Arctic seabirds show individual variation in foraging responses to glacial conditions without consequences for reproductive output
Data files
Dec 12, 2025 version files 147.81 MB
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Glacial_Discharge_Daily_2006_2022.csv
370.17 KB
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Glacier_visit_dates.csv
78.43 KB
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Glacier_Visits_per_bird_per_trip_GPS_600m.csv
43.27 KB
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ind_plasticity_estimates.csv
146.83 MB
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Kongsfjorden_Foraging_Trips_2014_2022_FinalVersion.csv
301.44 KB
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Kongsfjorden_Glacier_Visits_10mins.csv
87.20 KB
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Kongsfjorden_GPS_Trips_Time_to_reach_glacier.csv
24.54 KB
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Kongsfjorden_Reproductive_outcomes.csv
26.32 KB
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Kongsfjorden_Sex_Database.csv
8.82 KB
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Kongsfjorden_Zooplankton_2014_2021_Station_Averages.csv
8.41 KB
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MS_glacier_plasticity_data_preparation.Rmd
16.40 KB
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MS_glacier_plasticity_models.Rmd
10.57 KB
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README.md
8.42 KB
Abstract
Behavioural plasticity is predicted to be the primary mechanism by which long-lived species 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 glacier in the high Arctic, where profitability varies significantly across space and time. Understanding individual variation in plasticity is crucial to understand populations adaptability 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 individuals spent more time at glaciers with increasing discharge when zooplankton biomass was low in the fjord, the probability of glacial front use 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. With increasing rate of glacier retreat and shifts in food availability, less plastic individuals or those reliant on glacial fronts may face pressure in finding food and may thereby experience long-term declines in reproductive success. Understanding and quantifying the complex nature of individual variation in plasticity will provide more detailed insights into how species will survive in rapidly changing environments.
Dataset DOI: 10.5061/dryad.0rxwdbsc2
Description of the data and file structure
All Datasets provided can be used to recreate the analysis described in the associated article.
Files and variables
Any NA values included within the datasets refer to non available data points. This could be for a variety of reasons such as it not being possible to gather environmental data on a given day or there not being a confirmed sex for an individual.
File: Kongsfjorden_Zooplankton_2014_2021_Station_Averages.csv
Description: This dataset contains information from zooplankton samples, including the location, date, and depth when the sample was created. It contains the total biomass of zooplankton that kittiwakes feed on from those samples.
Variables
- Station: Location of zooplankton sampling station
- Sampling.date: Date of sampling
- Max.depth: Max depth the zooplankton sampling reached in metres
- Min.depth: Minimum depth of zooplankton sampler in metres
- Station.biomass.depth.cat.KI: Biomass of zooplankton fed on by kittiwakes in mg m^−3 ^
- depth.ave.sum.station.zoop: Depth-averaged sum of zooplankton biomass by station in mg m-3
File: Kongsfjorden_Sex_Database.csv
Description: Contains the identified sex of all individuals included in the analysis.
Variables
- BirdID: Individual ID
- Sex: Sex of Individual
File: Kongsfjorden_Reproductive_outcomes.csv
Description: Contains data on breeding success from all of the monitored nests within Kongsfjorden during the study period. It also contains information on environmental conditions during the sampling years.
Variables
- Nest.ID: Nest identifier
- ACRNNID: Colony specific nest identifier
- Breed.year: Year of breeding attempt
- Hatch..Success: Binary hatching success
- Breeding.Success: Binary breeding success
- N.of.Chicks: Number of chicks in nest
- Day.Criteria: Date of final check
- Colony: Which colony
- Comments:
- zoop.annual.mean: DA zooplankton mean in mg m-3
- scaled.zoop: DA zooplankton mean scaled
- zoop.annual.mean.inner: DA zooplankton mean of inner stations in mg m-3
- scaled.zoop.inner: DA zooplankton mean of inner stations scaled
- BirdID: Individual ID
- n.year: Year
- bird.years: how many years of data we have per bird
- BirdID.year: Individual ID and year
- breed.ave.diff: Difference in breeding success to the average
- chick.1.0: Binary, did they produce one or more chicks
File: Kongsfjorden_GPS_Trips_Time_to_reach_glacier.csv
Description: This dataset contains information on how long it took an individual to reach a glacier within a given trip.
Variables
- birdTrip: Trip within each deployment
- time.to.visit.glacier: Time spent at the glacier in minutes
File: Glacier_Visits_per_bird_per_trip_GPS_600m.csv
Description: This dataset contains information on how many different glaciers an individual visited during a trip and which one they visited most.
Variables
- birdTrip: Trip within each deployment
- different.glaciers.600: How many glaciers did they visit
- Predominant.glacier: What glacier did they spend the most time at?
File: Glacier_visit_dates.csv
Description: This dataset gives the date and time that an individual visited a glacier.
Variables
- GlacierVisitID: Glacier visit for an individual in a trip
- datetime: Date and time variable
- g.visit.date: Date they visited the glacier
File: Kongsfjorden_Glacier_Visits_10mins.csv
Description: This dataset contains information on unique visits to a glacier front and how long each visit happened for.
Variables
- ring: Individual Identifier
- birdTrip: Individual trip within deployment
- GlacierVisitID: Unique glacier visit identifier
- TimeAtGlacier: Time spent at the glacier during that visit in minutes
- Name: Name of glacier
- year: Year
File: Kongsfjorden_Foraging_Trips_2014_2022_FinalVersion.csv
Description: This dataset gives summary information for each GPS trip throughout the study period, including basic metrics (i.e., trip length) as well as basic environmental conditions during the trip.
Variables
- Year: Year
- colony: Colony
- BirdID: Individual Identifier
- birdTrip: Trip within deployment
- TripLength: Length of foraging trip in minutes
- maxDist: Maximum distance reached from colony in km
- TripDistance: Maximum distance from colony in km
- stage: Breeding stage
- GlacierProp.trip: Proportion of the trip at the glacier
- GlacierFeed.consec: How many visits to a glacier
- minLong: Minimum longitude during trip
- date: Date
- number.trips: Number of trips during that deployment
- TripType: Whether the trip was in the fjord, glacier, or pelagic
- chlorophyll: Chlorophyll values for fjord on that day in mg m-3
- week.chloro.mean: Weekly average of chlorophyll in mg m-3
- Year.mean: Mean of chlorophyll for that season in mg m-3
- month: Month
- daily.temp.ave: Average temperature in the fjord that day in degrees Celsius
- daily.sal.ave: Average salinity in fjord that day in practical salinity units
- roll.mean.temp.7: Rolling mean of temperature across last 7 days in degrees Celsius
- roll.mean.sal.7: Rolling mean of salinity across the last 7 days in practical salinity units
- Time.at.glacier: Time spent at the glacier during the foraging trip
File: Glacial_Discharge_Daily_2006_2022.csv
Description: This dataset gives information on daily variation in discharge rates within the fjord.
Variables
- : The first column is an index column
- Date: Date
- Fjord: Which fjord within Svalbard
- day: Day
- month: Month
- Year: Year
- julianday: Day of the year
- Ave.Daily.runoff: Average runoff in the fjord on that date in m3 s-1
File: ind_plasticity_estimates.csv
Description: This dataset is the model estimates for individual plasticity in glacial front use. It contains each iteration produced by the model described in the manuscript.
Variables
- chain: Chain of model
- iteration: Iteration within chain
- draw: Predicted draw within chain
- b_Intercept: Model intercept
- b_scaled.Daily.runoff: Value of runoff
- BirdID.year: BirdID by year
- BirdID.year_Intercept: BirdID ID Intercept
- BirdID.year_slope: BirdID ID slope
- BirdID: Individual ID
- TAG: Predicted time spent at the glacier in minutes
- conditional_slope: Plasticity estimate
- meanslope: mean plasticity estimate
- mean.ind.slope: mean plasticity estimate for the individual without effects
- mean.TAG: mean predicted time spent at the glacier in minutes
- ave.GR: Average glacial run off during deployment in m3 s-1
- range.GR: Range of glacial run off experienced in m3 s-1
- colony: Breeding colony of an individual
File: MS_glacier_plasticity_data_preparation.Rmd
- A markdown document that contains the scripts to prepare the inputted data for running the models specified in the Analysis
- This includes code for how the dataframes are merged together, how variables are standardised across datasets, and removes any NA values
File: MS_glacier_plasticity_models.Rmd
- A Markdown document that contains the scripts for running the models specified in the analysis
- Datasets produced by the previous markdown document are loaded in preparation for running the models.
- Priors for the models are set
- Models are run
- Model diagnostics are conducted
Code/software
All analysis was run in R
All packages are listed below:
"ggplot2", "dplyr", "tidyr", "lubridate", "magrittr", "brms", "lme4", "arm", "MuMIn", "plyr", "broom", "coda", "grid", "gridExtra", "broom.mixed", "merTools", "parallel", "tidybayes", "wesanderson", "rptR", "MCMCglmm", "performance", "see", "rstan", "StanHeaders", "zoo", "sjPlot", "bayestestR", "MASS", "reshape2", "reshape", "plotly", "ggmcmc", "mcmcplots", "climwin", "ggpubr", "cowplot", "stringr", "ggExtra"
All models were run in the brms package.
Access information
Other publicly accessible locations of the data:
