishes suggests the phenomenon is widespread among freshwater fishes. PLOS ONE 13, e0204247. Shannon, G., McKenna, M. F., Angeloni, L. M., Crooks, K. R., Fristrup, K. M., Brown, E., Warner, K. A., Nelson, M. D., White, C. R., Briggs, J., McFarland, S. & Wittemyer, G. (2016). A synthesis of two decades of research documenting the effects of noise on wildlife. Biological Reviews 91, 982-1005. Silva, J. F., Hamilton, S., Rocha, J. V., Borie, A., Travassos, P., Soares, R. & Peixoto, S. (2019). Acoustic characterization of feeding activity of Litopenaeus vannamei in captivity. Aquaculture 501, 76-81. Tindle, C. T., Guthrie, K. M., Bold, M. D., Jones, J. D., Dixon, K. O. & Birdsall, T. G. (1978). Measurements of the frequency dependence of normal modes. The Journal of the Acoustical Society of America 64, 1178-1185. Urick, R. J. (1983). Principles of Underwater Sound. New York: McGraw-Hill Inc. Vance, T. L. (2000). Variability in Stridulatory Sound Production in the Channel Catfish, Ictalurus punctatus. Bios 71, 79-84. Verpoorter, C., Kutser, T., Seekell, D. A. & Tranvik, L. J. (2014). A global inventory of lakes based on highresolution satellite imagery. Geophysical Research Letters 41, 6396-6402. Wenz, G. M. (1962). Acoustic Ambient Noise in the Ocean: Spectra and Sources. The Journal of the Acoustical Society of America 34, 1936-1956. Wenz, G. M. (1972). Review of underwater acoustics research: noise. The Journal of the Acoustical Society of America 51, 1010-1024. Weston, D. E. (1971). Intensity-range relations in oceanographic acoustics. Journal of Sound and Vibration 18, 271-287. Weyhenmeyer, G. A., Livingstone, D. M., Meili, M., Jensen, O., Benson, B. & Magnuson, J. J. (2011). Large geographical differences in the sensitivity of ice-covered lakes and rivers in the Northern Hemisphere to temperature changes. Global Change Biology 17, 268-275. Wysocki, L. E., Amoser, S. & Ladich, F. (2007). Diversity in ambient noise in European freshwater habitats: Noise levels, spectral profiles, and impact on fishes. The Journal of the Acoustical Society of America 121, 2559-2566. Wysocki, L. E., Dittami, J. P. & Ladich, F. (2006). Ship noise and cortisol secretion in European freshwater fishes. Biological Conservation 128, 501-508.
Journal Pre-proof
Leora Island Beauty Sandy Barrs Spring Briar Bob Spring II Mirror
St Louis
-92.307 -92.635
46.867
-92.290
47.094
-92.540
47.104
-92.408
47.230
-93.025
47.225 46.534
-93.076 -92.554
47.058
-91.967
47.065
-92.009
47.072 46.633
-91.961 -92.616
46.535
-92.525
46.966
-92.173
10.6
Boat ramps per km shoreline 7.0
Nearest arterial (main) road (km) 1.6
6.73
7.3
13.3
2.19
6.4
9.0
17.3
15.2
2.4
2.14 1.99
8.1 18.3
13.1 8.0
11.2 27.7
10.6 22.0
10.1 0.2
1.70
9.4
7.9
13.3
10.0
3.9
1.07
10.7
4.6
20.3
14.2
4.7
0.90
19.8
8.4
6.9
6.0
16.3
0.88
9.4
5.2
5.2
6.2
16.0
0.52 0.51
8.2 7.0
3.3 4.4
7.0 11.2
4.5 7.8
4.4 17.6
of
Nichols
46.903 46.707
Houses per km shoreline
ro
Big Pike
-92.397
Shoreline length (km)
re
Caribou
46.875
Max. depth (m)
0.40
7.6
4.7
4.9
4.0
19.1
0.32
6.0
4.2
15.9
12.6
17.3
0.31 0.15
9.0 7.6
2.4 1.9
1.6 0.5
2.1 0.5
3.9 5.8
8.2
1.3
3.8
3.1
15.0
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St Louis St Louis Carlton St Louis St Louis St Louis St Louis St Louis Carlton St Louis St Louis St Louis Carlton Carlton
Lake area 2 (km )
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Grand
Position of hydrophone Latitude Longitude
0.08
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County
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Lake
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Table 1: Metrics for the 15 study lakes listed in order of decreasing lake area.
Journal Pre-proof Table 2: Broadband sound levels (100 – 12,000 Hz, rms dB re 1μPa) for each lake in summer and winter recordings compared to environmental data (wind speeds and coverage for winter).
2.68 2.68 3.58 4.02 3.58 3.58 11.62 2.24 6.71 5.36 4.47 4.47 3.58 6.71 7.15 3.13 4.92 5.81 3.13 5.81 5.81 4.47 4.47 4.647 3.58 4.92 4.92 4.02 4.47 2.24
6.26 5.81 6.71 7.60 6.26 6.26 17.88 5.36 14.30 9.39 8.05 8.05 6.71 14.30 11.18 7.60 8.94 11.18 8.05 11.18 11.18 8.94 8.94 8.94 7.15 9.39 9.39 7.60 8.94 5.36
Ice thickness (m)
Snow thickness (m)
n/a
n/a
0.72 0.64 0.53 0.60 0.67 0.61 0.48 0.66 0.55 0.59 0.61 0.60 0.45 0.53 0.48
0.25 0.22 0.32 0.13 0.18 0.20 0.23 0.18 0.23 0.30 0.35 0.26 0.32 0.27 0.14
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Maximum wind speed (m/s)
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Winter
Average wind speed (m/s)
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Grand Caribou Big Pike Nichols Leora Island Beauty Sandy Barrs Spring Briar Bob Spring II Mirror Grand Caribou Big Pike Nichols Leora Island Beauty Sandy Barrs Spring Briar Bob Spring II Mirror
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Summer
Broadband SPL (100 – 12,000 Hz, rms dB re 1μPa) 71.5 64.2 71.7 60.9 78.1 72.4 58.8 67.4 65.0 74.4 66.0 63.0 66.7 59.8 65.1 57.6 50.7 55.5 60.0 55.9 52.9 57.4 55.0 55.2 56.8 58.0 65.2 55.7 55.6 62.7
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Lake
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Season
Journal Pre-proof Table 3: Comparisons among median sound levels for each octave band from summer and winter recordings. Octave band levels 1, 2 and 3 are shown in grey owing to these frequencies
4
125.0
5
250.0
6
500.0
7
1000.0
8
2000.0
9
4000.0
10
8000.0
Winter
32.7
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U value
13.1
< 0.001
8
13.9
< 0.001
15
9.2
< 0.001
17
7.6
< 0.001
18
4.8
< 0.001
30
6.9
< 0.001
28
8.1
< 0.001
22
6.8
< 0.001
19
7.3
< 0.001
8
6.1
< 0.001
5
of
63.0
P value
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3
Difference (dB)
re
31.5
Summer Winter Summer Winter Summer Winter Summer Winter Summer Winter Summer Winter Summer Winter Summer Winter Summer Winter Summer
Median octave band level (dB re 1μPa) 76.1 63.0 76.3 62.4 66.4 57.2 59.7 52.1 52.4 47.7 51.1 44.2 48.0 39.9 43.9 37.1 42.8 35.4 38.8
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2
Season
na
1
Center Frequency (Hz) 16.0
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Octave Band
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falling below the low-frequency cut-off in water 6 m deep.
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Figures
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Figure 1: Map of 15 study lakes within Minnesota, inset map on lower left corner shows in relation to the United States map. Individual maps of each lake can be found in the
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Supplementary Information Figures 1 - 15.
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Figure 2: Broadband (100 – 12,000 Hz) root-mean-square (RMS) sound pressure levels (SPL) (dB re 1μPa) from the 60 – minute recordings taken in winter and summer at each lake. Significant differences according to Holm-Šídák post-hoc test between the two seasons are represented by * (P <0.05).
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Figure 3: Comparison of sound levels for ten octave bands measured over an hour period on a weekday afternoon in summer (orange filled) and winter (blue not filled). Significant differences according to Holm-Šídák post-hoc test between the two seasons are represented by * (P <0.05). The shaded region represents the low frequency-cut off < 100 Hz.
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Figure 4: Comparison of broadband sound pressure level (100 – 12,000 Hz, rms dB re 1μPa) to weather metrics in summer (orange circles) and winter (blue triangles). Each individual point
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represents the single value for each lake. Results of Pearson’s Product Correlation shown in upper right corner of each graph.
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Figure 5: Comparison of broadband sound pressure level (100 – 12,000 Hz, rms dB re 1μPa) to anthropogenic metrics in summer (orange circles) and winter (blue triangles). Each individual point represents the single value for each lake. Results of Pearson’s Product Correlation shown in upper right corner of each graph.
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Figure 6: Comparison of power spectral density (dB re 1µPa2/Hz) recorded during ambient and anthropogenic activities in summer and winter. Ambient summer and winter represents the median power spectral density taken from all 15 lakes over each 1-hour recording, auger winter represents the median power spectral density taken over a 10-minute segment from the Pike Lake recording, and motorboat summer represents the median power spectral density taken over a 10-minute segment from the Pike Lake recording. The grey box shows the frequencies in the low-frequency cut off.
Journal Pre-proof Figure captions Figure 1: Map of 15 study lakes within Minnesota, inset map on lower left corner shows in relation to the United States map. Individual maps of each lake can be found in the Supplementary Information Figures 1 - 15. Figure 2: Broadband (100 – 12,000 Hz) root-mean-square (RMS) sound pressure levels (SPL) (dB re 1μPa) from the 60 – minute recordings taken in winter and summer at each lake. Significant differences according to Holm-Šídák post-hoc test between the two seasons are represented by * (P <0.05).
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Figure 3: Comparison of sound levels for ten octave bands measured over an hour period on a
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weekday afternoon in summer (orange filled) and winter (blue not filled). Significant differences according to Holm-Šídák post-hoc test between the two seasons are represented by * (P
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<0.05). The shaded region represents the low frequency-cut off < 100 Hz.
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Figure 4: Comparison of broadband sound pressure level (100 – 12,000 Hz, rms dB re 1μPa) to weather metrics in summer (orange circles) and winter (blue triangles). Each individual point upper right corner of each graph.
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represents the single value for each lake. Results of Pearson’s Product Correlation shown in
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Figure 5: Comparison of broadband sound pressure level (100 – 12,000 Hz, rms dB re 1μPa) to anthropogenic metrics in summer (orange circles) and winter (blue triangles). Each individual
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point represents the single value for each lake. Results of Pearson’s Product Correlation shown in upper right corner of each graph.
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Figure 6: Comparison of power spectral density (dB re 1µPa2/Hz) recorded during ambient and anthropogenic activities in summer and winter. Ambient summer and winter represents the median power spectral density taken from all 15 lakes over each 1-hour recording, auger winter represents the median power spectral density taken over a 10-minute segment from the Pike Lake recording, and motorboat summer represents the median power spectral density taken over a 10-minute segment from the Pike Lake recording. The grey box shows the frequencies in the low-frequency cut off.
Journal Pre-proof Highlights
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Passive acoustic monitoring of freshwater ecosystems is a largely unexplored field. Small freshwater lakes in Minnesota have a relatively pristine soundscape Sound pressure levels were lower in winter compared to summer recordings Sound from anthropogenic activity increased intensity by 10 dB Understanding baseline sound levels is needed to determine consequences of noise
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Putland, Mensinger PII:
S1574-9541(19)30329-2
DOI:
https://doi.org/10.1016/j.ecoinf.2019.101018
Reference:
ECOINF 101018
To appear in:
Ecological Informatics
Received date:
22 April 2019
Revised date:
9 September 2019
Accepted date:
30 September 2019
Please cite this article as: Putland, and Mensinger, Exploring the soundscape of small freshwater lakes, Ecological Informatics(2019), https://doi.org/10.1016/ j.ecoinf.2019.101018
This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
© 2019 Published by Elsevier.
Journal Pre-proof Exploring the soundscape of small freshwater lakes Putland, R.L.* [email protected], Mensinger, A.F. Department of Biology, Swenson Science Building, University of Minnesota Duluth, MN, USA, 55812
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*Corresponding author.
Journal Pre-proof Abstract Monitoring freshwater ecosystems using passive acoustics is a largely unexplored approach, despite having the potential to yield information about the biological, geological and anthropogenic activity of a lake or river system. The state of Minnesota, located in the upper Midwest of the USA and nicknamed ‘land of 10,000 lakes’, provides an interesting case study for soundscape research, because lakes offer ecological, recreational and economic value throughout the area. The underwater soundscape was monitored at fifteen small lakes < 10 km2 on representative days in winter (during 100 % ice cover) and summer 2018 using a
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hydrophone suspended 2 m below the water’s surface. Median broadband sound pressure level (100 – 12,000 Hz) was significantly lower in winter (57.2 dB re 1μPa) compared to summer
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(66.7 dB re 1μPa), possibly because low frequency wind sounds were reduced in winter. Recordings suggest small freshwater lakes in Minnesota have a relatively pristine soundscape,
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where vocalizing aquatic animals may hold acoustic niches. However, sound from anthropogenic activity was also present in the study lakes. Ice auger and motorboat sound
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increased the intensity of the soundscape by 10 dB and overlapped the frequency range (300 –
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1,000 Hz) of biological sounds in the environment, that may be important to aquatic life. Understanding current baseline sound levels in ecologically significant freshwater lakes, like those in this study, is the first step in determining any potential consequences of anthropogenic
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sound. Moving forward, baseline sound levels provide vital evidence for scientists and governing bodies to make proactive decisions for soundscape conservation.
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Keywords: Underwater sound, freshwater, lakes, ambient sound, anthropogenic noise
Journal Pre-proof 1. Introduction Passive acoustic monitoring technology is a valuable and flexible tool in ecology, providing the unique opportunity to rapidly quantify and compare sound across habitats, space and time (Browning and Gibb, 2018). Soundscape ecology encompasses all sounds in an environment: including geological (weather and geophysical activity), biological (vocalizations and mechanical sounds produced by animals) and anthropogenic (sounds produced by human activity) (Pijanowski et al., 2011). Ecologists and conservation managers recognize that soundscapes provide both ecological and social value (Dumyahn and Pijanowski, 2011). However, little is
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known about the soundscape of freshwater environments, despite lakes and rivers being home to a diverse array of wildlife and a place for both recreational and commercial activities.
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Freshwater acoustic research has focused principally on characterizing the bioacoustics of
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individual species, behavior or sound production mechanisms. Many freshwater animals produce sound, such as turtles (Giles et al., 2009), amphibians (Narins and Feng, 2006; Bee,
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2012), arthropods (Alexander, 1967; Popov, 1990), crustaceans (Buscaino et al., 2012; Silva et al., 2019) and fish (Mann et al., 2007; Cott et al., 2013). Sound is either produced passively
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during feeding or movement (Rice and Lobel, 2003; Rountree et al., 2018; Silva et al., 2019), or actively to communicate with conspecifics, attract mates or defend territory (Desjonquères et al.,
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2019). However, the diversity and dynamics of freshwater soundscapes is mostly inferred from marine or terrestrial recordings of abiotic sound. One study conducted in Austria suggested sound levels were higher in rivers with fast flowing water (110 dB re 1μPa) than stagnant water
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bodies such as lakes (100 dB re 1μPa) (Wysocki et al., 2007). However, ten of the twelve
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deployment sites were < 2 m water depth meaning low frequency sound could not be analyzed because the frequency cut-off would be ~2,000 Hz (Weston, 1971; Urick, 1983) and recordings only lasted up to 3 minutes. Additionally, the habitats differed in size, bathymetry, sediment composition and water depth, which all influence sound propagation (Farcas et al., 2016). Sound levels in freshwater habitats depend primarily on hydrology, most notably the volume and speed of water flow. For example, higher spectral levels (80 dB re 1μPa) were recorded in Lake Traunsee, Austria in the lower frequencies (< 1,000 Hz) because of water flowing into the lake from a stream (Amoser et al., 2004). Sound levels are also influenced by meteorology; in Lake Clinton, IL, USA spectral levels ranged from 40 – 60 dB re 1μPa (1 Hz bands) during rainfall compared to 30 – 50 dB re 1μPa when no rain was falling (Nystuen, 1986). These recordings were taken in very shallow water environments, < 2 m deep, therefore sounds caused by wind generated waves may have become distorted by surface and bottom reflections however, when
Journal Pre-proof rain hits the water’s surface it produces a cavitation bubble that increases the sound levels at frequencies > 200 Hz (Ma et al., 2005). It is also imperative to understand the role of ice formation in lake soundscape ecology, because of the world’s 117 million lakes (Verpoorter et al., 2014), almost half periodically freeze (Weyhenmeyer et al., 2011). Yet, comparatively few ecological studies have been conducted during winter because limnology fieldwork during ice cover is logistically difficult (Block et al., 2019). Winter is an important period for limnological processes ranging from biogeochemistry to fish ecology, therefore understanding seasonal variation in soundscape ecology is important.
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The first study to describe the under ice acoustic soundscape of a freshwater lake was conducted in the Northwest Territories, Canada. Ambient sound was influenced by ice cracking
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(160 dB re 1μPa) as well as anthropogenic activity (150 dB re 1μPa) (Martin and Cott, 2016). In winter, sounds are associated with ice augers to open fishing holes, pedestrian and vehicle
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traffic, whereas, in summer recreational and commercial vessel activity is prevalent. To date, the variation of ambient sound in lakes during the year has received little attention. However,
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passive acoustic monitoring allows remote areas to be surveyed over extended timescales.
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The growing understanding that sound plays a critical role in the life history of many aquatic animals has led to the recognition of anthropogenic sound as a major pollutant of international
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concern (Buxton et al., 2017). Acute impacts of exposure of anthropogenic sound have include physical injury and hearing damage whereas chronic impacts of increased sound levels have
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been indirectly related to ecology, behavior and fitness (Shannon et al., 2016; Gurule-Small and Tinghitella, 2019). For example, rudd (Scardinius erythrophthalmus) and roach (Rutilus rutilus)
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actively avoided high speed boating traffic in the Meuse River, Belgium (Boussard, 1981) and exposure to boat sound resulted in an increase in cortisol secretion in European freshwater fishes such as common carp (Cyprinus carpio), gudgeon (Gobio gobio) and perch (Perca fluviatilis) (Wysocki et al., 2006). Anthropogenic sound generally lies between 10 – 1,000 Hz, which directly overlaps the frequency range used by many species of aquatic animals for communication and if sufficiently intense, may result in acoustic masking, which impedes an individual’s ability to effectively perceive, recognize or decode sounds of interest (Clark et al., 2009). Animals may adapt to overlapping sound signals by using different frequencies, increasing the amplitude of their vocalizations [otherwise known as the Lombard effect (Lombard, 1911)] or producing sound during quieter time (Naguib, 2013; Holt and Johnston, 2014). Furthermore, there may be a trade-off between the detection and recognition of acoustic signals and hearing sensitivity, with animals developing lower sensitivity in ‘noisy’ environments
Journal Pre-proof (Lugli, 2019). A recent review on the effects of anthropogenic sound on freshwater fish provided an in-depth analysis on changes in behavioral and physiological outputs (Mickle and Higgs, 2017). Additionally, an investigation on freshwater soundscapes (near Puget Sound, Washington, USA) found sound levels correlated to urbanization within a 10 km radius (Kuehne et al., 2013). Lakes categorized as low, medium or high urbanization (percent land cover within 10k m radius with impervious surface) had 0 %, 17 % and 67 % of hourly measurements respectively, over the noise threshold of 55 dB (Kuehne et al., 2013). Scientists and land managers have emphasized the importance of small inland lakes in terms of aesthetic, cultural, educational, economic and environmental interests (Dudgeon et al., 2007). This study was
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designed to establish a baseline for ambient sound in 15 small freshwater lakes in Minnesota
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and compare sound spectra between summer and winter. Data collected will provide information for management on the potential effect of anthropogenic sound and how to prioritize
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soundscape conservation of freshwater ecosystems in the future.
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2. Materials and Methods 2.1 Data collection
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Sound was recorded at 15 lakes in St Louis and Carlton counties, Minnesota, USA (Figure 1). The lakes were chosen to reflect different urbanization patterns (number of houses, boat ramps
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and proximity to arterial roads (defined as single lane highways), while being < 7 km2 in surface area (Table 1) to facilitate access to the deployment sites without motorized vehicles.
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Additionally, all lakes contain a diverse range of fish species (Supplementary Information Table 1) and are regularly used for recreational fishing and motorboat activities (per obs. Putland
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2019).
Acoustic recordings were taken at each lake using an omni-directional hydrophone (ST300, Ocean Instruments, NZ); for one hour between 13:00 and 17:00 on a single day in winter (13th February – 9th March 2018) and summer (20th August – 20th September 2018). Recordings were conducted on weekdays to minimize the influence of recreational activity and allow an accurate background sound baseline (ambient sound minus anthropogenic activity). For all deployments, recording apparatus (hydrophone, battery, recorder and timer) was attached to a weighted rope two meters below the water surface (or base of ice in winter), with deployment location chosen to be the deepest point on each lake (Supplementary Information Figures 1 – 15). In winter, all lakes were completely covered with ice and site access was on foot, therefore the weighted rope was lowered through an 0.25 m diameter hole drilled using an electric auger, and measurements were taken of snow and ice thickness (m) (Supplementary Figure 16). In
Journal Pre-proof summer, deployments at the same GPS location were conducted from a Kevlar canoe to avoid introducing sound into the recordings (Supplementary Figures 2 - 15), except at Grand Lake where owing to the presence of recreational canoeists a different position was used for the summer recording (Supplementary Figure 1). The hydrophone was programmed to sample continuously at 24,000 Hz for the duration of each deployment. The hydrophone had a flat -3dB frequency response between 10 – 72,000 Hz and was calibrated prior and post deployment using a pistonphone with a 1,000 Hz tone (GRAS 42AA). Self- noise of the hydrophone was < 32 dB / Hz re 1µPa above 2,000 Hz (Ocean Instruments, NZ). The low frequency cut-off for underwater sound is inversely proportional to water depth, each lake was > 6 m water depth
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therefore the minimum low frequency cut-off was 100 Hz (Tindle et al., 1978; Urick, 1983). Sound speed profiles of the water column were calculated using salinity and temperature data
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collected with a YSI meter (± 0.1 ˚C), with readings taken at meter intervals from the surface (0
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m) to lake bottom (6 – 19 m) (Supplementary Information Table 2, Figures 17 - 18). No specific permits were required for the described field sampling. All lakes were publicly accessible via
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boat launches maintained by the Minnesota Department for Natural Resources. No biological
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samples were collected and therefore state scientific collection permits were not needed.
2.2 Data analysis
All acoustic data were reviewed aurally and visually using a scrolling spectrographic display of
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10 seconds (Hanning window, FFT length = 512 with 50% overlap, providing a frequency resolution of 46.8 Hz and a time resolution of 0.4 ms) in Raven Pro (version 1.5.0) to assess the
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proportion of recordings where anthropogenic and biological sounds were present. Spectrograms were also produced to show qualitatively the variability in sound within each 1-
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hour recording. All sound recordings (60 x 1-minute files from each lake) were then analyzed using MATLAB software (version 2015a) to determine acoustic metrics. Each sound recording were high-pass filtered at 100 Hz owing to the low-frequency cut-off and analyzed up to the Nyquist frequency of 12,000 Hz. A Friedman’s two-way ANOVA (p < 0.05) was performed to determine whether sound [broadband (100 – 12,000 Hz) root-mean-squared (RMS) sound pressure level (SPL)] varied between season and lake. Following this, a multiple comparison Holm-Šídák post hoc test (p < 0.05) was conducted to determine individual effects. To determine whether the sound pressure level varied with frequency, power spectral density (PSD) (dB re 1μPa2/Hz) was calculated over sixty, 1-minute integration periods for the entire 1 hour recording between 10 – 12,000 Hz using a fast Fourier transformation of 30 second samples, creating 1 Hz frequency resolution and applying a Hanning window with 50% overlap.
Journal Pre-proof Octave band sound pressure levels (16, 31.5, 63, 125, 250, 500, 1000, 2000, 4000, 8000 Hz) (dB re 1μPa) were subsequently calculated for each recording using PSDs. Median octave band levels were then compared between summer and winter using a Mann-Whitney U test. Abiotic sounds including weather or water movement can alter the soundscape (Wenz, 1962; Ma et al., 2005). Therefore, to assess how weather may have altered the soundscape of the freshwater lakes, wind speed and direction was compared to broadband SPL using a Pearson’s product correlation. Data was collected from the NOAA weather station in Duluth, MN (46.837, 92.211) , which despite being 6 km away from the nearest lake provides a proxy of weather in
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the area for the duration of each deployment. Ice and snow thickness measurements taken at using a Pearson’s product correlation (p < 0.05).
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each deployment site in winter also were compared to broadband SPL of winter recordings
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All recordings were inspected aurally and visually using scrolling spectrograms (fast Fourier transformation (FFT) length = 4096, Hanning window, 50% overlap) and sounds matched to
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fieldnotes of anthropogenic activity at the time. Average PSD was calculated for each type of sound identified and used to compare different sounds. Furthermore, to determine whether
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sound recorded was affected by the number of houses, boat ramps on the lake or proximity to the nearest arterial road, defined as single lane highway, broadband SPL was compared to
3. Results
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these factors using a Pearson’s product correlation (p < 0.05).
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There was a significant difference in broadband SPL according to season and lake (two-way ANOVA F1 = 2880.673, p < 0.001 and F14 = 73.726, p < 0.001) as well as the interaction
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between lake and season (two-way ANOVA F14 = 113.969, p < 0.001) (Figure 2). During summer, the median broadband SPL across all lakes was 66.7 re 1μPa, with highest sound levels recorded at Barrs, Big, Nichols, Grand and Leora (71.5 – 78.1 dB re 1μPa) and the lowest recorded at Pike, Island and Spring II (59.8 – 60.9 dB re 1μPa) (Table 2). In comparison, during the winter the median broadband SPL across all the lakes was 57.2 dB re 1μ, with higher sound levels recorded at Briar, Mirror and Pike Lake (60. 0 – 65.2 dB re 1μPa) compared to all the other lakes (50.7 – 58.0 dB re 1μPa) (Table 2). To isolate the effect of season from lake, a multiple comparison Holm-Sidak post-hoc test was performed, with all lakes, except Pike Lake, found to have a significant difference between summer and winter recordings (Supplementary Table 1). Median broadband SPL at Pike Lake was 60.9 dB re 1μPa in summer and in winter was 60.0 dB re 1μPa (Table 2).
Journal Pre-proof Frequency (octave band level) was found to be a significant factor in sound level recorded between seasons (Mann-Whitney U > 5.00, p < 0.001) with the median octave band level at least 4.8 dB re 1μPa higher in summer compared to winter (Figure 3, Table 3). The greatest difference (up to 13.9 dB) between seasons was seen in the lower frequencies (16.0, 31.5, 63.0 Hz). Furthermore, there was a steady decrease in sound level as the octave band frequency increased. For example, the median octave band level for 125 Hz was 59.7 and 52.1 dB re 1μPa for summer and winter respectively, compared to 38.3 and 32.7 dB re 1μPa at 8000 Hz, which was very close to the noise floor of the instrument (32 dB > 2000 Hz) (Table 4).
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Broadband SPL was compared for all lakes to weather metrics using a Pearson’s correlation coefficient. There was no significant correlation between sound recorded and average wind
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speed (r = -0.246 p = 0.225), maximum wind speed (r = -0.339 p = 0.091), ice thickness (r = 0.277 p = 0.337) or snow thickness (r = -0.233 p = 0.423) (Figure 4). However, at similar wind
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speeds, broadband SPL was noted as lower in winter compared to summer (Figure 4). For example, when the average wind speed was 4.47 m/s, SPL was 66.0 dB re 1μPa on Spring
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Lake in summer, compared to 55.0 dB re 1μPa on Beauty Lake in winter.
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Broadband SPL also was compared to anthropogenic metrics of land use each lake: there was no significant correlation between broadband SPL recorded and the number of houses/shoreline
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length (r = 0.050, p = 0.773), number of boat ramps/shoreline length (r = 0.020, p = 0.898) and nearest arterial road (r = 0.075, p = 0.693) (Figure 5). However, the PSD recorded during
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example anthropogenic activities (auger or motorboat) was higher across all frequencies (10 – 12,000 Hz) when compared to the median ambient soundscape recorded at all lakes (Figure 6,
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Supplementary Figure 19). Motorboat sound PSD was also > 10 dB higher than summer ambient sound between 300 – 1,000 Hz and auger sound was > 10 dB higher than winter ambient sound across the entire frequency spectrum (Figure 6). Motorboat sound was detected for the following percentages of summer recording at Pike (10 %), Spring (13 %), Briar (22 %) and Bob (7 %) lake and auger sound was present at Grand (3 %), Big (8 %), Pike (22 %), Nichols (10 %), Briar (7 %) and Bob (13 %) lake during the winter recordings (Supplementary Table 3). 26 sources of biological sound were also detected from visual and aural analysis ranging between 81 – 1406 Hz and 0.1 – 1.5 seconds in duration, although sources were unknown. In winter, 21 of the sounds were detected, and in summer, 5 sounds were detected (Supplementary Table 4). Furthermore, potential biological sound was present in 12 of the 15 lakes during the winter: Grand (3 %), Caribou (7 %), Pike (2 %), Nichols (3 %), Island (2 %), Beauty (2 %), Sandy (2 %), Barrs (3 %), Spring (2 %), Briar (3 %), Spring II (2 %), Mirror (5%)
Journal Pre-proof during the summer. Whereas, in summer potential biological sound was present in 2 of the 15 lakes: Pike (2 %) and Mirror (7 %). Overall, visual and aural inspection of spectrograms highlighted the limited variability of SPL within the 1-hour sound recordings (Supplementary Information Figures 20 – 34) despite the present of anthropogenic and biological sounds.
4. Discussion This study provides insight into the baseline ambient sound level of freshwater lakes and has application to the development of policy initiatives to monitor and regulate the soundscape. There was a significant difference in broadband SPL according to season, with the median SPL
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recorded in summer 9.5 dB louder than in winter, equating to a tenfold increase in power (Urick,
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1983). Additionally, at all lakes recorded, the highest sound levels (55 - 90 dB re 1μPa) were observed in the low frequency region (< 125 Hz), with a steady decline in the sound level up to
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12,000 Hz (35 - 45 dB re 1μPa) (Figure 3), suggesting geophony (weather and open water movements) may have played a large role in the soundscape (Wenz, 1962; Urick, 1983). It
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would be expected that other temperate lakes (< 7 km2) will have a similar range in sound level recorded during the summer and winter months, especially if they are subject to 100% ice cover
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because ice and snow provides an additional layer between the underwater and terrestrial environment for sound to reflect and refract from.
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Recordings were conducted during minimal human activity and in the absence of atmospheric disturbances other than wind to achieve the goal of measuring baseline sound levels. The major
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abiotic variable was wind speed which varied from 5.3 to 17.8 m/s (Table 2) however, in this study there was no signification correlation between wind speed and broadband sound level.
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Wind speed is a crude measurement of surface agitation which also depends on factors such as duration and constancy of the wind and its direction in relation to near shore topography (Wenz, 1972). Furthermore, a limitation of recording the soundscape of small shallow lakes is that sound propagation is strongly influenced by variation in sound speed profile within the water column. In this study, maximum water depth ranged between 6 – 18 m (Briar Lake – Island Lake, Table 1) resulting in a frequency cut-off of approximately 100 Hz (Urick, 1983), therefore sounds caused by wind generated waves may have become distorted by surface and bottom reflections. In winter, the presence, thickness and movement of ice can also contribute significantly to ambient sound levels, while ice and snow on the lake surface may function to dampen or eliminate sound created by wind generated waves (Martin and Cott, 2016). For
Journal Pre-proof example, when the same wind speed (4.47 m/s) was recorded in summer and winter, broadband SPL was 66.0 and 55.0 dB re 1μPa (100 – 12,000 Hz) respectively (Figure 4). In this study, the focus was to measure the baseline sound level across as many small lakes within approximately the same seasonal window (i.e. short time frame), maximize equipment security and optimize deployment logistics. Comparing the fifteen freshwater lakes, the broadband SPL of ambient sound ( 100 – 12,000 Hz) was similar, ranging from 58 – 74 dB re 1μPa in summer and 50 – 65 dB re 1μPa in winter, despite differences in lake area, water depth and bottom type (Figure 2). Summer recordings taken in Lake Sarzana, Italy, 40 - 50 dB re
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1μPa (Bom, 1969), Lake Clinton, IL, USA, 40 - 60 dB re 1μPa (Nystuen, 1986) and the Niger River, Mali, 40 – 70 dB re 1μPa (Crawford et al., 1997) showed very similar sound levels with
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high amounts of energy in the low frequencies, followed by a rapid decline to higher frequencies and a more gradual decline above 10 kHz. However, in the literature there are limited winter
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recordings available to compare to this study. In temperate marine environments, ambient sound levels are often higher during summer than winter, according to the behavior of both
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resident and transient soniferous animals (Radford et al., 2008; Haxel et al., 2013). In the
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freshwater lakes recorded in this study, sound levels were significantly lower in the winter which may provide an acoustic refuge for soniferous fish species that spawn in the winter months, such as burbot (Lota lota) (Bergersen et al., 1993; Cott et al., 2014; Cott et al., 2015) and allow
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individual fish to communicate over a wider area (Putland et al., 2017). Lower sound levels may be the result of soniferous animals being less active or unable to generate sound in cold water
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conditions. However, more potential biological sounds were detected during winter month therefore a reduction in the contribution of geophony to the soundscape may be more plausible.
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Moving forward, it is understood that long term acoustic monitoring is needed to record day and night, bolster conclusions, as well during a greater variety of meteorological events, such as storms and ice formation. For example, ice cracking produced 0.05 – 1 second duration pulses between 800 – 8,000 Hz, in recordings conducted in Great Slave Lake, Canada (Martin and Cott, 2016) and sound pressure levels of ice cracking sounds exceeded 160 dB re 1μPa in recordings taken in Kennady Lake, Canada (Mann et al., 2009). Although, in the winter recordings taken for this study ice sounds were not recorded probably because thick full ice coverage on the lakes minimized cracking. Additionally, sounds produced as ice forms during the transition from open (summer) to closed (winter) water conditions have not previously been recorded in a lake environment. With technological improvements in remote acoustic monitoring and a greater interest in winter limnology, it is expected that winter sampling will increase
Journal Pre-proof (Magnuson et al., 2000; Jensen et al., 2007; Hewitt et al., 2018; Block et al., 2019) allowing questions about temporal and spectral characteristics of ice formation to be answered. Freshwater lakes and ponds are home to a diverse array of wildlife with 162 species of fishes, 78 mammals, 22 amphibians, 31 reptiles and 428 bird species resident to Minnesota waters (Minnesota DNR, 2019). Many of these animals reliant on sound for interspecific communication or to mediate predator/prey interactions. Potential fish sounds may have been produced from catostomidae, centrarchidae, ictaluridae and lottidae, which inhabited some of all the lakes sampled. White sucker (Catastomus commersonnii) produce sound (~1500 Hz, duration 0.014
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s) via air movement with air bubbles released from the mouth and gills as they descend to depths (Rountree et al., 2018). Six different species of sunfish including green sunfish (Lepomis
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cyanellus) were found to produce grunts (<1000 Hz and <0.3 s) during active courtship by nesting males. Channel catfish (Ictalurus punctatus) produce stridulatory sounds (30 – 300 Hz)
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by rubbing the base of their pectoral spine against their pectoral gridle (Fine et al., 1997; Vance, 2000). Burbot (Lota lota) have a wide repertoire of vocalizations, from slow knocks (< 500 Hz, <
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1 s) to a complex hum (< 1000 Hz, < 10 s) (Cott et al., 2014). 87 freshwater species have been
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reported to produce sounds in North America and Europe over the last 200 years, accounting for 5% of known freshwater fish species (Rountree et al., 2019). In this study, 26 unique sounds were recorded between 100 – 2,000 Hz that maybe biological in origin, based on lower
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frequencies as all were < 1500 Hz and had a limited durations < 1 s, although sources were unknown. The challenge to deciphering species specific recordings is greater when you
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consider that other freshwater organisms such as turtles, birds amphibians, aquatic insects, crustaceans and some mammals also produce underwater sounds which are poorly
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documented (Rountree et al., 2019). However, using acoustic recorders to monitor soniferous species could give an indication of biodiversity in each lake recorded especially during the winter months when it is difficult to use other sampling methods, and the ice cover greatly reduces background sound. For example, burbot are a freshwater gadoid fish that spawns in the winter months (Cott et al., 2014). Their relatively sensitive hearing (Cott et al., 2013) and the presence of drumming muscles suggest that sound production and reception plays a primary role for burbot in communication (Cott et al., 2014). Therefore, passive acoustic monitoring has the potential to document mating vocalizations to aid in modelling the reproductive success of fishes in a particular lake system (Putland et al., 2018). In future studies, by combining video footage with acoustic recordings, scientists could unravel sources of unknown sounds as well as associated behavior, to create a catalog of freshwater fish sounds. In the present study, motorboat and ice auger sound increased the ambient sound level by >10 dB between 300 –
Journal Pre-proof 1,000 Hz and 100 – 12,000 Hz respectively (Figure 6) within 100 – 200 m from the source. Within each study lake, aquatic life has limited area to move from a sound source, which could result in chronic (sustained) sound exposure and subsequent ecological consequences. Some of the most popular recreational fish species in Minnesota, such as walleye (Sander vitreus) and northern pike (Esox lucuis) (Minnesota DNR, 2019), inhabit the study lakes (Supplementary Information Table 2). Walleye and northern pike have auditory sensitivity within 10 – 300 Hz (Mann et al., 2007) and 100 – 1,000 Hz (Popper et al., 2005; Mann et al., 2007) respectively, therefore have the potential to be impacted by sound produced by motorboats and augers (Figure 6). However, behavioral disturbance and physiological stress caused by anthropogenic
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sound is unpredictable. By combining information on the auditory sensitivity of lake inhabitants
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with long-term recordings of the soundscape, future studies could evaluate how individuals and/or populations may perceive and respond to sound.
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Multi-day sampling would offer the opportunity to investigate the frequency, timing and intensity of biological and anthropogenic activity on each lake (Joo et al., 2011). The short-term data
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reported may also not reflect differences across entire seasons, therefore longer-term
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monitoring is needed to confirm that the differences described in the present study reflect typical seasonal differences. One of the biggest challenges faced by those charged with monitoring ecosystems is decision making in the absence of essential information. This study focused on
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establishing the baseline ambient sound level for small lakes in summer and winter, providing managers with a starting point for understanding how geological (wind, rain and ice formation),
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biological (comprised of mostly unknown sources for the freshwater environment) and anthropogenic sounds (ice auger, fishing and boat sound) make up the ambient soundscape. It
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is expected that the sound levels of other small (< 7km2) lakes in temperate regions will also range between 55 – 90 dB re 1µPa with a significant difference between winter and summer recordings owing to the ice/snow interface between the underwater and terrestrial environments. Baseline sound levels also offer the potential to track changes in soundscape ecology in response to other large-scale environmental processes such as climate change or urban development.
Journal Pre-proof Acknowledgements Special thanks to Donn Branstrator for advice on winter limnology and Samuel Seddon for producing maps of the different lakes. We would also like to thank Emily Fleissner, Noland Michels, Alayna Mackiewicz and Loranzie Rogers for helping with fieldwork. Funding was
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provided by a Mistletoe Research Fellowship awarded to RLP.
Journal Pre-proof References
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Alexander, R. D. (1967). Acoustical communication in arthropods. Annual Review of Entomology 12, 495526. Amoser, S., Wysocki, L. E. & Ladich, F. (2004). Noise emission during the first powerboat race in an Alpine lake and potential impact on fish communities. The Journal of the Acoustical Society of America 116, 3789-3797. Bee, M. A. (2012). Sound source perception in anuran amphibians. Current Opinion in Neurobiology 22, 301-310. Bergersen, E. P., Cook, M. F. & Baldes, R. J. (1993). Winter movements of burbot (Lota lota) during an extreme drawdown in Bull Lake, Wyoming, USA. Ecology of Freshwater Fish 2, 141-145. Block, B. D., Denfeld, B. A., Stockwell, J. D., Flaim, G., Grossart, H. F., Knoll, L. B., Maier, D. B., North, R. L., Rautio, M., Rusak, J. A., Sadro, S., Weyhenmeyer, G. A., Bramburger, A. J., Branstrator, D. K., Salonen, K. & Hampton, S. E. (2019). The unique methodological challenges of winter limnology. Limnology and Oceanography: Methods 17, 42-57. Bom, N. (1969). Effect of rain on underwater noise level. Journal of the Acoustical Society of America 45, 150-156. Boussard, A. (1981). The reactions of roach (Rutilus rutilus) and rudd (Scardinius erythrophtalmus) to noises produced by high speed boating. In Proceedings of 2nd British Freshwater Fish Conference, pp. 188-200. Browning, E. & Gibb, R. (2018). Monitoring ecosystems through sound: the present and future of passive acoustics. Methods Blog. Buscaino, G., Filiciotto, F., Buffa, G., Di Stefano, V., Maccarrone, V., Buscaino, C., Mazzola, S., Alonge, G., D’Angelo, S. & Maccarrone, V. (2012). The underwater acoustic activities of the red swamp crayfish Procambarus clarkii. The Journal of the Acoustical Society of America 132, 1792-1798. Buxton, R. T., McKenna, M. F., Mennitt, D., Fristrup, K. M., Crooks, K. R., Angeloni, L. & Wittemyer, G. (2017). Noise pollution is pervasive in U.S. protected areas. Science 356, 531. Clark, C., Ellison, W., Southall, B., Hatch, L., Parijs, S., Frankel, A. & Ponirakis, D. (2009). Acoustic masking in marine ecosystems: intuitions, analysis, and implication. Mar Ecol Prog Ser 395. Cott, P. A., Guzzo, M. M., Chapelsky, A. J., Milne, S. W. & Blanchfield, P. J. (2015). Diel bank migration of Burbot (Lota lota). Hydrobiologia 757, 3-20. Cott, P. A., Hawkins, A. D., Zeddies, D. G., Martin, B., Johnston, T. A., Reist, J. D., Gunn, J. M. & Higgs, D. M. (2014). Song of the burbot: Under-ice acoustic signaling by a freshwater gadoid fish. Journal of Great Lakes Research 40, 435-440. Cott, P. A., Johnston, T. A., Gunn, J. M. & Higgs, D. M. (2013). Hearing sensitivity of the burbot. Transactions of the American Fisheries Society 142, 1699-1704. Crawford, J. D., Philippe, J. & Vincent, B. (1997). Sound production and reproductive ecology of strongly acoustic fish in Africa: Pollimyrus isidori, mormyridae. Behaviour 134, 677-725. Desjonquères, C., Gifford, T. & Linke, S. (2019). Passive acoustic monitoring as a potential tool to survey animal and ecosystem processes in freshwater environments. Freshwater Biology 0. Dudgeon, D., Arthington, A. H., Gessner, M. O., Kawabata, Z., Knowler, D. J., Lévêque, C., Naiman, R. J., Prieur-Richard, A., Soto, D., Stiassny, M. L. J. & Sullivan, C. A. (2007). Freshwater biodiversity: importance, threats, status and conservation challenges. Biological Reviews 81, 163-182. Dumyahn, S. L. & Pijanowski, B. C. (2011). Soundscape conservation. Landscape Ecology 26, 1327. Farcas, A., Thompson, P. M. & Merchant, N. D. (2016). Underwater noise modelling for environmental impact assessment. Environmental Impact Assessment Review 57, 114-122. Fine, M. L., Friel, J. P., McElroy, D., King, C. B., Loesser, K. E. & Newton, S. (1997). Pectoral spine locking and sound production in the channel catfish Ictalurus punctatus. Copeia 1997, 777-790.
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Giles, J. C., Davis, J. A., McCauley, R. D. & Kuchling, G. (2009). Voice of the turtle: The underwater acoustic repertoire of the long-necked freshwater turtle, Chelodina oblonga. The Journal of the Acoustical Society of America 126, 434-443. Gurule-Small, G. A. & Tinghitella, R. M. (2019). Life history consequences of developing in anthropogenic noise. Global Change Biology 25, 1957-1966. Haxel, J. H., Dziak, R. P. & Matsumoto, H. (2013). Observations of shallow water marine ambient sound: the low frequency underwater soundscape of the central Oregon coast. The Journal of the Acoustical Society of America 133, 2586-2596. Hewitt, B. A., Lopez, L. S., Gaibisels, K. M., Murdoch, A., Higgins, S. N., Magnuson, J. J., Paterson, A. M., Rusak, J. A., Yao, H. & Sharma, S. (2018). Historical trends, drivers, and future projections of ice phenology in small north temperate lakes in the Laurentian Great Lakes region. Water 10. Holt, D. E. & Johnston, C. E. (2014). Evidence of the Lombard effect in fishes. Behavioral Ecology 25, 819826. Jensen, O. P., Benson, B. J., Magnuson, J. J., Card, V. M., Futter, M. N., Soranno, P. A. & Stewart, K. M. (2007). Spatial analysis of ice phenology trends across the Laurentian Great Lakes region during a recent warming period. Limnology and Oceanography 52, 2013-2026. Joo, W., Gage, S. H. & Kasten, E. P. (2011). Analysis and interpretation of variability in soundscapes along an urban–rural gradient. Landscape and Urban Planning 103, 259-276. Kuehne, L. M., Padgham, B. L. & Olden, J. D. (2013). The soundscapes of lakes across an urbanization gradient. PLOS ONE 8, e55661. Lombard, E. (1911). Le signe de l'élévation de la voix. Annales des Maladies de L'Oreille et du Larynx 37, 101-119. Lugli, M. (2019). How ambient noise may shape peripheral auditory sensitivity: a theoretical model on the trade-off between signal detection and recognition. Evolutionary Ecology 33, 173-194. Ma, B. B., Nystuen, J. A. & Lien, R. (2005). Prediction of underwater sound levels from rain and wind. The Journal of the Acoustical Society of America 117, 3555-3565. Magnuson, J. J., Robertson, D. M., Benson, B. J., Wynne, R. H., Livingstone, D. M., Arai, T., Assel, R. A., Barry, R. G., Card, V. M., Kuusisto, E., Granin, N. G., Prowse, T. D., Stewart, K. M. & Vuglinski, V. S. (2000). Historical trends in lake and river ice cover in the Northern Hemisphere. Science 289, 1743. Mann, D. A., Cott, P. A., Hanna, B. W. & Popper, A. N. (2007). Hearing in eight species of northern Canadian freshwater fishes. Journal of Fish Biology 70, 109-120. Mann, D. A., Cott, P. A. & Horne, B. (2009). Under-ice noise generated from diamond exploration in a Canadian sub-arctic lake and potential impacts on fishes. The Journal of the Acoustical Society of America 126, 2215-2222. Martin, B. S. & Cott, P. A. (2016). The under-ice soundscape in Great Slave Lake near the city of Yellowknife, Northwest Territories, Canada. Journal of Great Lakes Research 42, 248-255. Mickle, M. F. & Higgs, D. M. (2017). Integrating techniques: a review of the effects of anthropogenic noise on freshwater fish. Canadian Journal of Fisheries and Aquatic Sciences. Minnesota DNR (2019). Species found in Minnesota https://www.dnr.state.mn.us/animals/index.html. Naguib, M. (2013). Living in a noisy world: indirect effects of noise on animal communication. Behaviour 150, 1069-1084. Narins, P. M. & Feng, A. S. (2006). Hearing and sound communication in amphibians: prologue and prognostication. In Hearing and Sound Communication in Amphibians (Narins, P. M., Feng, A. S., Fay, R. R. & Popper, A. N., eds.), pp. 1-11. New York, NY: Springer New York. Nystuen, J. A. (1986). Rainfall measurements using underwater ambient noise. The Journal of the Acoustical Society of America 79, 972-982. Pijanowski, B. C., Farina, A., Gage, S. H., Dumyahn, S. L. & Krause, B. L. (2011). What is soundscape ecology? An introduction and overview of an emerging new science. Landscape Ecology 26, 1213-1232.
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Leora Island Beauty Sandy Barrs Spring Briar Bob Spring II Mirror
St Louis
-92.307 -92.635
46.867
-92.290
47.094
-92.540
47.104
-92.408
47.230
-93.025
47.225 46.534
-93.076 -92.554
47.058
-91.967
47.065
-92.009
47.072 46.633
-91.961 -92.616
46.535
-92.525
46.966
-92.173
10.6
Boat ramps per km shoreline 7.0
Nearest arterial (main) road (km) 1.6
6.73
7.3
13.3
2.19
6.4
9.0
17.3
15.2
2.4
2.14 1.99
8.1 18.3
13.1 8.0
11.2 27.7
10.6 22.0
10.1 0.2
1.70
9.4
7.9
13.3
10.0
3.9
1.07
10.7
4.6
20.3
14.2
4.7
0.90
19.8
8.4
6.9
6.0
16.3
0.88
9.4
5.2
5.2
6.2
16.0
0.52 0.51
8.2 7.0
3.3 4.4
7.0 11.2
4.5 7.8
4.4 17.6
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Nichols
46.903 46.707
Houses per km shoreline
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Big Pike
-92.397
Shoreline length (km)
re
Caribou
46.875
Max. depth (m)
0.40
7.6
4.7
4.9
4.0
19.1
0.32
6.0
4.2
15.9
12.6
17.3
0.31 0.15
9.0 7.6
2.4 1.9
1.6 0.5
2.1 0.5
3.9 5.8
8.2
1.3
3.8
3.1
15.0
lP
St Louis St Louis Carlton St Louis St Louis St Louis St Louis St Louis Carlton St Louis St Louis St Louis Carlton Carlton
Lake area 2 (km )
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Grand
Position of hydrophone Latitude Longitude
0.08
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County
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Lake
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Table 1: Metrics for the 15 study lakes listed in order of decreasing lake area.
Journal Pre-proof Table 2: Broadband sound levels (100 – 12,000 Hz, rms dB re 1μPa) for each lake in summer and winter recordings compared to environmental data (wind speeds and coverage for winter).
2.68 2.68 3.58 4.02 3.58 3.58 11.62 2.24 6.71 5.36 4.47 4.47 3.58 6.71 7.15 3.13 4.92 5.81 3.13 5.81 5.81 4.47 4.47 4.647 3.58 4.92 4.92 4.02 4.47 2.24
6.26 5.81 6.71 7.60 6.26 6.26 17.88 5.36 14.30 9.39 8.05 8.05 6.71 14.30 11.18 7.60 8.94 11.18 8.05 11.18 11.18 8.94 8.94 8.94 7.15 9.39 9.39 7.60 8.94 5.36
Ice thickness (m)
Snow thickness (m)
n/a
n/a
0.72 0.64 0.53 0.60 0.67 0.61 0.48 0.66 0.55 0.59 0.61 0.60 0.45 0.53 0.48
0.25 0.22 0.32 0.13 0.18 0.20 0.23 0.18 0.23 0.30 0.35 0.26 0.32 0.27 0.14
-p
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of
Maximum wind speed (m/s)
re
Winter
Average wind speed (m/s)
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Grand Caribou Big Pike Nichols Leora Island Beauty Sandy Barrs Spring Briar Bob Spring II Mirror Grand Caribou Big Pike Nichols Leora Island Beauty Sandy Barrs Spring Briar Bob Spring II Mirror
na
Summer
Broadband SPL (100 – 12,000 Hz, rms dB re 1μPa) 71.5 64.2 71.7 60.9 78.1 72.4 58.8 67.4 65.0 74.4 66.0 63.0 66.7 59.8 65.1 57.6 50.7 55.5 60.0 55.9 52.9 57.4 55.0 55.2 56.8 58.0 65.2 55.7 55.6 62.7
ur
Lake
Jo
Season
Journal Pre-proof Table 3: Comparisons among median sound levels for each octave band from summer and winter recordings. Octave band levels 1, 2 and 3 are shown in grey owing to these frequencies
4
125.0
5
250.0
6
500.0
7
1000.0
8
2000.0
9
4000.0
10
8000.0
Winter
32.7
ur
U value
13.1
< 0.001
8
13.9
< 0.001
15
9.2
< 0.001
17
7.6
< 0.001
18
4.8
< 0.001
30
6.9
< 0.001
28
8.1
< 0.001
22
6.8
< 0.001
19
7.3
< 0.001
8
6.1
< 0.001
5
of
63.0
P value
-p
3
Difference (dB)
re
31.5
Summer Winter Summer Winter Summer Winter Summer Winter Summer Winter Summer Winter Summer Winter Summer Winter Summer Winter Summer
Median octave band level (dB re 1μPa) 76.1 63.0 76.3 62.4 66.4 57.2 59.7 52.1 52.4 47.7 51.1 44.2 48.0 39.9 43.9 37.1 42.8 35.4 38.8
lP
2
Season
na
1
Center Frequency (Hz) 16.0
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Octave Band
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falling below the low-frequency cut-off in water 6 m deep.
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Figure 1: Map of 15 study lakes within Minnesota, inset map on lower left corner shows in relation to the United States map. Individual maps of each lake can be found in the
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Supplementary Information Figures 1 - 15.
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Figure 2: Broadband (100 – 12,000 Hz) root-mean-square (RMS) sound pressure levels (SPL) (dB re 1μPa) from the 60 – minute recordings taken in winter and summer at each lake. Significant differences according to Holm-Šídák post-hoc test between the two seasons are represented by * (P <0.05).
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Figure 3: Comparison of sound levels for ten octave bands measured over an hour period on a weekday afternoon in summer (orange filled) and winter (blue not filled). Significant differences according to Holm-Šídák post-hoc test between the two seasons are represented by * (P <0.05). The shaded region represents the low frequency-cut off < 100 Hz.
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Figure 4: Comparison of broadband sound pressure level (100 – 12,000 Hz, rms dB re 1μPa) to weather metrics in summer (orange circles) and winter (blue triangles). Each individual point
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represents the single value for each lake. Results of Pearson’s Product Correlation shown in upper right corner of each graph.
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Figure 5: Comparison of broadband sound pressure level (100 – 12,000 Hz, rms dB re 1μPa) to anthropogenic metrics in summer (orange circles) and winter (blue triangles). Each individual point represents the single value for each lake. Results of Pearson’s Product Correlation shown in upper right corner of each graph.
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Figure 6: Comparison of power spectral density (dB re 1µPa2/Hz) recorded during ambient and anthropogenic activities in summer and winter. Ambient summer and winter represents the median power spectral density taken from all 15 lakes over each 1-hour recording, auger winter represents the median power spectral density taken over a 10-minute segment from the Pike Lake recording, and motorboat summer represents the median power spectral density taken over a 10-minute segment from the Pike Lake recording. The grey box shows the frequencies in the low-frequency cut off.
Journal Pre-proof Figure captions Figure 1: Map of 15 study lakes within Minnesota, inset map on lower left corner shows in relation to the United States map. Individual maps of each lake can be found in the Supplementary Information Figures 1 - 15. Figure 2: Broadband (100 – 12,000 Hz) root-mean-square (RMS) sound pressure levels (SPL) (dB re 1μPa) from the 60 – minute recordings taken in winter and summer at each lake. Significant differences according to Holm-Šídák post-hoc test between the two seasons are represented by * (P <0.05).
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Figure 3: Comparison of sound levels for ten octave bands measured over an hour period on a
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weekday afternoon in summer (orange filled) and winter (blue not filled). Significant differences according to Holm-Šídák post-hoc test between the two seasons are represented by * (P
-p
<0.05). The shaded region represents the low frequency-cut off < 100 Hz.
re
Figure 4: Comparison of broadband sound pressure level (100 – 12,000 Hz, rms dB re 1μPa) to weather metrics in summer (orange circles) and winter (blue triangles). Each individual point upper right corner of each graph.
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represents the single value for each lake. Results of Pearson’s Product Correlation shown in
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Figure 5: Comparison of broadband sound pressure level (100 – 12,000 Hz, rms dB re 1μPa) to anthropogenic metrics in summer (orange circles) and winter (blue triangles). Each individual
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point represents the single value for each lake. Results of Pearson’s Product Correlation shown in upper right corner of each graph.
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Figure 6: Comparison of power spectral density (dB re 1µPa2/Hz) recorded during ambient and anthropogenic activities in summer and winter. Ambient summer and winter represents the median power spectral density taken from all 15 lakes over each 1-hour recording, auger winter represents the median power spectral density taken over a 10-minute segment from the Pike Lake recording, and motorboat summer represents the median power spectral density taken over a 10-minute segment from the Pike Lake recording. The grey box shows the frequencies in the low-frequency cut off.
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Passive acoustic monitoring of freshwater ecosystems is a largely unexplored field. Small freshwater lakes in Minnesota have a relatively pristine soundscape Sound pressure levels were lower in winter compared to summer recordings Sound from anthropogenic activity increased intensity by 10 dB Understanding baseline sound levels is needed to determine consequences of noise
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