Fine-scale vertical movements highlight the importance of prey encounters on the ascent for pursuit diving seabirds.

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Astrid Dedieu, Mark Jessopp, Sam L Cox This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8591637/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract Understanding how diving predators exploit prey in dynamic marine environments provides insight into their foraging strategies, energetic trade-offs, and capacity to adapt to changing conditions. While traditional classifications of dive shape have provided valuable albeit coarse insights, fine-scale behaviours embedded within dives, such as prey encounter/capture attempts, can be overlooked. We used time-depth recorders (TDR) deployed on Atlantic puffins ( Fratercula arctica ) to detect fine-scale vertical undulations (“wiggles”) in dives as proxies for prey-capture attempts. We used a broken-stick model to divide dives into segments and built a custom function to identify wiggles within segments. Wiggle occurrence differed significantly among dive phases ( p < 0.001). Wiggles were most frequent during the ascent and bottom phases. Less than 10% of wiggles occurred during descent phases, despite descent making up 30.3% of the time spent diving. Our findings indicate that puffins, like other pursuit-diving predators, forage not only during the bottom phase but extensively while surfacing. More broadly, the integration of broken-stick segmentation with wiggle detection provides a simple, transferable framework for quantifying prey-capture attempts across pursuit-diving predators. This approach enhances inference from TDR data and contributes to a comparative understanding of how diving predators partition foraging effort within dives. Atlantic puffin seabird auk wiggles TDR dive patterns foraging Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction To meet their metabolic demands, marine predators must locate and capture enough prey within vast and often unpredictable environments. This challenge is amplified for species that must also provision offspring, requiring efficient foraging strategies to balance self-maintenance with parental care (Wischnewski et al., 2019 ). Among these predators, seabirds face particularly demanding trade-offs as they search for prey in highly dynamic marine systems while returning regularly to breeding colonies to provision chicks (Burke and Montevecchi, 2009 ). Diving behaviour provides one of the clearest behavioural windows into at-sea foraging ecology of diving predators, with dive shape and fine-scale movement patterns offering insight into how predators exploit prey patches, adjust search effort, and maximise energy intake (Chimienti et al., 2017 ; Heerah et al. 2019 ). For air-breathing marine predators, such as seabirds and marine mammals, these behaviours are further shaped by the need to regularly return to the surface to breathe, creating a trade-off between time spent foraging at depth and the physiological limits of oxygen storage. Dive profiles, reconstructed from time-depth recorders (TDRs) or accelerometers, are typically described by their overall shape, which reflects the allocation of time to different phases of the dive. Three general dive types are most often recognised among pursuit diving predators: complex W-shaped dives, V-shaped dives involving rapid descents and ascents with minimal time spent at depth, and U-shaped dives, which include a distinct bottom phase at or near maximum depth (Heerah et al., 2014 ). Typically, V-shaped dives are interpreted as exploratory or targeted foraging dives and W-/U- shaped dives are considered foraging dives, as this is where time is spent searching for or handling prey (Schreer et al., 2001 ; Elliott et al., 2008 ; Shoji et al., 2015 ; Cox et al., 2016 ). While this dichotomy has proven useful in distinguishing between exploratory and foraging dives, any dive can involve prey encounters, and coarse classification does not capture the full behavioural complexity that occurs within dives (Godard et al., 2020 ). To address this limitation, there has been an increasing focus on fine-scale behaviours embedded within dive profiles (Heerah et al., 2014 ). Among these, vertical undulations known as “wiggles” have emerged as promising proxies for prey capture attempts (Halsey et al. 2007 ; Bost et al. 2007 ). Wiggles represent rapid, small-scale reversals in vertical direction, which are thought to correspond to pursuit or handling of individual prey items. In marine mammals, wiggles show strong correlations with independently verified feeding events and are now widely used to infer foraging success (Heerah et al., 2014 ; Carter et al., 2016 ). Similarly, in penguins, wiggles have been linked to prey encounters, as individuals adjust their trajectories to chase or capture prey (Carroll et al., 2014 ; Del Cano et al., 2021), alongside beak opening events and oesophageal temperature drops in king penguins ( Aptenodytes patagonicus ; Hanuise et al., 2010 ; Brisson-Curadeau et al., 2021 ) and Adelie penguins ( Pygoscelis adeliae ; Bost et al., 2007 ). Despite their potential, the use of wiggles as behavioural indicators in pursuit-diving seabirds remains relatively underexplored compared to marine mammals with most work concentrating on flightless seabirds (i.e. penguins). Integrating wiggle detection with dive shape analysis could provide a much more nuanced picture of foraging effort, distinguishing exploratory dives, prey-search phases, and active capture attempts. This would be especially valuable for both a range of far/free ranging species alongside small-bodied animals (e.g. auks), for which direct observation at sea is challenging and accelerometry data are often unavailable due to limitations in data transmission and tag size. In this study, we develop a tailored algorithm to identify wiggles in Atlantic puffin (Fratercula arctica ) dives, separating fine-scale reversals from the broader oscillations that define dive shape. By detecting not only the occurrence but also the position of wiggles within dives, our method provides a framework for inferring foraging strategies and the associated energetic trade-offs, such as distinguishing benthic versus pelagic search, or identifying phases of intensive prey pursuit. This approach offers new insight into the fine-scale foraging ecology of puffins and other pursuit-diving seabirds. 2. Methods 2.1. Data collection A total of 38 Atlantic puffins ( Fratercula arctica ) were equipped with tags on Skellig Michael, County Kerry, Ireland (51.7707°N, 10.5405°W) across four field seasons: 10 individuals in July 2021, 10 in June 2023, 8 in July 2024 and 10 in June 2025. Birds were fitted with combined GPS-time-depth recorders (TDR) devices (geoFIX, PathTrack), each weighing 3.5 g (< 1.5% of average adult body mass, 335–425 g). Capture and recapture were conducted during the chick-rearing period by hand-extracting adults ('grubbing') from their burrows, and in some cases the use of hand nets outside burrows. Individuals were weighed to the nearest 5 g using a spring balance, and devices were attached to the lower back feathers using tesa® 4651 tape. TDRs sampled depth every 2 seconds when submerged below 1 meter. Deployment duration differed across years: tags were left on for 24–48 hours in 2021, 2023 and 2025, and for 4–6 days in 2024. Of the 38 puffins tagged with TDRs, 10, 8, 7 and 5 were successfully recovered in 2021, 2023, 2024 and 2025 respectively (four recovered TDRs failed to record dive data in 2025, leaving 1 working tag). All data processing and analysis were conducted using R version 4.4.0. 2.2. Data processing and analysis 2.2.1. Dive data processing Individual dives were separated based on a minimum surface interval of 5 seconds (Petalas et al., 2025 ). Dives containing fewer than 4 data points or with a maximum depth of less than 2 m were omitted from analyses to exclude cleaning behaviour and potential recording errors near the surface (Bennison et al., 2019 ). 2.2.2. Wiggle identification function and application To split dives into segments with overall consistent movements patterns (e.g. descent, ascent, w-sections, etc) within which small vertical fluctuations (‘wiggles’) could be identified, we applied a broken stick model (BSM) to dive profiles (Photopoulou et al., 2014 ). Dives with fewer than 10 data points were excluded prior to analysis to aid model fitting. For each dive, we applied an automated broken-stick segmentation approach (Photopoulou et al., 2014 ) using the optBrokenstick function (SESman, 2017), which implements an iterative optimization process to approximate the shape of a dive profile using a series of straight-line segments. The algorithm begins with a minimum of three points ( npmin = 3) and sequentially adds breakpoints to minimize the residual distance between the observed profile and its piecewise-linear fit. Model improvement is evaluated using a cost function ( max_dist_cost ), which quantifies the maximum deviation between the segmented model and the observed trajectory. To constrain model complexity so that the small vertical movements attributed to wiggles do not wrongly trigger the addition of breakpoints, we defined a maximum number of allowed breakpoints ( npmax = 6) and a stopping threshold ( threshold = 9), such that additional breakpoints were only included if they produced a meaningful reduction in model error. The algorithm terminated when these criteria were met or when further additions no longer improved the fit, as confirmed by visual inspection. The BSM identified turning points in depth–time space, partitioning each dive into distinct linear segments. This approach effectively captured the major structural phases of each dive; descent, bottom phase, and ascent while avoiding overfitting of small-scale variability that is of interest in wiggle identification. BSM segments containing fewer than four data points were excluded from wiggle identification as a minimum number of 4 points per BSM segment is required to establish baseline direction of the segment and changes in trajectory associated with wiggles. We developed a custom R function to detect wiggles, as small reversals in vertical movement within individual broken-stick segments. For each segment, the function calculated depth differences between successive time points to identify local turning points in the vertical trajectory. Depending on whether the segment represented a descent or ascent, different criteria were applied to define a wiggle. A wiggle was recorded when a local change in slope direction (e.g., from descending to ascending or vice-versa) exceeded a threshold of 0.25m, and the bird subsequently returned to its initial direction of movement within the BSM segment, ensuring that only complete reversals were classified as wiggles. This threshold was chosen following an iterative process, examining the trade-off between wiggle identification and the recording frequency of the TDRs used. The start and end times of each wiggle were recorded, alongside total duration, total depth change, and the dive phase in which the wiggle occurred (i.e. descent, bottom, or ascent; see classification of dives phases below). 2.2.3. Dive phase and wiggle allocation Descent, bottom and ascent phase were identified for all dives. The bottom phase was identified as 80–100% of the maximum depth (Kuroki et al., 2003 ; Karnovsky et al., 2025 ), “descent” was allocated to any point that came before the bottom, and “ascent” assigned to points following the bottom phase. Dives were standardised as percentage of dive duration and percentage of maximum depth to visualize where wiggles occurred throughout the dives. Each wiggle was allocated a dive phase based on when it occurred during the dive. 2.3. Generalized mixed linear model We used generalized linear mixed models ( glmmTMB package in R) to test whether wiggle occurrence differed among dive phases (descent, bottom, ascent). Wiggle presence (0/1) within each phase was modelled as a binomial response, with dive ID nested within bird ID as random intercepts. The logarithm of phase duration was included as an offset. A null model without dive phase was compared to the full model using Akaike’s Information Criterion (AIC) and a likelihood ratio test ( p < 0.05). 3. Results Dives were only recorded between 4 am and 10 pm and puffins did not dive during hours of darkness. A total of 16,659 dives were recorded by 26 TDRs, with 13,975 retained for further analysis following data cleaning and processing. 30.3% of dive time was spent in the descent phase, 38.2% in bottom phase and 31.5% in ascent phase (Fig. 1). Figure 1 Histograms showing the percentage of wiggles occurring across different dive phases (left) and the mean percentage of time spent in each phase (right). Dives were split into 28,939 broken stick segments (Fig. 2 b) suitable for wiggle detection, with 10,239 wiggles identified across 7,130 dives (51% of analysed dives; Fig. 3 ). Within these dives, the number of wiggles per dive ranged from 0 to 7, with a maximum of 7 and an average of 1.45 wiggles per dive (median = 1). Table 1 Summary of all dive data. Year n° of birds n° of dives Mean max depth and range (in m) Mean duration and range (in s) 2021 10 4,131 26.8 (2.07–59.6) 69.6 (6-141) 2023 8 4,649 13.2 (2.02–39.8) 36 (5-125) 2024 7 7,724 13.3 (2-40.3) 43.5 (5-135) 2025 1 155 20.4 (2.1–34,4) 62.5 (6-110) Table 2 Results from GLMM of wiggle presence with dive phase. From left to right: estimate and standard errors from GLMM summary table, change in AIC against the null model, p-value from likelihood ratio test against the null model. Estimate Std. error ΔAIC p-value Dive phase Ascent (intercept) - -3.647 - 0.0226 2195.2 < 0.001 Bottom -0.380 0.0291 Descent -1.569 0.0371 Probability of wiggle occurrence differed significantly among dive phases (ΔAIC = 2195.2, p < 0.001; Table 2 , Fig. 4 ). Wiggles were more likely during a dive’s ascent, with 53.7% of wiggles recorded during this phase (Figs. 1 & 4 ). A concentration of wiggles was evident near the surface, at the end of the dive (Fig. 3 ). 37.1% of wiggles were recorded during bottom phases. Wiggles were least likely during the descent, with only 9.2% of wiggles occurring during this phase (Fig. 1, 2 c & 4 ). 4. Discussion By developing a customised framework to identify fine-scale vertical undulations or “wiggles” as proxies for prey capture attempts, we show that while active foraging by a pursuit diving predator occurs in all three dive phases, it is disproportionately concentrated during ascent and, to a lesser extent, bottom phases. Over half of all wiggles (53.7%) were recorded on the ascent, compared to 37.1% in the bottom phase and only 9.2% during descent. The predominance of wiggles during ascents suggests puffins frequently forage while returning to the surface, rather than concentrating prey pursuit within descent phases. This pattern is consistent with findings from other diving seabirds: African penguins often herd fish schools upwards and capture prey on the ascent (McInnes et al., 2017 ), while razorbills and guillemots also exhibit prey captures late in dives, including during ascent dive phases (Chimienti et al., 2017 ). Several mechanisms may explain why the ascent is an important foraging window for puffins. First, prey items flushed upwards from the bottom may remain accessible as the bird ascends and escapees break off from their schools (McInnes et al., 2017 ). Second, increasing light levels closer to the surface may enhance prey contrast (i.e. downwelling light creating a silhouette), improving detectability (Darby et al., 2022 ). Atlantic puffins, like many other pursuit-diving seabirds, are efficient visual predators whose foraging behaviour is constrained to daylight hours, which is supported by the foraging patterns observed in our data and elsewhere (Croll et al., 1992 ; Shoji et al., 2015 ). Third, ascent-foraging may represent an energy-efficient strategy, combining the locomotion required for surfacing with buoyancy to aid prey pursuit (Lovvorn et al., 2004 ; Wilson et al., 2011 ). Wiggles recorded in the bottom phase likely correspond to active prey search or handling within discrete prey patches, potentially at or near the benthos. The relatively low frequency of wiggles on descent aligns with this expectation, as puffins are likely prioritizing efficient travel to depth rather than engaging in prey capture (Hanuise and Handrich, 2013 ). Many wiggles were observed near the surface at the end of dives, suggesting that puffins may exploit prey layers or trapped individuals against the surface (Chimienti et al., 2017 ; McInnes and Pistorius, 2019 ) immediately before re-emerging or as a strategy to avoid predators or minimize interactions with other birds sitting on the water. The presence of dives with no detected wiggles indicates potential exploratory dives or that puffins may employ alternative foraging strategies, such as targeting benthic prey patches without frequent reversals or capturing prey during smoother trajectories. Overall, these patterns highlight the importance of considering ascent behaviour as a key component of the foraging ecology of visual, pursuit-diving seabirds such as the Atlantic puffin. Beyond puffins, our approach demonstrates the use of combining broken stick segmentation with wiggle detection for studying fine-scale foraging behaviour across a wide range of diving seabirds and marine predators. Many air-breathing pursuit divers share the constraint of limited oxygen stores and must balance travel efficiency with prey capture (Green at al., 2005). Applying a standardized framework for identifying wiggles allows for comparisons across taxa, study systems, and environments. However, while wiggles provide a potentially useful proxy for prey-capture attempts, and their use has been validated elsewhere (Brisson-Curadeau et al., 2021 ; Del Cano et al., 2021), a lack of accelerometer data prevents us from identifying finer-scale ‘jerk’, which can be indicative of prey capture attempts (Viviant et al., 2010; Ydesen et al. 2014 ; Oosthuizen et al., 2025 ). Owing to stringent tag-weight constraints in puffins, the deployment of cameras or additional high-resolution sensors was not possible. While the interpretation of wiggles as prey-capture attempts is supported by validation studies in other pursuit-diving seabirds and marine predators, we cannot directly confirm these detections in the absence of on-animal validation. The 2-second sampling interval of the TDR’s used here, although relatively fine, may also underestimate the frequency of short-duration events. Increasing the resolution of data recording to 0.5-1 s would likely capture more subtle undulations. Nonetheless, the wiggle framework presented here provides a valuable tool for quantifying how diving predators partition foraging effort across dive phases and how these strategies vary with prey type, distribution, or depth. More broadly, applying this approach to extensive existing TDR datasets can enhance comparative analyses across diving species and improve understanding of the energetic and ecological drivers of underwater foraging. 5. Conclusion Our study demonstrates the value of combining dive shape classification with fine-scale wiggle detection to investigate the foraging ecology of Atlantic puffins. Puffins performed wiggles disproportionately during bottom and, especially, ascent phases, which likely reflects adaptive strategies to balance prey capture and oxygen constraints. Wiggle identification offers a straightforward proxy for prey-capture attempts, complementing conventional dive-shape metrics. Future work integrating accelerometry, higher-resolution sensors, and prey field data will refine wiggle-based proxies and further elucidate links between diving behaviour, foraging success, and ecological context. Declarations Competing interests: The authors have no relevant financial or non-financial interests to disclose. Ethics statement The procedures were reviewed and approved by the University College Cork animal ethics committee (application #2019-001) and the British Trust for Ornithology Special Methods Committee prior to being undertaken under permits from the BTO (C/6143, C/7158) and National Parks & Wildlife Service (C145/2024, 044/2024, C114/2023, 020/2023, C155/2021, 54/2021). Author contribution statement Dr Mark Jessopp purchased the equipment. Astrid Dedieu and Dr Mark Jessopp collected the data. Astrid Dedieu and Dr Sam L Cox conceived the methodological approach and analysed the data. Astrid Dedieu led the writing of the manuscript. All authors contributed to the drafts and gave final approval for publication. Funding: This work was funded by the Sustainable Energy Authority of Ireland under the SEAI Research, Development & Demonstration Funding Programme 2021, Grant number 21/RDD/67. Acknowledgements This research is funded by the Sustainable Energy Authority of Ireland under the SEAI Research, Development & Demonstration Funding Programme 2021 (Grant number 21/RDD/670) and is publication #5 of the SEAI-funded CETUS Project. SL Cox was funded through the European Union’s Horizon 2020 research and innovation programme (101029802 - SEAFICS). We would like to thank the Office of Public Works for facilitating the fieldwork and all those who helped collect the data, particularly Jamie Darby, Manon Clairbaux and Emma Murphy. Data availability statement: Both the data and the code are uploaded as separate supplementary material with the following names: data - "puffin_dives_labelled.csv"; code - "wiggles_code.txt" and "functions_for_wigglescode.txt". Additionally, the data is already available upon request on the seabird tracking database ( https://data.seabirdtracking.org/dataset/2244 ). Dataset ID 2244. The code and data used for this project will be made freely available upon publication. References Bennison A, Quinn JL, Debney A, Jessopp M (2019) Tidal drift removes the need for area-restricted search in foraging Atlantic puffins. Biology Letters, 15 (7), 20190208. https://doi.org/.1098/rsbl.2019.0208 Bost CA, Handrich Y, Butler PJ, Fahlman A, Halsey LG, Woakes AJ, Ropert-Coudert Y (2007) Changes in dive profiles as an indicator of feeding success in king and Adélie penguins. 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J Exp Biol 217(13):2239–2243. https://doi.org/10.1242/jeb.099804 Supplementary Files functionsforwigglescode.txt puffindivelabelled.csv wigglescode.txt Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 21 Jan, 2026 Reviewers invited by journal 21 Jan, 2026 Editor assigned by journal 14 Jan, 2026 First submitted to journal 13 Jan, 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. 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Dedieu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAArklEQVRIiWNgGAWjYFCCBAaJBAYGOSibBC3GJGoBkokNRDuLvz354I0HFYfTNxw//IDhQQURWiTOPEu2SDhzOHfDmTQDhoQzxFhzI8dMIrENqOUGD9B5bUTokL+R/00i8d/hdAOwln9EaDG4kcMmkdhwOAGipYEILYZnnhlbJBxLN5wJ9MuBhGNEaJE7nvzw5o8aa3m+44cfPvxRQ4QWFHCAVA2jYBSMglEwCnAAABalOwrs4t/xAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0009-0006-0538-9168","institution":"University College Cork","correspondingAuthor":true,"prefix":"","firstName":"Astrid","middleName":"","lastName":"Dedieu","suffix":""},{"id":578381630,"identity":"468251e7-00de-4256-bfc2-45c4ad488c51","order_by":1,"name":"Mark Jessopp","email":"","orcid":"","institution":"University College Cork","correspondingAuthor":false,"prefix":"","firstName":"Mark","middleName":"","lastName":"Jessopp","suffix":""},{"id":578381631,"identity":"caad9957-d06a-4288-aac2-92b4328205b1","order_by":2,"name":"Sam L Cox","email":"","orcid":"","institution":"University College Cork","correspondingAuthor":false,"prefix":"","firstName":"Sam","middleName":"L","lastName":"Cox","suffix":""}],"badges":[],"createdAt":"2026-01-13 11:55:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8591637/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8591637/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":100995134,"identity":"7e0af421-a896-43d4-a580-5d49a6c602b9","added_by":"auto","created_at":"2026-01-23 15:13:16","extension":"xml","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":8140,"visible":true,"origin":"","legend":"","description":"","filename":"mabiMABID2600030.xml","url":"https://assets-eu.researchsquare.com/files/rs-8591637/v1/e31242eabd20ab1def07b97e.xml"},{"id":100995089,"identity":"50271216-b7f2-47a8-aaae-845ce1011a3e","added_by":"auto","created_at":"2026-01-23 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09:39:12","extension":"jpeg","order_by":11,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":48834,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8591637/v1/2dfa3be1e3557a319e60d976.jpeg"},{"id":101204239,"identity":"f1e08003-8f5c-435f-8539-e43750368336","added_by":"auto","created_at":"2026-01-27 09:42:05","extension":"xml","order_by":16,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":90383,"visible":true,"origin":"","legend":"","description":"","filename":"MABID26000300structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8591637/v1/44497635b67848f8bb1b181a.xml"},{"id":100995090,"identity":"c216f204-d657-4eae-a41a-8d6453b1c419","added_by":"auto","created_at":"2026-01-23 15:13:01","extension":"html","order_by":17,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":98120,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8591637/v1/1d7930ce44ba8ae80bdbbb31.html"},{"id":100995085,"identity":"f7e1616f-fe66-41d6-9343-ce2024f1b3aa","added_by":"auto","created_at":"2026-01-23 15:13:01","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":13682,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eHistograms showing the percentage of wiggles occurring across different dive phases (left) and the mean percentage of time spent in each phase (right).\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8591637/v1/baa08ee03e150d0878079c4f.png"},{"id":100995133,"identity":"33989625-5e61-4912-80f3-800cda9673df","added_by":"auto","created_at":"2026-01-23 15:13:16","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":23397,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eDive profile plots of three example dives. Row a) shows phase classification with an 80% bottom phase. Row b) shows broken-stick segmentation and row c) highlights where wiggles were recorded in the dives.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8591637/v1/f2a00e0fd9cd6ad3da2fbf3c.png"},{"id":100995088,"identity":"8fb9a329-785f-4b46-8813-2842dd375f21","added_by":"auto","created_at":"2026-01-23 15:13:01","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":62674,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eStandardised visualization of wiggle occurrence throughout dives with percentage of maximum depth and percentage of total dive duration.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8591637/v1/173e91b49156a4a8d60d4caa.png"},{"id":100995086,"identity":"7658582d-fe6e-4466-b5f7-616b523bcc16","added_by":"auto","created_at":"2026-01-23 15:13:01","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":6053,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003ePredicted occurrence of wiggles for each dive phase. Error bars represent 95% confidence interval.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8591637/v1/6a606afeb27a238ce86d0309.png"},{"id":101943000,"identity":"7abd2d47-9b5e-4e1d-b21d-79d642a339d4","added_by":"auto","created_at":"2026-02-05 09:39:24","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":716218,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8591637/v1/d6305f4a-3009-40bb-b2ed-19867c68f29b.pdf"},{"id":100995087,"identity":"6559a668-1182-4d68-b248-0cfcb0a703cd","added_by":"auto","created_at":"2026-01-23 15:13:01","extension":"txt","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":86538,"visible":true,"origin":"","legend":"","description":"","filename":"functionsforwigglescode.txt","url":"https://assets-eu.researchsquare.com/files/rs-8591637/v1/d1591b2d1a62c2990793e500.txt"},{"id":101203462,"identity":"ba03956d-1859-499a-ae63-6dcff75ef40b","added_by":"auto","created_at":"2026-01-27 09:39:48","extension":"csv","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":25780664,"visible":true,"origin":"","legend":"","description":"","filename":"puffindivelabelled.csv","url":"https://assets-eu.researchsquare.com/files/rs-8591637/v1/04d1b78e2d0c3382cd1580a3.csv"},{"id":100995093,"identity":"ebe34c09-ce20-4044-93dc-16ba25d38ce0","added_by":"auto","created_at":"2026-01-23 15:13:02","extension":"txt","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":13946,"visible":true,"origin":"","legend":"","description":"","filename":"wigglescode.txt","url":"https://assets-eu.researchsquare.com/files/rs-8591637/v1/19f466a111e278152433ec9c.txt"}],"financialInterests":"","formattedTitle":"Fine-scale vertical movements highlight the importance of prey encounters on the ascent for pursuit diving seabirds.","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eTo meet their metabolic demands, marine predators must locate and capture enough prey within vast and often unpredictable environments. This challenge is amplified for species that must also provision offspring, requiring efficient foraging strategies to balance self-maintenance with parental care (Wischnewski et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Among these predators, seabirds face particularly demanding trade-offs as they search for prey in highly dynamic marine systems while returning regularly to breeding colonies to provision chicks (Burke and Montevecchi, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Diving behaviour provides one of the clearest behavioural windows into at-sea foraging ecology of diving predators, with dive shape and fine-scale movement patterns offering insight into how predators exploit prey patches, adjust search effort, and maximise energy intake (Chimienti et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Heerah et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). For air-breathing marine predators, such as seabirds and marine mammals, these behaviours are further shaped by the need to regularly return to the surface to breathe, creating a trade-off between time spent foraging at depth and the physiological limits of oxygen storage.\u003c/p\u003e \u003cp\u003eDive profiles, reconstructed from time-depth recorders (TDRs) or accelerometers, are typically described by their overall shape, which reflects the allocation of time to different phases of the dive. Three general dive types are most often recognised among pursuit diving predators: complex W-shaped dives, V-shaped dives involving rapid descents and ascents with minimal time spent at depth, and U-shaped dives, which include a distinct bottom phase at or near maximum depth (Heerah et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Typically, V-shaped dives are interpreted as exploratory or targeted foraging dives and W-/U- shaped dives are considered foraging dives, as this is where time is spent searching for or handling prey (Schreer et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Elliott et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Shoji et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Cox et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). While this dichotomy has proven useful in distinguishing between exploratory and foraging dives, any dive can involve prey encounters, and coarse classification does not capture the full behavioural complexity that occurs within dives (Godard et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTo address this limitation, there has been an increasing focus on fine-scale behaviours embedded within dive profiles (Heerah et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Among these, vertical undulations known as \u0026ldquo;wiggles\u0026rdquo; have emerged as promising proxies for prey capture attempts (Halsey et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Bost et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Wiggles represent rapid, small-scale reversals in vertical direction, which are thought to correspond to pursuit or handling of individual prey items. In marine mammals, wiggles show strong correlations with independently verified feeding events and are now widely used to infer foraging success (Heerah et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Carter et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Similarly, in penguins, wiggles have been linked to prey encounters, as individuals adjust their trajectories to chase or capture prey (Carroll et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Del Cano et al., 2021), alongside beak opening events and oesophageal temperature drops in king penguins (\u003cem\u003eAptenodytes patagonicus\u003c/em\u003e; Hanuise et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Brisson-Curadeau et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and Adelie penguins (\u003cem\u003ePygoscelis adeliae\u003c/em\u003e; Bost et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2007\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDespite their potential, the use of wiggles as behavioural indicators in pursuit-diving seabirds remains relatively underexplored compared to marine mammals with most work concentrating on flightless seabirds (i.e. penguins). Integrating wiggle detection with dive shape analysis could provide a much more nuanced picture of foraging effort, distinguishing exploratory dives, prey-search phases, and active capture attempts. This would be especially valuable for both a range of far/free ranging species alongside small-bodied animals (e.g. auks), for which direct observation at sea is challenging and accelerometry data are often unavailable due to limitations in data transmission and tag size.\u003c/p\u003e \u003cp\u003eIn this study, we develop a tailored algorithm to identify wiggles in Atlantic puffin \u003cem\u003e(Fratercula arctica\u003c/em\u003e) dives, separating fine-scale reversals from the broader oscillations that define dive shape. By detecting not only the occurrence but also the position of wiggles within dives, our method provides a framework for inferring foraging strategies and the associated energetic trade-offs, such as distinguishing benthic versus pelagic search, or identifying phases of intensive prey pursuit. This approach offers new insight into the fine-scale foraging ecology of puffins and other pursuit-diving seabirds.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Data collection\u003c/h2\u003e \u003cp\u003eA total of 38 Atlantic puffins (\u003cem\u003eFratercula arctica\u003c/em\u003e) were equipped with tags on Skellig Michael, County Kerry, Ireland (51.7707\u0026deg;N, 10.5405\u0026deg;W) across four field seasons: 10 individuals in July 2021, 10 in June 2023, 8 in July 2024 and 10 in June 2025. Birds were fitted with combined GPS-time-depth recorders (TDR) devices (geoFIX, PathTrack), each weighing 3.5 g (\u0026lt;\u0026thinsp;1.5% of average adult body mass, 335\u0026ndash;425 g). Capture and recapture were conducted during the chick-rearing period by hand-extracting adults ('grubbing') from their burrows, and in some cases the use of hand nets outside burrows. Individuals were weighed to the nearest 5 g using a spring balance, and devices were attached to the lower back feathers using tesa\u0026reg; 4651 tape. TDRs sampled depth every 2 seconds when submerged below 1 meter.\u003c/p\u003e \u003cp\u003eDeployment duration differed across years: tags were left on for 24\u0026ndash;48 hours in 2021, 2023 and 2025, and for 4\u0026ndash;6 days in 2024. Of the 38 puffins tagged with TDRs, 10, 8, 7 and 5 were successfully recovered in 2021, 2023, 2024 and 2025 respectively (four recovered TDRs failed to record dive data in 2025, leaving 1 working tag). All data processing and analysis were conducted using R version 4.4.0.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Data processing and analysis\u003c/h2\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e2.2.1. Dive data processing\u003c/h2\u003e \u003cp\u003eIndividual dives were separated based on a minimum surface interval of 5 seconds (Petalas et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Dives containing fewer than 4 data points or with a maximum depth of less than 2 m were omitted from analyses to exclude cleaning behaviour and potential recording errors near the surface (Bennison et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.2.2. Wiggle identification function and application\u003c/h2\u003e \u003cp\u003eTo split dives into segments with overall consistent movements patterns (e.g. descent, ascent, w-sections, etc) within which small vertical fluctuations (\u0026lsquo;wiggles\u0026rsquo;) could be identified, we applied a broken stick model (BSM) to dive profiles (Photopoulou et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Dives with fewer than 10 data points were excluded prior to analysis to aid model fitting. For each dive, we applied an automated broken-stick segmentation approach (Photopoulou et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) using the \u003cem\u003eoptBrokenstick\u003c/em\u003e function (SESman, 2017), which implements an iterative optimization process to approximate the shape of a dive profile using a series of straight-line segments. The algorithm begins with a minimum of three points (\u003cem\u003enpmin\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3) and sequentially adds breakpoints to minimize the residual distance between the observed profile and its piecewise-linear fit. Model improvement is evaluated using a cost function (\u003cem\u003emax_dist_cost\u003c/em\u003e), which quantifies the maximum deviation between the segmented model and the observed trajectory. To constrain model complexity so that the small vertical movements attributed to wiggles do not wrongly trigger the addition of breakpoints, we defined a maximum number of allowed breakpoints (\u003cem\u003enpmax\u003c/em\u003e\u0026thinsp;=\u0026thinsp;6) and a stopping threshold (\u003cem\u003ethreshold\u003c/em\u003e\u0026thinsp;=\u0026thinsp;9), such that additional breakpoints were only included if they produced a meaningful reduction in model error. The algorithm terminated when these criteria were met or when further additions no longer improved the fit, as confirmed by visual inspection. The BSM identified turning points in depth\u0026ndash;time space, partitioning each dive into distinct linear segments. This approach effectively captured the major structural phases of each dive; descent, bottom phase, and ascent while avoiding overfitting of small-scale variability that is of interest in wiggle identification. BSM segments containing fewer than four data points were excluded from wiggle identification as a minimum number of 4 points per BSM segment is required to establish baseline direction of the segment and changes in trajectory associated with wiggles.\u003c/p\u003e \u003cp\u003eWe developed a custom R function to detect wiggles, as small reversals in vertical movement within individual broken-stick segments. For each segment, the function calculated depth differences between successive time points to identify local turning points in the vertical trajectory. Depending on whether the segment represented a descent or ascent, different criteria were applied to define a wiggle. A wiggle was recorded when a local change in slope direction (e.g., from descending to ascending or vice-versa) exceeded a threshold of 0.25m, and the bird subsequently returned to its initial direction of movement within the BSM segment, ensuring that only complete reversals were classified as wiggles. This threshold was chosen following an iterative process, examining the trade-off between wiggle identification and the recording frequency of the TDRs used.\u003c/p\u003e \u003cp\u003eThe start and end times of each wiggle were recorded, alongside total duration, total depth change, and the dive phase in which the wiggle occurred (i.e. descent, bottom, or ascent; see classification of dives phases below).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.2.3. Dive phase and wiggle allocation\u003c/h2\u003e \u003cp\u003eDescent, bottom and ascent phase were identified for all dives. The bottom phase was identified as 80\u0026ndash;100% of the maximum depth (Kuroki et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Karnovsky et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), \u0026ldquo;descent\u0026rdquo; was allocated to any point that came before the bottom, and \u0026ldquo;ascent\u0026rdquo; assigned to points following the bottom phase. Dives were standardised as percentage of dive duration and percentage of maximum depth to visualize where wiggles occurred throughout the dives. Each wiggle was allocated a dive phase based on when it occurred during the dive.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Generalized mixed linear model\u003c/h2\u003e \u003cp\u003eWe used generalized linear mixed models (\u003cem\u003eglmmTMB\u003c/em\u003e package in R) to test whether wiggle occurrence differed among dive phases (descent, bottom, ascent). Wiggle presence (0/1) within each phase was modelled as a binomial response, with dive ID nested within bird ID as random intercepts. The logarithm of phase duration was included as an offset. A null model without dive phase was compared to the full model using Akaike\u0026rsquo;s Information Criterion (AIC) and a likelihood ratio test (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003e \u003c/p\u003e \u003cp\u003eDives were only recorded between 4 am and 10 pm and puffins did not dive during hours of darkness. A total of 16,659 dives were recorded by 26 TDRs, with 13,975 retained for further analysis following data cleaning and processing. 30.3% of dive time was spent in the descent phase, 38.2% in bottom phase and 31.5% in ascent phase (Fig.\u0026nbsp;1).\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eFigure\u0026nbsp;1\u003c/strong\u003e \u003cp\u003e \u003cem\u003eHistograms showing the percentage of wiggles occurring across different dive phases (left) and the mean percentage of time spent in each phase (right).\u003c/em\u003e \u003c/p\u003e \u003c/p\u003e \u003cp\u003eDives were split into 28,939 broken stick segments (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003eb) suitable for wiggle detection, with 10,239 wiggles identified across 7,130 dives (51% of analysed dives; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Within these dives, the number of wiggles per dive ranged from 0 to 7, with a maximum of 7 and an average of 1.45 wiggles per dive (median\u0026thinsp;=\u0026thinsp;1).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary of all dive data.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026minus;\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003en\u0026deg; of birds\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003en\u0026deg; of dives\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean max depth and range (in m)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMean duration and range (in s)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e2021\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4,131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e26.8 (2.07\u0026ndash;59.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e \u003cp\u003e69.6 (6-141)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e2023\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4,649\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13.2 (2.02\u0026ndash;39.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e \u003cp\u003e36 (5-125)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e2024\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7,724\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13.3 (2-40.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e \u003cp\u003e43.5 (5-135)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e2025\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e155\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20.4 (2.1\u0026ndash;34,4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e \u003cp\u003e62.5 (6-110)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults from GLMM of wiggle presence with dive phase. From left to right: estimate and standard errors from GLMM summary table, change in AIC against the null model, p-value from likelihood ratio test against the null model.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eEstimate\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eStd. error\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eΔAIC\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep-value\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDive phase\u003c/p\u003e \u003cp\u003e\u003cem\u003eAscent (intercept)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003cp\u003e-3.647\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003cp\u003e0.0226\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2195.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eBottom\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.380\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0291\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eDescent\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.569\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0371\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eProbability of wiggle occurrence differed significantly among dive phases (ΔAIC\u0026thinsp;=\u0026thinsp;2195.2, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Wiggles were more likely during a dive\u0026rsquo;s ascent, with 53.7% of wiggles recorded during this phase (Figs.\u0026nbsp;1 \u0026amp; \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003e). A concentration of wiggles was evident near the surface, at the end of the dive (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e). 37.1% of wiggles were recorded during bottom phases. Wiggles were least likely during the descent, with only 9.2% of wiggles occurring during this phase (Fig.\u0026nbsp;1, \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003ec \u0026amp; \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eBy developing a customised framework to identify fine-scale vertical undulations or \u0026ldquo;wiggles\u0026rdquo; as proxies for prey capture attempts, we show that while active foraging by a pursuit diving predator occurs in all three dive phases, it is disproportionately concentrated during ascent and, to a lesser extent, bottom phases. Over half of all wiggles (53.7%) were recorded on the ascent, compared to 37.1% in the bottom phase and only 9.2% during descent. The predominance of wiggles during ascents suggests puffins frequently forage while returning to the surface, rather than concentrating prey pursuit within descent phases. This pattern is consistent with findings from other diving seabirds: African penguins often herd fish schools upwards and capture prey on the ascent (McInnes et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), while razorbills and guillemots also exhibit prey captures late in dives, including during ascent dive phases (Chimienti et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Several mechanisms may explain why the ascent is an important foraging window for puffins. First, prey items flushed upwards from the bottom may remain accessible as the bird ascends and escapees break off from their schools (McInnes et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Second, increasing light levels closer to the surface may enhance prey contrast (i.e. downwelling light creating a silhouette), improving detectability (Darby et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Atlantic puffins, like many other pursuit-diving seabirds, are efficient visual predators whose foraging behaviour is constrained to daylight hours, which is supported by the foraging patterns observed in our data and elsewhere (Croll et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e1992\u003c/span\u003e; Shoji et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Third, ascent-foraging may represent an energy-efficient strategy, combining the locomotion required for surfacing with buoyancy to aid prey pursuit (Lovvorn et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Wilson et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWiggles recorded in the bottom phase likely correspond to active prey search or handling within discrete prey patches, potentially at or near the benthos. The relatively low frequency of wiggles on descent aligns with this expectation, as puffins are likely prioritizing efficient travel to depth rather than engaging in prey capture (Hanuise and Handrich, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Many wiggles were observed near the surface at the end of dives, suggesting that puffins may exploit prey layers or trapped individuals against the surface (Chimienti et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; McInnes and Pistorius, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) immediately before re-emerging or as a strategy to avoid predators or minimize interactions with other birds sitting on the water. The presence of dives with no detected wiggles indicates potential exploratory dives or that puffins may employ alternative foraging strategies, such as targeting benthic prey patches without frequent reversals or capturing prey during smoother trajectories. Overall, these patterns highlight the importance of considering ascent behaviour as a key component of the foraging ecology of visual, pursuit-diving seabirds such as the Atlantic puffin.\u003c/p\u003e \u003cp\u003eBeyond puffins, our approach demonstrates the use of combining broken stick segmentation with wiggle detection for studying fine-scale foraging behaviour across a wide range of diving seabirds and marine predators. Many air-breathing pursuit divers share the constraint of limited oxygen stores and must balance travel efficiency with prey capture (Green at al., 2005). Applying a standardized framework for identifying wiggles allows for comparisons across taxa, study systems, and environments. However, while wiggles provide a potentially useful proxy for prey-capture attempts, and their use has been validated elsewhere (Brisson-Curadeau et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Del Cano et al., 2021), a lack of accelerometer data prevents us from identifying finer-scale \u0026lsquo;jerk\u0026rsquo;, which can be indicative of prey capture attempts (Viviant et al., 2010; Ydesen et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Oosthuizen et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Owing to stringent tag-weight constraints in puffins, the deployment of cameras or additional high-resolution sensors was not possible. While the interpretation of wiggles as prey-capture attempts is supported by validation studies in other pursuit-diving seabirds and marine predators, we cannot directly confirm these detections in the absence of on-animal validation. The 2-second sampling interval of the TDR\u0026rsquo;s used here, although relatively fine, may also underestimate the frequency of short-duration events. Increasing the resolution of data recording to 0.5-1 s would likely capture more subtle undulations. Nonetheless, the wiggle framework presented here provides a valuable tool for quantifying how diving predators partition foraging effort across dive phases and how these strategies vary with prey type, distribution, or depth. More broadly, applying this approach to extensive existing TDR datasets can enhance comparative analyses across diving species and improve understanding of the energetic and ecological drivers of underwater foraging.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eOur study demonstrates the value of combining dive shape classification with fine-scale wiggle detection to investigate the foraging ecology of Atlantic puffins. Puffins performed wiggles disproportionately during bottom and, especially, ascent phases, which likely reflects adaptive strategies to balance prey capture and oxygen constraints. Wiggle identification offers a straightforward proxy for prey-capture attempts, complementing conventional dive-shape metrics. Future work integrating accelerometry, higher-resolution sensors, and prey field data will refine wiggle-based proxies and further elucidate links between diving behaviour, foraging success, and ecological context.\u003c/p\u003e"},{"header":"Declarations","content":" \u003ch2\u003eCompeting interests:\u003c/h2\u003e \u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eEthics statement\u003c/strong\u003e \u003cp\u003eThe procedures were reviewed and approved by the University College Cork animal ethics committee (application #2019-001) and the British Trust for Ornithology Special Methods Committee prior to being undertaken under permits from the BTO (C/6143, C/7158) and National Parks \u0026amp; Wildlife Service (C145/2024, 044/2024, C114/2023, 020/2023, C155/2021, 54/2021).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eAuthor contribution statement\u003c/strong\u003e \u003cp\u003eDr Mark Jessopp purchased the equipment. Astrid Dedieu and Dr Mark Jessopp collected the data. Astrid Dedieu and Dr Sam L Cox conceived the methodological approach and analysed the data. Astrid Dedieu led the writing of the manuscript. All authors contributed to the drafts and gave final approval for publication.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e \u003cp\u003eThis work was funded by the Sustainable Energy Authority of Ireland under the SEAI Research, Development \u0026amp; Demonstration Funding Programme 2021, Grant number 21/RDD/67.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eThis research is funded by the Sustainable Energy Authority of Ireland under the SEAI Research, Development \u0026amp; Demonstration Funding Programme 2021 (Grant number 21/RDD/670) and is publication #5 of the SEAI-funded CETUS Project. SL Cox was funded through the European Union\u0026rsquo;s Horizon 2020 research and innovation programme (101029802 - SEAFICS). We would like to thank the Office of Public Works for facilitating the fieldwork and all those who helped collect the data, particularly Jamie Darby, Manon Clairbaux and Emma Murphy.\u003c/p\u003e\u003ch2\u003eData availability statement:\u003c/h2\u003e \u003cp\u003eBoth the data and the code are uploaded as separate supplementary material with the following names: data - \"puffin_dives_labelled.csv\"; code - \"wiggles_code.txt\" and \"functions_for_wigglescode.txt\". Additionally, the data is already available upon request on the seabird tracking database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://data.seabirdtracking.org/dataset/2244\u003c/span\u003e\u003cspan address=\"https://data.seabirdtracking.org/dataset/2244\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Dataset ID 2244. The code and data used for this project will be made freely available upon publication.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBennison A, Quinn JL, Debney A, Jessopp M (2019) Tidal drift removes the need for area-restricted search in foraging Atlantic puffins. \u003cem\u003eBiology Letters, 15\u003c/em\u003e(7), 20190208. https://doi.org/.1098/rsbl.2019.0208\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBost CA, Handrich Y, Butler PJ, Fahlman A, Halsey LG, Woakes AJ, Ropert-Coudert Y (2007) Changes in dive profiles as an indicator of feeding success in king and Ad\u0026eacute;lie penguins. 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J Exp Biol 217(13):2239\u0026ndash;2243. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1242/jeb.099804\u003c/span\u003e\u003cspan address=\"10.1242/jeb.099804\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"marine-biology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mabi","sideBox":"Learn more about [Marine Biology](https://www.springer.com/journal/227)","snPcode":"227","submissionUrl":"https://submission.nature.com/new-submission/227/3","title":"Marine Biology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Atlantic puffin, seabird, auk, wiggles, TDR, dive patterns, foraging","lastPublishedDoi":"10.21203/rs.3.rs-8591637/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8591637/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eUnderstanding how diving predators exploit prey in dynamic marine environments provides insight into their foraging strategies, energetic trade-offs, and capacity to adapt to changing conditions. While traditional classifications of dive shape have provided valuable albeit coarse insights, fine-scale behaviours embedded within dives, such as prey encounter/capture attempts, can be overlooked. We used time-depth recorders (TDR) deployed on Atlantic puffins (\u003cem\u003eFratercula arctica\u003c/em\u003e) to detect fine-scale vertical undulations (\u0026ldquo;wiggles\u0026rdquo;) in dives as proxies for prey-capture attempts. We used a broken-stick model to divide dives into segments and built a custom function to identify wiggles within segments. Wiggle occurrence differed significantly among dive phases (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Wiggles were most frequent during the ascent and bottom phases. Less than 10% of wiggles occurred during descent phases, despite descent making up 30.3% of the time spent diving. Our findings indicate that puffins, like other pursuit-diving predators, forage not only during the bottom phase but extensively while surfacing. More broadly, the integration of broken-stick segmentation with wiggle detection provides a simple, transferable framework for quantifying prey-capture attempts across pursuit-diving predators. This approach enhances inference from TDR data and contributes to a comparative understanding of how diving predators partition foraging effort within dives.\u003c/p\u003e","manuscriptTitle":"Fine-scale vertical movements highlight the importance of prey encounters on the ascent for pursuit diving seabirds.","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-23 15:12:52","doi":"10.21203/rs.3.rs-8591637/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2026-01-21T23:37:08+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-21T22:32:20+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-14T06:45:22+00:00","index":"","fulltext":""},{"type":"submitted","content":"Marine Biology","date":"2026-01-13T06:53:22+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"marine-biology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mabi","sideBox":"Learn more about [Marine Biology](https://www.springer.com/journal/227)","snPcode":"227","submissionUrl":"https://submission.nature.com/new-submission/227/3","title":"Marine Biology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"1ca6fe74-9fa9-4957-b886-861f1eef33bd","owner":[],"postedDate":"January 23rd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-25T16:46:44+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-23 15:12:52","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8591637","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8591637","identity":"rs-8591637","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}