Evidence of predation events by marine mammals at offshore wind farms

Evidence of predation events by marine mammals at offshore wind farms | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Evidence of predation events by marine mammals at offshore wind farms Anthony W. J. Bicknell, Robert Main, Samuel Gierhart, Paul Thompson, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9214554/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract Anthropogenic structures in the marine environment alter the availability and distribution of food for marine animals with potential consequences for individual fitness, population and community dynamics. Knowledge of how existing wind farm sites influence the behaviour or foraging activities of marine mammals is valuable if predictions on the effect future large-scale renewable energy development will have on local or regional populations are to be made. During acoustic tracking of demersal gadoid fish at two offshore wind farms in Scotland, six Atlantic cod were predated by a marine mammal close to or in the vicinity of turbine foundations. The distinct change in the temperature, depth and movement pattern of the acoustic tags allowed estimations of the time and location of predation (and likely predator) and provided details of post consumption behaviour. The detection of tags (predator) at multiple turbines within a relatively short period, post consumption, suggests the targeted use of turbine foundations as foraging sites where prey fish are known to aggregate. Moreover, the bias in predation of cod over haddock, and evidence for the cod being of higher energetic quality, provide rare evidence for prey selection by a marine mammal predator(s). These data provide further evidence for how wind farms and other introduced structures influence the behaviour of marine mammals with potential consequences for individuals and populations through changes in the distribution of prey. The ultimate consequences are to be determined but need consideration with the continued development of offshore wind farm sites. Biological sciences/Ecology Earth and environmental sciences/Ecology Earth and environmental sciences/Ocean sciences Acoustic tracking gadoid fish marine mammals offshore wind farms predation prey selection Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Coastal and offshore seascapes are changing rapidly around the world with the introduction of artificial structures to harness wind, wave, and tidal energy 1,2 . Wind turbine foundations and subsea infrastructure, e.g. scour protection and cables, are known to effect the presence and distribution of marine species 3 but how these changes will ultimately alter population dynamics, community structure, behaviour and predator/prey interactions at a local or regional scales is still to be determined. Knowledge of how marine mammals may be affected by offshore wind infrastructure is important across European waters given they are protected under the Habitats Directive (92/43/EEC), and their consideration is a requirement for Environmental Impact Assessments (EIA) during consenting. Currently, most attention has focussed on the potential impacts of underwater noise and vessel traffic during wind farm construction and operation 4-7 . However, it is also important to understand how wind farm infrastructure may influence foraging behaviour through changes in prey availability. The distribution of fish and other prey species can be altered through aggregation effects at wind turbine foundations or subsea cables 8-13 . These in turn may have consequences for food availability, individual energy budgets and potential population effects for marine mammals 14 . Movement and inferred foraging behaviour of two species of seal (Harbour Phoca vitulina and grey Halichoerus grypus) has been shown to concentrate around wind turbine foundations and cables 15 providing evidence for such behavioural changes. Similarly, higher densities of harbour porpoise have been recorded during operational phases at some North Sea wind farms 16 . However, direct evidence of predation around structures is lacking, and it remains possible that increased use of wind farms may also be driven by reductions in disturbance from other factors such as changes in fisheries or vessel traffic intensity 4,5 . Acoustic telemetry is widely used to study the ecology and movements of marine animals 17 and provides opportunities to explore how fish movement and behaviour can be altered by the introduction of offshore wind farm structures 18 . Tags are attached or inserted into individual fish and subsequently emit coded acoustic signals for several months or years. These signals can be detected by fixed hydrophone receivers at strategic locations, or on mobile receivers deployed on vessels or animals 18,19 . Tag variants also provide sensor data including depth and temperature at the time of detection, which can reveal movements throughout the water column and alterations in the ambient temperature the fish is experiencing. When assessed in combination, the temperature, depth and location data can identify changes in expected fish behaviour that could infer mortality 20,21 or consumption by a warm blooded predator, e.g. cetacean or pinniped 18,21,22 . Temperature of (non-acoustic) archival satellite tags has also been used to identify predation of European eels Anguilla anguilla by endothermic fish (10 °C increase from ambient) or marine mammals (> 30°C) during annual migration 23,24 . Here, data from a study of the movements of two demersal gadoid species around northern North Sea offshore wind farms were used to explore patterns of marine mammal predation. The study area is known to support a range of marine mammal species, with harbour seal, grey seal and harbour porpoise Phocoena phocoena occurring most commonly across this region 25-27 . The primary aim was to use fish acoustic tag detection and sensor data to provide direct evidence of marine mammal foraging activity. In addition, these data were explored to assess whether they could provide insights into the identity of the predators and/or evidence of prey selection. Results General detection patterns of tagged fish Of the 217 tagged fish, 104 were detected for 14 days or more (either contiguous or non-contiguous) and used in subsequent analysis. Of the 104 fish, 57 (~55%) were tagged in Beatrice and 47 (~45%) in Moray East wind farm. These included 7 cod and 28 haddock captured in 2022, and 8 cod and 61 haddock captured in 2024. The overall mean length for the 15 tagged cod were 30cm (range 24-36cm), and for the 89 haddock were 31cm (range 24-38cm) (Supplementary Table S2). Once filtered for potential false detections, the array receivers recorded 2,285,988 detections of which 1,142,783 contained temperature data and 1,143,205 contained depth data. The maximum detection period for fish tagged in April 2022 was 422 days, in June 2022 was 301 days, and in April 2024 was 396 days ( Table 1 ). The detections for individual cod and haddock tagged in each year overlapped in time and the array clusters on which they were recorded (Supplementary Table S2; Supplementary Figure S3 & S4). Predation events on tagged fish Detections where the tag temperature exceeded 30 °C indicated that six of the 104 fish detected for more than 14 days were predated by a marine mammal: three in the 2022 cohort of tagged fish and three in the 2024 cohort ( Figure 2; Supplementary Figure S4). In all but one case, tagged fish were detected regularly within the wind farm up until the point at which the temperature increased to 30 °C. In the remaining case (ID 65594; Supplementary Figure S5c), the fish appeared to have left the study array for around five months before the tag, at this point registering a temperature of >30 °C, was again detected on the array. In two cases (ID 65594 & ID 70942), tags were subsequently detected at ambient temperature after periods above 30 °C ( Figure 2c & f ; Supplementary Figure S5c & S6d), indicating potential expulsion of the tag by the predator through regurgitation or defecation. Tags registering > 30° C were recorded at multiple receivers within both offshore wind farms ( Figure 3 ). In four of the six cases, a rapid increase in tag temperature occurred within 3 hrs of a prior detection at ambient sea water temperature, either at the same receiver station (ID 65589 & 70865) or at another receiver in the same cluster (ID 65596 & 70942), providing evidence for the location and approximate time of predation ( Table 2 ;Supplementary Figure S6 and S7). The two remaining tags had a longer delay between the temperature change detections ( Table 2; Supplementary Figure S6 and S7). Tag 70861 was detected within the same array cluster after 1.7 days, indicating that predation could have recently taken place within the wind farm (Supplementary Figure S6b). In contrast, tag 65594 was detected with an elevated temperature several months after the previous detection at ambient sea water temperature, providing little insight on where or when predation occurred (Supplementary Figure S5c & Sd). Movements of predators following ingestion of tagged fish. Following ingestion by predators, tags were detected from 4 - 123 times on 1 to 5 days ( Table 3 ). Although limited in extent, data from three tags with >20 detections provide evidence that predators moved between turbine locations over these relatively short time scales ( Figure 3; Supplementary Figure S7). Tag 65589 provided a ~4 day detection period after temperature increase ( Table 3 ), with detections on six receivers located between ~1.13 – 6.5km apart across two array clusters ( Figure 3a; Supplementary Figure S7b). Tag 65594 was detected for the longest post temperature increase (~6 days; Table 3 ) at six receivers at distances from ~1.12km within a cluster to over 10 km between the different clusters ( Figure 3b; Supplementary Figure S7d). Finally, tag 65596 was detected for 24 hours after the temperature increase on nine different receivers that were located between ~1.16 – 3.1 km apart in array cluster B1 ( Figure 3c; Supplementary Figure S7f). Data on the depth of tags following predations were sparse, with between one and 68 depth detections per tag ( Table 3 ). The overall depth distribution of all detections from tags registering > 30°C shows that most tags were detected while at depths below 30 m, at or close the seabed around the receivers ( Figure 4a ). This depth distribution was more similar to previously published data on dive patterns of harbour seals ( Figure 4b ) than those from harbour porpoises ( Figure 4c ). Only one tag (65596) provided regular depth readings through the 24 hours over which sustained high temperatures indicated that it remained within the predator. These revealed eight vertical ascents to the surface with subsequent return to below 40m ( Figure 5a ), and detections on nine different receivers located between ~1.16 – 3.1 km apart in array cluster B1 ( Figure 5b) . The temperature detections showed variation post increase with two noteworthy declines below 34 °C, one of which coincided with 3 vertical movements from the surface to below 40m ( Figure 5a ). Prey selection All six observed predation events involved tagged cod (6 of 15 tagged cod). No haddock were identified as being predated during their detection periods (0 of 89 tagged haddock). The probability of detecting zero haddock predation events based on the observed ratios was very low (Fishers exact test: p-value <0.0001; Supplementary Table S3), suggesting a bias towards predation upon tagged cod given their availability to marine mammal predators. There was no significant difference between the lengths of all tagged cod or just predated cod, and haddock (tagged cod vs haddock: p-value 0.54; predated cod vs haddock: p-value 0.88; Figure 6a ). However, there was significant differences between the energy estimates for tagged cod or predated cod when compared to haddock (tagged cod vs haddock: p-value <0.0001; predated cod vs haddock: p-value <0.0001; Figure 6b ). Discussion Changes in temperature, depth profile and spatial movement of 104 cod and haddock acoustically tagged and released within two operational wind farms indicated that 6 cod had been predated by a marine mammal. Two of these events could be identified as taking place close to wind turbine foundations (<400m) where the fish where in proximity, and location detections post-consumption of one cod revealed putative attendance at multiple turbine foundations in quick succession suggesting prospecting. To our knowledge, this is the first direct evidence of marine mammal predation of fish at wind turbine foundations, and further evidence that they represent foraging sites for some individuals 15 . Proximity to turbine foundation and foraging The probability of detecting the acoustic tag transmissions on a receiver within the OWF array was high (>85%) up to 400m away and declined sharply between 400-600 m (Supplementary Figure S 2). The combination of range test results and removing detections on more than one receiver within a short interval, provided confidence in that most tag detections were within 400 m of a receiver deployed next to a turbine foundation. Finer scale position data (i.e. accuracy greater than 400 m) are not available using the array design deployed in this study, and as such closer attendance of the fish or marine mammal to the turbine foundation is not certain. However, it is known that cod and haddock are attracted to within 100m of turbine foundations due the reefing effect that creates hard substrate habitat and/or patches of enhanced prey availability in soft sediment environments 8,28-30 . Cod have been particularly well studied at turbine foundations in the southern North Sea, with evidence of strong attraction and residency within 50 m or less of the structures 28,31 . Similar residency behaviour has been found close to hard substrate artificial reefs 32 , and much higher abundance within 100m has been observed at an oil and gas platform 33 . Three of the six predated cod (65589, 65596, 70942; Supplementary Figure S 5 & S6) demonstrated periods (months) of consistent detections on single receivers, with only limited detections on neighbouring receivers signifying local residency and proximity to the turbine foundations. After being predated upon, the detections for two of these tags contrasted with the previous local residency behaviour, with one being detected on nine receivers within a day ( Figure 3; Table 3 ), indicating the predator was moving throughout the wind farm, and coming in proximity to other turbine foundations. Furthermore, two shorter periods of lower temperature for this individual ( Figure 5 ) may be related to prey and ambient water entering the stomach during subsequent capture events while foraging 22,34,35 , which also align with depth profiles of dive behaviour ( Figure 6 ). Marine mammal predator The primary identifier of predation by a marine mammal was the marked increase in tag temperature from periods of temperature equivalent to the ambient water. Several marine mammal species frequent the Moray Firth region, but there are three species that are most likely to occur within these wind farm sites harbour porpoise Phocoena phocoena L.: 26, grey seal Halichoerus grypus & harbour seal Phoca vitulina: 27, bottlenose dolphin Tursiops truncatus: 36 . Cod have been identified in the diet of all three of these species, both in the Moray Firth (Santos et al., 2004; Thompson et al., 1996; Tollit et al., 1998; Wilson and Hammond, 2019) and other regions of the North Sea 37-44 . However, other cetaceans such as bottlenose dolphins, white-beaked dolphins Lagenorhynchus albirostris , common dolphins Delphinus delphis , Rissos’ dolphins Grampus griseus and killer whales Orcinus orca are occasionally recorded in the Outer Moray Firth 45,46 , and all these could potentially be predating upon this size class of cod. Whilst the body temperature of different marine mammals may vary slightly 47 , tag temperatures cannot be used to differentiate between potential species of marine mammal predators. Similarly, neither predation events ( Table 3 ) nor the presence of different marine mammals demonstrates repeated seasonal patterns, so this did not provide insights to the potential identity of the predators. Low tag transmission rates (~3/ minute) also limit the potential to relate the time-series of acoustic detections to dive profiles of potential predators. Nonetheless, available data suggest that the depth distribution of predated tags is more likely to represent that of a diving seal than a small cetacean ( Figure 4 ). Information on the amount of time that marine mammals spend at different waters depths is available for a limited number of species. However, dive profiles of harbour seals and grey seals indicate that they generally feed close to the bottom and spend much of the dive at depth when in shallow coastal waters e.g. 41 . This is illustrated in Figure 4b using data for harbour seals taken from Thompson, et al. 48 ), which provides information on the proportion of a predator’s time that is spent at different water depths. In contrast, harbour porpoises and other small cetaceans typically spend the majority of their time in the upper layers of the water column in similar shallow coastal waters 49-51 . For example, Teilmann, et al. 50 ) showed that tagged harbour porpoises spent 45-63% of their time in the upper 2m, and depth distributions of porpoises taken from their study highlight that much less time is spent at depth compared to seals (Figure 4c). Without further evidence and sampling it is not possible to confirm the identity (or identities) of the species responsible. Pinnipeds are known to interact with OWF turbine foundations and cable infrastructure 15,52 . Based on the depth profiles of tag detections and tagged seals and porpoises, we suggest that tagged fish were most likely to be predated by either harbour or grey seals. However, we cannot exclude the possibility that some fish were taken by other predators such as harbour porpoises given they are known to forage close (<200m) to older structures in this area 53 . Evidence for prey selection The sample of tagged fish available to marine mammal predators at the study site was heavily skewed towards haddock, yet all the fish that were predated were cod. Thus, although the identity of the predators remains uncertain, these data provide rare evidence for prey selection in a wild marine mammal. Prey acquisition is a crucial component of marine mammal bioenergetics, balancing foraging effort (efficiency) with ingested energy required for survival and reproduction 54,55 . Targeting prey with an optimal balance between energetic quality and capture effort is therefore expected Optimal foraging theory; 56 51,57,58 and the preferential selection of certain species and/or age classes by marine mammals means they are likely to have an important role in shaping marine communities 59,60 . To date, evidence for prey selection has been constrained by uncertainties over the true availability of different prey to marine mammal predators. For example, there is known to be potential bias in fisheries surveys where these are used to characterise prey availability for comparison with marine mammal diet (Spitz et al. 2010). Previously, the only studies which have precise data on the relative abundance of different prey species are those that have directly fed animals either in captivity 61 around fisheries vessels 55 . Although previous diet studies indicate that marine mammal populations generally consume a wide range of species, individuals often show distinct preferences for particular species when offered a choice of prey 55 . Our data suggest that the marine mammal(s) foraging at this wind farm site showed a preference for cod over haddock. Cod and haddock tagged in this study did not differ significantly in length, but energy estimates where significantly higher for cod (predated or not) indicating prey quality could be a reason for this apparent selection. The length-weight relationship and energy estimates were derived from a relatively small number of cod from the study area, so could be viewed with caution. However, the difference holds when using additional samples (haddock = 26; cod = 10) from another North Sea site as part of same energetic study 62 suggesting a reliable relationship. Selection could also be influenced by differences in the behaviour of the prey species that affect catchability, or other functional traits affecting choice 63 . A synergistic effect of quality and behaviour of prey may enhance selection for certain species and could be evident for cod in this study, which had higher energetic estimates and show residency to a relatively small area close to a few (sometimes 1) turbine foundations. Nevertheless, this new evidence for the likely existence of prey selection for cod is important to consider when assessing predator-prey dynamics and interactions between competing predators 64 or with commercial fisheries 40,65 . Conclusion The most likely predators of the tagged cod in this study are seals (grey or common) that commonly occur in the Moray Firth, and have been observed and tracked in the offshore wind farm boundaries 27,66 . The predation events occurred close to wind turbine foundations where cod have been shown to often reside, attracted by the hard substrate structures and possible enhanced feeding opportunities 28 . Lack of predation of the more numerous haddock suggests prey selection, and the higher energetic estimates for the cod provide support for optimal foraging of high-quality prey. The apparent prospecting of one seal post consumption of the tagged cod, provide further evidence that offshore wind farm foundations offer feeding opportunities for predators by aggregating prey species. The consequences of this behaviour for prey and predator populations are still to be determined but needs consideration with the continued large-scale development of offshore wind farms. Methods Study location The study took place within the Beatrice and Moray East offshore wind farms in the Moray Firth, Scotland (Figure 1). The wind farms are located 7.5-23 nautical miles offshore in water depth of 35-60 metres. Beatrice operates 84 7MW turbines with 4-leg jacket foundations that were installed between August 2017 and July 2018. Moray East operates 100 9.5MW turbines with 3 leg jackets that were installed between July and December 2020. Commercial fishing is permitted within the wind farm boundaries, with scallop dredging, trawling and creel potting known to take place 67 , sometimes close to the turbines (<50m; pers comm). The seabed substratum across the wind farms comprised two biotopes: medium to fine sands and muddy sands; or cobbles and pebbles, gravels and coarse sands 68 . Acoustic telemetry array and tagging In March 2022 an array of 84 VR2AR receivers (69 kHz, InnovaSea Systems Inc., USA) was deployed in the Beatrice and Moray East wind farms. The receivers were positioned 50 metres northeast of a wind turbine foundation in five clusters spread across the two wind farms (2 in Beatrice and 3 in Moray East: Figure 1b). The mean distance between receivers in the clusters was: B1 = 1.16km, B2 = 1.16km, ME1 = 1.55km, ME2 = 1.53km & ME3 = 1.52km. Receivers were deployed with ~65 kg of chain ballast and moored using an acoustic release canister (RS Aqua ARC; www.rsaqua.co.uk). The array was maintained by replacing receivers and downloading detection data every 6 to 12 months (depending upon weather) between deployment in March 2022 until final retrieval in June 2025. Fish were captured by rod and line within the Beatrice and Moray East wind farms and tagged with Innovasea V9TP 2X (69 kHz) acoustic transmitters during 2022 (April and June) and 2024 (April). In total, 21 Atlantic cod Gadus morhua (cod) (2022 n =7, 2024 n=14)and196 haddock Melanogrammus aeglefinus (haddock) (2022 n=46, 2024 n=150) were tagged.Prior to tagging, fish were held in aerated holding tanks before being transferred to a shallow anaesthetic bath dosed with 140 mg/1l MS222 (Manufacturer, Country). Once anaesthetised fish were measured and placed dorsally on a V-shaped cradle, where they were ram-ventilated with maintenance anaesthetic dose of MS-222. The acoustic transmitters were inserted into the peritoneal cavity via an incision (10-15 mm) and closed using two absorbable monofilament sutures. Lidocaine analgesic (2% spray) was topically applied to the surgical site prior to incision. After the surgical procedure, fish were transferred to a recovery, held for a short period prior to decompression becoming apparent and returned to the seabed in a release cage that opened 2-3 hours after descent. Fish tagging activities were reviewed and signed off by University of Exeter Ethical Review Committee. All procedures were performed in accordance with UK Home office project licences held by Scottish Marine Directorate and University of Exeter (PP9073566). and personal individual licences held by project team members (PILh). These licence permits, and procedures within, are regulated under the UK Animals (Scientific procedures) Act 1986. Tag data, range test and analyses The V9TP 2X tags transmit their unique identity code with associated temperature or depth (pressure) data alternately every 180 seconds (150 minimum – 210 maximum intervals). When within detection range, data from these detections were stored on the VR2AR receiver until download during maintenance cruises. Range detection tests conducted within the study array indicate that there was a high detection probability (≥80%) at distances ≤400m from receivers and near-zero detection probability (≤1%) beyond 750m (see Supplementary Materials for full methods; Supplementary Figure S2). Downloaded detections from implanted tags were first filtered to remove potential false detections when either: 1) only a single detection was received in a day; 2) detections were heard on two or three different receivers within 2.5 minutes or 3) and detections fell outside realistic intervals between detections considering distances, elapsed time and known maximum speed for each species. In this study, only those fish that were detected within the study array on at least 14 days were used in subsequent analysis. Predation identification Tag temperatures were expected to track the surrounding water when within an ectothermic fish. However, following previous work by Righton et al. (2016, if the tag temperature ever exceeded 30 °C, the fish was assumed to have been consumed by an endothermic marine mammal ~37 °C; 47 . For those cases where a tag temperature of > 30 °C demonstrated that a predation event had occurred, the time-series of detection locations, depths, and temperatures was further explored to investigate the nature of these likely predator prey events. First, the pattern of temperature and locations were used to determine whether predation may have occurred within the wind farms. Second, the distribution of depths before and after predation were compared with available data on dive profiles of seals and small cetaceans to explore the identity of the predator. Prey selection The relative abundance of cod and haddock that were available to marine mammal predators in the study area was estimated based on the proportion of each species tagged and detected for over 14 days (i.e. 15 cod vs 89 haddock). If marine mammal predators exhibit no preference between these prey species, then detections of predation events would be expected to occur at the same ratio. A one-tailed Fisher’s exact test was used to compare whether observed predation events differed from this expected ratio. Factors that might influence prey selection within the sample of tagged fish were also explored. First, species differences in the length of tagged fish were compared. These data were then used to explore potential species differences in calorific content using energy values of local cod and haddock of known size. Prey energy density (kJ/g) values were obtained using standard bomb calorimetry methods of 56 (haddock n =40, cod n =16) fish samples collected in summer and autumn (periods of predation events) of 2021 to 2024 within the study area for related studies of prey energy landscapes. These were used to derive length–total energy (kJ) relationships 62 that were applied to the measured lengths of the tagged fish to estimate their total energy content. Analysis of variance (ANOVA) models were used to compare length and energy between tagged cod and haddock, and predated cod and haddock. All data processing and analysis was implemented in R version 4.4.1 using Tidyverse packages for data manipulation and visualization 69 , and splines for flexible modelling 70 . Declarations Acknowledgements Thanks to the funders, PrePARED project partners, the skipper and crew of the vessel Waterfall (Moray First Marine Ltd). Author contributions A.W.J. Bicknell : Conceptualisation, Funding acquisition, Investigation, Data curation, Formal analysis, Visualization, Writing - Original Draft. R. Main : Investigation, Data curation, Writing – review & editing. S. Gierhart : Investigation, Data curation. M. P. Thompson : Conceptualisation, Funding acquisition, Visualization, Writing – review & editing. G. Hastie : Writing – review & editing. P. Wright : Data curation, Formal analysis, Writing – review & editing. M.J. Witt : Conceptualisation, Funding acquisition, Writing – review & editing. Data availability statement Data available via designated repository: https://doi.org/10.6084/m9.figshare.31860610 Ethics statement Fish tagging activities were reviewed and signed off by University of Exeter Ethical Review Committee. Tagging of fish was conducted under UK Home office project licences held by Scottish Marine Directorate and University of Exeter (PP9073566), and personal individual licences held by project team members (PILh). These licence permits, and procedures within, are regulated under the UK Animals (Scientific procedures) Act 1986. Declaration of competing interest The PrePARED project funder, the Crown Estate (Scotland), are the legal proprietors of the leased seabed on which the offshore wind farms in this study are located. The authors have no other competing interests. 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Habitat and resource segregation of two sympatric seals in the North Sea. Science of The Total Environment 764 , 142842, doi:https://doi.org/10.1016/j.scitotenv.2020.142842 (2021). Nilssen, K. T. et al. Diet and prey consumption of grey seals (Halichoerus grypus) in Norway. Marine Biology Research 15 , 137-149, doi:10.1080/17451000.2019.1605182 (2019). Smout, S., Rindorf, A., Hammond, P. S., Harwood, J. & Matthiopoulos, J. Modelling prey consumption and switching by UK grey seals. ICES Journal of Marine Science 71 , 81-89, doi:10.1093/icesjms/fst109 (2013). Tollit, D. J. et al. Variations in harbour seal Phoca vitulina diet and dive-depths in relation to foraging habitat. Journal of Zoology 244 , 209-222, doi:https://doi.org/10.1111/j.1469-7998.1998.tb00026.x (1998). Thompson, P. M. et al. Comparative distribution, movements and diet of harbour and grey seals from Moray Firth, NE Scotland. Journal of Applied Ecology , 1572-1584 (1996). Andreasen, H. et al. Diet composition and food consumption rate of harbor porpoises (Phocoena phocoena) in the western Baltic Sea. Marine Mammal Science 33 , 1053-1079, doi:https://doi.org/10.1111/mms.12421 (2017). Santos, M. B. et al. Variability in the diet of harbor porpoises ( Phocoena phocoena ) in Scottish Waters 1992–2003. Marine Mammal Science 20 , 1-27, doi:https://doi.org/10.1111/j.1748-7692.2004.tb01138.x (2004). Robinson, K. P. et al. Killer whale (Orcinus orca) occurrence in the Moray Firth, Northeast Scotland: incidental sightings, behavioural observations, and photo-identification. Aquatic Mammals 43 , 26 (2017). Thompson, P. M., Brookes, K. L. & Cordes, L. S. Integrating passive acoustic and visual data to model spatial patterns of occurrence in coastal dolphins. ICES Journal of Marine Science 72 , 651-660, doi:10.1093/icesjms/fsu110 (2014). Davis, R. W. & Thomson, D. Marine mammals . (Springer, 2019). Thompson, D., Onoufriou, J., Brownlow, A. & Morris, C. Data based estimates of collision risk: an example based on harbour seal tracking data around a proposed tidal turbine array in the Pentland Firth . (Scottish Natural Heritage, 2016). Hastie, G. D., Wilson, B. & Thompson, P. M. Diving deep in a foraging hotspot: acoustic insights into bottlenose dolphin dive depths and feeding behaviour. Marine Biology 148 , 1181-1188, doi:10.1007/s00227-005-0143-x (2006). Teilmann, J., Larsen, F. & Desportes, G. Time allocation and diving behaviour of harbour porpoises (Phocoena phocoena) in Danish and adjacent waters. Journal of Cetacean Research and Management 9 , 201-210 (2007). Rojano-Doñate, L. et al. Low hunting costs in an expensive marine mammal predator. Science Advances 10 , eadj7132, doi:doi:10.1126/sciadv.adj7132 (2024). Arnould, J. P. Y. et al. Use of Anthropogenic Sea Floor Structures by Australian Fur Seals: Potential Positive Ecological Impacts of Marine Industrial Development? PLOS ONE 10 , e0130581, doi:10.1371/journal.pone.0130581 (2015). Fernandez-Betelu, O., Graham, I. M. & Thompson, P. M. Reef effect of offshore structures on the occurrence and foraging activity of harbour porpoises. Frontiers in Marine Science 9 , doi:10.3389/fmars.2022.980388 (2022). Booth, C. G. et al. Estimating energetic intake for marine mammal bioenergetic models. Conservation Physiology 11 , coac083, doi:https://doi.org/10.1093/conphys/coac083 (2023). Corkeron, P. J., Bryden, M. & Hedstrom, K. in The Bottlenose Dolphin 329-336 (Elsevier, 1990). MacArthur, R. H. & Pianka, E. R. On Optimal Use of a Patchy Environment. The American Naturalist 100 , 603-609, doi:10.1086/282454 (1966). Spitz, J., Mourocq, E., Leauté, J.-P., Quéro, J.-C. & Ridoux, V. Prey selection by the common dolphin: Fulfilling high energy requirements with high quality food. Journal of Experimental Marine Biology and Ecology 390 , 73-77, doi:https://doi.org/10.1016/j.jembe.2010.05.010 (2010). Spitz, J. et al. Cost of Living Dictates what Whales, Dolphins and Porpoises Eat: The Importance of Prey Quality on Predator Foraging Strategies. PLOS ONE 7 , e50096, doi:10.1371/journal.pone.0050096 (2012). Bundy, A., Heymans, J. J., Morissette, L. & Savenkoff, C. Seals, cod and forage fish: A comparative exploration of variations in the theme of stock collapse and ecosystem change in four Northwest Atlantic ecosystems. Progress in Oceanography 81 , 188-206, doi:https://doi.org/10.1016/j.pocean.2009.04.010 (2009). Bowen, W. D. Role of marine mammals in aquatic ecosystems. Marine Ecology Progress Series 158 , 267-274 (1997). Gallon, S. L., Thompson, D. & Middlemas, S. J. What should I eat? Experimental evidence for prey selection in grey seals. Animal Behaviour 123 , 35-41, doi:https://doi.org/10.1016/j.anbehav.2016.09.012 (2017). Wright, P. F. C. et al. Quantifying prey energetic quality: energy density and size-energy relationships for marine fish and cephalopods. (In review). Spitz, J., Ridoux, V. & Brind'Amour, A. Let's go beyond taxonomy in diet description: testing a trait-based approach to prey–predator relationships. Journal of Animal Ecology 83 , 1137-1148, doi:https://doi.org/10.1111/1365-2656.12218 (2014). Langley, I. et al. Temporal changes in the dietary niche of sympatric seals provides insight into the role of competition in population declines. Oikos (2026). Cook, R. M., Holmes, S. J. & Fryer, R. J. Grey seal predation impairs recovery of an over-exploited fish stock. Journal of Applied Ecology 52 , 969-979, doi:https://doi.org/10.1111/1365-2664.12439 (2015). Carter, M. I. D. et al. At-sea distribution of seals on the Northwest European Shelf: Towards transboundary conservation and management. Journal of Applied Ecology 63 , e70236, doi:https://doi.org/10.1111/1365-2664.70236 (2026). Dunkley, F. & Solandt, J.-L. Windfarms, fishing and benthic recovery: Overlaps, risks and opportunities. Marine Policy 145 , 105262, doi:https://doi.org/10.1016/j.marpol.2022.105262 (2022). Parry, M. E. V. Guidance on Assigning Benthic Biotopes using EUNIS or the Marine Habitat Classification of Britain and Ireland (revised 2019). Report No. JNCC Report No. 546, (JNCC, Peterborough, 2019). Wickham, H. et al. Welcome to the Tidyverse. Journal of open source software 4 , 1686 (2019). R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, Vienna, Austria, 2025). Tables Tables 1 to 3 are available in the supplementary files section Additional Declarations Competing interest reported. The PrePARED project funder, the Crown Estate (Scotland), are the legal proprietors of the leased seabed on which the offshore wind farms in this study are located. The authors have no other competing interests. Supplementary Files BicknelletalSRSupplementarytMaterials.docx Tables.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 14 May, 2026 Reviewers agreed at journal 29 Apr, 2026 Reviewers agreed at journal 21 Apr, 2026 Reviewers invited by journal 07 Apr, 2026 Editor assigned by journal 07 Apr, 2026 Editor invited by journal 07 Apr, 2026 Submission checks completed at journal 30 Mar, 2026 First submitted to journal 30 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9214554","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":622159504,"identity":"56c433f1-4793-4938-aaaf-d71eb2eeb293","order_by":0,"name":"Anthony W. J. Bicknell","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9klEQVRIie2PsWrDMBCGzxzIi5ysChT7FWQEzevIFDplK5QMhRgKypLueo1SyKwgSBfTOUOWUGhXmUDwUErtJkOHqPXYQd9tP/dx9wMEAv8SCqYdGMRofsbyb4Ug6bZ4PwWOCuX9lGGZrExTbdNWqV/p9CMdl7hzkbr2KswM5GqxeRMEk6dcV1xcGCJYpCb+vwzlFpwtFCZLViteaIBLiNTUa2QnZaaQvjfFJ59piA+/Kvxb2VjZdiFQl1wyoN0V/2O57bpUNldIxEivRa6R3jD54q+fPj9Y16xtNozvd3t6l2Ysnj86d3vlr49nE+kXAoFAINCDL8ukTvQeVjjHAAAAAElFTkSuQmCC","orcid":"","institution":"University of Exeter","correspondingAuthor":true,"prefix":"","firstName":"Anthony","middleName":"W. J.","lastName":"Bicknell","suffix":""},{"id":622159505,"identity":"d901a94f-6e30-4d82-99cd-b185c5a577b5","order_by":1,"name":"Robert Main","email":"","orcid":"","institution":"Scottish Government","correspondingAuthor":false,"prefix":"","firstName":"Robert","middleName":"","lastName":"Main","suffix":""},{"id":622159506,"identity":"bb493856-c766-4c86-9db0-2353fe1397f0","order_by":2,"name":"Samuel Gierhart","email":"","orcid":"","institution":"University of Exeter","correspondingAuthor":false,"prefix":"","firstName":"Samuel","middleName":"","lastName":"Gierhart","suffix":""},{"id":622159507,"identity":"63eed440-dbbf-4127-b0fd-32981f6d5424","order_by":3,"name":"Paul Thompson","email":"","orcid":"","institution":"University of Aberdeen","correspondingAuthor":false,"prefix":"","firstName":"Paul","middleName":"","lastName":"Thompson","suffix":""},{"id":622159508,"identity":"7dde7ada-af2d-464d-a9c9-8856dac9614b","order_by":4,"name":"Gordon Hastie","email":"","orcid":"","institution":"University of St Andrews","correspondingAuthor":false,"prefix":"","firstName":"Gordon","middleName":"","lastName":"Hastie","suffix":""},{"id":622159509,"identity":"d2ec2384-61a1-48de-a48d-c306bec95ab7","order_by":5,"name":"Philippa F. C. Wright","email":"","orcid":"","institution":"University of St Andrews","correspondingAuthor":false,"prefix":"","firstName":"Philippa","middleName":"F. C.","lastName":"Wright","suffix":""},{"id":622159510,"identity":"96c12b87-501e-4fa8-abfd-59d5ac070fc8","order_by":6,"name":"Matthew J. Witt","email":"","orcid":"","institution":"University of Exeter","correspondingAuthor":false,"prefix":"","firstName":"Matthew","middleName":"J.","lastName":"Witt","suffix":""}],"badges":[],"createdAt":"2026-03-24 16:40:04","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9214554/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9214554/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106963134,"identity":"63e8ad25-ab36-43eb-b488-e9f1b2fba3ad","added_by":"auto","created_at":"2026-04-15 09:42:20","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":56095095,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ea\u003c/strong\u003e. Offshore wind farms around the UK and status as of \u0026nbsp;25th\u003csup\u003eth\u003c/sup\u003e August 2025 (EMODnet Human Activities Portal, \u0026nbsp;\u003ca href=\"https://emodnet.ec.europa.eu/en/human-activities\"\u003ehttps://emodnet.ec.europa.eu/en/human-activities\u003c/a\u003e). Beatrice and Moray East operational wind farms indicated in white with red boundaries. \u003cstrong\u003eb\u003c/strong\u003e. Beatrice and Moray East wind farm boundaries with turbine and cable configuration. Location of acoustic receivers (array) indicated with orange filled circles with names of 5 array clusters.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-9214554/v1/cd4b7cd5f664dd87665e0a1a.png"},{"id":106962129,"identity":"79113bce-cb70-451e-8843-df8cc2b37305","added_by":"auto","created_at":"2026-04-15 09:34:25","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":14489006,"visible":true,"origin":"","legend":"\u003cp\u003eVariation in tag temperature through time for six tagged fish where an increase in tag temperature above 30°C (shown by the red dashed line) confirmed predation by a marine mammal. Dark blue circles = fish (pre-predation), light blue circles = mammal/external (post-predation), red cross = first detection after 10°C tag termperature increase.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-9214554/v1/948da5ff3e3ca6486f25c855.png"},{"id":106962134,"identity":"797d908c-51bf-4de0-b2ee-aee2ca44914a","added_by":"auto","created_at":"2026-04-15 09:34:34","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":10686971,"visible":true,"origin":"","legend":"\u003cp\u003eLocations of tags where repeated detections with temperature readings of above 30°C indicated that they were within the stomach of a marine mammal. Data are shown for the three tags with at least 20 detections, over periods of \u003cstrong\u003ea.\u003c/strong\u003e 4 days, \u003cstrong\u003eb.\u003c/strong\u003e6 days and \u003cstrong\u003ec.\u003c/strong\u003e 24 hours. Green cross = release location, red cross = location of first detection after 10°C tag termperature increase, light blue line = mammal movement based on subsequent detections.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-9214554/v1/6fa8585ec4e019ef396cb32b.png"},{"id":106964680,"identity":"eb31c99a-59e7-4f20-95a5-7acb9d5e526a","added_by":"auto","created_at":"2026-04-15 09:51:08","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":210392,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ea\u003c/strong\u003e. Depth distribution of all detections of tags registering \u0026gt; 30° C which were assumed to have been predated and transmitted from within a marine mammal. These can be compared with available data on the time that potential predators may be spending at different depths when foraging\u0026nbsp;in similar\u0026nbsp;shallow coastal environments. \u003cstrong\u003eb.\u003c/strong\u003e Re-presents data from Figure 7 in Thompson, et al. \u003csup\u003e48\u003c/sup\u003e), which were collected from 14 GSM-GPS tagged harbour seals diving in the Pentland Firth, Scotland. \u003cstrong\u003ec).\u003c/strong\u003e re-presents data form Figure 6 of Teilmann, et al. \u003csup\u003e50\u003c/sup\u003e), collected from 14 porpoises equipped with satellite linked time-at-depth recorders that were diving in Danish coastal waters.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-9214554/v1/a8566d0113fd15970126e337.png"},{"id":106966408,"identity":"da0a4a6e-fbe6-4916-9451-cac627a19fd3","added_by":"auto","created_at":"2026-04-15 09:58:51","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":27361608,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ea\u003c/strong\u003e. Depth and temperature timeseries panels for tag 65596 after predation (dark grey shading = night, light grey = dawn/dusk, red rectangles = areas of notable temperature reduction and associated depth detections). \u003cstrong\u003eb\u003c/strong\u003e. Spatial illustration of detection locations across the B1 receiver cluster for tag 65596 after predation (small light grey filled circles = wind turbines, black dot = detection location (amount not shown), light blue line with direction arrows = mammal movement). Coloured filled circles = receiver station (see legend), green cross = cod release location, red cross = first detection after consumption by marine mammal.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-9214554/v1/0683bdcd646acb43b5fc2ee1.png"},{"id":106964694,"identity":"ea77dc79-946a-419d-b1f3-01c1e1c25e6b","added_by":"auto","created_at":"2026-04-15 09:51:12","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":7268281,"visible":true,"origin":"","legend":"\u003cp\u003eBox and whisker plots for length (a) and estimated energy (b) of all tagged Atlantic cod and haddock. \u0026nbsp;Red dots = predated Atlantic cod; Green dots = mean values.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-9214554/v1/f5b455f995e0a42a4927f240.png"},{"id":106885884,"identity":"2a0c13cb-344a-4da5-a1e6-7377afefd706","added_by":"auto","created_at":"2026-04-14 12:35:14","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":714530,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9214554/v1/833c4c37-cfd7-4bd9-a4c3-0f2d2de45992.pdf"},{"id":106964365,"identity":"320f00c3-f1d6-4d27-a5e1-a85e42ccea68","added_by":"auto","created_at":"2026-04-15 09:50:05","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":2634850,"visible":true,"origin":"","legend":"","description":"","filename":"BicknelletalSRSupplementarytMaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-9214554/v1/d06b0f013e761cf022d4112f.docx"},{"id":106963173,"identity":"e0af1b14-2f82-415d-be85-5fc2735192e3","added_by":"auto","created_at":"2026-04-15 09:42:40","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":26945,"visible":true,"origin":"","legend":"","description":"","filename":"Tables.docx","url":"https://assets-eu.researchsquare.com/files/rs-9214554/v1/52f18fa919488c1be7895ac1.docx"}],"financialInterests":"Competing interest reported. The PrePARED project funder, the Crown Estate (Scotland), are the legal proprietors of the leased seabed on which the offshore wind farms in this study are located. The authors have no other competing interests.","formattedTitle":"Evidence of predation events by marine mammals at offshore wind farms","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCoastal and offshore seascapes are changing rapidly around the world with the introduction of artificial structures to harness wind, wave, and tidal energy \u003csup\u003e1,2\u003c/sup\u003e. Wind turbine foundations and subsea infrastructure, e.g. scour protection and cables, are known to effect the presence and distribution of marine species \u003csup\u003e3\u003c/sup\u003e but how these changes will ultimately alter population dynamics, community structure, behaviour and predator/prey interactions at a local or regional scales is still to be determined. \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eKnowledge of how marine mammals may be affected by offshore wind infrastructure is important across European waters given they are protected under the Habitats Directive (92/43/EEC), and their consideration is a requirement for Environmental Impact Assessments (EIA) during consenting. Currently, most attention has focussed on the potential impacts of underwater noise and vessel traffic during wind farm construction and operation \u003csup\u003e4-7\u003c/sup\u003e. However, it is also important to understand how wind farm infrastructure may influence foraging behaviour through changes in prey availability. The distribution of fish and other prey species can be altered through aggregation effects at wind turbine foundations or subsea cables \u003csup\u003e8-13\u003c/sup\u003e. These in turn may have consequences for food availability, individual energy budgets and potential population effects for marine mammals \u003csup\u003e14\u003c/sup\u003e. Movement and inferred foraging behaviour of two species of seal (Harbour \u003cem\u003ePhoca vitulina\u003c/em\u003e and grey \u003cem\u003eHalichoerus grypus)\u003c/em\u003e has been shown to concentrate around wind turbine foundations and cables \u003csup\u003e15\u003c/sup\u003e providing evidence for such behavioural changes. Similarly, higher densities of harbour porpoise have been recorded during operational phases at some North Sea wind farms \u003csup\u003e16\u003c/sup\u003e. However, direct evidence of predation around structures is lacking, and it remains possible that increased use of wind farms may also be driven by reductions in disturbance from other factors such as changes in fisheries or vessel traffic intensity \u003csup\u003e4,5\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAcoustic telemetry is widely used to study the ecology and movements of marine animals \u003csup\u003e17\u003c/sup\u003e and provides opportunities to explore how fish movement and behaviour can be altered by the introduction of offshore wind farm structures \u003csup\u003e18\u003c/sup\u003e. Tags are attached or inserted into individual fish and subsequently emit coded acoustic signals for several months or years. These signals can be detected by fixed hydrophone receivers at strategic locations, or on mobile receivers deployed on vessels or animals \u003csup\u003e18,19\u003c/sup\u003e. Tag variants also provide sensor data including depth and temperature at the time of detection, which can reveal movements throughout the water column and alterations in the ambient temperature the fish is experiencing. When assessed in combination, the temperature, depth and location data can identify changes in expected fish behaviour that could infer mortality \u003csup\u003e20,21\u003c/sup\u003e or consumption by a warm blooded predator, e.g. cetacean or pinniped \u003csup\u003e18,21,22\u003c/sup\u003e. Temperature of (non-acoustic) archival satellite tags has also been used to identify predation of European eels \u003cem\u003eAnguilla anguilla\u003c/em\u003e by endothermic fish (10 °C increase from ambient) or marine mammals (\u0026gt; 30°C) during annual migration \u003csup\u003e23,24\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eHere, data from a study of the movements of two demersal gadoid species around northern North Sea offshore wind farms were used to explore patterns of marine mammal predation. The study area is known to support a range of marine mammal species, with harbour seal, grey seal and harbour porpoise \u003cem\u003ePhocoena phocoena\u003c/em\u003e occurring most commonly across this region \u003csup\u003e25-27\u003c/sup\u003e. The primary aim was to use fish acoustic tag detection and sensor data to provide direct evidence of marine mammal foraging activity. In addition, these data were explored to assess whether they could provide insights into the identity of the predators and/or evidence of prey selection.\u0026nbsp;\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cem\u003eGeneral detection patterns of tagged fish\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eOf the 217 tagged fish, 104 were detected for 14 days or more (either contiguous or non-contiguous) and used in subsequent analysis. Of the 104 fish, 57 (~55%) were tagged in Beatrice and 47 (~45%) in Moray East wind farm. These included 7 cod and 28 haddock captured in 2022, and 8 cod and 61 haddock captured in 2024. The overall mean length for the 15 tagged cod were 30cm (range 24-36cm), and for the 89 haddock were 31cm (range 24-38cm) (Supplementary Table S2). Once filtered for potential false detections, the array receivers recorded 2,285,988 detections of which 1,142,783 contained temperature data and 1,143,205 contained depth data. The maximum detection period for fish tagged in April 2022 was 422 days, in June 2022 was 301 days, and in April 2024 was 396 days (\u003cstrong\u003eTable 1\u003c/strong\u003e). The detections for individual cod and haddock tagged in each year overlapped in time and the array clusters on which they were recorded (Supplementary Table S2; Supplementary Figure S3 \u0026amp; S4).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003ePredation events on tagged fish\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eDetections where the tag temperature exceeded 30 °C indicated that six of the 104 fish detected for more than 14 days were predated by a marine mammal: three in the 2022 cohort of tagged fish and three in the 2024 cohort (\u003cstrong\u003eFigure 2;\u0026nbsp;\u003c/strong\u003eSupplementary Figure S4). \u0026nbsp;In all but one case, tagged fish were detected regularly within the wind farm up until the point at which the temperature increased to 30 °C. In the remaining case (ID 65594; Supplementary Figure S5c), the fish appeared to have left the study array for around five months before the tag, at this point registering a temperature of \u0026gt;30 °C, was again detected on the array. \u0026nbsp;In two cases (ID 65594 \u0026amp; ID 70942), tags were subsequently detected at ambient temperature after periods above 30 °C (\u003cstrong\u003eFigure 2c \u0026amp; f\u003c/strong\u003e;\u0026nbsp;Supplementary Figure S5c\u0026nbsp;\u0026amp; S6d), indicating potential expulsion of the tag by the predator through regurgitation or defecation. \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTags registering \u0026gt; 30° C were recorded at multiple receivers within both offshore wind farms (\u003cstrong\u003eFigure 3\u003c/strong\u003e). \u0026nbsp;In four of the six cases, a rapid increase in tag temperature occurred within 3 hrs of a prior detection at ambient sea water temperature, either at the same receiver station (ID 65589 \u0026amp; 70865) or at another receiver in the same cluster (ID 65596 \u0026amp; 70942), providing evidence for the location and approximate time of predation (\u003cstrong\u003eTable 2\u003c/strong\u003e;Supplementary Figure S6 and S7). The two remaining tags had a longer delay between the temperature change detections (\u003cstrong\u003eTable 2;\u0026nbsp;\u003c/strong\u003eSupplementary Figure S6 and S7). Tag 70861 was detected within the same array cluster after 1.7 days, indicating that predation could have recently taken place within the wind farm (Supplementary Figure S6b). In contrast, tag 65594 was detected with an elevated temperature several months after the previous detection at ambient sea water temperature, providing little insight on where or when predation occurred (Supplementary Figure S5c \u0026amp; Sd). \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eMovements of predators following ingestion of tagged fish.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eFollowing ingestion by predators, tags were detected from 4 - 123 times on 1 to 5 days (\u003cstrong\u003eTable 3\u003c/strong\u003e). Although limited in extent, data from three tags with \u0026gt;20 detections provide evidence that predators moved between turbine locations over these relatively short time scales (\u003cstrong\u003eFigure 3;\u0026nbsp;\u003c/strong\u003eSupplementary Figure S7). Tag 65589 provided a ~4 day detection period after temperature increase (\u003cstrong\u003eTable 3\u003c/strong\u003e), with detections on six receivers located between ~1.13 – 6.5km apart across two array clusters (\u003cstrong\u003eFigure 3a;\u0026nbsp;\u003c/strong\u003eSupplementary Figure S7b). Tag 65594 was detected for the longest post temperature increase (~6 days; \u003cstrong\u003eTable 3\u003c/strong\u003e) at six receivers at distances from ~1.12km within a cluster to over 10 km between the different clusters (\u003cstrong\u003eFigure 3b;\u0026nbsp;\u003c/strong\u003eSupplementary Figure S7d). Finally, tag 65596 was detected for 24 hours after the temperature increase on nine different receivers that were located between ~1.16 – 3.1 km apart in array cluster B1 (\u003cstrong\u003eFigure 3c;\u0026nbsp;\u003c/strong\u003eSupplementary Figure S7f). \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eData on the depth of tags following predations were sparse, with between one and 68 depth detections per tag (\u003cstrong\u003eTable 3\u003c/strong\u003e). The overall depth distribution of all detections from tags registering \u0026gt; 30°C shows that most tags were detected while at depths below 30 m, at or close the seabed around the receivers (\u003cstrong\u003eFigure 4a\u003c/strong\u003e). \u0026nbsp;This depth distribution was more similar to previously published data on dive patterns of harbour seals (\u003cstrong\u003eFigure 4b\u003c/strong\u003e) than those from harbour porpoises (\u003cstrong\u003eFigure 4c\u003c/strong\u003e).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOnly one tag (65596) provided regular depth readings through the 24 hours over which sustained high temperatures indicated that it remained within the predator. These revealed eight vertical ascents to the surface with subsequent return to below 40m (\u003cstrong\u003eFigure 5a\u003c/strong\u003e), and detections on nine different receivers located between ~1.16 – 3.1 km apart in array cluster B1 (\u003cstrong\u003eFigure 5b)\u003c/strong\u003e. The temperature detections showed variation post increase with two noteworthy declines below 34 °C, one of which coincided with 3 vertical movements from the surface to below 40m (\u003cstrong\u003eFigure 5a\u003c/strong\u003e). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003ePrey selection\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAll six observed predation events involved tagged cod (6 of 15 tagged cod). No haddock were identified as being predated during their detection periods (0 of 89 tagged haddock). The probability of detecting zero haddock predation events based on the observed ratios was very low (Fishers exact test: p-value \u0026lt;0.0001; Supplementary Table S3), suggesting a bias towards predation upon tagged cod given their availability to marine mammal predators.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThere was no significant difference between the lengths of all tagged cod or just predated cod, and haddock (tagged cod vs haddock: p-value 0.54; predated cod vs haddock: p-value 0.88; \u003cstrong\u003eFigure 6a\u003c/strong\u003e). However, there was significant differences between the energy estimates for tagged cod or predated cod when compared to haddock (tagged cod vs haddock: p-value \u0026lt;0.0001; predated cod vs haddock: p-value \u0026lt;0.0001; \u003cstrong\u003eFigure 6b\u003c/strong\u003e).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eChanges in temperature, depth profile and spatial movement of 104 cod and haddock acoustically tagged and released within two operational wind farms indicated that 6 cod had been predated by a marine mammal. Two of these events could be identified as taking place close to wind turbine foundations (\u0026lt;400m) where the fish where in proximity, and location detections post-consumption of one cod revealed putative attendance at multiple turbine foundations in quick succession suggesting prospecting. To our knowledge, this is the first direct evidence of marine mammal predation of fish at wind turbine foundations, and further evidence that they represent foraging sites for some individuals \u003csup\u003e15\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eProximity to turbine foundation and foraging\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe probability of detecting the acoustic tag transmissions on a receiver within the OWF array was high (\u0026gt;85%) up to 400m away and declined sharply between 400-600 m (Supplementary Figure\u003cstrong\u003e\u0026nbsp;S\u003c/strong\u003e2). The combination of range test results and removing detections on more than one receiver within a short interval, provided confidence in that most tag detections were within 400 m of a receiver deployed next to a turbine foundation. Finer scale position data (i.e. accuracy greater than 400 m) are not available using the array design deployed in this study, and as such closer attendance of the fish or marine mammal to the turbine foundation is not certain. However, it is known that cod and haddock are attracted to within 100m of turbine foundations due the reefing effect that creates hard substrate habitat and/or patches of enhanced prey availability in \u0026nbsp;soft sediment environments \u003csup\u003e8,28-30\u003c/sup\u003e. Cod have been particularly well studied at turbine foundations in the southern North Sea, with evidence of strong attraction and residency within 50 m or less of the structures \u003csup\u003e28,31\u003c/sup\u003e. Similar residency behaviour has been found close to hard substrate artificial reefs \u003csup\u003e32\u003c/sup\u003e, and much higher abundance within 100m has been observed at an oil and gas platform \u003csup\u003e33\u003c/sup\u003e. Three of the six predated cod (65589, 65596, 70942; Supplementary Figure\u003cstrong\u003e\u0026nbsp;S\u003c/strong\u003e5 \u0026amp; S6) demonstrated periods (months) of consistent detections on single receivers, with only limited detections on neighbouring receivers signifying local residency and proximity to the turbine foundations. After being predated upon, the detections for two of these tags contrasted with the previous local residency behaviour, with one being detected on nine receivers within a day (\u003cstrong\u003eFigure 3; Table 3\u003c/strong\u003e), indicating the predator was moving throughout the wind farm, and coming in proximity to other turbine foundations. Furthermore, two shorter periods of lower temperature for this individual (\u003cstrong\u003eFigure 5\u003c/strong\u003e) may be related to prey and ambient water entering the stomach during subsequent capture events while foraging \u003csup\u003e22,34,35\u003c/sup\u003e, which also align with depth profiles of dive behaviour (\u003cstrong\u003eFigure 6\u003c/strong\u003e). \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eMarine mammal predator\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe primary identifier of predation by a marine mammal was the marked increase in tag temperature from periods of temperature equivalent to the ambient water. Several marine mammal species frequent the Moray Firth region, but there are three species that are most likely to occur within these wind farm sites harbour porpoise Phocoena phocoena L.: \u003csup\u003e26,\u003c/sup\u003egrey seal Halichoerus grypus \u0026amp; harbour seal Phoca vitulina: \u003csup\u003e27,\u003c/sup\u003ebottlenose dolphin Tursiops truncatus: \u003csup\u003e36\u003c/sup\u003e. Cod have been identified in the diet of all three of these species, both in the Moray Firth (Santos et al., 2004; Thompson et al., 1996; Tollit et al., 1998; Wilson and Hammond, 2019)\u0026nbsp;and other regions of the North Sea \u003csup\u003e37-44\u003c/sup\u003e. However, other cetaceans such as bottlenose dolphins, white-beaked dolphins \u003cem\u003eLagenorhynchus albirostris\u003c/em\u003e, common dolphins \u003cem\u003eDelphinus delphis\u003c/em\u003e, Rissos’ dolphins \u003cem\u003eGrampus griseus\u003c/em\u003e and killer whales \u003cem\u003eOrcinus orca\u003c/em\u003e are occasionally recorded in the Outer Moray Firth \u003csup\u003e45,46\u003c/sup\u003e, and all these could potentially be predating upon this size class of cod.\u003c/p\u003e\n\u003cp\u003eWhilst the body temperature of different marine mammals may vary slightly \u003csup\u003e47\u003c/sup\u003e, tag temperatures cannot\u0026nbsp;be used to differentiate between potential species of marine mammal predators. Similarly, neither predation events (\u003cstrong\u003eTable 3\u003c/strong\u003e) nor the presence of different marine mammals demonstrates repeated seasonal patterns, so this did not provide insights to the potential identity of the predators. Low tag transmission rates (~3/ minute) also limit the potential to relate the time-series of acoustic detections to dive profiles of potential predators. Nonetheless, available data suggest that the depth distribution of predated tags is more likely to represent that of a diving seal than a small cetacean (\u003cstrong\u003eFigure 4\u003c/strong\u003e). Information on the amount of time that marine mammals spend at different waters depths is available for a limited number of species. However, dive profiles of harbour seals and grey seals indicate that they generally feed close to the bottom and spend much of the dive at depth when in shallow coastal waters e.g. \u003csup\u003e41\u003c/sup\u003e. \u0026nbsp;This is illustrated in \u003cstrong\u003eFigure 4b\u003c/strong\u003e using data for harbour seals taken from Thompson, et al. \u003csup\u003e48\u003c/sup\u003e), which provides information on the proportion of a predator’s time that is spent at different water depths. In contrast, harbour porpoises and other small cetaceans typically spend the majority of their time in the upper layers of the water column in similar shallow coastal waters \u003csup\u003e49-51\u003c/sup\u003e. For example, Teilmann, et al. \u003csup\u003e50\u003c/sup\u003e) showed that tagged harbour porpoises spent 45-63% of their time in the upper 2m, and depth distributions of porpoises taken from their study highlight that much less time is spent at depth compared to seals (Figure 4c). Without further evidence and sampling it is not possible to confirm the identity (or identities) of the species responsible. Pinnipeds are known to interact with OWF turbine foundations and cable infrastructure \u003csup\u003e15,52\u003c/sup\u003e. Based on the depth profiles of tag detections and tagged seals and porpoises, we suggest that tagged fish were most likely to be predated by either harbour or grey seals. However, we cannot exclude the possibility that some fish were taken by other predators such as harbour porpoises given they are known to forage close (\u0026lt;200m) to older structures in this area \u003csup\u003e53\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eEvidence for prey selection\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe sample of tagged fish available to marine mammal predators at the study site was heavily skewed towards haddock, yet all the fish that were predated were cod. Thus, although the identity of the predators remains uncertain, these data provide rare evidence for prey selection in a wild marine mammal. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePrey acquisition is a crucial component of marine mammal bioenergetics, balancing foraging effort (efficiency) with ingested energy required for survival and reproduction \u0026nbsp;\u003csup\u003e54,55\u003c/sup\u003e. Targeting prey with an optimal balance between energetic quality and capture effort is therefore expected Optimal foraging theory; \u003csup\u003e56\u003c/sup\u003e \u003csup\u003e51,57,58\u003c/sup\u003e and the preferential selection of certain species and/or age classes by marine mammals means they are likely to have an important role in shaping marine communities \u003csup\u003e59,60\u003c/sup\u003e. To date, evidence for prey selection has been constrained by uncertainties over the true availability of different prey to marine mammal predators. For example, there is known to be potential bias in fisheries surveys where these are used to characterise prey availability for comparison with marine mammal diet (Spitz et al. 2010). Previously, the only studies which have precise data on the relative abundance of different prey species are those that have directly fed animals either in captivity \u003csup\u003e61\u003c/sup\u003e around fisheries vessels \u003csup\u003e55\u003c/sup\u003e. Although previous diet studies indicate that marine mammal populations generally consume a wide range of species, individuals often show distinct preferences for particular species when offered a choice of prey \u003csup\u003e55\u003c/sup\u003e. Our data suggest that the marine mammal(s) foraging at this wind farm site showed a preference for cod over haddock. Cod and haddock tagged in this study did not differ significantly in length, but energy estimates where significantly higher for cod (predated or not) indicating prey quality could be a reason for this apparent selection. The length-weight relationship and energy estimates were derived from a relatively small number of cod from the study area, so could be viewed with caution. However, the difference holds when using additional samples (haddock = 26; cod = 10) from another North Sea site as part of same energetic study \u003csup\u003e62\u003c/sup\u003e suggesting a reliable relationship. Selection could also be influenced by differences in the behaviour of the prey species that affect catchability, or other functional traits affecting choice \u003csup\u003e63\u003c/sup\u003e. A synergistic effect of quality and behaviour of prey may enhance selection for certain species and could be evident for cod in this study, which had higher energetic estimates and show residency to a relatively small area close to a few (sometimes 1) turbine foundations. Nevertheless, this new evidence for the likely existence of prey selection for cod is important to consider when assessing predator-prey dynamics and interactions between competing predators \u003csup\u003e64\u003c/sup\u003e or with commercial fisheries \u003csup\u003e40,65\u003c/sup\u003e.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe most likely predators of the tagged cod in this study are seals (grey or common) that commonly occur in the Moray Firth, and have been observed and tracked in the offshore wind farm boundaries \u003csup\u003e27,66\u003c/sup\u003e. The predation events occurred close to wind turbine foundations where cod have been shown to often reside, attracted by the hard substrate structures and possible enhanced feeding opportunities \u003csup\u003e28\u003c/sup\u003e. Lack of predation of the more numerous haddock suggests prey selection, and the higher energetic estimates for the cod provide support for optimal foraging of high-quality prey. The apparent prospecting of one seal post consumption of the tagged cod, provide further evidence that offshore wind farm foundations offer feeding opportunities for predators by aggregating prey species. The consequences of this behaviour for prey and predator populations are still to be determined but needs consideration with the continued large-scale development of offshore wind farms.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cem\u003eStudy location\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe study took place within the Beatrice and Moray East offshore wind farms in the Moray Firth, Scotland (Figure 1). The wind farms are located 7.5-23 nautical miles offshore in water depth of 35-60 metres. Beatrice operates 84 7MW turbines with 4-leg jacket foundations that were installed between August 2017 and July 2018. Moray East operates 100 9.5MW turbines with 3 leg jackets that were installed between July and December 2020. Commercial fishing is permitted within the wind farm boundaries, with scallop dredging, trawling and creel potting known to take place \u003csup\u003e67\u003c/sup\u003e, sometimes close to the turbines (\u0026lt;50m; pers comm). The seabed substratum across the wind farms comprised two biotopes: medium to fine sands and muddy sands; or cobbles and pebbles, gravels and coarse sands \u003csup\u003e68\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAcoustic telemetry array and tagging\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eIn March 2022 an array of 84 VR2AR receivers (69 kHz, InnovaSea Systems Inc., USA) was deployed in the Beatrice and Moray East wind farms. The receivers were positioned 50 metres northeast of a wind turbine foundation in five clusters spread across the two wind farms (2 in Beatrice and 3 in Moray East: Figure 1b). The mean distance between receivers in the clusters was: B1 = 1.16km, B2 = 1.16km, ME1 = 1.55km, ME2 = 1.53km \u0026amp; ME3 = 1.52km. Receivers were deployed with ~65 kg of chain ballast and moored using an acoustic release canister (RS Aqua ARC; www.rsaqua.co.uk). The array was maintained by replacing receivers and downloading detection data every 6 to 12 months (depending upon weather) between deployment in March 2022 until final retrieval in June 2025.\u003c/p\u003e\n\u003cp\u003eFish were captured by rod and line within the Beatrice and Moray East wind farms and tagged with Innovasea V9TP 2X (69 kHz) acoustic transmitters during 2022 (April and June) and 2024 (April). In total, 21 Atlantic cod \u003cem\u003eGadus morhua\u0026nbsp;\u003c/em\u003e(cod) (2022 n =7, 2024 n=14)and196 haddock \u003cem\u003eMelanogrammus aeglefinus\u0026nbsp;\u003c/em\u003e(haddock) (2022 n=46, 2024 n=150) were tagged.Prior to tagging, fish were held in aerated holding tanks before being transferred to a shallow anaesthetic bath dosed with 140 mg/1l MS222 (Manufacturer, Country). Once anaesthetised fish were measured and placed dorsally on a V-shaped cradle, where they were ram-ventilated with maintenance anaesthetic dose of MS-222. The acoustic transmitters were inserted into the peritoneal cavity via an incision (10-15 mm) and closed using two absorbable monofilament sutures. Lidocaine analgesic (2% spray) was topically applied to the surgical site prior to incision. After the surgical procedure, fish were transferred to a recovery, held for a short period prior to decompression becoming apparent and returned to the seabed in a release cage that opened 2-3 hours after descent.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFish tagging activities were reviewed and signed off by University of Exeter Ethical Review Committee. All procedures were performed in accordance with UK Home office project licences held by Scottish Marine Directorate and University of Exeter (PP9073566). and personal individual licences held by project team members (PILh). These licence permits, and procedures within, are regulated under the UK Animals (Scientific procedures) Act 1986.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eTag data, range test and analyses\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe V9TP 2X tags transmit their unique identity code with associated temperature or depth (pressure) data alternately every 180 seconds (150 minimum \u0026ndash; 210 maximum intervals). When within detection range, data from these detections were stored on the VR2AR receiver until download during maintenance cruises. Range detection tests conducted within the study array indicate that there was a high detection probability (\u0026ge;80%) at distances \u0026le;400m from receivers and near-zero detection probability (\u0026le;1%) beyond 750m (see Supplementary Materials for full methods; Supplementary Figure S2).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDownloaded detections from implanted tags were first filtered to remove potential false detections when either: 1) only a single detection was received in a day; 2) detections were heard on two or three different receivers within 2.5 minutes or 3) and detections fell outside realistic intervals between detections considering distances, elapsed time and known maximum speed for each species. In this study, only those fish that were detected within the study array on at least 14 days were used in subsequent analysis.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003ePredation identification\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTag temperatures were expected to track the surrounding water when within an ectothermic fish. However, following previous work by Righton et al. (2016, if the tag temperature ever exceeded 30 \u0026deg;C, the fish was assumed to have been consumed by an endothermic marine mammal ~37 \u0026deg;C; \u003csup\u003e47\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFor those cases where a tag temperature of \u0026gt; 30 \u0026deg;C demonstrated that a predation event had occurred, the time-series of detection locations, depths, and temperatures was further explored to investigate the nature of these likely predator prey events. First, the pattern of temperature and locations were used to determine whether predation may have occurred within the wind farms. Second, the distribution of depths before and after predation were compared with available data on dive profiles of seals and small cetaceans to explore the identity of the predator. \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003ePrey selection\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe relative abundance of cod and haddock that were available to marine mammal predators in the study area was estimated based on the proportion of each species tagged and detected for over 14 days (i.e. 15 cod vs 89 haddock). If marine mammal predators exhibit no preference between these prey species, then detections of predation events would be expected to occur at the same ratio. A one-tailed Fisher\u0026rsquo;s exact test was used to compare whether observed predation events differed from this expected ratio.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFactors that might influence prey selection within the sample of tagged fish were also explored. First, species differences in the length of tagged fish were compared. These data were then used to explore potential species differences in calorific content using energy values of local cod and haddock of known size. Prey energy density (kJ/g) values were obtained using standard bomb calorimetry methods of 56 (haddock \u003cem\u003en\u003c/em\u003e=40, cod \u003cem\u003en\u003c/em\u003e=16) fish samples collected in summer and autumn (periods of predation events) of 2021 to 2024 within the study area for related studies of prey energy landscapes. \u0026nbsp;These were used to derive length\u0026ndash;total energy (kJ) relationships \u003csup\u003e62\u003c/sup\u003e that were applied to the measured lengths of the tagged fish to estimate their total energy content. Analysis of variance (ANOVA) models were used to compare length and energy between tagged cod and haddock, and predated cod and haddock. \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAll data processing and analysis was implemented in R version 4.4.1 using Tidyverse packages for data manipulation and visualization \u003csup\u003e69\u003c/sup\u003e, and splines for flexible modelling \u003csup\u003e70\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThanks to the funders, PrePARED project partners, the skipper and crew of the vessel Waterfall (Moray First Marine Ltd).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA.W.J. Bicknell\u003c/strong\u003e: Conceptualisation, Funding acquisition, Investigation, Data curation, Formal analysis, Visualization, Writing - Original Draft. \u003cstrong\u003eR. Main\u003c/strong\u003e: Investigation, Data curation, Writing \u0026ndash; review \u0026amp; editing. \u003cstrong\u003eS. Gierhart\u003c/strong\u003e: Investigation, Data curation. \u003cstrong\u003eM. P. Thompson\u003c/strong\u003e: Conceptualisation, Funding acquisition, Visualization, Writing \u0026ndash; review \u0026amp; editing. \u003cstrong\u003eG. Hastie\u003c/strong\u003e: Writing \u0026ndash; review \u0026amp; editing. \u003cstrong\u003eP. Wright\u003c/strong\u003e: Data curation, Formal analysis, Writing \u0026ndash; review \u0026amp; editing. \u003cstrong\u003eM.J. Witt\u003c/strong\u003e: Conceptualisation, Funding acquisition, Writing \u0026ndash; review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData available via designated repository: https://doi.org/10.6084/m9.figshare.31860610\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFish tagging activities were reviewed and signed off by University of Exeter Ethical Review Committee. Tagging of fish was conducted under UK Home office project licences held by Scottish Marine Directorate and University of Exeter (PP9073566), and personal individual licences held by project team members (PILh). These licence permits, and procedures within, are regulated under the UK Animals (Scientific procedures) Act 1986.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of competing interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe PrePARED project funder, the Crown Estate (Scotland), are the legal proprietors of the leased seabed on which the offshore wind farms in this study are located. The authors have no other competing interests.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was funded by the Crown Estate and Crown Estate (Scotland) as part of the Offshore Wind Evidence \u0026amp; Change programme\u0026rsquo;s (UK) Predators and Prey Around Renewable Energy Developments (PrePARED) project.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003ePaolo, F.\u003cem\u003e et al.\u003c/em\u003e Satellite mapping reveals extensive industrial activity at sea. \u003cem\u003eNature\u003c/em\u003e \u003cstrong\u003e625\u003c/strong\u003e, 85-91, doi:10.1038/s41586-023-06825-8 (2024).\u003c/li\u003e\n\u003cli\u003eCui, Y. \u0026amp; Zhao, H. 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C.\u003cem\u003e et al.\u003c/em\u003e Quantifying prey energetic quality: energy density and size-energy relationships for marine fish and cephalopods. (In review).\u003c/li\u003e\n\u003cli\u003eSpitz, J., Ridoux, V. \u0026amp; Brind\u0026apos;Amour, A. Let\u0026apos;s go beyond taxonomy in diet description: testing a trait-based approach to prey\u0026ndash;predator relationships. \u003cem\u003eJournal of Animal Ecology\u003c/em\u003e \u003cstrong\u003e83\u003c/strong\u003e, 1137-1148, doi:https://doi.org/10.1111/1365-2656.12218 (2014).\u003c/li\u003e\n\u003cli\u003eLangley, I.\u003cem\u003e et al.\u003c/em\u003e Temporal changes in the dietary niche of sympatric seals provides insight into the role of competition in population declines. \u003cem\u003eOikos\u003c/em\u003e (2026).\u003c/li\u003e\n\u003cli\u003eCook, R. M., Holmes, S. J. \u0026amp; Fryer, R. J. 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Guidance on Assigning Benthic Biotopes using EUNIS or the Marine Habitat Classification of Britain and Ireland (revised 2019). Report No. JNCC Report No. 546, (JNCC, Peterborough, 2019).\u003c/li\u003e\n\u003cli\u003eWickham, H.\u003cem\u003e et al.\u003c/em\u003e Welcome to the Tidyverse. \u003cem\u003eJournal of open source software\u003c/em\u003e \u003cstrong\u003e4\u003c/strong\u003e, 1686 (2019).\u003c/li\u003e\n\u003cli\u003eR: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, Vienna, Austria, 2025).\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTables 1 to 3 are available in the supplementary files section\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Acoustic tracking, gadoid fish, marine mammals, offshore wind farms, predation, prey selection","lastPublishedDoi":"10.21203/rs.3.rs-9214554/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9214554/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAnthropogenic structures in the marine environment alter the availability and distribution of food for marine animals with potential consequences for individual fitness, population and community dynamics. Knowledge of how existing wind farm sites influence the behaviour or foraging activities of marine mammals is valuable if predictions on the effect future large-scale renewable energy development will have on local or regional populations are to be made.\u003c/p\u003e\n\u003cp\u003eDuring acoustic tracking of demersal gadoid fish at two offshore wind farms in Scotland, six Atlantic cod were predated by a marine mammal close to or in the vicinity of turbine foundations. The distinct change in the temperature, depth and movement pattern of the acoustic tags allowed estimations of the time and location of predation (and likely predator) and provided details of post consumption behaviour.\u003c/p\u003e\n\u003cp\u003eThe detection of tags (predator) at multiple turbines within a relatively short period, post consumption, suggests the targeted use of turbine foundations as foraging sites where prey fish are known to aggregate. Moreover, the bias in predation of cod over haddock, and evidence for the cod being of higher energetic quality, provide rare evidence for prey selection by a marine mammal predator(s). These data provide further evidence for how wind farms and other introduced structures influence the behaviour of marine mammals with potential consequences for individuals and populations through changes in the distribution of prey. The ultimate consequences are to be determined but need consideration with the continued development of offshore wind farm sites.\u003c/p\u003e","manuscriptTitle":"Evidence of predation events by marine mammals at offshore wind farms","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-14 12:35:09","doi":"10.21203/rs.3.rs-9214554/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-05-14T13:58:48+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"49686558410805089188276446398692270190","date":"2026-04-29T21:04:16+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"143423495310782844208525812489552100131","date":"2026-04-21T10:11:00+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-07T15:28:47+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-07T15:21:58+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-04-07T14:08:12+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-30T11:28:36+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2026-03-30T11:20:37+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e107b638-10d9-4cf0-8758-79c4bb863037","owner":[],"postedDate":"April 14th, 2026","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-05-14T13:58:48+00:00","index":64,"fulltext":""},{"type":"reviewerAgreed","content":"49686558410805089188276446398692270190","date":"2026-04-29T21:04:16+00:00","index":61,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":66205231,"name":"Biological sciences/Ecology"},{"id":66205232,"name":"Earth and environmental sciences/Ecology"},{"id":66205233,"name":"Earth and environmental sciences/Ocean sciences"}],"tags":[],"updatedAt":"2026-04-14T12:35:10+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-14 12:35:09","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9214554","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9214554","identity":"rs-9214554","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}