DOI: 10.5061/dryad.ksn02v7fk
title: Winter survival of a small predator is determined by the amount of food in hoards

description: The hoarding behaviour of animals has evolved to reduce starvation risk
 when food resources are scarce, but food limitation on survival of
 hoarding animals is poorly understood. Eurasian pygmy owls (Glaucidium
 passerinum) hoard small mammals and birds in natural cavities and
 nest-boxes in late autumn for later use in the following winter. We
 studied the relative influence of the food biomass in hoards of pygmy owls
 on their over-winter and over-summer apparent survival. We also tested
 whether this influence is modulated by intrinsic (age, sex) traits or
 extrinsic factors (winter temperature, snow depth). We measured biomass of
 prey items in pygmy owl food-hoards during autumns 2003 – 2023 in
 west-central Finland. We individually marked and recaptured pygmy owls
 both at nests in the breeding season and at food-hoards. Our dataset
 included a total of 407 pygmy owls which were all captured from a
 food-hoard at least once during their capture history. The mean biomass of
 the annual food-hoards associated with one individual was 443 g (sd = 523
 g, range from 3.5 to 4505 g) and was markedly higher in autumns of vole
 abundance than in those of vole scarcity. Hoard size had a positive effect
 on apparent survival of owls over consecutive winter, whereas it did not
 affect apparent survival over next summer. Hoard size was a better
 predictor of apparent survival than vole abundance (main food of pygmy
 owls) in the field. Male owls had higher overall apparent survival rates
 than female owls, particularly when food-hoards were small. That hoard
 size was a better predictor of apparent survival than vole abundance
 indicates that the hoards are critical for pygmy owls during winter,
 likely because they are unable to hunt voles below deep snow cover. The
 positive relationship between apparent survival of owl individuals and
 their hoard size during winter (when the hoard is being consumed), but not
 summer, indicates that the hoard size has a true positive effect on
 survival, and does not only reflect latent inter-individual differences
 and/or dissimilarities in their environments. We conclude that food
 limitation during hoarding essentially regulates apparent over-winter
 survival of pygmy owl individuals.
description: # Winter survival of a small predator is determined by the amount of food
 in hoards
 [https://doi.org/10.5061/dryad.ksn02v7fk](https://doi.org/10.5061/dryad.ksn02v7fk) ## Description of the data and file structure This dataset contains data used in the following publication: Korpimaki, E., Piironen, A. & Laaksonen, T. 2025: Winter survival of a small predator is determined by the amount of food in hoards. Journal of Animal Ecology. The data is a capture-mark-recapture data collected by catching pygmy owls from the nest boxes during spring and fall. Also, the biomass of prey items in the food hoards was recorded as well as relative vole density in the field in every spring and fall. See a detailed description of the methods from the manuscript. ##### Author information for the data: Name: Antti Piironen Institution: University of Saskatchewan, University of Turku Email: [antti.p.piironen@utu.fi](mailto:antti.p.piironen@utu.fi), [antti.p.piironen@gmail.com](mailto:antti.p.piironen@gmail.com) Name: Erkki Korpimaki Institution: University of Turku Email: [ekorpi@utu.fi](mailto:ekorpi@utu.fi) ### Files and variables ##### Date of data collection: Years 2003-2022 ##### Geographic location of data collection: Finland ##### SHARING/ACCESS INFORMATION 1\. Links to publications that cite or use the data: Korpimaki, E., Piironen, A. & Laaksonen, T. 2025: Winter survival of a small predator is determined by the amount of food in hoards. Journal of Animal Ecology. 2\. Links to other publicly accessible locations of the data: None 4\. Links/relationships to ancillary data sets: None 5\. Was data derived from another source: No ##### FILE OVERVIEW 1\. File List: capture-histories.Rdata storage-sizes.Rdata vole-index.Rdata Kauhava lentokentta_1.11.2003 - 1.4.2023_lumensyvyys.csv Kauhava lentokentta_1.11.2003 - 1.4.2023_paivan_keskilampotila.csv 2\. Relationship between files, if important: No 3\. Additional related data collected that was not included in the current data package: None ###### DATA-SPECIFIC INFORMATION FOR: capture-histories.Rdata 1\. Number of variables: 44 2\. Number of cases rows: 555 3\. Variable List: \* ring: individual identifier \* sex: sex of the individual \* age: age of the individual (at banding) \* columns from '2003' to '2023': individual encounter histories in spring (1 = recaptured/banded, 0 = not recaptured). The first '1' in each row represents banding. \* columns from '2003-04' to '2022-23': individual encounter histories in fall (1 = recaptured/banded, 0 = not recaptured). The first '1' in each row represents banding. ###### DATA-SPECIFIC INFORMATION FOR: storage-sizes.Rdata 1\. Number of variables: 44 2\. Number of cases rows: 555 3\. Variable List: \* ring: individual identifier \* sex: sex of the individual \* age: age of the individual (at banding) \* columns from '2003' to '2023': Redundant (all NA). \* columns from '2003-04' to '2022-23': the total biomass of prey items in hoards associated with the individual (in grams) in the fall. 4\. Missing data codes: In this file, missing values (=empty cells) represents cases where an individual was not associated to any hoard in a given sampling occasion. As the file is in .Rdata-format, R will automatically correctly read the missing values. ###### DATA-SPECIFIC INFORMATION FOR: vole-index.Rdata 1\. Number of variables: 41 2\. Number of cases rows: 1 3\. Variable List: \* vole-idx: Z-standardized value of relative vole abundance in spring and fall in the 2003-2023. The first value represents the spring 2003 and the last value the spring 2023. ###### DATA-SPECIFIC INFORMATION FOR: Kauhava lentokentta\_1.11.2003 - 1.4.2023\_lumensyvyys.csv 1\. Number of variables: 6 2\. Number of cases/rows: 6996 3\. Variable List: \* weather station: The name of the weather station \* year: Year \* month: Month \* day: Day of month \* time: Local time (HH:MM) \* snow depth [cm]: Snow depth (in centimeters) 4\. Missing data codes: Empty cell. Empty cells only occur in the column "snow depth [cm]" and represent missing snow depth measurements. ###### DATA-SPECIFIC INFORMATION FOR: Kauhava lentokentta\_1.11.2003 - 1.4.2023\_paivan\_keskilampotila.csv 1\. Number of variables: 6 2\. Number of cases/rows: 7091 3\. Variable List: \* weather station: The name of the weather station \* year: Year \* month: Month \* day: Day of month \* time: Local time (HH:MM) \* temperature [C]: Daily mean air temperature (in Celsius degrees) 4\. Missing data codes: Empty cell. Empty cells only occur in the column "temperature [C]" and represent missing temperature measurements. ## Code/software To be able to run the analysis, R software version 4.3.3 or newer is needed. All needed R packages are mentioned in the attached R script (glapas-code.R).