Published February 1, 2024 | Version v1
Software Open

Data for: Temporal variations in female moose responses to roads and logging in the absence of wolves

  • 1. Université du Québec à Rimouski
  • 2. Ministère de l'Environnement, de la Lutte contre les changements climatiques, de la Faune et des Parcs

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.

Notes

Funding provided by: Ministère des Transports
Crossref Funder Registry ID: https://ror.org/05sgcwh30
Award Number: N/A

Funding provided by: Natural Sciences and Engineering Research Council
Crossref Funder Registry ID: https://ror.org/01h531d29
Award Number: 2016-05196

Funding provided by: Ministère de l'Environnement, de la Lutte contre les changements climatiques, de la Faune et des Parcs
Crossref Funder Registry ID: https://ror.org/00ra8zc69
Award Number: N/A

Funding provided by: Natural Sciences and Engineering Research Council
Crossref Funder Registry ID: https://ror.org/01h531d29
Award Number: 2022-04307

Methods

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 (4th and 5th 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. 

Files

Files (212.7 kB)

Name Size Download all
md5:0801e30720079e93e1d162ffdf672823
6.3 kB Download
md5:062414ab16956786d857c11a6e0ee7f4
1.2 kB Download
md5:3b61106c4630c8182629efa34afd78a1
1.2 kB Download
md5:dc72d76ce2b336ccb29ded29761c92b9
13.9 kB Download
md5:40fef9bfbb6dcac04f51e19357ceb7d4
14.1 kB Download
md5:f26dce0959bdadb1d854b1ce3a91c043
9.4 kB Download
md5:0cf20e58fb1104c6b2af4f780e2ce4e5
3.2 kB Download
md5:5c3289967820ab1c2e9648aac1372eb1
3.2 kB Download
md5:cb70b6df76389564accd9f87667afbe7
3.1 kB Download
md5:11f16b0d9f5044c6b227669cb5b54ff4
3.2 kB Download
md5:6215c4f723cf477204cc77580e1951e1
3.2 kB Download
md5:dab0a35c726158b9c1e1e87a0e76b292
3.5 kB Download
md5:4f13a2be4aad9f583b6a2ab3e0ecc3d8
4.7 kB Download
md5:91595fd8ddc3458474d5c98d678f9dbb
5.9 kB Download
md5:8e48964460f3c9df6a00cbf53670795e
10.6 kB Download
md5:59851e1d29678ab707af5c38f209df33
10.5 kB Download
md5:029029ab5aa8fd91dbf827e08bfbaa6a
10.0 kB Download
md5:3ef41f6ca5ff39c8e9b16c169a7945e7
5.9 kB Download
md5:d70600aaa4f0d458dd8a1892f113e82c
4.3 kB Download
md5:9905efc4c04dd5f3a67780a122029462
4.7 kB Download
md5:219eeac1854f23f581e02cdcc1c8df29
4.3 kB Download
md5:16141b0c7d87b33d880749f95baac34c
4.7 kB Download
md5:060664f5b7d8055d4359f15cdfd719b9
4.7 kB Download
md5:392ad40886b30c53eebca4fbd2d95eda
8.7 kB Download
md5:9c7e1b426d7e24b759542aa3b1aadbd1
33.0 kB Download
md5:693f2d1909e2e8edd4bdb1a996f7245f
8.8 kB Download
md5:f8eb776cbef9caa3614901119d9c95f3
8.7 kB Download
md5:218ea8fcc99cbc6a4e7c863da418e145
8.8 kB Download
md5:093ea1a99d64344957bfd3c06318b9f2
8.8 kB Download

Additional details

Related works

Is source of
10.5061/dryad.dfn2z357t (DOI)