Individual-level differences in size drive movements and spatial segregation of a pelagic seabird

Individual-level differences in size drive movements and spatial segregation of a pelagic seabird | 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 Research Article Individual-level differences in size drive movements and spatial segregation of a pelagic seabird Lucas Krüger This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3956269/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Individual-level differences in size play a crucial role in shaping the movements and spatial segregation of sexually dimorphic pelagic seabirds. This study investigated how size influences the response of Southern giant petrels ( Macronectes giganteus ) to environmental conditions, particularly wind speed and direction, during foraging trips in the Maritime Antarctic Peninsula. Utilizing tracking data from 36 breeding individuals in two seasons, was found that smaller males exhibited higher transit speeds in response to stronger winds, whereas females showed more efficient utilization of wind during transit independently of size. Additionally, smaller females engaged in longer foraging trips associated with higher chlorophyll-a concentrations, while larger females were associated with areas of sea ice. The results suggest that size-driven variability influences not only individual movement patterns but also spatial segregation within the same sex. These findings provide insights into the intricate relationship between size, environmental factors, and foraging behavior in pelagic seabirds, highlighting the importance of considering individual-level variability in understanding population dynamics and responses to environmental change. Understanding how individual differences in size shape seabird ecology is essential in the face of climate-induced alterations in wind patterns in the Southern Ocean. Antarctic Peninsula Foraging Ecology GPS tracking data Individual Ecology Southern Giant Petrel Wind Figures Figure 1 Figure 2 Figure 3 Figure 4 Background Procellariiformes are highly mobile pelagic seabirds that have adapted to take advantage of winds [ 1 ] to cover large distances spending relatively low energy [ 2 – 4 ]. They are able to optimize flight and maintain high speeds even when experiencing across- or head-winds by performing s-shaped dynamic soaring [ 2 , 5 ], differently of seabirds with no soaring capability which experience increasing energy expenditure if flying against the wind [ 1 , 6 – 8 ]. That ability allows procellariiformes to cover vast areas in search of food [ 4 ] or to move to distant feeding hotspots [ 9 ]. Morphological or anatomical variation that allows for differences in use of wind and space by seabirds are usually evaluated on an inter-specific basis [ 1 , 10 ], but it also has been shown to drive sex variability for sexually size dimorphic seabird species, including procellariform species [ 2 , 11 , 12 ]. Females are smaller and lighter and usually engage more frequently on long-distance foraging in comparison to males, which are heavier [ 12 – 14 ]. Few studies have evaluated whether individual variation in anatomical features affects how animals respond to environmental conditions at the individual level, it is, whether difference between individuals also occurs within sexes [ 15 ]. Giant petrels ( Macronectes spp.) are the seabirds with the most pronounced sex dimorphism; females have around 80% of males mass and 85% of males bill size [ 16 , 17 ]. Those size differences are of relevance to giant petrels ecology, as smaller females often engage on longer pelagic foraging trips compared to males that perform smaller coastal/land trips to hunt or scavenge, despite both sexes being able to make use of both strategies [ 13 , 18 ]. It has been suggested that intra-sexual variability in size also shapes distribution, as size determined the home range center of Southern giant petrels ( M. giganteus ) during the non-breeding season [ 15 ]. Therefore, Southern giant petrels are one of the best species of seabirds to understand how the size differences affect the relationship of individuals with their environment to shape their movements and exploratory behavior. In this study, tracking data of Southern giant petrels [ 19 ] breeding in the Maritime Antarctic Peninsula was used to evaluate whether wind speed and direction affected flight speed and covered distance, and whether distance also reflected in the type of habitat used by the individuals for foraging. Previous studies in the same colony [ 13 , 20 ] have shown that females in average engage in longer pelagic trips, but that is not a rule and both females and males are able to use both pelagic and coastal/land habitats foraging at longer or shorter distances. So far, what is the driver of those individual-level differences is not known. Therefore, individual variability in response to environmental variables was evaluated in contrast with tarsus length (a proxy for relative body size) and bill size. The hypothesis was that smaller individuals (from both sexes) were more likely to have increased flight speed and distance in response to increasing winds, in opposition to larger individuals, reaching farer foraging areas. Methods Study area Harmony point (62°17′60″ S, 59°10′60″ W) is an ice-free area in the West sector of Nelson Island, in the South Shetland Islands, maritime Antarctic Peninsula (Fig. 1 a). Harmony Point was given status of Antarctic Specially Protected Area due to its representativeness in seabird species [ 21 ]. Twelve seabird species nests in the area, including one of the largest known colonies of Chinstrap Penguin [ 22 ] and Southern giant petrels [ 23 ] in the South Shetland Islands. The Southern giant petrel colony at the area seem to be numerically stable, as most counts since the 1980s varied between 400 and 500 breeding pairs. Tracked Southern giant petrels [ 13 , 20 ] have been recorded hunting and scavenging on penguin colonies and seal haul-out sites nearby their breeding colony [ 20 ]. Capture and GPS deployment Between 2021 and 2023 50 breeding individuals were captured on the nest, following methods and protocols previously approved [ 24 ]. For capture, one researcher approached the colony, while the others remained at a distance, preferentially out of sight of the birds. Response behavior to the approaching researcher was used as a proxy for capture selection, for instance, animals that regurgitate or stand on the nest exposing the eggs when the researcher approaches are more likely to leave the nest or might take longer to return to the nest after releasing, increasing the risk of breeding failure (Krüger et al. in prep). Therefore, animals displaying those behaviors were not captured. Before capture, the egg was gently removed from below the bird, covered in thermal tissue and put inside a box, for protection from cold and predation. Then the bird was captured by hand and taken to the site where other two researchers were waiting. Animals had their eyes covered with a head hood. All captured animals were ringed with stainless steel bands (rings), preferentially both animals from the same nest each season. All captured animals had bill and tarsus measured with calipers. Bill was used to determine sex, since males have larger bills with no overlap of measures with females [ 16 , 25 ]. After release back to nest, behavioral status of the animal was observed, and after the animal remained sat at the nest, the egg was placed back. Bill size from pairs was used to determine a threshold value for identifying sex, assuming the smaller individual of a pair is a female. Females (or the smaller individual from a pair) had mean bill size of 85.27 mm ± 2.78 mm (80.0 mm to 90.7mm) and males 97.98 mm ± 3.49 mm (91.9mm to 104.6 mm). Therefore, any individual with bill size larger than 91 mm was considered male, and smaller than 91mm as female. This method can not account for same-sex pairs, which is known to occur with procellariforms [i.e., 6]. However, such pairs are likely a small proportion of one population. Bill and tarsus sizes are correlated with body size in Southern Giant Petrels [ 15 ], but as a general, tarsus length is a good indicator of body size for seabirds [ 27 – 29 ]. Bill size and shape reflect differences in diet for birds as a general ([ 30 , 31 ] but see [ 32 ]) and may indicate sex-level differences in diet for dimorphic seabirds [ 33 ]. In the particular case of Southern giant petrels, bill size might imply dominance and / or competitive advantage, as giant petrels often engage in intra-specific aggression when scavenging [ 34 ]. Bill force have been shown to be correlated with size in other species [ 35 ], therefore, for giant petrels, larger bill sizes might allow subduing larger prey, be it at sea (i.e. fishes or small seabirds ) or at land (seabird chicks and adults[ 36 , 37 ]). GPSs were deployed on the back of the birds using 3M Extreme Hold Duct Tape 2835-B (1.88 inches) and Loctite glue. In 2021/22 and 2022/2023 seasons 30 and 20 birds were tagged with GPSs, respectively. In 2021/22, 4 females lost the GPS, and in 2022/23 one male and one female lost the GPS. Therefore we had tracking data for 26 and 18 animals, respectively. Eight animals were repeated in both seasons, totalizing 36 animals tracked in the two seasons. CatLog-P Gen 2 epoxy cased GPSs loggers (Perthold Technology, 50g) and Sputnik XAIS GPSs (Sextant technology, 70g) were used. Southern giant petrel weight varies between 4 kg and 6 kg, therefore the heavier instrumentation on the lighter birds would represent less than 2% of body weight, percentage which is accepted to have no significant effect on large procellariforms [ 38 ]. Loggers were configurated to record one geographical fix each 5 minutes and were recovered from the birds after 2 to 4 weeks. GPS processing Tracking data [ 19 ] was processed using ‘Track2KBA’ R package [ 39 ] data for identification of foraging trips, for exclusion of points in the area of the colony (300 m radius, as the birds from this colony have been recorded feeding near the colony site[ 20 ]) and for calculation of trips summary statistics. A posteriori the function ‘as.ltraj’ from ‘adehabitatLT’ R package [ 40 , 41 ] was used to calculate heading direction and covered distance between consecutive points. Geographical fixes were converted to tracks using the ‘Tracking Analyst Tools’ and usage density was calculated using the ‘line density’ function on the ‘Spatial Analyst Tools’ in ArcMap 10.8.2; smooth distance (h) was 25 km. Behavioral states ‘EMbC’ R package [ 42 ] was used to apply speed/turn bivariate binary clustering (‘stbc’) over the tracking data, which identifies behavioral states based in low (L) or high (H) speed and turning angle to identify between foraging and transit states. In the case of Southern giant petrels, during scavenging (or hunting) animals can have very low speeds or even be stationary, if in land, which are states similar to resting (in water, resting animals are taken by the currents, which also would match speed and turning of an animal scavenging on the water), therefore very low speeds were not excluded from the analysis and where grouped in a separated category. After applying the ‘stbc’, each position was classified on those four categories (LL, LH, HL, HH), which were posteriorly named as transit (HL and HH), Foraging (LH), Scavenging or Resting (LL). Please see supplemental material 1 for further details. Environmental variables After having tracks processed, environmental variables were downloaded from Copernicus Marine Data Store ( https://data.marine.copernicus.eu/ ) using the Longitudinal, Latitudinal and temporal range of the tracks: hourly 0.125° grid Eastward and Northward Wind (m/s) from ‘Global Ocean Hourly Sea Surface Wind and Stress from Scatterometer and Model’ ( https://doi.org/10.48670/moi-00305 ); daily 0.083° grid Sea Ice Cover (proportion) from ‘Global Ocean Physics Analysis and Forecast’ ( https://doi.org/10.48670/moi-00016 ) and 0.25° grid Mass Concentration of Chlorophyll-a in Seawater (mg/m 3 ) from ‘Global Ocean Biogeochemistry Analysis and Forecast’ ( https://doi.org/10.48670/moi-00015 ). Downloads from those data banks generated NetCDF files with a grid file associated to each time unit (hour or day). Those Variables were extracted to the tracking data based on the matching date-time and geographical position. For instance, bird tracking data had one fix every 5 minutes, therefore 12 consecutive fixes had the same value for wind (which temporal resolution is one value per hour) if the bird was stationary. Eastward and Northward winds (EW and NW, respectively) were used to calculate wind speed [sqrt(EW 2 + NW 2 )]. Wind direction was calculated using the formula [arc tangent ((EW/NW) * 180) / (π*0.5)], posteriorly transformed in radians and used to calculate the intensity of the wind experienced by the bird in relation to its direction [cos (bird direction) – cos (wind direction) * wind speed]. Negative values indicated that the bird experienced wind from its front sectors (head wind) and positives from its rear sectors (tail winds), which were then classified as head or tails, and had the square root of its square value calculated, to eliminate negative values. Higher values of tail wind mean a bird experienced strong wind speeds from its back, and higher values of headwind mean a bird experienced strong wind speeds from its front. Please see supplemental material 1 for further details and for the exact routine for applying the processing of the data. Statistical Analysis As the broader temporal resolution from environmental data is days, after extracting environmental data into tracking data and calculating wind speeds and components, data was averaged per animal, per trip, per behavioral state and per day. Therefore, daily covered distance and daily mean speed of the birds during different behavioral states were used as response variables. Covered distance had a heavy-tailed distribution, and speed deviation dispersion along regression lines was not homogeneous, therefore transformations were conducted to reduce the violation of parametric models’ assumptions (Supplemental Material 2). As individual data is repeated through time, linear mixed models were used to account for the effect of individual variability affecting the population-level trend [response ~ explanatory + (explanatory | bird id)]. Effect of wind on bird speed and covered distance was tested using only transit periods, as one can assume wind influences travelling speed and distance. Effect of chlorophyll-a concentration and sea ice cover on covered distance was tested for non-transit periods (Foraging, Scavenging/Resting), as those variables are associated with foraging. Fixed effects were tested using the Satterthwaite approach [ 43 ]; random effects significance was tested using the Likelihood Ratio Test [ 44 ]. When individual variability had a significant effect on the population trend, the random slope ± standard deviation was extracted from the models. The slope was then plotted in relation to tarsus length for transit behavioral state (assuming tarsus length is a good proxy for body size, larger individuals are heavier and would experience different advantages or disadvantages from wind intensity and direction) and bill size for non-transit behavioral state (as bill size might indicate that a bird is more likely to hunt, scavenge or forage pelagically). All statistical analyses were conducted in R [ 45 ]. Please refer to Supplemental Materials 2 and 3 for the code of the statistical analysis in detail. Codes are also available as .rmd files in github [ 46 ], and post processed data is available in [ 47 ]. Results Distribution Range The distribution range of the 36 birds (Fig. 1 b) tracked encompassed over 5,830 kms in longitudinal range (from 51.35°W to 103.87°W) and 2105 kms in latitudinal range (from 54.00°S to 72.97°S). Most animals utilized the northern tip of the Antarctic Peninsula engaging in foraging trips in a 500km radius from the colony, but longer foraging trips occurred to the South America, Weddell Sea (Southeast) and Bellingshausen Sea (Southwest) (Fig. 1 b). Foraging Trips The 36 tracked birds realized a total of 196 foraging trips in the period between 13 December 2021 and 24 January 2022, and between16 December 2022 and 16 January 2023. While most trips were within a 500 km radius from the colony, 14 trips went over 500km, even reaching 2000kms in a straight line from the colony (Fig. 2ab). Trips covered in median 110.9 km (1st qu.=14.0, 3rd qu.=726.2) reaching a maximum of 10,036.2 km, taking from half a day (12h, 1st qu.=3.2, 3rd qu.=79.5) up to 17.3 days. Most trips headed southwest and southeast (176.7°, 1st qu.=136.7°, 3rd qu.=258.5°), but west and northwest also were frequent directions, nonetheless, longer trips headed south, southeast and southwest (Fig. 2bc). Behavioral states The STBC classification identified four levels of behavioral states: speed of 0.3 ± 0.19 m/s and turning angle of 0.13 ± 0.11 radians; speed of 0.10 ± 0.09 m/s and turning angle of 1.70 ± 0.11 radians; speed of 11.19 ± 4.84 m/s and turning angle of 0.13 ± 0.11 radians; speed of 6.15 ± 4.54 m/s and turning angle of 1.41 ± 0.11 radians. Both behavioral states with high speed were considered as transit (representing 53.6% of cases), low speed and high angle as foraging (22.63% of cases) and low speed and low angle as scavenging /resting (23.68% of cases). Transit and foraging Birds experienced during transit mean wind speeds of 7.11 m/s ± 3.60 m/s, with a maximum absolute value of 20.51 m/s. Higher wind speeds meant higher transit speeds; the effect of individual variability, significantly explained over 26% of the model’s variance (Table 1 ). Smaller males had a higher slope, indicating that smaller males were faster when experiencing higher wind speeds than larger males (Fig. 3 a). Tail winds significantly increased transit speeds, but there was no significant effect of individuals variability, meaning that all individuals responded similarly to the tail winds (Table 1 ). Head winds did not affect transit speeds, but individual variability significantly explained over 25% of models’ variance (Table 1 ), with smaller females being able to achieve higher speeds when experiencing stronger head winds in comparison to larger females and males (Fig. 3 b). Daily covered distance increased significantly in response to wind speed, tail and head winds, but effect of individual variability was significant only for wind speed and head winds (Table 1 ), again indicating that all individuals responded similarly to tail winds. Females on average had higher slopes than males in response to both wind speed and head winds, but male size influenced males response to wind speed and head winds, with a tendency that larger males had lower slopes (Fig. 3 c,d). Mean daily chlorophyll-a concentration during non-transit states was 1.66 ± 0.96 mg/m 3 , which was similar to the levels of chlorophyll-a concentration when in transit (1.64 ± 0.92 mg/m 3 ), and did not explained the covered distance in non-transit behavioral states (Table 1 ). But individual variability significantly explained 40.14% of models’ variance (Table 1 ), as random slope was inversely proportional to females’ size (Fig. 3 e). Sea ice cover had the highest fixed-effect slope and the highest percentage of models’ variance explained by the random effect, indicating a high relation between the distance covered and areas of sea ice cover (Table 1 ). Females size influenced proportionally the relation with sea ice (Fig. 3 d). Table 1 Statistical results for linear mixed models testing the effects of environmental conditions (wind speed WS, tail wind component TWC, head wind component HWC, chlorophyll-a concentration CHL and sea ice cover SIC) on mean daily Southern giant petrels ( Macronectes giganteus ) flight speed and daily covered distance in transit or foraging / scavenging / resting behavioral states. Results for the fixed effect are the F statistics, fixed effect slope estimate (β) and significance (P); random effect results are the % of variance explained by differences in individual variability (intercept and slope), likelihood ratio test (LRT) and significance (P). Fixed Random State Response Explanatory df F β P %var LRT P Transit Speed ~ WS 31,950 4.28 0.017 0.048 24.76 9.47 0.009 TWC 30,404 5.88 0.043 0.016 16.01 0.51 0.773 HWC 31,546 0.31 0.007 0.584 24.77 8.34 0.015 Distance ~ WS 31,950 12.04 0.011 0.001 21.26 15.19 < 0.001 TWC 30,404 29.34 0.033 < 0.001 12.91 < 0.01 0.997 HWC 31,546 11.65 0.015 0.002 15.25 13.13 0.001 Non-transit Distance ~ CHL 31,1969 1.42 0.020 0.244 40.14 46.59 < 0.001 SIC 31,1969 11.22 0.306 0.006 67.26 49.56 0%; trips targeting sea ice (Fig. 4 a) were performed by 13 and 7 individuals in 2021/22 and 2022/23 respectively, involving both long and short foraging trips. Birds would pass through areas of high chlorophyll-a concentration in any behavioral states (Fig. 4 b). Discussion Morphological variability is usually used for identifying differences of response to wind at the species-level [ 48 ]. Sex differences in at-sea distribution and foraging habitat also have been identified as result of difference in size between males and females of dimorphic species [ 12 ], but differences at the individual level within a species have seldomly been evaluated. In this study, measures as simples as bill size and tarsus length were good predictors of response to different environmental conditions, what might be a result of the role that size has on the foraging ecology of giant petrels [ 14 , 18 ]. Previous studies have focused mostly on sex-level differences of distribution and foraging habitat [ 14 , 16 , 49 ], but evidence that differences of size within the same sex influences distribution have already been identified during the non-breeding season [ 15 ]. This study showed that during the breeding season size is also of importance and might influence not only the movements but also spatial segregation, as size influenced individual responses to wind (mostly in males) and determined also foraging habitat segregation of females. Results indicated that while all animals were able to increase speed and distance when experiencing stronger winds, the amount of speed gained by using stronger winds also depended on size. That difference was not as clear for females as it was for males, suggesting that on average females were able to have faster speeds in response to stronger winds, and smaller males tended to be more similar to females in their response to wind, what has been previously indicated for the relation with temperature during non-breeding [ 15 ]. Therefore, females seemed to be more efficient in the usage of wind. That is consistent to what is known for the species [ 14 , 15 ] and also for other dimorphic seabird species [ 50 – 53 ], where females, for being smaller, are lighter, have a lower wing load and therefore are able to increase speed and distance in response to wind. For the non-transit behavioral modes, on the other hand, distance covered by females in response to chlorophyll-a and sea ice indicated a potential source of intra-sexual segregation, as smaller females had increased foraging distances associated with chlorophyll-a concentration whereas larger females were associated with sea ice. In sexual-dimorphic seabirds, larger individuals are often assumed to be more competitive, as they are able to gain advantage during foraging interactions [ 52 ]. Specifically to Southern giant petrels, foraging in sea ice might be related to the search of seal carcasses, placentae or feces, or for hunting penguins [ 13 ], which larger females would be in advantage in relation to smaller females. That suggest that smaller females prefer to increase foraging effort targeting areas of higher productivity or by increasing their area of search engaging in very long foraging trips through open ocean [ 4 , 11 ], as sea ice habitat has likely been occupied by more competitive females. Interesting to note that size and aggressiveness seem to be related in giant petrels, at least for animals from this colony (Finger et al. in review). Maximum wind speed experienced by Southern giant petrels in transit was similar to the maximum speeds that indicated wind avoidance in other procellariiformes species (above 20 m/s), which has been suggested to be the limit of wind speed that albatrosses and large petrels can withstand [ 1 , 2 ]. Wind speed in the Antarctic Peninsula can often surpass the 24 m/s during storms [ 54 ], but values as high as that never appeared in tracking data during transit nor foraging. While giant petrels take sex-level size differences to extreme, other dimorphic seabird species which sex-level differences in distribution [ 55 ], foraging habitat [ 51 ] and even response to wind [ 11 , 12 ] have been detected previously, could also present similar intra-sexual-level differences explained by size. In those cases where size difference is not as pronounced as it is for giant petrels, finer measurements that allow for detecting more subtle differences would be useful to try and apply an analytical approach similar to the one here; measures like hand-wing-index, wing load, bill width, bill shape and head size, can explain differences in dispersal ability and foraging strategies [ 29 , 32 , 56 ]. Finally, increases in wind speed have favored Albatrosses populations [ 11 ]. While there is a lot of variability in population trends of southern giant petrels throughout its range, some colonies have experienced increases in size in the region of the South America and Antarctic Peninsula [ 57 – 59 ], and those have been at least partially attributed to interaction with fisheries discards [ 60 , 61 ]. It is possible that similar to what have been found for wandering albatrosses ( Diomedea exulans ) 10 years ago, increases in wind speed in the Southern Ocean as a result of climate change could contribute to an increasing giant petrel population. At least for one site, climate change has favored some demographic parameters of giant petrel colonies, where Northern giant petrel ( M. hallii ) females survival was positively related to increases of meridional winds [ 49 ]. Wind speeds have increased in most of the Southern Ocean [ 62 , 63 ] and those changes might shape not only the movements of individuals but also influence population dynamics at the long run. Given the importance of giant petrels as predators of other seabird species, even threatened ones [ 36 , 64 ], understanding how those changes affects giant petrels populations have broader implications. Conclusions While all individuals had similar responses to some of the environmental factors, it is, wind speed and tail wind component meant higher flight speeds and covered distances for all individuals, individual-level differences were important, for both transit and foraging behavioral states. This explains individual level differences in movements and foraging strategies that are responsible for intra-sexual spatial and foraging segregation, which might have demographic consequence for this species. Similar approaches applied to other flying seabird species would allow to generalize this finding to other sexually dimorphic seabird species and aid in understanding how populations are organized in space and time. Declarations Ethics approval. This study received environmental permits from Instituto Antártico Chileno and was ethically approved by ethical committees of Universidad de Magallanes (069/CEC/2018) and of Instituto Milénio BASE (3/CBSCUA/2022) [ 24 ]. Competing interests. The author declare that he has no competing interests. Funding This study was funded by INACH (Programa Áreas Marinas Protegidas 24 03 052) and ANID – Programa Iniciativa Milenio – ICN2021_002 (BASE). Author Contribution LK idealized the study, analyzed the data and wrote the manuscript. Acknowledgements I thank INACH’s logistic team for their support in the organization of the camping, especially Pablo Espinosa, Mario Andrés Briones and Juan Bravo; I thank Karpuj team and INACH logistic team for deployment and aid with cargo in the camping site at Harmony Point. I was accompanied during fieldwork by several students, whose support was fundamental for collecting the data: Julia Victória Grohmann Finger (now a doctor), Solenne Belle, Cristina Belén, Elisa González and Albert Palomino. Availability of data and materials. All datasets and materials generated and analysed during the current studies are available as supplementary appendices to this manuscript and or deposited online in Zenodo and Github repositories listed on references [ 19 ], [ 46 ] and [ 47 ]. References Nourani E, Safi K, de Grissac S, Anderson DJ, Cole NC, Fell A, et al. Seabird morphology determines operational wind speeds, tolerable maxima, and responses to extremes. Curr Biol. 2023;33:1179–1184e3. Richardson PL, Wakefield ED, Phillips RA. Flight speed and performance of the wandering albatross with respect to wind. Mov Ecol. 2018;6:1–15. Weimerskirch H, Delord K, Guitteaud A, Phillips RA, Pinet P. Extreme variation in migration strategies between and within wandering albatross populations during their sabbatical year, and their fitness consequences. Sci Rep. 2015;5:1–7. Ventura F, Granadeiro JP, Padget O, Catry P. Gadfly petrels use knowledge of the windscape, not memorized foraging patches, to optimize foraging trips on ocean-wide scales. Proc R Soc B Biol Sci. 2020;287. Yonehara Y, Goto Y, Yoda K, Watanuki Y, Young LC, Weimerskirch H, et al. Flight paths of seabirds soaring over the ocean surface enable measurement of fine-scale wind speed and direction. Proc Natl Acad Sci U S A. 2016;113:9039–44. Amélineau F, Tarroux A, Lacombe S, Bråthen VS, Descamps S, Ekker M et al. Multi-colony tracking of two pelagic seabirds with contrasting flight capability illustrates how windscapes shape migratory movements at an ocean-basin scale. Ecography. 2023;1–15. Weimerskirch H, Guionnet T, Martin J, Shaffer SA, Costa DP. Fast and fuel efficient? Optimal use of wind by flying albatrosses. Proc R Soc B Biol Sci. 2000;267:1869–74. Amélineau F, Péron C, Lescroël A, Authier M, Provost P, Grémillet D. Windscape and tortuosity shape the flight costs of northern gannets. J Exp Biol. 2014;217:876–85. Grecian WJ, Witt MJ, Attrill MJ, Bearhop S, Becker PH, Egevang C, et al. Seabird diversity hotspot linked to ocean productivity in the Canary Current Large Marine Ecosystem. Biol Lett. 2016;12:86–90. Adams J, Flora S. Correlating seabird movements with ocean winds: Linking satellite telemetry with ocean scatterometry. Mar Biol. 2010;157:915–29. Weimerskirch H, Louzao M, de Grissac S, Delord K. Changes in Wind Pattern Alter Albatross Distribution and Life-History Traits. Science. 2012;335:211–4. De Pascalis F, Imperio S, Benvenuti A, Catoni C, Rubolini D, Cecere JG. Sex-specific foraging behaviour is affected by wind conditions in a sexually size dimorphic seabird. Anim Behav. 2020;166:207–18. Finger JVG, Krüger L, Corá DH, Petry MV. Habitat selection of southern giant petrels: Potential environmental monitors of the Antarctic Peninsula. Antarct Sci. 2023;14. González-Solís J, Croxall JP, Afanasyev V. Offshore spatial segregation in giant petrels Macronectes spp.: differences between species, sexes and seasons. Aquat Conserv Mar Freshw Ecosyst. 2008;17:22–36. Krüger, Paiva VH, Finger JVG, Petersen E, Xavier JC, Petry MV, et al. Intra-population variability of the non-breeding distribution of southern giant petrels Macronectes giganteus is mediated by individual body size. Antarct Sci. 2018;30:271–7. González-Solís J, Croxall JP, Wood AG. Sexual dimorphism and sexual segregation in foraging strategies of northern giant petrels, Macronectes halli , during incubation. Oikos. 2000;90:390–8. González-Solís J, Croxall J, Wood A. Foraging partitioning between giant petrels Macronectes spp. and its relationship with breeding population changes at Bird Island, South Georgia. Mar Ecol Prog Ser. 2000;204:279–88. Granroth-Wilding HMV, Phillips RA. Segregation in space and time explains the coexistence of two sympatric sub-Antarctic petrels. Ibis. 2019;161:101–16. Krüger L. GPS fixes for foraging Southern Giant Petrels ( Macronectes giganteus ) breeding in Nelson Island. Maritime Antarct Peninsula, 2021 to 2023 [Data set]. Zenodo. 2023. Corá DH, Finger JVG, Krüger L. Coprophagic behaviour of southern giant petrels ( Macronectes giganteus ) during breeding period. Polar Biol. 2020;43:2111–6. ATCM. Management Plan for Antarctic Specially Protected Area No. 133: Harmony Point, Neslons Island, South Shetland Islands [Internet]. 2022. Available from: https://www.ats.aq/devph/en/apa-database/38 . Silva MP, Favero M, Casaux R, Baroni A. The status of breeding birds at Harmony Point, Nelson Island, Antarctica in summer 1995/96. Mar Ornithol. 1998;26:75–8. Krüger L. An update on the Southern Giant Petrels Macronectes giganteus breeding at Harmony Point, Nelson Island, Maritime Antarctic Peninsula. Polar Biol. 2019;42:1205–8. Krüger L, Vianna JA, Cárdenas CA. General methodology for fieldwork with seabirds to monitor Antarctic and Subantarctic ecosystems and Ethical Certifications. Zenodo. 2024. Copello S, Quintana F, Somoza G. Sex determination and sexual size-dimorphism in Southern Giant-Petrels ( Macronectes giganteus ) from Patagonia. Argentina Emu. 2006;106:141–6. Young LC, Zaun BJ, VanderWerf EA. Successful same-sex pairing in Laysan albatross. Biol Lett. 2008;4:323–5. Gilliland SG, Ankney CD. Estimating Age of Young Birds with a Multivariate Measure of Body Size. Auk. 1992;109:444–50. Senar JC, Pascual J. Keel and tarsus length may provide a good predictor of avian body size. Ardea. 1997;85:269–74. Tobias JA, Sheard C, Pigot AL, Devenish AJM, Yang J, Sayol F, et al. AVONET: morphological, ecological and geographical data for all birds. Ecol Lett. 2022;25:581–97. Xu Y, Price M, Que P, Zhang K, Sheng S, He X et al. Ecological predictors of interspecific variation in bird bill and leg lengths on a global scale. Proc R Soc B Biol Sci. 2023;290. Van De Poll M, Ens BJ, Oosterbeek K, Brouwer L, Verhulst S, Tinbergen JM, et al. Oystercatchers’ bill shapes as a proxy for diet specialization: More differentiation than meets the eye. Ardea. 2009;97:335–47. Navalón G, Bright JA, Marugán-Lobón J, Rayfield EJ. The evolutionary relationship among beak shape, mechanical advantage, and feeding ecology in modern birds*. Evolution. 2019;73:422–35. Navarro J, Kaliontzopoulou A, González-Solís J. Sexual dimorphism in bill morphology and feeding ecology in Cory’s shearwater ( Calonectris diomedea ). Zoology. 2009;112:128–38. Bretagnolle V. Social Behaviour of the Southern Giant Petrel. Ostrich. 1988;59:116–25. Corbin CE, Lowenberger LK, Gray BL. Linkage and trade-off in trophic morphology and behavioural performance of birds. Funct Ecol. 2015;29:808–15. Risi MM, Jones CW, Osborne AM, Steinfurth A, Oppel S. Southern Giant Petrels Macronectes giganteus depredating breeding Atlantic Yellow-nosed Albatrosses Thalassarche chlororhynchos on Gough Island. Polar Biol. 2021;44:593–9. Finger JVG, Corá DH, Petry MV, Krüger L. Cannibalism in southern giant petrels ( Macronectes giganteus ) at Nelson Island, Maritime Antarctic Peninsula. Polar Biol. 2021;44:1219–22. Phillips RA, Xavier JC, Croxall JP. Effects of satellite transmitters on albatrosses and petrels. Auk. 2003;120:1082–90. Beal M, Oppel S, Handley J, Pearmain EJ, Morera-Pujol V, Carneiro APB, et al. track2KBA: An R package for identifying important sites for biodiversity from tracking data. Methods Ecol Evol. 2021;12:2372–8. Calenge C. Analysis of Animal Movements in R: the adehabitatLT Package. 2011;1–85. Available from: papers2://publication/uuid/36198CB2-5 C12-4441-9104-A50AE7C3E8F1. Calenge AC, Royer M. Package ‘ adehabitatLT.’ 2013. Garriga J, Palmer JRB, Oltra A, Bartumeus F. Expectation-maximization binary clustering for behavioural annotation. PLoS ONE. 2016;11:1–26. Luke SG. Evaluating significance in linear mixed-effects models in R. Behav Res Methods. 2017;49:1494–502. Kuriki S. Likelihood Ratio Tests for Covariance Structure in Random Effects Models. J Multivar Anal. 1993;46:175–97. R Core Team. R: A language and environment for statistical computing. [Internet]. Vienna: R Foundation for Statistical Computing. ; 2023. Available from: https://www.r-project.org/ . Krüger L. Codes for processing Southern Giant Petrel movements in relation to individual size [Internet]. Programing codes. 2024. Available from: https://github.com/drlucaskruger/SouthernGiantPetrel_Movements_and_Individual_Size.git . Krüger L. Processed tracking data of Southern Giant Petrels during breeding activity in Harmony Point, Nelson Island, between 2021 and 2023 [Internet]. Dataset. 2024. https://zenodo.org/doi/10.5281/zenodo.10657404 . Nourani E, Safi K, de Grissac S, Anderson DJ, Cole NC, Fell A, et al. Seabird morphology determines operational wind speeds, tolerable maxima, and responses to extremes. Curr Biol. 2023;33:1179–1184e3. Gianuca D, Votier SC, Pardo D, Wood AG, Sherley RB, Ireland L et al. Sex-specific effects of fisheries and climate on the demography of sexually dimorphic seabirds. J Anim Ecol. 2019;1366–78. Paz JA, Seco Pon JP, Krüger L, Favero M, Copello S. Is there sexual segregation in habitat selection by Black-browed Albatrosses wintering in the south-west Atlantic? Emu - Austral Ornithol. 2021;00:1–11. Paiva VH, Pereira J, Ceia FR, Ramos JA. Environmentally driven sexual segregation in a marine top predator. Sci Rep. 2017;7:2590. Reyes-González JM, De Felipe F, Morera-Pujol V, Soriano-Redondo A, Navarro-Herrero L, Zango L, et al. Sexual segregation in the foraging behaviour of a slightly dimorphic seabird: Influence of the environment and fishery activity. J Anim Ecol. 2021;90:1109–21. De Felipe F, Reyes-González JM, Militão T, Neves VC, Bried J, Oro D, et al. Does sexual segregation occur during the nonbreeding period? A comparative analysis in spatial and feeding ecology of three Calonectris shearwaters. Ecol Evol. 2019;9:10145–62. Turner J, Chenoli SN, Abu Samah A, Marshall G, Phillips T, Orr A. Strong wind events in the Antarctic. J Geophys Res Atmos. 2009;114. McKee JL, Tompkins EM, Estela FA, Anderson DJ. Age effects on Nazca booby foraging performance are largely constant across variation in the marine environment: Results from a 5-year study in Galápagos. Ecol Evol. 2023;13. Sheard C, Neate-Clegg MHC, Alioravainen N, Jones SEI, Vincent C, MacGregor HEA et al. Ecological drivers of global gradients in avian dispersal inferred from wing morphology. Nat Commun. 2020;11. Marin M. Breeding of southern giant petrel Macronectes giganteus in Southern Chile. Mar Ornithol. 2018;46:57–60. Petry MV, Valls FCL, Petersen ES, Finger JVG, Krüger L. Population trends of seabirds at Stinker Point, Elephant Island, Maritime Antarctica. Antarct Sci. 2018;7:1–7. Poncet S, Wolfaardt AC, Barbraud C, Reyes-Arriagada R, Black A, Powell RB, et al. The distribution, abundance, status and global importance of giant petrels ( Macronectes giganteus and M. halli ) breeding at South Georgia. Polar Biol. 2020;43:17–34. Krüger L, Paiva VH, Petry MV, Ramos JA. Seabird breeding population size on the Antarctic Peninsula related to fisheries activities in non-breeding ranges off South America. Antarct Sci. 2017;29:495–8. Krüger L, Paiva VH, Petry MV, Ramos JA. Strange lights in the night: using abnormal peaks of light in geolocator data to infer interaction of seabirds with nocturnal fishing vessels. Polar Biol. 2017;40:221–6. Yu L, Zhong S, Sun B. The climatology and trend of surface wind speed over antarctica and the southern ocean and the implication to wind energy application. Atmosphere. 2020;11. Zheng CW, Pan J, Li CY. Global oceanic wind speed trends. Ocean Coast Manag. 2016;129:15–24. Dilley BJ, Davies D, Connan M, Cooper J, de Villiers M, Swart L, et al. Giant petrels as predators of albatross chicks. Polar Biol. 2013;36:761–6. Additional Declarations No competing interests reported. Supplementary Files SupplementaryAppendix1GPSdataandEnviVarsProcessing.pdf SupplementaryAppendix2Variablestransformationandmodelsdiagnostics.pdf SupplementaryAppendix3Statisticalanalysisandplots.pdf Cite Share Download PDF Status: Posted Version 1 posted 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-3956269","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":273869901,"identity":"04043219-0ce5-43ef-8b5a-8cc141404639","order_by":0,"name":"Lucas Krüger","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAt0lEQVRIiWNgGAWjYBACPh4ILccGJJiBWIagFjaoFmOYFh6itSQ2kKDl7DPJLxX30vukmx9+LmCwI0ILb7uZtMyZ4tw2mWPG0jMYkonQws/GJi3ZlpDbJpFgxszDcIBYLf8S0tkk0r8RqYW3jU3yY0NCAptEDrG28BxjtmY4lmDYJpFTLM1jQIRf+HnSGG/+qEmQl5+RvvEzT4WdHEEtIMCMMNmAKA0MDIw/iFQ4CkbBKBgFIxQAALRiKPYpOG5xAAAAAElFTkSuQmCC","orcid":"","institution":"Instituto Antártico Chileno (INACH)","correspondingAuthor":true,"prefix":"","firstName":"Lucas","middleName":"","lastName":"Krüger","suffix":""}],"badges":[],"createdAt":"2024-02-14 14:04:55","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3956269/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3956269/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":51512473,"identity":"a02ef95c-266a-49d6-8d94-6c1cbe720743","added_by":"auto","created_at":"2024-02-22 21:17:29","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":831942,"visible":true,"origin":"","legend":"\u003cp\u003eLocation of the Southern giant petrels (\u003cem\u003eMacronectes giganteus\u003c/em\u003e) studied breeding colony (a) and track density (rescaled to the maximum value) of the Southern Giant Petrels tracked with GPSs loggers during December and January 2021/22 and 2022/23, plotted above the mean sea ice cover between 01 December 2021 until 31 January 2023 (b).\u003c/p\u003e","description":"","filename":"fig1mappetrelito.png","url":"https://assets-eu.researchsquare.com/files/rs-3956269/v1/6d423446ee298518ab9077f9.png"},{"id":51512476,"identity":"c88fa29c-d47b-4b1e-b468-07f03854f853","added_by":"auto","created_at":"2024-02-22 21:17:29","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1442070,"visible":true,"origin":"","legend":"\u003cp\u003eStraight line distance from the colony (a,b), trip direction, distance and duration (b,c) of the Southern Giant Petrels (\u003cem\u003eMacronectes giganteus\u003c/em\u003e) breeding in Harmony Point; 26 animals tracked in 2021/22 (a,c) and 18 animals tracked in 2022/23 (b,d). In ‘a’ and ‘b’ each color is one individual.\u003c/p\u003e","description":"","filename":"fig2TripSummaries2.png","url":"https://assets-eu.researchsquare.com/files/rs-3956269/v1/ab84058fc44d2945a8f0ab64.png"},{"id":51512480,"identity":"2c2466da-3f74-46b5-a8bc-a0773f78a318","added_by":"auto","created_at":"2024-02-22 21:17:30","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1000485,"visible":true,"origin":"","legend":"\u003cp\u003eLinear mixed models’ (LMMs) Individual random effect (slope) ± standard deviation in relation to tarsus (a-d) and bill (e,f) lengths of female (F, red circles) and male (M, blue triangles) Southern giant petrels (\u003cem\u003eMacronectes giganteus\u003c/em\u003e) during breeding in Harmony point, Nelson Island. LMMs tested the relation of bird speed during transit to wind speed (a) and head wind component (b), the relation of covered distance during transit to wind speed (c) and head wind component (d), and the relation of covered distance during foraging to chlorophyll-a concentration (e) and sea ice cover (f).\u003c/p\u003e","description":"","filename":"fig3randomeffectplotsportrait.png","url":"https://assets-eu.researchsquare.com/files/rs-3956269/v1/f772b67c5c4e04ececd30a94.png"},{"id":51512475,"identity":"44950c1d-5e39-4d42-8491-730b281dff3e","added_by":"auto","created_at":"2024-02-22 21:17:29","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1135333,"visible":true,"origin":"","legend":"\u003cp\u003eChronology of two Southern giant petrels (\u003cem\u003eMacronectes giganteus\u003c/em\u003e) foraging trips breeding in Harmony Point in relation to distance from colony (dark solid line), wind speed (dots and dotted grey line), sea ice cover (SIC) and chlorophyll-a concentration (CHL). In ‘a’ is one of the longer foraging trips whose bird targeted far areas of intermediary sea ice for foraging and scavenging (note few or no foraging points while distance quickly increases or decreases, and the use of higher wind speed when in transit during quick increase in distances), while in ‘b’ a trip in pelagic areas where the bird foraged as it moved.\u003c/p\u003e","description":"","filename":"fig4iceandchltripsexample.png","url":"https://assets-eu.researchsquare.com/files/rs-3956269/v1/7bda0c4a7d2fe28f43891191.png"},{"id":59813173,"identity":"fc762b18-c2a6-43f3-ab21-be53697bd53e","added_by":"auto","created_at":"2024-07-07 23:16:36","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4801274,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3956269/v1/2316277c-4748-4c3d-97d9-fe0693cf3919.pdf"},{"id":51512478,"identity":"0476114f-f150-43a8-b1ee-a2a57296f156","added_by":"auto","created_at":"2024-02-22 21:17:29","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":3512576,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryAppendix1GPSdataandEnviVarsProcessing.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3956269/v1/eaf8b641fbc9d1bbecdadf66.pdf"},{"id":51512474,"identity":"65f7f622-34bc-4d45-b298-6c41b0dca69d","added_by":"auto","created_at":"2024-02-22 21:17:29","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":1921229,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryAppendix2Variablestransformationandmodelsdiagnostics.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3956269/v1/c7712f2e1fb3bc837ec66330.pdf"},{"id":51512479,"identity":"b190aa8f-922b-4aa4-ac15-5dc08b351859","added_by":"auto","created_at":"2024-02-22 21:17:30","extension":"pdf","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":347698,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryAppendix3Statisticalanalysisandplots.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3956269/v1/4312034fe4c9aef7fdb05816.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Individual-level differences in size drive movements and spatial segregation of a pelagic seabird","fulltext":[{"header":"Background","content":"\u003cp\u003eProcellariiformes are highly mobile pelagic seabirds that have adapted to take advantage of winds [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] to cover large distances spending relatively low energy [\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. They are able to optimize flight and maintain high speeds even when experiencing across- or head-winds by performing s-shaped dynamic soaring [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], differently of seabirds with no soaring capability which experience increasing energy expenditure if flying against the wind [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. That ability allows procellariiformes to cover vast areas in search of food [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] or to move to distant feeding hotspots [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMorphological or anatomical variation that allows for differences in use of wind and space by seabirds are usually evaluated on an inter-specific basis [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], but it also has been shown to drive sex variability for sexually size dimorphic seabird species, including procellariform species [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Females are smaller and lighter and usually engage more frequently on long-distance foraging in comparison to males, which are heavier [\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Few studies have evaluated whether individual variation in anatomical features affects how animals respond to environmental conditions at the individual level, it is, whether difference between individuals also occurs within sexes [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eGiant petrels (\u003cem\u003eMacronectes\u003c/em\u003e spp.) are the seabirds with the most pronounced sex dimorphism; females have around 80% of males mass and 85% of males bill size [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Those size differences are of relevance to giant petrels ecology, as smaller females often engage on longer pelagic foraging trips compared to males that perform smaller coastal/land trips to hunt or scavenge, despite both sexes being able to make use of both strategies [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. It has been suggested that intra-sexual variability in size also shapes distribution, as size determined the home range center of Southern giant petrels (\u003cem\u003eM. giganteus\u003c/em\u003e) during the non-breeding season [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Therefore, Southern giant petrels are one of the best species of seabirds to understand how the size differences affect the relationship of individuals with their environment to shape their movements and exploratory behavior.\u003c/p\u003e \u003cp\u003eIn this study, tracking data of Southern giant petrels [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] breeding in the Maritime Antarctic Peninsula was used to evaluate whether wind speed and direction affected flight speed and covered distance, and whether distance also reflected in the type of habitat used by the individuals for foraging. Previous studies in the same colony [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] have shown that females in average engage in longer pelagic trips, but that is not a rule and both females and males are able to use both pelagic and coastal/land habitats foraging at longer or shorter distances. So far, what is the driver of those individual-level differences is not known. Therefore, individual variability in response to environmental variables was evaluated in contrast with tarsus length (a proxy for relative body size) and bill size. The hypothesis was that smaller individuals (from both sexes) were more likely to have increased flight speed and distance in response to increasing winds, in opposition to larger individuals, reaching farer foraging areas.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eStudy area\u003c/p\u003e \u003cp\u003eHarmony point (62\u0026deg;17\u0026prime;60\u0026Prime; S, 59\u0026deg;10\u0026prime;60\u0026Prime; W) is an ice-free area in the West sector of Nelson Island, in the South Shetland Islands, maritime Antarctic Peninsula (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea). Harmony Point was given \u003cem\u003estatus\u003c/em\u003e of Antarctic Specially Protected Area due to its representativeness in seabird species [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Twelve seabird species nests in the area, including one of the largest known colonies of Chinstrap Penguin [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] and Southern giant petrels [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] in the South Shetland Islands. The Southern giant petrel colony at the area seem to be numerically stable, as most counts since the 1980s varied between 400 and 500 breeding pairs. Tracked Southern giant petrels [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] have been recorded hunting and scavenging on penguin colonies and seal haul-out sites nearby their breeding colony [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eCapture and GPS deployment\u003c/p\u003e \u003cp\u003eBetween 2021 and 2023 50 breeding individuals were captured on the nest, following methods and protocols previously approved [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. For capture, one researcher approached the colony, while the others remained at a distance, preferentially out of sight of the birds. Response behavior to the approaching researcher was used as a proxy for capture selection, for instance, animals that regurgitate or stand on the nest exposing the eggs when the researcher approaches are more likely to leave the nest or might take longer to return to the nest after releasing, increasing the risk of breeding failure (Kr\u0026uuml;ger et al. in prep). Therefore, animals displaying those behaviors were not captured. Before capture, the egg was gently removed from below the bird, covered in thermal tissue and put inside a box, for protection from cold and predation. Then the bird was captured by hand and taken to the site where other two researchers were waiting. Animals had their eyes covered with a head hood. All captured animals were ringed with stainless steel bands (rings), preferentially both animals from the same nest each season. All captured animals had bill and tarsus measured with calipers. Bill was used to determine sex, since males have larger bills with no overlap of measures with females [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. After release back to nest, behavioral status of the animal was observed, and after the animal remained sat at the nest, the egg was placed back.\u003c/p\u003e \u003cp\u003eBill size from pairs was used to determine a threshold value for identifying sex, assuming the smaller individual of a pair is a female. Females (or the smaller individual from a pair) had mean bill size of 85.27 mm\u0026thinsp;\u0026plusmn;\u0026thinsp;2.78 mm (80.0 mm to 90.7mm) and males 97.98 mm\u0026thinsp;\u0026plusmn;\u0026thinsp;3.49 mm (91.9mm to 104.6 mm). Therefore, any individual with bill size larger than 91 mm was considered male, and smaller than 91mm as female. This method can not account for same-sex pairs, which is known to occur with procellariforms [i.e., 6]. However, such pairs are likely a small proportion of one population.\u003c/p\u003e \u003cp\u003eBill and tarsus sizes are correlated with body size in Southern Giant Petrels [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], but as a general, tarsus length is a good indicator of body size for seabirds [\u003cspan additionalcitationids=\"CR28\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Bill size and shape reflect differences in diet for birds as a general ([\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] but see [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]) and may indicate sex-level differences in diet for dimorphic seabirds [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. In the particular case of Southern giant petrels, bill size might imply dominance and / or competitive advantage, as giant petrels often engage in intra-specific aggression when scavenging [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Bill force have been shown to be correlated with size in other species [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], therefore, for giant petrels, larger bill sizes might allow subduing larger prey, be it at sea (i.e. fishes or small seabirds ) or at land (seabird chicks and adults[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]).\u003c/p\u003e \u003cp\u003eGPSs were deployed on the back of the birds using 3M Extreme Hold Duct Tape 2835-B (1.88 inches) and Loctite glue. In 2021/22 and 2022/2023 seasons 30 and 20 birds were tagged with GPSs, respectively. In 2021/22, 4 females lost the GPS, and in 2022/23 one male and one female lost the GPS. Therefore we had tracking data for 26 and 18 animals, respectively. Eight animals were repeated in both seasons, totalizing 36 animals tracked in the two seasons. CatLog-P Gen 2 epoxy cased GPSs loggers (Perthold Technology, 50g) and Sputnik XAIS GPSs (Sextant technology, 70g) were used. Southern giant petrel weight varies between 4 kg and 6 kg, therefore the heavier instrumentation on the lighter birds would represent less than 2% of body weight, percentage which is accepted to have no significant effect on large procellariforms [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Loggers were configurated to record one geographical fix each 5 minutes and were recovered from the birds after 2 to 4 weeks.\u003c/p\u003e \u003cp\u003eGPS processing\u003c/p\u003e \u003cp\u003eTracking data [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] was processed using \u0026lsquo;Track2KBA\u0026rsquo; R package [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e] data for identification of foraging trips, for exclusion of points in the area of the colony (300 m radius, as the birds from this colony have been recorded feeding near the colony site[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]) and for calculation of trips summary statistics. \u003cem\u003eA posteriori\u003c/em\u003e the function \u0026lsquo;as.ltraj\u0026rsquo; from \u0026lsquo;adehabitatLT\u0026rsquo; R package [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e] was used to calculate heading direction and covered distance between consecutive points. Geographical fixes were converted to tracks using the \u0026lsquo;Tracking Analyst Tools\u0026rsquo; and usage density was calculated using the \u0026lsquo;line density\u0026rsquo; function on the \u0026lsquo;Spatial Analyst Tools\u0026rsquo; in ArcMap 10.8.2; smooth distance (h) was 25 km.\u003c/p\u003e \u003cp\u003eBehavioral states\u003c/p\u003e \u003cp\u003e\u0026lsquo;EMbC\u0026rsquo; R package [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e] was used to apply speed/turn bivariate binary clustering (\u0026lsquo;stbc\u0026rsquo;) over the tracking data, which identifies behavioral states based in low (L) or high (H) speed and turning angle to identify between foraging and transit states. In the case of Southern giant petrels, during scavenging (or hunting) animals can have very low speeds or even be stationary, if in land, which are states similar to resting (in water, resting animals are taken by the currents, which also would match speed and turning of an animal scavenging on the water), therefore very low speeds were not excluded from the analysis and where grouped in a separated category. After applying the \u0026lsquo;stbc\u0026rsquo;, each position was classified on those four categories (LL, LH, HL, HH), which were posteriorly named as transit (HL and HH), Foraging (LH), Scavenging or Resting (LL). Please see supplemental material 1 for further details.\u003c/p\u003e \u003cp\u003eEnvironmental variables\u003c/p\u003e \u003cp\u003eAfter having tracks processed, environmental variables were downloaded from Copernicus Marine Data Store (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://data.marine.copernicus.eu/\u003c/span\u003e\u003cspan address=\"https://data.marine.copernicus.eu/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) using the Longitudinal, Latitudinal and temporal range of the tracks: hourly 0.125\u0026deg; grid Eastward and Northward Wind (m/s) from \u0026lsquo;Global Ocean Hourly Sea Surface Wind and Stress from Scatterometer and Model\u0026rsquo; (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.48670/moi-00305\u003c/span\u003e\u003cspan address=\"10.48670/moi-00305\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e); daily 0.083\u0026deg; grid Sea Ice Cover (proportion) from \u0026lsquo;Global Ocean Physics Analysis and Forecast\u0026rsquo; (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.48670/moi-00016\u003c/span\u003e\u003cspan address=\"10.48670/moi-00016\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and 0.25\u0026deg; grid Mass Concentration of Chlorophyll-a in Seawater (mg/m\u003csup\u003e3\u003c/sup\u003e) from \u0026lsquo;Global Ocean Biogeochemistry Analysis and Forecast\u0026rsquo; (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.48670/moi-00015\u003c/span\u003e\u003cspan address=\"10.48670/moi-00015\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Downloads from those data banks generated NetCDF files with a grid file associated to each time unit (hour or day). Those Variables were extracted to the tracking data based on the matching date-time and geographical position. For instance, bird tracking data had one fix every 5 minutes, therefore 12 consecutive fixes had the same value for wind (which temporal resolution is one value per hour) if the bird was stationary.\u003c/p\u003e \u003cp\u003eEastward and Northward winds (EW and NW, respectively) were used to calculate wind speed [sqrt(EW\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;+\u0026thinsp;NW\u003csup\u003e2\u003c/sup\u003e)]. Wind direction was calculated using the formula [arc tangent ((EW/NW) * 180) / (π*0.5)], posteriorly transformed in radians and used to calculate the intensity of the wind experienced by the bird in relation to its direction [cos (bird direction) \u0026ndash; cos (wind direction) * wind speed]. Negative values indicated that the bird experienced wind from its front sectors (head wind) and positives from its rear sectors (tail winds), which were then classified as head or tails, and had the square root of its square value calculated, to eliminate negative values. Higher values of tail wind mean a bird experienced strong wind speeds from its back, and higher values of headwind mean a bird experienced strong wind speeds from its front.\u003c/p\u003e \u003cp\u003ePlease see supplemental material 1 for further details and for the exact routine for applying the processing of the data.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eAs the broader temporal resolution from environmental data is days, after extracting environmental data into tracking data and calculating wind speeds and components, data was averaged per animal, per trip, per behavioral state and per day. Therefore, daily covered distance and daily mean speed of the birds during different behavioral states were used as response variables. Covered distance had a heavy-tailed distribution, and speed deviation dispersion along regression lines was not homogeneous, therefore transformations were conducted to reduce the violation of parametric models\u0026rsquo; assumptions (Supplemental Material 2).\u003c/p\u003e \u003cp\u003eAs individual data is repeated through time, linear mixed models were used to account for the effect of individual variability affecting the population-level trend [response\u0026thinsp;~\u0026thinsp;explanatory + (explanatory | bird id)]. Effect of wind on bird speed and covered distance was tested using only transit periods, as one can assume wind influences travelling speed and distance. Effect of chlorophyll-a concentration and sea ice cover on covered distance was tested for non-transit periods (Foraging, Scavenging/Resting), as those variables are associated with foraging. Fixed effects were tested using the Satterthwaite approach [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]; random effects significance was tested using the Likelihood Ratio Test [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWhen individual variability had a significant effect on the population trend, the random slope\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation was extracted from the models. The slope was then plotted in relation to tarsus length for transit behavioral state (assuming tarsus length is a good proxy for body size, larger individuals are heavier and would experience different advantages or disadvantages from wind intensity and direction) and bill size for non-transit behavioral state (as bill size might indicate that a bird is more likely to hunt, scavenge or forage pelagically).\u003c/p\u003e \u003cp\u003eAll statistical analyses were conducted in R [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Please refer to Supplemental Materials 2 and 3 for the code of the statistical analysis in detail. Codes are also available as .rmd files in github [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e], and post processed data is available in [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eDistribution Range\u003c/p\u003e \u003cp\u003eThe distribution range of the 36 birds (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb) tracked encompassed over 5,830 kms in longitudinal range (from 51.35\u0026deg;W to 103.87\u0026deg;W) and 2105 kms in latitudinal range (from 54.00\u0026deg;S to 72.97\u0026deg;S). Most animals utilized the northern tip of the Antarctic Peninsula engaging in foraging trips in a 500km radius from the colony, but longer foraging trips occurred to the South America, Weddell Sea (Southeast) and Bellingshausen Sea (Southwest) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb).\u003c/p\u003e \u003cp\u003eForaging Trips\u003c/p\u003e \u003cp\u003eThe 36 tracked birds realized a total of 196 foraging trips in the period between 13 December 2021 and 24 January 2022, and between16 December 2022 and 16 January 2023. While most trips were within a 500 km radius from the colony, 14 trips went over 500km, even reaching 2000kms in a straight line from the colony (Fig.\u0026nbsp;2ab). Trips covered in median 110.9 km (1st qu.=14.0, 3rd qu.=726.2) reaching a maximum of 10,036.2 km, taking from half a day (12h, 1st qu.=3.2, 3rd qu.=79.5) up to 17.3 days. Most trips headed southwest and southeast (176.7\u0026deg;, 1st qu.=136.7\u0026deg;, 3rd qu.=258.5\u0026deg;), but west and northwest also were frequent directions, nonetheless, longer trips headed south, southeast and southwest (Fig.\u0026nbsp;2bc).\u003c/p\u003e \u003cp\u003eBehavioral states\u003c/p\u003e \u003cp\u003eThe STBC classification identified four levels of behavioral states: speed of 0.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.19 m/s and turning angle of 0.13\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11 radians; speed of 0.10\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09 m/s and turning angle of 1.70\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11 radians; speed of 11.19\u0026thinsp;\u0026plusmn;\u0026thinsp;4.84 m/s and turning angle of 0.13\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11 radians; speed of 6.15\u0026thinsp;\u0026plusmn;\u0026thinsp;4.54 m/s and turning angle of 1.41\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11 radians. Both behavioral states with high speed were considered as transit (representing 53.6% of cases), low speed and high angle as foraging (22.63% of cases) and low speed and low angle as scavenging /resting (23.68% of cases).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTransit and foraging\u003c/p\u003e \u003cp\u003eBirds experienced during transit mean wind speeds of 7.11 m/s\u0026thinsp;\u0026plusmn;\u0026thinsp;3.60 m/s, with a maximum absolute value of 20.51 m/s. Higher wind speeds meant higher transit speeds; the effect of individual variability, significantly explained over 26% of the model\u0026rsquo;s variance (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Smaller males had a higher slope, indicating that smaller males were faster when experiencing higher wind speeds than larger males (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). Tail winds significantly increased transit speeds, but there was no significant effect of individuals variability, meaning that all individuals responded similarly to the tail winds (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Head winds did not affect transit speeds, but individual variability significantly explained over 25% of models\u0026rsquo; variance (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), with smaller females being able to achieve higher speeds when experiencing stronger head winds in comparison to larger females and males (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb). Daily covered distance increased significantly in response to wind speed, tail and head winds, but effect of individual variability was significant only for wind speed and head winds (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), again indicating that all individuals responded similarly to tail winds. Females on average had higher slopes than males in response to both wind speed and head winds, but male size influenced males response to wind speed and head winds, with a tendency that larger males had lower slopes (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec,d).\u003c/p\u003e \u003cp\u003eMean daily chlorophyll-a concentration during non-transit states was 1.66\u0026thinsp;\u0026plusmn;\u0026thinsp;0.96 mg/m\u003csup\u003e3\u003c/sup\u003e, which was similar to the levels of chlorophyll-a concentration when in transit (1.64\u0026thinsp;\u0026plusmn;\u0026thinsp;0.92 mg/m\u003csup\u003e3\u003c/sup\u003e), and did not explained the covered distance in non-transit behavioral states (Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). But individual variability significantly explained 40.14% of models\u0026rsquo; variance (Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), as random slope was inversely proportional to females\u0026rsquo; size (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ee). Sea ice cover had the highest fixed-effect slope and the highest percentage of models\u0026rsquo; variance explained by the random effect, indicating a high relation between the distance covered and areas of sea ice cover (Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Females size influenced proportionally the relation with sea ice (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ed).\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\u003eStatistical results for linear mixed models testing the effects of environmental conditions (wind speed WS, tail wind component TWC, head wind component HWC, chlorophyll-a concentration CHL and sea ice cover SIC) on mean daily Southern giant petrels (\u003cem\u003eMacronectes giganteus\u003c/em\u003e) flight speed and daily covered distance in transit or foraging / scavenging / resting behavioral states. Results for the fixed effect are the F statistics, fixed effect slope estimate (β) and significance (P); random effect results are the % of variance explained by differences in individual variability (intercept and slope), likelihood ratio test (LRT) and significance (P).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\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=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eFixed\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003eRandom\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eState\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eResponse\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExplanatory\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003edf\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e%var\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eLRT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTransit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSpeed ~\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31,950\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e4.28\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.017\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.048\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e24.76\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e9.47\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e0.009\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTWC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30,404\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e5.88\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.043\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.016\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e16.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.773\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHWC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31,546\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.584\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e24.77\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e8.34\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e0.015\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDistance ~\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31,950\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e12.04\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.011\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e21.26\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e15.19\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTWC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30,404\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e29.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e12.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.997\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHWC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31,546\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e11.65\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.015\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e15.25\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e13.13\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-transit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDistance ~\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCHL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31,1969\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.244\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e40.14\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e46.59\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSIC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31,1969\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e11.22\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.306\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.006\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e67.26\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e49.56\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\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\u003e88% of the tracked animals overlapped with areas of sea ice\u0026thinsp;\u0026gt;\u0026thinsp;0%; trips targeting sea ice (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea) were performed by 13 and 7 individuals in 2021/22 and 2022/23 respectively, involving both long and short foraging trips. Birds would pass through areas of high chlorophyll-a concentration in any behavioral states (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eMorphological variability is usually used for identifying differences of response to wind at the species-level [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Sex differences in at-sea distribution and foraging habitat also have been identified as result of difference in size between males and females of dimorphic species [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], but differences at the individual level within a species have seldomly been evaluated. In this study, measures as simples as bill size and tarsus length were good predictors of response to different environmental conditions, what might be a result of the role that size has on the foraging ecology of giant petrels [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Previous studies have focused mostly on sex-level differences of distribution and foraging habitat [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e], but evidence that differences of size within the same sex influences distribution have already been identified during the non-breeding season [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. This study showed that during the breeding season size is also of importance and might influence not only the movements but also spatial segregation, as size influenced individual responses to wind (mostly in males) and determined also foraging habitat segregation of females.\u003c/p\u003e \u003cp\u003eResults indicated that while all animals were able to increase speed and distance when experiencing stronger winds, the amount of speed gained by using stronger winds also depended on size. That difference was not as clear for females as it was for males, suggesting that on average females were able to have faster speeds in response to stronger winds, and smaller males tended to be more similar to females in their response to wind, what has been previously indicated for the relation with temperature during non-breeding [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Therefore, females seemed to be more efficient in the usage of wind. That is consistent to what is known for the species [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] and also for other dimorphic seabird species [\u003cspan additionalcitationids=\"CR51 CR52\" citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e], where females, for being smaller, are lighter, have a lower wing load and therefore are able to increase speed and distance in response to wind.\u003c/p\u003e \u003cp\u003eFor the non-transit behavioral modes, on the other hand, distance covered by females in response to chlorophyll-a and sea ice indicated a potential source of intra-sexual segregation, as smaller females had increased foraging distances associated with chlorophyll-a concentration whereas larger females were associated with sea ice. In sexual-dimorphic seabirds, larger individuals are often assumed to be more competitive, as they are able to gain advantage during foraging interactions [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. Specifically to Southern giant petrels, foraging in sea ice might be related to the search of seal carcasses, placentae or feces, or for hunting penguins [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], which larger females would be in advantage in relation to smaller females. That suggest that smaller females prefer to increase foraging effort targeting areas of higher productivity or by increasing their area of search engaging in very long foraging trips through open ocean [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], as sea ice habitat has likely been occupied by more competitive females. Interesting to note that size and aggressiveness seem to be related in giant petrels, at least for animals from this colony (Finger et al. in review).\u003c/p\u003e \u003cp\u003eMaximum wind speed experienced by Southern giant petrels in transit was similar to the maximum speeds that indicated wind avoidance in other procellariiformes species (above 20 m/s), which has been suggested to be the limit of wind speed that albatrosses and large petrels can withstand [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Wind speed in the Antarctic Peninsula can often surpass the 24 m/s during storms [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e], but values as high as that never appeared in tracking data during transit nor foraging.\u003c/p\u003e \u003cp\u003eWhile giant petrels take sex-level size differences to extreme, other dimorphic seabird species which sex-level differences in distribution [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e], foraging habitat [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e] and even response to wind [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] have been detected previously, could also present similar intra-sexual-level differences explained by size. In those cases where size difference is not as pronounced as it is for giant petrels, finer measurements that allow for detecting more subtle differences would be useful to try and apply an analytical approach similar to the one here; measures like hand-wing-index, wing load, bill width, bill shape and head size, can explain differences in dispersal ability and foraging strategies [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFinally, increases in wind speed have favored Albatrosses populations [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. While there is a lot of variability in population trends of southern giant petrels throughout its range, some colonies have experienced increases in size in the region of the South America and Antarctic Peninsula [\u003cspan additionalcitationids=\"CR58\" citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e], and those have been at least partially attributed to interaction with fisheries discards [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. It is possible that similar to what have been found for wandering albatrosses (\u003cem\u003eDiomedea exulans\u003c/em\u003e) 10 years ago, increases in wind speed in the Southern Ocean as a result of climate change could contribute to an increasing giant petrel population. At least for one site, climate change has favored some demographic parameters of giant petrel colonies, where Northern giant petrel (\u003cem\u003eM. hallii\u003c/em\u003e) females survival was positively related to increases of meridional winds [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. Wind speeds have increased in most of the Southern Ocean [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e] and those changes might shape not only the movements of individuals but also influence population dynamics at the long run. Given the importance of giant petrels as predators of other seabird species, even threatened ones [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e], understanding how those changes affects giant petrels populations have broader implications.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eWhile all individuals had similar responses to some of the environmental factors, it is, wind speed and tail wind component meant higher flight speeds and covered distances for all individuals, individual-level differences were important, for both transit and foraging behavioral states. This explains individual level differences in movements and foraging strategies that are responsible for intra-sexual spatial and foraging segregation, which might have demographic consequence for this species. Similar approaches applied to other flying seabird species would allow to generalize this finding to other sexually dimorphic seabird species and aid in understanding how populations are organized in space and time.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eEthics approval.\u003c/h2\u003e \u003cp\u003eThis study received environmental permits from Instituto Ant\u0026aacute;rtico Chileno and was ethically approved by ethical committees of \u003cem\u003eUniversidad de Magallanes\u003c/em\u003e (069/CEC/2018) and of \u003cem\u003eInstituto Mil\u0026eacute;nio BASE\u003c/em\u003e (3/CBSCUA/2022) [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e \u003ch2\u003eCompeting interests.\u003c/h2\u003e \u003cp\u003eThe author declare that he has no competing interests.\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis study was funded by INACH (Programa \u0026Aacute;reas Marinas Protegidas 24 03 052) and ANID \u0026ndash; Programa Iniciativa Milenio \u0026ndash; ICN2021_002 (BASE).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eLK idealized the study, analyzed the data and wrote the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eI thank INACH\u0026rsquo;s logistic team for their support in the organization of the camping, especially Pablo Espinosa, Mario Andr\u0026eacute;s Briones and Juan Bravo; I thank Karpuj team and INACH logistic team for deployment and aid with cargo in the camping site at Harmony Point. I was accompanied during fieldwork by several students, whose support was fundamental for collecting the data: Julia Vict\u0026oacute;ria Grohmann Finger (now a doctor), Solenne Belle, Cristina Bel\u0026eacute;n, Elisa Gonz\u0026aacute;lez and Albert Palomino.\u003c/p\u003e\u003ch2\u003eAvailability of data and materials.\u003c/h2\u003e \u003cp\u003eAll datasets and materials generated and analysed during the current studies are available as supplementary appendices to this manuscript and or deposited online in Zenodo and Github repositories listed on references [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e] and [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e].\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eNourani E, Safi K, de Grissac S, Anderson DJ, Cole NC, Fell A, et al. Seabird morphology determines operational wind speeds, tolerable maxima, and responses to extremes. Curr Biol. 2023;33:1179\u0026ndash;1184e3.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRichardson PL, Wakefield ED, Phillips RA. Flight speed and performance of the wandering albatross with respect to wind. Mov Ecol. 2018;6:1\u0026ndash;15.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWeimerskirch H, Delord K, Guitteaud A, Phillips RA, Pinet P. Extreme variation in migration strategies between and within wandering albatross populations during their sabbatical year, and their fitness consequences. Sci Rep. 2015;5:1\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVentura F, Granadeiro JP, Padget O, Catry P. Gadfly petrels use knowledge of the windscape, not memorized foraging patches, to optimize foraging trips on ocean-wide scales. Proc R Soc B Biol Sci. 2020;287.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYonehara Y, Goto Y, Yoda K, Watanuki Y, Young LC, Weimerskirch H, et al. Flight paths of seabirds soaring over the ocean surface enable measurement of fine-scale wind speed and direction. Proc Natl Acad Sci U S A. 2016;113:9039\u0026ndash;44.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAm\u0026eacute;lineau F, Tarroux A, Lacombe S, Br\u0026aring;then VS, Descamps S, Ekker M et al. Multi-colony tracking of two pelagic seabirds with contrasting flight capability illustrates how windscapes shape migratory movements at an ocean-basin scale. Ecography. 2023;1\u0026ndash;15.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWeimerskirch H, Guionnet T, Martin J, Shaffer SA, Costa DP. Fast and fuel efficient? Optimal use of wind by flying albatrosses. Proc R Soc B Biol Sci. 2000;267:1869\u0026ndash;74.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAm\u0026eacute;lineau F, P\u0026eacute;ron C, Lescro\u0026euml;l A, Authier M, Provost P, Gr\u0026eacute;millet D. Windscape and tortuosity shape the flight costs of northern gannets. J Exp Biol. 2014;217:876\u0026ndash;85.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGrecian WJ, Witt MJ, Attrill MJ, Bearhop S, Becker PH, Egevang C, et al. Seabird diversity hotspot linked to ocean productivity in the Canary Current Large Marine Ecosystem. Biol Lett. 2016;12:86\u0026ndash;90.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAdams J, Flora S. Correlating seabird movements with ocean winds: Linking satellite telemetry with ocean scatterometry. Mar Biol. 2010;157:915\u0026ndash;29.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWeimerskirch H, Louzao M, de Grissac S, Delord K. Changes in Wind Pattern Alter Albatross Distribution and Life-History Traits. Science. 2012;335:211\u0026ndash;4.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDe Pascalis F, Imperio S, Benvenuti A, Catoni C, Rubolini D, Cecere JG. Sex-specific foraging behaviour is affected by wind conditions in a sexually size dimorphic seabird. Anim Behav. 2020;166:207\u0026ndash;18.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFinger JVG, Kr\u0026uuml;ger L, Cor\u0026aacute; DH, Petry MV. Habitat selection of southern giant petrels: Potential environmental monitors of the Antarctic Peninsula. Antarct Sci. 2023;14.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGonz\u0026aacute;lez-Sol\u0026iacute;s J, Croxall JP, Afanasyev V. Offshore spatial segregation in giant petrels \u003cem\u003eMacronectes\u003c/em\u003e spp.: differences between species, sexes and seasons. Aquat Conserv Mar Freshw Ecosyst. 2008;17:22\u0026ndash;36.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKr\u0026uuml;ger, Paiva VH, Finger JVG, Petersen E, Xavier JC, Petry MV, et al. Intra-population variability of the non-breeding distribution of southern giant petrels \u003cem\u003eMacronectes giganteus\u003c/em\u003e is mediated by individual body size. Antarct Sci. 2018;30:271\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGonz\u0026aacute;lez-Sol\u0026iacute;s J, Croxall JP, Wood AG. Sexual dimorphism and sexual segregation in foraging strategies of northern giant petrels, \u003cem\u003eMacronectes halli\u003c/em\u003e, during incubation. Oikos. 2000;90:390\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGonz\u0026aacute;lez-Sol\u0026iacute;s J, Croxall J, Wood A. Foraging partitioning between giant petrels \u003cem\u003eMacronectes\u003c/em\u003e spp. and its relationship with breeding population changes at Bird Island, South Georgia. Mar Ecol Prog Ser. 2000;204:279\u0026ndash;88.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGranroth-Wilding HMV, Phillips RA. Segregation in space and time explains the coexistence of two sympatric sub-Antarctic petrels. Ibis. 2019;161:101\u0026ndash;16.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKr\u0026uuml;ger L. GPS fixes for foraging Southern Giant Petrels (\u003cem\u003eMacronectes giganteus\u003c/em\u003e) breeding in Nelson Island. Maritime Antarct Peninsula, 2021 to 2023 [Data set]. Zenodo. 2023.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCor\u0026aacute; DH, Finger JVG, Kr\u0026uuml;ger L. Coprophagic behaviour of southern giant petrels (\u003cem\u003eMacronectes giganteus\u003c/em\u003e) during breeding period. Polar Biol. 2020;43:2111\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eATCM. Management Plan for Antarctic Specially Protected Area No. 133: Harmony Point, Neslons Island, South Shetland Islands [Internet]. 2022. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ats.aq/devph/en/apa-database/38\u003c/span\u003e\u003cspan address=\"https://www.ats.aq/devph/en/apa-database/38\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSilva MP, Favero M, Casaux R, Baroni A. The status of breeding birds at Harmony Point, Nelson Island, Antarctica in summer 1995/96. Mar Ornithol. 1998;26:75\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKr\u0026uuml;ger L. An update on the Southern Giant Petrels \u003cem\u003eMacronectes giganteus\u003c/em\u003e breeding at Harmony Point, Nelson Island, Maritime Antarctic Peninsula. Polar Biol. 2019;42:1205\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKr\u0026uuml;ger L, Vianna JA, C\u0026aacute;rdenas CA. General methodology for fieldwork with seabirds to monitor Antarctic and Subantarctic ecosystems and Ethical Certifications. Zenodo. 2024.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCopello S, Quintana F, Somoza G. Sex determination and sexual size-dimorphism in Southern Giant-Petrels (\u003cem\u003eMacronectes giganteus\u003c/em\u003e) from Patagonia. Argentina Emu. 2006;106:141\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYoung LC, Zaun BJ, VanderWerf EA. Successful same-sex pairing in Laysan albatross. Biol Lett. 2008;4:323\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGilliland SG, Ankney CD. Estimating Age of Young Birds with a Multivariate Measure of Body Size. Auk. 1992;109:444\u0026ndash;50.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSenar JC, Pascual J. Keel and tarsus length may provide a good predictor of avian body size. Ardea. 1997;85:269\u0026ndash;74.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTobias JA, Sheard C, Pigot AL, Devenish AJM, Yang J, Sayol F, et al. AVONET: morphological, ecological and geographical data for all birds. Ecol Lett. 2022;25:581\u0026ndash;97.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXu Y, Price M, Que P, Zhang K, Sheng S, He X et al. Ecological predictors of interspecific variation in bird bill and leg lengths on a global scale. Proc R Soc B Biol Sci. 2023;290.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVan De Poll M, Ens BJ, Oosterbeek K, Brouwer L, Verhulst S, Tinbergen JM, et al. Oystercatchers\u0026rsquo; bill shapes as a proxy for diet specialization: More differentiation than meets the eye. Ardea. 2009;97:335\u0026ndash;47.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNaval\u0026oacute;n G, Bright JA, Marug\u0026aacute;n-Lob\u0026oacute;n J, Rayfield EJ. The evolutionary relationship among beak shape, mechanical advantage, and feeding ecology in modern birds*. Evolution. 2019;73:422\u0026ndash;35.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNavarro J, Kaliontzopoulou A, Gonz\u0026aacute;lez-Sol\u0026iacute;s J. Sexual dimorphism in bill morphology and feeding ecology in Cory\u0026rsquo;s shearwater (\u003cem\u003eCalonectris diomedea\u003c/em\u003e). Zoology. 2009;112:128\u0026ndash;38.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBretagnolle V. Social Behaviour of the Southern Giant Petrel. Ostrich. 1988;59:116\u0026ndash;25.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCorbin CE, Lowenberger LK, Gray BL. Linkage and trade-off in trophic morphology and behavioural performance of birds. Funct Ecol. 2015;29:808\u0026ndash;15.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRisi MM, Jones CW, Osborne AM, Steinfurth A, Oppel S. Southern Giant Petrels \u003cem\u003eMacronectes giganteus\u003c/em\u003e depredating breeding Atlantic Yellow-nosed Albatrosses \u003cem\u003eThalassarche chlororhynchos\u003c/em\u003e on Gough Island. Polar Biol. 2021;44:593\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFinger JVG, Cor\u0026aacute; DH, Petry MV, Kr\u0026uuml;ger L. Cannibalism in southern giant petrels (\u003cem\u003eMacronectes giganteus\u003c/em\u003e) at Nelson Island, Maritime Antarctic Peninsula. Polar Biol. 2021;44:1219\u0026ndash;22.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePhillips RA, Xavier JC, Croxall JP. Effects of satellite transmitters on albatrosses and petrels. Auk. 2003;120:1082\u0026ndash;90.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBeal M, Oppel S, Handley J, Pearmain EJ, Morera-Pujol V, Carneiro APB, et al. track2KBA: An R package for identifying important sites for biodiversity from tracking data. Methods Ecol Evol. 2021;12:2372\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCalenge C. Analysis of Animal Movements in R: the adehabitatLT Package. 2011;1\u0026ndash;85. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003epapers2://publication/uuid/36198CB2-5\u003c/span\u003e\u003cspan address=\"http://papers2://publication/uuid/36198CB2-5\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003eC12-4441-9104-A50AE7C3E8F1.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCalenge AC, Royer M. Package \u0026lsquo; adehabitatLT.\u0026rsquo; 2013.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGarriga J, Palmer JRB, Oltra A, Bartumeus F. Expectation-maximization binary clustering for behavioural annotation. PLoS ONE. 2016;11:1\u0026ndash;26.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLuke SG. Evaluating significance in linear mixed-effects models in R. Behav Res Methods. 2017;49:1494\u0026ndash;502.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKuriki S. Likelihood Ratio Tests for Covariance Structure in Random Effects Models. J Multivar Anal. 1993;46:175\u0026ndash;97.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eR Core Team. R: A language and environment for statistical computing. [Internet]. Vienna: R Foundation for Statistical Computing. ; 2023. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.r-project.org/\u003c/span\u003e\u003cspan address=\"https://www.r-project.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKr\u0026uuml;ger L. Codes for processing Southern Giant Petrel movements in relation to individual size [Internet]. Programing codes. 2024. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/drlucaskruger/SouthernGiantPetrel_Movements_and_Individual_Size.git\u003c/span\u003e\u003cspan address=\"https://github.com/drlucaskruger/SouthernGiantPetrel_Movements_and_Individual_Size.git\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKr\u0026uuml;ger L. Processed tracking data of Southern Giant Petrels during breeding activity in Harmony Point, Nelson Island, between 2021 and 2023 [Internet]. Dataset. 2024. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://zenodo.org/doi/10.5281/zenodo.10657404\u003c/span\u003e\u003cspan address=\"https://zenodo.doi/10.5281/zenodo.10657404\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNourani E, Safi K, de Grissac S, Anderson DJ, Cole NC, Fell A, et al. Seabird morphology determines operational wind speeds, tolerable maxima, and responses to extremes. Curr Biol. 2023;33:1179\u0026ndash;1184e3.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGianuca D, Votier SC, Pardo D, Wood AG, Sherley RB, Ireland L et al. Sex-specific effects of fisheries and climate on the demography of sexually dimorphic seabirds. J Anim Ecol. 2019;1366\u0026ndash;78.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePaz JA, Seco Pon JP, Kr\u0026uuml;ger L, Favero M, Copello S. Is there sexual segregation in habitat selection by Black-browed Albatrosses wintering in the south-west Atlantic? Emu - Austral Ornithol. 2021;00:1\u0026ndash;11.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePaiva VH, Pereira J, Ceia FR, Ramos JA. Environmentally driven sexual segregation in a marine top predator. Sci Rep. 2017;7:2590.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eReyes-Gonz\u0026aacute;lez JM, De Felipe F, Morera-Pujol V, Soriano-Redondo A, Navarro-Herrero L, Zango L, et al. Sexual segregation in the foraging behaviour of a slightly dimorphic seabird: Influence of the environment and fishery activity. J Anim Ecol. 2021;90:1109\u0026ndash;21.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDe Felipe F, Reyes-Gonz\u0026aacute;lez JM, Milit\u0026atilde;o T, Neves VC, Bried J, Oro D, et al. Does sexual segregation occur during the nonbreeding period? A comparative analysis in spatial and feeding ecology of three \u003cem\u003eCalonectris\u003c/em\u003e shearwaters. Ecol Evol. 2019;9:10145\u0026ndash;62.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTurner J, Chenoli SN, Abu Samah A, Marshall G, Phillips T, Orr A. Strong wind events in the Antarctic. J Geophys Res Atmos. 2009;114.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcKee JL, Tompkins EM, Estela FA, Anderson DJ. Age effects on Nazca booby foraging performance are largely constant across variation in the marine environment: Results from a 5-year study in Gal\u0026aacute;pagos. Ecol Evol. 2023;13.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSheard C, Neate-Clegg MHC, Alioravainen N, Jones SEI, Vincent C, MacGregor HEA et al. Ecological drivers of global gradients in avian dispersal inferred from wing morphology. Nat Commun. 2020;11.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMarin M. Breeding of southern giant petrel \u003cem\u003eMacronectes giganteus\u003c/em\u003e in Southern Chile. Mar Ornithol. 2018;46:57\u0026ndash;60.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePetry MV, Valls FCL, Petersen ES, Finger JVG, Kr\u0026uuml;ger L. Population trends of seabirds at Stinker Point, Elephant Island, Maritime Antarctica. Antarct Sci. 2018;7:1\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePoncet S, Wolfaardt AC, Barbraud C, Reyes-Arriagada R, Black A, Powell RB, et al. The distribution, abundance, status and global importance of giant petrels (\u003cem\u003eMacronectes giganteus\u003c/em\u003e and \u003cem\u003eM. halli\u003c/em\u003e) breeding at South Georgia. Polar Biol. 2020;43:17\u0026ndash;34.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKr\u0026uuml;ger L, Paiva VH, Petry MV, Ramos JA. Seabird breeding population size on the Antarctic Peninsula related to fisheries activities in non-breeding ranges off South America. Antarct Sci. 2017;29:495\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKr\u0026uuml;ger L, Paiva VH, Petry MV, Ramos JA. Strange lights in the night: using abnormal peaks of light in geolocator data to infer interaction of seabirds with nocturnal fishing vessels. Polar Biol. 2017;40:221\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYu L, Zhong S, Sun B. The climatology and trend of surface wind speed over antarctica and the southern ocean and the implication to wind energy application. Atmosphere. 2020;11.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZheng CW, Pan J, Li CY. Global oceanic wind speed trends. Ocean Coast Manag. 2016;129:15\u0026ndash;24.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDilley BJ, Davies D, Connan M, Cooper J, de Villiers M, Swart L, et al. Giant petrels as predators of albatross chicks. Polar Biol. 2013;36:761\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Antarctic Peninsula, Foraging Ecology, GPS tracking data, Individual Ecology, Southern Giant Petrel, Wind","lastPublishedDoi":"10.21203/rs.3.rs-3956269/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3956269/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIndividual-level differences in size play a crucial role in shaping the movements and spatial segregation of sexually dimorphic pelagic seabirds. This study investigated how size influences the response of Southern giant petrels (\u003cem\u003eMacronectes giganteus\u003c/em\u003e) to environmental conditions, particularly wind speed and direction, during foraging trips in the Maritime Antarctic Peninsula. Utilizing tracking data from 36 breeding individuals in two seasons, was found that smaller males exhibited higher transit speeds in response to stronger winds, whereas females showed more efficient utilization of wind during transit independently of size. Additionally, smaller females engaged in longer foraging trips associated with higher chlorophyll-a concentrations, while larger females were associated with areas of sea ice. The results suggest that size-driven variability influences not only individual movement patterns but also spatial segregation within the same sex. These findings provide insights into the intricate relationship between size, environmental factors, and foraging behavior in pelagic seabirds, highlighting the importance of considering individual-level variability in understanding population dynamics and responses to environmental change. Understanding how individual differences in size shape seabird ecology is essential in the face of climate-induced alterations in wind patterns in the Southern Ocean.\u003c/p\u003e","manuscriptTitle":"Individual-level differences in size drive movements and spatial segregation of a pelagic seabird","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-02-22 21:17:24","doi":"10.21203/rs.3.rs-3956269/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"91c7c2cc-f9a7-41a6-b66e-8d5420f2001a","owner":[],"postedDate":"February 22nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-07-16T03:21:05+00:00","versionOfRecord":[],"versionCreatedAt":"2024-02-22 21:17:24","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3956269","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3956269","identity":"rs-3956269","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}