# Arctic-breeding black-legged Kittiwakes show individual variation in foraging responses to glacial conditions without consequences for reproductive output

Frederick C Mckendrick, Sébastien Descamps, Kathryn E Arnold, Stéphanie Jenouvrier, Stephanie M Harris, Philip Bertrand, Jack Kohler, Ward Van Pelt, Olivier Chastel, William Jouanneau, et al.

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Frederick C Mckendrick, Sébastien Descamps, Kathryn E Arnold, Stéphanie Jenouvrier, Stephanie M Harris, et al., Arctic-breeding black-legged Kittiwakes show individual variation in foraging responses to glacial conditions without consequences for reproductive output. Oikos, 2025, 10.1002/oik.11558. hal-05273414

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OIKOS

Research article

# Arctic-breeding black-legged Kittiwakes show individual variation in foraging responses to glacial conditions without consequences for reproductive output

Frederick C. Mckendrick✉<fnref n="1" />, Sébastien Descamps ID<fnref n="2" />, Kathryn E. Arnold ID<fnref n="3" />, Stéphanie Jenouvrier ID<fnref n="4" />, Stephanie M. Harris ID<fnref n="5" />, Philip Bertrand ID<fnref n="2" />,<fnref n="6" />, Jack Kohler ID<fnref n="2" />, Ward van Pelt ID<fnref n="7" />, Olivier Chastel<fnref n="8" />, William Jouanneau ID<fnref n="8" />, Don-Jean Léandri-Breton ID<fnref n="8" />,<fnref n="9" />, Pierre Blévin<fnref n="10" />, Natasha E. Gillies ID<fnref n="1" />, Haakon Hop ID<fnref n="2" />, Philipp Assmy ID<fnref n="2" />, Allison Bailey ID<fnref n="2" />, Anette Wold ID<fnref n="2" />, Hallvard Strøm ID<fnref n="2" /> and Samantha C. Patrick<fnref n="1" />

Correspondence: Frederick C. Mckendrick (Freddiemckendrick1@gmail.com)

Oikos

2025: e11558

doi: 10.1002/oik.11558

Subject Editor: Jiri Reif

Editor-in-Chief: Pedro Peres-Neto

Accepted 13 August 2025

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www.oikosjournal.org

Behavioural plasticity is likely to influence how individuals continue to access resources under rapid climate change. Plasticity will be particularly important at highly dynamic, prey-rich foraging areas such as upwelling fronts of marine-terminating glaciers in the high Arctic, where profitability varies significantly across space and time. Understanding individual variation in plasticity and its adaptive potential is crucial to understand a populations flexibility to future climate scenarios. By analysing GPS data from 186 black-legged Kittiwakes *Rissa tridactyla* breeding in the high Arctic over six years, we quantified individual variation in behavioural plasticity in use of glacial fronts and its relationship with the number of chicks produced. Variation in the relationship between glacial use and levels of discharged meltwater was primarily explained by differences in food availability between years. Whereas there was no significant relationship between discharge rates and glacier use in years of low zooplankton biomass, the probability of glacial front use and time spent at glaciers decreased in years when food was more abundant, despite high discharge and likely good conditions at the front. Interestingly, neither glacial use nor plasticity in foraging during the breeding season correlated with the number of surviving chicks, suggesting that all individuals still obtained enough food for reproduction. Understanding the complex nature of

© 2025 The Author(s). Oikos published by John Wiley & Sons Ltd on behalf of Nordic Society Oikos.

This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

<footnote n="1">¹School of Environmental Science, University of Liverpool, Liverpool, UK</footnote>

<footnote n="2">²Norwegian Polar Institute, Fram Centre, Tromsø, Norway</footnote>

<footnote n="3">³Department of Environment and Geography, University of York, York, UK</footnote>

<footnote n="4">⁴Biology Department, Woods Hole Oceanographic Institute, Woods Hole, MA, USA</footnote>

<footnote n="5">⁵School of Ocean Sciences, Bangor University, Bangor, Wales, UK</footnote>

<footnote n="6">⁶Département de Biologie, Chimie et Géographie and Centre d'Études Nordiques, Université du Québec à Rimouski, Rimouski, QC, Canada</footnote>

<footnote n="7">⁷Department of Earth Sciences, Uppsala University, Uppsala, Sweden</footnote>

<footnote n="8">⁸Centre d'Etudes Biologiques de Chizé (CEBC), UMR 7372 CNRS- La Rochelle Université, Villiers-en-Bois, France</footnote>

<footnote n="9">⁹Department of Natural Resource Sciences, McGill University, Ste-Anne-de-Bellevue, QC, Canada</footnote>

<footnote n="10">¹⁰Akvaplan-niva AS, Fram Centre, Tromsø, Norway</footnote>
individual variation in plasticity and when it is likely to be adaptive will be the first step in highlighting when plasticity can be
used to predict how species will respond to rapidly changing environments.

Keywords: black-legged Kittiwakes, environmental change, glacial fjords, movement behaviour, plasticity, Svalbard

**Introduction**

Anthropogenic climate change represents a major challenge to individuals' abilities to find and consume food (Cahill et al. 2012, Radchuk et al. 2019). Increasing temperatures, weather variability and habitat degradation all play a role in changing the spatial and temporal availability of resources (Peterson and Lowe 2009, Smith et al. 2009, Bryndum-Buchholz et al. 2019). Consequently, animals must adjust their behaviour in response to these environmental changes to maintain adequate resource acquisition for both survival and reproduction (Wong and Candolin 2015, Beever et al. 2017). The adaptation of populations to climate change will, therefore, be governed by individuals' capability to respond to such changes (van de Pol et al. 2017).

Behavioural plasticity, e.g. a within-individual behav-
ioural response to a change in environmental conditions
(Nussey et al. 2007), is thus likely to be the dominant driver
of how a species will maximise resource acquisition in the face
of climate change. This will especially be true in long-lived
animals such as seabirds, as rapid environmental changes
and long generation times limit the capacity for evolutionary
adaptation (Gilg et al. 2012, Munday et al. 2013). Plasticity
in foraging in seabirds, for example, may increase the rela-
tive ability of individuals to access prey (Paiva et al. 2010,
Kowalczyk et al. 2015). Therefore, variation in plasticity
among individuals will likely result in disparity in survival and
reproduction in the context of climate change (Nussey et al.
2007, Muñoz et al. 2015), although this remains understud-
ied in seabirds (Grémillet and Charmantier 2010, reviewed
by Orgeret et al. 2022).

Significant among-individual variation in foraging plastic-
ity has been observed in multiple seabird species (common
murre *Uria aalge*: Gulka and Davoren 2019; little penguin
*Eudyptula minor*: Cambrasse et al. 2017, yellow-eyed pen-
guin *Megadyptes antipodes*: Muller et al. 2021, northern gan-
net *Morus bassanus*: Patrick et al. 2014, wandering albatross
*Diomedea exulans*: Gillies et al. 2023). However, most of these
studies quantify plasticity in behaviour across time (temporal
plasticity; Stamps 2016) as opposed to behavioural differ-
ences across environmental gradients (exogenous plasticity;
Stamps 2016). Studies of temporal plasticity often suggest
but do not empirically test the links to environmental charac-
teristics that are likely driven by large-scale climate variability,
e.g. the timing and magnitude of peak capelin biomass avail-
able to foraging common murre (Gulka and Davoren 2019).
Quantifying variation in exogenous plasticity to specific and
measurable components of the environment will facilitate
our understanding of how populations will cope under future
climate scenarios, particularly in ecosystems that are chang-
ing the fastest.

Plasticity is only adaptive if individuals can match their
behaviour to the conditions they experience, which are, in
turn, potentially limited by the availability or reliability of
alternative foraging areas (Ghalambor et al. 2007, Auld et al.
2010, Murren et al. 2015). The costs of changing behaviour,
emerging through changes in energy use, must therefore
at least match the potential energy gain to enhance fitness
(DeWitt et al. 1998). However, few studies have explored
the fitness consequences of foraging or movement plastic-
ity in the wild (Betini and Norris 2012, Little et al. 2022,
Laforge et al. 2023, Gillies et al. 2024). As the adaptive
benefit of plasticity in behaviour is highly dependent on the
specific conditions an individual experiences (Vinton et al.
2022), we require studies exploring individual behaviour,
fine-scale environmental changes and the subsequent impact
on fitness traits.

Plasticity in foraging behaviour may provide the most pro-
nounced benefits to individuals in rich resource areas in rap-
idly changing regions (Grémillet et al. 2015). For instance, in
the Arctic, the fastest changing region on the planet (Post et al.
2009, Deb and Bailey 2023), marine-terminating glacier
fronts provide important refugia to seabirds through enhanced
food availability (Arimitsu et al. 2012, Renner et al. 2012).
Meltwater discharge drives the advection of freshwater from
beneath the glacier, pulling nutrients, zooplankton and fish
to the water's surface (Hop et al. 2023). Increased discharge
subsequently accelerates this process leading to greater poten-
tial abundance of prey to be accessible to foraging seabirds
(Urbanski et al. 2017). Stochasticity in discharge conditions,
which is likely to be exacerbated by climate change, leads to
striking changes therefore in the availability of prey and sub-
sequent foraging behaviour of seabirds (Gremillet et al. 2015,
Urbanski et al. 2017). Furthermore, other factors, includ-
ing both resource abundance (Bertrand et al. 2021b) and
the advection of warm Atlantic water masses to the Arctic
(Stempniewicz et al. 2021), also play a role in determining
the profitability of foraging at glacier fronts. The sensitiv-
ity of glaciers to the current conditions highlights therefore
the importance of individual capability to respond to these
changes to access resources provided by them.

In this study we explored individual variation in plasticity
of foraging behaviour and its consequences for reproductive
performance in Arctic-breeding populations of black-legged
kittiwake *Rissa tridactyla* (hereafter ‘kittiwakes’). We used a
long-term tracking dataset to explore how individuals vary
in their daily use of a key foraging habitat, marine-termin-
nating glacial fronts, in response to daily changes in glacial
discharge, an expected predictor of the profitability of avail-
able foraging habitat to seabirds. While previous work in this
study area has highlighted inter-annual variation in glacial
use (Bertrand et al. 2021b), identifying how individuals
respond to daily discharge will improve our understanding of how both dynamic conditions at glacier fronts and prey abundance shape resource availability at the finer scales to which seabirds aggregate to feed.

We first investigated the environmental drivers of glacier front use (discharge and prey densities in the fjord as a whole) and predicted that the use of glaciers will increase with daily variation in sub-glacial discharge when prey becomes more accessible (Urbanski et al. 2017), but that this relationship would be weaker in years of high resource availability when more food is abundant across the ecosystem beyond the glaciers (Bertrand et al. 2021b). We then quantified inter-individual variation in the plasticity of foraging behaviour at glaciers to daily levels of glacial discharge and expected individuals to differ accordingly in their level of plasticity (Stamps 2016). We finally examined the relationship between glacier use and chick-rearing success, predicting that individuals that spend more time at glacier fronts or exhibit stronger plasticity (increased glacier use with increasing glacial discharge) would raise more chicks. While feeding at glaciers is known to be important to Kittiwakes and other seabirds breeding in

Svalbard, its adaptive benefits, particularly in terms of reproductive success, is yet to be studied.

# Material and methods

## Study system

Kittiwakes were tracked at four colonies within a glacial fjord in the Arctic, Kongsfjorden, on the Svalbard archipelago, between 2015 and 2021 (Ossian Sarsfjellet 78°55'N, 12°26'E [OSS]; Observasjonsholmen 78°56'N, 12°16'E [OBS]; Krykkjefjellet 78°54'N, 12°11'E [KRY]; Blomstrandhalvøya 79°00'N, 12°07'E [BLM]; Fig. 1). Breeding colony sizes range from approximately 150 (OBS) – 2000 breeding pairs (OSS) (Bertrand et al. 2021a). Six glacial fronts are present within Kongsfjorden and are all used by Kittiwakes and other marine predators (Hamilton et al. 2019, Bertrand et al. 2021a). The fjord has two basins with the inner basin starting at Lovénøyane and characterised by shallower water (< 100 m) and the presence of two glacial bays on either side of Ossian Sarsfjellet (Fig. 1; Hop et al. 2023). Glaciers form

<figure><img src="image_6.png" /><figcaption>Figure 1. Kongsfjorden, Svalbard, with the location of the four black-legged Kittiwake colonies (circles, BLM: Blomstrandhalvøya (pink), KRY: Krykkjefjellet (blue), OBS: Observasjonsholmen (yellow), OSS: Ossian Sarsfjellet (green)), six monitored glacial fronts (blue polygons) and zooplankton sampling stations (black squares). Three zooplankton sampling stations are in the outer fjord (Kb1, Kb2, Kb3) and three in the inner fjord east of Lovénøyane (Kb5, Kb6, Kb7). Inset map depicts Svalbard with the location of Kongsfjorden (red box).</figcaption></figure>
prominent but variable parts of Kittiwake foraging habitats with individuals visiting glaciers on 33–86% of trips between years (Bertrand et al. 2021a) and provide important refuges for zooplankton prey (Hop et al. 2023) and fish species like polar cod *Boreogadus saida* (Bouchard et al. 2023). Polar cod make up the main dietary component of Kittiwakes within the fjord alongside a variety of zooplankton species and increasing numbers of Atlantic species like capelin *Mallotus villosus*, and herring *Clupea harengus* (Vihtakari et al. 2018). When not feeding at glacier fronts, individuals switch between feeding within the open fjord (maximum distance ~25 km) to pelagic trips to the continental shelf break around 100 km from Kongsfjorden (Bertrand et al. 2025, Supporting information).

## GPS tracking

GPS devices were used to track 186 individuals between 2015–2021, with individuals being tracked for 1–3 deployments within the study period (total deployments = 238, mean = 1.3). We tracked 84 females and 102 males, with 92 and 146 deployments during the incubating and chick-rearing phases of breeding, respectively (full summary of tracking can be found in the Supporting information). Five logger types were used (gt-120 i-GOTu [Mobile action], AxyTrek Marine [Technosmart], GEO+RF[Pathtrack], Catlog Gen1 + 2 [mr-lee]) with masses ranging from 3.28–18.6 g (0.9–4.9% of mean Kittiwake body mass). GPS mass did not significantly influence the likelihood of visiting glacier fronts nor impact how long individuals spent at glaciers (Supporting information). GPS devices were deployed for a mean of 75 h (range: 23–263 h). Sampling resolution ranged from 1 to 10-min location fixes depending on year and breeding stage. To ensure there were no erroneous fixes, a maximum speed filter threshold was set so that all consecutive points where individuals moved faster than 80 km h⁻¹ were removed (Christensen-Dalsgaard et al. 2018, Harris et al. 2020, Bertrand et al. 2021b). On average 8% of programmed fixes per trip could not find a GPS signal and were therefore missed. The R package ‘adehabitatLT’ (Calenge 2006) was used to linearly interpolate these missing values (Harris et al. 2020, Bertrand et al. 2021a). A further 15% of fixes were interpolated so that all tracks were standardised to 10 min (Bertrand et al. 2021b). A buffer of 200 m was set to identify when individuals were at the colony. Only trips outside of this area for ≥ 50 min were included as foraging trips based on a bootstrapping method highlighted in Bertrand et al. (2021b). To avoid retaining trips from the colony that were not for foraging (i.e. freshwater bathing and nest material collection), 135 trips that were > 66% over land were removed (9% of trips). The 66% figure was chosen based on the frequency distribution of all trips that went further than 200 m from the colony (Bertrand et al. 2021b). This resulted in a total of 1257 foraging trips over the six-year period.

## Classification of glacial use

To quantify glacier use, we defined the front area as a 600 m radius from the yearly delimited glacial fronts based on a

sensitivity analysis that compared glacier use across increasing radii sizes (Supporting information). The glacial front lines were determined for the six glaciers within Kongsfjorden by manually digitising a single satellite image from between 15 August–15 September each year (Moholdt et al. 2021). Although the front lines are highly dynamic within- and between-years, we assume that an image during this period is representative of the glacial frontline in that given year (Moholdt et al. 2021). Full methodology for this process can be found in Andreassen et al. (2021). In brief, the calving fronts were identified, drawn and vectorised based on imagery derived from Sentinel-2 satellites (Andreassen et al. 2021).

## Quantifying the use of glacier fronts

As measures of glacier front use, we quantified whether and for how long an individual visited a glacier per foraging trip. For any trip, an individual was categorised as using a glacial front if it was within the 600 m buffer for two consecutive GPS fixes, i.e. for at least 10 min (Bertrand et al. 2021b). We categorised consecutive fixes as glacier visits because this was more likely to represent foraging than commuting through the glacier front. Time spent at glaciers across a foraging trip was calculated by multiplying the total number of GPS fixes within the 600 m front buffer by the standardised interval of 10 min.

## Modelling of glacial discharge

To quantify the variability in upwelling conditions at glacier fronts, daily measures of discharge rates were modelled. Glacial discharge is the exchange of water from land to the sea through glacial channels and is calculated using a coupled energy balance–subsurface model (van Pelt et al. 2019). In brief, glacial mass balance was modelled for the whole of Svalbard on a 1-km grid using a combination of digital elevation models and meteorological forcings with calibration from in situ measurements. Mean surface discharge represents runoff of melt and rainwater that did not refreeze or became stored in snow/firn. Simulated discharge in each 1-km grid cell was then interpolated onto a 250-m grid and routed to glacier fronts along flow paths determined by the MATLAB package ‘TopoToolbox’ (Schwanghart and Kuhn 2010), which accounts for pressure gradients in subglacial drainage systems. Modelled surface or snow discharge reaches its respective glacial front instantly, while in reality delays would be anywhere between hours and one to two days. Using sliding-window analysis (Bailey and van de Pol 2016) we did not find a significant difference in glacial use when lagging or averaging glacial discharge over the previous two days and, therefore, daily discharge rates were used in the analysis. Full details of the sliding window analysis can be found in the Supporting information.

The discharge rates for the whole fjord were calculated by averaging discharge from the six major glacial outlets within the fjord – Blomstrandbreen, Conwaybreen, Kongsbreen North, Kongsbreen, South Kronebreen and Kongsvegen (Fig. 1b). Discharge rates for Kongsbreen North and South were combined as the discharge comes from the same source.
Discharge rates between the five outlets differed in magnitude (Supporting information) but were all significantly correlated (Pearson correlation coefficient range: 0.884–0.998; Supporting information) and all measures were averaged to give daily discharge rates for the whole fjord. Whilst there is some preference in the fjord for Kittiwakes to use the Kronebreen/Kongsvegen front, all glaciers are used to a varying extent (Supporting information).

## Zooplankton biomass

Zooplankton biomass was used as an index of yearly variation in food availability to assess its impact on glacier front use by Kittiwakes. Meso- and macrozooplankton abundance have been sampled annually within Kongsfjorden since 1996 (Hop et al. 2019). During the study period (2015–2021), zooplankton were sampled annually in summer (July–August) from a research vessel at six stations within the fjord (three inner, three outer) at depths ranging 0–300 m (except in 2021, when the inner stations Kb6 and Kb7 were not sampled). The inner stations (Kb5, Kb6, Kb7) should be most representative of prey availability at glacier fronts (Hop et al. 2023). However, to make up for inconsistent sampling at inner stations in 2021, our measure of prey availability used zooplankton biomass averaged for all sampling stations in the fjord, both inner and outer basins. Using only inner stations in the models did not change any of the inferences we make (Supporting information).

Zooplankton were collected by vertical tows of plankton nets (200 µm mesh) along the entire water column, divided into 3–6 depth strata. Nets were either the multiple plankton sampler (MultiNet type Midi, 0.25 m² opening, HydroBios Kiel) or a closing WP2 net (0.255 m² opening, HydroBios, Kiel). Following capture, samples were preserved in a 4% hexamethylenetetramine-buffered formalin solution in seawater. All zooplankton (meso- and macrozooplankton) were identified and counted under a stereomicroscope equipped with ocular micrometre according to standard procedure (Postel 2000) to the lowest possible taxonomic level. Mesozooplankton (size < 5 mm, including copepods and juvenile stages of pteropods, euphausiids, ostracods, amphipods and chaetognaths) were identified and counted in a subsamples until at least 500 individuals had been identified per sample. Macrozooplankton (size > 5 mm, including adult stages of the same taxonomic groups as well as appendicularians) were sorted and identified from the whole sample. Although macrozooplankton, those that Kittiwakes feed on, are underrepresented in the MultiNet and WP2 due to the small mesh sizes, the same mesh size was used throughout the timeseries making it possible to compare between years.

Zooplankton biomass estimates for all samples were calculated using dry mass conversion factors gathered from both published sources and those measured by Hop et al. (2019). We restricted our calculations to species known to be consumed by Kittiwakes within Kongsfjorden based on dietary samples analysed in Vihtakari et al. (2018) (full list can be found in the Supporting information). Depth-averaged biomass for every species was then calculated for each station

and averaged across the fjord to give a measure of annual zooplankton biomass availability for the fjord (Supporting information). Although zooplankton is an important component of the diet of Kittiwakes within the fjord, with some species such as krill *Thysanoessa inermis* present in 22% of diet samples, the predominant prey in Kittiwake's diet are fishes during most of the season (Vihtakari et al. 2018). Whilst data exists on the diet of fish species within the Arctic (Dalpadado et al. 2016, Cusa et al. 2019), there is relatively little research on the spatial and temporal variation in fish abundance within Svalbard fjords (Mueter et al. 2021). Growth and survival of polar cod are positively correlated with zooplankton biomass, based on results from the Canadian Arctic (LeBlanc et al. 2020). As polar cod is the predominant prey of Kittiwake within Kongsfjorden (Vihtakari et al. 2018), the availability of fish for Kittiwakes is thus likely higher in years of high zooplankton abundance. We utilised samples from across the water column as both zooplankton and fish species known to prey on zooplankton exhibit regular vertical migrations and so utilising all samples allowed us to present total potential food availability to Kittiwakes without it being influenced by sampling time (Darnis et al. 2017, Gorska et al. 2023).

The data suggested that there are annual peaks in zooplankton biomass (dry mass), with four years of our study having a dry weight less than 13.46 mg m⁻³ and three years greater than 84.89 mg m⁻³ (Supporting information). Hence later in the text we refer to high and low zooplankton years being above 84.49 mg m⁻³ and below 13.46 mg m⁻³, respectively.

## Reproductive output

To assess the potential adaptive benefit of plasticity in foraging behaviour, reproductive outcome was measured for a subset of 80 individuals (30% tracked in incubation and 70% in chick rearing) between 2017 and 2021. Three individuals were tracked in multiple years for which they had reproductive outcomes giving a total of 83 breeding events across the four years. Breeding success was monitored at Krykkjefjellet, Ossian Sarsfjellet and Observasjonsholmen in 2017, at Krykkjefjellet and Observasjonsholmen in 2018 and only at Krykkjefjellet in 2020 and 2021. Nest contents was recorded every 3–5 days. While in some years nests were followed to near fledgling, due to differences in the length of nest recording between years, we standardised reproductive output as the number of chicks raised to a minimum of 15 days (Moe et al. 2009, Mercier et al. 2021, Descamps et al. 2022). The majority of Kittiwake chick mortality tends to occur in the first 10 days (Barrett and Runde 1980, Coulson and Porter 1985) so a 15-day proxy is likely to be a good measurement of the number of chicks that fledge from a nest.

Of 83 breeding attempts, only two nests failed to raise any chicks to 15 days and three had more than two chicks. Subsequently most of our variation in reproductive output was between nests raising one or more than one chick. We subsequently removed the two failed breeding attempts and grouped individuals with three-chick broods with two-chick broods. While not a true measure of reproductive success, as all individuals raised chicks, the energetic requirements of
raising more than one chick likely benefits from increased resource acquisition through plasticity in foraging behaviour. Our measure of reproductive output was thus defined as one or more than one chick being raised.

## Statistical analysis

All statistical analysis were done in R ver. 4.2.2 (www.r-project.org). Regression models were conducted under Bayesian frameworks and fitted in the 'brms' package (Bürkner 2017). Bayesian techniques facilitate greater model flexibility and more reliable modelling of uncertainty at small sample sizes (McElreath 2016). All models are labelled as Mx Responses variable with x being the order that model is present in the manuscript.

## Population-level responses to glacial discharge

To investigate the drivers of population-level glacial use by kitiwakes, we modelled how daily variation in glacial discharge and prey availability influenced both 1) the decision to visit a glacier (binary yes/no) during a foraging trip (M1_Use_of_glacier_YN) and 2) time spent at glacial fronts during a foraging trip (M2_Time_at_glaciers). Breeding stage was used to account for the changes in spatial and temporal constraints on foraging trips across the breeding season by the presence of chicks later in the season (Harris et al. 2020). Sex- and colony-specific variation in foraging behaviour and habitat selection occurs regularly in seabirds (Patrick and Weimerskirch 2014, Cleasby et al. 2015, Bertrand et al. 2021a) therefore

sex and colony identity were included in both models as fixed effects. We checked for spatial autocorrelation within our data set using Moran's I test all of which showed near 0 values and p-values of > 0.05. Trip duration was also included in initial models, but showed high multicollinearity (ViF >3), likely due to shorter trips occurring later in the season during chick-rearing, and so was subsequently removed from the models (Zuur et al. 2010). All continuous variables (i.e. discharge and zooplankton) were scaled by mean centring and dividing by two SDs. Dividing by two rather than one SD allows numeric variables to be directly compared to untransformed binary variables (Gelman 2008). Individual ID was included as a random effect in both models to account for repeated measures per individual. Full model structure for both models can be found in Table 1. We explored the possibility of temporal autocorrelation in glacier use across the study period due to increases in average discharge but found this did not influence our findings (Supporting information).

M1_Use_of_glacier_YN and M2_Time_at_glaciers assumed Bernoulli and Gamma distributions, respectively, as they represented multiple binary outcomes and non-zero, right-skewed time data, respectively. Generalized linear mixed effects models were fitted using a Bayesian framework with 10 000 iterations across four chains, with a warmup period of 2000 iterations, and thinned every fourth iteration. M1_Use_of_glacier_YN used a logit link and weakly informative priors (normal[0,5]) for the fixed effects and a weakly informative half-Cauchy prior (half-Cauchy[1,1]) for the

<table><caption>Table 1. Breakdown of models fitted in the analysis. All explanatory variables are labelled as either fixed or random effects. Model families are included in italic brackets underneath the response variable.</caption><thead><tr><th>Response</th><th></th><th>Explanatory variables</th></tr></thead><tbody><tr><td>Use of glacier (Y/N)<br>(Bernoulli)<br>(M1_Use_of_glacier_YN)</td><td>Fixed</td><td>Daily glacial discharge<br>Zooplankton biomass<br>Glacial discharge × Zooplankton biomass<br>Sex<br>Colony<br>Breeding stage</td></tr><tr><td>Time spent at glacier (mins)<br>(Gamma)<br>(M2_Time_at_glaciers)</td><td>Random intercept<br>Fixed</td><td>Individual ID<br>Daily glacial discharge<br>Zooplankton biomass<br>Glacial discharge × Zooplankton biomass<br>Sex<br>Colony<br>Breeding stage</td></tr><tr><td>Time spent at glacier (mins)<br>(Gamma)<br>(M3_Time_at_glacier_plasticity)</td><td>Random intercept<br>Fixed</td><td>Individual ID<br>Daily glacial discharge<br>Zooplankton biomass<br>Glacial discharge × Zooplankton biomass<br>Sex<br>Colony<br>Breeding stage</td></tr><tr><td>Reproductive success (number of chicks at 15 days)<br>(Bernoulli)<br>(M4 Number_of_chicks)</td><td>Random slope<br>Random intercept<br>Fixed</td><td>Glacial discharge | Individual year ID<br>Individual ID<br>Time spent at glacier<br>Plasticity estimate<br>Zooplankton biomass<br>Time spent at glacier front × Zooplankton biomass<br>Plasticity estimate × Zooplankton biomass<br>Colony</td></tr></tbody></table>
random effect of individual ID. M2_Time_at_glaciers used a log-link and weakly informative normal priors for the fixed effects (Normal[0,1]), an informative prior for the intercept (Normal[3.5,1]) and a weakly informative half-Cauchy prior (half-Cauchy[1,1]) for the random effect of individual ID. The chosen priors improved model convergence and the posterior estimates did not differ significantly from when alternate priors were used (Supporting information).

## Individual variation in plasticity

Variation in foraging behaviour between trips in response to changes in environmental conditions at glacial fronts was quantified using a random regression mixed effects model, named M3_Time_at_glacier_plasticity. Only individuals that experienced varying levels of glacial discharge when foraging at glacial fronts were included in this analysis (n=101 versus n=143 in M2_Time_at_glaciers). The same fixed-effect structure as in M2_Time_at_glaciers was used. However, to account for individual variability in plasticity and the different relationships with glacial discharge between years, daily glacial discharge was incorporated into a year-specific individual ID random effect. This represents a unique ID that an individual has for each year that it was tracked. To account for repeated measures per individual within and between years, individual ID was also included as a final random effect. The full model structure is summed up in Table 1.

M3_Time_at_glaciers_plasticity assumed a Gamma distribution as the time spent at glaciers in a foraging trip represented non-zero right-skewed time data where the variance increased with the mean (Nakagawa et al. 2017). The model was fitted for 10 000 iterations across four chains, with a warmup period of 2000 iterations and thinned every fourth iteration. As in M2_Time_at_glaciers, we used normally-distributed priors for the fixed effect and intercept, normal[0,1] and normal[3.5,1] respectively. For the random effect structure, we used half-Cauchy priors, half-Cauchy[0.5,0.1] for individual ID and year-specific individual ID and half-Cauchy[0,1] for the glacial discharge coefficient of the year-specific individual ID. Less informative priors were tested and although they did not influence the posterior estimates, they led to poor model convergence (Supporting information).

## Glacier use and reproductive output

We fitted a linear model to quantify whether glacier use, both mean and plasticity, had an influence on individual reproductive output. This model was fitted 1000 times with each model including a different estimate sampled from the posterior distribution of mean time spent at glaciers and individual plasticity extracted from M3_Time_at_glacier_plasticity. Plasticity estimates represent the magnitude and direction of the slope for each individual between time at glaciers and discharge and are extracted from the individual ID random effect of slope (Hertel et al. 2020). Repeating this model 1000 times using the posterior distribution helps minimise potential bias of single point estimates (commonly termed best linear unbiased predictors or BLUPs, Houslay and

Wilson 2017, Hadfield et al. 2010), reducing the likelihood of non-conservative estimates of statistical relationships. The distribution dataset produced from M3_Time_at_glacier_plasticity was subset to only include individuals tracked in a year of data for which they also had reproductive outcome. This gave a dataset of glacier use estimates for individuals concurrent with 80 breeding attempts.

Random samples from the posterior distribution were incorporated as a predictor into M4Number_of_chicks with birds' reproductive output (either 1 or 2+ chicks raised to 15 days) as the response variable. Zooplankton biomass and colony were included as additional fixed effects. As we found an interaction between glacial discharge, zooplankton abundance and use of glaciers (Table 2, Fig. 2a–b), we also included an interaction for both glacier use and plasticity with zooplankton abundance to quantify if a particular strategy was beneficial in a given year. The model assumed a Bernoulli distribution, one chick versus two or more chicks. Models were fit using 10 000 iterations across four chains after a warmup of 2000 iterations and thinned every second iteration. A normally-distributed prior was used for the fixed effects with mean of 0 and SD of 2. A normally-distributed prior was also used for the Intercept with a mean and SD of 1.

## Model assessment

To assess convergence for all models, we first ensured that all parameters in the model exceeded a minimum effective sample size of 1000 for the bulk and tail of the model (Bürkner 2017). We then visually observed trace plots to ensure the model mixed suitably and explored all possible posterior values. To test convergence between and within chains we used Gelman–Rubin (Rhat <0.01) and Geweke (−2 < Z-score < 2) diagnostic tools respectively (Geweke 1991, Gelman and Rubin 1992). We define fixed effects as statistically significant if the equal tailed 89% credible intervals did not overlap 0. We used 89% credible intervals as suggested in McElreath (2016) as they produce more reliable estimates than other size-credible intervals, particularly when effective sample sizes are small (< 10 000; Kruschke 2014).

## Results

### Population-level responses to daily glacial discharge

Individuals visited the glacier fronts during 700 of the 1257 foraging trips and time spent at glaciers during a foraging trip lasted for a mean of 161 min (range 20–1380 min). 81% of tracked individuals visited glaciers at least once. The effect of daily glacial discharge on both use of glaciers and time spent at glaciers varied as a function of the yearly zooplankton biomass with a significant estimate for the interaction (M1_Use_of_Glaciers_YN: -1.35 [89% CI -2.17, -0.53]; M2_Time_at_glaciers: -0.89 [-1.21, -0.56]) (Table 2, Fig. 2). In years of low zooplankton biomass there appeared to be no relationship between the likelihood of visiting a glacial front and glacial discharge. However, a strong negative
<table><caption>Table 2. Model outputs from the four models conducted in the analysis and outlined in Table 1. Estimate refers to the mean of the posterior distribution. Female is the comparison factor for sex, Blomstrand is the comparison for colony and chick rearing is the comparison for breeding stage. Estimates for M1 are on the logit scale and M2/M3 are on the log scale. Significant fixed effects are highlighted in bold and determined by when the 89% credible intervals do not overlap 0. The shape parameter of the models using a gamma distribution is equal to the inverse of the dispersion. Glacial discharge and zooplankton biomass were both mean centred in all models so that any main effect where there is an interaction represents the effect of either variable when the other is at its mean.</caption><thead><tr><th>Model</th><th>Parameter</th><th>Estimate</th><th>5.5% CI</th><th>94.5% CI</th></tr></thead><tbody><tr><td rowspan="10">M1_Use_of_glacier_YN</td><td>Intercept</td><td><strong>2.03</strong></td><td><strong>0.80</strong></td><td><strong>3.34</strong></td></tr><tr><td>Glacial discharge</td><td>0.09</td><td>-0.43</td><td>0.62</td></tr><tr><td><strong>Zooplankton biomass</strong></td><td><strong>-1.39</strong></td><td><strong>-2.09</strong></td><td><strong>-0.70</strong></td></tr><tr><td><strong>Colony [Krykkjefjellet]</strong></td><td><strong>-1.65</strong></td><td><strong>-2.95</strong></td><td><strong>-0.38</strong></td></tr><tr><td><strong>Colony [Observasjonsholmen]</strong></td><td><strong>-2.23</strong></td><td><strong>-3.56</strong></td><td><strong>-0.93</strong></td></tr><tr><td>Colony [Ossian Sars]</td><td>-0.55</td><td>-1.93</td><td>0.77</td></tr><tr><td>Male</td><td>-0.11</td><td>-0.71</td><td>0.50</td></tr><tr><td><strong>Breeding stage [Incubation]</strong></td><td><strong>-0.68</strong></td><td><strong>-1.33</strong></td><td><strong>-0.02</strong></td></tr><tr><td><strong>Glacial discharge × Zooplankton biomass</strong></td><td><strong>-1.38</strong></td><td><strong>-2.43</strong></td><td><strong>-0.38</strong></td></tr><tr><td>Individual ID</td><td>1.60</td><td>1.30</td><td>1.95</td></tr><tr><td rowspan="10">M2_Time_at_glaciers</td><td>Intercept</td><td><strong>4.84</strong></td><td><strong>4.43</strong></td><td><strong>5.25</strong></td></tr><tr><td>Glacial discharge</td><td>0.03</td><td>-0.14</td><td>0.20</td></tr><tr><td><strong>Zooplankton biomass</strong></td><td><strong>0.52</strong></td><td><strong>0.23</strong></td><td><strong>0.81</strong></td></tr><tr><td>Colony [Krykkjefjellet]</td><td>-0.16</td><td>-0.58</td><td>0.27</td></tr><tr><td>Colony [Observasjonsholmen]</td><td>-0.09</td><td>-0.53</td><td>0.34</td></tr><tr><td>Colony [Ossian Sars]</td><td>0.27</td><td>-0.15</td><td>0.70</td></tr><tr><td>Male</td><td>-0.08</td><td>-0.29</td><td>-0.13</td></tr><tr><td><strong>Breeding stage [Incubation]</strong></td><td><strong>0.71</strong></td><td><strong>0.48</strong></td><td><strong>0.95</strong></td></tr><tr><td><strong>Glacial discharge × Zooplankton biomass</strong></td><td><strong>-0.57</strong></td><td><strong>-0.97</strong></td><td><strong>-0.15</strong></td></tr><tr><td>Individual ID</td><td>0.52</td><td>0.41</td><td>0.65</td></tr><tr><td>Shape</td><td>2.53</td><td>2.25</td><td>2.82</td></tr><tr><td rowspan="10">M3_Time_at_glacier_plasticity</td><td>Intercept</td><td><strong>4.88</strong></td><td><strong>4.49</strong></td><td><strong>5.29</strong></td></tr><tr><td>Glacial discharge</td><td>0.11</td><td>-0.14</td><td>0.37</td></tr><tr><td>Zooplankton biomass</td><td>0.18</td><td>-0.17</td><td>0.52</td></tr><tr><td>Colony [Krykkjefjellet]</td><td>-0.21</td><td>-0.64</td><td>0.21</td></tr><tr><td>Colony [Observasjonsholmen]</td><td>-0.09</td><td>-0.54</td><td>0.34</td></tr><tr><td>Colony [Ossian Sars]</td><td>0.22</td><td>-0.22</td><td>0.64</td></tr><tr><td>Male</td><td>-0.01</td><td>-0.22</td><td>0.20</td></tr><tr><td><strong>Breeding stage [Incubation]</strong></td><td><strong>0.69</strong></td><td><strong>0.41</strong></td><td><strong>0.98</strong></td></tr><tr><td><strong>Glacial discharge × Zooplankton biomass</strong></td><td><strong>-0.61</strong></td><td><strong>-1.18</strong></td><td><strong>-0.03</strong></td></tr><tr><td>Individual ID</td><td>0.32</td><td>0.05</td><td>0.50</td></tr><tr><td>Individual year ID</td><td>0.25</td><td>0.02</td><td>0.48</td></tr><tr><td>Glacial discharge | Individual year ID</td><td>0.82</td><td>0.54</td><td>1.11</td></tr><tr><td>Shape</td><td>2.85</td><td>2.51</td><td>3.21</td></tr><tr><td rowspan="10">M4 Number_of_chicks</td><td>Intercept</td><td>-0.15</td><td>-4.95</td><td>4.56</td></tr><tr><td>Time spent at glacier front</td><td>-0.11</td><td>-1.02</td><td>0.83</td></tr><tr><td>Plasticity</td><td>0.10</td><td>-0.48</td><td>0.68</td></tr><tr><td>Zooplankton biomass</td><td>0.10</td><td>-0.48</td><td>0.68</td></tr><tr><td><strong>Krykkjefjellet</strong></td><td><strong>1.38</strong></td><td><strong>1.23</strong></td><td><strong>1.58</strong></td></tr><tr><td><strong>Observasjonsholmen</strong></td><td><strong>-0.90</strong></td><td><strong>-1.08</strong></td><td><strong>-0.70</strong></td></tr><tr><td><strong>Ossian Sars</strong></td><td><strong>-0.96</strong></td><td><strong>-1.14</strong></td><td><strong>-0.74</strong></td></tr><tr><td>Time spent at glacier front × Zooplankton biomass</td><td>0.35</td><td>0.43</td><td>-0.47</td></tr><tr><td>Plasticity × Zooplankton biomass</td><td>0.65</td><td>-0.74</td><td>2.03</td></tr></tbody></table>

relationship was seen between visiting a glacier front and discharge in years of high zooplankton biomass (Fig. 2a). Similar relationships were seen between time spent at glaciers: in years with low zooplankton biomass in the fjord, individuals did not vary their time spent at glaciers with increased glacial discharge, but there was a strong negative relationship when zooplankton biomass was greater (Fig. 2b). Whilst considering the interaction with glacial discharge, kittwakes were less likely to use glacier fronts in years of high zooplankton biomass when glacial discharge was at mean levels (-1.40

[-1.94, -0.88]) but likely to spend more time at glaciers on each visit (0.75 [0.53, 0.98]).

The likelihood of individuals visiting a glacier front while foraging differed significantly between colonies with birds from Krykkjefjellet (-1.40 [-2.47, -0.36]) and Observasjonsholmen (-2.04 [-3.13, -0.98]) visiting glaciers less often than those from Blomstrand (Table 2). Birds were also more likely to visit glacier fronts later in the season during chick rearing (estimate of the incubation breeding stage effect: -0.68 [-1.33, -0.02]) (Table 2).
<figure><img src="image_7.png" /></figure>

Figure 2. Predicted probability of using glaciers (A) and time spent at glacial fronts (B) based on the interaction between daily glacial discharge and zooplankton biomass. Thick and thin lines represent the mean and 100 estimates of the relationships between the variables as predicted by the respective models. For simplicity, high (yellow) and low (green) zooplankton biomass have been grouped as greater or less than the inter-annual mean zooplankton biomass of 84.89 and 13.46 mg m⁻³, respectively (Low: 2015, 2018, 2021; High: 2016, 2017, 2020). Relationships for specific zooplankton biomasses are shown in the Supporting information. Points in (A) represent whether an individual visited a glacier during a foraging trip (1) or not (0). Points and lines in (B) represent the average and SEs of time spent at glaciers across foraging trips that have been categorised in bins of 4 m³ s⁻¹ for visualisation purpose only.

## Individual variation in plasticity of glacial foraging effort

Individuals showed moderate repeatability in their time spent at glacial fronts within a given season ($R_{adj}$: 0.45[0.36, 0.54], Supporting information). They differed in the magnitude and direction to which they varied their time spent at glaciers in relation to daily glacial discharge (Table 2, Fig. 3).

## Glacier use and reproductive outcomes

Neither time spent at glacier fronts (−0.11 [−1.02, 0.83]) nor individual plasticity (0.10 [−0.48, 0.68]) in glacier front use influenced the number of chicks raised to 15 days and this relationship did not vary with zooplankton biomass (Table 2).

## Discussion

Understanding individual variation in behavioural plasticity is likely to be a key component for predicting how populations will respond to climate change (Buchholz et al. 2019). This is likely to be particularly important for variation in foraging behaviour due to wide-spread changes in the availability and distribution of prey resources (Brierley and Kingsford 2009, Evans and Moustakas 2018). Here we showed that black-legged Kittiwakes were highly plastic in their use of glaciers in response to glacial discharge but that these responses were dependent on yearly variation in food availability. Although individuals exhibited large variation in their use of glacier fronts for foraging, this was not correlated with reproductive success. Among successful breeders, those varying their use of glacier fronts in response to the environment and, therefore,

potentially maximising resource acquisition did not raise significantly more chicks to 15 days.

Understanding not only when, but how, individuals utilise key resource areas may shed a light on the availability of resources within ecosystems (Chimienti et al. 2017). Although we show individuals were on average less likely to visit glaciers in years of high zooplankton biomass, individuals had longer foraging bouts at fronts during those years (Fig. 2). Spending longer at glacier fronts may represent more intense foraging effort via intense competition in these areas (Lydersen et al. 2014) as well as increasing the mortality risk of birds close to the calving edge (Stott 1936). This suggests that although they are less likely to be visited, glacier fronts are still valuable foraging areas to Kittiwakes, even under high levels of discharge (Fig. 2b). Increasing discharge in high zooplankton biomass years, therefore may alleviate competition at glacier fronts through the advection of prey across the whole fjord and an improved accessibility of resources to individuals not foraging at glacier fronts (Bertrand et al. 2021b).

It is feasible that high levels of discharge may prove costly for individuals, increasing water turbidity and subsequently limiting the ability of visual feeding seabirds to forage successfully (Darby et al. 2022). However, turbidity level has been shown to be positively correlated with the density of foraging Kittiwakes within Arctic fjords (Nishizawa et al. 2020). We therefore suggest that individuals switch foraging sites away from glaciers during these high biomass years, not because there is an increasing cost to feeding at glaciers, but through the presence of foraging opportunities away from fronts that may be amplified through the dispersal of resources at high levels of discharge. When not feeding at glacier fronts, there was no
<figure><img src="image_8.png" /><figcaption>Figure 3. Individual time spent at glaciers in response to glacial discharge extracted from M3_Time_at_glacier_plasticity. Each individual in a given year is represented as an individual line, with predicted slopes for each individual being created using the 'posterior_linpred' function in R from the 'rstantools' package. Shaded areas represent 89% credible intervals. Yellow and green colours indicate high and low zooplankton abundances, respectively, grouped as greater or less than the inter-annual mean zooplankton biomass of 84.89 mg m⁻³ and 13.46 mg m⁻³ respectively (Low: 2015, 2018, 2021; High: 2016, 2017, 2020). Density plots on the outside of the graph represent the distributions of the raw data.</figcaption></figure>

specific targeting of other microhabitats suggesting Kittiwakes may be utilising ephemeral resource patches (Supporting information). Accessibility and subsequent competition for resources within Arctic fjord ecosystems will therefore be sensitive to changes in both glacial dynamics and prey abundance.

Kittiwakes within Kongsfjorden utilize glacier fronts regularly for foraging, especially during years with low food concentrations (Fig. 2A). Glacier fronts likely provide the most consistent foraging patches in the fjord due to the entrainment of food via the glacial pump even at low discharge levels (Everett et al. 2018). Limited foraging opportunities may therefore constrain the expression of plasticity as the energetic costs of changing behaviour and searching for more sparsely distributed resources increases (Chevin et al. 2010, Vinton et al. 2022). As individuals tend to spend less time during each visit to a glacier front compared to high resource years (Fig. 2B), visiting glacial fronts may be a strategy to make the best of a bad situation as even though food may not be abundant, it may be consistent. In comparison during high zooplankton biomass, Kittiwakes likely benefit from abundant food throughout the fjord with minimal costs for flight and search. As resource availability changes in marine ecosystems, this may either promote or constrain the exhibition of plasticity especially for species that rely on historically rich foraging hotspots.

Predicting shifts in foraging patches due to climate change is key to understanding how individuals will continue to acquire resources (Stillman 2008, Brambilla et al. 2018). In Kongsfjorden, we predict that increases in glacial discharge (Supporting information) and zooplankton biomass

(Hop et al. 2019) will decrease the reliance on glacial fronts as foraging sites for Kittiwakes and other surface-feeding seabirds such as Arctic terns *Sterna paradisaea* (Fig. 2a). This effect might intensify as tidewater glacier fronts retreat onto land, stopping the upwelling of zooplankton at tidewater glacier fronts and reducing the attractiveness of glacier fronts as ‘climate refugia for Arctic marine predators (Torsvik et al. 2019, Hop et al. 2023). Furthermore, changes in water circulation could alter primary production, with wide-ranging impacts not only for Kittiwakes but with food-web effects cascading to other marine predators (Halbach et al. 2019, Torsvik et al. 2019, Meire et al. 2023). So, although reductions in glacial use by Kittiwakes may reduce the reliance on them as foraging hotspots, and therefore reduce the potential impact of their loss, greater understanding of other changes within fjord ecosystems is needed.

Our finding of a significant interactive effect between glacial discharge and yearly variation in zooplankton biomass on the use of glaciers (Fig. 2) adds another layer of complexity to how we assess relationships of plasticity in wild-living species. We are regularly interested in how individuals respond to a specific measurable component of the environment as we have good predictions of how this will change through time (Urban et al. 2024), in our case glacial discharge. However, these results may only be relevant once accounting for the wider ecological context (Bonamour et al. 2019), e.g. the food availability. For instance, advancing phenology with increasing temperatures is one of the most studied examples of phenotypic plasticity (Charmantier et al. 2008, Romano et al. 2023) and varies among individuals
(Brommer et al. 2008). However, the direction and strength of this plasticity can be highly dependent on other biotic or abiotic factors (Bonamour et al. 2019, Cuchot et al. 2024). Fitting plastic responses in behaviour within wider contexts may therefore help understand when plasticity is both likely to emerge and provide beneficial adaptations to species.

Within a breeding season, individuals are likely to alter foraging strategies as the constraints they face change between self-maintenance and provision to chicks (Harris et al. 2020). Here we show that individuals may capitalise on productive resource patches when they are constrained to feeding chicks with a higher probability of using glacier fronts during chick-rearing compared to incubation (Table 2). Similar patterns can be found in other seabird species, for instance razorbills *Alca torda* prioritise areas of high primary productivity during chick rearing to improve foraging efficiency (Shoji et al. 2016). Reliance on glaciers as profitable foraging areas may therefore be particularly pronounced when individuals have to feed chicks and changes in resource availability during these periods may have a significant impact on the ability to efficiently provide chicks with food (Bertrand et al. 2025).

Although many habitats experience significant fine-scale temporal variation in resource availability, individual plasticity in movement is rarely measured on such short time scales (Patrick et al. 2014). We showed that Kittiwakes change their foraging sites in response to daily variations in glacial discharge and consistent with other species, individuals differed in the magnitude of their response (Nakayama et al. 2016, Chambault et al. 2020, Jablonszky et al. 2022). As the relative accessibility of prey is highly important to top predators (Real et al. 2022, Riaz et al. 2023) and is primarily governed by fine-scale spatial or temporal features (Muška et al. 2018), it is important to account for these dynamic environmental features to understand how individuals may differ in how they gather resources (Gillies et al. 2024). While we could not directly link foraging plasticity with resource consumption, understanding resource gain may help us understand both the potential benefits of plasticity and how individuals may be able to capitalise on the highly stochastic nature of resource accessibility at glacier fronts.

One issue in the quantification of individual plasticity in movement behaviour surrounds the conflict with habitat matching. Individuals can either change their behaviour in response to their environment (plasticity) or can select their habitat based on the present environmental conditions, requiring no change in behaviour (habitat matching) (Edelaar et al. 2017). While both represent potentially adaptive behavioural strategies, for an individual that habitat matches, we would not observe them to be plastic as they would exhibit limited change in time spent at glaciers and discharge because they only choose to visit glaciers under select conditions. Evidence for habitat matching in wild systems is rare (Edelaar et al. 2017) and we did explore this possibility but found no relationship between the mean or range of glacial conditions that an individual experienced and their level of plasticity, and the consequences and caveats of this are discussed in the Supporting information. Given the general

reliance on glaciers within and between years, it is unlikely that habitat matching will influence how Kittiwakes forage within Arctic fjords.

Despite pronounced differences in glacial use along gradients of both discharge and resource availability, we detected no effect of glacier use on the number of chicks raised. Individual plasticity may not provide significant benefits for chick survival when food is generally abundant as has been the case in Kongsfjorden (Gluchowska et al. 2016, Wiencke and Hop 2016). As almost all individuals in our study successfully raised at least one chick and our measurement of reproduction was restricted to the survival of one or more chick, it might be that plasticity is more beneficial early in the season during egg laying before we recorded nests when individuals can be more flexible (Shoji et al. 2016). Whilst plasticity may benefit other traits important for Kittiwake fitness not measured here, such as chick growth rates or physiological condition (Young et al. 2017), variation in reproductive performance among individuals is generally lower when the environmental quality is high (Griffen and Norelli 2015). Current conditions may allow both plastic and non-plastic phenotypes to persist and the potential benefits of plasticity may only begin to manifest as the Kongsfjorden ecosystem changes over the next few decades and greater resources can be gained through behavioural flexibility.

Over the next 50 years we expect to see rapid shifts in the ecological conditions experienced by all animals occupying the high Arctic with particularly significant changes to the availability of key foraging areas such as glaciers. Here we show that at an individual level, Kittiwakes alter their use of glaciers in response to variability in glacial discharge and that the extent and direction of this behavioural response varies among individuals. Although this might suggest that individuals can respond to future changes to the ecosystem, the relative benefit of behavioural responses to glacial discharge will be highly dependent on ongoing changes in resource availability highlighting the complex nature of using plasticity to predict population viability into the future.

**Acknowledgements** – We would like to thank a large number of fieldworkers over the years without which this work would not be possible particularly Maite Cerezo Araujo, Saga Svavarsdóttir, Delphin Ruche and Joanna Sulich. Also, to the Norwegian Polar Institute logistics department and specifically Erlend Lorentzen for providing logistical support. We would like to thank Jack Thorley, Fionnuala McCully, Eve Merrall, Linnet Jessell, Jonathon Green and the rest of the SEGUL research group for statistical, writing, graphing help whenever called upon during the preparation and presentation of this manuscript. We would like to thank Dr Ian Smith for support and maintenance of the High Throughput Computing service HTCondor at the University of Liverpool: http://condor.liv.ac.uk/.

**Funding** – FM was funded by a NERC grant (NE/S00713X/1) through the ACCE doctoral training partnership. The Norwegian Research Council provided funding for fieldwork to F.M and S.D, project number 333121. PA was funded by Centre for Ice, Cryosphere, Carbon and Climate (iC3) supported by the Research Council of Norway through its Centres of Excellence
funding scheme, project number 332635. SJ was funded by an
NSF ORCC no. 2222057 grant. NG was funded by the BBSRC
Doctoral Career Development Fund (DCDF) administered by the
Oxford Interdisciplinary Biosciences Doctoral Training Partnership
(grant number: BB/M011224/1). PB was funded by the Fonds
de recherche due Quebec-Nature et technologies (FRQNT) and
Natural Sciences and Engineering Research Council of Canada
(NSERC). OC was financially and logistically supported by the
IPEV no. 330 Ornitho-Endocrino programme and all work and
Krykkjefjellet is part of the long-term Studies in Ecology and
Evolution (SEE-Life) program of the CNRS. Fieldwork in the other
colonies was funded by programme SEAPOP (www.seapop.no).

*Permits* – The study was performed under permit nos. 6439, 8602, 15503 and 29584 from the Norwegian Food Safety Authority. Annual permits were also acquired by the Sysselmesteren for research programs 361 and 11820.

Author contributions

**Frederick C. Mckendrick:** Conceptualization (lead); Data curation (supporting); Formal analysis (lead); Funding acquisition (supporting); Investigation (lead); Methodology (supporting); Project administration (supporting); Visualization (lead); Writing – original draft (lead); Writing – review and editing (lead). **Sébastien Descamps:** Conceptualization (equal); Data curation (equal); Funding acquisition (equal); Investigation (equal); Methodology (equal); Project administration (equal); Supervision (equal); Writing – review and editing (equal). **Kathryn E. Arnold:** Conceptualization (supporting); Investigation (supporting); Supervision (equal); Writing – review and editing (equal). **Stéphanie Jenouvrier:** Conceptualization (supporting); Investigation (supporting); Supervision (equal); Writing – review and editing (equal).

**Stephanie M. Harris:** Conceptualization (supporting); Data curation (supporting); Funding acquisition (supporting); Investigation (supporting); Supervision (supporting); Writing – review and editing (equal). **Philip Bertrand:** Data curation (supporting); Funding acquisition (supporting); Investigation (supporting); Writing – review and editing (equal). **Jack Kohler:** Data curation (supporting); Formal analysis (supporting); Funding acquisition (supporting); Methodology (supporting); Writing – review and editing (equal). **Ward van Pelt:** Data curation (supporting); Formal analysis (supporting); Methodology (supporting); Writing – review and editing (equal).

**Jack Kohler:** Data curation (supporting); Formal analysis (supporting); Funding acquisition (supporting); Methodology (supporting); Writing – review and editing (equal). **Ward van Pelt:** Data curation (supporting); Formal analysis (supporting); Methodology (supporting); Writing – review and editing (equal). **Don-Jean Léandri-Breton:** Data curation (supporting); Investigation (supporting); Methodology (supporting); Writing – review and editing (equal). **Pierre Blévin:** Data curation (supporting); Investigation (supporting); Methodology (supporting); Writing – review and editing (equal).

**William Jouanneau:** Data curation (supporting); Investigation (supporting); Methodology (supporting); Writing – review and editing (equal). **Don-Jean Léandri-Breton:** Data curation (supporting); Investigation (supporting); Methodology (supporting); Writing – review and editing (equal). **Pierre Blévin:** Data curation (supporting); Investigation (supporting); Methodology (supporting); Writing – review and editing (equal). **Natasha E. Gillies:** Conceptualization (supporting); Data curation (supporting); Investigation (supporting); Supervision (supporting); Writing – review and editing (equal). **Haakon Hop:** Data curation (supporting); Funding acquisition (equal); Investigation (supporting); Writing – review and editing (supporting).

acquisition (equal); Investigation (supporting); Methodology
(supporting); Writing – review and editing (supporting).

**Philipp Assmy:** Data curation (supporting); Funding acquisition (supporting); Investigation (supporting); Methodology (supporting); Writing – review and editing (equal).

**Allison Bailey:** Data curation (supporting); Investigation (supporting); Methodology (supporting); Writing – review and editing (equal). **Anette Wold:** Data curation (supporting); Investigation (supporting); Methodology (supporting); Writing – review and editing (equal).

**Hallvard Strøm:** Data curation (equal); Funding acquisition (equal); Investigation (equal); Methodology (equal); Writing – review and editing (equal). **Samantha C. Patrick:** Conceptualization (equal); Data curation (equal); Formal analysis (supporting); Funding acquisition (supporting); Investigation (supporting); Methodology (supporting); Project administration (equal); Supervision (lead); Writing – original draft (supporting); Writing – review and editing (equal).

Data availability statement

Data are available from the Dryad Digital Repository: https://doi.org/10.5061/dryad.0rxwdbsc2 (Mckendrick et al. 2025).

Supporting information

The Supporting information associated with this article is
available with the online version.

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