DOI: 10.5061/dryad.brv15dvgm
title: Humans drive spatial variation in mortality risk for a threatened wolf population in a Canis hybrid zone

description: Large carnivores often exhibit high survival rates in protected areas,
 whereas intentional and unintentional human-caused mortality may be
 greater in adjacent areas. These patterns can result in source-sink
 dynamics and limit population expansion beyond protected areas. We used
 telemetry data from 438 canids in 141 packs collected from 2002–2020 to
 evaluate mortality risk for wolves, coyotes, and admixed canids in a
 3-species hybrid zone in and adjacent to a large protected area in
 Ontario, Canada. The hybrid zone is occupied by most of the remaining
 eastern wolves (Canis lycaon), a rare, threatened species that hybridizes
 with sympatric eastern coyotes (C. latrans) and Great Lakes gray wolves
 (C. lupus). Within Algonquin Provincial Park (APP), annual human-caused
 mortality from harvest and vehicles was low (0.06, 95% CI [0.03, 0.08]),
 whereas annual human-caused mortality was higher in adjacent areas (0.31,
 95% CI [0.25, 0.37]). Smaller protected areas implemented to help protect
 eastern wolves did not significantly reduce mortality. Eastern wolves
 survived poorly relative to other canids and dispersing canids survived
 poorly relative to residents. Mortality risk was greater when canids were
 closer to roads. Mortality risk was also increased or reduced by the
 strength of individual-level selection or avoidance of roads relative to
 their availability, respectively. Our results provide a comprehensive
 evaluation of factors influencing spatial variation in mortality risk for
 canids to inform eastern wolf recovery efforts. Additionally, we developed
 a novel modeling approach for investigating the influence of resource
 selection on mortality risk, which highlighted that individual-level
 responses to risk can strongly influence population-level mortality
 patterns. Synthesis and applications. Despite being listed as ‘threatened’
 under the Ontario Endangered Species Act, eastern wolves are still legally
 trapped and shot outside protected areas in central Ontario. Eastern
 wolves and dispersing canids survive poorly outside of APP, primarily from
 human-caused mortality. These results, along with the apparent inadequacy
 of the smaller protected areas, suggest that expanding the threatened
 eastern wolf population outside APP is unlikely under current management
 conditions. Protecting eastern wolves from human-caused mortality is
 complicated as it would require a harvest ban for all canids, including
 coyotes.
description: # Humans drive spatial variation in mortality risk for a threatened wolf
 population in a Canis hybrid zone
 Description of the data
 and file structure: These data are the files needed to reproduce the
 results of the mortality risk modeling, survival rates, and cause-specific
 mortality rates provided in Benson et al. (2024). Humans drive spatial
 variation in mortality risk for a threatened wolf population in a Canis
 hybrid zone. There are 7 files which correspond to the 6 mortality risk
 model sets (suitable for Cox proportional hazards modeling and
 Kaplan-Meier Survival rate estimation) presented and explained in Benson
 et al. (2024) and the cause-specific mortality analysis. Here are
 descriptions of the data contained in each column in each file. Many of
 the column names and data are the same in different files, and in these
 cases, they are only described once. Then in subsequent files, only the
 new columns are described. **A.
 ModelSet1_SurvivalData_RecoveryZone_ResidentsNonResidents** * ID = unique
 animal ID number * Pack = name of canid social group or
 "transient" for solitary animals that are not part of a group *
 Assignment = genetic assignment to one of the following classes: EW =
 eastern wolf, EC = eastern coyote, GLW = great lakes gray wolf, CH =
 admixed coyote, WH = admixed wolf * EW = dummy coded variable for eastern
 wolves (1 = eastern wolf, 0 = other canid) * Year = biological year
 corresponding to survival data (2002-2021) * post2016 = represents whether
 the data was collected after the new harvest protection for canids was
 implemented in June, 2016 * jStart = day of biological year that telemetry
 monitoring began (0-365) * jEnd = day of biological year that telemetry
 monitoring ended (0-365) * Event = whether mortality occurred on last day
 of monitoring (0 = censor, 1 = event) * Resident = dummy coded variable
 indicating whether animal was resident (1) or non-resident (0) * Protected
 = dummy coded variable indicating whether animal was in protected area (1)
 or not in protected area (0) * protection_APP = whether animal was in
 large protected area of Algonquin Provincial Park (1) or outside park (0)
 * protection_NonAPP = whether animal was in smaller protected areas
 outside of Algonquin Park (1) or not in these areas (0) * Sex_M = dummy
 coded sex variable (1 = Male, 0 = Female) * Adult = dummy coded age class
 (1 = adult, 0 = yearling) * Yearling = dummy coded age class (1 =
 yearling, 0 = adult) **B.
 ModelSet2_SurvivalData_APP_ResidentsNonResidents** All columns are
 consistent with ModelSet1 (explained above) except for the addition of: *
 ID_year: simply merges the animal ID number and year of monitoring **C.
 ModelSet3_SurvivalData_OutsideAPP_ResidentsNonResidents** All columns are
 consistent with ModelSet1 and ModelSet2 (explained above). **D.
 ModelSet4_SurvivalData_RecoveryZone_ResidentsOnly** All columns are
 consistent with ModelSet1, ModelSet2, and ModelSet3 (explained above)
 except for the addition of: * densSecRds = density of secondary roads
 within the animal's home range **E.
 ModelSet5_SurvivalData_RecoveryZone_GPSonly** All columns are consistent
 with ModelSet1, ModelSet2, and ModelSet3 (explained above) except for the
 addition of: * Sex = sex of animal (M = male, F = female) * Age_Class =
 age class of animal (Adult or yearling) * DistRD = daily mean distance to
 secondary roads in KM * DistRD = daily distance to secondary roads in KM,
 rescaled by subtracing mean and dividing by standard deviation *
 log_distRds = natural log distance to roads in KM * selectionRds = daily
 selection ratio (used/available) for secondary roads * log_selectRds =
 natural log of selection ratio for secondary roads * fill_7days = dummy
 coded variable for whether data were filled in from a rolling average of
 the last 7 days to replace missing distance to roads data (1 =
 filled/replaced, 0 = not filled/replaced) **F.
 ModelSet6_SurvivalData_OutsideAPP_GPSonly** All columns are consistent
 with ModelSet1, ModelSet2, ModelSet3, ModelSet4, and ModelSet5 (explained
 above). **G. CSMDataRecoveryArea** All columns consistent with ModelSet1,
 Modelset3, and ModelSet4 except: * COD = cause of death (Drop = collar
 drop, Fail = collar fail, Censor = censor, or causes of death
 corresponding to those explained in detail in paper