# The Freshwater Sounds Archive

Jack A Greenhalgh, Mauricio S Akmentins, Martin Boullhesen, Gabriel Lourenço Brejão, Jacob C Bowman, Robert A Briers, Katie Campbell, Ambrosine Clark, Mackenzie Coen, Camille Desjonquères, et al.

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## To cite this version:

Jack A Greenhalgh, Mauricio S Akmentins, Martin Boullhesen, Gabriel Lourenço Brejão, Jacob C Bowman, et al. The Freshwater Sounds Archive. 2025. hal-05343638

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1

The Freshwater Sounds Archive

2 Jack A. Greenhalgh<sup>1,2*</sup>, Mauricio S. Akmentins<sup>3</sup>, Martin Boullhesen<sup>3</sup>, Gabriel Lourenço

3 Brejão<sup>20</sup>, Jacob C. Bowman<sup>4</sup>, Robert A. Briers<sup>5</sup>, Katie Campbell<sup>6</sup>, Ambrosine Clark<sup>7</sup>,

4 Mackenzie Coen<sup>8</sup>, Camille Desjonquères<sup>18</sup>, Soledad Gastón<sup>3</sup>, Benjamin L. Gottesman<sup>17</sup>, Ian

5 T. Jones<sup>15</sup>, José J. Lahoz-Monfort<sup>1</sup>, Elliot Lindsay<sup>2</sup>, Fernando Macías Rodríguez<sup>9</sup>, Francisco

6 Navarrete-Mier<sup>9</sup>, Melissa Norton<sup>2</sup>, Maria Carolina Las Casas e Novaes<sup>20</sup>, Shun Okazaki<sup>2</sup>,

7 Jernej Polajnar<sup>10</sup>, Milton Cezar Ribeiro<sup>20</sup>, Louise Roberts<sup>7</sup>, David Rothenberg<sup>19</sup>, Saeed

8 Shafiei Sabet<sup>11</sup>, Ryan Satish<sup>16</sup>, Brittnie Spriel<sup>14</sup>, David Stanković<sup>10</sup>, Kees te Velde<sup>12</sup>,

9 Jonathan H. Timperley<sup>7</sup>, Katie Turlington<sup>13</sup>, Jack R. Walker<sup>7</sup>, Marisol P. Valverde<sup>17</sup>, Kieran

10 Cox<sup>14</sup>, Audrey Looby<sup>14</sup>

11 ¹Instituto Pirenaico de Ecología (CSIC), Avenida Nuestra Señora de la Victoria, 22700,

12 Jaca, Huesca, Spain.

13 ²Department of Biology, McGill University, 1205 Dr Penfield Ave, Montreal, Quebec, H3A

14 1B1, Canada.

15 ³Instituto de Ecorregiones Andinas (INECOA), Universidad Nacional de Jujuy, Consejo

16 Nacional de Investigaciones Científicas y Técnicas, Canónigo Gorriti 237 (PC 4600), San

17 Salvador de Jujuy, Argentina.

18 ⁴Environmental and Life Sciences Graduate Program, Trent University, 1600 West Bank

19 Drive, Peterborough, Ontario K9L 0G2, Canada.

20 ⁵Centre for Conservation & Restoration Science, School of Applied Sciences, Edinburgh

21 Napier University, Edinburgh, EH11 4BN, United Kingdom.

22 ⁶Department of Renewable Resources, University of Alberta, 116 85 Ave Edmonton, Alberta,

23 T6G 2R3, Canada.

24 ⁷Department of Earth, Ocean, and Ecological Sciences, University of Liverpool, Liverpool, L3

25 5DA, United Kingdom.

26 ⁸Florida State University, 222 S. Copeland Street, Tallahassee, FL, United States.

27 ⁹Equatorial Biome and Ocean Acidification - EBIOAC Lab, Facultad de Ciencias de la Vida y

28 Tecnologías, Universidad Laica Eloy Alfaro de Manabí - ULEAM, Ecuador.

29 ¹⁰Department of Organisms and Ecosystems Research, National Institute of Biology, Večna

30 pot 121, Ljubljana, Slovenia.
31 $^{11}$Fisheries Department, Faculty of Natural Resources, University of Guilan, Sowmeh Sara,
32 1144, Iran.

33 $^{12}$Institute of Biology Leiden, Leiden University, Leiden Sylviusweg 72, 2333 BE, The
34 Netherlands.

35 $^{13}$Australian Rivers Institute, School of Environment and Science, Griffith University,
36 Australia.

37 $^{14}$Department of Biology, University of Victoria, Victoria, BC, V8P 3E6, Canada.

38 $^{15}$University of New Hampshire, Center for Acoustics Research and Education, 8 College Rd,
39 Durham, New Hampshire, United States.

40 $^{16}$Indian Institute for Human Settlements, IIHS, Sadashivanagar Campus, Bengaluru No.
41 197/36, 2nd Main Road, Sadashivanagar, Bengaluru, 560 080, Karnataka, India.

42 $^{17}$Department of Ecology and Evolutionary Biology and K. Lisa Yang Center for Conservation
43 Bioacoustics, Cornell University, 215 Tower Road, Ithaca, New York, United States.

44 $^{18}$Université Grenoble Alpes, Université Savoie Mont Blanc, CNRS, LECA, F-38000
45 Grenoble, France.

46 $^{19}$Department of Humanities and Social Sciences, New Jersey Institute of Technology,
47 Newark, NJ, United States.

48 $^{20}$São Paulo State University (Unesp), Institute of Biosciences, Rio Claro, Avenida 24A, n°
49 1515, Bela Vista, Rio Claro, SP, Brazil.

50 KC and AL are joint last authors.

51 **Keywords:** Freshwater ecoacoustics, biological archive, biomonitoring, passive acoustic
52 monitoring.

53 **Abstract**

54 Freshwater ecosystems are full of underwater sounds produced by amphibians, aquatic arthropods,
55 reptiles, plants, fishes, and methane bubbles escaping from the sediment. Although much headway
56 has been made in recent years investigating the overall soundscapes of various freshwater
57 ecosystems around the world, there remains a significant knowledge gap in our collective inability to
58 accurately and reliably link recorded sounds with the species that produced them. Here, we present
59 The Freshwater Sounds Archive, a new global initiative, which seeks to address this knowledge gap
60 by collating species-specific freshwater sound recordings into a publicly available database. By
61 means of metadata collection, we also present a snapshot of the species studied, the recording
62 equipment, and recording parameters used by freshwater ecoacousticians globally. In total, 61 entries
were submitted to the archive between the 4th of March 2023 and the 30th of April 2025, representing 63 16 countries and 6 continents. The most numerous taxonomic group was arthropods (29 entries), 64 followed by fishes (14 entries), amphibians (10 entries), macrophytes (7 entries), and a freshwater 65 mollusk (1 entry). The majority of the submissions were from European countries (27 entries), of 66 which the United Kingdom was the most represented with 14 entries. The next most represented 67 region was North America (11 entries), followed by South America (8 entries), Oceania and Asia (5 68 entries each), Africa (3 entries), and the Middle East and Central America with 1 entry each. The 69 global south, polar regions, and areas with an elevation >500 m (asl) were underrepresented. The 70 field of freshwater ecoacoustics to date has largely focused on the analysis of ‘sound types’ due to a 71 current lack of knowledge of species-specific sounds. The Freshwater Sounds Archive presents an 72 opportunity to move beyond the ‘sound type’ approach, and towards an approach with higher 73 taxonomic resolution, ultimately resulting in species-specific descriptions. Furthermore, The 74 Freshwater Sounds Archive will provide freshwater ecoacousticians with one of the main tools 75 required to start creating annotated training datasets for machine learning models from soundscape 76 recordings by referring to known species sounds present in the archive. In the long-term, this will 77 result in the automatic detection and classification of species-specific freshwater sounds from 78 soundscape recordings, such as indicator, invasive, and endangered species. 79

80 **Introduction**

Previous research has shown that freshwater ecosystems are full of underwater sounds 81 produced by amphibians, aquatic arthropods, (semi)aquatic mammals, reptiles, plants, fish, 82 and methane bubbles escaping from the sediment (Decker et al., 2020; Desjonquères et al., 83 2019; Gottesman et al., 2020; Greenhalgh et al., 2020; 2021; 2023; Linke et al., 2018; 84 Marian et al., 2021; Putland et al., 2020; Rountree et al 2019; te Velde et al., 2024). The 85 soundscape, or all the sounds in an environment, comprises a combination of different 86 sound sources: sounds produced by biological (biophony), sounds produced by climatic 87 conditions (geophony), and sounds produced by humans (anthrophony) (Pijanowski et al., 88 2011; Rountree et al., 2020). 89

Although much headway has been made in recent years investigating the overall 90 soundscapes of various freshwater ecosystems around the world, there remains a significant 91 knowledge gap in our collective ability to accurately and reliably link recorded sounds with 92 the species that produced them. The need for an archive of freshwater species-specific 93 sounds has been identified by many authors (Linke et al., 2018; Gottesman et al, 2018; 94 Greenhalgh et al., 2021). To address the current lack of a biological sound archive 95 specifically dedicated to underwater sounds produced by freshwater species, here we 96 present The Freshwater Sounds Archive (https://fishsounds.net/freshwater.js). The 97 Freshwater Sounds Archive initiative will collate sound recordings from around the world and 98 provide opportunities for researchers, particularly from countries with little or no current 99
100 representation within the freshwater ecoacoustics literature, to collaborate and form
101 networks with other researchers working in a diversity of freshwater ecosystems. These
102 collaborations will help facilitate the adoption of freshwater ecoacoustics as an effective
103 survey method more broadly within conservation biology and environmental management.

104 Central to the ability to understand sounds generated by the natural world is the
documentation and cataloguing of sounds produced by animals, plants, and methane
105 bubbles associated with decompositional processes. As such, the establishment and
106 maintenance of biological sound archives is an integral part of deriving ecologically
107 meaningful conclusions from large acoustic datasets. Since the creation of the Macaulay
108 Library (https://www.macaulaylibrary.org/) by Cornell University, Ithaca, New York, in 1929—
109 which is primarily dedicated to the cataloguing of bird sounds—many other biological sound
110 libraries have been established encompassing a wide array of ecosystems and species,
111 such as marine mammals in ocean environments (https://dosits.org/), bats in temperate
112 woodlands (https://www.bats.org.uk/resources/sound-library), and frogs in tropical
113 rainforests (https://www.fonozoo.com/index_eng.php) (Greenhalgh et al., 2024). Cataloguing
114 species-specific sounds is essential if ecoacoustics is to be used in future biodiversity
115 assessments alongside other conventional methodologies used to survey freshwater
116 environments.
117

118 Conventionally, the ecological status of freshwater ecosystems is derived by monitoring
119 aquatic species indicative of specific environmental conditions due to variations in their
120 abundance, presence, or absence along an environmental gradient (Bal et al., 2018). In fact,
121 multiple indices can be used, such as the Whalley, Hawkes, Paisley & Trigg (WHPT) index
122 (Paisley et al., 2014), that scores aquatic invertebrates based on their preferences for
123 varying habitat and water qualities to infer ecological status to meet legal environmental
124 monitoring requirements, such as the Water Framework Directive (Directive 2000/60/EC,
125 2003). Monitoring freshwater species using conventional methods is labour-intensive,
126 invasive, and expensive (Greenhalgh et al., 2020). Consequently, this limits the number of
127 sites that can be surveyed, and there is a bias towards excluding hard-to-access sites from
128 sampling campaigns that require regular visitation. Moreover, conventional methods only
129 capture a single snapshot in time. As a result, freshwater ecologists are now supplementing
130 conventional methods with new cutting-edge technologies, such as camera traps,
131 environmental DNA, and passive acoustic monitoring, which involves recording all the
132 sounds in an environment, to address ecological questions (Greenhalgh et al., 2021).
133 Passive acoustic monitoring is rapidly becoming an affordable and effective method for non-
134 invasively monitoring ecosystems at large spatial and temporal scales, as inexpensive
135 acoustic sensors can be deployed for months at a time in many locations simultaneously
136 (Browning et al., 2017; Hill et al., 2018). Additionally, passive acoustic monitoring facilitates
137 behavioural tracking, providing insights into vital behaviours such as mating rituals, predator
138 avoidance, and foraging (Gibb et al., 2019).

139 There is a wealth of ecological information that can be derived from passive acoustic
140 monitoring of freshwater ecosystems because sounds are produced by species across
141 multiple trophic levels and include a wide range of behaviours (e.g., mating and foraging)
142 and processes (e.g., photosynthesis and decomposition) (Gibb et al., 2019; Greenhalgh
143 2023). As such, the development of the Freshwater Sounds Archive is a crucial stepping
144 stone towards deriving meaningful ecological conclusions from passive acoustic monitoring
145 data collected in freshwater environments. The aims of The Freshwater Sounds Archive are
146 to: 1) Establish the world's first global archive dedicated specifically to sounds produced
147 underwater by freshwater species, 2) Quantitatively analyse sounds and metadata submitted
148 to the archive, and 3) Make all the submitted recordings publicly available via the
149 FishSounds platform to increase awareness and access.

150 Methods

151 Establishing the archive

152 Contributors to the archive were found by publishing an online form on social media
153 platforms, and spreading the word within special working groups in bio/ecoacoustics
154 communities for people interested in contributing. Once the initial interest had been
155 assessed and enthusiasm for the development of an archive was made clear, collaborations
156 with members in the community involved in running and maintaining biological sound
157 archives followed. Namely, this involved establishing a collaboration with Audrey Looby and
158 Kieran Cox of FishSounds (https://fishsounds.net/), an online platform that offers a
159 comprehensive, global inventory of fish sound production research (Looby et al., 2023a).
160 Collaboration with FishSounds therefore facilitated the future hosting of The Freshwater
161 Sounds Archive.

162 A metadata file was created by JAG, AL, and KC to be filled in by all contributors submitting
163 recordings to the archive, which can be found in the Supplementary Material (S1). Metadata
164 parameters were decided based on their relevance for their information regarding: 1)
165 Geography (region, country, latitude, longitude, elevation), 2) Equipment (hydrophone), 3)
166 Recording parameters (gain, sampling rate) 4) Habitat, and 5) Taxonomy (order, family,
167 genus, species).

168 Geographical distribution
169 Recordings submitted to the archive via a Google Drive folder were then downloaded, along 170 with the associated metadata. A world map was produced in QGIS (version 3.36.3) using the 171 latitude and longitude coordinates associated with each recording and were projected onto a 172 Google Satellite image world map. Additionally, elevation data (metres above sea level) for 173 each recording submitted to the archive were calculated from latitude and longitude 174 coordinates using FreeMapTools (https://www.freemaptools.com/elevation-finder.htm).

175 Taxonomic representation

176 Taxonomic metadata were visualised in R Studio by creating a pie chart using the ggplot2 177 package (Wickham et al., 2016).

178 Species-specific spectrograms

179
180 Finally, species-specific spectrograms were produced in R Studio using the spectro function
181 in the Seewave package (Sueur et al., 2008) with an FFT of 512.

182 Results

183 Geographical distribution

184 In total, 61 entries were submitted to the archive between the 4th of March 2023 and the 185 30th of April 2025, representing 16 countries and 6 continents (Figure 1). A full species list 186 along with associated metadata is available in the Supplementary Materials (S2). The most 187 numerous taxonomic group was arthropods (29 entries), followed by fishes (14 entries), 188 amphibians (10 entries), macrophytes (7 entries), and a freshwater mollusk (1 entry). The 189 majority of the submissions were from European countries (27 entries), of which the United 190 Kingdom was the most represented with 14 entries. The next most represented region was 191 North America (11 entries), followed by South America (8 entries), Oceania and Asia (5 192 entries each), Africa (3 entries), and the Middle East and Central America with 1 entry each. 193 The recording captured at the highest elevation was 3,797.2 m (asl) in the Argentinian 194 Andes, and the lowest was captured at ~0 m (asl) in an aquarium in southern Japan. The 195 median elevation at which submitted recordings were captured was 50.3 m asl, the Q25 was 196 7.7 m asl, and the Q75 was 240.8 m asl. The majority of recordings submitted to the archive 197 were collected in the northern hemisphere between 30 and 60 degrees latitude. The global 198 south, polar regions, and areas with an elevation >500 m (asl) were underrepresented.

199 Taxonomic representation

200 The 61 entries submitted represented 21 orders, including two unknown sounds (one likely
201 made by a fish, and the others by plants; Figure 2). The most numerous taxonomic group
202 was arthropods (29 entries), followed by fishes (14 entries), amphibians (10 entries),
203 macrophytes (7 entries), and a freshwater mollusk (1 entry). No sounds were submitted by
204 (semi)aquatic mammals or reptiles. In total, 4 orders and 9 different families were
205 represented among the freshwater arthropods. The order Hemiptera (water boatmen) was
206 the most numerous with 21 entries, followed by Coleoptera (predaceous diving beetles with
207 5 entries). The next most numerous group with 14 entries, fishes, was represented by 9
208 orders and 11 families, of which Characiformes (characins) were the most numerous with 3
209 entries. Amphibians were represented by one order and 7 families, of which Ranidae was
210 the most numerous with 4 entries. Macrophytes were represented by 4 orders and 5
211 families. Freshwater mollusks were the least represented with one entry in one family.

212 **Figure 2.** Taxonomic representation of current submissions (n=61) to The Freshwater
213 Sounds Archive.

214 **Species-specific spectrograms**

215
216 Species-specific spectrograms showed distinct patterns between taxonomic groups (Figure
217 3).

218 **Recording metadata**

219
220 In total, 16 different hydrophones were used to collect the submitted recordings. The most
221 popular choice was HydroMoth (12 entries), followed by the Aquarian H2a (11 entries), the
222 HTI-96-Min (8 entries), the Aquarian H2d (6 entries), the Soundtrap 300STD (4 entries), the
223 Aquabeat, Jez-Riley-French standard, and a custom-made hydrophone (3 entries each), the
224 SQ26-08 Cetacean Research Tech and the Reson TC4033 (2 entries each), and the
225 Aquarian AS-1, the Aquarian H1a, the Brüel & Kjaer Type 8103, the GoPro Hero 7, the
226 GoPro Hero 8, and the HTI-94-SSQ (1 entry each). One entry did not disclose the
227 hydrophone model used to collect the sounds. In total, 6 different sampling rates were used
228 to collect the recordings. The most popular choice was 48.0 kHz (25 entries), followed by
229 44.1 kHz (23 entries), 96.0 kHz (10 entries), and 192.0 kHz, 45.1 kHz, and 16.0 kHz (1 entry
230 each).

231
232 Aquaria were the most popular recording environment, with 23 entries. The next most
233 numerous recording environment was rivers (18 entries), followed by ponds (12 entries),
234 lakes (7 entries), and one recording that was capture in a cenote.

235
236 **Discussion**
237

238 This global collaborative effort to launch the first biological sounds archive for underwater
239 freshwater species sounds has resulted in submissions from 35 contributors represented by
240 16 countries and every continent except Antarctica. The establishment of a biological sound
241 archive is essential for deriving more detailed ecological conclusions from passive acoustic
242 monitoring data and understanding freshwater soundscape ecology.

243

244 Describing complex and largely unknown systems

245

246 The field of freshwater ecoacoustics to date has largely focused on the analysis of 'sound
types' (Desjonquères et al., 2015; Gottesman et al., 2020; Greenhalgh et al., 2021) due to a
247 current lack of knowledge of species-specific sounds. In other nascent fields, similar 'type
248 classification' techniques have been employed to help decode complex and largely unknown
249 biological systems before a higher taxonomic level approach can be readily adopted. In
250 molecular ecology, specifically environmental DNA and metabarcoding, 'Operational
251 Taxonomic Units' were defined to permit the inference of taxa from unique genetic barcodes
252 without direct reference to the species (Blaxter et al., 2005). And in remote sensing, spectral
253 signatures (a combination of light wavelength and reflectance) are used as proxies to detect
254 native and invasive plant species from satellite images (Iqbal et al., 2021). Whereas in
255 complex microbial systems, in which many thousands of largely unknown bacterial and viral
256 species are present, ecological clusters are defined as 'phylotypes' to begin decoding the
257 rich biodiversity (Wu et al., 2020).
258

259

260 These approaches have their merits in the early development of a monitoring technique in
describing largely unknown complex systems. In the field of freshwater ecoacoustics for
261 example, the classification of 'sound types' has been used to make inferences of relative
262 sound type abundance (acoustic activity) and richness across sites (Desjonquères et al.,
263 2015), time periods (Linke et al., 2020; Gottesman et al., 2020), environmental gradients
264 (Desjonquères et al. 2018) and management activities (Greenhalgh et al., 2021). However,
265 this 'sound type' approach presents significant limitations in the ecological conclusions that
266 can be derived due to a lack of taxonomic resolution. Furthermore, the classification of
267 'sound types' is prone to subjectivity, with sound types often receiving subjective labels such
268 as 'grunt' and 'croak', the interpretation of which has the potential to vary between authors. It
269 is also very likely that different sound types produced by the same species are labelled as
270 two or more sound types, artificially increasing the derived impression of species richness
271 (Looby et al., 2023b).
272

273
274 Acoustic indices as proxies for species-specific detection

275

276 Acoustic indices—mathematical functions that consider variations in amplitude and
277 frequency of a recording over time—have also been used to study complex soundscapes
278 with little or no prior knowledge of the sound producers (Buxton et al., 2018). Much has been
discussed in the literature on the use of acoustic indices to assess the health of coral reef
279 soundscapes (Bertucci et al., 2016; Lamont et al., 2022), and infer avian species richness
280 (Alcocer et al., 2022; Buxton et al., 2018; Eldridge et al., 2018). Although a suite of acoustic
281 indices has been shown to be successful in the prediction of avian species richness where
282 species-specific sounds are well described in the United Kingdom, they were unsuccessful in
283 a more complex and unknown tropical rainforest soundscape in Ecuador (Eldridge et al.,
284 2018). Moreover, a recent comprehensive review on the use of acoustic indices highlighted
285 their limitations in inferring species diversity metrics (Alcocer et al., 2022). Soundscapes are
286 complex systems that are influenced by many variables in addition to species diversity, such
287 as variation in relative species abundance, vocal repertoire (number of ‘sound types’),
288 distance from the sensor, modification by habitat components such as vegetation, and
289 anthropogenic and natural noise, which can alter the signal to noise ratio of species-specific
290 calls in the soundscape (Alcocer et al., 2022).
291

292

293 Most acoustic indices were also originally designed for the detection of bird song in
294 terrestrial environments making them largely unsuited for the detection of aquatic fauna in
295 freshwater ecosystems (Greenhalgh et al., 2020). Additionally, short repeating phrases that
296 occupy a continuous frequency band with little or no variation in amplitude, such as those
297 produced by aquatic insect stridulation, can be difficult to accurately characterise using some
298 acoustic indices (Desjonquères et al., 2020; Ferreira et al., 2018). This is because indices
299 like the Acoustic Complexity Index are designed to ignore consistent sounds in the recorded
300 bandwidth to reduce the influence of anthropogenic sounds on calculated values (Pieretti et
301 al., 2011). While some studies have successfully detected soniferous fishes and aquatic
302 insects in freshwaters using the Acoustic Complexity Index with specially adapted
303 parameters (Linke et al., 2020), other studies have failed to do so (Karaconstantis et al.,
304 2020: Greenhalgh et al., 2023).
305

306

307 Towards a species-specific approach

308

309 The Freshwater Sounds Archive presents an opportunity to move beyond the ‘sound type’ or
acoustic indices approaches, and towards an approach with higher taxonomic resolution,
310 ultimately resulting in species-specific descriptions. In anuran amphibians, the advertisement
call is a well-recognised species-specific character used for species identification (Köhler et al., 2017). In the case of aquatic frogs, such as the genus *Telmatobius*, studies on vocal behaviour are in early stages, with some distinctions noted among species (Akmentins et al., 2024). Underwater calling represents an incipient field of work for anurans, facilitated by emergent recording technologies (Lamont et al., 2022). We have known for decades that different species of aquatic insects must be able to produce different sounds because identification guidebooks draw upon the differences in their sound-producing anatomy to distinguish between species (Savage, 1990). If different species have different anatomical structures related to sound production, then logically it follows that most must be producing species-specific sounds that can be described. Foundational work by Antti Jansson provided the first descriptions of many sounds produced by lesser water boatmen (Corixidae) (Jansson 1974, Pajunen & Huldén, 2002; Rothenberg, 2021). Multiple reviews of aquatic insect sound production since have demonstrated the widespread adoption of stridulatory behaviour by many taxa, including a recent review that estimated that more than 7,000 species of aquatic insect are likely to produce sound worldwide (Aiken 1985; Desjonquères et al., 2024). Once species-specific sounds have been identified, the large-scale annotation of soundscape recordings has the potential to generate large amounts of training data for machine learning models to automatically detect species' sounds and potentially assess the ecological condition of freshwater habitats. The application of automated species-specific sound detection in freshwater environments has great promise in the detection of indicator, invasive, and endangered freshwater species sounds.

Deep learning: The future of freshwater ecoacoustics analysis

One of the main challenges associated with passive acoustic monitoring is the vast amount of data that are produced, making it impossible to manually analyse recordings (Stowell, 2022). Therefore, novel computational solutions are required in the large-scale analysis of ecoacoustics data to automate large parts of the analysis workflow. Deep learning is a subset of machine learning that uses very large datasets, such as audio data collected in passive acoustic monitoring surveys, to learn from data and to make predictions using neural networks (Stowell, 2022; Dufourq et al., 2022). The most common use of deep learning techniques in ecoacoustic studies are classification and detection (Ruff et al., 2021). This is achieved by training models using pre-labelled data, such as species-specific sounds that have been annotated from soundscape recordings (Stowell, 2022). In addition, novel techniques such as transfer learning of pre-trained convolutional neural networks are promising for classification tasks (Kath et al., 2024). Biological sound archives play a crucial
347 role in this effort by providing a starting point from which an annotated species-specific call
348 library can be created and used to train deep learning models (Cañas et al., 2023).

349
350 Since the wider adoption of deep learning techniques within ecoacoustic studies in 2016
351 (Goëau et al., 2016), many authors have applied them to automatically detect species-
352 specific sounds produced by a diversity of taxa. A recent review of the use of deep learning
353 in ecoacoustics (Stowell, 2022) showed that of the taxa whose species-specific sounds have
354 been studied using deep learning, birds were the most studied group (with 65 studies),
355 followed by marine mammals (30 studies), amphibians (8 studies), bats (7 studies),
356 arthropods (7 studies), and fishes (3 studies). However, deep learning methods are rarely
357 used in the analysis of freshwater soundscapes. Nevertheless, Parcerisas et al., (2024)
358 recently demonstrated that a deep learning model trained to detect underwater sound events
359 in marine environments can transfer effectively to freshwater habitats. They further applied
360 unsupervised clustering to identify novel 'sound types' based on acoustic feature similarity, a
361 method that can aid in discovering novel sounds and looking for temporal and spatial
362 patterns in sound events of potentially biological origin. This could be especially useful in
363 underexplored freshwater environments. Although these results confirm that deep learning-
364 based sound event detection works in freshwater systems, and provides a useful explorative
365 tool, more meaningful ecological insights into species distributions and behaviours will
366 require models trained on annotated, species-specific datasets.

367
368 As such, The Freshwater Sounds Archive will provide freshwater ecoacousticians with one
369 of the main tools required to start creating annotated training datasets for deep learning
370 models from soundscape recordings by referring to known species sounds present in the
371 archive. Soon, long alphanumeric codes called Application Programming Interface keys (API
372 keys) will facilitate the automatic downloading of annotated species-specific calls from
373 biological sound archives for training in machine learning models to detect species-specific
374 calls in the form of installable packages via platforms like R and Python (Stowell, 2022). API
375 keys also facilitate data sharing between biological sound archives, which means specialised
376 archives, such as The Freshwater Sounds Archive that mobilise a specific subset of the
377 scientific community, can contribute to larger archives in the long-term (Scott et al., 2019).
378 There is huge untapped potential to harness recent advances in deep learning to provide
379 automated, scalable, and non-invasive freshwater ecological assessment. However, much
380 work is still required to better understand species-specific sounds and build reliable
381 annotated training data libraries.

382
383 **Challenges and limitations**
384
385 In the initial stages of the archive development, it was not always possible to achieve
386 species-level descriptions due to the challenges associated with isolating species in tanks
387 and recording them separately. While captive auditioning has helped identify several
388 species-specific sounds, many behaviours (e.g., mating, territory defence, distress calls) are
389 context-dependent and less likely to occur in captivity. As such, where a species description
390 was not possible, such as sounds submitted from soundscape recordings, an educated
391 guess was made as to the next highest taxonomic level (at least to order level) based on
392 physical observations of the recording environment and previous experience in listening to
393 freshwater species' sounds. To overcome any inaccuracies associated with this approach,
394 all the sounds submitted to the archive will be published online via FishSounds.com and a
395 forum will be established to permit future taxonomic revisions as more data become
396 available and assist in the identification of new sounds. It is critically important at this early
397 stage to avoid the mislabelling of species-specific sounds that could result in perpetuating
398 errors throughout the wider literature. Therefore, we believe that a more conservative
399 approach to taxonomic labelling is appropriate, unless the species has been recorded in
400 isolation and identified by a trained professional.

401
402 Conclusions

403
404 Passive acoustic monitoring in freshwater environments has revealed a rich diversity of
405 sounds produced by taxa and processes that represent multiple trophic levels. This includes
406 sounds produced by primary producers (macrophytes), primary consumers (aquatic insects),
407 secondary consumers (fishes and amphibians), and even decompositional processes in the
408 form of methane bubbles. Although there is great promise in the wealth of ecological data
409 that freshwater soundscape monitoring provides, there is a considerable knowledge gap in
410 the current inability to relate species-specific sounds with the species that produces them.
411 The Freshwater Sound Archive therefore presents an opportunity to move past the current
412 'sound type' or acoustic index-based approach of classifying and quantifying freshwater
413 soundscapes towards a taxonomic approach, which will lead to the inference of more
414 meaningful ecological conclusions. Furthermore, the archive will provide freshwater
415 ecoacousticians with the tools required to start creating annotated training datasets for
416 machine learning models from soundscape recordings by referring to known species sounds
417 present in the archive. In the long-term, this will result in the automatic detection and
418 classification of species-specific freshwater sounds, such as indicator, invasive, and
419 endangered species from soundscape recordings, as successfully achieved in other fields,
420 such as with bird and bat species identification in woodlands, fish and marine mammals in
421 the oceans, and frogs in rainforests.

422

423 Author contributions

424 JAG: Conceptualization; Data Curation; Investigation; Methodology; Resources; Formal

425 Analysis; Writing: Original Draft; Writing: Review & Editing; Visualization; Supervision;

426 Project Administration. AL, KC: Conceptualization; Methodology; Resources; Writing:

427 Review & Editing; Supervision; Project Administration. JCB, SSS, MN, FN-M, RB, AC, JT,

428 LR, KtV, KC, MC, ITJ, JP, DS, RS, MPV, CD: Data Curation; Investigation; Resources,

429 Writing: Review & Editing. DR, EL, MB, MA, SG, SO, JRW, KT, BG: Data Curation;

430 Investigation; Resources. JJL-M: Resources.

431

432 Acknowledgements

433 AL is supported by a Banting postdoctoral fellowship. Data collection by JCB was supported

434 by donations from members of the Stoney Lake community, Ontario, Canada. MC was

435 supported by the National Science Foundation Research Experience for Undergraduates

436 program: Sensors in Earth, Oceans, and Space Science, at the University of New

437 Hampshire, U.S. RB was partially funded by the UK Acoustics Network (UKAN+) via EPSRC

438 grant number EP/V007866/1. RS carried out data collection with support of an internal

439 research grant at IIHS and support by Viral Joshi and Isha Bopardikar. LR, AC, JW and JT

440 were funded by the University of Liverpool. Data collection by MPV was supported by a

441 Kieckhefer Adirondack Fellowship.

442

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<figure><img src="image_3.png" /><figcaption>**Figure 1.** Global distribution of current submissions to The Freshwater Sounds Archive (n=61). Numbers within circles represent the number of records from each location.</figcaption></figure>

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<figure><img src="image_4.png" /><figcaption>**Figure 2.** Taxonomic representation of current submissions (n=61) to The Freshwater Sounds Archive.</figcaption></figure>

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<figure><img src="image_5.png" /></figure>

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705

706 **Figure 3.** Example sounds of A) European perch (*Perca fluviatilis*), B) Rusted frog (*Telmatobius rubigo*), C) Water boatman (*Sigara concinna*), D) Broad-leaved pondweed (*Potamogeton natans*). Spectrogram parameters: window type: "Hanning", window length:

707

708 (*Potamogeton natans*). Spectrogram parameters: window type: "Hanning", window length:

709 512. Note that the frequency and time axes are not consistent across plots.

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