DOI: 10.5061/dryad.dfn2z357t
title: Data for: Temporal variations in female moose responses to roads and logging in the absence of wolves

description: Animal movements, needed to acquire food resources, avoid predation risk,
 and find breeding partners, are influenced by annual and circadian cycles.
 Decisions related to movement reflect a quest to maximize benefits while
 limiting costs, especially in heterogeneous landscapes. Predation by
 wolves (Canis lupus) has been identified as the major driver of moose
 (Alces alces) habitat selection patterns, and linear features have been
 shown to increase wolf efficiency to travel, hunt and kill prey. However,
 few studies have described moose behavioral response to roads and logging
 in Canada in the absence of wolves. We thus characterized temporal changes
 (i.e., day phases and biological periods) in eastern moose (Alces alces
 americana) habitat selection and space use patterns near a road network in
 a wolf-free area located south of the St. Lawrence River (eastern Canada).
 We used telemetry data collected on 18 females between 2017 and 2019 to
 build resource selection functions and mixed linear regressions to explain
 variations in habitat selection patterns, home-range size and movement
 rates. Female moose selected forest stands providing forage when movement
 was not impeded by snow cover (i.e., spring/green-up, summer/rearing,
 fall/rut) and stands offering protection against incidental predation
 during calving. In winter, home-range size decreased with an increasing
 proportion of stands providing food and shelter against harsh weather,
 limiting the energetic costs associated with movement. Our results
 reaffirmed the year-round aversive effect of roads, even in the absence of
 wolves, but the magnitude of this avoidance differed between day phases,
 being lower during the “dusk-night-dawn” phase, perhaps due to a lower
 level of human activity on and near roads. Female moose behavior in our
 study area was similar to what was observed in landscapes where moose and
 wolves cohabit, suggesting that the risk associated with humans, perceived
 as another type of predator, and with incidental predators (coyote Canis
 latrans, black bear Ursus americanus), equates that of wolf predation in
 heavily managed landscapes.
description: Telemetry data were collected on 20 moose (2 males, 18 females;
 151,029 GPS locations) between 2017 and 2019. Capture and handling took
 place in February and March 2017, and our protocols were approved by the
 ministère de l’Environnement, de la Lutte contre les Changements
 Climatiques, de la Faune et des Parcs (hereafter MELCCFP; wildlife
 management permit SEG # 2017-02-10-010-01-S-F) and by the Animal Welfare
 Committee of the Université du Québec à Rimouski (hereafter UQAR;
 certificate CPA #68-17-183). We delineated 5 biological
 periods (winter, spring/green-up, calving, summer/rearing, fall/rut) and 2
 day phases (day, dusk-night-dawn) in order to consider the temporal
 variations in moose behavior associated with these factors. We determined
 the cut-off dates of the biological periods by identifying breaks in the
 distribution of mean movement rates in function of Julian days and using
 the available knowledge on moose ecology (Hundertmark, 2007; Leblond et
 al., 2010); we did so for each individual-year combination. We defined day
 phases using the official sunrise and sunset times (National Research
 Council Canada, 2021): the day was bounded by the 60-minute period
 following sunrise to the 60-minute period preceding sunset, and
 dusk-night-dawn was bounded by the 60-minute period preceding sunset to
 the 60-minute period following sunrise.  We defined
 landcover types using 1: 20,000 ecoforestry maps published by the
 ministère des Ressources naturelles et des Forêts (hereafter MRNF) and
 combined information from two mapping exercises
 (4<sup>th</sup> and 5<sup>th</sup> decennial
 inventories) to create updated annual maps to account for anthropogenic
 disturbance, and fit the GPS data collected from our collared moose (from
 2017 to 2019). We regrouped the map polygons into a total of 8 landcover
 types relevant to moose ecology based on stand cover, composition, height,
 age, disturbance, land types, and representativity. We
 estimated movement rates (in m/h) for each individual and for each step
 (i.e., the trajectory linking two successive locations spaced by a 2 h
 interval) using Euclidean distances. We delineated seasonal home ranges
 using the kernel method based on Brownian bridges (Horne et al., 2007).
  We characterized moose habitat selection patterns using resource
 selection functions (hereafter RSF; Manly et al., 2002) with the different
 landcover types and other covariates (elevation, slope, day phase,
 presence of forest and paved roads in buffer zone) for each biological
 period. For each biological period, we retained only combinations of
 individual ID – year for which we had data for the entire or nearly the
 entire duration of the biological period (i.e., ~4% of the ID – year were
 removed from the dataset for the statistical analyses). We compared moose
 space use patterns (movement rates and home-range sizes) between
 biological periods using an analysis of variance (ANOVA) with repeated
 measures followed by a multiple comparison test (Tukey). The RSF we used
 to describe the habitat selection patterns was a mixed logistic regression
 contrasting GPS locations (coded 1) with random points (coded 0) with
 different combinations of the following independent variables: landcover
 types, topography variables, day phases, and the presence of forest and
 paved roads in the buffer zone around each location. 
description: # Data for: "Temporal variations in moose responses to roads and
 logging in the absence of wolves"
 We have uploaded the
 datasets used for each analysis for each biological period. The GPS
 telemetry data are not joined, but all the subsquent data resulting from
 the data management prior to analyses are shared, as well as the Rscripts.
 ## Description of the data and file structure Data files are structured
 for each analysis (MovementRate, HomeRangeSize, ResourceSelectionFunction
 - RSF), and for each biological period (Winter, Spring, Calving, Rearing,
 Rut). Scripts can be found in each "analysis" folder and
 associated datasets too. As native French speakers, our scripts are
 annotated in French (!!) and the title of some datasets are also using
 French acronyms/abbreviations. So Movement Rate is referred to as
 "MR" in the script names but "TM" in the dataset names
 (for "Taux de mouvement"), and Home-range Size is referred to as
 "HRS" in the script names but "DV" in the dataset
 names (for "Domaine Vitaux"). ### Habitat selection files * id:
 individual identification * date: day, month, and year * day: day phase
 (coded 1 if yes and 0 if not) * period: biological periods * presence: GPS
 locations (coded 1) and random points (coded 0) * stand0: presence of
 0-4 m habitats (coded 1 if yes and 0 if not) * c4: presence of 4-7 m
 coniferous stands (coded 1 if yes and 0 if not) * dm4: presence of 4-7 m
 deciduous and mixed stands (coded 1 if yes and 0 if not) * dm7: presence
 of 7-12 m deciduous and mixed stands (coded 1 if yes and 0 if not) * dm12:
 presence of +12 m deciduous and mixed stands (coded 1 if yes and 0 if not)
 * notdense_c: presence of +7 m low density coniferous stands (coded 1 if
 yes and 0 if not) * dense_c: presence of +7 m high density coniferous
 stands (coded 1 if yes and 0 if not) * water: presence of wetlands and
 water bodies (coded 1 if yes and 0 if not) * slope: slope (º) * elev_km:
 elevation (km) * slope_sd: centered slope * idyear: individual-year
 combination * dawndusknight: dusk, dawn, and night phases (coded 1 if yes
 and 0 if not) * pav_km: minimum Euclidian distances to the nearest paved
 road in km * frt_km: minimum Euclidian distances to the nearest forest
 road in km * pav400: presence of paved roads in buffer zone width 0-400 m
 (coded 1 if yes and 0 if not) * pav250: presence of paved roads in buffer
 zone width 0-250 m (coded 1 if yes and 0 if not) * frt250: presence of
 forest roads in buffer zone width 0-250 m (coded 1 if yes and 0 if not) *
 pav150: presence of paved roads in buffer zone width 0-150 m (coded 1 if
 yes and 0 if not) * frt150: presence of forest roads in buffer zone width
 0-150 m (coded 1 if yes and 0 if not) * pav100: presence of paved roads in
 buffer zone width 0-100 m (coded 1 if yes and 0 if not) * frt100: presence
 of forest roads in the buffer zone width 0-100 m (coded 1 if yes and 0 if
 not) * frt75: presence of forest roads in buffer zone width 0-75 m (coded
 1 if yes and 0 if not) ### Home range files * id: individual
 identification * period: biological periods * notdense_c: proportion of
 +7 m low density coniferous stands * stand0: proportion of 0-4 m habitats
 * dm4: proportion of 4-7 m deciduous and mixed stands * dense_c:
 proportion of +7 m high density coniferous stands * water: proportion of
 wetlands and water bodies * dm7: proportion of 7-12 m deciduous and mixed
 stands * c4: proportion of 4-7 m coniferous stands * dm12: proportion of
 +12 m deciduous and mixed stands * elev_km: mean elevation (km) * slope:
 mean slope (º) * slope_sd: centered slope * idyear: individual-year
 combination * area_km: home-range sizes in km2 * dense_frt: density of
 forest roads in the home range * dense_pav: density of paved roads in the
 home range * dense_roads: density of paved and forest roads in the home
 range * area_log: log-transformed home-range sizes ### Movement rate files
 * area: ellipses area around each step in m2 * date: day, month and year *
 id: individual identification * day: day phase (coded 1 if yes and 0 if
 not) * period: biological periods * time: hour, minute and second *
 notdense_c: proportion of +7 m low density coniferous stands * stand0:
 proportion of 0-4 m habitats * dm4: proportion of 4-7 m deciduous and
 mixed stands * dense_c: proportion of +7 m high density coniferous stands
 * water: proportion of wetlands and water bodies * dm7: proportion of
 7-12 m deciduous and mixed stands * c4: proportion of 4-7 m coniferous
 stands * dm12: proportion of +12 m deciduous and mixed stands * elev_km:
 mean elevation (km) * slope: mean slope (º) * slope_sd: centered slope *
 idyear: individual-year combination * rate_mh: estimated movement rates in
 m/h * rate_kmh: estimated movement rates in km/h * frt_km: minimum
 Euclidian distances to the nearest forest road in km * pav_km: minimum
 Euclidian distances to the nearest paved road in km * rate_log:
 log-transformed movement rates * slope_coeff: coefficients of variation of
 slope * dawndusknight: dusk, dawn, and night phases (coded 1 if yes and 0
 if not) * pav400: presence of paved roads in buffer zone width 0-400 m
 (coded 1 if yes and 0 if not) * pav250: presence of paved roads in buffer
 zone width 0-250 m (coded 1 if yes and 0 if not) * frt250: presence of
 forest roads in buffer zone width 0-250 m (coded 1 if yes and 0 if not) *
 pav150: presence of paved roads in buffer zone width 0-150 m (coded 1 if
 yes and 0 if not) * frt150: presence of forest roads in buffer zone width
 0-150 m (coded 1 if yes and 0 if not) * pav100: presence of paved roads in
 buffer zone width 0-100 m (coded 1 if yes and 0 if not) * frt100: presence
 of forest roads in the buffer zone width 0-100 m (coded 1 if yes and 0 if
 not) * frt75: presence of forest roads in buffer zone width 0-75 m (coded
 1 if yes and 0 if not) ## Code/Software Joined are the R scripts that were
 used to manage and analyse the data.