DOI: 10.5061/dryad.ksn02v7g1
title: Data from: Movement-integrated habitat selection reveals wolves balance ease of travel with human avoidance in a risk-reward trade-off

description: Anthropogenic linear features often alter wildlife behaviour and movement.
 Landscape features, such as habitat, can have important mediating effects
 on wildlife response to disturbance and yet are rarely explicitly
 considered in how habitat and disturbance interact. We tested the
 movement and space-use responses of GPS-collared wolves to linear features
 with respect to adjacent habitat variation. We simultaneously modeled wolf
 movement and selection within a conditional logistic regression framework
 (integrated Step Selection Analysis). We explicitly considered how
 adjacent habitat alters these responses through putative effects such as
 movement friction. Classifying linear features based on the
 selection and movement response of wolves revealed that pairing
 transmission lines and primary roads increased the avoidance response to
 be greater than either feature on their own and provided evidence of a
 semi-permeable barrier to movement. In contrast, features with reduced
 human activity, including secondary and tertiary roads were highly
 selected for and function as movement corridors. Synthesis and
 applications: Explicitly parameterizing adjacent habitats provides
 evidence that where a linear feature is routed and which habitats it
 interacts with will have the greatest implications for wolf behavioural
 responses. Reduced avoidance behaviour in highly risky environments
 signifies the importance of habitat for maintaining landscape
 connectivity, particularly when routing multiple different features
 parallel and near each other. Increased vegetation density along linear
 features also reduced movement advantages putatively by increasing
 friction, indicating that actively decommissioning other features such as
 secondary roads could be an effective mitigation strategy for reducing
 wolf encounters with prey. Knowing the influence of adjacent habitats on
 the likelihood of wolves selecting for a given linear feature creates a
 context to minimize the impact of new anthropogenic features on behaviour.
description: We captured and collared 46 wolves across 12 packs in eastern
 Manitoba between 2014-2019. Each wolf was fit with a GPS telemetry collar
 programmed to collect a GPS relocation every two hours across all
 seasons. To produce the dataset, GPS locations were
 used to generate steps (linear connection between consecutive locations)
 using the integrated Step Selection Function (iSSA) framework. For a given
 used step, we randomly generated ten steps based on observed distributions
 of individual-level movement behaviour. We then extracted habitat
 covariates for each start and end point of a step, including proportion of
 forest, distance to linear features, and time of day. 
description: # Movement-integrated habitat selection reveals wolves balance ease of
 travel with human avoidance in a risk-reward trade-off
 [https://doi.org/10.5061/dryad.ksn02v7g1](https://doi.org/10.5061/dryad.ksn02v7g1) ## Description of the data and file structure We captured and collared 46 wolves across 12 packs in eastern Manitoba between 2014-2019. Each wolf was fit with a GPS telemetry collar programmed to collect a GPS relocation every two hours across all seasons. To produce the dataset, GPS locations were used to generate steps (linear connection between consecutive locations) using the integrated Step Selection Function (iSSA) framework. For a given used step, we randomly generated ten steps based on observed distributions of individual-level movement behaviour. We then extracted habitat covariates for each start and end point of a step, including proportion of forest, distance to linear features, and time of day.  GPS locations have been removed for conservation purposes. Please contact authors directly if required. ### Files and variables #### File: JAPPL-2024-00408\_Wolf-Crossing-Dataset.csv **Description:** Dataset used to run wolf iSSA model for crossing linear features ##### Variables * uniqueID: unique row ID * Date: date of GPS location in YYYY-MM-DD format * Animal_ID: unique ID for each tagged animal * burst_: unique identifier for each step burst * sl_: step length in meters * ta_: turn angle in radians * t1_: timestamp (date and time in CST) at start of step * t2_: timestamp (date and time in CST) at end of step * dt_: time difference between t1 and t2 * step_id_: unique identifier for a given step * case_: logical; indicates used (TRUE) or available (FALSE) step * cos_ta: cosine transformed turn angle * log_sl: log transformed step length * lnDistTo_PR_start: log transformed distance to nearest primary road at the start of the step * lnDistTo_SR_start: log transformed distance to nearest secondary road at the start of the step * lnDistTo_TL_start: log transformed distance to line 77 at the start of the step  * lnDistTo_TLAll_start: log transformed distance to nearest transmission line at the start of the step * lnDistTo_TLN_start: log transformed distance to line PQ95 at the start of the step * lnDistTo_TLP_start: log transformed distance to line L5/L47 at the start of the step * lnDistTo_TR_start: log transformed distance to nearest tertiary road at the start of the step * lnDistTo_W_Temp_start: log transformed distance to nearest waterway at the start of the step * lnDistTo_PR_end: log transformed distance to nearest primary road at the end of the step * lnDistTo_SR_end: log transformed distance to nearest secondary road at the end of the step * lnDistTo_TL_end: log transformed distance to line 77 at the end of the step  * lnDistTo_TLAll_end: log transformed distance to nearest transmission line at the end of the step * lnDistTo_TLN_end: log transformed distance to line PQ95 at the end of the step * lnDistTo_TLP_end: log transformed distance to line L5/L47 at the end of the step * lnDistTo_TR_end: log transformed distance to nearest tertiary road at the start of the step * lnDistTo_W_Temp_end: log transformed distance to nearest waterway at the end of the step * OpenBuff_St: proportion of open habitat within a 100 m buffer of the start of the step * ConiferBuff_St: proportion of coniferous habitat within a 100 m buffer of the start of the step * DecidBuff_St: proportion of deciduous habitat within a 100 m buffer of the start of the step * MixedBuff_St: proportion of mixed habitat within a 100 m buffer of the start of the step * OpenBuff_End: proportion of open habitat within a 100 m buffer of the end of the step * ConiferBuff_End: proportion of coniferous habitat within a 100 m buffer of the end of the step * DecidBuff_End: proportion of deciduous habitat within a 100 m buffer of the end of the step * MixedBuff_End: proportion of mixed habitat within a 100 m buffer of the end of the step * Time: time in CST of location fix * Day: binary; 1 if location was taken during daylight, 0 otherwise * Night: binary; 1 if location was taken during night, 0 otherwise * Twilight: binary; 1 if location was taken during twilight, 0 otherwise * PRcross: binary; 1 if step crossed a primary road, 0 otherwise * SRcross: binary; 1 if step crossed a secondary road, 0 otherwise * TRcross: binary; 1 if step crossed a tertiary road, 0 otherwise * TLcross: binary; 1 if step crossed Line 77, 0 otherwise * TLPcross: binary; 1 if step crossed Line L5/L47, 0 otherwise * TLNcross: binary; 1 if step crossed Line PQ95, 0 otherwise * TLAllcross: binary; 1 if step crossed a transmission line, 0 otherwise * Wcross: binary; 1 if step crossed a waterway, 0 otherwise * ToD: factor with 3 levels; Day, Night, or Twilight * wolf_step_id: unique identifier for a combination of animal ID and step ID #### File: JAPPL-2024-00408\_Wolf-Proximity-Dataset.csv **Description:** Dataset used to run wolf iSSA model for linear feature proximity. ##### Variables * uniqueID: unique row ID * Date: date of GPS location in YYYY-MM-DD format * Animal_ID: unique ID for each tagged animal * burst_: unique identifier for each step burst * sl_: step length in meters * ta_: turn angle in radians * t1_: timestamp (date and time in CST) at start of step * t2_: timestamp (date and time in CST) at end of step * dt_: time difference between t1 and t2 * step_id_: unique identifier for a given step * case_: logical; indicates used (TRUE) or available (FALSE) step * cos_ta: cosine transformed turn angle * log_sl: log transformed step length * lnDistTo_PR_start: log transformed distance to nearest primary road at the start of the step * lnDistTo_SR_start: log transformed distance to nearest secondary road at the start of the step * lnDistTo_TL_start: log transformed distance to line 77 at the start of the step  * lnDistTo_TLAll_start: log transformed distance to nearest transmission line at the start of the step * lnDistTo_TLN_start: log transformed distance to line PQ95 at the start of the step * lnDistTo_TLP_start: log transformed distance to line L5/L47 at the start of the step * lnDistTo_TR_start: log transformed distance to nearest tertiary road at the start of the step * lnDistTo_W_Temp_start: log transformed distance to nearest waterway at the start of the step * lnDistTo_PR_end: log transformed distance to nearest primary road at the end of the step * lnDistTo_SR_end: log transformed distance to nearest secondary road at the end of the step * lnDistTo_TL_end: log transformed distance to line 77 at the end of the step  * lnDistTo_TLAll_end: log transformed distance to nearest transmission line at the end of the step * lnDistTo_TLN_end: log transformed distance to line PQ95 at the end of the step * lnDistTo_TLP_end: log transformed distance to line L5/L47 at the end of the step * lnDistTo_TR_end: log transformed distance to nearest tertiary road at the start of the step * lnDistTo_W_Temp_end: log transformed distance to nearest waterway at the end of the step * OpenBuff_St: proportion of open habitat within a 100 m buffer of the start of the step * ConiferBuff_St: proportion of coniferous habitat within a 100 m buffer of the start of the step * DecidBuff_St: proportion of deciduous habitat within a 100 m buffer of the start of the step * MixedBuff_St: proportion of mixed habitat within a 100 m buffer of the start of the step * OpenBuff_End: proportion of open habitat within a 100 m buffer of the end of the step * ConiferBuff_End: proportion of coniferous habitat within a 100 m buffer of the end of the step * DecidBuff_End: proportion of deciduous habitat within a 100 m buffer of the end of the step * MixedBuff_End: proportion of mixed habitat within a 100 m buffer of the end of the step * Time: time in CST of location fix * Day: binary; 1 if location was taken during daylight, 0 otherwise * Night: binary; 1 if location was taken during night, 0 otherwise * Twilight: binary; 1 if location was taken during twilight, 0 otherwise * ToD: factor with 3 levels; Day, Night, or Twilight * wolf_step_id: unique identifier for a combination of animal ID and step ID ## Code/software R programming language is required to run the files. Code and scripts can be found on Zenodo: [https://doi.org/10.5281/zenodo.14201743](https://doi.org/10.5281/zenodo.14201743)