Philippa Kaur

BioassaysSalmon-louse bioassays were performed by the BC Centre for Aquatic Health Sciences (CAHS) as described in Saksida et al.10. Briefly, motile (i.e., pre-adult and adult) L. salmonis were collected from 11 salmon farms in the Broughton Archipelago (BA) between 2010 and 2021 and transported to CAHS in Campbell River, BC. Within 18 h of collection, healthy lice were separated by sex and randomly placed into petri dishes each containing approximately 10 lice (mean ± SD = 9.6 ± 1.1) and subjected to one of six EMB concentrations (either 0, 31.3, 62.5, 125, 250, and 500 ppb or 0, 62.5, 125, 250, 500, and 1000 ppb, depending on suspected variation in EMB sensitivity11). Each collection corresponded to one bioassay, and each bioassay contained roughly four replicates for each sex (4.0 ± 1.3 for females and 3.6 ± 0.9 for males). After 24 h of EMB exposure, lice were classified as alive if they could swim and attach to the petri dish, or moribund/dead otherwise. Lice were kept at 10 °C throughout the process. In total, 34 bioassays were conducted from 11 farms between October 2010 and November 2021.We analysed the proportion of lice that survived exposure to EMB, using standard statistical descriptions that accounted for within-assay dependencies (generalized linear mixed models (GLMMs) with logit link functions, fitted separately to the data from each bioassay). The models included fixed effects for EMB concentration, sex, and the interaction between the two, as well as a random intercept for petri dish. For each analysis, we centered concentration values and scaled them by one standard deviation. We used the GLMM fits to calculate the effective concentrations at which 50% of the lice survived (EC50) in each bioassay. The GLMM for one bioassay produced a singular fit because there was not enough variation in the female survival data to warrant the random-effects structure. We retained the EC50 values resulting from this singular fit because re-fitting without the random intercept yielded identical EC50 values, and removing the entire bioassay from the overall dataset did not qualitatively affect the subsequent analysis.To assess whether the sensitivity of salmon lice to EMB has decreased over time, we fitted a set of five standard GLMs with gamma error distributions and log link functions to the maximum-likelihood EC50 estimates. Each of these five models included binary effects for sex and for whether the farm’s stock had previously been treated, since both affect EMB sensitivity in lice10. The first model included only these two effects and served as a null model that assumed lice did not evolve EMB resistance over time. The second model added a fixed effect for time (i.e., the number of days since January 1, 2010), while the third model included an interaction between time and sex. The fourth and fifth models were identical to the second and third, but with a quadratic effect for time, to account for possible first-order nonlinearity. We were unable to add an effect for farm due to small sample sizes. We performed model selection using the Akaike Information Criterion penalized for small sample sizes AICc25, treating AICc differences of less than two as being indistinguishable in terms of statistical support and selecting the least complex model when that was the case26. The ΔAICc values for the EC50 models were 48.1, 6.1, 4.9, 0, 1.75, respectively.Field efficacyWe used relative salmon-louse counts after EMB treatment (i.e., the post-treatment count divided by the pre-treatment count) as our measure of EMB field resistance between 2010 and 2021 (higher relative counts imply lower treatment efficacy). We defined “pre-treatment” as one month prior to treatment and “post-treatment” as three months after treatment (roughly when one would expect to find the lowest counts in louse populations previously unexposed to EMB), as in Saksida et al.10. We excluded EMB treatments for which an additional, non-EMB treatment was performed within the following three months. In total, there were 73 EMB treatments for which we were able to calculate relative post-treatment counts.Salmon-louse counts were performed by farm staff as described by Godwin et al.27. In short, salmon-louse counts were usually performed at least one per month by capturing 20 stocked fish in each of three net pens using a box seine net, then placing the fish in an anesthetic bath of tricaine methanesulfonate (TMS, or MS-222) and assessing the fish for motile (i.e., pre-adult and adult) L. salmonis by eye.The treatment dataset included the date and type of every treatment that has been performed on a BA farm (i.e., not just the 11 farms with bioassay data). In total, 88 EMB treatments were conducted between 2010 and 2021, of which we were able to calculate relative post-treatment counts for 73 because some months lacked counts or had a non-EMB treatment performed within the following three months. An additional 22 non-EMB treatments (e.g., freshwater and hydrogen baths) were performed, all since the beginning of 2019, but we excluded these data from our analysis.To determine whether field efficacy of EMB treatments has decreased over time, we used GLM-based “hurdle models”—standard statistical descriptions used to accommodate an over-abundance of zeroes in data being analysed. A hurdle model uses two components—one model for whether a count is nonzero and another for the value of the nonzero count—to predict overall mean count. To this end, we fitted three binomial GLMs paired with three gamma GLMs to the relative-count data, each of the paired models being structurally identical in terms of predictors. All of these submodels included a binary fixed effect for previous treatment, as in the EC50 models. The null pair of submodels included no additional terms, the second pair of submodels included a fixed effect for time (i.e., the number of days since January 1, 2010), and the third pair of submodels included a quadratic effect of time (again, to account for possible first-order deviations nonlinearity). We were unable to add an effect for farm due to small sample sizes. We performed model selection of the hurdle models, again using the Akaike Information Criterion penalized for small sample sizes. The ΔAICc values for the three hurdle models were 39.6, 18.3, and 0, respectively. We performed our analyses in R 3.6.028, using the lme4 package29. More

Ethics oversightThis study was carried out within the frame of our housing and breeding permit (311.4-si) granted by the Landratsamt Starnberg, Germany. Attachment of backpacks was approved by the Regierung von Oberbayern, Germany (ROB-55-2-2532. Vet_02-17-211).Study populationsWe used four zebra finch populations that are genetically differentiated due to founder effects and selection (see Supplementary Fig. 1 & Fig. 2): two domesticated populations (D1 and D2) that have been maintained in captivity in Europe for about 150 years and two populations (W1 and W2) that have been taken from the wild about 10–30 years ago (see Supplementary Fig. 1). We ran all experiments in two independent replicates. We used individuals from populations D1 and W1 for replicate 1 and individuals from D2 and W2 for replicate 2.Breeding experiment Generation 1We created four groups of 36 individuals (9 males and 9 females from both a domesticated and a wild-derived population, two groups within each replicate) and put each group separately in an indoor aviary (5 m × 2.0 m × 2.5 m). All individuals had been reared normally by their genetic parents in similar breeding aviaries, were inexperienced (never mated before) and unfamiliar to all opposite-sex individuals. In replicate 1 (W1 – D1, starting December 2016), birds were 142 ± 32 days old at the start of the experiment (range: 101–191 days); in replicate 2 (W2 – D2, starting March 2017), birds were 241 ± 47 days old (range: 151–306 days). In each aviary, we provided nest material and nest boxes to stimulate breeding and observed pair-bonding behaviour for ca. 60 h spread over 14 days. Two observers recorded all instances of allopreening, sitting in bodily contact, and visiting a nest box together, which reflects pair bonding64.In total, we observed 3166 instances of heterosexual association among the 4 × 36 individuals (Supplementary Table 3). We defined a pair-bond between two opposite-sex individuals if they were recorded in pair-bonding behaviour at least five times (mean: 22 ± 14 SD, range: 5 – 73). This cut-off was chosen (blind to the outcome of data analysis) based on the frequency distribution showing a clear deviation from a random, zero-truncated Poisson distribution (Supplementary Fig. 8). Using this definition, we identified a total of 60 pairs (30 in each replicate). Of all females, 48 and 6 had a pair-bond with one and two males, respectively (18 females remained unpaired). Conversely, 34, 10, and 2 males had a pair-bond with one, two, and three females, respectively (26 males remained unpaired).Cross-fostering for Generation 2 experimentsAfter the breeding experiment of Generation 1, in 2017, we established two different cultural lineages within each genetic population by cross-fostering eggs, either within or between populations (Fig. 3). For this purpose, we used 16 aviaries (four per population), each containing 8 males and 8 females of the same population (Generation 1). Individuals were allowed to freely form pairs and breed. We reciprocally exchanged eggs shortly after laying between two aviaries per population (within-population cross-fostering) and between pairs of aviaries from different populations (between-population cross-fostering). This resulted in four cultural lineages per replicate (DD, DW, WD, and WW; Fig. 3). Each lineage was maintained in two separate breeding aviaries to ensure the availability of unfamiliar opposite-sex Generation 2 individuals from the same line. Offspring remained with their foster parents until they reached sexual maturity, when the following experiment started.Social experiment Generation 2Between December 2017 and March 2018, we put four groups of individuals (two groups for each replicate) in indoor aviaries (same as in Generation 1 experiment). Each group consisted of 10 males and 10 females from each of the cross-fostered groups DD, WW, DW and WD, i.e., a total of 80 birds per aviary, except that one aviary of replicate 2 only consisted of 63 individuals (7DD, 8WW, 8DW and 8WD) due to a shortage of birds. In replicate 1 (W1 – D1, starting December 2017), birds were 170 ± 25 days old at the start of the experiment (range: 105–199 days); in replicate 2 (W2 – D2, starting January 2018), birds were 200 ± 29 days old (range: 120–241 days). We recorded the position of individuals using an automated barcode-based tracking system31. We fitted each individual with a unique machine-readable barcode (Supplementary Fig. 4a) and placed eight cameras (8-megapixel Camera Module V2; RS Components Ltd and Allied Electronics Inc.), each connected to a Raspberry Pi (Raspberry Pi 3 Model Bs; Raspberry Pi Foundation) in each aviary. For 30 consecutive days, the cameras recorded individuals at six perches and at two feeders (Supplementary Fig. 4b, c). Between 05:30 and 20:00, when lights were switched on, each camera took a picture every two seconds.Each day, pictures stored on the Raspberry Pis were downloaded to a central server and processed using customised scripts. The customised software used the PinPoint library in Python65 to identify each barcode in each picture, allowing us to simultaneously track the position and orientation of each individual (Supplementary Fig. 4b) for the duration of the experiment. The tracking system generated 118 million observations across all four aviaries (Supplementary Fig. 4c). From these data, we extracted the average distance between the male and the female (in mm) for each male-female dyad, either daily or across the entire 30-day period (for comparison, such distance data were also extracted for all male-male and all female-female dyads). We used this dataset to identify the nearest opposite-sex individual for each of 151 males and females (55% of these 151 associations were reciprocal). Out of 151 nearest males to females, 74 (49%) paired with that female in the following breeding experiment (see below) and this proportion strongly increased as the average distance between partners decreased (Supplementary Fig. 9).Breeding experiment Generation 2Immediately after the social experiment, we moved each group into a separate semi-outdoor aviary (5 m × 2.5 m × 2.5 m) and provided nest material and nest boxes. During the next 2 months, three observers scored heterosexual associations to identify pair bonds as described for ‘breeding experiment Generation 1’ (ca 300 h per replicate). In total, we observed 6072 associations involving 284 individuals (Supplementary Table 3). Consistent with the previous experiment, we defined a pair-bond when a male-female dyad was observed in pair-bonding behaviour at least five times during the entire experiment (mean: 18 ± 13 SD range: 5 – 61; Supplementary Fig. 8). Using this definition, we identified 147 pairs (79 pairs in replicate 1 and 68 in replicate 2). Of all males, 97, 22 and 2 had a pair-bond with 1, 2 and 3 females, respectively (27 males remained unpaired). Conversely, 99, 21 and 2 females had a pair-bond with 1, 2 and 3 males (26 females remained unpaired).Breeding experiment Generation 3Between April and December 2018, we housed the four cultural lineages (DD, WW, DW and WD) separately again. We placed 8 males and 8 females in each of 16 breeding aviaries (four per lineage) and allowed them to freely form pairs and breed. The offspring belong to four lineages (Fig. 3): two lineages with individuals that were raised by parents that had not been cross-fostered between the domestic and wild-derived population (DDD and WWW) and two lineages with individuals from the same genetic background, but where their parents had been cross-fostered and raised by the other population (DDW and WWD).Between December 2018 and February 2019, we put four groups of 36 birds (two per replicate, i.e., 2 with 18 DDD and 18 DDW individuals and 2 with 18 WWW and 18 WWD individuals; 9 males and 9 females per lineage; Supplementary Table 3) in an outdoor aviary (same as above). In replicate 1 (W1 – D1, starting December 2018), birds were 172 ± 44 days old at the start of the experiment (range: 131–195 days); in replicate 2 (W2 – D2, starting January 2019), birds were 191 ± 40 days old (range: 122–230 days). During 14 days, two observers recorded all pair-bond behaviours as described under ‘breeding experiment Generation 1’. In total, we observed 3378 instances of pair-bond behaviour involving 137 individuals (Supplementary Table 3). As above, we defined a pair-bond when a male-female dyad was observed in pair-bonding behaviour at least five times during the entire experiment (mean: 18 ± 11 SD, range: 5 – 47; Supplementary Fig. 8). We identified 82 pair bonds (37 in replicate 1 and 45 in replicate 2). Of all males, 34, 16, 4 and 1 had a pair-bond with 1, 2, 3 and 4 females, respectively (17 males remained unpaired). Conversely, 42, 16, 1 and 1 females had a pair-bond with 1, 2, 3 and 5 males, respectively (12 females remained unpaired).Morphological measurementsAfter birds had reached sexual maturity ( >100 days of age), we measured body mass (to the nearest 0.1 g), tarsus length (to the nearest 0.1 mm), and wing length (to the nearest 0.5 mm) of all individuals (all measured by WF). We included these three variables in a principal component analysis (PCA) and used the first principal component (PC1, 67% of variation explained) as a measure of body size.Song recording and analysis approachWe recorded the songs of the parental males from Generation 1 (16 aviaries x 8 males = 128 males, of which 122 were successfully recorded between November and December in 2017) and of their offspring (Generation 2; 146 out of 152 males were successfully recorded between March and May 2018). To elicit courtship song, each male was placed together with an unfamiliar female in a metal wire cage (50 cm × 30 cm × 40 cm) equipped with three perches and containing food and water. The cage was placed within one of two identical sound-attenuated chambers. We mounted a Behringer condenser microphone (TC20, Earthworks, USA) at a 45° angle between the ceiling and the side wall of the chamber, such that the distance to each perch was approximately 35 cm. The microphone was connected to a PR8E amplifier (SM Pro Audio, Melbourne, Australia) from which we recorded directly through a M-Audio Delta 44 sound card (AVID Technology GmbH, Hallbergmoos, Germany) onto the hard drive of a computer.Previous studies that quantified differentiation of songs between zebra finch populations using specific song parameters (e.g., duration and frequency measures) largely failed to detect prominent differences12,49,50. We, therefore, used the following two approaches (Sound Analysis Pro and Machine Learning) to quantify the extent to which a given male’s song resembled the songs of other males.Song similarity analysis with SAPUsing Sound Analysis Pro (SAP) version 2011.10427, we quantified song similarity (ranging from 0 to 100) by direct pairwise comparison of song motifs (the main part of a male’s song that is stereotypically repeated and about 0.8 s long, excluding introductory syllables). Pair-wise comparisons of two males (based on one representative motif recording per male) revealed higher within-population similarity than between-population similarity (Supplementary Table 2, data from Generation 1). Further, for offspring that were cross-fostered between populations (N = 73 males from Generation 2) song similarity to their foster father was higher than song similarity to their genetic father (80 versus 68, paired t-test: p  More

Laboratory for Integrative Biodiversity Research (LIBRe), Finnish Museum of Natural History (LUOMUS), University of Helsinki, Helsinki, FinlandStefano Mammola, Jagoba Malumbres-Olarte, Pedro Cardoso, Caroline S. Fukushima, Tuuli Korhonen, Marija Miličić & Joni A. SaarinenMolecular Ecology Group (MEG), Water Research Institute, National Research Council of Italy (CNR-IRSA), Largo Tonolli 50, 28922, Verbania Pallanza, ItalyStefano Mammola & Alejandro MartínezCE3C – Centre for Ecology, Evolution and Environmental Changes / Azorean Biodiversity Group and Universidade dos Açores, Angra do Heroísmo, Azores, PortugalJagoba Malumbres-OlarteAlbert Katz International School for Desert Studies, Ben-Gurion University of the Negev, Sede Boqer Campus, Beersheba, IsraelValeria ArabeskyBlaustein Institutes for Desert Research, Ben-Gurion University of the Negev, Sede Boqer Campus, Beersheba, IsraelValeria Arabesky & Yael LubinColección Nacional de Arácnidos, Instituto de Biología, Universidad Nacional Autónoma de México (UNAM), Mexico City, MexicoDiego Alejandro Barrales-AlcaláEnvironmental Biology Division, Institute of Biological Sciences, College of Arts and Sciences and Museum of Natural History, University of the Philippines Los Banos, 4031, Los Baños, PhilippinesAimee Lynn Barrion-DupoCentro Universitario de Rivera, Universidad de la República, Montevideo, UruguayMarco Antonio BenamúLab. Ecotoxicología de Artrópodos Terrestres, Centro Univeritario de Rivera, Universidad de la República, Montevideo, UruguayMarco Antonio BenamúLaboratorio Ecología del Comportamiento, Instituto de Investigaciones Biológicas clemente Estable (IIBCE), Montevideo, UruguayMarco Antonio BenamúDitsong National Museum of Natural History, PO Box 4197, Pretoria, 0001, South AfricaTharina L. BirdDepartment of Zoology and Entomology, University of Pretoria, Private Bag X20, Hatfield, 0028, South AfricaTharina L. BirdFreelance translator, Verbania Pallanza, ItalyMaria BogomolovaDepartment of Molecular Biology and Genetics, Democritus University of Thrace, Komotini, GreeceMaria ChatzakiDepartment of Life sciences, National Chung Hsing University, No.145 Xingda Rd., South Dist., Taichung City, 402204, TaiwanRen-Chung Cheng & Tien-Ai ChuDepartment of Biology, Macelwane Hall, 3507 Laclede Avenue, Saint Louis University, St. Louis, MO, 63103, USALeticia M. Classen-RodríguezCroatian Biospeleological Society, Rooseveltov trg 6, Zagreb, CroatiaIva Čupić & Martina PavlekProgram Sarjana, Fakultas Biologi, Universitas Gadjah Mada, Yogyakarta, IndonesiaNaufal Urfi Dhiya’ulhaqInsectarium de Montréal, Espace pour la vie, 4101, rue Sherbrooke Est, Montréal, Québec, H1X 2B2, CanadaAndré-Philippe Drapeau PicardSerket, Arachnid Collection of Egypt (ACE), Cairo, EgyptHisham K. El-HennawyErzincan Binali Yıldırım University, Faculty of Science and Arts, Biology Department, 24002, Erzincan, TurkeyMert ElvericiThe National Natural History Collections, The Hebrew University of Jerusalem, Edmond J. Safra Campus, Givat Ram, Jerusalem, 9190401, IsraelZeana Ganem & Efrat Gavish-RegevThe Department of Ecology, Evolution and Behavior, The Hebrew University of Jerusalem, Edmond J. Safra Campus, Givat Ram, Jerusalem, 9190401, IsraelZeana GanemBotswana International University of Science and Technology, Palapye, BotswanaNaledi T. GonnyeUMR CNRS 6553 Ecobio, Université de Rennes, 263 Avenue du Gal Leclerc, CS 74205, 35042, Rennes Cedex, FranceAxel Hacala & Julien PétillonDepartment of Zoology and Entomology, University of the Free State, P.O. Box 339, Bloemfontein, 9300, South AfricaCharles R. Haddad & Zingisile MboDepartment of Zoology, University of Oxford, Oxford, OX1 3PS, United KingdomThomas HesselbergDepartment of Biological Sciences, National University of Singapore, 14 Science Drive 4, Singapore, 117543, SingaporeTammy Ai Tian HoDepartment of Biotechnology, Faculty of Science and Technology, Thammasat University, Rangsit, Pathum Thani, 12121, ThailandThanakorn Into & Booppa PetcharadDept. of Life Science and Systems Biology, University of Torino, Via Accademia Albertina, 13 – 10123, Torino, ItalyMarco Isaia & Veronica NanniUnit of Conservation Biology, Department of Zoology, Bharathiar University, Coimbatore, 641046, Tamilnadu, IndiaDharmaraj JayaramanNational Museum of Namibia, Windhoek, NamibiaNanguei Karuaera5A Sagar Sangeet, SBS Marg, Mumbai, 400005, IndiaRajashree Khalap & Kiran KhalapDepartment of Biological Sciences, Ajou University, Suwon, Republic of KoreaDongyoung KimResearch Centre of the Slovenian Academy of Sciences and Arts, Jovan Hadži Institute of Biology, Ljubljana, SloveniaSimona Kralj-FišerUniversity of Greifswald, Zoological Institute and Museum, General and Systematic Zoology, Loitzerstrasse 26, 17489, Greifswald, GermanyHeidi Land, Shou-Wang Lin & Gabriele UhlDepartment of Natural Resource Sciences, McGill University, 21 111 Lakeshore Road, Sainte-Anne-de-Bellevue, Quebec, H9X 3V9, CanadaSarah Loboda & Catherine ScottDepartment of Biological Science, Macquarie University, Sydney, NSW, 2122, AustraliaElizabeth LoweMitrani Department of Desert Ecology, University in Midreshet Ben-Gurion, Midreshet Ben-Gurion, IsraelYael LubinBioSense Institute – Research Institute for Information Technologies in Biosystems, University of Novi Sad, Dr Zorana Đinđića 1, 21000, Novi Sad, SerbiaMarija MiličićNational Museums of Kenya, Museum Hill, P.O. BOX 40658- 00100, Nairobi, KenyaGrace Mwende KiokoSchool for Advanced Studies IUSS, Science, Technology and Society Department, 25100, Pavia, ItalyVeronica NanniInstitute of Biological Sciences, Faculty of Science, University of Malaya, 50603, Kuala Lumpur, MalaysiaYusoff Norma-RashidDepartment of Animal and Environmental Biology, Federal University, Oye-Ekiti, Ekiti State, NigeriaDaniel NwankwoTe Aka Mātuatua School of Science, University of Waikato, Private Bag 3105, Hamilton, 3240, New ZealandChristina J. PaintingIndependent researcher, Toronto, CanadaAleck PangMuseo Civico di Scienze Naturali “E. Caffi”, Piazza Cittadella, 10, I-24129, Bergamo, ItalyPaolo PantiniRuđer Bošković Institute, Bijenička cesta 54, 10000, Zagreb, CroatiaMartina PavlekBiodiversity Research Laboratory, Moreton Morrell, Warwickshire College University Centre, Warwickshire, United KingdomRichard PearceInstitute for Coastal and Marine Research, Nelson Mandela University, Port Elizabeth, South AfricaJulien PétillonDepartment of Entomology, University of Antananrivo, Antananarivo, MadagascarOnjaherizo Christian RaberahonaSchool of Biological Sciences, University of Nebraska-Lincoln, Lincoln, Nebraska, 68588, United StatesLaura Segura-HernándezDepartment of Biological Sciences, University of Toronto Scarborough, 1265 Military Trail, Scarborough, Ontario, M1C 1A4, CanadaLenka SentenskáNatural Sciences, Auckland War Memorial Museum, Parnell, Auckland, 1010, New ZealandLeilani WalkerTe Pūnaha Matatini, University of Auckland, Auckland, New ZealandLeilani WalkerMurang’a University of Technology, Department of Physical & Biological Sciences, P.O.Box 75-10200, Murang’a, KenyaCharles M. WaruiInstitute of Biology and Earth Sciences, Pomeranian University in Słupsk, Arciszewskiego 22a, 76-200, Słupsk, PolandKonrad WiśniewskiZoological Museum, Biodiversity Unit, FI-20014, University of Turku, Turku, FinlandAlireza ZamaniDepartment of Psychology, University of Tennessee, Knoxville, Tennessee, USAAngela ChuangDepartment of Entomology and Nematology, Citrus Research and Education Center, University of Florida, Lake Alfred, Florida, USAAngela ChuangConceptualization: SM, JM-O, CS, AC; Data collection & validation: all authors; Data management: SM, VN, AC; Data analysis & visualization (Figs. 2–5): SM; Summary illustration (Fig. 1): JM-O; Writing (first draft): SM; Writing, contributions: JM-O, CS, AC; All authors read the text, provided comments, suggestions, and corrections, and approved the final version. More

Marine Ecology Research Group and Centre for Integrative Ecology, School of Biological Sciences, University of Canterbury, Christchurch, New ZealandMads S. Thomsen, Luca Mondardini, David R. Schiel & Alfonso SicilianoDepartment of Bioscience, Aarhus University, 4000, Roskilde, DenmarkMads S. ThomsenSmithsonian Tropical Research Institute, Apartado, 0843-03092, Balboa, Ancon, Republic of PanamaAndrew H. Altieri, Viktoria M. M. Frühling, Seamus B. Harrison & Gerhard ZotzEnvironmental Engineering Sciences, University of Florida, Gainesville, FL, USAAndrew H. Altieri & Christine AngeliniDepartment of Biological Sciences, Macquarie University, Sydney, NSW, AustraliaMelanie J. Bishop & Semonn OleksynDipartimento di Biologia, Università di Pisa, CoNISMa, Via Derna 1, 56126, Pisa, ItalyFabio Bulleri & Joachim LangeneckMarine Sciences, University of Georgia, Athens, GA, USARoxanne FarhanCentre for Marine Science and Innovation, School of Biological, Earth and Environmental Sciences, University of New South Wales, Sydney, NSW, AustraliaPaul E. Gribben & Brendan S. LanhamSydney Institute of Marine Science, Chowder Bay Road, Mosman, 2088, Sydney, NSW, AustraliaPaul E. Gribben & Brendan S. LanhamCoastal Ecology Lab, MOE Key Laboratory for Biodiversity Science and Ecological Engineering, School of Life Sciences, Fudan University, 2005 Songhu Road, 200438, Shanghai, ChinaQiang HeInstitute for Biology and Environmental Sciences, Carl von Ossietzky University Oldenburg, Oldenburg, GermanyMoritz Klinghardt, Tristan Schneider & Gerhard ZotzSchool of Biological Sciences and UWA Oceans Institute, University of Western Australia, Perth, WA, AustraliaYannick Mulders & Thomas WernbergDepartment of Biology and Marine Biology, University of North Carolina Wilmington, Wilmington, NC, USAAaron P. RamusNicholas School of the Environment, Duke University, 135 Duke Marine Lab Road, Beaufort, NC, USABrian R. Silliman & Stacy ZhangMarine Biological Association of the United Kingdom, The Laboratory, Citadel Hill, Plymouth, PL1 2PB, UKDan A. SmaleCawthron Institute, Nelson, New ZealandPaul M. South More

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