Foraging flexibility in response to at-sea constraints in a deep diver, the king penguin: an experimental study

Foraging flexibility in response to at-sea constraints in a deep diver, the king penguin: an experimental study | 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 Foraging flexibility in response to at-sea constraints in a deep diver, the king penguin: an experimental study Camille Lemonnier, Charles-André Bost, Nicolas Joly, Antoine Stier, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4704667/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 03 Jul, 2025 Read the published version in Oecologia → Version 1 posted 4 You are reading this latest preprint version Abstract When foraging, marine top predators rely on increasingly unpredictable oceanographic structures. Central place foragers are particularly affected. Their efficiency at replenishing their body reserves at sea while feeding their offspring on land relies on accurately targeting predictable foraging locations. Therefore, increased time and effort spent searching for resources is likely to compromise reproduction. Here, we used an experimental design to assess the flexibility of breeding king penguin (Aptenodytes patagonicus) foraging behavior in response to harsh conditions at sea, and examined the consequences on the growth and survival of their chick. We tested for behavioral adjustments to compensate for experimentally increased foraging workload, obtained by the application of a hydrodynamic drag effect. Compared to controls, treated adults more directly targeted a predictable hydrographic feature, the Polar Front, while limiting the increased costs of deep diving. Treated adults significantly increased hunting activity at shallower depths where the effect of treatment on diving efficiency was neglectable. Our experiment resulted in decreased body mass gain during the brooding stage of chicks raised by treated parents compare to controls, with no direct effects on chick survival up to the winter period, but significant negative effects during winter. We identified two different strategies for foraging in king penguins: 1) foraging at the Polar Front where prey patches are more predictable and accessible at shallower depths or 2) foraging closer to the colony by targeting preys at deeper depths. These results highlight the possibility of a trade-off between distance and depth in breeding king penguin foraging behavior. diving behavior foraging oceanographic fronts plasticity seabird biologging Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction The current intensification of ocean warming rates (Cheng et al. 2022) and associated changes in oceanographic conditions have major repercussions on primary productivity, rippling through the food web all the way to top predators (Hazen et al. 2019; Parmesan 2006). This is particularly the case in the Southern Ocean where unique circumpolar currents support both large-scale (Polar Front, Antarctic convergence) and meso-scale (eddies, filaments) oceanographic structures (Song et al. 2020; Sturm et al. 2023) of high primary productivity (Pinkerton et al. 2021) on which major food webs rely (Bost et al. 2009; Murphy 1995). In particular, many marine mammals and seabirds are central-place foragers, spatially constrained in their foraging range by the requirements of tending to their offspring on-land at a fixed central place site during the breeding season. Therefore, these central-place foragers are particularly sensitive to local variations in the availability and distribution of their prey. Understanding the phenotypic flexibility (and limit thereof) of such species to current changes in oceanographic conditions is key in forecasting future population trends and potential changes in life-history strategies (Forcada & Trathan 2009; Massardier-Galatà et al. 2017; Péron et al. 2012; Trathan et al. 2007). King penguins ( Aptenodytes patagonicus ) are top marine predators, long distance central place foragers and deep divers, that breed on remote circumpolar islands throughout the Southern Ocean (Bost et al. 2012). During the austral summer breeding season, both parents alternate foraging trips at sea, lasting between a week and 10 days, and guarding shifts on land to tend and feed the chick (Stonehouse 1960; Weimerskirch et al. 1992). Timing is critical: a delay in the duration of a parental foraging trip entails the likely abandonment of the egg or chick by the fasting partner remaining on-land, and the loss of reproduction for the season (Groscolas et al. 2008; Olsson 1997). Adults thus mainly travel southward to reach their preferred foraging grounds, the frontal zones where they feed on small mesopelagic fish, caught at shallower depths (Bost et al. 1997, 2002, 2009; Charrassin & Bost 2001). These frontal zones are where prey aggregate at shallower depths are particularly important as they allow birds to spend a greater fraction of their diving time foraging (deep dives are energetically less efficient because actual, profitable, foraging time at the bottom is reduced compared to the costs of commuting from and to the surface) (Kooyman 2012). Because parents are constrained both by time and limited areas within their reach to forage (Weimerskirch 2007), they are highly sensitive to the temporal and spatial predictability of these foraging grounds (Bost et al. 2015; Oliver et al. 2019). Thus, being able to accurately predict and target areas of high prey accessibility means the difference between breeding success or failure. Long-term monitoring of the foraging behavior of king penguins in Crozet have shown that adults principally target the Antarctic Polar Front every year, regardless of its position relative to the colony (Bost et al. 2015). As a result, in years when the Polar Front is especially distant from the colony, birds struggle to efficiently provision their chicks, and breeding success is lower (Bost et al. 2015). Climate models predict the Polar Front to move further and further from the colonies in Crozet over the next hundred years (Péron et al. 2012). The travel distance between favorable foraging locations and the colony are expected to double – a prohibitive distance for king penguins in Crozet – and to negatively affect breeding success, ultimately leading to local population extinction in the near future (Péron et al. 2012). Here, we used an experimental approach to investigate the short-term (intra-annual) foraging flexibility and response of breeding king penguins to variable (favorable or harsh) conditions at-sea. Over two breeding seasons (2019 and 2022), we experimentally increased the foraging workload of a group of chick-brooding parents, and compared them to a group not subject to this constraint. We fitted birds with a dummy logger aimed at increasing the individual’s drag coefficient (Wilson et al. 1986) when swimming and/or foraging under water (see Methods). Our rationale was to decrease the benefit/cost ratio of foraging for a group of experimental birds by increasing the travelling costs they faced compared to a control group monitored at the same time and under similar environmental conditions, but not subject to this constraint. Manipulating the benefit/cost ratio of foraging by making experimental birds work harder was used to mimic the effects of what birds might encounter during a bad year at sea. We hypothesized that (i) if experimental birds were observed to adjust their foraging behavior compared to control birds, and (ii) if that modification allowed them to compensate for harsh conditions with no observable consequences on chick growth and survival, this response could be interpreted as behavioral flexibility. As the treatment was expected to reduce swimming speed due to increased drag, and more so with increasing depth as the device was incompressible, we expected birds to maximize their foraging activity in large oceanographic structures like the Polar Front (where prey is accessible at shallower depths) compared to controls. In addition, we expected treated birds to spend more time in the bottom phase of their dives relative to ascent and descent phases to maximize foraging time and maintain their energy balance. We also tested whether treated birds were able to compensate for the treatment, by examining the effect of the treatment on chick body mass gain and survival. As king penguins are long lived seabirds, they behave as prudent parents (Drent & Daan 1980) and tend to abandon reproduction when conditions are difficult (Groscolas et al. 2000, 2008) . If birds were able to adjust to the treatment (via modifications in their foraging behavior) without additional costs, we expected that chick body mass gain and survival would not differ between treated and control groups. This experiment was conducted over two very contrasted breeding seasons (2022 being an overall harsher climatic year than 2019, see results), providing us with the opportunity to test for the adaptive scope and limits of foraging flexibility. We expected parents to work harder in 2022 (a year marked by overall poor reproductive success at our study site) but to perhaps be able to compensate in 2019 (a year of overall relatively good reproductive success). Methods Study site and adult monitoring The study was conducted during the 2019-20 and 2022-23 breeding seasons in the “Baie du Marin” king penguin colony, on Possession Island (46°26′S, 51°52′E) in the Crozet archipelago, home to ~24,000 breeding pairs (Barbraud et al. 2020). King penguin pairs (N = 14 in 2019 and 21 in 2022) and their chicks were monitored from courtship (November) to the beginning of the Austral winter (~March). Birds were early breeders that had laid their egg from Nov 21st to Dec 11th in 2019 and from Nov 17th to Dec 26th in 2022. We selected pairs haphazardly once they had settled on their final breeding territory, shortly before egg-laying. Males and females were temporarily marked on the chest, from some 3-m distance using a telescopic perch and a brush dipped in animal spray paint (Porcimark®, Kruuse, Langeskov, Denmark). Once the egg was laid, we caught males and females over two successive incubation shifts, while their partner was foraging at sea. Birds were only caught on the 3rd day after they settled for their first respective incubation shift, ensuring they were motivated to incubate the egg. Upon capture, the egg was carefully removed from the brood pouch and replaced by a warm dummy plaster egg during bird handling to avoid accidental breakage. Beak and both flipper lengths were then measured to the nearest 1-mm using a metal ruler, body girth was measured to the nearest 1-mm with a measuring tape and birds were marked on the chest using water resistant hair-dye (Garnier®). Experimental design Monitored pairs were assigned to either control or treated groups, ensuring a balanced spatial and temporal distribution between groups. Inter-group consistency in adults’ initial condition was statistically verified using Principal Components Analyses (PCA; see Section S1 ESM ). To ensure that we recorded the first brooding trip at sea after the egg had hatched, we caught birds at day 50 of incubation (incubation is typically 53 days in king penguin; Stonehouse 1960). Adults were then instrumented with loggers according to the following experimental protocol: (i) control birds (N = 12 and 22 birds belonging to 7 and 13 breeding pairs in 2019 and 2022, respectively) were equipped with an Axy-Trek Marine High Depth data logger (TechnoSmArt Europe srl, Rome, Italy); (ii) treated birds (N = 13 and 20 birds belonging to 7 and 11 breeding pairs in 2019 and 2022 respectively) were equipped both with an Axy-Trek and a dummy device (see Fig.1 ). Then, both partners were alternatively equipped according to their group (control or treated) in order to record two successive brooding shifts (see Fig.S2 ESM ) and ensure they had undergone the same manipulations. Axy-Trek Marine loggers are small (65 x 40 x 15 mm, 45 g in air) GPS loggers combined with tri-axial accelerometers and pressure sensors allowing to record both penguin movement and diving profiles at sea. The GPS was set to acquire satellite fixes at a rate of 1 fix/15 minutes when the bird was not submerged; pressure was recorded at a rate of 1 Hz. The dummy device (designed for treated bids) was a P.V.C cylinder of 3 cm diameter; and a cross-sectional area of 28.3 cm 2 , i.e. roughly 2.8% of the cross-sectional area of an average king penguin. Position of loggers on the back of penguins was standardized to ensure repeatability over individuals. Axy-Trek loggers were always fitted first, just above the tail articulation, and dummy loggers were added in front of the Axy-Trek and at the back of the maximal cross section area of the bird. Loggers were externally attached to the feathers using waterproof tape (Tesa tape, 4651; Tesa) and instant adhesive glue (Loctite, 401; Henkel, Germany). The equipment period was stopped and all loggers removed before chick thermal independence. Chick monitoring Upon hatching, chicks of the monitored pairs were captured at days 4, 10, 20 and 35. At each capture, marked chicks were weighed to the nearest 1-g using a spring-slide Pesola® scale. When possible, i.e. when it coincided, chick captures were performed during deployment or retrieval of equipment to reduce the number of interventions in the colony. At day 20, chicks were identified using color-coded fish tag (Floy Tag and MFG, Inc., Seattle, WA, USA) attached subcutaneously to their upper-back to allow monitoring after formation of the creches (Stier et al . 2014; Viblanc et al . 2020). The survival of chicks was re-determined at the onset of winter (day 100, late April-early May) and at fledging (December-January the subsequent year). Logger data processing Of the 67 birds that were successfully fitted with a logger, 45 had successfully recorded data of the first brooding trip at sea. Logger data were extracted according to the first and last GPS position at sea to remove locations taken when the bird was still on-land in the colony. GPS data: When bird’s return to the colony was not recorded from GPS data (battery or memory shortage, n = 21 tracks of the 45 recorded tracks) and the return phase from the foraging trip at sea was sufficiently advanced ( i.e. at least 70% of total trip duration recorded, n = 15 tracks) we estimated the return date as the date of first sighting in the colony. Otherwise, logger data was discarded from the study (n = 5 tracks), the final dataset contained 40 tracks including 14 incomplete tracks (see table S1.1 ESM ). GPS data were filtered according to horizontal speed to remove erroneous positions (filter threshold = 14 km h -1 ; see Cotté et al. 2007; Kooyman et al. 1992) and interpolated to correct for irregular fix frequencies (GPS switched off when the bird was diving) using the “ move ” R package (fix frequency = 1 fix/15 minutes). For each foraging trip, trip duration (days), maximum distance to the colony (km), total distance traveled (km), and mean horizontal speed (km h -1 ) were extracted. Time-depth recorder (TDR) data: TDR data were extracted and filtered to remove the time spent in the colony (see above for details). Correction of the 0-offset ( i.e. the gradual shift of the 0 m over time due to logger accuracy) of the TDR data was performed using the diveMove R package TDRcalibrate function. Then, dives and dive parameters were extracted using a custom-made R function. For each dive, the timestamp of begin and end, dive and post-dive durations (time spent at the surface between two consecutive dives in seconds), bottom duration (s), maximum depth (m), vertical speed during ascent and descent ( ), number of wiggles were extracted following the definitions in (Halsey et al. 2007a), and each dive was associated to a single dive ID. Wiggles are rapid changes in depth, usually in the deepest portion of a dive profile that are the signature of hunting activity. In the dive profile, a wiggle translates into 3 consecutive points where the depth derivative (vertical speed) cancels out and changes sign (Halsey et al. 2007a). Diving efficiency was calculated for each dive as: (Halsey et al. 2007a). The dive efficiency represents the proportion of time in a dive cycle spent in the bottom phase, i.e. the phase of active foraging during a dive. More efficient penguins are those that spend a higher proportion of diving time actively foraging. Dives with maximum depth 50m were considered foraging dives (Charrassin et al. 1998) and included in statistical analysis. Marine environmental data Bathymetry data around Crozet were obtained from NOAA National Centers for Environmental Information with a 0.0042° x 0.0042° grid resolution (2022: ETOPO 2022 15 Arc-Second Global Relief Model, https://doi.org/10.25921/fd45-gt74). Maps using bathymetry data were generated using the ggOceanMaps (Vihtakari et al. 2024) and ggOceanData packages (https://mikkovihtakari.github.io/ggOceanMaps/). The isotherm 2°C at 200 m depth was considered the signature of the position of the Polar Front (Belkin & Gordon 1996; Orsi et al. 1995). Daily Sea Potential Temperature at 200m (SPT, in °C) were obtained from the E.U. Copernicus Marine Service Information (Global Ocean Physics Reanalysis https://doi.org/10.48670/moi-00021 for 2019 and Global Ocean Physics Analysis and Forecast, https://doi.org/10.48670/moi-00016 for 2022) with a 0.083° × 0.083° grid resolution, between the 1 st of January and the 1 st of June for each breeding season (encompassing all foraging trips). Environmental data were processed in R using the terra package (Hijmans et al. 2024a) as Spatial Raster objects. Mean SPT was computed for each grid cell over the period of the recording of foraging behavior (2020-01-20 to 2020-03-05, and 2023-01-14 to 2023-03-14). Statistical Analysis All statistical analyses were performed in R (v. 4.2.2). Generalized linear (GLMM) and linear (LMM) mixed models were run using the ‘lme4’ package (Bates et al. 2015). In general, our models tested for an effect of Treatment (Control/Treated), Year (2019/2022) and Sex (M/F) on bird foraging behavior.Two-way interactions Treatment x Sex and Treatment x Year were initially considered in the models to test if the effect of the treatment differed between the sexes, and/or was more pronounced in 2022 (poor year) than in 2019 (normal year). However, as these interactions were never significant (all p > 0.150), they were removed from all final models; thereby only main effects of Treatment, Sex and Year are presented below. Bird ID nested within in Pair ID was specified as a random intercept in all models to account for repeated measurements (multiple dives per individual) and the non-independence of partners within a pair. When explaining trivial amounts of variance, nested effects were removed and we specified Bird ID on its own to allow model convergence. As models on foraging trip parameters did not rely on repeated measures over individual ( e.g. , trip duration has only one value per trip) or were averaged over the entire trip ( e.g. , mean horizontal speed), only Pair ID was included as a random intercept. Where appropriate, Tukey HSD contrasts between groups and marginal means for Treatment and Year were assessed using the emmeans package in R (Lenth et al. 2025). We ensured that model residuals were normally distributed by visual inspection of density distributions, Q–Q plots, cumulative distribution functions, and P–P plots using the fitdistrplus package in R (Delignette-Muller et al. 2025), or alternative distributions were specified as appropriate (see below). Results are presented either as raw or marginal (estimated) means ± standard error (se) as appropriate. Effects were considered statistically significant for p < 0.05. Oceanographic conditions and habitat use To test how our experimental treatment affected the marine habitat use of king penguins, we proceeded in a two-step analyses: (1) First, the spatial distribution of foraging dives around Crozet was mapped using kernel density estimation (package adehabitatHR v.0.4.21; Calenge & Fortmann-Roe, 2023). To do so, the GPS positions of foraging dives over entire foraging trips were approximated from the interpolated GPS data based on timestamps of foraging dives and GPS fixes. Kernel densities with probabilities of 0.25, 0.50 and 0.75 were then calculated for each Year and Treatment (2019 treated vs. 2019 control vs. 2022 treated vs. 2022 control ). (2) Second, the distances of foraging dives from the signature of the Polar front were computed using the geosphere package (Hijmans et al. 2024b, function dist2line with geographic distance). We then assessed the effects of Treatment , Sex and Year on the distances of foraging dives from the Polar front using an LMM (see above). Foraging trip parameters We investigated the effects of Treatment , Sex , and Year on foraging trip duration (days), maximum foraging range (km), mean horizontal speed ( ), and mean diving depth of foraging dives (m) over the trip using linear mixed models (LMMs). Trip duration was log-transformed to ensure normality of residuals; results are given for the back-transformed estimate and marginal means. For the model on mean horizontal speed, Pair ID explained trivial amounts of variance so that we resorted to a simpler linear model (LM). For maximum range, the distribution of model residuals showed the existence of two outliers (i.e., two birds performed trips of over 740 km, superior to mean + 2*sd (= 686.35 km) of the overall data). However, running the models with or without those data points led to similar results, so that we chose to keep them in the analyses. Diving parameters We tested the effects of Treatment , Sex , and Year on diving parameters (dive duration, post-dive duration, bottom duration, diving efficiency, number of wiggles, descent and ascent rates) using LMMs or GLMMs (see below). As diving parameters are markedly influenced by the maximum depth of the dive (Halsey et al. 2007b; Kooyman et al. 1992; Sato et al. 2002; Zimmer et al. 2008, 2010), foraging dives were grouped in three depth categories according to the maximum depth reached during the dive: Shallow (50-125m, n = 7746 dives), Mid (125-200m, n = 15762 dives) and Deep (>200m, n = 6965 dives) dives, group limits corresponding to 1 st and 3 rd quantiles of the maximum depth distribution respectively (see Fig.S3 ESM ). Analyses were then run within each depth category to ensure dives were comparable. The number of wiggles and post dive duration were analyzed with Poisson distributions in GLMM, as appropriate for count data and given the distribution of the raw data. Chick growth To test how our experimental treatment affected chick growth, we proceeded in a two-step analyses: (1) First, we compared chick body mass (in g) between treated and control pairs at days 4, 10, 20 and 35. We ran an LMM with Treatment , Year and Stage , as explanatory variables. We further included two-way interactions Treatment x Year and Treatment x Stage (days 4, 10, 20 and 35) to test if changes in chick mass due to the treatment were more pronounced in 2022 (harsh year) than in 2019 (normal year), and whether the effect of the treatment was more pronounced towards the end of growth as offspring nutritional requirements increased. Chick ID was included as a random intercept to account for repeated measures on individual chicks. (2) Second, we analyzed the rate of chick body mass gain (in g/day) over the monitoring period. To do this, we ran a LMM specified as Body Mass (g) ~ Age (days) + (Age|ID) and extracted the random slope estimates ( Age|ID ) for each individual chick as a proxy for their linear phase of body mass gain between day 4 and day 35. Then, we ran a linear model (LM) to compare chick body mass gain depending on the Treatment and Year controlling for chick body mass at day 4 as a covariate to account for inter-individual differences in initial body mass. Chick survival Chick survival was assessed using a COX hazard model with mortality events noted as 1 and age of mortality being the age of the chick at the last sighting alive in the colony. A first COX model was run including data from hatching to 35-days to consider the fate of chicks at the end of the chick-brooding phase and experimental treatment. To consider longer-term effects, we ran an additional COX model including data from hatching to fledging which included over winter survival probability. Results Oceanographic conditions and habitat use The signature of the Polar Front was located farther south of the colony in 2022 (642.1 59.1 km) than in 2019 (560.6 61.7 km). This increase of +164km (14.6%) in 2022 compared to 2019 in the theoretical distance birds had to travel to reach their foraging grounds made for more difficult foraging conditions in 2022 ( Fig. 2 ). In both years, the 90% kernel distributions of control birds’ foraging dive locations were continuous and elongated in a North-South position, whereas they were more clustered for treated birds ( Fig. 2 ). This indicated that control birds tended to forage both during the travelling and central phases of their foraging trips, whereas treated birds foraged less during travelling phases, and, in both years, nearest to the front ( Fig. 2 ). However, the foraging dives of control birds were performed over a larger latitudinal gradient in 2022 (25% kernel ranging below 51°S) than in 2019 (25% kernel ranging to 50°S). Foraging dives also extended further south in 2022 for treated birds, with a foraging hotspot (90% kernel) located at 51°S (510 km from the colony). On average, treated birds foraged closer (marginal mean = 262.6 18.5 km) to the Polar Front compared to controls (317.5 16.5 km) (LMM; t = -2.23 and p = 0.034, see Table 1 ). Similarly, birds foraged closer to the Polar front in 2022 (marginal mean = 262.3 16.3 km compared to 2019 (317.8 18.8 km, t = -2.28 and p = 0.030, Table 1 ). Overall, this was consistent with the relative position of the 90% kernel density of foraging dives ( Fig. 2 ). Foraging behavior Overall, birds performed shallower foraging dives (-14.3%; LMM: estimate = -24.12 5.85 m, t = 4.12 and p < 0.001) and had shorter maximum prospection ranges from the colony (-24.6%, estimate = -84.51 34.62 km, t = 2.44 and p = 0.023) in 2019 than in 2022 ( Table 1, Fig. 3 ). Further, treated birds performed overall (both in 2019 and 2022) longer foraging trips (LMM: back-transformed estimate = +1.49 days, t = 3.68 and p = 0.001), travelled farther from the colony (+25.9%, LMM: estimate = 88.31 34.49 km, t = 2.56 and p = 0.018), exhibited slower mean horizontal speeds (-8.6%, LM: estimate = -0.33 0.12 km h -1 , t = -2.82 and p = 0.008) and performed shallower foraging dives than controls (-9.2%, LMM: estimate = -15.84 5.57 m, p = 0.009) (see Table 1, Fig. 3 ). We observed a significant effect of Sex on maximum depth of foraging dives (estimate = +25.96 5.49 m for males, t = 4.73 and p < 0.001, Table 1 ) and on mean horizontal speed (estimate = +0.30 0.12 km.h -1 for males, t = 2.49 and p = 0.018, Table 1 ). Interestingly, Pair ID explained more than 50% of the residual variance for trip duration and maximum range, but not for mean depth of foraging dives (<7%) or horizontal speed (Pair ID not kept in model, Table 1 ). Diving capacities Models were run separately for each dive parameter and for each depth category (Shallow (50-125m), Mid (125-200m) and Deep (>200m)), standardized estimates or incidence rates ratio are provided in Fig. 4 for comparison of the effects across depth categories. Summaries of all the different models are provided in Supplementary Materials (ESM S4) . Diving efficiency Dive efficiency was significantly lower for treated birds during deep dives (-12.5%, marginal mean = 0.0722 ± 0.004; LMM; t = -2.42 and p = 0.022, see Fig. 4 ; ESM S4 ) than for control birds (0.0835 ± 0.004), but not significantly during mid-dives (marginal means = 0.1220 ± 0.005 and 0.1360 ± 0.005 for treated and control birds, respectively; LMM: t = -1.89 and p = 0.066) and shallow dives (0.1910 ± 0.007 and 0.1880 ± 0.007 for treated and control birds; LMM: t = 0.38 and p = 0.709).Similarly, males showed higher diving efficiency (marginal mean = 0.0834 ± 0.003) than females (0.0724 ±0.005) in deep dives only (15.2%, LMM: t = 2.26 and p = 0.031), and Year had no effect on diving efficiency for all depth categories (all p > 0.220) ( Fig. 4 & ESM S4 ). It is interesting to note that the effect of Treatment appeared to increase with increasing depth (see model estimates Fig. 4 ). Dive, bottom and post-dive duration Dive duration (s) was not influenced by Treatment (all p > 0.470) or Year (all p > 0.110), regardless of depth category. However, males performed longer dives than females at all diving depths (all p < 0.005, Fig. 4 & ESM S4 ). The duration of the bottom phase (s) was significantly reduced for treated (marginal mean = 33.8 2.1 s) birds compared to controls (40.2 1.8 s) during deep dives (-15.9%, LMM: t = -2.14 and p = 0.041), but not during mid dives (marginal means = 53.5 2.1 s and 47.7 2.3 s for control and treated birds respectively, LMM: t = -1.93 and p = 0.062) and shallow dives (marginal means = 60.3 2.2 s and 59.9 2.3 s for control and treated birds respectively, LMM: t = -0.15 and p = 0.880). Similar to diving efficiency, the effect of the Treatment on bottom duration appeared to increase with increasing depth (see model estimates Fig. 4 ). Post dive duration, on the contrary, was significantly reduced (-16.3%) for treated birds during shallow dives (marginal means = 141.3 11.9 s and 118.2 11.0 s for control and treated birds respectively, GLM: z = -2.43 and p = 0.015), but not during mid dives (marginal means = 134.6 11.7 s and 126.6 11.4 s for control and treated birds respectively, GLM: z = -1.09 and p = 0.274) and deep dives (marginal means = 160.3 12.7 s and 137.1 11.8 s , GLM: z = -1.83 and p = 0.068) ( Fig. 4 ). Descent and ascent rate Descent and ascent rate followed the same trend, with treated birds showing lower vertical speed than control birds during mid dives (descent: -4.5% marginal means = 1.32 0.02 m/s and 1.26 0.02 m/s for control and treated birds respectively, LMM: t = -2.45 and p = 0.003 and ascent: -7.9% marginal means = 1.38 0.02 m/s and 1.27 0.02 m/s for control and treated birds respectively, LMM: t = -3.59 and p = 0.001) and deep dives (descent: -5.0%, marginal means = 1.41 0.02 m/s and 1.34 0.02 m/s for control and treated birds respectively, LMM: t = -2.33 and p = 0.028 and ascent: -8.2%, marginal means = 1.46 0.02 m/s and 1.34 0.02 m/s for control and treated birds respectively, LMM: t = -4.64 and p < 0.001), but not during shallow dives (descent: LMM: t = -0.28 and p = 0.784 and ascent: LMM: t = -0.65 and p = 0.523) ( Fig. 4 & ESM S4 ). Hunting activity The number of wiggles was significantly decreased in treated compared to control birds during mid dives (-12.9%, marginal means = 3.1 1.9 and 2.7 1.8 for control and treated birds respectively, GLM: z = -2.95 and p = 0.003) and deep dives (-16.0%, marginal means = 2.5 1.8 and 2.1 1.6 for control and treated birds respectively, GLM: z = -2.65 and p = 0.008) but not in shallow dives (GLM: z = -0.60 and p = 0.550) ( Fig. 4 & ESM S4 ). Chick growth The influence of the treatment on chick body mass differed across the growth period ( Stage: day 4, 10, 20 and 35) as indicated by the significant interaction between Treatment and growth stage (LMM; Treatment x Stage : F = 2.84 and p = 0.042). Whereas chicks from control and treated pairs started out at a similar body mass, i.e. not significantly different at day 4 and 10 (post-hoc contrasts: p = 0.940 and 0.274), chicks from treated pairs showed significantly lower body mass than controls at day 20 (-0.354 0.114 kg (-26.5%), post hoc contrasts: t = 3.11 and p = 0.002) and at day 35 (-0.366 0.129 kg (-18.4%), t = 2.84 and p = 0.005) (see Fig. 5.A & ESM S5 ). Chick body mass gain per day (g/day) from day 4 to day 35 (brooding phase), was significantly lower for chicks from treated pairs (LMM; estimate = -17 5 g/day, t = -2.12 and p = 0.048). Chick body mass gain per day was not significantly influenced by the year or initial body mass at day 4 (p = 0.327 and 0.419 respectively) ( Fig 5.B & ESM S5 ). Chick survival Chick short-term survival up until day 35, was not significantly affected by Treatment or Year (COX hazard model: coef = 1.11 0.84 and 0.52 0.84 and p = 0.184 and 0.531 respectively). In contrast, chick longer-term survival until fledging (~300 days, after the winter fast and molt in next summer) was significantly affected by Treatment (COX hazard model: coef = 0.89 0.39 and p = 0.024). Chicks from treated parents were twice more likely (odds ratio = 2.43) to die before fledging. Mortality risk before fledging was not significantly different between years (COX hazard model: coef = 0.68 0.41, p = 0.095), being on average slightly higher in 2022 than in 2019 (odds ratio = 1.97). Mortality peaked right after thermal independence (~day 30) with 15 (out of 37) reported dead chicks from day 35 to day 100 (onset of winter period and after the treatment period of parents) compared to 9 chicks lost during the brooding phase (chick with parents). Discussion From an optimal foraging and energetics perspective, unfavorable foraging conditions can be defined as periods when resource availability or quality decreases or energy investments into foraging increase (or both) (Pyke 1984; Pyke et al. 1977). We studied the flexibility of foraging behavior in breeding king penguins using an experimental protocol aimed at mimicking harsh conditions at sea. Since manipulating resources at sea is not possible, we experimentally increased foraging workload for a group of breeding adults during chick brooding (treated), and compared them to a group of breeding adults not subject to this constraint at the same moment (control). This mimicked the effects of a poor year by decreasing the benefit/cost ratio of foraging through an increase in travelling and especially diving costs relative to energy acquisition. Thus, treated birds exhibited lower horizontal and vertical speed due to increased hydrodynamic drag (Bannasch et al. 1994). In addition, given that the zone of the Polar Front, where food resources are concentrated at shallower depth (Bost et al. 1997, 2009; Charrassin & Bost 2001), was substantially further from the colony in 2022 (~640 km, similar during “extreme” years; Bost et al. 2015) than in 2019 (~560 km, intermediate between normal and extreme years), birds theoretically had to travel a greater distance to rely on the same foraging grounds and dive at deeper depth.This provided us with the unique opportunity to test for cumulative effects of a decrease in resource availability per se , and an increase in energy requirements to forage, especially at depth. Coping with harsh conditions at sea Experimentally increasing hydrodynamic drag and foraging energy costs in our study led to similar effects to what is usually observed in poor breeding years in king penguins at the scale of the foraging trip (Bost et al. 2015; Brisson-Curadeau et al. 2024). Specifically, both foraging trip duration and maximum foraging range increased in treated birds compared to controls, imposing additional constraints on breeding birds that had direct, and indirect, effects on reproduction. Chicks from treated pairs exhibited overall lower body mass gain and body mass, likely due to the inability of treated parents to provision their chick as much as controls (Gauthier-Clerc et al. 2002). Chick survival at thermal independence (35 days) was not significantly affected, though survival until fledging was significantly reduced for chicks from treated parents. Chick body mass gain before winter is critical for chick survival through winter and until fledging (Weimerskirch et al. 1992). The reduction in chick survival probability until fledging is probably due to chicks from treated parents showing reduced body mass gain from day 4 to day 35 and reduced absolute body mass at both 20 and 35 days: those chicks may not have been able to withstand the constrains imposed by the winter period (winter fast and predation pressure). Especially, most of the chick mortality occurred between 35 and 100 days, at the beginning of the crèche period, during which small chicks are more vulnerable to predation by giant petrels ( Macronectes giganteus ) (Hunter 1991). These results confirm the critical importance of travel distance and duration for central place forager breeding success, as observed in other seabird (Eby et al. 2023; Fayet et al. 2021; Fromant et al. 2021) and mammal (Massardier-Galatà et al. 2017) species. Yet, in response to increased foraging workload, we found that king penguins displayed substantial phenotypic flexibility. Treated birds adopted different foraging behavior than controls as early as their first foraging trip with the equipment, indicating a direct response to experimentally increased workload. Specifically, in contrast to controls, treated birds maximized their hunting activity (foraging dives) at or near the Polar Front, where prey is known to occur at shallower depths (Bost et al. 1997, 2009; Charrassin & Bost 2001), and foraged less during commuting trips. This difference in foraging locations led to observed differences in maximum diving depth. Indeed, while all birds generally increased maximum diving depth in 2022 compared to 2019, treated birds exhibited decreased maximum diving depths compared to controls in both years. Although predators may also alter their foraging strategies by switching to different preys, this scenario is unlikely as king penguins show little variation in diet, even when facing harsh environmental conditions (Brisson-Curadeau et al. 2024). Therefore, the observed shift in foraging locations for treated birds is more likely due to flexibility in foraging strategy and effort, rather than modifications in targeted prey per se . Interestingly, for trip parameters strongly related to trip length (trip duration in days and maximum range in km), the identity of the breeding pair explained more than 50% of the residual variance, indicating that these metrics are highly dependent on the performance of the other partner (i.e., longer foraging trips imply longer fasting period on land and potentially a longer period necessary to replenish energy stores), leaving hardly any room for sex-specific or individual compensation. However, other parameters (horizontal speed or diving depth) were rather a reflection of individual performance (breeding pair explaining less than 7% of residual variance) and could be used as adjustment variables. In addition to shifts in foraging grounds, birds facing an experimental increase in foraging workload also exhibited marked differences in their diving profiles. First, treated birds showed significantly lower vertical speed (both ascent and descent rates) for all dives >125m. Decreased vertical speed is the direct consequence of increased drag effect when diving, this effect being even more striking in deep foraging dives which are both energetically more constraining and for which descent and ascent phases are longer. A greater effect of the dummy device at deeper depth is also to be expected as the device is incompressible, and drag effect increases with depth. Decreased vertical speed may also be a byproduct of decreased swimming speed. As drag is markedly affected by swimming speed, birds may mitigate the energy demands imposed by the dummy logger by decreasing swimming speed. Consequently, treated birds showed reduced bottom duration (s) for deep foraging dives (significantly for >200m and marginally for 125-200m), while both dive and post dive duration remained unchanged for these depths (though post dive duration was also marginally reduced for treated birds in deep dives), ultimately leading to decreased diving efficiency (marginally for 125-200m). As penguins are air-breathing deep divers, dive duration is limited by the physiological ability of the species. Similarly, post dive duration corresponds to surface recovery between series of foraging dives when in a diving bout, and is constrained by birds’ physiological abilities. Therefore, diving time lost to transiting from and to the surface was principally gained by shortening bottom time, affecting birds’ ability to actively forage. Indeed, treated birds showed significantly reduced mean number of wiggles per dive for dives deeper than 125m. Overall, treated birds were less efficient (compared to controls) in dives deeper than 125m with regards to optimal foraging theory ( i.e. , maximize time spent in profitable areas) and therefore tended to maximize their foraging activity at 50-125m depths, corresponding to the typical depth of the thermocline, where prey aggregate, at the Polar Front. The distance – depth trade-off: will foraging at the Polar Front be optimal in the future? Theoretical studies of foraging behavior in diving birds and mammals indicate that optimized strategies maximize the time spent in favorable depth for foraging relative to time spent commuting or recovering at the surface (Cornick & Horning 2003; Doniol-Valcroze et al. 2011; Halsey & Butler 2006; Hanuise et al. 2013; Watanabe et al. 2014). Overall, treated birds were more efficient at diving at shallow depths, with a reduction in the duration of the transit phase. This enabled them to increase their foraging activity and thus maximize their diving efficiency at these depths. As prey is more readily available at shallow depths at the Polar Front, treated birds were found to feed preferentially at the Polar Front. Treated birds avoided losing energy during the travelling phase by avoiding deep dives, observed in control birds, that are energetically more draining. However, targeting the distant Polar Front can also be energetically constraining, especially in years where it is further from the colony ( i.e. , in 2022), and can result in longer foraging trips and impaired chick provisioning during the chick brooding phase. As a result, breeding birds are faced with a trade-off between having to dive deeper while staying closer to the colony or travel more distance to forage at shallower depths. It appeared that adults faced with increased workload ( i.e. , treated birds) modified their foraging behavior in response to the constrain, and this, as soon as the first foraging trip with the equipment. This response was consistent between the two years, with a cumulative effect in case of already poor conditions at sea that can have consequences for breeding on-land. In our case, the negative cumulative effect of the treatment and the environmental conditions on chick growth and survival were limited during the monitoring period, but were more striking after the winter period. Most breeding pairs managed to compensate for the added constraint and were able to successfully raise their chick until the onset of winter. However, it appeared that treated birds paid a cost during winter, since chick overwinter survival was lower than for controls. In addition, although parents were found to compensate for harsher conditions during one breeding season, it is not excluded that they paid the cost of increased reproduction effort in their following breeding attempts (Daan et al. 1996). Moreover, during a more “extreme” year ( i.e. 1997, Polar Front below 53°S) it is likely that adults faced with an increased constraint would have failed their breeding attempt if they adopted the same strategy ( i.e. , targeting the Polar Front in order to dive at shallower depths), as chick survival is greatly reduced when fasting period exceeds 20 days during chick brooding (Gauthier-Clerc et al. 2002). This is because parents of the same breeding pair alternate between foraging at sea and brooding on land, so that in bad years, the individuals on land may not be able to fast long enough to wait for their partner to return from foraging at sea (Groscolas et al. 2008; Olsson 1997) and small chicks are quickly predated if unguarded by a parent (Hunter 1991). In the short term, targeting the Polar Front zone thus allows these long-lived seabirds to replenish their energy stores favoring survival over reproduction in the case of limited available energy (Drent & Daan 1980; Olsson 1997). This strategy would be adaptive if detrimental conditions at sea occurred sporadically, allowing individuals to resume reproduction in favorable years, and if no long-term change in oceanographic conditions occurred. However, climate modelling projections show that changes in the location of the Polar Front over the next hundred years will strongly impact the northern range of king penguins, moving further and further away from the colonies at Crozet, and doubling by 2100 (passing below 54°S by 2070, Péron et al. 2012). Thus, the traveling distance and time required to reach foraging grounds will eventually reach a critical point beyond which breeding failure is inevitable for most breeding birds when foraging exclusively at the Polar Front zone. Continuing to rely on the Polar Front during summer as the main foraging ground may thus constitute an ecological trap, as breeding adults would take more time travelling further away from the colony leading to repeated breeding failure over the years that might eventually lead to the disappearance of the king penguins population in Crozet (Péron et al . 2012). Despite a possibly bleak outlook for the king penguin populations of Crozet, our study also showed that some individuals did not (or hardly) forage at the Polar Front, and yet still replenished their energy stores and successfully fed their chick. This hints towards other aspects of changes in king penguin foraging behavior that have yet to be studied in the context of climate change. Especially, king penguins have shown exceptional foraging flexibility in Tierra del Fuego, Chile (Pütz et al. 2021). Further studies on the specific case of king penguins at Crozet are needed to determine if different strategies may exist in the population, and to better characterize the oceanic structures targeted by the birds. Declarations Funding: This work was supported by the French Polar Institute (IPEV) and the Zone Atelier Antarctique (LTSER France, CNRS-EE) through a collaboration between the ECONERGY 119 and OISEAUX PLONGEURS 394 polar projects, and by the French National Center for Scientific Research (CNRS). CL was supported by a PhD scholarship from the Ecole Normale Supérieure (ENS-Lyon). We are grateful to the Terres Australes et Antarctiques Françaises (TAAF) for providing logistical support in the field. The 394 and 119 projects on king penguin are part of the long-term studies in Ecology and Evolution (SEE-Life) program of the CNRS. Conflicts of interest: Authors declare that they have no conflict of interest. Ethics approval: All applicable institutional and/or national guidelines for the care and use of animals were followed (Ethical approval: 2019: APAFIS#16465–2018080111195526v4 and 2022: APAFIS#31268-2021042117037897v3). 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For each parameter, estimates and standard error (as est se), degree of freedom (df), statistic (t) and p-value are presented. Reference levels are Female for effect of Sex , Control for Treatment , and 2019 for Year . For all models, Pair ID was categorized as a random intercept except for the distance of foraging dives where individual ID nested in Pair ID was included as a random intercept to account for repeated measures over individuals and for non-independence of breeding partners. Results are given as % of residual variance explained by the random factor. For Mean horizontal speed, model including Pair ID as a random intercept failed to converge, random effect was therefore removed and simple Linear Model was run. Trip duration was log-transformed to fit model assumptions (normality). est ± se df t value p Distance of foraging dives to the Polar Front (km) Intercept (57.06 ± 24.89)E03 29.45 2.293 0.029 Year [2022] -28.07 ± 12.31 29.44 -2.280 0.030 Treatment [TREATED] -54.75 ± 24.55 28.28 -2.230 0.034 Sex [Male] -12.36 ± 15.35 16.16 -0.805 0.432 Random: Pair ID:ID 10.6% Random: Pair ID 22.8% Trip duration (days) (log-transformed) Intercept 2.00 ± 0.11 35.34 17.89 < 0.001 Year [2022] 0.22 ± 0.11 26.06 2.01 0.055 Treatment [TREATED] 0.40 ± 0.11 24.17 3.68 0.001 Sex [Male] -0.10 ± 0.07 15.82 -1.41 0.178 Random: Pair ID 55.9% Maximum range (km) Intercept 253.86 ± 35.68 33.62 7.12 < 0.001 Year [2022] 84.51 ± 34.62 23.84 2.44 0.023 Treatment [TREATED] 88.31 ± 34.49 22.16 2.56 0.018 Sex [Male] 42.35 ± 24.08 14.28 1.76 0.100 Random: Pair ID 53.9% Mean horizontal speed (km.h-1) Intercept 3.50 ± 0.14 36.00 24.44 < 0.001 Year [2022] 0.04 ± 0.12 36.00 0.29 0.774 Treatment [TREATED] -0.33 ± 0.12 36.00 -2.82 0.008 Sex [Male] 0.30 ± 0.12 36.00 2.49 0.018 Mean depth of foraging dives (m) Intercept 139.33 ± 6.69 35.85 20.81 < 0.001 Year [2022] 24.12 ± 5.85 27.19 4.12 < 0.001 Treatment [TREATED] -15.84 ± 5.57 22.63 -2.84 0.009 Sex [Male] 25.96 ± 5.49 22.59 4.73 < 0.001 Random: Pair ID 6.9% Supplementary Files ForagingFlexibilitySUPPLEMENTARYMATERIALS.docx Cite Share Download PDF Status: Published Journal Publication published 03 Jul, 2025 Read the published version in Oecologia → Version 1 posted Reviewers agreed at journal 04 May, 2025 Reviewers invited by journal 29 Apr, 2025 Editor assigned by journal 17 Apr, 2025 First submitted to journal 06 Apr, 2025 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. 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d'Etudes Biologiques de Chize","correspondingAuthor":false,"prefix":"","firstName":"Charles-André","middleName":"","lastName":"Bost","suffix":""},{"id":449537704,"identity":"642b4ac8-dae2-41cc-abf1-8baddaf8d333","order_by":2,"name":"Nicolas Joly","email":"","orcid":"","institution":"IPHC DEPE: Institut Pluridisciplinaire Hubert Curien Departement Ecologie Physiologie et Ethologie","correspondingAuthor":false,"prefix":"","firstName":"Nicolas","middleName":"","lastName":"Joly","suffix":""},{"id":449537705,"identity":"b9802096-3f33-475a-bacd-6f80f2840843","order_by":3,"name":"Antoine Stier","email":"","orcid":"","institution":"IPHC DEPE: Institut Pluridisciplinaire Hubert Curien Departement Ecologie Physiologie et Ethologie","correspondingAuthor":false,"prefix":"","firstName":"Antoine","middleName":"","lastName":"Stier","suffix":""},{"id":449537706,"identity":"b3707da8-0e62-4064-88ae-0620dbe36b88","order_by":4,"name":"Jean-Patrice Robin","email":"","orcid":"","institution":"IPHC DEPE: 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10:11:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4704667/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4704667/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00442-025-05754-9","type":"published","date":"2025-07-03T15:58:06+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":81688877,"identity":"f44c7db8-036a-4804-b8cc-760234561c02","added_by":"auto","created_at":"2025-04-30 11:19:36","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":64979,"visible":true,"origin":"","legend":"\u003cp\u003eAttachment positions of both Axy-Trek and dummy loggers on the back of king penguins (\u003cem\u003eAptenodytes patagonicus\u003c/em\u003e).\u003c/p\u003e","description":"","filename":"Picture1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4704667/v1/6d67f6638623721978440fa9.jpg"},{"id":81690172,"identity":"c1f5ee5b-4daf-45c5-9490-784ddc2d6843","added_by":"auto","created_at":"2025-04-30 11:27:36","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":86837,"visible":true,"origin":"","legend":"\u003cp\u003ePosition of the main foraging areas for studied king penguins (\u003cem\u003eAptenodytes patagonicus\u003c/em\u003e) depending on the year (2019 and 2022) and the treatment (Control/Treated). Areas correspond to kernel densities (25%, 50% and 75% on the position of foraging dives), dashed lines correspond to the position of the signature of the Polar Front (2°C isotherm at 200m).\u003c/p\u003e","description":"","filename":"Picture2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4704667/v1/2079ebec48dce282f0ae6d24.jpg"},{"id":81688879,"identity":"514554d1-d15f-4adf-ab6c-767532340d25","added_by":"auto","created_at":"2025-04-30 11:19:36","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":51981,"visible":true,"origin":"","legend":"\u003cp\u003eAdult king penguin (\u003cem\u003eAptenodytes patagonicus\u003c/em\u003e) trip parameters (\u003cstrong\u003eA.\u003c/strong\u003e Trip duration (days), \u003cstrong\u003eB. \u003c/strong\u003eMaximum foraging range (km), \u003cstrong\u003eC.\u003c/strong\u003e Mean horizontal speed (km.h\u003csup\u003e-1\u003c/sup\u003e) and \u003cstrong\u003eD.\u003c/strong\u003e Mean maximum depth of foraging dives). Colors represent treatment group (blue = Control, orange = Treated) and point shape the year (circles = 2019 and triangles = 2022). For all parameters both raw data and marginal mean and standard errors from LMMs are provided. For trip duration, marginal means were back transformed (but standard error are still provided on the log scale).\u003c/p\u003e","description":"","filename":"Picture3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4704667/v1/8746c5b076206f663d807d90.jpg"},{"id":81688878,"identity":"0641423a-58a7-4891-9735-b9c7495a530c","added_by":"auto","created_at":"2025-04-30 11:19:36","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":65180,"visible":true,"origin":"","legend":"\u003cp\u003eForest plot of the different model outputs (see Results) for each diving parameter (first row Linear Mixed Models and second row Generalized Linear Mixed Models) of king penguins (\u003cem\u003eAptenodytes patagonicus\u003c/em\u003e) for each depth category Shallow (50-125m, n = 7746 dives), Mid (125-200m, n = 15762 dives) and Deep (\u0026gt;200m, n = 6965 dives). Effects of explanatory variables (y-axis) are given for each dependent variable (color, diving parameters), effects are given relative to a reference category (Control, 2019 and Female for Treatment, Year and Sex respectively). Standardized estimates are given for the scaled variable to facilitate visualization, so that effect sizes are directly comparable across models. Estimates (for LMMs) and incidence rate ratios (for GLMMs) are given along with their 95% confidence interval. All model summaries can be found in \u003cstrong\u003eSupplementary Materials ESM S4\u003c/strong\u003e.\u003c/p\u003e","description":"","filename":"Picture4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4704667/v1/a634a5b92f9d74aadfa2eba4.jpg"},{"id":81688881,"identity":"414ce6ab-0fe8-46ee-af49-7cb642ea9011","added_by":"auto","created_at":"2025-04-30 11:19:36","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":34426,"visible":true,"origin":"","legend":"\u003cp\u003eKing penguin (\u003cem\u003eAptenodytes patagonicus\u003c/em\u003e) chick body mass (kg) (\u003cstrong\u003eA.\u003c/strong\u003e) and body mass gain (g/day) (\u003cstrong\u003eB.\u003c/strong\u003e) during the monitoring period (day 4 to day 35), colors represent treatment group and point shape the year (circles = 2019 and triangles = 2022) sample sizes are provided for each age. Both raw data (scatter plot) and marginal mean and standard error from Linear Models are presented. Linear Model for chick body mass included \u003cem\u003eYear\u003c/em\u003e, \u003cem\u003eTreatment\u003c/em\u003e and \u003cem\u003eAge\u003c/em\u003e (day 4, 10, 20 and 35) as explanatory variables. The interaction terms \u003cem\u003eTreatment \u003c/em\u003ex\u003cem\u003e Age\u003c/em\u003e and \u003cem\u003eTreatment\u003c/em\u003ex \u003cem\u003eYear \u003c/em\u003ewere also included in the model to evaluate the evolution of the effect of Treatment over chick growth and whether the effect of the treatment depended on the year. The interaction terms \u003cem\u003eTreatment \u003c/em\u003ex\u003cem\u003e Year\u003c/em\u003e was not retained in the final model. Linear Model for chick body mass gain (g/day) included Year and Treatment as explanatory variables and chick body mass at day 4 as a covariate. All model summaries can be found in \u003cstrong\u003eSupplementary Materials ESM S5\u003c/strong\u003e.\u003c/p\u003e","description":"","filename":"Picture5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4704667/v1/665a47c79271ff6ad4740434.jpg"},{"id":86179137,"identity":"8002a566-ae2e-4b8b-a7c8-d83a4657292f","added_by":"auto","created_at":"2025-07-07 16:16:13","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1451502,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4704667/v1/38d4a557-684b-40f5-abec-b2804402a15d.pdf"},{"id":81690173,"identity":"22f0e34c-1cd3-4e95-8b5c-584d24024411","added_by":"auto","created_at":"2025-04-30 11:27:36","extension":"docx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":1067016,"visible":true,"origin":"","legend":"","description":"","filename":"ForagingFlexibilitySUPPLEMENTARYMATERIALS.docx","url":"https://assets-eu.researchsquare.com/files/rs-4704667/v1/76447e5c043fa56e3e829337.docx"}],"financialInterests":"","formattedTitle":"Foraging flexibility in response to at-sea constraints in a deep diver, the king penguin: an experimental study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe current intensification of ocean warming rates (Cheng \u003cem\u003eet al.\u003c/em\u003e 2022) and associated changes in oceanographic conditions have major repercussions on primary productivity, rippling through the food web all the way to top predators (Hazen \u003cem\u003eet al.\u003c/em\u003e 2019; Parmesan 2006). This is particularly the case in the Southern Ocean where unique circumpolar currents support both large-scale (Polar Front, Antarctic convergence) and meso-scale (eddies, filaments) oceanographic structures (Song \u003cem\u003eet al.\u003c/em\u003e 2020; Sturm \u003cem\u003eet al.\u003c/em\u003e 2023) of high primary productivity (Pinkerton \u003cem\u003eet al.\u003c/em\u003e 2021) on which major food webs rely (Bost \u003cem\u003eet al.\u003c/em\u003e 2009; Murphy 1995). In particular, many marine mammals and seabirds are central-place foragers, spatially constrained in their foraging range by the requirements of tending to their offspring on-land at a fixed central place site during the breeding season. Therefore, these central-place foragers are particularly sensitive to local variations in the availability and distribution of their prey. Understanding the phenotypic flexibility (and limit thereof) of such species to current changes in oceanographic conditions is key in forecasting future population trends and potential changes in life-history strategies (Forcada \u0026amp; Trathan 2009; Massardier-Galatà \u003cem\u003eet al.\u003c/em\u003e 2017; Péron \u003cem\u003eet al.\u003c/em\u003e 2012; Trathan \u003cem\u003eet al.\u003c/em\u003e 2007).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eKing penguins (\u003cem\u003eAptenodytes patagonicus\u003c/em\u003e) are top marine predators, long distance central place foragers and deep divers, that breed on remote circumpolar islands throughout the Southern Ocean (Bost \u003cem\u003eet al.\u003c/em\u003e 2012). During the austral summer breeding season, both parents alternate foraging trips at sea, lasting between a week and 10 days, and guarding shifts on land to tend and feed the chick (Stonehouse 1960; Weimerskirch \u003cem\u003eet al.\u003c/em\u003e 1992). Timing is critical: a delay in the duration of a parental foraging trip entails the likely abandonment of the egg or chick by the fasting partner remaining on-land, and the loss of reproduction for the season (Groscolas \u003cem\u003eet al.\u003c/em\u003e 2008; Olsson 1997). Adults thus mainly travel southward to reach their preferred foraging grounds, the frontal zones where they feed on small mesopelagic fish, caught at shallower depths (Bost \u003cem\u003eet al.\u003c/em\u003e 1997, 2002, 2009; Charrassin \u0026amp; Bost 2001). These frontal zones are where prey aggregate at shallower depths are particularly important as they allow birds to spend a greater fraction of their diving time foraging (deep dives are energetically less efficient because actual, profitable, foraging time at the bottom is reduced compared to the costs of commuting from and to the surface) (Kooyman 2012). Because parents are constrained both by time and limited areas within their reach to forage (Weimerskirch 2007), they are highly sensitive to the temporal and spatial predictability of these foraging grounds (Bost \u003cem\u003eet al.\u003c/em\u003e 2015; Oliver \u003cem\u003eet al.\u003c/em\u003e 2019). Thus, being able to accurately predict and target areas of high prey accessibility means the difference between breeding success or failure.\u003c/p\u003e\n\u003cp\u003eLong-term monitoring of the foraging behavior of king penguins in Crozet have shown that adults principally target the Antarctic Polar Front every year, regardless of its position relative to the colony (Bost \u003cem\u003eet al.\u003c/em\u003e 2015). As a result, in years when the Polar Front is especially distant from the colony, birds struggle to efficiently provision their chicks, and breeding success is lower (Bost \u003cem\u003eet al.\u003c/em\u003e 2015). Climate models predict the Polar Front to move further and further from the colonies in Crozet over the next hundred years (Péron \u003cem\u003eet al.\u003c/em\u003e 2012). The travel distance between favorable foraging locations and the colony are expected to double – a prohibitive distance for king penguins in Crozet – and to negatively affect breeding success, ultimately leading to local population extinction in the near future (Péron \u003cem\u003eet al.\u003c/em\u003e 2012).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHere, we used an experimental approach to investigate the short-term (intra-annual) foraging flexibility and response of breeding king penguins to variable (favorable or harsh) conditions at-sea. Over two breeding seasons (2019 and 2022), we experimentally increased the foraging workload of a group of chick-brooding parents, and compared them to a group not subject to this constraint. We fitted birds with a dummy logger aimed at increasing the individual’s drag coefficient (Wilson \u003cem\u003eet al.\u003c/em\u003e 1986) when swimming and/or foraging under water (see Methods). Our rationale was to decrease the benefit/cost ratio of foraging for a group of experimental birds by increasing the travelling costs they faced compared to a control group monitored at the same time and under similar environmental conditions, but not subject to this constraint. Manipulating the benefit/cost ratio of foraging by making experimental birds work harder was used to mimic the effects of what birds might encounter during a bad year at sea.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe hypothesized that (i) if experimental birds were observed to adjust their foraging behavior compared to control birds, and (ii) if that modification allowed them to compensate for harsh conditions with no observable consequences on chick growth and survival, this response could be interpreted as behavioral flexibility. As the treatment was expected to reduce swimming speed due to increased drag, and more so with increasing depth as the device was incompressible, we expected birds to maximize their foraging activity in large oceanographic structures like the Polar Front (where prey is accessible at shallower depths) compared to controls. In addition, we expected treated birds to spend more time in the bottom phase of their dives relative to ascent and descent phases to maximize foraging time and maintain their energy balance. We also tested whether treated birds were able to compensate for the treatment, by examining the effect of the treatment on chick body mass gain and survival. As king penguins are long lived seabirds, they behave as prudent parents (Drent \u0026amp; Daan 1980) and tend to abandon reproduction when conditions are difficult (Groscolas \u003cem\u003eet al.\u003c/em\u003e 2000, 2008) . If birds were able to adjust to the treatment (via modifications in their foraging behavior) without additional costs, we expected that chick body mass gain and survival would not differ between treated and control groups.\u003c/p\u003e\n\u003cp\u003eThis experiment was conducted over two very contrasted breeding seasons (2022 being an overall harsher climatic year than 2019, see results), providing us with the opportunity to test for the adaptive scope and limits of foraging flexibility. We expected parents to work harder in 2022 (a year marked by overall poor reproductive success at our study site) but to perhaps be able to compensate in 2019 (a year of overall relatively good reproductive success).\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eStudy site and adult monitoring\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was conducted during the 2019-20 and 2022-23 breeding seasons in the \u0026ldquo;Baie du Marin\u0026rdquo; king penguin colony, on Possession Island (46\u0026deg;26\u0026prime;S, 51\u0026deg;52\u0026prime;E) in the Crozet archipelago, home to ~24,000 breeding pairs (Barbraud \u003cem\u003eet al.\u003c/em\u003e 2020). King penguin pairs (N = 14 in 2019 and 21 in 2022) and their chicks were monitored from courtship (November) to the beginning of the Austral winter (~March). Birds were early breeders that had laid their egg from Nov 21st to Dec 11th in 2019 and from Nov 17th to Dec 26th in 2022. We selected pairs haphazardly once they had settled on their final breeding territory, shortly before egg-laying. Males and females were temporarily marked on the chest, from some 3-m distance using a telescopic perch and a brush dipped in animal spray paint (Porcimark\u0026reg;, Kruuse, Langeskov, Denmark). Once the egg was laid, we caught males and females over two successive incubation shifts, while their partner was foraging at sea. Birds were only caught on the 3rd day after they settled for their first respective incubation shift, ensuring they were motivated to incubate the egg. Upon capture, the egg was carefully removed from the brood pouch and replaced by a warm dummy plaster egg during bird handling to avoid accidental breakage. Beak and both flipper lengths were then measured to the nearest 1-mm using a metal ruler, body girth was measured to the nearest 1-mm with a measuring tape and birds were marked on the chest using water resistant hair-dye (Garnier\u0026reg;).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eExperimental design\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMonitored pairs were assigned to either control or treated groups, ensuring a balanced spatial and temporal distribution between groups. Inter-group consistency in adults\u0026rsquo; initial condition was statistically verified using Principal Components Analyses (PCA; see \u003cstrong\u003eSection S1\u003c/strong\u003e\u003cstrong\u003eESM\u003c/strong\u003e). To ensure that we recorded the first brooding trip at sea after the egg had hatched, we caught birds at day 50 of incubation (incubation is typically 53 days in king penguin; Stonehouse 1960). Adults were then instrumented with loggers according to the following experimental protocol: (i) control birds (N = 12 and 22 birds belonging to 7 and 13 breeding pairs in 2019 and 2022, respectively) were equipped with an Axy-Trek Marine High Depth data logger (TechnoSmArt Europe srl, Rome, Italy); (ii) treated birds (N = 13 and 20 birds belonging to 7 and 11 breeding pairs in 2019 and 2022 respectively) were equipped both with an Axy-Trek and a dummy device (see \u003cstrong\u003eFig.1\u003c/strong\u003e). Then, both partners were alternatively equipped according to their group (control or treated) in order to record two successive brooding shifts (see \u003cstrong\u003eFig.S2\u003c/strong\u003e\u003cstrong\u003eESM\u003c/strong\u003e) and ensure they had undergone the same manipulations. \u003c/p\u003e\n\u003cp\u003eAxy-Trek Marine loggers are small (65 x 40 x 15 mm, 45 g in air) GPS loggers combined with tri-axial accelerometers and pressure sensors allowing to record both penguin movement and diving profiles at sea. The GPS was set to acquire satellite fixes at a rate of 1 fix/15 minutes when the bird was not submerged; pressure was recorded at a rate of 1 Hz. The dummy device (designed for treated bids) was a P.V.C cylinder of 3 cm diameter; and a cross-sectional area of 28.3 cm\u003csup\u003e2\u003c/sup\u003e, \u003cem\u003ei.e. \u003c/em\u003eroughly 2.8% of the cross-sectional area of an average king penguin. Position of loggers on the back of penguins was standardized to ensure repeatability over individuals. Axy-Trek loggers were always fitted first, just above the tail articulation, and dummy loggers were added in front of the Axy-Trek and at the back of the maximal cross section area of the bird. Loggers were externally attached to the feathers using waterproof tape (Tesa tape, 4651; Tesa) and instant adhesive glue (Loctite, 401; Henkel, Germany). The equipment period was stopped and all loggers removed before chick thermal independence. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eChick monitoring\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUpon hatching, chicks of the monitored pairs were captured at days 4, 10, 20 and 35. At each capture, marked chicks were weighed to the nearest 1-g using a spring-slide Pesola\u0026reg; scale. When possible, \u003cem\u003ei.e.\u003c/em\u003e when it coincided, chick captures were performed during deployment or retrieval of equipment to reduce the number of interventions in the colony. At day 20, chicks were identified using color-coded fish tag (Floy Tag and MFG, Inc., Seattle, WA, USA) attached subcutaneously to their upper-back to allow monitoring after formation of the creches (Stier \u003cem\u003eet al\u003c/em\u003e\u003cem\u003e.\u003c/em\u003e 2014; Viblanc \u003cem\u003eet al\u003c/em\u003e\u003cem\u003e.\u003c/em\u003e 2020). The survival of chicks was re-determined at the onset of winter (day 100, late April-early May) and at fledging (December-January the subsequent year).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLogger data processing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOf the 67 birds that were successfully fitted with a logger, 45 had successfully recorded data of the first brooding trip at sea. Logger data were extracted according to the first and last GPS position at sea to remove locations taken when the bird was still on-land in the colony.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eGPS data:\u003c/em\u003e When bird\u0026rsquo;s return to the colony was not recorded from GPS data (battery or memory shortage, n = 21 tracks of the 45 recorded tracks) and the return phase from the foraging trip at sea was sufficiently advanced (\u003cem\u003ei.e.\u003c/em\u003e at least 70% of total trip duration recorded, n = 15 tracks) we estimated the return date as the date of first sighting in the colony. Otherwise, logger data was discarded from the study (n = 5 tracks), the final dataset contained 40 tracks including 14 incomplete tracks (see \u003cstrong\u003etable S1.1 ESM\u003c/strong\u003e). GPS data were filtered according to horizontal speed to remove erroneous positions (filter threshold = 14 km h\u003csup\u003e-1\u003c/sup\u003e; see Cott\u0026eacute; \u003cem\u003eet al.\u003c/em\u003e 2007; Kooyman \u003cem\u003eet al.\u003c/em\u003e 1992) and interpolated to correct for irregular fix frequencies (GPS switched off when the bird was diving) using the \u0026ldquo;\u003cem\u003emove\u003c/em\u003e\u0026rdquo; R package (fix frequency = 1 fix/15 minutes). For each foraging trip, trip duration (days), maximum distance to the colony (km), total distance traveled (km), and mean horizontal speed (km h\u003csup\u003e-1\u003c/sup\u003e) were extracted.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eTime-depth recorder (TDR) data: \u003c/em\u003eTDR data were extracted and filtered to remove the time spent in the colony (see above for details). Correction of the 0-offset (\u003cem\u003ei.e. \u003c/em\u003ethe gradual shift of the 0 m over time due to logger accuracy) of the TDR data was performed using the \u003cem\u003ediveMove \u003c/em\u003eR package \u003cem\u003eTDRcalibrate\u003c/em\u003e function. Then, dives and dive parameters were extracted using a custom-made R function. For each dive, the timestamp of begin and end, dive and post-dive durations (time spent at the surface between two consecutive dives in seconds), bottom duration (s), maximum depth (m), vertical speed during ascent and descent ( ), number of wiggles were extracted following the definitions in (Halsey \u003cem\u003eet al.\u003c/em\u003e 2007a), and each dive was associated to a single dive ID. Wiggles are rapid changes in depth, usually in the deepest portion of a dive profile that are the signature of hunting activity. In the dive profile, a wiggle translates into 3 consecutive points where the depth derivative (vertical speed) cancels out and changes sign (Halsey \u003cem\u003eet al.\u003c/em\u003e 2007a). Diving efficiency was calculated for each dive as: (Halsey \u003cem\u003eet al.\u003c/em\u003e 2007a). The dive efficiency represents the proportion of time in a dive cycle spent in the bottom phase, i.e. the phase of active foraging during a dive. More efficient penguins are those that spend a higher proportion of diving time actively foraging. Dives with maximum depth 50m were considered foraging dives (Charrassin \u003cem\u003eet al.\u003c/em\u003e 1998) and included in statistical analysis. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMarine environmental data\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBathymetry data around Crozet were obtained from NOAA National Centers for Environmental Information with a 0.0042\u0026deg; x 0.0042\u0026deg; grid resolution (2022: ETOPO 2022 15 Arc-Second Global Relief Model, https://doi.org/10.25921/fd45-gt74). Maps using bathymetry data were generated using the \u003cem\u003eggOceanMaps \u003c/em\u003e(Vihtakari \u003cem\u003eet al.\u003c/em\u003e 2024) and \u003cem\u003eggOceanData\u003c/em\u003e packages (https://mikkovihtakari.github.io/ggOceanMaps/).\u003c/p\u003e\n\u003cp\u003eThe isotherm 2\u0026deg;C at 200 m depth was considered the signature of the position of the Polar Front (Belkin \u0026amp; Gordon 1996; Orsi \u003cem\u003eet al.\u003c/em\u003e 1995). Daily Sea Potential Temperature at 200m (SPT, in \u0026deg;C) were obtained from the E.U. Copernicus Marine Service Information (Global Ocean Physics Reanalysis https://doi.org/10.48670/moi-00021 for 2019 and Global Ocean Physics Analysis and Forecast, https://doi.org/10.48670/moi-00016 for 2022) with a 0.083\u0026deg; \u0026times; 0.083\u0026deg; grid resolution, between the 1\u003csup\u003est\u003c/sup\u003e of January and the 1\u003csup\u003est\u003c/sup\u003e of June for each breeding season (encompassing all foraging trips). \u003c/p\u003e\n\u003cp\u003eEnvironmental data were processed in R using the \u003cem\u003eterra\u003c/em\u003e package (Hijmans \u003cem\u003eet al.\u003c/em\u003e 2024a) as Spatial Raster objects. Mean SPT was computed for each grid cell over the period of the recording of foraging behavior (2020-01-20 to 2020-03-05, and 2023-01-14 to 2023-03-14).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll statistical analyses were performed in R (v. 4.2.2). Generalized linear (GLMM) and linear (LMM) mixed models were run using the \u0026lsquo;lme4\u0026rsquo; package (Bates \u003cem\u003eet al.\u003c/em\u003e 2015). In general, our models tested for an effect of \u003cem\u003eTreatment\u003c/em\u003e (Control/Treated), \u003cem\u003eYear\u003c/em\u003e (2019/2022) and \u003cem\u003eSex\u003c/em\u003e (M/F) on bird foraging behavior.Two-way interactions \u003cem\u003eTreatment \u003c/em\u003ex\u003cem\u003e Sex \u003c/em\u003eand \u003cem\u003eTreatment \u003c/em\u003ex\u003cem\u003e Year \u003c/em\u003ewere initially considered in the models to test if the effect of the treatment differed between the sexes, and/or was more pronounced in 2022 (poor year) than in 2019 (normal year). However, as these interactions were never significant (all p \u0026gt; 0.150), they were removed from all final models; thereby only main effects of \u003cem\u003eTreatment, Sex\u003c/em\u003e and \u003cem\u003eYear\u003c/em\u003e are presented below. \u003cem\u003eBird ID\u003c/em\u003e nested within in \u003cem\u003ePair ID\u003c/em\u003e was specified as a random intercept in all models to account for repeated measurements (multiple dives per individual) and the non-independence of partners within a pair. When explaining trivial amounts of variance, nested effects were removed and we specified \u003cem\u003eBird ID\u003c/em\u003e on its own to allow model convergence. As models on foraging trip parameters did not rely on repeated measures over individual (\u003cem\u003ee.g.\u003c/em\u003e, trip duration has only one value per trip) or were averaged over the entire trip (\u003cem\u003ee.g.\u003c/em\u003e, mean horizontal speed), only \u003cem\u003ePair ID \u003c/em\u003ewas included as a random intercept. Where appropriate, Tukey HSD contrasts between groups and marginal means for \u003cem\u003eTreatment and Year \u003c/em\u003ewere assessed using the \u003cem\u003eemmeans\u003c/em\u003e package in R (Lenth \u003cem\u003eet al.\u003c/em\u003e 2025). We ensured that model residuals were normally distributed by visual inspection of density distributions, Q\u0026ndash;Q plots, cumulative distribution functions, and P\u0026ndash;P plots using the \u003cem\u003efitdistrplus\u003c/em\u003e package in R (Delignette-Muller \u003cem\u003eet al.\u003c/em\u003e 2025), or alternative distributions were specified as appropriate (see below). Results are presented either as raw or marginal (estimated) means \u0026plusmn; standard error (se) as appropriate. Effects were considered statistically significant for \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eOceanographic conditions and habitat use\u003c/em\u003e \u003c/p\u003e\n\u003cp\u003eTo test how our experimental treatment affected the marine habitat use of king penguins, we proceeded in a two-step analyses: (1) First, the spatial distribution of foraging dives around Crozet was mapped using kernel density estimation (package \u003cem\u003eadehabitatHR\u003c/em\u003e v.0.4.21; Calenge \u0026amp; Fortmann-Roe, 2023). To do so, the GPS positions of foraging dives over entire foraging trips were approximated from the interpolated GPS data based on timestamps of foraging dives and GPS fixes. Kernel densities with probabilities of 0.25, 0.50 and 0.75 were then calculated for each \u003cem\u003eYear\u003c/em\u003e and \u003cem\u003eTreatment \u003c/em\u003e(2019\u003csub\u003etreated\u003c/sub\u003e\u003cem\u003evs.\u003c/em\u003e 2019\u003csub\u003econtrol\u003c/sub\u003e\u003cem\u003evs.\u003c/em\u003e 2022\u003csub\u003etreated\u003c/sub\u003e\u003cem\u003evs.\u003c/em\u003e 2022\u003csub\u003econtrol\u003c/sub\u003e). (2) Second, the distances of foraging dives from the signature of the Polar front were computed using the \u003cem\u003egeosphere\u003c/em\u003e package (Hijmans \u003cem\u003eet al.\u003c/em\u003e 2024b, function\u003cem\u003e dist2line\u003c/em\u003e with geographic distance). We then assessed the effects of \u003cem\u003eTreatment\u003c/em\u003e, \u003cem\u003eSex\u003c/em\u003e and \u003cem\u003eYear\u003c/em\u003e on the distances of foraging dives from the Polar front using an LMM (see above). \u003c/p\u003e\n\u003cp\u003e\u003cem\u003eForaging trip parameters\u003c/em\u003e \u003c/p\u003e\n\u003cp\u003eWe investigated the effects of \u003cem\u003eTreatment\u003c/em\u003e, \u003cem\u003eSex\u003c/em\u003e, and \u003cem\u003eYear\u003c/em\u003e on foraging trip duration (days), maximum foraging range (km), mean horizontal speed ( ), and mean diving depth of foraging dives (m) over the trip using linear mixed models (LMMs). Trip duration was log-transformed to ensure normality of residuals; results are given for the back-transformed estimate and marginal means. For the model on mean horizontal speed, \u003cem\u003ePair ID\u003c/em\u003e explained trivial amounts of variance so that we resorted to a simpler linear model (LM). For maximum range, the distribution of model residuals showed the existence of two outliers (i.e., two birds performed trips of over 740 km, superior to \u003cem\u003emean + 2*sd\u003c/em\u003e (= 686.35 km) of the overall data). However, running the models with or without those data points led to similar results, so that we chose to keep them in the analyses.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eDiving parameters\u003c/em\u003e \u003c/p\u003e\n\u003cp\u003eWe tested the effects of \u003cem\u003eTreatment\u003c/em\u003e, \u003cem\u003eSex\u003c/em\u003e, and \u003cem\u003eYear\u003c/em\u003e on diving parameters (dive duration, post-dive duration, bottom duration, diving efficiency, number of wiggles, descent and ascent rates) using LMMs or GLMMs (see below). As diving parameters are markedly influenced by the maximum depth of the dive (Halsey \u003cem\u003eet al.\u003c/em\u003e 2007b; Kooyman \u003cem\u003eet al.\u003c/em\u003e 1992; Sato \u003cem\u003eet al.\u003c/em\u003e 2002; Zimmer \u003cem\u003eet al.\u003c/em\u003e 2008, 2010), foraging dives were grouped in three depth categories according to the maximum depth reached during the dive: Shallow (50-125m, n = 7746 dives), Mid (125-200m, n = 15762 dives) and Deep (\u0026gt;200m, n = 6965 dives) dives, group limits corresponding to 1\u003csup\u003est\u003c/sup\u003e and 3\u003csup\u003erd\u003c/sup\u003e quantiles of the maximum depth distribution respectively (see \u003cstrong\u003eFig.S3\u003c/strong\u003e\u003cstrong\u003eESM\u003c/strong\u003e). Analyses were then run within each depth category to ensure dives were comparable. The number of wiggles and post dive duration were analyzed with Poisson distributions in GLMM, as appropriate for count data and given the distribution of the raw data. \u003c/p\u003e\n\u003cp\u003e\u003cem\u003eChick growth\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTo test how our experimental treatment affected chick growth, we proceeded in a two-step analyses: (1) First, we compared chick body mass (in g) between treated and control pairs at days 4, 10, 20 and 35. We ran an LMM with \u003cem\u003eTreatment\u003c/em\u003e, \u003cem\u003eYear\u003c/em\u003e and \u003cem\u003eStage\u003c/em\u003e, as explanatory variables. We further included two-way interactions \u003cem\u003eTreatment \u003c/em\u003ex\u003cem\u003e Year and Treatment\u003c/em\u003e x\u003cem\u003e Stage \u003c/em\u003e(days 4, 10, 20 and 35) to test if changes in chick mass due to the treatment were more pronounced in 2022 (harsh year) than in 2019 (normal year), and whether the effect of the treatment was more pronounced towards the end of growth as offspring nutritional requirements increased. \u003cem\u003eChick ID\u003c/em\u003e was included as a random intercept to account for repeated measures on individual chicks. (2) Second, we analyzed the rate of chick body mass gain (in g/day) over the monitoring period. To do this, we ran a LMM specified as \u003cem\u003eBody Mass (g)\u003c/em\u003e ~\u003cem\u003eAge (days)\u003c/em\u003e + \u003cem\u003e(Age|ID)\u003c/em\u003e and extracted the random slope estimates (\u003cem\u003eAge|ID\u003c/em\u003e) for each individual chick as a proxy for their linear phase of body mass gain between day 4 and day 35. Then, we ran a linear model (LM) to compare chick body mass gain depending on the \u003cem\u003eTreatment\u003c/em\u003e and \u003cem\u003eYear\u003c/em\u003e controlling for chick body mass at day 4 as a covariate to account for inter-individual differences in initial body mass.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eChick survival\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eChick survival was assessed using a COX hazard model with mortality events noted as 1 and age of mortality being the age of the chick at the last sighting alive in the colony. A first COX model was run including data from hatching to 35-days to consider the fate of chicks at the end of the chick-brooding phase and experimental treatment. To consider longer-term effects, we ran an additional COX model including data from hatching to fledging which included over winter survival probability.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eOceanographic conditions and habitat use\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe signature of the Polar Front was located farther south of the colony in 2022 (642.1\u0026nbsp;\u0026nbsp;59.1 km) than in 2019 (560.6\u0026nbsp;\u0026nbsp;61.7\u0026nbsp;km). This increase of +164km (14.6%) in 2022 compared to 2019 in the theoretical distance birds had to travel to reach their foraging grounds made for more difficult foraging conditions in 2022 (\u003cstrong\u003eFig. 2\u003c/strong\u003e).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn both years, the 90% kernel distributions of control birds’ foraging dive locations were continuous and elongated in a North-South position, whereas they were more clustered for treated birds (\u003cstrong\u003eFig. 2\u003c/strong\u003e). This indicated that control birds tended to forage both during the travelling and central phases of their foraging trips, whereas treated birds foraged less during travelling phases, and, in both years, nearest to the front (\u003cstrong\u003eFig. 2\u003c/strong\u003e). However, the foraging dives of control birds were performed over a larger latitudinal gradient in 2022 (25% kernel ranging below 51°S) than in 2019 (25% kernel ranging to 50°S). Foraging dives also extended further south in 2022 for treated birds, with a foraging hotspot (90% kernel) located at 51°S (510 km from the colony).\u003c/p\u003e\n\u003cp\u003eOn average, treated birds foraged closer (marginal mean = 262.6\u0026nbsp;\u0026nbsp;\u0026nbsp;18.5 km) to the Polar Front compared to controls (317.5\u0026nbsp;\u0026nbsp;16.5 km) (LMM; t = -2.23 and p = 0.034, see \u003cstrong\u003eTable 1\u003c/strong\u003e). Similarly, birds foraged closer to the Polar front in 2022 (marginal mean = 262.3\u0026nbsp;\u0026nbsp;\u0026nbsp;16.3 km compared to 2019 (317.8\u0026nbsp;\u0026nbsp;\u0026nbsp;18.8 km, t = -2.28 and p = 0.030, \u003cstrong\u003eTable 1\u003c/strong\u003e). Overall, this was consistent with the relative position of the 90% kernel density of foraging dives (\u003cstrong\u003eFig. 2\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eForaging behavior\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOverall, birds performed shallower foraging dives (-14.3%; LMM: estimate = -24.12\u0026nbsp;\u0026nbsp;\u0026nbsp;5.85 m, t = 4.12 and p \u0026lt; 0.001) and had shorter maximum prospection ranges from the colony (-24.6%, estimate = -84.51\u0026nbsp;\u0026nbsp;\u0026nbsp;34.62 km, t = 2.44 and p = 0.023) in 2019 than in 2022 (\u003cstrong\u003eTable 1, Fig. 3\u003c/strong\u003e). Further, treated birds performed overall (both in 2019 and 2022) longer foraging trips (LMM: back-transformed estimate = +1.49 days, t = 3.68 and p = 0.001), travelled farther from the colony (+25.9%, LMM: estimate = 88.31\u0026nbsp;\u0026nbsp;\u0026nbsp;34.49\u0026nbsp;km, t = 2.56 and p = 0.018), exhibited slower mean horizontal speeds (-8.6%, LM: estimate = -0.33\u0026nbsp;\u0026nbsp;\u0026nbsp;0.12\u0026nbsp;km\u0026nbsp;h\u003csup\u003e-1\u003c/sup\u003e, t = -2.82 and p = 0.008) and performed shallower foraging dives than controls (-9.2%, LMM: estimate = -15.84\u0026nbsp;\u0026nbsp;\u0026nbsp;5.57\u0026nbsp;m, p = 0.009) (see \u003cstrong\u003eTable 1, Fig. 3\u003c/strong\u003e).\u0026nbsp;\u0026nbsp;We observed a significant effect of \u003cem\u003eSex\u003c/em\u003e on maximum depth of foraging dives (estimate = +25.96 \u0026nbsp;\u0026nbsp;5.49\u0026nbsp;m for males, t = 4.73 and p \u0026lt; 0.001, \u003cstrong\u003eTable 1\u003c/strong\u003e) and on mean horizontal speed (estimate = +0.30\u0026nbsp;\u0026nbsp;\u0026nbsp;0.12\u0026nbsp;km.h\u003csup\u003e-1\u003c/sup\u003e for males, t = 2.49 and p = 0.018, \u003cstrong\u003eTable 1\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003eInterestingly, Pair ID explained more than 50% of the residual variance for trip duration and maximum range, but not for mean depth of foraging dives (\u0026lt;7%) or horizontal speed \u003cem\u003e(Pair ID\u0026nbsp;\u003c/em\u003enot kept in model, \u003cstrong\u003eTable 1\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDiving capacities\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eModels were run separately for each dive parameter and for each depth category (Shallow (50-125m), Mid (125-200m) and Deep (\u0026gt;200m)), standardized estimates or incidence rates ratio are provided in \u003cstrong\u003eFig. 4\u003c/strong\u003e for comparison of the effects across depth categories. Summaries of all the different models are provided in \u003cstrong\u003eSupplementary Materials (ESM S4)\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eDiving efficiency\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDive efficiency was significantly lower for treated birds during deep dives (-12.5%, marginal mean = 0.0722\u0026nbsp;±\u0026nbsp;0.004; LMM; t = -2.42 and p = 0.022, see \u003cstrong\u003eFig. 4\u003c/strong\u003e; \u003cstrong\u003eESM S4\u003c/strong\u003e) than for control birds (0.0835\u0026nbsp;±\u0026nbsp;0.004), but not \u0026nbsp;significantly during mid-dives (marginal means = 0.1220\u0026nbsp;±\u0026nbsp;0.005 and 0.1360\u0026nbsp;±\u0026nbsp;0.005 for treated and control birds, respectively; LMM: t = -1.89 and p = 0.066) and shallow dives (0.1910\u0026nbsp;±\u0026nbsp;0.007 and 0.1880\u0026nbsp;±\u0026nbsp;0.007 for treated and control birds; LMM: t = 0.38 and p = 0.709).Similarly, males showed higher diving efficiency (marginal mean = 0.0834\u0026nbsp;±\u0026nbsp;0.003) than females (0.0724\u0026nbsp;±0.005) in deep dives only (15.2%, LMM: t = 2.26 and p = 0.031), and \u003cem\u003eYear\u0026nbsp;\u003c/em\u003ehad no effect on diving efficiency for all depth categories (all p \u0026gt; 0.220) (\u003cstrong\u003eFig. 4\u0026nbsp;\u003c/strong\u003e\u0026amp; \u003cstrong\u003eESM S4\u003c/strong\u003e). It is interesting to note that the effect of \u003cem\u003eTreatment\u003c/em\u003e appeared to increase with increasing depth (see model estimates \u003cstrong\u003eFig. 4\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eDive, bottom and post-dive duration\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDive duration (s) was not influenced by \u003cem\u003eTreatment\u0026nbsp;\u003c/em\u003e(all p \u0026gt; 0.470) or \u003cem\u003eYear\u0026nbsp;\u003c/em\u003e(all p \u0026gt; 0.110), regardless of depth category. However, males performed longer dives than females at all diving depths (all p \u0026lt; 0.005, \u003cstrong\u003eFig. 4\u003c/strong\u003e \u0026amp; \u003cstrong\u003eESM S4\u003c/strong\u003e). The duration of the bottom phase (s) was significantly reduced for treated (marginal mean = 33.8\u0026nbsp;\u0026nbsp;\u0026nbsp;2.1 s) birds compared to controls (40.2\u0026nbsp;\u0026nbsp;\u0026nbsp;1.8 s) during deep dives (-15.9%, LMM: t = -2.14 and p = 0.041), but not during mid dives (marginal means = 53.5\u0026nbsp;\u0026nbsp;\u0026nbsp;2.1 s and 47.7\u0026nbsp;\u0026nbsp;\u0026nbsp;2.3 s for control and treated birds respectively, LMM: t = -1.93 and p = 0.062) and shallow dives (marginal means = 60.3\u0026nbsp;\u0026nbsp;2.2 s and 59.9\u0026nbsp;\u0026nbsp;\u0026nbsp;2.3 s\u0026nbsp;for control and treated birds respectively, LMM: t = -0.15 and p = 0.880). Similar to diving efficiency, the effect of the \u003cem\u003eTreatment\u003c/em\u003e on bottom duration appeared to increase with increasing depth (see model estimates \u003cstrong\u003eFig. 4\u003c/strong\u003e). Post dive duration, on the contrary, was significantly reduced (-16.3%) for treated birds during shallow dives (marginal means = 141.3\u0026nbsp;11.9 s and\u0026nbsp;118.2\u0026nbsp;11.0 s\u0026nbsp;for control and treated birds respectively,\u0026nbsp;GLM: z = -2.43 and p = 0.015), but not during mid dives (marginal means = 134.6\u0026nbsp;11.7 s and 126.6\u0026nbsp;11.4 s\u0026nbsp;for control and treated birds respectively,\u0026nbsp;GLM: z = -1.09 and p = 0.274) and deep dives (marginal means = 160.3\u0026nbsp;12.7 s and 137.1\u0026nbsp;11.8 s\u0026nbsp;, GLM: z = -1.83 and p = 0.068) (\u003cstrong\u003eFig. 4\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eDescent and ascent rate\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDescent and ascent rate followed the same trend, with treated birds showing lower vertical speed than control birds during mid dives (descent: -4.5% marginal means = 1.32\u0026nbsp;0.02 m/s and 1.26\u0026nbsp;0.02 m/s for control and treated birds respectively, LMM: t = -2.45 and p = 0.003 and ascent: -7.9% marginal means = 1.38\u0026nbsp;0.02 m/s and 1.27\u0026nbsp;0.02 m/s for control and treated birds respectively, LMM: t = -3.59 and p = 0.001) and deep dives (descent: -5.0%, marginal means = 1.41\u0026nbsp;0.02 m/s and 1.34\u0026nbsp;0.02 m/s for control and treated birds respectively,\u0026nbsp;LMM: t = -2.33 and p = 0.028 and ascent: -8.2%, marginal means = 1.46\u0026nbsp;0.02 m/s and 1.34\u0026nbsp;0.02 m/s for control and treated birds respectively,\u0026nbsp;LMM: t = -4.64 and p \u0026lt; 0.001), but not during shallow dives (descent: LMM: t = -0.28 and p = 0.784 and ascent: LMM: t = -0.65 and p = 0.523) (\u003cstrong\u003eFig. 4\u0026nbsp;\u003c/strong\u003e\u0026amp;\u003cstrong\u003e\u0026nbsp;ESM S4\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eHunting activity\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe number of wiggles was significantly decreased in treated compared to control birds during mid dives (-12.9%, marginal means = 3.1\u0026nbsp;\u0026nbsp;1.9 and 2.7\u0026nbsp;1.8 for control and treated birds respectively, GLM: z = -2.95 and p = 0.003) and deep dives (-16.0%, marginal means = 2.5\u0026nbsp;1.8 and 2.1\u0026nbsp;1.6 for control and treated birds respectively,\u0026nbsp;GLM:\u0026nbsp;z = -2.65 and p = 0.008) but not in shallow dives (GLM: z = -0.60 and p = 0.550) (\u003cstrong\u003eFig. 4\u0026nbsp;\u003c/strong\u003e\u0026amp;\u003cstrong\u003e\u0026nbsp;ESM S4\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eChick growth\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe influence of the treatment on chick body mass differed across the growth period (\u003cem\u003eStage:\u003c/em\u003e day 4, 10, 20 and 35) as indicated by the significant interaction between \u003cem\u003eTreatment\u003c/em\u003e and growth stage (LMM; \u003cem\u003eTreatment\u0026nbsp;\u003c/em\u003ex\u003cem\u003e\u0026nbsp;Stage\u003c/em\u003e: F = 2.84 and p = 0.042). Whereas chicks from control and treated pairs started out at a similar body mass, i.e. not significantly different at day 4 and 10 (post-hoc contrasts: p = 0.940 and 0.274), chicks from treated pairs showed significantly lower body mass than controls at day 20 (-0.354\u0026nbsp;\u0026nbsp;0.114 kg (-26.5%), post hoc contrasts: t = 3.11 and p = 0.002) and at day 35 (-0.366\u0026nbsp;\u0026nbsp;0.129 kg (-18.4%), t = 2.84 and p = 0.005) (see \u003cstrong\u003eFig. 5.A\u0026nbsp;\u003c/strong\u003e\u0026amp;\u003cstrong\u003e\u0026nbsp;ESM S5\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003eChick body mass gain per day (g/day) from day 4 to day 35 (brooding phase), was significantly lower for chicks from treated pairs (LMM;\u0026nbsp;estimate =\u0026nbsp;-17\u0026nbsp;\u0026nbsp;\u0026nbsp;5 g/day, t = -2.12 and p = 0.048). Chick body mass gain per day was not significantly influenced by the year or initial body mass at day 4 (p = 0.327 and 0.419 respectively) (\u003cstrong\u003eFig 5.B\u0026nbsp;\u003c/strong\u003e\u0026amp;\u003cstrong\u003e\u0026nbsp;ESM S5\u003c/strong\u003e).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eChick survival\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eChick short-term survival up until day 35, was not significantly affected by \u003cem\u003eTreatment\u003c/em\u003e or \u003cem\u003eYear\u003c/em\u003e (COX hazard model: coef = 1.11\u0026nbsp;\u0026nbsp;0.84 and 0.52\u0026nbsp;\u0026nbsp;\u0026nbsp;0.84\u0026nbsp;and p = 0.184 and 0.531 respectively). In contrast, chick longer-term survival until fledging (~300 days, after the winter fast and molt in next summer) was significantly affected by \u003cem\u003eTreatment\u003c/em\u003e (COX hazard model: coef = 0.89 \u0026nbsp; 0.39 and p = 0.024). Chicks from treated parents were twice more likely (odds ratio = 2.43) to die before fledging. Mortality risk before fledging was not significantly different between years (COX hazard model: coef = 0.68 \u0026nbsp; 0.41, p = 0.095), being on average slightly higher in 2022 than in 2019 (odds ratio = 1.97). Mortality peaked right after thermal independence (~day 30) with 15 (out of 37) reported dead chicks from day 35 to day 100 (onset of winter period and after the treatment period of parents) compared to 9 chicks lost during the brooding phase (chick with parents).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eFrom an optimal foraging and energetics perspective, unfavorable foraging conditions can be defined as periods when resource availability or quality decreases or energy investments into foraging increase (or both) (Pyke 1984; Pyke \u003cem\u003eet al.\u003c/em\u003e 1977). We studied the flexibility of foraging behavior in breeding king penguins using an experimental protocol aimed at mimicking harsh conditions at sea. Since manipulating resources at sea is not possible, we experimentally increased foraging workload for a group of breeding adults during chick brooding (treated), and compared them to a group of breeding adults not subject to this constraint at the same moment (control). This mimicked the effects of a poor year by decreasing the benefit/cost ratio of foraging through an increase in travelling and especially diving costs relative to energy acquisition. Thus, treated birds exhibited lower horizontal and vertical speed due to increased hydrodynamic drag (Bannasch \u003cem\u003eet al.\u003c/em\u003e 1994). In addition, given that the zone of the Polar Front, where food resources are concentrated at shallower depth (Bost \u003cem\u003eet al.\u003c/em\u003e 1997, 2009; Charrassin \u0026amp; Bost 2001), was substantially further from the colony in 2022 (~640 km, similar during “extreme” years; Bost \u003cem\u003eet al.\u003c/em\u003e 2015) than in 2019 (~560 km, intermediate between normal and extreme years), birds theoretically had to travel a greater distance to rely on the same foraging grounds and dive at deeper depth.This provided us with the unique opportunity to test for cumulative effects of a decrease in resource availability \u003cem\u003eper se\u003c/em\u003e, and an increase in energy requirements to forage, especially at depth.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCoping with harsh conditions at sea\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eExperimentally increasing hydrodynamic drag and foraging energy costs in our study led to similar effects to what is usually observed in poor breeding years in king penguins at the scale of the foraging trip (Bost \u003cem\u003eet al.\u003c/em\u003e 2015; Brisson-Curadeau \u003cem\u003eet al.\u003c/em\u003e 2024). Specifically, both foraging trip duration and maximum foraging range increased in treated birds compared to controls, imposing additional constraints on breeding birds that had direct, and indirect, effects on reproduction. Chicks from treated pairs exhibited overall lower body mass gain and body mass, likely due to the inability of treated parents to provision their chick as much as controls (Gauthier-Clerc \u003cem\u003eet al.\u003c/em\u003e 2002). Chick survival at thermal independence (35 days) was not significantly affected, though survival until fledging was significantly reduced for chicks from treated parents. Chick body mass gain before winter is critical for chick survival through winter and until fledging (Weimerskirch \u003cem\u003eet al.\u003c/em\u003e 1992). The reduction in chick survival probability until fledging is probably due to chicks from treated parents showing reduced body mass gain from day 4 to day 35 and reduced absolute body mass at both 20 and 35 days: those chicks may not have been able to withstand the constrains imposed by the winter period (winter fast and predation pressure). Especially, most of the chick mortality occurred between 35 and 100 days, at the beginning of the crèche period, during which small chicks are more vulnerable to predation by giant petrels (\u003cem\u003eMacronectes giganteus\u003c/em\u003e) (Hunter 1991). These results confirm the critical importance of travel distance and duration for central place forager breeding success, as observed in other seabird (Eby \u003cem\u003eet al.\u003c/em\u003e 2023; Fayet \u003cem\u003eet al.\u003c/em\u003e 2021; Fromant \u003cem\u003eet al.\u003c/em\u003e 2021) and mammal (Massardier-Galatà \u003cem\u003eet al.\u003c/em\u003e 2017) species.\u003c/p\u003e\n\u003cp\u003eYet, in response to increased foraging workload, we found that king penguins displayed substantial phenotypic flexibility. Treated birds adopted different foraging behavior than controls as early as their first foraging trip with the equipment, indicating a direct response to experimentally increased workload. Specifically, in contrast to controls, treated birds maximized their hunting activity (foraging dives) at or near the Polar Front, where prey is known to occur at shallower depths (Bost \u003cem\u003eet al.\u003c/em\u003e 1997, 2009; Charrassin \u0026amp; Bost 2001), and foraged less during commuting trips. This difference in foraging locations led to observed differences in maximum diving depth. Indeed, while all birds generally increased maximum diving depth in 2022 compared to 2019, treated birds exhibited decreased maximum diving depths compared to controls in both years. Although predators may also alter their foraging strategies by switching to different preys, this scenario is unlikely as king penguins show little variation in diet, even when facing harsh environmental conditions (Brisson-Curadeau \u003cem\u003eet al.\u003c/em\u003e 2024). Therefore, the observed shift in foraging locations for treated birds is more likely due to flexibility in foraging strategy and effort, rather than modifications in targeted prey \u003cem\u003eper se\u003c/em\u003e. Interestingly, for trip parameters strongly related to trip length (trip duration in days and maximum range in km), the identity of the breeding pair explained more than 50% of the residual variance, indicating that these metrics are highly dependent on the performance of the other partner (i.e., longer foraging trips imply longer fasting period on land and potentially a longer period necessary to replenish energy stores), leaving hardly any room for sex-specific or individual compensation. However, other parameters (horizontal speed or diving depth) were rather a reflection of individual performance (breeding pair explaining less than 7% of residual variance) and could be used as adjustment variables.\u003c/p\u003e\n\u003cp\u003eIn addition to shifts in foraging grounds, birds facing an experimental increase in foraging workload also exhibited marked differences in their diving profiles. First, treated birds showed significantly lower vertical speed (both ascent and descent rates) for all dives \u0026gt;125m. Decreased vertical speed is the direct consequence of increased drag effect when diving, this effect being even more striking in deep foraging dives which are both energetically more constraining and for which descent and ascent phases are longer. A greater effect of the dummy device at deeper depth is also to be expected as the device is incompressible, and drag effect increases with depth. Decreased vertical speed may also be a byproduct of decreased swimming speed. As drag is markedly affected by swimming speed, birds may mitigate the energy demands imposed by the dummy logger by decreasing swimming speed. Consequently, treated birds showed reduced bottom duration (s) for deep foraging dives (significantly for \u0026gt;200m and marginally for 125-200m), while both dive and post dive duration remained unchanged for these depths (though post dive duration was also marginally reduced for treated birds in deep dives), ultimately leading to decreased diving efficiency (marginally for 125-200m). As penguins are air-breathing deep divers, dive duration is limited by the physiological ability of the species. Similarly, post dive duration corresponds to surface recovery between series of foraging dives when in a diving bout, and is constrained by birds’ physiological abilities. Therefore, diving time lost to transiting from and to the surface was principally gained by shortening bottom time, affecting birds’ ability to actively forage. Indeed, treated birds showed significantly reduced mean number of wiggles per dive for dives deeper than 125m. Overall, treated birds were less efficient (compared to controls) in dives deeper than 125m with regards to optimal foraging theory (\u003cem\u003ei.e.\u003c/em\u003e, maximize time spent in profitable areas) and therefore tended to maximize their foraging activity at 50-125m depths, corresponding to the typical depth of the thermocline, where prey aggregate, at the Polar Front.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe distance – depth trade-off: will foraging at the Polar Front be optimal in the future?\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTheoretical studies of foraging behavior in diving birds and mammals indicate that optimized strategies maximize the time spent in favorable depth for foraging relative to time spent commuting or recovering at the surface (Cornick \u0026amp; Horning 2003; Doniol-Valcroze \u003cem\u003eet al.\u003c/em\u003e 2011; Halsey \u0026amp; Butler 2006; Hanuise \u003cem\u003eet al.\u003c/em\u003e 2013; Watanabe \u003cem\u003eet al.\u003c/em\u003e 2014). Overall, treated birds were more efficient at diving at shallow depths, with a reduction in the duration of the transit phase. This enabled them to increase their foraging activity and thus maximize their diving efficiency at these depths. As prey is more readily available at shallow depths at the Polar Front, treated birds were found to feed preferentially at the Polar Front. Treated birds avoided losing energy during the travelling phase by avoiding deep dives, observed in control birds, that are energetically more draining. However, targeting the distant Polar Front can also be energetically constraining, especially in years where it is further from the colony (\u003cem\u003ei.e.\u003c/em\u003e, in 2022), and can result in longer foraging trips and impaired chick provisioning during the chick brooding phase. As a result, breeding birds are faced with a trade-off between having to dive deeper while staying closer to the colony or travel more distance to forage at shallower depths. It appeared that adults faced with increased workload (\u003cem\u003ei.e.\u003c/em\u003e, treated birds) modified their foraging behavior in response to the constrain, and this, as soon as the first foraging trip with the equipment. This response was consistent between the two years, with a cumulative effect in case of already poor conditions at sea that can have consequences for breeding on-land. In our case, the negative cumulative effect of the treatment and the environmental conditions on chick growth and survival were limited during the monitoring period, but were more striking after the winter period. Most breeding pairs managed to compensate for the added constraint and were able to successfully raise their chick until the onset of winter. However, it appeared that treated birds paid a cost during winter, since chick overwinter survival was lower than for controls. In addition, although parents were found to compensate for harsher conditions during one breeding season, it is not excluded that they paid the cost of increased reproduction effort in their following breeding attempts (Daan \u003cem\u003eet al.\u003c/em\u003e 1996). Moreover, during a more “extreme” year (\u003cem\u003ei.e.\u003c/em\u003e 1997, Polar Front below 53°S) it is likely that adults faced with an increased constraint would have failed their breeding attempt if they adopted the same strategy (\u003cem\u003ei.e.\u003c/em\u003e, targeting the Polar Front in order to dive at shallower depths), as chick survival is greatly reduced when fasting period exceeds 20 days during chick brooding (Gauthier-Clerc \u003cem\u003eet al.\u003c/em\u003e 2002). This is because parents of the same breeding pair alternate between foraging at sea and brooding on land, so that in bad years, the individuals on land may not be able to fast long enough to wait for their partner to return from foraging at sea (Groscolas \u003cem\u003eet al.\u003c/em\u003e 2008; Olsson 1997) and small chicks are quickly predated if unguarded by a parent (Hunter 1991). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn the short term, targeting the Polar Front zone thus allows these long-lived seabirds to replenish their energy stores favoring survival over reproduction in the case of limited available energy (Drent \u0026amp; Daan 1980; Olsson 1997). This strategy would be adaptive if detrimental conditions at sea occurred sporadically, allowing individuals to resume reproduction in favorable years, and if no long-term change in oceanographic conditions occurred. However, climate modelling projections show that changes in the location of the Polar Front over the next hundred years will strongly impact the northern range of king penguins, moving further and further away from the colonies at Crozet, and doubling by 2100 (passing below 54°S by 2070, Péron \u003cem\u003eet al.\u003c/em\u003e 2012). Thus, the traveling distance and time required to reach foraging grounds will eventually reach a critical point beyond which breeding failure is inevitable for most breeding birds when foraging exclusively at the Polar Front zone. Continuing to rely on the Polar Front during summer as the main foraging ground may thus constitute an ecological trap, as breeding adults would take more time travelling further away from the colony leading to repeated breeding failure over the years that might eventually lead to the disappearance of the king penguins population in Crozet (Péron\u0026nbsp;\u003cem\u003eet al\u003c/em\u003e\u003cem\u003e.\u003c/em\u003e 2012).\u003c/p\u003e\n\u003cp\u003eDespite a possibly bleak outlook for the king penguin populations of Crozet, our study also showed that some individuals did not (or hardly) forage at the Polar Front, and yet still replenished their energy stores and successfully fed their chick. This hints towards other aspects of changes in king penguin foraging behavior that have yet to be studied in the context of climate change. Especially, king penguins have shown exceptional foraging flexibility in Tierra del Fuego, Chile (Pütz \u003cem\u003eet al.\u003c/em\u003e 2021). Further studies on the specific case of king penguins at Crozet are needed to determine if different strategies may exist in the population, and to better characterize the oceanic structures targeted by the birds.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eThis work was supported by the French Polar Institute (IPEV) and the Zone Atelier Antarctique (LTSER France, CNRS-EE) through a collaboration between the ECONERGY 119 and OISEAUX PLONGEURS 394 polar projects, and by the French National Center for Scientific Research (CNRS). CL was supported by a PhD scholarship from the Ecole Normale Supérieure (ENS-Lyon). We are grateful to the Terres Australes et Antarctiques Françaises (TAAF) for providing logistical support in the field. The 394 and 119 projects on king penguin are part of the long-term studies in Ecology and Evolution (SEE-Life) program of the CNRS.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of interest:\u0026nbsp;\u003c/strong\u003eAuthors declare that they have no conflict of interest.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval:\u0026nbsp;\u003c/strong\u003eAll applicable institutional and/or national guidelines for the care and use of animals were followed (Ethical approval: 2019: APAFIS#16465–2018080111195526v4 and 2022: APAFIS#31268-2021042117037897v3). The study was approved by TAAF prefectural decrees (N°2019-166 and N°2022-69).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material:\u0026nbsp;\u003c/strong\u003eData and code will be made available upon publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors’ contributions:\u0026nbsp;\u003c/strong\u003eDesigned the study: VAV, PB, CAB, AS, JP, YH, CL; collected the data: CL, AC, MM, JPR; analyzed the data: CL, NJ; wrote the paper: CL. All authors commented on and approved of the final draft.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eBannasch, R., Wilson, R.P. \u0026amp; Culik, B. (1994). Hydrodynamic Aspects of Design and Attachment of A Back-Mounted Device in Penguins. \u003cem\u003eJ. Exp. Biol.\u003c/em\u003e, 194, 83\u0026ndash;96.\u003c/li\u003e\n \u003cli\u003eBarbraud, C., Delord, K., Bost, C.A., Chaigne, A., Marteau, C. \u0026amp; Weimerskirch, H. (2020). 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Seeing the light: depth and time restrictions in the foraging capacity of emperor penguins at Pointe G\u0026eacute;ologie, Antarctica. \u003cem\u003eAquat. Biol.\u003c/em\u003e, 3, 217\u0026ndash;226.\u003c/li\u003e\n \u003cli\u003eZimmer, I., Wilson, R.P., Beaulieu, M., Ropert-Coudert, Y., Kato, A., Ancel, A., \u003cem\u003eet al.\u003c/em\u003e (2010). Dive efficiency versus depth in foraging emperor penguins. \u003cem\u003eAquat. Biol.\u003c/em\u003e, 8, 269\u0026ndash;277.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1.\u0026nbsp;\u003c/strong\u003eResults from Linear Mixed Models (or Linear Models) for the trip parameters of the first foraging trip during brooding of king penguins (Aptenodytes patagonicus). For each parameter, estimates and standard error (as est \u0026nbsp; se), degree of freedom (df), statistic (t) and \u003cem\u003ep-value\u003c/em\u003e are presented. Reference levels are Female for effect of \u003cem\u003eSex\u003c/em\u003e, Control for \u003cem\u003eTreatment\u003c/em\u003e, and 2019 for \u003cem\u003eYear\u003c/em\u003e. For all models, Pair ID was categorized as a random intercept except for the distance of foraging dives where individual ID nested in Pair ID was included as a random intercept to account for repeated measures over individuals and for non-independence of breeding partners. Results are given as % of residual variance explained by the random factor. For Mean horizontal speed, model including Pair ID as a random intercept failed to converge, random effect was therefore removed and simple Linear Model was run. Trip duration was log-transformed to fit model assumptions (normality).\u0026nbsp;\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"578\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 155px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 114px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eest \u0026plusmn; se\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cstrong\u003edf\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cstrong\u003et value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"6\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDistance of foraging dives to the Polar Front (km)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 155px;\"\u003e\n \u003cp\u003e\u003cem\u003eIntercept\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 114px;\"\u003e\n \u003cp\u003e(57.06 \u0026plusmn; 24.89)E03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003e29.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003e2.293\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.029\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 155px;\"\u003e\n \u003cp\u003eYear [2022]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 114px;\"\u003e\n \u003cp\u003e-28.07 \u0026plusmn; 12.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003e29.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003e-2.280\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.030\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 155px;\"\u003e\n \u003cp\u003eTreatment [TREATED]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 114px;\"\u003e\n \u003cp\u003e-54.75 \u0026plusmn; 24.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003e28.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003e-2.230\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.034\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 155px;\"\u003e\n \u003cp\u003eSex [Male]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 114px;\"\u003e\n \u003cp\u003e-12.36 \u0026plusmn; 15.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003e16.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003e-0.805\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 61px;\"\u003e\n \u003cp\u003e0.432\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 155px;\"\u003e\n \u003cp\u003eRandom: \u003cem\u003ePair ID:ID\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 340px;\"\u003e\n \u003cp\u003e\u003cem\u003e10.6%\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 155px;\"\u003e\n \u003cp\u003eRandom: \u003cem\u003ePair ID\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 340px;\"\u003e\n \u003cp\u003e\u003cem\u003e22.8%\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTrip duration (days) (log-transformed)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 155px;\"\u003e\n \u003cp\u003e\u003cem\u003eIntercept\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 114px;\"\u003e\n \u003cp\u003e2.00 \u0026plusmn; 0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003e35.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003e17.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 155px;\"\u003e\n \u003cp\u003eYear [2022]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 114px;\"\u003e\n \u003cp\u003e0.22 \u0026plusmn; 0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003e26.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003e2.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 61px;\"\u003e\n \u003cp\u003e0.055\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 155px;\"\u003e\n \u003cp\u003eTreatment [TREATED]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 114px;\"\u003e\n \u003cp\u003e0.40 \u0026plusmn; 0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003e24.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003e3.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 155px;\"\u003e\n \u003cp\u003eSex [Male]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 114px;\"\u003e\n \u003cp\u003e-0.10 \u0026plusmn; 0.07\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003e15.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003e-1.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 61px;\"\u003e\n \u003cp\u003e0.178\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 155px;\"\u003e\n \u003cp\u003eRandom: \u003cem\u003ePair ID\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 340px;\"\u003e\n \u003cp\u003e\u003cem\u003e55.9%\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMaximum range (km)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 155px;\"\u003e\n \u003cp\u003e\u003cem\u003eIntercept\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 114px;\"\u003e\n \u003cp\u003e253.86 \u0026plusmn; 35.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003e33.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003e7.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 155px;\"\u003e\n \u003cp\u003eYear [2022]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 114px;\"\u003e\n \u003cp\u003e84.51 \u0026plusmn; 34.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003e23.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003e2.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.023\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 155px;\"\u003e\n \u003cp\u003eTreatment [TREATED]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 114px;\"\u003e\n \u003cp\u003e88.31 \u0026plusmn; 34.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003e22.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003e2.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.018\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 155px;\"\u003e\n \u003cp\u003eSex [Male]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 114px;\"\u003e\n \u003cp\u003e42.35 \u0026plusmn; 24.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003e14.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003e1.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 61px;\"\u003e\n \u003cp\u003e0.100\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 155px;\"\u003e\n \u003cp\u003eRandom: \u003cem\u003ePair ID\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 340px;\"\u003e\n \u003cp\u003e\u003cem\u003e53.9%\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean horizontal speed (km.h-1)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 155px;\"\u003e\n \u003cp\u003e\u003cem\u003eIntercept\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e3.50 \u0026plusmn; 0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e36.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e24.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 155px;\"\u003e\n \u003cp\u003eYear [2022]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e0.04 \u0026plusmn; 0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e36.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.774\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 155px;\"\u003e\n \u003cp\u003eTreatment [TREATED]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e-0.33 \u0026plusmn; 0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e36.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e-2.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.008\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 155px;\"\u003e\n \u003cp\u003eSex [Male]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e0.30 \u0026plusmn; 0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e36.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e2.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.018\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean depth of foraging dives (m)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 155px;\"\u003e\n \u003cp\u003e\u003cem\u003eIntercept\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 114px;\"\u003e\n \u003cp\u003e139.33 \u0026plusmn; 6.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003e35.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003e20.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 155px;\"\u003e\n \u003cp\u003eYear [2022]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 114px;\"\u003e\n \u003cp\u003e24.12 \u0026plusmn; 5.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003e27.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003e4.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 155px;\"\u003e\n \u003cp\u003eTreatment [TREATED]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 114px;\"\u003e\n \u003cp\u003e-15.84 \u0026plusmn; 5.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003e22.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003e-2.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.009\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 155px;\"\u003e\n \u003cp\u003eSex [Male]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 114px;\"\u003e\n \u003cp\u003e25.96 \u0026plusmn; 5.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003e22.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003e4.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 155px;\"\u003e\n \u003cp\u003eRandom: \u003cem\u003ePair ID\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 340px;\"\u003e\n \u003cp\u003e\u003cem\u003e6.9%\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"oecologia","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"oeco","sideBox":"Learn more about [Oecologia](https://www.springer.com/journal/442)","snPcode":"442","submissionUrl":"https://submission.nature.com/new-submission/442/3","title":"Oecologia","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"diving behavior, foraging, oceanographic fronts, plasticity, seabird, biologging","lastPublishedDoi":"10.21203/rs.3.rs-4704667/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4704667/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"When foraging, marine top predators rely on increasingly unpredictable oceanographic structures. Central place foragers are particularly affected. Their efficiency at replenishing their body reserves at sea while feeding their offspring on land relies on accurately targeting predictable foraging locations. Therefore, increased time and effort spent searching for resources is likely to compromise reproduction. Here, we used an experimental design to assess the flexibility of breeding king penguin (Aptenodytes patagonicus) foraging behavior in response to harsh conditions at sea, and examined the consequences on the growth and survival of their chick. We tested for behavioral adjustments to compensate for experimentally increased foraging workload, obtained by the application of a hydrodynamic drag effect. Compared to controls, treated adults more directly targeted a predictable hydrographic feature, the Polar Front, while limiting the increased costs of deep diving. Treated adults significantly increased hunting activity at shallower depths where the effect of treatment on diving efficiency was neglectable. Our experiment resulted in decreased body mass gain during the brooding stage of chicks raised by treated parents compare to controls, with no direct effects on chick survival up to the winter period, but significant negative effects during winter. We identified two different strategies for foraging in king penguins: 1) foraging at the Polar Front where prey patches are more predictable and accessible at shallower depths or 2) foraging closer to the colony by targeting preys at deeper depths. These results highlight the possibility of a trade-off between distance and depth in breeding king penguin foraging behavior.","manuscriptTitle":"Foraging flexibility in response to at-sea constraints in a deep diver, the king penguin: an experimental study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-30 11:19:31","doi":"10.21203/rs.3.rs-4704667/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2025-05-04T22:35:59+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-04-29T07:52:23+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-04-18T01:40:21+00:00","index":"","fulltext":""},{"type":"submitted","content":"Oecologia","date":"2025-04-06T11:44:52+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"oecologia","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"oeco","sideBox":"Learn more about [Oecologia](https://www.springer.com/journal/442)","snPcode":"442","submissionUrl":"https://submission.nature.com/new-submission/442/3","title":"Oecologia","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"8a19c2ba-64c2-42e6-96f1-4bf35c039a67","owner":[],"postedDate":"April 30th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-07-07T16:05:13+00:00","versionOfRecord":{"articleIdentity":"rs-4704667","link":"https://doi.org/10.1007/s00442-025-05754-9","journal":{"identity":"oecologia","isVorOnly":false,"title":"Oecologia"},"publishedOn":"2025-07-03 15:58:06","publishedOnDateReadable":"July 3rd, 2025"},"versionCreatedAt":"2025-04-30 11:19:31","video":"","vorDoi":"10.1007/s00442-025-05754-9","vorDoiUrl":"https://doi.org/10.1007/s00442-025-05754-9","workflowStages":[]},"version":"v1","identity":"rs-4704667","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4704667","identity":"rs-4704667","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}