# 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, Jean-Patrice Robin, Yves Handrich, Anne Cillard, Marine Montblanc, Pierre Bize, Vincent A Viblanc

► To cite this version:

Camille Lemonnier, Charles-André Bost, Nicolas Joly, Antoine Stier, Jean-Patrice Robin, et al. Foraging flexibility in response to at-sea constraints in a deep diver, the king penguin: an experimental study. Oecologia, 2025, 207 (7), pp.123. 10.1007/s00442-025-05754-9. hal-05167623

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

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Camille Lemonnier¹, Charles-André Bost*², Nicolas Joly¹, Antoine Stier¹, Jean-Patrice

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Robin¹, Yves Handrich¹, Anne Cillard¹, Marine Montblanc¹, Pierre Bize³,⁴, Vincent A

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Viblanc¹*

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¹Université de Strasbourg, CNRS, IPHC UMR 7178, F-67000 Strasbourg, France

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²Centre d'Etudes Biologiques de Chizé (CEBC), UMR 7372 CNRS-La Rochelle Université,

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79360 Villiers-en-Bois, France

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³Swiss Ornithological Institute, Sempach, Switzerland

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⁴Departement of Ecology & Evolution, University of Lausanne, Switzerland

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**\*co-senior authors**

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**Corresponding author:** Lemonnier Camille, camille.lemonnier@iphc.cnrs.fr ;
clemonnieracad@gmail.com

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**Author contributions:** Designed the study: VAV, PB, CAB, AS, JPR, 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.

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**Acknowledgments**

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We are grateful to the Terres Australes et Antarctiques Françaises (TAAF) for providing
logistical support in the field and to the different field assistants and collaborators that helped
in this study. We thank Dr Andreas Nord and three anonymous reviewers for their
constructive comments and suggestions on previous versions of this paper. This study is part

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22 of the long-term Studies in Ecology and Evolution (SEE-Life) program of CNRS and the

23 Zone Atelier Antarctique (LTSER France).

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ABSTRACT

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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 negligible. Our experiment resulted in decreased body mass gain during the brooding stage of chicks raised by treated parents compared 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.

**Keywords:** diving behavior, foraging, oceanographic fronts, plasticity, seabird, biologging
47 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 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 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, 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 has shown
that adults primarily 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
farther and farther 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 can adjust their foraging behavior compared to control birds, and (ii) if that modification allows 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
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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).

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METHODS

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Study site and adult monitoring

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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 all 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
randomly 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 had 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 length and 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®).

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147 **Experimental design**

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Monitored pairs were assigned to either control or treated groups, ensuring a balanced
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spatial and temporal distribution between groups. Inter-group consistency in adults' initial
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condition was statistically verified using Principal Components Analyses (PCA; see **Section
151** **S1 ESM**). To ensure that we recorded the first foraging trip at sea after the egg had hatched,
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we caught birds at day 50 of incubation (**Fig 1.A.**, incubation is typically 53 days in king
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penguin; Stonehouse 1960). Adults were then instrumented with loggers according to the
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following experimental protocol: (i) control birds (N = 12 and 22 birds belonging to 7 and 13
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breeding pairs in 2019 and 2022, respectively) were equipped with an Axy-Trek Marine High
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Depth data logger (TechnoSmArt Europe srl, Rome, Italy); whereas (ii) treated birds (N = 13
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and 20 birds belonging to 7 and 11 breeding pairs in 2019 and 2022 respectively) were
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equipped both with an Axy-Trek and a dummy device (see **Fig 1.B.**) (see below).

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Axy-Trek Marine loggers are small (65 x 40 x 15 mm, 45 g in air) GPS loggers
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combined with tri-axial accelerometers and pressure sensors allowing to record both penguin
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movement and diving profiles at sea. The GPS was set to acquire satellite fixes at a rate of 1
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fix/15 minutes when the bird was not submerged; pressure was recorded at a rate of 1 Hz. The
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dummy device (designed for treated bids) was a P.V.C cylinder of 3 cm diameter; and a cross-
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sectional area of 28.3 cm², i.e. roughly 2.8% of the cross-sectional area of an average king
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penguin. Position of loggers on the back of penguins was standardized to ensure repeatability
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over individuals. Axy-Trek loggers were always fitted first, just above the tail articulation,
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and dummy loggers were added in front of the Axy-Trek and at the back of the maximal cross
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section area of the bird (see **Fig 1.B.**). Loggers were externally attached to the feathers using
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waterproof tape (Tesa tape, 4651; Tesa) and instant adhesive glue (Loctite, 401; Henkel,
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Germany).
Within a pair, both partners were alternatively equipped according to their group, i.e., the two partners either received an Axy-Trek (control birds); or an Axy-Trek plus dummy device (treated birds). Overall, both parents were equipped one after the other, twice in a row, allowing two successive foraging trips per parent to be recorded during four chick brooding shifts (see Fig 1.A.). This ensured that both partners of the pair had undergone the same manipulations. However, since most data-loggers malfunctioned during the second foraging trip of a parent (only 15 second trips successfully recorded), only logger data from the first trip at sea of each parent were considered in the present analyses.

The equipment period was stopped and all loggers removed before chick thermal independence (at around 35-days of age).

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## 182 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 autumn (day 100, late April-early May) and at fledging (December-January the subsequent year).

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## 193 Logger data processing
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Of the 67 birds that were successfully equipped with a logger, 45 had successfully recorded data of the first foraging 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.

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**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.

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**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 (*m* · *s*⁻¹), 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

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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: $Diving efficiency = \frac{Bottom duration}{dive duration+post dive duration}$ (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.

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## 228 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° x 0.083° grid resolution, between the 1st of January and the 1st 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).

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246 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 as well as 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
267 below). Results are presented either as raw or marginal (estimated) means ± standard error
268 (se) as appropriate. Effects were considered statistically significant for *p* < 0.05.

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270 *Oceanographic conditions and habitat use*

271 To test how our experimental treatment affected the marine habitat use of king penguins, we
272 proceeded in a two-step analyses: (1) First, the spatial distribution of foraging dives around
273 Crozet was mapped using kernel density estimation (package *adehabitatHR* v.0.4.21; Calenge
274 & Fortmann-Roe, 2023). To do so, the GPS positions of foraging dives over entire foraging
275 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
276 calculated for each *Year* and *Treatment* (2019<sub>treated</sub> vs. 2019<sub>control</sub> vs. 2022<sub>treated</sub> vs. 2022<sub>control</sub>).
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278 (2) Second, the distances of foraging dives from the signature of the Polar front were
279 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
280 distances of foraging dives from the Polar front using an LMM (see above).
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283 *Foraging trip parameters*

284 We investigated the effects of *Treatment*, *Sex*, and *Year* on foraging trip duration (days),
285 maximum foraging range (km), mean horizontal speed (km·h⁻¹), and mean diving depth of
286 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
287 estimate and marginal means. For the model on mean horizontal speed, *Pair ID* explained
288 trivial amounts of variance so that we resorted to a simpler linear model (LM). For maximum
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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).

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However, running the models with or without those data points led to similar results, so that we chose to keep them in the analyses.

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## Diving parameters

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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 (Charrassin *et al.* 2002; 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), group limits corresponding to 1st and 3rd quantiles of the maximum depth distribution respectively (see **Fig.S2 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.

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## Chick growth

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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.

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326 *Chick survival*

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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.

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334 RESULTS

335 Oceanographic conditions and habitat use
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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).

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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 farther south in 2022 for treated birds, with a foraging hotspot (90% kernel) located at 51°S (510 km from the colony).

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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).

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## Foraging behavior

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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

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(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**).

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372 **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. Results for each dive parameter are described in the following paragraphs.
Summaries of all the different models are provided in **Supplementary Materials (ESM S3)**.

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379 *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 S3). It is interesting to note that the effect of Treatment appeared to increase with increasing depth (see model estimates Fig. 4).

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## Durations of dive, bottom and post-dive phases

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 S3**). 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
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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).

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## Descent and ascent rate

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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 S3).

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## Hunting activity

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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 S3).

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## Chick growth
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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 S4**).

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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 S4**).

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Chick survival

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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

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(onset of winter period and after the treatment period of parents) compared to 9 chicks lost during the brooding phase (chick with parents).

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DISCUSSION

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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 in the foraging behavior of 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 trips 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 Polar Front was substantially farther 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 depths. 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 by adjusting foraging behavior
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Experimentally increasing hydrodynamic drag and foraging energy costs in our study led to similar effects to those typically observed in poor breeding years in king penguins at the scale of the foraging trip (Bost *et al.* 2015; Brisson-Curadeau *et al.* 2024). 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.

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In response to increased foraging workload, king penguins displayed marked phenotypic flexibility. As predicted, treated birds tended to maximize their diving activity in areas of high prey availability as early as their first foraging trip, avoiding deep dives that were both time and energy consuming. Unfortunately, as many of our loggers failed during the second trip, we were unable to assess the potentially cumulative effect of harsh conditions over the scale of multiple foraging trips. Nonetheless, compared 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 phases of the trip. This difference in foraging locations is also consistent with observed differences in maximum diving depth. While all birds generally increased maximum diving depth in 2022 compared to 2019, treated birds exhibited shallower maximum diving depths compared to controls in both years. It is unlikely birds altered their foraging strategies by switching to different preys, since 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 was more likely due to flexibility in foraging location and effort, rather than modifications in targeted prey *per se*.

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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 depended strongly on the performance of the
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other partner, 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 to compensate for partners' difficulties.

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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 slower vertical speed (both ascent and descent rates) for all dives >125m. Treated birds also 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, it is not surprising that dive duration and post-dive recovery were only slightly affected by the treatment, since both components are limited by the physiological ability of the species. In contrast, decreased vertical speed in treated birds could be explained by increased drag when diving, this effect being more pronounced in deep foraging dives, as the dummy device is incompressible and the ratio between its cross-sectional area and that of the bird increases with increasing depth. Non-exclusively, decreased vertical speed may also be a byproduct of decreased overall swimming speed as a behavioral adaptation to mitigate the drag effects imposed by the dummy logger when diving. Therefore, it appeared that diving time lost to transiting from and to the surface was principally gained by shortening bottom time, affecting birds' ability to actively forage, especially at deeper depths. This was confirmed by the findings that treated birds showed significantly reduced mean number of wiggles per dive for dives deeper than 125m.
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Thus, consistent with our predictions (and optimal foraging theory (Pyke 1984)),
treated birds avoided increased energy costs imposed by our experimental design by
concentrating their foraging activity at shallower 50-125m depths, corresponding to the
typical depth of the thermocline where prey aggregate at the Polar Front, and avoiding deeper
dives less (>125m) where they were overall less efficient foragers than controls.

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Consequences on reproduction

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If behavioral flexibility were sufficient to compensate for harsh conditions at sea, we
would have expected little differences in breeding success between control and treated birds.

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However, it appeared that parents were not fully able to compensate for increased foraging
costs, despite modifications in their foraging behavior. Chicks from treated pairs exhibited
overall lower body mass gain and body mass over the brooding period, likely due to the
inability of treated parents to provision their chick as much as controls (Gauthier-Clerc et al.

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2002). Chick survival at thermal independence (35 days) was not significantly affected,
though survival until fledging was reduced for chicks from treated parents. Our manipulation
over two consecutive foraging trips during chick brooding for each parent would have
mimicked conditions where, for experimental birds, the cost/benefit ratio of foraging would
have been high over most of the pre-winter chick-rearing period. The observed reduction in
chick survival probability until fledging can be due to the observed reduced body mass gain
until day-35 for chicks from treated parents, as chick body mass gain before winter is critical
for chick survival until fledging (Weimerskirch et al. 1992).

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In addition, most of chick mortality occurred between 35 and 100 days, at the
beginning of the crèche period, during which smaller chicks are more vulnerable to predation
by giant petrels (Macronectes giganteus) (Hunter 1991). Thus, it is possible that chicks from

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treated birds suffered from the cumulative direct and indirect (viz. through predation) effects
of reduced growth on survival. Interestingly, the fact that survival costs were not detectable
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over the short-term (35-days) suggests that parents might be able to accommodate for
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increased foraging costs over shorter periods, such as those that might be experienced during
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punctually deteriorated conditions at sea over a few weeks.
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**The distance – depth trade-off: will foraging at the Polar Front be optimal in the future?**

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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 shallower depths, reducing the duration (and energy) spent in 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. However, targeting the distant Polar Front could also be energetically constraining, especially in years where it is farther from the colony (i.e., in 2022), resulting 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.

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In our study, it appeared that treated birds faced with increased workload modified
their foraging behavior to target the Polar Front more directly, this response being consistent
between the two years. Targeting the Polar Front to replenish energy stores, and favor
survival over reproduction might be an adaptive strategy for these long-lived birds (Drent &

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Daan 1980; Olsson 1997), if detrimental conditions at sea occurred only 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 farther and farther 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 the increasing distance travelled from the colony over the years would eventually be prohibitive for breeding, possibly leading 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
597 119 and OISEAUX PLONGEURS 394 polar projects, and by the French National Center for
598 Scientific Research (CNRS). CL was supported by a PhD scholarship from the Ecole Normale
599 Supérieure (ENS-Lyon). We are grateful to the Terres Australes et Antarctiques Françaises
600 (TAAF) for providing logistical support in the field. The 394 and 119 projects on king
601 penguin are part of the long-term studies in Ecology and Evolution (SEE-Life) program of the
602 CNRS.

603

604 **Conflicts of interest:** Authors declare that they have no conflict of interest.

605

606 **Ethics approval:** All applicable institutional and/or national guidelines for the care and use
607 of animals were followed (Ethical approval: 2019: APAFIS#16465-2018080111195526v4
608 and 2022: APAFIS#31268-2021042117037897v3). The study was approved by TAAF
609 prefectural decrees (N°2019-166 and N°2022-69).

610

611 **Availability of data and material:** Data and code will be made available upon publication.

612

613 **References**

614 Bannasch, R., Wilson, R.P. & Culik, B. (1994). Hydrodynamic Aspects of Design and Attachment of A
615 Back-Mounted Device in Penguins. *J. Exp. Biol.*, **194**, 83–96.

616 Barbraud, C., Delord, K., Bost, C.A., Chaigne, A., Marteau, C. & Weimerskirch, H. (2020). Population
trends of penguins in the French Southern Territories. *Polar Biol.*, **43**, 835–850.

618 Bates, D., Mächler, M., Bolker, B. & Walker, S. (2015). Fitting Linear Mixed-Effects Models Using
619 lme4. *J. Stat. Softw.*, **67**, 1–48.

620 Belkin, I.M. & Gordon, A.L. (1996). Southern Ocean fronts from the Greenwich meridian to Tasmania.
*J. Geophys. Res. Oceans*, **101**, 3675–3696.

622 Bohec, C.L., Gauthier-Clerc, M., Grémillet, D., Pradel, R., Béchet, A., Gendner, J.-P., et al. (2007).
623 Population Dynamics in a Long-Lived Seabird: I. Impact of Breeding Activity on Survival and
624 Breeding Probability in Unbanded King Penguins. *J. Anim. Ecol.*, **76**, 1149–1160.
625 Bókony, V., Lendvai, Á.Z., Liker, A., Angelier, F., Wingfield, J.C. & Chastel, O. (2009). Stress Response and the Value of Reproduction: Are Birds Prudent Parents? *Am. Nat.*, 173, 589–598.

626 Bost, C., Georges, J.-Y., Guinet, C., Cherel, Y., Pütz, K., Charrassin, J.-B., *et al.* (1997). Foraging habitat and food intake of satellite-tracked king penguins during the summer at Crozet Archipelago. *Mar. Ecol. Prog. Ser.*, 150, 21–33.

630 Bost, C., Karine, D., Cotte, C., Péron, C. & Weimerskirch, H. (2012). The King Penguin: Life History, current status and priority conservation actions. In: *Penguins Book*.

632 Bost, C.A., Cotté, C., Bailleul, F., Cherel, Y., Charrassin, J.B., Guinet, C., *et al.* (2009). The importance of oceanographic fronts to marine birds and mammals of the southern oceans. *J. Mar. Syst.*, 634 Special Issue on Observational Studies of Oceanic Fronts, 78, 363–376.

635 Bost, C.A., Cotté, C., Terray, P., Barbraud, C., Bon, C., Delord, K., *et al.* (2015). Large-scale climatic anomalies affect marine predator foraging behaviour and demography. *Nat. Commun.*, 6, 8220.

638 Bost, C.A., Zorn, T., Maho, Y.L. & Duhamel, G. (2002). Feeding of diving predators and diel vertical migration of prey: King penguins’ diet versus trawl sampling at Kerguelen Islands. *Mar. Ecol. Prog. Ser.*, 227, 51–61.

641 Brisson-Curadeau, É., Bost, C.-A., Cherel, Y. & Elliott, K. (2024). King Penguins adjust foraging effort rather than diet when faced with poor foraging conditions. *Ibis*, 166, 723–731.

643 Calenge, C. & Fortmann-Roe, contributions from S. (2023). *adehabitatHR: Home Range Estimation*.

644 Charrassin, J.-B. & Bost, C.-A. (2001). Utilisation of the oceanic habitat by king penguins over the annual cycle. *Mar. Ecol. Prog. Ser.*, 221, 285–298.

646 Charrassin, J.-B., Bost, C.A., Pütz, K., Lage, J., Dahier, T., Zorn, T., *et al.* (1998). Foraging strategies of incubating and brooding king penguins Aptenodytes patagonicus. *Oecologia*, 114, 194–201.

648 Charrassin, J.-B., Le Maho, Y. & Bost, C.-A. (2002). Seasonal changes in the diving parameters of king penguins (Aptenodytes patagonicus). *Mar. Biol.*, 141, 581–589.

650 Cheng, L., von Schuckmann, K., Abraham, J.P., Trenberth, K.E., Mann, M.E., Zanna, L., *et al.* (2022). Past and future ocean warming. *Nat. Rev. Earth Environ.*, 3, 776–794.

652 Cornick, L.A. & Horning, M. (2003). A test of hypotheses based on optimal foraging considerations for a diving mammal using a novel experimental approach. *Can. J. Zool.*, 81, 1799–1807.

654 Cotté, C., Park, Y.-H., Guinet, C. & Bost, C.-A. (2007). Movements of foraging king penguins through marine mesoscale eddies. *Proc. R. Soc. B Biol. Sci.*, 274, 2385–2391.

656 Delignette-Muller, M.-L., Dutang, C., Pouillot, R., Denis, J.-B. & Siberchicot, A. (2025). *fitdistrplus: Help to Fit of a Parametric Distribution to Non-Censored or Censored Data*.

658 Doniol-Valcroze, T., Lesage, V., Giard, J. & Michaud, R. (2011). Optimal foraging theory predicts diving and feeding strategies of the largest marine predator. *Behav. Ecol.*, 22, 880–888.

660 Drent, R.H. & Daan, S. (1980). The prudent parent: Energetic adjustments in avian breeding. *Ardea - Wagening.*, 68, 225–252.

662 Forcada, J. & Trathan, P.N. (2009). Penguin responses to climate change in the Southern Ocean. *Glob. Change Biol.*, 15, 1618–1630.

664 Gauthier-Clerc, M., Le Maho, Y., Gendner, J.-P. & Handrich, Y. (2002). Moultling fast and time constraint for reproduction in the king penguin. *Polar Biol.*, 25, 288–295.

666 Groscolas, R., Decrock, F., Thil, M.-A., Fayolle, C., Boissery, C. & Robin, J.-P. (2000). Refeeding signal in fasting-incubating king penguins: changes in behavior and egg temperature. *Am. J. Physiol.-Regul. Integr. Comp. Physiol.*, 279, R2104–R2112.

669 Groscolas, R., Lacroix, A. & Robin, J.-P. (2008). Spontaneous egg or chick abandonment in energy-depleted king penguins: A role for corticosterone and prolactin? *Horm. Behav.*, 53, 51–60.

671 Halsey, L.G., Bost, C.-A. & Handrich, Y. (2007a). A thorough and quantified method for classifying seabird diving behaviour. *Polar Biol.*, 30, 991–1004.

673 Halsey, L.G. & Butler, P.J. (2006). Optimal diving behaviour and respiratory gas exchange in birds. *Respir. Physiol. Neurobiol.*, *Frontiers in Comparative Physiology II: Respiratory Rhythm, Pattern and Responses to Environmental Change*, 154, 268–283.
676 Halsey, L.G., Handrich, Y., Fahlman, A., Schmidt, A., Bost, C.-A., Holder, R.L., et al. (2007b). Fine-scale
677 analyses of diving energetics in king penguins Aptenodytes patagonicus: how behaviour
678 affects costs of a foraging dive. *Mar. Ecol. Prog. Ser.*, 344, 299–309.
679 Hanuise, N., Bost, C.-A. & Handrich, Y. (2013). Optimization of transit strategies while diving in
680 foraging king penguins. *J. Zool.*, 290, 181–191.
681 Hazen, E.L., Abrahms, B., Brodie, S., Carroll, G., Jacox, M.G., Savoca, M.S., et al. (2019). Marine top
682 predators as climate and ecosystem sentinels. *Front. Ecol. Environ.*, 17, 565–574.
683 Hijmans, R.J., Bivand, R., Dyba, K., Pebesma, E. & Sumner, M.D. (2024a). terra: Spatial Data Analysis.
684 Hijmans, R.J., Karney (GeographicLib), C., Williams, E. & Vennes, C. (2024b). geosphere: Spherical
685 Trigonometry.
686 Hunter, S. (1991). The impact of avian predator-scavengers on King Penguin Aptenodytes
687 patagonicus chicks at Marion Island. *Ibis*, 133, 343–350.
688 Kooyman, G.L. (2012). *Diverse Divers: Physiology and behavior*. Springer Science & Business Media.
689 Kooyman, G.L., Cherel, Y., Maho, Y.L., Croxall, J.P., Thorson, P.H., Ridoux, V., et al. (1992). Diving
690 Behavior and Energetics During Foraging Cycles in King PENGUINS. *Ecol. Monogr.*, 62, 143–163.
691 Lenth, R.V., Banfai, B., Bolker, B., Buerkner, P., Giné-Vázquez, I., Herve, M., et al. (2025). emmeans:
692 Estimated Marginal Means, aka Least-Squares Means.
693 Massardier-Galatà, L., Morinay, J., Bailleul, F., Wajnberg, E., Guinet, C. & Coquillard, P. (2017).
694 Breeding success of a marine central place forager in the context of climate change: A
695 modeling approach. *PLOS ONE*, 12, e0173797.
696 Murphy, E.J. (1995). Spatial Structure of the Southern Ocean Ecosystem: Predator-Prey Linkages in
697 Southern Ocean Food Webs. *J. Anim. Ecol.*, 64, 333–347.
698 Oliver, M.J., Kohut, J.T., Bernard, K., Fraser, W., Winsor, P., Statscewich, H., et al. (2019). Central
699 place foragers select ocean surface convergent features despite differing foraging strategies.
700 *Sci. Rep.*, 9, 157.
701 Olsson, O. (1997). Effects of food availability on fledging condition and post-fledging survival in king
702 penguin chicks. *Polar Biol.*, 18, 161–165.
703 Orsi, A.H., Whitworth, T. & Nowlin, W.D. (1995). On the meridional extent and fronts of the Antarctic
704 Circumpolar Current. *Deep Sea Res. Part Oceanogr. Res. Pap.*, 42, 641–673.
705 Parmesan, C. (2006). Ecological and Evolutionary Responses to Recent Climate Change. *Annu. Rev.
706 Ecol. Evol. Syst.*, 37, 637–669.
707 Péron, C., Weimerskirch, H. & Bost, C.-A. (2012). Projected poleward shift of king penguins'
708 (Aptenodytes patagonicus) foraging range at the Crozet Islands, southern Indian Ocean. *Proc.
709 R. Soc. B Biol. Sci.*, 279, 2515–2523.
710 Pinkerton, M.H., Boyd, P.W., Deppeler, S., Hayward, A., Höfer, J. & Moreau, S. (2021). Evidence for
711 the Impact of Climate Change on Primary Producers in the Southern Ocean. *Front. Ecol. Evol.*,
712 9, 134.
713 Pütz, K., Gherardi-Fuentes, C., García-Borboroglu, P., Godoy, C., Flagg, M., Pedrana, J., et al. (2021).
714 Exceptional foraging plasticity in King PENGUINS (Aptenodytes patagonicus) from a recently
715 established breeding site in Tierra del Fuego, Chile. *Glob. Ecol. Conserv.*, 28, e01669.
716 Pyke, G.H. (1984). Optimal Foraging Theory: A Critical Review. *Annu. Rev. Ecol. Syst.*, 15, 523–575.
717 Pyke, G.H., Pulliam, H.R. & Charnov, E.L. (1977). Optimal Foraging: A Selective Review of Theory and
718 Tests. *Q. Rev. Biol.*, 52, 137–154.
719 Sato, K., Naito, Y., Kato, A., Niizuma, Y., Watanuki, Y., Charrassin, J.B., et al. (2002). Buoyancy and
720 maximal diving depth in penguins: do they control inhaling air volume? *J. Exp. Biol.*, 205,
721 1189–1197.
722 Song, H., Marshall, J., McGillicuddy, D.J. & Seo, H. (2020). Impact of Current-Wind Interaction on
723 Vertical Processes in the Southern Ocean. *J. Geophys. Res. Oceans*, 125, e2020JC016046.
724 Stier, A., Viblanc, V.A., Massemin-Challet, S., Handrich, Y., Zahn, S., Rojas, E.R., et al. (2014). Starting
725 with a handicap: phenotypic differences between early- and late-born king penguin chicks
726 and their survival correlates. *Funct. Ecol.*, 28, 601–611.
727 Stonehouse, B. (1960). *The King Penguin (Aptenodytes patagonica) of South Georgia: I. Breeding
behaviour and development*. HMSO, London.

728 Sturm, D., de Vries, J., Balch, W.M., Wheeler, G. & Brownlee, C. (2023). Mesoscale oceanographic
meanders influence protist community function and structure in the southern Indian Ocean.
*Environ. Microbiol.*, n/a.

732 Trathan, P.N., Forcada, J. & Murphy, E.J. (2007). Environmental forcing and Southern Ocean marine
predator populations: effects of climate change and variability. *Philos. Trans. R. Soc. Lond. B*
*Biol. Sci.*, 362, 2351-2365.

735 Viblanc, V.A., Schull, Q., Stier, A., Durand, L., Lefol, E., Robin, J.-P., et al. (2020). Foster rather than
biological parental telomere length predicts offspring survival and telomere length in king
penguins. *Mol. Ecol.*, 29, 3155-3167.

738 Vihtakari, M., Bivand, R. & Wickham, H. (2024). ggOceanMaps: Plot Data on Oceanographic Maps
using "ggplot2."

740 Watanabe, Y.Y., Ito, M. & Takahashi, A. (2014). Testing optimal foraging theory in a penguin-krill
system. *Proc. R. Soc. B Biol. Sci.*, 281, 20132376.

742 Weimerskirch, H. (2007). Are seabirds foraging for unpredictable resources? *Deep Sea Res. Part II*
*Top. Stud. Oceanogr.*, Bio-logging Science: Logging and Relaying Physical and Biological Data
*Using Animal-Attached Tags*, 54, 211-223.

745 Weimerskirch, H., Stahl, J.C. & Jouventin, P. (1992). The breeding biology and population dynamics of
King Penguins Aptenodytes patagonica on the Crozet Islands. *Ibis*, 134, 107-117.

747 Wilson, R.P., Grant, W.S. & Duffy, D.C. (1986). Recording Devices on Free-Ranging Marine Animals:
Does Measurement Affect Foraging Performance? *Ecology*, 67, 1091-1093.

749 Zimmer, I., Wilson, R.P., Beaulieu, M., Ancel, A. & Plötz, J. (2008). Seeing the light: depth and time
restrictions in the foraging capacity of emperor penguins at Pointe Géologie, Antarctica.
*Aquat. Biol.*, 3, 217-226.

752 Zimmer, I., Wilson, R.P., Beaulieu, M., Ropert-Coudert, Y., Kato, A., Ancel, A., et al. (2010). Dive
efficiency versus depth in foraging emperor penguins. *Aquat. Biol.*, 8, 269-277.

754
<caption>**Table 1.**</caption>

Results 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 ± 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).

<table><thead><tr><th></th><th></th><th>est ± se</th><th>df</th><th>t value</th><th>p</th></tr></thead><tbody><tr><td rowspan="6">Distance of foraging dives to the Polar Front (km)</td><td>Intercept</td><td>(57.06 ± 24.89)E03</td><td>29.45</td><td>2.293</td><td><strong>0.029</strong></td></tr><tr><td>Year [2022]</td><td>-28.07 ± 12.31</td><td>29.44</td><td>-2.280</td><td><strong>0.030</strong></td></tr><tr><td>Treatment [TREATED]</td><td>-54.75 ± 24.55</td><td>28.28</td><td>-2.230</td><td><strong>0.034</strong></td></tr><tr><td>Sex [Male]</td><td>-12.36 ± 15.35</td><td>16.16</td><td>-0.805</td><td>0.432</td></tr><tr><td>Random: Pair ID:ID</td><td></td><td>10.6%</td><td></td><td></td></tr><tr><td>Random: Pair ID</td><td></td><td>22.8%</td><td></td><td></td></tr><tr><td rowspan="6">Trip duration (days) (log-transformed)</td><td>Intercept</td><td>2.00 ± 0.11</td><td>35.34</td><td>17.89</td><td><strong>&lt; 0.001</strong></td></tr><tr><td>Year [2022]</td><td>0.22 ± 0.11</td><td>26.06</td><td>2.01</td><td>0.055</td></tr><tr><td>Treatment [TREATED]</td><td>0.40 ± 0.11</td><td>24.17</td><td>3.68</td><td><strong>0.001</strong></td></tr><tr><td>Sex [Male]</td><td>-0.10 ± 0.07</td><td>15.82</td><td>-1.41</td><td>0.178</td></tr><tr><td>Random: Pair ID</td><td></td><td>55.9%</td><td></td><td></td></tr><tr><td>Maximum range (km)</td><td>Intercept</td><td>253.86 ± 35.68</td><td>33.62</td><td>7.12</td><td><strong>&lt; 0.001</strong></td></tr><tr><td rowspan="6">Mean horizontal speed (km.h-1)</td><td>Year [2022]</td><td>84.51 ± 34.62</td><td>23.84</td><td>2.44</td><td><strong>0.023</strong></td></tr><tr><td>Treatment [TREATED]</td><td>88.31 ± 34.49</td><td>22.16</td><td>2.56</td><td><strong>0.018</strong></td></tr><tr><td>Sex [Male]</td><td>42.35 ± 24.08</td><td>14.28</td><td>1.76</td><td>0.100</td></tr><tr><td>Random: Pair ID</td><td></td><td>53.9%</td><td></td><td></td></tr><tr><td>Intercept</td><td>3.50 ± 0.14</td><td>36.00</td><td>24.44</td><td><strong>&lt; 0.001</strong></td></tr><tr><td>Year [2022]</td><td>0.04 ± 0.12</td><td>36.00</td><td>0.29</td><td>0.774</td></tr><tr><td rowspan="6">Mean depth of foraging dives (m)</td><td>Treatment [TREATED]</td><td>-0.33 ± 0.12</td><td>36.00</td><td>-2.82</td><td><strong>0.008</strong></td></tr><tr><td>Sex [Male]</td><td>0.30 ± 0.12</td><td>36.00</td><td>2.49</td><td><strong>0.018</strong></td></tr><tr><td>Intercept</td><td>139.33 ± 6.69</td><td>35.85</td><td>20.81</td><td><strong>&lt; 0.001</strong></td></tr><tr><td>Year [2022]</td><td>24.12 ± 5.85</td><td>27.19</td><td>4.12</td><td><strong>&lt; 0.001</strong></td></tr><tr><td>Treatment [TREATED]</td><td>-15.84 ± 5.57</td><td>22.63</td><td>-2.84</td><td><strong>0.009</strong></td></tr><tr><td>Sex [Male]</td><td>25.96 ± 5.49</td><td>22.59</td><td>4.73</td><td><strong>&lt; 0.001</strong></td></tr><tr><td></td><td>Random: Pair ID</td><td></td><td>6.9%</td><td></td><td></td></tr></tbody></table>
766 **Figure Caption**

767

768 **Figure 1.** Schematic view of the equipment protocol. **A.** Schematic view of the timing of bird capture and logger deployment. Both 3rd and 4th brooding shift were not presented in this study because of insufficient data retrieved from deployed loggers, parents were however equipped according to their group (treated/control) over the entire monitoring period. Last loggers (after emancipation) were opportunistically retrieved when parent was on land to feed the chick. For most of the monitored pairs, the last incubation shift was carried out by the female, but for some pairs the brooding shifts are inverted. **B.** Attachment position of both Axy-Trek and dummy loggers on the back of king penguins (*Aptenodytes patagonicus*).

776

777 **Figure 2.** Position of the main foraging areas for studied king penguins (*Aptenodytes patagonicus*) 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).

781

782 **Figure 3.** Adult king penguin (*Aptenodytes patagonicus*) trip parameters (**A.** Trip duration (days), **B.** Maximum foraging range (km), **C.** Mean horizontal speed (km.h⁻¹) and **D.** 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 and confidence interval were back-transformed to the response scale.

788

789 **Figure 4.** Forest 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
791 (Aptenodytes patagonicus) for each depth category Shallow (50-125m, n = 7746 dives), Mid (125-792 200m, n = 15762 dives) and Deep (>200m, n = 6965 dives). Effects of explanatory variables (y-axis) 793 are given for each dependent variable (color, diving parameters), effects are given relative to a 794 reference category (Control, 2019 and Female for Treatment, Year and Sex respectively). 795 Standardized estimates are given for the scaled variable to facilitate visualization, so that effect sizes 796 are directly comparable across models. Estimates (for LMMs) and incidence rate ratios (for GLMMs) 797 are given along with their 95% confidence interval. All model summaries can be found in 798 **Supplementary Materials ESM S4.** 799

800 **Figure 5.** King penguin (*Aptenodytes patagonicus*) chick body mass (kg) (A.) and body mass gain (g/day) (B.) 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 *Year*, *Treatment* and *Age* (day 4, 10, 20 and 35) as explanatory variables. The interaction terms *Treatment* x *Age* and *Treatment* x *Year* were also included in the model to evaluate the changes in the effect of *Treatment* over chick growth and whether the effect of the treatment depended on the year. The interaction terms *Treatment* x *Year* 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 **Supplementary Materials ESM S5**.
<figure><img src="image_2.png" /><figcaption>Figure 1</figcaption></figure>

812

813

814
<figure><img src="image_3.png" /><figcaption>Figure 2</figcaption></figure>
<figure><img src="image_4.png" /></figure>

<figure><img src="image_5.png" /></figure>

<figure><img src="image_6.png" /></figure>

<figure><img src="image_7.png" /></figure>

Treatment CONTROL TREATED
Sexe F M

817

<caption>818 **Figure 3.**</caption>
<figure><img src="image_8.png" /><figcaption>Figure 4.</figcaption></figure>
<figure><img src="image_9.png" /></figure>

<figure><img src="image_10.png" /><figcaption>Figure 5.</figcaption></figure>