Data from: Den attendance by Arctic foxes experiencing 10 years of increasing tourism
Data files
Apr 08, 2025 version files 63.86 KB
Abstract
Wildlife tourism is a growing industry, and an increasing number of people seek to observe and interact with wild animals in their natural surroundings. In Iceland, the native Arctic fox (Vulpes lagopus) is widespread and has been under heavy hunting pressure for centuries. However, in Hornstrandir nature reserve (66° 44'N, 22° 42'W), foxes have been protected from hunting since 1994. These foxes are relatively tame and have become popular amongst tourists who want to observe and take close-up photos of young cubs. Breeding foxes, therefore, face increasing human disturbance within their active den area. In this study, parental visits to fox dens and tourist activities in the area were monitored in 2008 and then annually for a ten-year period (2010-2019). The data demonstrated an increase in tourism near fox dens, and a decrease in the frequency with which fox parents visit the den. The results further suggest that foxes visited the den less often when tourists were present (reducing from 0.23 visits per hour by females in the peak time of day without tourists, to 0.18 visits per hour with tourists present). This study gives support to our hypothesis that tourist activity disturbs the frequency of parental visits to their offspring, but at current levels of tourism, this may not be affecting the frequency of parental feeding.
Dataset DOI: 10.5061/dryad.ht76hdrt7
Description of the data and file structure
There are two CSV files. Both can be loaded into R
foxData <- read.csv(“den_attendance_by_arctic_foxes_experiencing_increasing_tourism_foxData.csv”,
stringsAsFactors=TRUE)
foxDataMF <- read.csv(“den_attendance_by_arctic_foxes_experiencing_increasing_tourism_foxDataMF.csv”,
stringsAsFactors=TRUE)
- foxData has one row per observation of a fox den for a 4 hour duration (or shorter).
Variables in foxData
- year : numeric, year
- yearSince2008 : numeric, year rescaled so 2008 becomes year 0
- denID : character, identifier for den
- observationDay : numeric, day on which observation took place (1-5 within the observed period)
- timePeriodOfDay : character, time of day (period1 is 10:00 to 14:00, 2 is 14:00 to 18:00, 3 is 18:00 to 22:00)
- observationDurationMins : numeric, duration of observation (up to 240 minutes)
- logObservationTime : numeric, log of above
- foggy : logical, TRUE if observation affected by fog
- countVisitsMale : numeric, count of visits by male parent fox
- countFeedingMale : numeric, count of visits by male parent fox with food
- countVisitsFemale : numeric, as above female
- countFeedingFemale : numeric, as above female
- countNursing : numeric, count of visits by female parent fox to nurse cubs
- countTourists : numeric, count of unique individual tourists in the area near den
- touristDurationMins : numeric, duration of time within the period at least one tourist was present
- countTouristArrivalEvents : numeric, count of events at which one or more tourist arrived in area (also add 1 if tourists present at start of observation period)
- foxDataMF is one row for each parent, per observation of a fox den (twice as many rows as foxData)
Most variables as above.
Variables in foxDataMF
- year
- yearSince2008
- denID
- observationDay
- timePeriodOfDay
- observationDurationMins
- logObservationTime
- foggy
- sex : character, sex of the parent that this row refers to
- countVisits : numeric, count of visits by this parent fox
- countFeeding : numeric, count of visits by this parent fox with food
- countTourists
- touristDurationMins
- countTouristArrivalEvents
Code/software
Supplemental data and full R codes with packages are in the Supplemental information file which is accessible in open access with the paper
Data is on CSV format, readable into R and Excel or other spreadsheet formats
Libraries in R
library(dplyr) # data preparation
library(glmmTMB) # model fitting with glmmTMB()
library(DHARMa) # tests of over-dispersal and zero-inflation
library(car) # Anova()
library(emmeans) # to compare parameter confidence intervals
library(ggplot2) # figures