# Worldwide Soundscapes: a synthesis of passive acoustic monitoring across realms

Kevin Fa Darras, Rodney Rountree, Steven van Wilgenburg, Anna F Cord, Frederik Pitz, Youfang Chen, Lijun Dong, Amandine Gasc, Tzu-Hao Lin, Patrick Mauritz Diaz, et al.

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## To cite this version:

Kevin Fa Darras, Rodney Rountree, Steven van Wilgenburg, Anna F Cord, Frederik Pitz, et al. "Worldwide Soundscapes: a synthesis of passive acoustic monitoring across realms." 2025. hal-04903892

HAL Id: hal-04903892

https://hal.science/hal-04903892v1

Preprint submitted on 21 Jan 2025

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1 Worldwide Soundscapes: a synthesis of
2 passive acoustic monitoring across realms

3

4 Authors & affiliations

5 Kevin FA Darras¹,²,³c, Rodney Rountree⁴,⁵, Steven Van Wilgenburg⁶, Anna F Cord³,⁷, Frederik

6 Pitz³, Youfang Chen², Lijun Dong⁹, Amandine Gasc¹⁰, Tzu-Hao Lin¹¹, Patrick Mauritz Diaz¹²,

7 Shih-Hung Wu¹³, Marcus Salton¹⁴, Sarah Marley¹⁵, Laura Schillé¹⁶, Paul Jacobus

8 Wensveen¹⁷, Camille Desjonquères¹⁸,¹⁹, Orlando Acevedo-Charry²⁰,²¹, Matyáš Adam²²,

9 Jacopo Aguzzi²³, Michel André²⁴, Alexandre Antonelli²⁵,²⁶,²⁷, Leandro Aparecido Do

10 Nascimento²⁸, Giulliana Appel²⁹, Christos Astaras³⁰, Andrey Atemasov³¹,³², Luc Barbaro³³,

11 Fritjof Basan³⁴, Carly Batist³⁵,³⁶, Adriá López Baucells³⁷, Julio Ernesto Baumgarten³⁸, Just T

12 Bayle Sempere³⁹, Kristen Bellisario⁴⁰, Asaf Ben David⁴¹, Oded Berger-Tal⁴², Matthew G

13 Betts⁴³, Iqbal Bhalla⁴⁴, Thiago Bicudo³⁵, Marta Bolgan⁴⁵, Sara Bombaci⁴⁶, Martin

14 Boullhesen⁴⁷, Tom Bradfer-Lawrence⁴⁸,⁴⁹, Robert A Briers⁵⁰, Michal Budka⁵¹, Katie

15 Burchard⁵², Alice Calvente⁵³, Maite Cerezo-Araujo⁵⁴, Gunnar Cerwén⁵⁵, Maria Chistopolova⁵⁶,

16 Christopher W Clark⁵⁷, Benjamin Cretois⁵⁸, Chapin Czarnecki⁵⁹, Luis P da Silva⁶⁰,⁶¹, Wigna da

17 Silva⁶², Laurence H De Clippele⁶³, David de la Haye⁶⁴, Ana Silvia de Oliveira Tissiani⁶⁵, Devin

18 de Zwaan⁶⁶,⁶⁷, Ricardo Díaz-Delgado⁶⁸, Pedro Diniz⁶⁹, Dorgival Diógenes Oliveira-Júnior⁷⁰,⁷¹,

19 Thiago Dorigo⁷², Saskia Dröge⁷³, Marina Duarte⁷⁴,⁷⁵, Adam Duarte⁷⁶, Kerry Dunleavy⁷⁷,

20 Robert Dziak⁷⁸, Simon Elise⁷⁹,⁸⁰, Hiroto Enari⁸, Haruka S Enari⁸, Florence Erbs²⁴, Nina

21 Ferrari⁴³, Luane Ferreira⁶², Abram B Fleishman⁷⁷, Bárbara Freitas⁸¹,⁸²,⁸³, Nick Friedman⁸⁴,

22 Jérémy SP Froidevaux⁴⁸,⁸⁵, Svetlana Gogoleva⁸⁶, Maria Isabel Gonçalves³⁸, Carolina

23 Gonzaga⁷⁴, José Miguel González Correa³⁹, Eben Goodale⁸⁷, Benjamin Gottesman⁵⁷, Ingo

24 Grass⁸⁸, Jack Greenhalgh⁸⁹, Jocelyn Gregoire⁹⁰, Jonas Hagge⁹¹, William Halliday⁹², Antonia

25 Hammer⁹³, Tara Hanf-Dressler⁹⁴, Samara Haver⁹⁵, Daniel Hending⁹⁶, Jose Hernandez-

26 Blanco⁵⁶, Thomas Hiller⁸⁸, Joe Chun-Chia Huang⁹⁷, Katie Lois Hutchinson⁹⁸, Janet Jackson⁹⁹,

27 Alain Jacot¹⁰⁰, Olaf Jahn¹⁰¹,¹⁰², Jasper Kanes^, Ellen Kenchington¹⁰³, Sebastian Kepfer-

28 Rojas¹⁰⁴, Justin Kitzes⁵⁹, Tharaka Kusuminda¹⁰⁵, Yael Lehnardt⁴², Paula Leitman⁷², José

29 Leon¹⁰⁶, Cicero Simão Lima-Santos⁷⁰,⁷¹, Kyle John Lloyd¹⁰⁷,¹⁰⁸,¹⁰⁹, Audrey Looby¹¹⁰,¹¹¹, David

30 López-Bosch³⁷, Tatiana Maeda¹¹², Christos Mammides¹¹³, Gabriel Marcacci¹⁰⁰, Matthias

31 Markolf¹¹⁴,¹¹⁵, Martinez Isaac Marques⁶⁵, Charles W Martin¹¹⁰, Dominic A Martin¹¹⁶,¹¹⁷, Kathy

32 Martin⁶⁶,¹¹⁸, Matthew McKown⁷⁷, Logan JT McLeod¹¹⁹, Oliver Metcalf¹²⁰, Christoph Meyer¹²¹,

33 Grzegorz Mikusinski¹²², Larissa Sayuri Moreira Sugai⁵⁷, Dave Morris⁹⁹, Sandra Müller¹²³,

34 Kelsie A Murchy¹²⁴, Maria Mas Navarro³⁷, Rym Nouioua¹²⁵, Julian D Olden¹²⁶, Steffen

35 Oppel⁴⁹,¹⁰⁰, Anna N Osiecka¹²⁷, Miles Parsons⁹², Filipa Isabel Pereira Samarra¹⁷, Cristian

36 Pérez-Granados¹²⁸, Liliana Piatti¹²⁹, Mauro Pichorim⁷⁰,⁷¹, Thiago Pinheiro⁶², Jean-Nicolas

37 Pradervand¹⁰⁰, Craig Radford¹³⁰, Emiliano Ramalho³⁵, Laura K Reynolds¹³¹, Klaus Riede¹⁰¹,

38 Talen Rimmer¹²⁴, Ricardo Rocha⁹⁶, Luciana Rocha⁶², Samuel RP-J Ross¹³², Carolyn

39 Rosten¹³³, Carlos Salustio-Gomes⁷⁰,⁷¹, Philip Samartzis¹³⁴, José Santos⁶², Kevin

40 Scharffenberg¹³⁵, Renée P Schoeman¹³⁶, Karl-Ludwig Schuchmann⁶⁵, Esther Sebastián-

41 González¹²⁸, Fannie Shabangu¹³⁷,¹³⁸, Taylor Shaw¹²³, Xiaoli Shen¹³⁹, David Singer⁹¹, Jenni

42 Stanley¹⁴⁰, Karolin Thomisch¹⁴¹, Laura Torrent³⁷, Juan Traba¹⁴²,¹⁴³, Junior A Tremblay¹⁴⁴,

43 Sunny Tseng¹⁴⁵, Mao-Ning Tuanmu¹⁴⁶, Marisol Valverde¹⁴⁷, Ben Vernasco⁷⁶,¹⁴⁸, Raiane Vital

44 da Paz⁷⁰,⁷¹, Matthew Ward¹⁴⁹, Matthew Weldy⁴³,¹⁵⁰, Julia Wiel⁵⁸, Jacob Willie¹⁵¹, Heather

45 Wood¹⁵², Songhai Li⁹c, Renata Sousa-Lima⁶²c, Thomas Cherico Wanger²c

46

47 c: corresponding authors

48

49 1: EFNO, ECODIV, INRAE, Domaine des Barres, Nogent-sur-Vernisson, Centre-Val de

50 Loire, 45290, France
51 2: Sustainable Agricultural Systems & Engineering Lab, School of Engineering, Westlake

52 University, No. 600 Dunyu Road, Sandun Town, Xihu District, Hangzhou, Zhejiang

53 province, , China

54 3: Chair of Computational Landscape Ecology, Faculty of Environmental Sciences, Dresden

55 University of Technology, Helmholtzstr. 10, Dresden, 01069, Germany

56 4: Biology Department, University of Victoria, Cunningham Building, Room 202, PO Box

57 1700 STN CSC, Victoria, BC, V8W 2Y2, Canada

58 5: The Fish Listener, , 23 Joshua Lane, Waguoit, MA, 2536, U.S.A.

59 6: Terrestrial Unit, Prairie Region, Canadian Wildlife Service, Environment & Climate

60 Change Canada, Prairie & Northern Wildlife Research Centre, 115 Perimeter Road,

61 Saskatoon, Saskatchewan, S7N 0X4, Canada

62 7: Agro-ecological Modeling Group, Faculty of Agriculture, University of Bonn, Niebuhrstr.

63 1a, Bonn, 53113, Germany

64 8: Wildlife Ecology & Management, Faculty of Agriculture, Yamagata University, 1-23

65 Wakabamachi, Tsuruoka, Yamagata, 9978555, Japan

66 9: Marine Mammal and Marine Bioacoustics Laboratory, Department of Deep Sea Science,

67 Institute of Deep-sea Science and Engineering Chinese Academy of Sciences, 28 Luhuitou

68 Road, Sanya, Hainan, 572000, China

69 10: Institut Méditerranéen de Biodiversité et d'Ecologie marine et continentale, , Aix

70 Marseille Univ, Avignon Univ, CNRS, IRD, Campus Aix Technopôle de l'environnement

71 Arbois Méditerranée Avenue Louis Philibert Bât Villemin, Aix-en-Provence, 13545, France

72 11: Marine Ecoacoustics and Informatics Lab, Biodiversity Research Center, Academia

73 Sinica, 128 Academia Road, Section 2, Nankang, Taipei, , 11529, Taiwan

74 12: Climate Change and Biodiversity, Hanns R. Neumann Stiftung Indonesia, , Muaradua,

75 OKU Selatan, South Sumatera, 32212, Indonesia

76 13: Biodiversity Indicator Lab, Division of Habitats and Ecosystems, Endemic Species

77 Research Institute, 1 Minsheng East Road, Jiji Town, Nantou County, Taiwan, 552005,

78 Taiwan

79 14: Australian Antarctic Division, Science Branch, Department of Climate Change, Energy,

80 Environment and Water, Channel Highway, Kingston, Tasmania, 7050, Australia

81 15: Scotland's Rural College, Craibstone Estate, Aberdeen, AB21 9YA, United Kingdom

82 16: BIOGECO, INRAE, University of Bordeaux, 69 route d'Arcachon, Cestas, 33610, France

83 17: Westman Islands Research Centre, , University of Iceland, Ægisgata 2, Vestmannaeyjar,

84 900, Iceland

85 18: Institut de Systématique, Évolution, Biodiversité, , Muséum National d'Histoire naturelle,

86 Rue Buffon, Paris, 75005, France

87 19: Université Grenoble Alpes, Université Savoie Mont Blanc, CNRS, LECA, , , Grenoble,

88 38000, France

89 20: Colección de Sonidos Ambientales Mauricio Álvarez-Rebolledo - Colecciones

90 Biológicas, Subdirección de Investigaciones, Instituto de Investigación de Recursos

91 Biológicos Alexander von Humboldt, Claustro de San Agustín, Villa de Leyva, Boyacá, ,

92 Colombia

93 21: Quantitative Ecology Lab & Ordway Lab of Ecosystem Conservation, School of Natural

94 Resources and Environment, Department of Wildlife Ecology and Conservation, & Florida

95 Museum of Natural History, University of Florida, 715 Newell Dr, Building 866 & 1659

96 Museum Rd, Dickinson Hall 315, Gainesville, Florida, 32611, U.S.A.

97 22: Department of Environmental Security, Faculty of Logistics and Crisis Management,

98 Tomas Bata University, Studentské nám. 1532, Uherské Hradiště, , 68601, Czech Republic

99 23: Marine Science Institute, ICM, CSIC, ICM, CSIC, Paseig Barceloneta, Barcelona, , Spain

100 24: Laboratory of Applied Bioacoustics, , Polytechnic University of Catalonia,

101 BarcelonaTech, Rambla Exposicion, 24, Vilanova I la Geltrú, Barcelona, 8800, Spain

102 25: Royal Botanic Gardens, Kew, Richmond, Surrey, TW9 3AE, United Kingdom, , Kew,

103 Richmond, Surrey, TW9 3AE, United Kingdom

104 26: Gothenburg Global Biodiversity Centre, Department of Biological and Environmental

105 Sciences, University of Gothenburg, Box 461, Göteborg, SE 40530, Sweden
106 27: Department of Plant, University of Oxford, South Parks Road, Oxford, OX1 3RB, United Kingdom
107
108 28: Bioacoustics Group, Science Department, Biometrio.earth, Dr. Schönemann Str. 38,
109 Saarbrücken, 66123, Germany
110 29: Biodiversidade e Serviços Ecossistêmicos, Instituto Tecnológico Vale, Boaventura da Silva, 955, Belém, PA, 66055-090, Brazil
112 30: Wildlife Lab, Forest Research Institute, Hellenic Agricultural Organization - DIMITRA (ELGO-DIMITRA), Vasilika, Thessaloniki, TK 57006, Greece
113
114 31: Department of Zoology and Animal Ecology, School of Biology, V.N.Karazin Kharkiv
115 National University, 4 Svobody Square, Kharkiv, 61022, Ukraine
116 32: Research Department, National Park ‘Homilshanski Lisy’, 27 Monastyrska Street,
117 Koropove village, Kharkiv region, 63437, Ukraine
118 33: DYNAFOR, University of Toulouse, INRAE, Chemin de Borde-Rouge, Castanet-Tolosan,
119 F-31326, France
120 34: Federal Maritime and Hydrographic Agency (BSH), Bernhard-Nocht Straße 78,
121 Hamburg, 20359, Germany
122 35: Rainforest Connection, Science department, 440 Cobia Drive, Suite 1902, TX, 77494,
123 U.S.A.
124 36: Arbimon, U.S.A.
125 37: BiBio Research Group, Natural Sciences Museum of Granollers, Av. Francesc Macià
126 51, Granollers, Catalonia, 08402, Spain
127 38: Applied Ecology & Conservation Lab, Biological Sciences, Universidade Estadual de Santa Cruz, Rodovia Jorge Amado km 16, Salobrinho, Ilhéus, Bahia, 45662900, Brazil
128
129 39: Marine Biological Acoustic, Marine Sciences and Applied Biology, University of Alicante,
130 C/ San Vicente del Raspeig s/n, San Vicente del Raspeig, Alicante, 3690, Spain
131 40: HIFI Lab, John Martinson Honors College, Purdue University West Lafayette Indiana,
132 1101 Third Street, West Lafayette, Indiana, 47906, U.S.A.
133 41: Tamar Dayan’s Lab, School of zoology, Steinhardt Museum of Natural History, Tel-Aviv
134 university, Klausner 12, Tel-Aviv, Tel-Aviv, 6997801, Israel
135 42: The Conservation Behavior Research Group, Mitrani Department of Desert Ecology,
136 Jacob Blaustein Institutes for Desert Research, Ben-Gurion University of the Negev,
137 Midreshet Ben-Gurion, 8499000, Israel
138 43: Forest Landscape Ecology Lab, Department of Forest Ecosystems and Society, Oregon
139 State University, U.S.A.
140 44: School of Geography and the Environment, Oxford University, South Parks Rd, Oxford,
141 OX1 3QY, United Kingdom
142 45: Ocean Science Consulting Limited, Spott Road, Dunbar, EH42 1RR, United Kingdom
143 46: Equity in Ecological Research Lab, Department of Fish, Wildlife, and Conservation
144 Biology, Colorado State University, 1474 Campus Delivery, Fort Collins, Colorado, 80521,
145 U.S.A.
146 47: Instituto de Ecorregiones Andinas (INECOA-UNJu-CONICET), Facultad de Ingeniería,
147 Universidad Nacional de Jujuy, Gorriti 237, San Salvador de Jujuy, Jujuy, 4600, Argentina
148 48: Biological and Environmental Sciences, University of Stirling, Stirling, Stirlingshire, FK9
149 4LA, Scotland
150 49: Centre for Conservation Science, RSPB, 2 Lochside View, Edinburgh Park, Edinburgh,
151 EH12 9DH, United Kingdom
152 50: Centre for Conservation and Restoration Science, School of Applied Sciences,
153 Edinburgh Napier University, Sighthill Campus, Edinburgh, EH11 4BN, United Kingdom
154 51: Department of Behavioural Ecology, Faculty of Biology, Adam Mickiewicz University,
155 Uniwersytetu Poznańskiego 6, Poznań, 61614, Poland
156 52: Northeast Fisheries Science Center, Narragansett Laboratory, 28 Tarzwell Drive,
157 Narragansett, RI, 2882, U.S.A.
158 53: Laboratório de Botânica Sistemática, Departamento de Botânica, Ecologia e Zoologia,
159 Universidade Federal do Rio Grande do Norte, Av. Sen. Salgado Filho 3000, Natal, Rio
160 Grande do Norte, 59078-900, Brazil
161 54: South Iceland Research Centre, University of Iceland, Lindarbraut 4, Laugarvatn, 840,

162 Iceland

163 55: Sensola SLU Multisensory Outdoor Laboratory, Department of Landscape Architecture,

164 Planning and Management, Swedish University of Agricultural Sciences, SLU, Sundsvägen

165 4-6, Alnarp, 23053, Sweden

166 56: , , , ,

167 57: K. Lisa Yang Center for Conservation Bioacoustics, Cornell Lab of Ornithology, Cornell

168 University, 159 Sapsucker Woods Rd, New York, 14850, U.S.A.

169 58: Miljødata, Norwegian Institute for Nature Research, Høgskoleringen 9, Trøndelag,

170 Trondheim, 7034, Norway

171 59: Department of Biological Sciences, University of Pittsburgh, Clapp Hall, Fifth and Ruskin

172 Avenues, Pittsburgh, PA, 15237, U.S.A.

173 60: CIBIO, Centro de Investigação em Biodiversidade e Recursos Genéticos, InBIO

174 Laboratório Associado, Campus de Vairão, Universidade do Porto-InBIO, Universidade do

175 Porto, Campus de Vairão, Rua Padre Armando Quintas, Porto, 4485-661 Vairão, Portugal

176 61: BIOPOLIS Program in Genomics, Biodiversity and Land Planning, CIBIO, University of

177 Porto, Campus de Vairão, Rua Padre Armando Quintas, Porto, 4485-661 Vairão, Portugal

178 62: EcoAcoustic Research Hub (EAR Hub), Biosciences Center, Universidade Federal do

179 Rio Grande do Norte, Av. Sen. Salgado Filho 3000, Natal, Rio Grande do Norte, 59078-900,

180 Brazil

181 63: School of Biodiversity, One Health & Veterinary Medicine, University of Glasgow,

182 Graham Kerr Building, Hillhead street 82, Scotland, United Kingdom, G12 8QQ, United

183 Kingdom

184 64: School of Arts and Cultures, Newcastle University, Newcastle upon Tyne, NE1 7RU,

185 United Kingdom

186 65: INAU Pantanal Biodata Center, National Institute of Science and Technology in

187 Wetlands (INAU), Computational Bioacoustics Research Unit (CO.BRA), Federal University

188 of Mato Grosso (UFMT), Cuiabá, 2367 Fernando Correa da Costa Ave, Mato Grosso,

189 78060-900, Brazil

190 66: Forest and Conservation Sciences, University of British Columbia, 2424 Main Mall,

191 Vancouver, British Columbia, V6T 1Z4, Canada

192 67: Department of Biology, Mount Allison University, 63B York Street, Sackville, New

193 Brunswick, E4L 1E7, Canada

194 68: Estación Biológica de Doñana-CSIC, CSIC, c/ Americo Vespucio 26, Sevilla, 41092,

195 Spain

196 69: Laboratório de Comportamento Animal, Departamento de Zoologia, Universidade de

197 Brasília, Instituto de Ciências Biológicas, Distrito Federal, 70910-900, Brazil

198 70: Laboratório de Ornitologia, Departamento de Botânica e Zoologia, Universidade Federal

199 do Rio Grande do Norte, Av. Sen. Salgado Filho 3000, Natal, RN, 59078-900, Brazil

200 71: Programa de Pós-Graduação em Ecologia, Centro de Ciências, Universidade Federal

201 do Rio Grande do Norte, Av. Sen. Salgado Filho 3000, Natal, RN, 59078-900, Brazil

202 72: Lifeplan Rio de Janeiro / Araçá project, Estrada Hans Garlipp, s/n, Macaé de Cima,

203 Fazenda Bacchus, Nova Friburgo, 28605-270, Brazil

204 73: Forest, Nature and Landscape, KU Leuven, Celestijnenlaan 200e, Leuven, 3001,

205 Belgium

206 74: ECOA - Laboratory of Bioacoustics, Post graduate Program in Vertebrate Biology /

207 Museum of Natural Sciences, Pontifical Catholic University of Minas Gerais, Minas

208 Gerais, Brazil

209 75: Environmental Studies Center (CEA), Sao Paulo State University - UNESP, Av. 24a,

210 1515, Bela Vista, Rio Claro, Sao Paulo, 13506-900, Brazil

211 76: Pacific Northwest Research Station, U.S.D.A. Forest Service, 3625 93rd Ave. SW,

212 Olympia, Washington, 98512, U.S.A.

213 77: Conservation Metrics, Inc., 145 McAllister Way, Santa Cruz, CA, 95060, U.S.A.

214 78: Acoustics Program, Pacific Marine Envrionmental Laboratory, National Oceanic and

215 Atmospheric Administration, 2030 SE Marine Science Drive, Newport, OR, 97365, U.S.A.
216 79: Reef Pulse S.A.S., NA, NA, Allée Mareau, Sainte-Clotilde, Reunion Island, 97490,

217 France

218 80: UMR 9220 ENTROPIE, Faculté des sciences et technologies, University of Reunion

219 Island, Avenue René Cassin, Sainte-Clotilde, Reunion Island, 97490, France

220 81: National Museum of Natural Sciences, Spanish National Research Council, Calle José

221 Gutiérrez Abascal 2, Madrid, 28006, Spain

222 82: Centre de Recherche sur la Biodiversité et l'Environnement (UMR 5300 CNRS-IRD-

223 TINPT-UPS), Université Paul Sabatier, 118 route de Narbonne, Toulouse, 31062 Toulouse

224 Cedex 9, France

225 83: Facultad de Ciencias, Universidad Autónoma de Madrid, C. Darwin 2, Madrid, 28049,

226 Spain

227 84: Centre for Taxonomy and Morphology, Leibniz Institute for the Analysis of Biodiversity

228 Change, Museum of Nature Hamburg, Martin-Luther-King-Platz 3, Hamburg, 20146,

229 Germany

230 85: Centre d'Ecologie et des Sciences de la Conservation (CESCO, UMR 7204), Museum

231 National d'Histoire Naturelle, Place de la Croix, Concarneau, Finistère, 29900, France

232 86: Laboratory of tropical ecology, , A.N. Severtsov Institute of Ecology and Evolution of

233 Russian Academy of Sciences, Leninsky Prospect, 33, Moscow, 119071, Russia

234 87: Department of Health and Environmental Science, , Xi'an Jiaotong-Liverpool University,

235 No. 8 Chongwen Road, Suzhou Industrial Park, Suzhou, Jiangsu, 215123, China

236 88: Ecology of Tropical Agricultural Systems, Institute of Agricultural Sciences in the Tropics,

237 University of Hohenheim, Garbenstrasse 13, Stuttgart, 70599, Germany

238 89: School of Biological Sciences, Life Sciences Building, University of Bristol, 24 Tyndall

239 Ave, Bristol, BS8 1TQ, United Kingdom

240 90: Migratory Bird Unit, Northern Region, Canadian Wildlife Service, Environment and

241 Climate Change Canada, 5019 - 52nd Street, Yellowknife, Northwest Territories, X1A 2P7,

242 Canada

243 91: Species and Habitat Conservation, Forest Nature Conservation, Northwest German

244 Forest Research Institute, Prof.-Oelkers-Str. 6, Hann. Münden, Lower Saxony, 34346,

245 Germany

246 92: Wildlife Conservation Society Canada, 169 Titanium Way, Whitehorse, Yukon, Y1A 0E9,

247 Canada

248 93: Applied Zoology and Nature Conservation, Zoological Institute and Museum, University

249 of Greifswald, Loitzer Strasse 26, Greifswald, 17489, Germany

250 94: Bat Lab, Department of Evolutionary Ecology, Leibniz Institute for Zoo and Wildlife

251 Research, Alfred-Kowalke-Straße 17, Berlin, 10315, Germany

252 95: Cooperative Institute for Marine Ecosystem and Resources Studies, , National Oceanic

253 and Atmospheric Administration Pacific Marine Environmental Laboratory and Oregon State

254 University, 2030 SE Marine Science Drive, Newport, OR, 97365, USA

255 96: Department of Biology, University of Oxford, , Oxford, OX1 3SZ, United Kingdom

256 97: Bat Squad Lab, Department of Life Science, National Taiwan Normal University, No. 88,

257 Section 4, Tingzhou Rd, Wenshan District, Taipei City, 116, Taiwan

258 98: Nature based Solutions Initiative, Department of Biology, University of Oxford, 11A

259 Mansfield Road, Oxford, OX13SZ, England

260 99: Centre for Northern Forest Ecosystem Research, Ministry of Northern Development,

261 Mines, Natural Resources and Forestry, Ontario Government, 103 - 421 James Street

262 South, Thunder Bay, Ontario, P7E 2V6, Canada

263 100: Swiss Ornithological Institute, Seerose 1, Sempach, 6204, Switzerland

264 101: Zoological Research Museum Alexander Koenig, , Leibniz Institute for the Analysis of

265 Biodiversity Change, Adenauerallee 160, Bonn, 53113, Germany

266 102: Museum für Naturkunde, , Leibniz-Institut für Evolutions- und Biodiversitätsforschung,

267 Invalidenstraße 43, Berlin, 10115, Germany

268 103: , , , ,

269 104: Ecology and Nature Management, Geosciences and Natural Resource Management,

270 University of Copenhagen, Rolighedsvej 23, Frederiksberg, 1958, Denmark
271 105: Department of Agricultural Biology, Faculty of Agriculture, University of Ruhuna,

272 Mapalana, Kamburupitiya, x, Sri Lanka

273 106: Fundación de Conservación Jocotoco, Fundación de Conservación Jocotoco,

274 Fundación de Conservación Jocotoco, Valladolid N24-414 y Luis Cordero, Quito, 170525,

275 Ecuador

276 107: Landscape Conservation Programme, Conservation Division, BirdLife South Africa,

277 Gauteng, South Africa

278 108: Afromontane Research Unit, Department of Zoology and Entomology, University of the

279 Free State, Phuthaditjhaba, South Africa

280 109: Centre for Statistics in Ecology, the Environment and Conservation, Department of

281 Statistical Sciences, University of Cape Town, Cape Town, South Africa

282 110: Nature Coast Biological Station, Institute of Food and Agricultural Sciences, University

283 of Florida, 552 1st Street, Cedar Key, Florida, 32625, U.S.A.

284 111: Fisheries and Aquatic Sciences, Institute of Food and Agricultural Sciences, University

285 of Florida, 136 Newins-Ziegler Hall, Florida, 32611, U.S.A.

286 112: Sound Forest Lab, The Nelson Institute of Environmental Studies and The Department

287 of Forest and Wildlife Ecologz, University of Wisconsin-Madison, 1650 Linden Drive,

288 Wisconsin, 53706, U.S.A.

289 113: Nature Conservation Unit, Frederick University, 7, Yianni Frederickou Street, Nicosia,

290 1036, Cyprus

291 114: Madagascar Programme, Chances for Nature, Brauweg 9a, Göttingen, 37073,

292 Germany

293 115: Cologne Zoo, Riehler Straße 173, Köln, Lower Saxony, 50735, Germany

294 116: Biodiversity, Macroecology and Biogeography, Faculty of Forest Sciences and Forest

295 Ecology, University of Goettingen, Büsgenweg 1, Göttingen, Niedersachsen, 37077,

296 Germany

297 117: Plant Ecology, Institute of Plant Sciences, University of Bern, Altenbergrain 21, Bern,

298 Bern, 3013, Switzerland

299 118: Pacific Wildlife Research Center, Environment and Climate Change Canada, 5421

300 Robertson Road, Delta, British Columbia, V4K 3Y3, Canada

301 119: Migratory Bird Unit, Northern Region, Canadian Wildlife Service, Environment and

302 Climate Change Canada, 91780 Alaska Highway, Whitehorse, Yukon Territory, Y1A 5B7,

303 Canada

304 120: Lancaster Environment Centre, Lancaster University, Library Ave, Bailrigg, Lancaster,

305 LA1 4YQ, United Kingdom

306 121: Environmental Research and Innovation Centre, School of Science, Engineering and

307 Environment, University of Salford, Peel Building, Salford, M5 4WT, United Kingdom

308 122: School for Forest Management, Swedish University of Agricultural Sciences SLU, Box

309 43, Skinnskatteberg, SE-739 21, Sweden

310 123: Chair of Geobotany, Chair of Geobotany, University of Freiburg, Schaenzlestr. 1,

311 Freiburg im Breisgau, 79104, Germany

312 124: Fisheries Ecology and Conservation Lab, Department of Biology, University of Victoria,

313 3800 Finnerty Rd., Victoria, British Columbia, V8P 5C2, Canada

314 125: Department of Botany and Biodiversity Research, Faculty of Life Sciences, University of

315 Vienna, Rennweg 14, Vienna, 1030, Austria

316 126: School of Aquatic and Fishery Sciences, University of Washington, 1122 NE Boat

317 Street, Seattle, Washington, 98105, U.S.A.

318 127: Polar Ecology Group, Department of Vertebrate Ecology and Zoology, University of

319 Gdańsk, Poland, Wita Stwosza 59, Gdańsk, 80-308, Poland

320 128: Department of Ecology, University of Alicante, Cra. San Vicente del raspeig sn,

321 Alicante, 3690, Spain

322 129: Laboratório de Ecologia, Instituto de Biociências, Universidade Federal de Mato Grosso

323 do Sul, , , , , Brazil

324 130: Institute of Marine Science, University of Auckland, Private Bag 92019. Auckland Mail

325 Centre, Auckland, 1142, New Zealand
326 131: Department of Soil, Water, and Ecosystem Sciences, Institute of Food and Agricultural Sciences, University of Florida, 2181 McCarty Hall A, Florida, 32611, U.S.A.

327

328 132: Integrative Community Ecology Unit, , Okinawa Institute of Science and Technology

329 Graduate University, 1919-1 Tancha, Onna-son, Kunigami-gun, Okinawa, 904-0495, Japan

330 133: Salmonid fishes, Norwegian Institute for Nature Research, Høgskoleringen 9,

331 Trøndelag, Trondheim, 7034, Norway

332 134: Cold Climate Research Lab, School of Art, RMIT University, 124 La Trobe Street,

333 Melbourne, Victoria, 3000, Australia

334 135: Fisheries and Oceans Canada, Freshwater Institute, 501 University Crescent,

335 Winnipeg, Manitoba, R3T 2N6, Canada

336 136: Centre for Marine Science and Technology, , Curtin University, , Perth, 6102, Western

337 Australia

338 137: Fisheries Management Branch, Department of Forestry, Fisheries and the

339 Environment, , Foreshore, Cape Town, 8000, South Africa

340 138: Mammal Research Institute Whale Unit, Department of Zoology and Entomology,

341 University of Pretoria, Hatfield, Pretoria, 28, South Africa

342 139: State Key Laboratory of Vegetation and Environmental Change, Institute of Botany,

343 Chinese Academy of Sciences, , Beijing, 100093, China

344 140: Coastal Marine Field Station, School of Science, University of Waikato, 58 Cross Road,

345 Sulphur Point, Tauranga, 3110, New Zealand

346 141: Ocean Acoustics Group, , Alfred Wegener Institute Helmholtz Centre for Polar and

347 Marine Research, Klussmannstr. 3d, Bremerhaven, 27570, Germany

348 142: Department of Ecology, Universidad Autónoma de Madrid, Darwin 2, Madrid, 28049,

349 Spain

350 143: Centro de Investigación en Biodiversidad y Cambio Global, Universidad Autónoma de

351 Madrid, Darwin 2, Madrid, 28049, Spain

352 144: Wildlife Research Division, Environment and Climate Change Canada, 801-1550,

353 avenue d'Estimauville, Québec (Québec), G1J 0C3, Canada

354 145: Natural Resources & Environmental Studies, University of Northern British Columbia,

355 3333 University Way, British Columbia, V2N 4Z9, Canada

356 146: Asian Soundscape Monitoring Network, Biodiversity Research Center, Academia

357 Sinica, 128 Academia Road, Section 2, Nankang, Taipei, , 11529, Taiwan

358 147: Department of Ecology and Evolutionary Biology & K. Lisa Yang Center for

359 Conservation Bioacoustics, , Cornell University, Corson Mudd, 215 Tower Road, Ithaca,

360 New York, 14853, U.S.A.

361 148: Department of Biology, Whitman College, 345 Boyer AVe, Walla Walla, Washington,

362 99362, U.S.A.

363 149: Talarak Foundation, Inc., Negros Forest Park, Bacolod City, Negros Occidental, 6100,

364 Philippines

365 150: Department of Forest Ecosystems and Society, Oregon State University, 3180 SW

366 Jefferson Way, Corvallis, Oregon, 97331, U.S.A.

367 151: Centre for Research and Conservation, Royal Zoological Society of Antwerp, , , , ,

368 Belgium

369 152: Department of Physical Geography, Stockholm University, SE-106 91 Stockholm,

370 Stockholm, 10691, Sweden

371

372 C: corresponding authors

373

374 Abstract

375 The urgency for remote, reliable, and scalable biodiversity monitoring amidst mounting

376 human pressures on climate and ecosystems has sparked worldwide interest in Passive

377 Acoustic Monitoring (PAM), but there has been no comprehensive overview of its coverage
378 across realms. We present metadata from 358 datasets recorded since 1991 in and above
379 land and water constituting the first global synthesis of sampling coverage across spatial,
380 temporal, and ecological scales. We compiled summary statistics (sampling locations,
381 deployment schedules, focal taxa, and recording parameters) and used eleven case studies
382 to assess trends in biological, anthropogenic, and geophysical sounds. Terrestrial sampling
383 is spatially denser (42 sites/M·km²) than aquatic sampling (0.2 and 1.3 sites/M·km² in
384 oceans and freshwater) with only one subterranean dataset. Although diel and lunar cycles
385 are well-covered in all realms, only marine datasets (65%) comprehensively sample all
386 seasons. Across realms, biological sounds show contrasting diel activity, while declining with
387 distance from the equator and anthropogenic activity. PAM can thus inform phenology,
388 macroecology, and conservation studies, but representation can be improved by widening
389 terrestrial taxonomic breadth, expanding coverage in the high seas, and increasing spatio-
390 temporal replication in freshwater habitats. Overall, PAM shows considerable promise to
391 support global biodiversity monitoring efforts.

392
393

# Main text

394

# Introduction

395

Sounds permeate all realms on Earth — terrestrial, freshwater, marine, and subterranean¹.

396

Passive Acoustic Monitoring (PAM) captures soundscapes that document soniferous life

397

("biophony"), human activities ("anthropophony"), and some geophysical events

398

("geophony")²⁻⁶. In ecoacoustics and soundscape ecology⁷,⁸, PAM can measure levels and

399

impacts of global change (e.g., climate change, urbanization, deep-sea mining)⁹⁻¹¹, monitor

400

ecosystem health, recovery, and restoration¹²⁻¹⁴, assess human-environment interactions

401

(public health, cultural ecosystem services)¹⁵,¹⁶ and guide conservation (protected area

402

management, landscape planning)¹⁷,¹⁸.

403

Despite the wide-ranging and increasing soundscape sampling effort, the data distribution

404

remains undescribed. Currently soundscape-recording communities are only networked

405

within realms and their methodologies differ. Previous reviews focused on single realms and

406

were either systematic¹⁹⁻²² or qualitative³,²³. Marine scientist networks using PAM exist²⁴, but

407

the freshwater community is nascent, and the terrestrial community grows faster than it can

408

unite. Acoustic calibration and sound propagation modeling are advanced in aquatic

409

studies²⁵ but seldom considered in terrestrial ones (except ²⁶,²⁷). Artificial intelligence can

410

identify increasing numbers of species on land²⁸ whereas aquatic sounds still challenge

411

identification²⁹,³⁰. Overall, there is much to gain from sharing data, experience, and methods

412

among PAM users.

413

Cross-realm PAM studies can yield new theoretical answers³¹ and applied solutions:

414

soundscapes track terrestrial and marine resilience to natural disasters³²; sound and silence

415

durations in multiple realms follow universal distributions³³. Transnational sampling could

416

form the basis for comprehensive soniferous biodiversity monitoring, just like community-

417

initiated telemetry databases³⁴, collaborative camera trap surveys³⁵, individual animal

418

observation networks³⁶, and invasive species control syntheses³⁷ advanced entire research

419

fields. A global PAM network could establish historical biodiversity baselines, support

420

systematic long-term and large-scale monitoring, and connect with the public through citizen

421

science. Such information is critical to inform global biodiversity policies such as the

422

Kunming-Montreal Global Biodiversity Framework.

423

We present the “Worldwide Soundscapes” project, the first global PAM meta-database and

424

network. We use it to quantify the known state of PAM efforts, highlight apparent sampling

425

gaps and biases, illustrate the potential of cross-realm PAM syntheses for research, and

426

federate PAM users. The project currently holds 307 contributors who collated metadata

427

about 358 passively-recorded, replicated soundscape datasets. Metadata describe the exact

428

spatio-temporal coverage, sampled ecosystems (IUCN Global Ecosystem Typology: GET),

429

transmission medium (air, water, or soil), focal taxa (IUCN Red list), recording settings, and

430

data and publication availability. We inferred coverage within administrative (Global

431

Administrative Database: GADM; International Hydrographic Organisation: IHO) and

432

protected areas (World Database on Protected Areas: WDPA) from geographic locations.

433

We selected recordings referenced in the meta-database to quantify soundscape

434

components (biophony, anthropophony, geophony) across eleven ecosystems from all

435

realms. We showcase their relevance to macroecology, conservation biology, and phenology

436

research and identify opportunities to advance the global PAM network. The publicly-
437 accessible meta-database³⁸,³⁹ continues to grow to enhance accessibility of data and
438 remains open for metadata contributions, facilitating future syntheses.

439 **Results**

440 **Summary dataset statistics**

441 To date, 358 validated soundscape meta-datasets (hereafter "datasets") have been
442 registered in our database from across the globe, dating back to 1991 (Fig. 1D). A dataset
443 gathers a team's metadata on a study or project. Metadata were validated by checking that
444 all fields were filled in and formatted correctly, and then cross-checked by their respective
445 contributors using site maps and recording timelines³⁹ on ecoSound-web⁴⁰. Based on the
446 GET definition of four 'core' realms, our database includes 259, 80, 18, and 1 validated
447 datasets from the terrestrial, marine, freshwater, and subterranean realms, respectively. The
448 transmission medium was air for terrestrial and subterranean datasets and mostly water for
449 aquatic datasets, as three datasets with aerial above-water recordings were included in the
450 freshwater and marine realms. The majority of datasets (84%) include both spatial and
451 temporal replicates (Fig. 1C). Few datasets have openly-accessible recordings (10-15%, Fig.
452 S1). Presently, few terrestrial and freshwater datasets (27% and 22% respectively) are
453 associated with DOI-referenced publications in contrast to marine datasets (44%) (Fig. S1).

454 **Spatial sampling coverage and density**

455 The database contains 11 093 sampling sites, including 147 polar, 8 455 temperate, and 2
456 491 tropical sites (Fig 1C). On land, 10 387 sites are located within 79 (out of 263) GADM
457 level 0 areas (Fig. 2B), primarily in the Northern Hemisphere (Table S1). Most terrestrial
458 sites occur in Canada (28%), followed by the United States (18%), but a significant
459 proportion is widely distributed (23% do not belong to the top 10 GADM areas). Few
460 terrestrial sites (8%) are located in WDPA category Ia, Ib, or II areas. Our database currently
461 lacks data from vast areas in Russia, Greenland, the Antarctic, interior Australia, North
462 Africa, and Central Asia. Site elevations range from sea level up to 3 420 m (Fig. 2A), but
463 mountains above 4 000 m in the Northern Hemisphere, above 2 000 m in the Southern
464 Hemisphere, as well as the Transantarctic Mountains are currently not represented in the
465 data. At sea, 469 sites are located within 32 (out of 101) IHO sea areas. Most marine sites
466 occur in the North Pacific Ocean (22%), followed by Southeast Alaskan and British
467 Columbian coastal waters (17%), but a significant proportion is widespread (17% do not
468 belong to the top 10 IHO areas). Many sites are situated in WDPA high-protection category
469 Ia and II areas (14%). Our database currently lacks datasets from Arctic waters off Eurasia,
470 the Southeast Pacific, and Southeast Asian coastal areas. Sampling sites span ocean
471 depths from sea water surface to depths of 10 090 m, but tropical bathypelagic and Southern
472 benthic areas are poorly represented (Table S2). Few GADM areas (11) are represented in
473 the freshwater datasets. Spain holds most freshwater sites (56%). Few freshwater sites (2%)
474 are in WDPA category II or Ib areas. Freshwater bodies are sampled at elevations from sea
475 level up to 950 m. Mountain freshwater bodies and those in Africa, Asia, and Oceania are
476 currently not represented in our database. The database contains seven subterranean sites
477 situated in Brazil between 277 and 810 m.

478 **Temporal sampling extent, coverage, and density**

479 We compare sampling coverage (in sampled years summed over sites) across time windows
480 of the diel cycle (dawn, dusk, day, night), lunar phases (bright and dark), and the seasons
(spring, summer, autumn, winter - only for temperate sites) (Fig. 3A). Dawn and dusk diel windows are shorter than day and night diel windows for most locations and less intensively sampled. The lunar phase cycle is evenly covered across realms and comprehensively covered within datasets (Fig. S2). In the terrestrial realm, daytime coverage surpasses nighttime coverage (611 vs. 398 years), while 75% of datasets sampled all diel time windows. Terrestrial datasets mostly sampled spring (415 years, 32%) and summer (564 years, 44%) while 24% sampled all seasons. Temporal coverage per site is highest in Japan (272 days) and Taiwan (250 days). In the marine realm, temporal coverage among diel time windows is even and 88% of marine datasets sampled all diel time windows. Marine datasets have high and similar coverage for winter and spring (117 and 122 years, combined 59% of seasonal coverage) and 65% cover the full seasonal cycle. In the freshwater realm, temporal coverage among diel time windows is even and 76% of freshwater datasets sampled all diel time windows. Similarly, 24% sampled all seasons. The subterranean tropical sites primarily covered the nighttime, across all seasons.

## Sampling in ecosystems

Our database includes 76 of the 110 GET functional groups and all biomes except anthropogenic shorelines and most subterranean biomes (Table S2). Sampling intensity differed across ecosystem levels - realms, biomes, and functional groups, with transitional realms representing the interface between core realms (Fig. 4A). The terrestrial realm has the third-largest extent and the highest spatial sampling density among realms (42 sites per Mkm²), but temporal coverage is comparatively low (17% sampled out of mean extent per site: 155 days). The most commonly sampled biome is temperate-boreal forests and woodlands (59% of sites). The marine realm is the most extensive and spatial sampling density is the lowest (0.2 sites per Mkm²), but temporal sampling extent and coverage are the highest among all realms (69% out of 497 days sampled). The most commonly sampled biomes are the marine shelf and pelagic ocean waters (43% and 42% of sites respectively). The freshwater realm has low spatial sampling densities (1.3 sites per Mkm²) and high temporal sampling densities (30% out of 115 days sampled). Rivers and streams are most commonly sampled (47% of sites). The terrestrial-freshwater realm, representing 81% of the area of non-subterranean realms, has the third-highest spatial sampling density (7.9 sites per Mkm²) and similar temporal sampling density to the terrestrial realm (16% days sampled out of 230). The subterranean realm, though second-largest, includes seven tropical sites in aerobic caves from one dataset.

## Target taxa and frequency ranges

Most marine datasets do not target specific taxa (70%) and use wide frequency ranges from 0.01 to 30 kHz (mean bounds of frequency ranges across datasets, Fig. 4B). Marine datasets that focus on single taxa comprise cetaceans (8%, 0.006 - 7 kHz) and fish (8%, 0.003 - 29 kHz). Similarly, most freshwater datasets are taxonomically unspecific (66%) and cover frequencies from 1 to 22 kHz. Some datasets (20%) focus on ray-finned fish, covering frequencies from 0.001 to 23 kHz. In contrast, terrestrial datasets mostly target single taxa and have narrow frequency ranges. Bird-focused datasets are most common (44%), spanning frequencies from 0.06 to 21 kHz, while bat-focused datasets are next (12%) and range from 5 to 139 kHz. Taxonomically unspecific datasets account for 24% of datasets, covering a broad range from 0.1 to 24 kHz. Generally, datasets targeting multiple taxa use wider frequency ranges than those targeting single taxa.
## 526 Soundscape case studies

527 We analysed 150 recordings from eleven different ecosystems representing the diversity of soundscapes on Earth, spanning latitudes from 69 degrees South to 67 degrees North (Table S3, Fig. 5A). Biophony dominated with an average soundscape occupation of 30% across all ecosystems, notably: photic coral reefs (Okinawa, Japan) with snapping shrimps and grunting fish choruses (75% soundscape occupancy)⁴¹; tropical lowland rainforests (Jambi, Indonesia) with buzzing insects and echoing bird and primate songs (61%). Only marine island slopes (off Sanriku, Japan) and polar outcrops (Antarctic) contained no or very little (2%) biophony, respectively. Geophony was absent in most soundscape samples, with the exception of high wind noise in polar outcrops (14%) and some wind in montane tropical forests (5%). Anthropophony occupied on average 9% of the soundscapes. Cities (Jambi, Indonesia; Montreal, Canada) exhibited the highest anthropophony (45%) with prevalent engine noise and human voices, while deep-sea mining and vessel communication caused high anthropophony in marine island slopes (32%)⁴². Silence occupancy was highest in polar outcrops (82%) and large lowland rivers (78%).

541 The selected soundscapes reveal greater biological activity closer to the equator, a negative relationship between biophony and anthropophony, and variable phenology of soniferous organisms over the diel cycle (Fig. 5). All Bayesian beta regression models for biophony occupancy converged. We detected a negative correlation of biophony occupancy with increasing distance from the equator ($P_{negative}=1$) and with anthropophony occupancy ($P_{negative}=1$). The phenology model predicted biophony occupancy values for each diel time window and realm (Fig. 5B), revealing similar phenology for the terrestrial and marine realm, and opposed freshwater and freshwater-marine realm phenology.

## 549 Discussion

550 The "Worldwide Soundscapes" project has — to our knowledge — assembled the first global meta-database of PAM datasets across realms. We analysed its current content to quantify sampling extent, coverage, and density across spatiotemporal and ecological scales. We annotated soundscapes from eleven ecosystems to investigate macroecological, conservation biology, and phenological trends. The database remains open for contributions³⁹ and can be openly accessed to source datasets and initiate collaborative studies³⁸. Next, we discuss the state and potential of PAM globally (Table 1).

557 Our results likely represent global PAM trends, even though spatial gaps reflect the background of the project contributors. Our database still misses some national programs (e.g., Australian Acoustic Observatory⁴³), but otherwise our terrestrial coverage is similar to a recent systematic review²¹. The gaps in North Africa and Northeastern Europe correspond with the paucity of bioacoustic datasets for these regions⁴⁴. Our database comprises 469 marine sampling locations while 991 were found in a recent systematic review²⁰, but most overlap (Fig S2). Marine tropical waters that are under-represented in our database reflect gaps found in the International Quiet Ocean Experiment network coverage⁴⁵. Our marine and terrestrial database coverage is thus broadly comparable with published data. However, as the database originates from an active network of researchers, it represents the current availability of mostly as-yet unpublished data (Fig. S1). To our knowledge, no other spatially-explicit review of freshwater sampling exists for comparison, and no other work quantified temporal coverage.
570 Geographic coverage strikingly differs between realms: marine coverage is sparse but 571 widespread; terrestrial coverage is comparatively intensive in the Americas and Western 572 Europe; freshwater coverage is scattered. This partly reflects funding priorities for where 573 conservation and active management priorities lie, for instance in biodiversity hotspots and 574 densely-populated areas. Technical limits in extreme environments also drive geographic 575 patterns: high latitudes and elevations entail extremely cold temperatures that present 576 challenges for operation and maintenance, some of which can be solved with robust power 577 setups (solar panels, freeze-resistant batteries). Marine deployments are generally even 578 more constrained due to costly and demanding underwater work, but some deployments 579 reach the poles as water temperatures are buffered below the freezing point. Affordable 580 underwater recorders⁴⁶ may help to intensify sampling of marine coastal areas and close 581 gaps in freshwater coverage. By contrast, terrestrial monitoring is generally straightforward. 582 Northern temperate areas outside of Northeastern Europe are comparatively better covered, 583 and the tropics outside of Africa better represented. It appears that gaps in North Africa, 584 Central Asia, and Northeastern Europe arise from differential research means and priorities 585 between countries. Taken together, these gaps help to correct spatial biases⁴⁷ and to identify 586 high-priority, unique research areas that should be included in global assessments. 587

587 Currently, only marine studies achieve relatively even coverage of temporal cycles. Indeed, 588 offshore deployments — especially in the deep sea — are expensive and limited duration 589 deployments are not cost-effective⁴⁸. Marine soundscapes fluctuate stochastically⁴⁹, but the 590 ocean buffers water temperatures so that animals retain a basal activity level year-round. 591 Although lunar phases affect marine life⁵⁰⁻⁵², we did not consider lunar tides, which affect 592 some ecosystems. In the terrestrial and freshwater realms, most deployments cover the 593 entire diel cycle but monitoring on land focuses either on diurnal birds or on nocturnal bats, 594 as found in the literature²¹. In contrast, although seasons drive activity cycles on land too⁵³,⁵⁴, 595 spring- and summertime monitoring is disproportionately common and we lack a thorough 596 understanding of seasonal dynamics. Terrestrial deployments in particular may be short for 597 logistic reasons: in the cold, batteries struggle and access is harder; recorders are at risk of 598 theft; and limited numbers may cycle between sites⁵⁵. Lunar phases — probably evenly 599 sampled by chance — also influence land animals⁵⁶ but should be explicitly considered. 600 Overall, we encourage longer-duration setups with regularly-spread sampling inside 601 temporal cycles to alleviate the higher expenses, energy consumption, storage, and traded- 602 off spatial coverage⁵⁷. Global changes impact soundscapes in largely unpredictable ways 603 through changing species distributions and phenology, necessitating higher and unbiased 604 coverage across multiple time scales — including inter-annual ones — to successfully 605 monitor ongoing changes⁵⁸. 606

606 Re-use of soundscape datasets is restricted by their taxonomic focus. Admittedly, 607 taxonomically untargeted, long, and regular deployments in oceans, coupled to large 608 detection ranges, concurrently sample many taxa⁵⁹. However, many soundscape recordings 609 sample particular frequencies, often in the human-audible range⁶⁰, although biophony ranges 610 from infrasound⁶¹ to ultrasound⁶². For instance, studies of toothed whales or bats often use 611 triggers and high-pass filters to record purely ultrasonic recordings only when signals are 612 detected, resulting in spectrally-restricted and temporally-biased soundscape recordings. 613 Less-studied taxa such as anurans and insects could effectively be co-sampled by adjusting 614 ongoing deployments, so we encourage terrestrial researchers to maximise frequency 615 ranges to enhance interdisciplinary collaboration. These collaborations can help to mutualise
616 resources for mitigating potentially prohibitive power, storage, transportation, and post- processing costs⁶³. Emerging embedded-Al audio detectors may offer an alternative⁶⁴, but 617 soundscape recordings will remain essential for broader application. 618

619 In every realm, ecosystems await acoustic discovery. Except for one dataset from aerobic 620 caves, we lack data from all subterranean realms (anthropogenic voids, ground streams, sea 621 caves), while endolithic systems may be the only irrelevant ecosystem for biophony. Access 622 is usually challenging or restricted for non-specialists, but subterranean biodiversity shows 623 high spatial turnover⁶⁵. Freshwater datasets were less rare, but several ones with 624 unreplicated sampling could not be included. Temporary, dynamic water bodies (seasonal, 625 episodic, and ephemeral ecosystems) are not yet well-studied⁶⁶ although they are accessible 626 from land. Notably, sounds can pass the boundary between recording media and be 627 captured in so-called holo-soundscapes in freshwater and shallow coastal areas⁵,⁶⁷,⁶⁸. 628 Advances are imminent as the freshwater acoustic research community is growing 629 rapidly⁶⁹,⁷⁰. In the oceans, sound propagates far and multiple biomes can be sampled at once 630 (e.g., recorders on the seafloor sample pelagic waters) so that most ecosystems are 631 covered. Still, coverage gaps may exist in rhodolith/Maërl beds, upwelling zones, and 632 deepwater coastal inlets. On land, sampling is biased towards biodiverse forests, and our 633 database coverage suggests that the most challenging terrestrial ecosystems, such as arid 634 zones (sclerophyll hot deserts and semi-deserts, semi-desert steppes), rocky habitats 635 (young rocky pavements, lava flows and screes) but also some vegetated temperate 636 ecosystems (cool temperate heathlands, temperate pyric humid forests, temperate 637 subhumid grasslands) are poorly sampled. Within the IUCN GET framework, soil 638 soundscapes also belong to the terrestrial realm and to date no eligible datasets are in our 639 database, despite recent studies⁷¹,⁷².

640 Our database highlights well-known global sampling biases⁷³ which could be resolved with 641 collaboration to remove cultural and socioeconomic barriers⁷⁴. Technological progress for 642 more affordable equipment renders PAM more accessible in lower-income countries. 643 However, high- and deep-sea work remains considerably more expensive, and tropical 644 developing countries in particular often lack funding for marine programmes requiring large 645 vessels, underwater vehicles, or cabled stations on the seafloor⁴⁸. Our network currently 646 consists of active, English-speaking members from 50 countries. Collaborative projects, 647 shared sea missions, and equipment loans should promote the establishment of soundscape 648 research communities⁷⁵. Increased international collaboration with scientists and local 649 stakeholders supporting citizen-science⁷⁶ in heavily underrepresented regions would improve 650 not only data coverage, but also representation and dialogue within the field.

651 Collaborative soundscape research relies on interoperable data. We harmonised metadata 652 with a bottom-up approach leading to our global inventory, but comprehensive standards for 653 PAM do not currently exist, even though initiatives for the marine realm are ongoing⁷⁷,⁷⁸. Few 654 affordable solutions exist for sharing large audio data volumes⁴⁰, underlining the need for 655 distributed soundscape recording repositories⁷⁹. Marine oil and gas industry projects 656 routinely upload data as part of their efforts to mitigate noise impacts on marine animals⁸⁰, 657 but these recordings often focus on frequencies relevant to seismic prospecting and access 658 may be restricted⁸¹,⁸². Furthermore, recording equipment requires calibration, sound 659 detection spaces need to be measured²⁶, and data privacy must be ensured on land⁸³. In 660 parallel, species sound libraries⁴⁴,⁸⁴⁻⁸⁶ grow and continue to provide invaluable acoustic and 661 taxonomic references. International organisations such as GBIF will be key to roll-out
662 standards (e.g., Darwin Core) in a top-down manner. For the moment, we encourage early
663 planning of data archival. In the future, the Worldwide Soundscapes will interoperate with
664 other databases to close remaining coverage gaps.

665 A unified approach of ecology with PAM is now possible. More comprehensive coverage is
666 needed to decisively answer research, conservation, and management questions that PAM
667 can address, so we encourage prospective contributors to archive their metadata and join
668 our inclusive project. We show that a large portion of the PAM community is willing to
669 collaborate across realms and form a global network. We advocate for a bolder PAM effort to
670 inform the agenda of soundscape ecology<sup>87</sup>, reaching out to places where no sound has
671 been recorded before as well as to urban settings<sup>88</sup>. The research community may open new
672 avenues to study environmental effects on acoustic activity<sup>58</sup>, social species interactions<sup>89</sup>,
673 human-wildlife relationships<sup>41</sup>, function and phylogeny<sup>90</sup>, soundscape effects on human
674 health<sup>91</sup>, acoustic adaptation and niche hypotheses<sup>92,93</sup>, macroecological patterns across
675 ecosystems<sup>1</sup>, and initiate an integrated approach to noise impacts on wildlife.

676 PAM is now an established method that can be applied over large spatial and temporal
677 scales. Consistent, large-scale monitoring of the Earth's soundscapes is essential to
678 establish baselines for historical trends<sup>94</sup> and quantify rapid changes in biodiversity and
679 natural systems. International funding schemes should integrate PAM into biodiversity
680 monitoring platforms such as GBIF<sup>95</sup> and GEO BON<sup>96,97</sup>. Soundscapes are just starting to be
681 used in legislation as an ecosystem feature to be preserved<sup>98</sup>. Occupancy maps for
682 soniferous wildlife obtained from PAM would underpin the evaluation of progress towards
683 threat reduction and ecosystem service provision of the Kunming-Montreal Global
684 Biodiversity Framework<sup>99</sup>. By building collaborations around the knowledge frontiers
685 identified here, we can aim to comprehensively describe and understand the acoustic make-
686 up of the planet.

687
688 Methods

689 Ethics & Inclusion statement

690 The present study has involved people who carried out PAM-based studies as primary
691 contributors. They could be corresponding authors for published studies, referred contacts
692 for unpublished studies, or principal investigators, and were asked to identify further primary
693 and secondary contributors of their study. Primary contributors could become co-authors,
694 and secondary contributors are acknowledged here. Some primary contributors were invited
695 as co-leads to be responsible for particular realms or biomes and are listed in the first-tier
696 authors list. Primary contributors who additionally provided soundscape recordings are also
697 listed in the first-tier authors list. All primary contributors were asked to identify further
698 contacts to reach a comprehensive coverage for the database. Given the nature of the study
699 (using passive acoustic monitoring methods), the research did not suffer from restrictions or
700 prohibitions in the setting of the researchers. Necessary permits from landowners or
701 environmental protection agencies, precautions to limit biological contaminations (e.g.,
702 biofilm on marine recorders) or for animal welfare, and local ethics committee reviews were
703 the responsibility of the respective primary contributors. None of the PAM activities resulted
704 in personal risk to participants; neither did they involve health, safety, security or other risk to
705 researchers. Local and regional research relevant to our study were taken into account in
706 citations throughout the main text.

707 Database construction

708 The database construction started in August 2021 within the frame of the Worldwide
709 Soundscapes project³⁸ using collaborative, peer-driven metadata collation. It represents the
710 current state of knowledge about publicly accessible meta-datasets for PAM within our
711 network. We started contacting contributors that we personally know, and co-lead authors
712 helped to contact potential contributors in the respective ecosystem types of their expertise
713 (i.e. terrestrial, urban, freshwater, and marine). We conducted focal publication searches to
714 actively plug coverage gaps by inviting the respective corresponding authors. We posted the
715 call for contributors on specialised ecoacoustics platforms and social media, and will keep
716 the project open for any contributor owning suitable soundscape recordings beyond the
717 present publication. We communicated mostly in English but also in Spanish, French,
718 Portuguese, German, Russian, and Chinese to gather metadata. We ended up including
719 metadata from larger groups such as the Silent Cities project, Ocean Networks Canada, the
720 Australian Acoustic Observatory, and Parks Canada.

721 Primary contributors provided the metadata that formed the basis for the database. Their
722 willingness to be informed through a mailing list, responsibility for their metadata, approval
723 for sharing the meta-data publicly, and willingness to participate in this study as co-authors
724 were explicitly stated in an online form-based collaboration agreement. Primary contributors
725 who became co-authors all fulfilled either data curation (e.g., as providers of structured
726 metadata) or project administration (e.g., as principal investigators designing the
727 corresponding study) roles and additionally a manuscript revision role. Primary contributors
728 cross-validated their metadata using maps and graphical timelines visualising their input. As
729 a result, apart from basic coherence checks conducted by the co-leads and research
730 assistants, the responsibility of the database content is borne by their contributors. The
731 database information page is integrated in our online collaborative ecoacoustics platform

732 ecoSound-web¹⁰⁰ that also hosts the soundscape recordings of the case study

733 (https://ecosound-web.de/ecosound_web/collection/show/49).

734 Our network consists of people reachable through email contact. Out of 588 contacted

735 contributors potentially involved in PAM, 298 (49%) provided some metadata. More datasets

736 exist but 24% of those contacted have not yet responded. This may be due to limitations in

737 sharing research data or, newcomers to PAM, or out-dated contact details. Older

738 soundscape datasets may be stored on analog media that are inaccessible over the internet,

739 but these are more often attended recordings⁵³ — thus not fulfilling our inclusion criteria (see

740 below). The missing share of datasets could be estimated with a systematic literature

741 search, but we posit that our dataset search was exhaustive, and we were able to fill gaps

742 more effectively due to focused calls for missing datasets in our large group of contributors.

743 Potential soundscape recording datasets were required to meet four criteria: 1) stationary —

744 mobile recorders would have complex, constantly changing spatial assignments, thus we

745 excluded recordings from cars, transect walks, or towed deployments; 2) passive —

746 obtained from automated, unattended recorders, without human presence near the acoustic

747 sensor; 3) ambient — with no particular recording direction or temporal selection, as

748 obtained from omnidirectional microphones and with non-triggered recordings; 4) from

749 spatially or temporally replicated study designs (Fig. 1). Datasets with spatial and temporal

750 replication are necessary to disentangle spatial and temporal effects from other soundscape

751 determinants. We considered datasets to have spatial replicates when several sites were

752 sampled simultaneously, and temporally replicated when one site was sampled over multiple

753 days at the same time of day. We chose sampling sites and days as the most elemental

754 units for defining replication in our unified analysis. However, we acknowledge that replicates

755 are defined differently in each study: for instance, sampling sites may only be spatial

756 replicates if they belong to the same category (i.e. habitat, management type). Temporal

757 replicates could also be defined at the scale of other solar and lunar cycles (e.g., multiple full

758 moons). Taken together, our requirements homogenise the dataset to enable statistical

759 analyses across datasets and future collaborative syntheses.

760 **Time and space**

761 Soundscapes are fundamentally determined by their time and location. Wildlife and human

762 activities are broadly determined by solar and lunar cycles³²,⁵⁴,⁵⁶,¹⁰¹, and geographical

763 positions on the planet relative to the poles or equator, or the land and water surface. For all

764 datasets, we determined their spatial coverage in terms of number of sites, and sampling

765 density as the number of sites within the realms' areal extent (as determined by IUCN GET

766 maps). Although spatial extent could be defined as the area bounded by the sites of a

767 dataset, challenges with calculating extents on the world sphere (especially for datasets with

768 very large extents spanning the globe), and unknown sampling areas covered in each site

769 led us to ignore spatial extents. Until sound detection spaces can be accurately measured,

770 our measure of spatial sampling density using points per area is thus provisional. Indeed,

771 acoustic sampling areas or volumes are extremely rarely measured in terrestrial sites and

772 seldom measured but sometimes simulated in marine environments¹⁰². They vary with sound

773 source intensity, frequency, directivity; recording medium temperature, currents, pressure,

774 humidity (for air); habitat structure such as topography and vegetation structure; ambient

775 sound level. Generally, detection ranges are greater in underwater environments due to the
776 higher density of the recording medium. For all datasets, we also determined their temporal
777 extent (i.e., the time range from the start of the first to the end of the last recording),
778 coverage (i.e., time sampled per site), and density (i.e., proportion of recorded time inside
779 the temporal extent).

780 We quantified the latitudinal and topographical coverage across realms by collecting latitude,
781 longitude and topography (above and below the sea level) data for each site. Contributors
782 could provide self-measured topography values for sites on land and gaps were filled in
783 using General Bathymetric Chart of the Oceans surface elevation data. For underwater sites,
784 depth values (below sea level for marine sites, below the surface for freshwater) were
785 provided by the contributors. We assigned sampling sites to administrative areas (GADM
786 divisions for freshwater and land, IHO for sea areas) and extracted their WDPA category.
787 Sites' climates were geographically classified into tropical (between -23.5° and 23.5°
788 latitude), polar (below -66.5° or above 6.5° latitude), and temperate (between polar and
789 tropical regions).

790 Contributors were asked to specify the timing of their deployments using start and end dates
791 and times, operation modes (continuous, scheduled, periodical). For scheduled operation
792 schedules (daily start and end times or durations) were required, and duty cycles were
793 required for periodical operation. Duty cycles could additionally be indicated for scheduled
794 deployments. Deployments were assigned to single sampling sites. Subsequently added
795 sites and sampling repetitions were considered as additional deployments within datasets. A
796 temporal framework was used to quantify sampling coverage in three solar and lunar cycles.
797 Temporal information was inferred from the timing of sound recordings relative to time
798 events that structure life and meteorological events on Earth (Fig. 3). Seasonal cycles were
799 inferred only for temperate sites, by splitting the year cycle into four meteorological seasons
800 (winter: December-February, spring: March-May, summer: June-August, fall: September-
801 November). The daily cycle was split into four windows delimiting dawn (from astronomical
802 dawn start at -18° solar altitude until 18° solar altitude), day, dusk (from 18° solar altitude to
803 astronomical dusk end at -18° solar altitude), and night. The lunar illumination cycle was split
804 into two time windows centred on the full and new moon phases. It follows that the extrema
805 and ecotones in the temporal cycles define the relevant sampling time windows, noting that
806 in temperate zones, the equinoxes roughly correspond to thermal ecotones. Seasonal cycles
807 in tropical and polar regions arising from precipitation patterns were not considered in this
808 analysis.

## 809 Ecological characterisation

810 We assigned individual sampling locations to ecosystem types following the IUCN GET
811 (https://global-ecosystems.org). Correspondingly, sites were assigned hierarchically to
812 realms (core or transitional ones), biomes, and functional groups. We calculated the major
813 and minor occurrence areas of all functional groups based on ecosystem maps¹,¹⁰³,¹⁰⁴. We
814 used these data to quantify spatio-temporal extent, coverage, and sampling density within
815 realms and biomes, and to identify sampling gaps. To identify which taxa acoustic
816 deployments were designed for, deployments were linked to IUCN taxa (class, order, family,
817 or genus) when applicable — some datasets were collected without taxonomic focus. We
818 use these data to depict the acoustic frequency ranges depending on the target taxon.
819 **Acoustic frequency ranges**

820 Sound recordings store a representation of the original soundscape, so we needed to
821 determine the spectral scope of the recording datasets. Microphones have variable
822 frequency responses, and for digital recorders the spectral scope of the recording is limited
823 by the sampling frequency. Contributors were asked to provide the audio parameters of their
824 deployments (sampling rate, high-pass filter) as well as auxiliary metadata about the
825 microphone and recorder model and brands. The frequency range is additionally determined
826 by microphone roll-off frequencies at the low end and in general, by their frequency
827 response, but we could not gather these detailed, often unpublished data for the high
828 number of equipment types used here.

829 **Soundscape case studies**

830 To illustrate how the database can be used for macroecology, conservation biology, and
831 phenology analyses, we selected 121 recordings across a variety of topographical,
832 latitudinal, and anthropisation conditions — all fundamental gradients of assembly filters in
833 both terrestrial and marine realms¹ — belonging to 10 functional groups. We aimed to select
834 datasets containing four spatial replicates within the same functional group, with 10-minute
835 recordings spanning all four diel time windows from the same date during the biologically
836 active season, but the available data did not always allow this (Table S2). We extracted
837 recordings starting at sunrise, solar noon, sunset, and solar midnight. Recordings covered
838 the audible frequency range with sampling frequencies of at least 44.1 kHz in single or dual
839 channel configuration. We acknowledge that this targeted, non-systematic selection is not
840 statistically representative of global patterns but rather illustrative of the database potential.

841 Each recording's spectrogram (i.e., visualisation of sound intensity along time and frequency
842 axes) was annotated with the three fundamental soundscape components: biophony,
843 anthropophony, and geophony. Soundscape recordings were uploaded to ecoSound-web
844 (https://ecosound-web.de/ecosound_web/collection/show/49)⁴⁰ for annotation: KD listened to
845 them and visually inspected the spectrograms (Fast Fourier Transform window size of 1 024)
846 at a visual density of 1 116 pixels per 10 minutes to create annotations. Annotations were
847 rectangular tags on the spectrogram, encompassing only the annotated sound, with defined
848 coordinates in the time and frequency dimensions. Annotations of different soundscape
849 components could overlap if they were simultaneously visible or audible. Annotations of the
850 same soundscape component were adjacent and non-overlapping to avoid double-counting.
851 Soundscape components occurring above 22.05 kHz were excluded from the analysis as we
852 focused on audible frequencies only, which were the frequencies targeted by most
853 recordings. Sounds caused by microphone or recorder self-noise were excluded. All
854 annotations were reviewed by the recording creators using the peer-review mode on
855 ecoSound-web, and only accepted ones were used in the analysis. The annotations were
856 exported and acoustic space occupancy for each soundscape component in each recording
857 was calculated as the proportion of the spectro-temporal space⁶⁰ (i.e., duration multiplied by
858 frequency range, divided by total area of spectrogram; range: 0-1).

859 All Bayesian beta regression models (4 chains of 1000 sampling iterations with 1000
860 warmup iterations) for biophony occupancy converged as determined by trace plots and R
861 hat values equaling 1. The models using latitude and anthropophony as predictors included
862 the functional group as a random intercept. The phenology model used diel time windows

863 and realms, as well as their interactions, as predictors.

864
Display items

865

866 Table 1: An agenda towards comprehensive coverage and a global PAM network

<table><thead><tr><td>Aims</td><td>Opportunities</td></tr></thead><tbody><tr><td>Increasing spatial and ecosystemic coverage</td><td><ul><li>Affordable underwater recorders</li><li>Autonomous setups for extreme environments on land</li><li>Deploy in subterranean realm</li><li>Deploy in dynamic freshwater bodies</li><li>Deploy at high latitude and elevation on land</li></ul></td></tr><tr><td>Increasing temporal and taxonomic coverage</td><td><ul><li>Longer deployments with duty cycles</li><li>Cover all seasons in freshwater and on land</li><li>Scale up to inter-annual cycles</li><li>Higher sampling frequencies</li><li>Disabling high-pass filters and triggers</li></ul></td></tr><tr><td>Increasing collaboration</td><td><ul><li>Work interdisciplinarily with speleologists, urban ecologists, soil and deep-sea benthic scientists</li><li>Foster international missions to work at large scales</li><li>Collaborate, invest, and fund work with under-represented countries</li><li>Disseminate results in more languages</li></ul></td></tr><tr><td>Interoperability</td><td><ul><li>Calibrate equipment and consider detection ranges</li><li>Build accessible and sustainable data repositories</li><li>Design and adopt standards</li><li>Integrate PAM into biodiversity and remote sensing databases</li></ul></td></tr></tbody></table>

867

868 **Figure 1: Overview of Worldwide Soundscapes meta-database:** A) Framework used to define spatial and temporal replicates. B) Number of datasets in each core realm for the different replication levels. C) Spatial extent and coverage, based on sampling sites, split by core realm. Due to their higher representation and to avoid overlapping site clusters, terrestrial site densities were plotted on a 3 degree resolution raster (Interactive map: https://ecosound-web.de/ecosound_web/collection/index/106). D) Temporal extent and coverage, based on recorded days, split by core realm. An enlarged version of panel D without terrestrial sites can be found in Fig S3. For panels C and D, Sites from transitional realms were assigned to their parent core realm.

877

878 **Figure 2: Spatial distribution of sampling sites.** A) Latitudinal and topographic distribution of sampling sites across core realms. Due to their higher representation and to avoid overlapping site clusters, terrestrial sites are shown with transparency. The minimum (deepest seafloor) and maximum (highest elevation of land or sea level) topographical limits (dark grey lines) are shown against latitude, based on General Bathymetric Chart of the Oceans data¹⁰⁵. Minimum topography above the sea level and maximum topography under the sea level were set to zero as the sea level represents the minimum and maximum in these cases. B) Number of sampling sites within different administrative regions (GADM
886 level 0 and IHO sea areas), split by core realm, across WDPA categories (Ia: strict nature
887 reserve; Ib: wilderness area, II: national park). The areas that do not belong to the top 10 in
888 terms of datasets have been aggregated under "others". One subterranean site in Brazil is
889 not shown. Sites from transitional realms were assigned to their parent core realm.

890

891 **Figure 3: Temporal sampling distribution.** A) Temporal sampling coverage across solar
892 and lunar cycles for the three core realms. Cycles consist of solar (daily and seasonal) and
893 lunar time cycles (lunar phase), subdivided in time windows. Seasons were only analysed in
894 temperate regions. Sampling coverage is represented with sampling days in number labels.
895 B) Mean number of sampling days per site within different administrative regions (GADM
896 level 0, IHO areas, WDPA categories), split by core realm. WDPA categories (Ia: strict
897 nature reserve; Ib: wilderness area, II: national park) are shown separately. One
898 subterranean site in Brazil is not shown. Numbers to the right of bars indicate the number of
899 sites the means were calculated from. Sites from transitional realms were assigned to their
900 parent core realm.

901

902 **Figure 4: Sampling distribution across ecological scales.** A) Spatial extent of realms, 903 based on major areas according to IUCN GET (coloured disk area proportional to area); 904 spatial sampling density (in sites per Mkm²) and coverage (in number of sites); temporal 905 extent (mean range between first and last recording day), coverage (days sampled per site 906 and density (proportion of days sampled per extent). B) Frequency ranges of datasets 907 across realms (using Nyquist frequency i.e., actual recorded frequencies) for the main 908 studied taxa. The dots at the ends of colored lines represent means of the lowest and 909 highest recorded frequencies, and the range between the minimum and maximum of these 910 values are indicated with black error bars. The limits of human hearing are indicated with 911 dashed lines. Number of datasets indicated above lines - datasets can be counted several 912 times if they contain deployments targeting different taxa.

913

914 **Figure 5: Soundscape components analysis.** A) Mean acoustic space occupancy of 915 soundscape components (biophony, geophony, anthropophony) as calculated from 916 annotations for the selected ecosystems, measured in proportion of spectro-temporal space 917 used, across eleven ecosystems over 150 recordings covering the time windows of the diel 918 cycle. Annotated recordings are accessible at 919 https://ecosound-web.de/ecosound_web/collection/show/49. Sample spectrograms are 920 shown in the background. B) Soundscape component occupancy data illustrate three 921 research questions linked to macroecology (i.e., biophony with distance from equator 922 relationship), conservation biology trade-offs (i.e., biophony with anthropophony 923 relationship); and phenological trends (i.e., mean biophony along diel time windows for each 924 realm). Gray ribbons indicate 95% credible intervals and numbers indicate probabilities of 925 positive or negative relationships.
927 **Data availability**

928 ● [Database metadata](#) at Zenodo (version 2.0.0)

929 ● [Demonstration collection](#)

930 **Acknowledgements**

931 We are thankful to our colleagues who supported our work: Carolina Gonzaga, Ellen

932 Kenchington, Jinshan Xu, Abigail Seybert, Abram B. Fleishman, Adriana C. Acero-Murcia,

933 Adrien Charbonneau, Adrià López-Baucells, Aklavik, Alain Paquette, Albert García,

934 Alexander C. Lees, Alexandra Buitrago-Cardona, Alfonso Zúñiga, Alfred Wegener Institute

935 Helmholtz Centre for Polar and Marine Research, Allan G Oliveira, Alpheous, Ambroise N.

936 Zongo, Ana Rainho, Ana Širović, Anamaria Dal Molin, Andros T Gianuca, Annalea Beard,

937 Anne Sourdril, Anthony Tan, Anushka Rege, Aran Mooney, Arne Wenzel, Astrid Brekke

938 Skrindo, Australian Institute of Marine Science, Base de Estudos do Pantanal - UFMS,

939 Bastien Castagneyrol, Bea Maas, Bibiana Gómez-Valencia, Bob Dziak, Bobbi J. Estabrook,

940 Borja Milá, Brian Miller, Bridget Maher, Brittnie Spriel, C. Lisa Mahon, Carolina Ocampo-

941 Ariza, Carolline Z Fieker, Catherine Potvin, Catrin Westphal, Charlotte Roemer, Chistophe

942 Thébaud, Chong Chen, Christina Ieronymidou, Christine Cassin, Christine Erbe, Christopher

943 Clark, Cicero Simao, Claire Attridge, Claudia A. Medina-Uribe, Clément Lemarchand, Colin

944 Bates, Colin KC Wen, Conner Partaker, Cowichan Tribes Lulumexun department - Fisheries

945 and Marine projects staff, Craig A Radford, Curtin University, Cécile Albert, Dadang Dwi

946 Putra, Daisy Dent, Dan Warren, Daniel Mihai Toma, Danny Swainson, Dawit Yemane,

947 Deanna Clement, Dedi Rahman, Dennis Higgs, Dennis Kühnel, Dexter Hodder, Diego

948 Alejandro Gómez-Montes, Diego Balbuena, Diego Gil, Diego Pavón Jórdan, Diogo Provete,

949 Dorgival Diogenes, Dustin Whalen, Edho Walesa Prabowo, Eduardo Rodríguez Martinez,

950 Elizabeth Clingham, Eliziane Oliveira, Ellen Mc Arthur, Elma Kay, Eloy Revilla, Emanuel

951 Stefan Baltag, Emilia Sokolowska, Emilio Tan, En-Hao Liu, Enoc Martinez, Eric Parmentier,

952 Eric Rakotomalala, Erich Fischer, Erika Berenguer, Estância Mimosa Ecoturismo, Evan

953 Economo, Fabio De Leo Cabrera, Faisal Ali Awanrali Khan, Fang-Yee Lin, Fannie W.

954 Shabangu, Filipe F de Deus, Flávio Rodrigues, Francis Juanes, Frederic Sinniger, Frode

955 Fossøy, Gabriel Leite, Gabriel leite, George Best, Gerard Bota, Germany, Giselle Mahung,

956 Greg and Lisa Morgan, Groupe Chiroptères de Guyane, Guillermo McPherson, Hailey

957 Davies, Hajar, Hannah Reyes, Hanneline Smit-Robinson, Harris Papadopoulos, Heriniaina

958 Randrianarison, Hervé Jourdan, Hila shamon, Hiromi Kayama Watanabe, Holger Kreft, Ian

959 Avery Bick, Ilias Karmiris, Irfan Fitriawan, Isabelle Côté, Italo A A Rondon, Jacques Keumo

960 Kuenbou, Jakob Tougaard, Jamie Ratliff, Jamieson Atkinson, Janice Ser Huay Lee, Jason

961 Gedamke, Jean Pierre Castro Namuche, Jean-Yves Royer, Jeff Robinson, Jennifer Clark,

962 Jens-Georg Fischer, Jessica Deichmann, Jessica Qualley, Jinshan Xu, Joaquin del Rio,

963 Johan Diepstraten, Johanna Järnegren, John Ewen, John Hildebrand, John Ryan, Jos

964 Barlow, Jose Leon, Jose Manuel Ochoa-Quintero, José Luis Mena, José Nilson Santos,

965 José Wagner, Juan Diego Tovar, Juliana Carolina Herran García, Julie Perrot, Julio

966 Baumgarten, Junior Tremblay, Just T. Bayle Sempere, Justine Magbanua, Jérémy Anso,

967 Jérémy Froidevaux, Jérémy Froideveaux, Jérôme Sueur, Karen H. Beard, Katie Huong,

968 Katie Innes, Kelsie Murchie, Ken Otter, Ken P. Findlay, Kenneth Dudley, Kevin Darras,
969 Kevin G. Borja-Acosta, Kieran Cox, Kinga Buda, Kris Harmon, Laela Sayigh, Laure Desutter-

970 Grandcolas, Laurel Symes, Lauren Kuehne, Laurence H. De Clippele, Leeann Henry, Leila

971 Hatch, Lenka Brousset, Leonardo Trevelin, Liana Chesini Rossi, Linilson Padovese, Lisa

972 Loseto, Louise Emmerson, Luca Iacobucci, Lucia Di Iorio, Luciana Nascimento, Luis P. da

973 Silva, Mao-Ning Tuanmuu, Marc Lammers, Marcia Maia, Marconi Campos, Marconi Campos

974 Cerqueira, Marcos Vaira, Marcus Rowcliffe, Marlize Muller, Martim Melo, María Jesús

975 García Bianco, Matias Carandell, Matthew J. Weldy, Matthew K. Pine, Matthew Slater,

976 Mauricio Akmentins, Max Ritts, Michael Scherer-Lorenzen, Mike van der Schaar, Milton

977 Cezar Ribeiro, Mina Anders, Muhammad Justi Makmun Jusrin, Nadia Pieretti, Niaina Nirina

978 Mahefa Andriamavosoloarisoa, Nicholas Brown, Nicholas Friedman, Nick Gardner, Nicolas

979 Farrugia, Nicolas Pajusco, Nikos Fakotakis, Noam Leader, Nuno Faria, Ocean Acoustics

980 Group, Ocean Networks Canada Society, Olaf Boebel, Omar Hiram Martinez Castillo, Pedro

981 B. Lopes, Peter Ellis, Peter Taylor, Pooja Choksi, Pousada Xaraés, Pshemek Zdroik,

982 Rainforest Connection (RFCx), Refúgio Ecológico Caiman, Refúgio da Ilha Ecolodge, Rex K.

983 Andrew, Richard Kinsey, Rob Rempel, Robert John Young, Robert McCauley, Robert

984 Spaan, Robert Young, Rouvah Andriafanomezantsoa, Saki Harii, Samuel Challéat, Samuel

985 Haché, Sandra Mueller, Sara Gonçalves, Sarab Sethi, Sarah Rowley, Sarika Khanwilkar,

986 Sean Martin, Shannon MacPhee, Shinsuke Kawagucci, Sierra Hunicutt, Sierra Jarriel, Sigal

987 Balshine, Silent cities consortium, Simon Childerhouse, Sina Weier, Sofie Van Parijs, Stan

988 Dosso, Stanislas Wroza, Stephanie Plön, Stephen Insley, Stuart Marsden, Sérgio Moreira,

989 Tatiana Atemasova, Tetsuya Miwa, The Hunters and Trappers Committees of Inuvik, The

990 Inuvialuit-Canada Fisheries Joint Management Committee, Thiago M Ventura, Thomas

991 Sattler, Todor Ganchev, Tomas Altamirano, Tomasz Stanislaw Osiejuk, Tomaz Melo,

992 Tomonari Akamatsu, Tristan Louth-Robins, Tómas Grétar Gunnarsson, Valentin Chevalier,

993 Valerie Linden, Valéria Tavares, Vijay Ramesh, Vu Dinh Thong, Vy Tram Nguyen, Wen-Ling

994 Tsai, Will Duguid, Yenifer Herrera-Varón, You-Fang Chen, Yuhang Song, Zehava Sigal,

995 Zuania Colón-Piñeiro, Zuzana Burivalova, and Tuktoyaktuk.

996 The use of trade or firm names in this publication is for reader information and does not

997 imply endorsement by the U.S. Government of any product or service. The findings and

998 conclusions in this publication are those of the authors and should not be construed to

999 represent any official U.S. Government determination or policy. We acknowledge the NFDI

1000 Consortium Earth System Sciences — NFDI4Earth, coordinated by TU Dresden, funded by

1001 the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) — project

1002 number: 460036893. BIOMON is funded by the European Union's Horizon Europe

1003 programme under grant agreement 101090273. Larissa Sayuri M. Sugai and Liiana Piatti

1004 acknowledge grant Fundect T.O.:95/2023; SIAFEM: 33112. This paper is NOAA-PMEL

1005 contribution number 5948. Songhai Li acknowledges the National Natural Science

1006 Foundation of China (Grant numbers 42225604). Anna Franziska Cord was supported by

1007 the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under

1008 Germany's Excellence Strategy – EXC 2070 – 390732324. This research was also

1009 funded by a Westlake University Startup Fund (Thomas C Wanger).

## 1010 Author contributions

1011 Kevin FA Darras, Rodney Rountree did the conceptualisation. Kevin FA Darras, Steven Van

1012 Wilgenburg, Frederik Pitz, Youfang Chen, Lijun Dong, Amandine Gasc, Tzu-Hao Lin, Patrick

1013 Mauritz Diaz, Shih-Hung Wu, Marcus Salton, Sarah Marley, Laura Schillé, Paul Jacobus
1014 Wensveen, Camille Desjonquères, Orlando Acevedo-Charry, Matyáš Adam, Michel André,
1015 Alexandre Antonelli, Leandro Aparecido Do Nascimento, Giulliana Appel, Christos Astaras,
1016 Andrey Atemasov, Luc Barbaro, Fritjof Basan, Carly Batist, Adriá López Baucells, Julio
1017 Ernesto Baumgarten, Just T Bayle Sempere, Kristen Bellisario, Asaf Ben David, Oded
1018 Berger-Tal, Matthew G Betts, Iqbal Bhalla, Thiago Bicudo, Marta Bolgan, Sara Bombaci,
1019 Martin Boullhesen, Tom Bradfer-Lawrence, Robert A Briers, Michal Budka, Katie Burchard,
1020 Alice Calvente, Maite Cerezo-Araujo, Gunnar Cerwén, Maria Chistopolova, Christopher W
1021 Clark, Benjamin Cretois, Chapin Czarnecki, Luis P da Silva, Wigna da Silva, Laurence H De
1022 Clippele, David de la Haye, Ana Silvia de Oliveira Tissiani, Devin de Zwaan, Ricardo Díaz-
1023 Delgado, Pedro Diniz, Dorgival Diógenes Oliveira-Júnior, Thiago Dorigo, Saskia Dröge,
1024 Marina Duarte, Adam Duarte, Kerry Dunleavy, Robert Dziak, Simon Elise, Hiroto Enari,
1025 Haruka S Enari, Florence Erbs, Nina Ferrari, Luane Ferreira, Abram B Fleishman, Bárbara
1026 Freitas, Nick Friedman, Jérémy SP Froidevaux, Svetlana Gogoleva, Maria Isabel Gonçalves,
1027 Carolina Gonzaga, José Miguel González Correa, Eben Goodale, Benjamin Gottesman,
1028 Ingo Grass, Jack Greenhalgh, Jocelyn Gregoire, Jonas Hagge, William Halliday, Antonia
1029 Hammer, Tara Hanf-Dressler, Samara Haver, Daniel Hending, Jose Hernandez-Blanco,
1030 Thomas Hiller, Joe Chun-Chia Huang, Katie Lois Hutchinson, Janet Jackson, Alain Jacot,
1031 Olaf Jahn, Jasper Kanes, Ellen Kenchington, Sebastian Kepfer-Rojas, Tharaka Kusuminda,
1032 Yael Lehnardt, Paula Leitman, José Leon, Cicero Simão Lima-Santos, Kyle John Lloyd,
1033 Audrey Looby, David López-Bosch, Tatiana Maeda, Christos Mammides, Gabriel Marcacci,
1034 Matthias Markolf, Martinez Isaac Marques, Charles W Martin, Dominic A Martin, Kathy
1035 Martin, Logan JT McLeod, Oliver Metcalf, Christoph Meyer, Grzegorz Mikusinski, Larissa
1036 Sayuri Moreira Sugai, Dave Morris, Sandra Müller, Kelsie A Murchy, Maria Mas Navarro,
1037 Rym Nouioua, Julian D Olden, Steffen Oppel, Anna N Osiecka, Miles Parsons, Filipa Isabel
1038 Pereira Samarra, Cristian Pérez-Granados, Liliana Piatti, Mauro Pichorim, Thiago Pinheiro,
1039 Jean-Nicolas Pradervand, Craig Radford, Emiliano Ramalho, Laura K Reynolds, Klaus
1040 Riede, Talen Rimmer, Ricardo Rocha, Luciana Rocha, Samuel RP-J Ross, Carlos Salustio-
1041 Gomes, Philip Samartzis, José Santos, Kevin Scharffenberg, Renée P Schoeman, Karl-
1042 Ludwig Schuchmann, Esther Sebastián-González, Fannie Shabangu, Taylor Shaw, Xiaoli
1043 Shen, David Singer, Jenni Stanley, Karolin Thomisch, Laura Torrent, Juan Traba, Junior A
1044 Tremblay, Sunny Tseng, Mao-Ning Tuanmu, Marisol Valverde, Ben Vernasco, Raiane Vital
da Paz, Matthew Ward, Matthew Weldy, Julia Wiel, Jacob Willie, Heather Wood, Renata
1045 Sousa-Lima contributed to data curation. Kevin FA Darras did the formal analysis. Kevin FA
1046 Darras, Anna F Cord, Anna F Cord, Thomas Cherico Wanger acquired funding. Kevin FA
1047 Darras, Rodney Rountree, Steven Van Wilgenburg, Frederik Pitz, Youfang Chen, Lijun
1048 Dong, Amandine Gasc, Songhai Li conducted the investigation. Kevin FA Darras designed
1049 the methodology. Kevin FA Darras, Rodney Rountree, Steven Van Wilgenburg, Lijun Dong,
1050 Amandine Gasc, Jacopo Aguzzi, Robert Dziak, Jose Hernandez-Blanco, Ellen Kenchington,
1051 Justin Kitzes, Matthew McKown, Carolyn Rosten, Songhai Li, Renata Sousa-Lima
1052 administered the project or sub-projects (i.e., collections). Kevin FA Darras provided
1053 resources. Kevin FA Darras provided software. Kevin FA Darras, Anna F Cord, Anna F
1054 Cord, Songhai Li, Renata Sousa-Lima, Thomas Cherico Wanger conducted supervision.
1055 Kevin FA Darras, Frederik Pitz, Youfang Chen, Tzu-Hao Lin, Patrick Mauritz Diaz, Shih-
1056 Hung Wu, Marcus Salton, Laura Schillé carried out validation. Kevin FA Darras designed
1057 visualisations. Kevin FA Darras, Anna F Cord, Tom Bradfer-Lawrence, Miles Parsons,
1058 Samuel RP-J Ross, Anna F Cord wrote the original draft and Kevin FA Darras, Rodney
1059 Rountree, Steven Van Wilgenburg, Anna F Cord, Amandine Gasc, Tzu-Hao Lin, Patrick
1060 Mauritz Diaz, Shih-Hung Wu, Sarah Marley, Laura Schillé, Paul Jacobus Wensveen, Camille
1061
1062 Desjonquères, Orlando Acevedo-Charry, Matyáš Adam, Jacopo Aguzzi, Michel André,
1063 Alexandre Antonelli, Leandro Aparecido Do Nascimento, Giulliana Appel, Christos Astaras,
1064 Andrey Atemasov, Luc Barbaro, Fritjof Basan, Carly Batist, Adriá López Baucells, Julio
1065 Ernesto Baumgarten, Just T Bayle Sempere, Kristen Bellisario, Asaf Ben David, Oded
1066 Berger-Tal, Matthew G Betts, Iqbal Bhalla, Thiago Bicudo, Marta Bolgan, Sara Bombaci,
1067 Martin Boullhesen, Tom Bradfer-Lawrence, Robert A Briers, Michal Budka, Katie Burchard,
1068 Alice Calvente, Maite Cerezo-Araujo, Gunnar Cerwén, Maria Chistopolova, Christopher W
1069 Clark, Benjamin Cretois, Chapin Czarnecki, Luis P da Silva, Wigna da Silva, Laurence H De
1070 Clippele, David de la Haye, Ana Silvia de Oliveira Tissiani, Devin de Zwaan, Ricardo Díaz-
1071 Delgado, Pedro Diniz, Dorgival Diógenes Oliveira-Júnior, Thiago Dorigo, Saskia Dröge,
1072 Marina Duarte, Adam Duarte, Kerry Dunleavy, Robert Dziak, Simon Elise, Hiroto Enari,
1073 Haruka S Enari, Florence Erbs, Nina Ferrari, Luane Ferreira, Abram B Fleishman, Bárbara
1074 Freitas, Nick Friedman, Jérémy SP Froidevaux, Svetlana Gogoleva, Maria Isabel Gonçalves,
1075 Carolina Gonzaga, José Miguel González Correa, Eben Goodale, Benjamin Gottesman,
1076 Ingo Grass, Jack Greenhalgh, Jocelyn Gregoire, Jonas Hagge, William Halliday, Antonia
1077 Hammer, Tara Hanf-Dressler, Samara Haver, Daniel Hending, Jose Hernandez-Blanco,
1078 Thomas Hiller, Joe Chun-Chia Huang, Katie Lois Hutchinson, Janet Jackson, Alain Jacot,
1079 Olaf Jahn, Jasper Kanes, Ellen Kenchington, Sebastian Kepfer-Rojas, Justin Kitzes,
1080 Tharaka Kusuminda, Yael Lehnardt, Paula Leitman, José Leon, Cicero Simão Lima-Santos,
1081 Kyle John Lloyd, Audrey Looby, David López-Bosch, Tatiana Maeda, Christos Mammides,
1082 Gabriel Marcacci, Matthias Markolf, Marinez Isaac Marques, Charles W Martin, Dominic A
1083 Martin, Kathy Martin, Matthew McKown, Logan JT McLeod, Oliver Metcalf, Christoph Meyer,
1084 Grzegorz Mikusinski, Larissa Sayuri Moreira Sugai, Dave Morris, Sandra Müller, Kelsie A
1085 Murchy, Maria Mas Navarro, Rym Nouioua, Julian D Olden, Steffen Oppel, Anna N Osiecka,
1086 Miles Parsons, Filipa Isabel Pereira Samarra, Cristian Pérez-Granados, Liliana Piatti, Mauro
1087 Pichorim, Thiago Pinheiro, Jean-Nicolas Pradervand, Craig Radford, Emiliano Ramalho,
1088 Laura K Reynolds, Klaus Riede, Talen Rimmer, Ricardo Rocha, Luciana Rocha, Samuel
1089 RP-J Ross, Carolyn Rosten, Carlos Salustio-Gomes, Philip Samartzis, José Santos, Kevin
1090 Scharffenberg, Renée P Schoeman, Karl-Ludwig Schuchmann, Esther Sebastián-González,
1091 Fannie Shabangu, Taylor Shaw, Xiaoli Shen, David Singer, Jenni Stanley, Karolin Thomisch,
1092 Laura Torrent, Juan Traba, Junior A Tremblay, Sunny Tseng, Mao-Ning Tuanmu, Marisol
1093 Valverde, Ben Vernasco, Raiane Vital da Paz, Matthew Ward, Matthew Weldy, Julia Wiel,
1094 Jacob Willie, Heather Wood, Anna F Cord, Renata Sousa-Lima edited it.

1095 Competing interests

1096 The authors declare to have no competing interests. All authors have seen and approved the
1097 manuscript; it hasn't been accepted or published elsewhere.
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A- Framework

C- Spatial extent and coverage of sampling sites

<figure><img src="image_3.png" /></figure>

<figure><img src="image_4.png" /></figure>

B- Replication (dataset level)

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

Temporal extent

<figure><img src="image_6.png" /></figure>

D- Temporal extent and coverage (day level)

<figure><img src="image_7.png" /></figure>

Core reslms: Freeseval1, Marmite1, Subterranes1, Terrestrial1
A - Topographical and latitudinal distribution of sampling sites

<figure><img src="image_8.png" /></figure>

B - Top 10 administrative regions for spatial sampling coverage

<figure><img src="image_9.png" /></figure>
## A- Sampling coverage within time cycles

<figure><img src="image_10.png" /></figure>

## B- Top 10 administrative regions for temporal sampling coverage

<figure><img src="image_11.png" /></figure>
A - Sampling intensity across realms

<figure><img src="image_12.png" /></figure>

B - Frequency range and target taxon in core realms (dataset level)

<figure><img src="image_13.png" /></figure>
A - Case study soundscapes

<figure><img src="image_14.png" /><figcaption>Soundscapes component: ■ anthrophony ■ biophony ■ geography</figcaption></figure>

B - Illustrative analyses

<figure><img src="image_15.png" /><figcaption>Realms: ■ Freshwater ■ Freshwater-Marine ■ Marine ■ Subterranean ■ Terrestrial</figcaption></figure>