DOI: 10.5061/dryad.1ns1rn92h
title: Data from: Human avoidance, selection for darkness and prey activity explain wolf diel activity in a highly cultivated landscape

description: Wildlife that share habitats with humans with limited options for spatial
 avoidance must either tolerate frequent human encounters or concentrate
 their activity on those periods with the least risk of encountering
 people. Based on 5,259 camera trap images of adult wolves from eight
 territories, we analyzed the extent to which diel activity patterns in a
 highly cultivated landscape with extensive public access (Denmark) could
 be explained by diel variation in darkness, human activity, and prey
 (deer) activity. A resource selection function that contrasted every
 camera observation (use) with 24 alternative hourly observations from the
 same day (availability), revealed that diel activity correlated with all
 three factors simultaneously with human activity having the strongest
 effect (negative), followed by darkness (positive) and deer activity
 (positive). A model incorporating these three effects had lower parsimony
 and classified use and availability observations just as well as a
 ‘circadian’ model that smoothed the use-availability ratio as a function
 of time of the day. Most of the selection for darkness was explained by
 variation in human activity, supporting the notion that nocturnality
 (proportion of observations registered at night vs. day at the equinox) is
 a proxy for temporal human avoidance. Contrary to our expectations, wolves
 were no more nocturnal in territories with unrestricted public access than
 in territories where public access was restricted to roads, possibly
 because wolves in all territories had few possibilities to walk more than
 a few hundred meters without crossing roads. Overall, Danish wolf packs
 were 6.5 (95% CI: 4.6-9.6) times more active at night than at daylight,
 which makes them amongst the most nocturnally active wolves reported so
 far. These results confirm the prediction that wolves in habitats with
 limited options for spatial human avoidance, invest more in temporal
 avoidance.
description: <strong>Population monitoring and data
 collection</strong> Since 2017, the Natural
 History Museum Aarhus and Aarhus University have monitored all wolves in
 Denmark for the Danish Environmental Protection Agency. The occurrence and
 turnover of individuals are registered from genetic markers obtained from
 scat, hair, saliva, or urine samples collected by systematic patrolling of
 forest roads and by snow tracking (active monitoring) as well as saliva
 samples from livestock kills obtained by the Danish Nature
 Agency. A territory was defined as the area patrolled
 by a single wolf, pair, or pack for a minimum of six months. The core
 areas and approximate territory extensions were estimated from the
 distribution of wolf signs (scats, tracks, kills, photos, etc.) within the
 landscape. With permission from the landowners, we placed wildlife cameras
 in places known (from the appearance of footprints, scats, or other signs)
 or suspected (leading lines in the landscape which from experience are
 known to be used by wolves when commuting, e.g. forest roads) to be used
 by the wolves within the territories. At locations with public access,
 visitors were informed about the presence of the cameras through signs
 containing project information and our contact details. We used cameras
 with fast trigger times able to record fast-moving species, that recorded
 videos and/or multiple pictures. Cameras were usually visited every two to
 six weeks, checking battery levels and changing memory cards. Where
 possible, the wolves on the images were identified to age defined as pup
 (born same calendar year) or adult (not a pup, hence all grown-up wolves
 observed January-June were coded as adults) and coded in the database. If
 multiple wolves on the same photo or video sequence were identified as
 different ages or individuals, they were registered as different records
 in the database. Prior to the analyses, such doublets or triplets were
 removed, so only one unique camera observation entered the analysis as an
 observation unit. As the cameras were placed to
 maximize the number of wolf observations, sampling effort was concentrated
 in the central parts of the territories where wolf sign concentrations
 were highest. Cameras aimed at recording wolves were usually placed along
 trails and forest roads used by wolves when traversing their territories
 and at places with a high density of scats and footprints, that indicated
 frequent use by wolves at a given time. In a subset of the territories we
 also had cameras placed in the terrain, optimized to register all large
 and medium-sized mammal species. For wolf population monitoring purposes,
 observations from both types of surveys were entered into the Danish
 National Database of Wolf Observations. The effort expended in terms of
 camera days was not registered in this database. Observations of general
 wildlife were logged in a separate database, which also included
 information on the effort expended in terms of the number of camera days
 (26,210 in total). Due to resource constraints, this database only
 contained a subset of the total number of camera observations available in
 the raw data. The wolf data used for this analysis were therefore drawn
 from the first database. The number of different camera locations,
 resulting in wolf observations varied from 26 to 198 per territory
 (median: 51) and the total area covered (100% minimum convex polygon) by
 cameras delivering wolf data for the analysis, ranging from 6.5 to 79.3
 (median: 21.3) km<sup>2</sup> per territory.
 <strong>Selection of observations for analyses
 </strong> We selected wolf camera trap data
 separated by a minimum of 5 minutes from eight independent territories
 from six areas (one territorial area was occupied by three different
 constellations of individuals during different time periods. This
 selection resulted in 5,259 camera observations of adults, 1,814
 observations only showing pups (representing five litters from four
 territories), and 158 observations where the age could not be determined
 (excluded from the analyses). Of the 5,259 observations of adult wolves,
 3,280 (62%) originated from cameras for monitoring wolves, 1,257 (24%)
 from cameras for monitoring general wildlife, and 722 (14%) from cameras
 where the initial purpose had not been recorded. As any dependence between
 observations within territories was accounted for in the statistical
 analyses by stating territory as random effect (see below), we decided to
 use the full data set rather than a reduced data set based on observations
 separated by 30 minutes, as often recommended to avoid serial dependence
 of observations. Under all circumstances, increasing the minimum sampling
 interval from 5 to 30 minutes only reduced the data set by 250
 observations, and did not change the outcome of any of the statistical
 analyses. Digitized data on wildlife and human activity
 was available from three of the six territory areas (five of eight
 territories. As ungulates, especially cervids (Cervidae, ‘deer’ hereafter)
 constitute the main and most selected prey type of wolves in Central
 Europe, we used 11,315 camera observations of deer (species composition:
 red deer <em>Cervus elaphus</em>: 60%, roe deer
 <em>Capreolus capreolus</em>: 31%, fallow deer <em>Dama
 dama</em>: 4%, unidentified deer species: 5%) to represent diel
 activity of prey. The 15,017 observations of humans were divided between
 48% pedestrians, 16% bicyclists, 31% motorized vehicles, and 5% horse
 riders. <strong>Seasonal
 definition</strong> To account for seasonal
 variation, we divided the year into quartiles: November-January (mean day
 length at 56°N: 8.47 hours; range: 7.92-9.68 hours), February-April
 (11.97; 9.22-14.77), May-July (16.01; 14.83-16.55) and August-October
 (12.57; 9.73-15.32). These not only contrasted the two three-month periods
 with the shortest and longest daylengths, but also provided a good
 division of the ecological and reproductive annual cycle for wolf packs
 that have young offspring in May-July, mobile offspring (frequenting
 rendezvous sites) in August-October, increasingly independent offspring
 through November-January, and a pre-parturition period from
 February-April, when last year’s offspring have attained full
 independence. <strong>Statistical analysis of
 diel activity patterns of wolves, deer, and
 humans</strong> For each three-month period, we
 quantified the general variation in the diel activity of juvenile and
 adult wolves (W hereafter), deer (D), and humans (H), by modelling the
 relative frequency of observations per one-hour-interval from midnight to
 midnight (0: 00:00-00:59, 1: 01:00-01:59, etc.). We used the R package
 ‘mgcv’ v. 1.8 to fit generalized additive models (GAM) with beta
 distribution and logit-link, an adaptive cyclical cubic smoothing spline
 for time, and territory ID as random effect. Models were visually
 validated by plotting standardized model residuals against fitted
 values. As data for human and deer activity was not
 available for all areas, extrapolation was necessary. As the
 season-specific diel activity curves for humans were highly correlated
 between the different study areas (Supporting information), we produced
 one season-specific diel activity function based on all data pooled. Among
 the deer, diel activity correlated less between fenced and unfenced areas.
 As we know from GPS-data that red deer in fenced areas move shorter hourly
 distances around dusk and dawn than red deer in unfenced nature areas (P.
 Sunde and R.M. Mortensen, unpublished), we created one activity
 distribution for deer based on data from all three areas (“Deer-Total”,
 abbreviated to “DT”), and one differentiated between fenced and unfenced
 areas (“Deer-Local”, abbreviated to “DL”). To quantify
 the extent to which diel activity levels of adult wolves, deer, and humans
 were associated with light conditions and correlated internally throughout
 the year, we created a correlation matrix comprised of 24 * 365 = 8,760
 hourly time observations, covering the entire year (1 January-31
 December). For each hourly time observation, we assigned light conditions
 (categorical variable: night [sun angle &lt; 0°, coded as 1] vs. day
 [sun angle ³ 0, coded as 0], “ND”) and the relative diel activity of W, H,
 DT, and DL (separated between fenced and unfenced areas) as predicted for
 each of the four seasons by the GAM-models.
 <strong>Statistical analysis of predictors of timescape
 selection </strong> We analysed the extent to
 which wolves selected to be active as a function of ND, H, and DT/DL by
 means of a Resource Selection Function (RSF) based on use-availability.
 For each wolf observation (used) we created 24 pseudo-observations,
 representing every hour for the same date and geographical location of the
 wildlife camera image. We used mixed-effect binary
 logistic regression (response variable: used vs. unused) with territory ID
 as random intercept. As models with camera type as random effect resulted
 in similar results as models without camera type, we did not include
 camera type as random effect. We evaluated the extent to which a fixed
 effect (ND, DT or DL, H) predicted variation in diel activity from the
 magnitude of its selection coefficient in the logistic regression equation
 and considered it important if deviating significantly
 (<em>p</em> &lt; 0.05) from 0. To establish the raw
 effects, we first ran models containing each of the three types of fixed
 effects in isolation (four different models in total as deer activity was
 represented by two alternative variables). To establish the extent to
 which the magnitude of a given fixed effect remained (or was reduced) when
 controlling for one or both two other competing fixed effects, we
 furthermore ran models representing all possible combinations of main
 effects of ND, H, and DT/DL. If a fixed effect remained statistically
 significant when controlling for the other two fixed effects, we took it
 as an indication that it contributed genuinely to explaining the variation
 in diel activity. We used the R package ‘mgcv’ v. 1.8 to fit generalized
 mixed models (GLM) with binomial distribution, logit-link, and territory
 ID as random effects. Models were visually validated by plotting
 standardized model residuals against fitted values. To
 explore the predictive power of H, ND, and DT/DL (in isolation as well as
 in combination), we calculated Somers’ D, which is {Institute, 2013 #568}a
 nonparametric index of a model’s ability to correctly classify the
 dependent variable, derived as D = 2 (AUC – 0.5) where AUC is the area
 under the model’s receiver operation curve. If D = 1, all observations are
 correctly classified by the model, whereas D = 0 indicates a
 non-informative model. To compare the predictive effects of the three
 fixed-effects variables with a model based on mere smoothing of the basic
 circadian pattern, we contrasted the D-values of the models based on
 different combinations of the three fixed effects with those of a
 mixed-effects logistic GAM model where activity was smoothed as a function
 of time of day. The GAM model was fit with an adaptive cyclical cubic
 smoothing spline for time, and territory ID as a random effect. If Somers’
 D for a fixed effects model approached the D-value of the GAM model, we
 took that as an indication that the fixed effects model was able to
 capture most of the diel activity variation. For all models considered, we
 calculated AICc. We considered a difference in AICc-values (DAICc)
 &gt; 7 as substantial evidence for the model with the lowest
 AICc-value to have the most support in data.
 <strong>Analysis of variation in
 nocturnality</strong> Using nocturnality as a
 proxy for temporal human avoidance, we modelled the conditional
 probability that a wolf image was registered at night (sun angle &lt;
 0°; p<sub>n</sub>) as opposed to at day (sun angle ³ 0°) as a
 logistic regression function, where we included the logit-transformed
 night length (logit [NL], the logit-transformed proportion of the day the
 sun is below the horizon) as a nuisance variable to adjust for variable
 night length. From the logistic regression equation, we derived
 conditional nocturnality (N) as the difference on a logit-scale between
 the predicted probability that an observation under given circumstances
 would be nocturnal, relative to what should be expected from the length of
 the night (<em>N</em> = logit [p<sub>n</sub>] –
 logit [NL]). From simple algebra, it follows that the activity selection
 ratio between night and day (SR<sub>N:D</sub>: how many more
 times wolves were observed per time unit at night as opposed to daytime)
 can be derived as R<sub>N:D</sub> = exp
 [<em>N</em>].  At the equinox (where NL = 0.5, hence logit
 [NL] = 0), this expression could be simplified to
 <em>N<sub>(NL = 0.5)</sub></em> = logit
 [p<sub>n</sub>] = ln (p<sub>n </sub>/
 [1-p<sub>n</sub>]) Û R<sub>N:D</sub> =
 p<sub>n </sub>/ (1- p<sub>n</sub>). As an example,
 if a sample of study subjects was observed equally often at day and night,
 standardized at the equinox, p<sub>n[NL=0.5] </sub>= 0.5 and
 SR<sub>N:D</sub> = 0.5 / (1-0.5) = 1. In comparison,
 situations where 80% and 90% of the observations at the equinox were
 nocturnal corresponded to wolves being 4 (0.8/0.2) and 9 (0.9/0.1) times
 more active during darkness than in daylight, respectively.
 The analysis was run as a mixed effect logistic regression
 function with territory ID as random intercept, using the GLIMMIX
 procedure in SAS 9.4 with a logit link function, binomial error
 distribution, using Satterthwaite’s approximation to estimate degrees of
 freedom. As fixed effects, we included logit [NL], social status (single
 wolf, pair, or pack), human access (restricted or free), and camera
 purpose (wolf monitoring, general wildlife monitoring, not registered). As
 models with camera type as random effect resulted in similar results as
 models without camera type, we did not include camera type as random
 effect.
description: # Data from: Human avoidance, selection for darkness and prey activity
 explain wolf diel activity in a highly cultivated landscape ###
 "Hour\_count.csv" A data set containing hourly observations from
 camera traps. The file contains the following variables: * Hour = hour of
 the day (0-23) * Species = Observation category (Deer, Human, Wolf_adult,
 Wolf_pup) * Season = Season of the year, quaternary (1:Feb-Apr, 2:May-Jul,
 3:Aug-Oct, 4:Nov-Jan) * Location = Name of the sampling region * Count =
 number of observations within the given hour ### "rsf\_5min.csv"
 A data set containing wolf observations filtered for 5-minute independent
 observations together with corresponding 24 hourly pseudo-observations.
 The file contains the following variables: * MuseumsID = Observation id *
 Dato = date of observation in the format DD-MM-YYYY * SurveyFixPoint =
 name of camera location * CamType = camera model * SampleAge = If
 observable, the age group of the observed wolf. "Voksen" =
 adult, "Hvalp" = pup, "Ukendt" = unknown age. *
 Individual = If observable, the individual ID of the observed wolf *
 Territory = name of territory * HH = Hour of the day * minute = minute of
 the day * MM = Month of the year * julday = Julian day * season = Season
 category: 1:Feb-Apr, 2:May-Jul, 3:Aug-Oct, 4:Nov-Jan * year = year of
 observation * U_A = Used/available (Observation/pseudo-observation) *
 daylength = relative day length (hours of daylight/24) of the day of
 observation * fit_deer1 = estimated hourly deer activity * fit_human1 =
 estimated hourly human activity * sunangle = angle of the sun of
 observation * fit_deer_local = estimated hourly local deer activity
 (fenced or unfenced locations) * socialstatus = social status of the
 observed wolf * DN = Day/Night category, Day = Day, Nig = Night *
 logit_daylength = logit hours of daylight * logit_nightlength = logit
 hours of darkness * Season = Season category: 1:Feb-Apr, 2:May-Jul,
 3:Aug-Oct, 4:Nov-Jan * fit_deer2 = standardised and centered fit_deer1 *
 fit_deer_local2 = standardised and centered fit_deer_local * fit_human2 =
 standardised and centered fit_human1 ### "rsf\_30min.csv" A data
 set containing wolf observations filtered for 30-minute independent
 observations together with corresponding 24 hourly pseudo-observations.
 The file contains the same variables as the "rsf_5min.csv"
 described above. ### "Clean\_wolf\_activity\_rsf.R" R-scripts
 for analyses of diel activity variation. ### "Allwolves5min.csv"
 A data set (CSV file) containing wolf observations filtered for 5-minute
 independent observations used for analyses of nocturnality * MuseumsID =
 Observation id * SurveyFixPoint = name of camera location * CamType =
 camera model * SampleAge = If observable, the age group of the observed
 wolf (only adult wolves ("Voksen") are used in the analysis of
 nocturnality) * Individual = If observable, the individual ID of the
 observed wolf * Territory = name of territory * Date = date of observation
 in the format DD-MM-YYYY * HH = Hour of the day of observation * minute =
 minute of the day of observation * MM = Month of the year of observation *
 julday = Julian day of observation * season = Season category: 1:Feb-Apr,
 2:May-Jul, 3:Aug-Oct, 4:Nov-Jan * year = year of observation * sunangle =
 angle of the sun of observation * daylength = relative day length (hours
 of daylight/24) of the day of observation * socialstatus = social status
 of the observed wolf (territorial, pair, or single) * logit_nightlength =
 logit transformed relative night length (= ln(NL/[1-NL], where NL is the
 number of hours where the sun is below the horizon/24) of the day of
 observation * DN = Day/Night category (D: day; N: night) * night =
 Night/day category coded as a numerical variable (1: night, 0: day) *
 access = Public access (Restricted or free) * purpose = purpose of camera
 location (wolf population monitoring, general wildlife observations, not
 registered or mixed purpose) ### "Allwolves5min.sas7bdat" A data
 set (SAS data file) containing wolf observations filtered for 5-minute
 independent observations used for analyses of nocturnality * MuseumsID =
 Observation id * SurveyFixPoint = name of camera location * CamType =
 camera model * SampleAge = If observable, the age group of the observed
 wolf (only adult wolves ("Voksen") are used in the analysis of
 nocturnality) * Individual = If observable, the individual ID of the
 observed wolf * Territory = name of territory * HH = Hour of the day of
 observation * MM = Month of the year of observation * day = day of the
 month of observation * julday = Julian day of observation * season =
 Season category: 1:Feb-Apr, 2:May-Jul, 3:Aug-Oct, 4:Nov-Jan * year = year
 of observation * minute = minute of the day of observation * sun_angle =
 angle of the sun of observation * daylength = relative day length (hours
 of daylight/24) of the day of observation * date = date of observation in
 the format DD-MM-YYYY * socialstatus = social status of the observed wolf
 (territorial, pair, or single) * logit_nightlength = logit transformed
 relative night length (= ln(NL/[1-NL], where NL is the number of hours
 where the sun is below the horizon/24) of the day of observation * DN =
 Day/Night category (D: day; N: night) * night = Night/day category coded
 as a numerical variable (1: night, 0: day) * access = Public access
 (Restricted or free) * purpose = purpose of camera location (wolf
 population monitoring, general wildlife observations, not registered or
 mixed purpose) ### "SAS-code for analysis of nocturnality in adult
 wolves\_.sas" SAS-script used for analyses of nocturnality ###
 "output from SAS script.pdf" PDF with output from analyses of
 nocturnality conducted in SAS