Humans drive spatial variation in mortality risk for a threatened wolf population in a Canis hybrid zone
Data files
Jan 12, 2024 version files 15.81 MB
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CSMData_RecoveryArea.csv
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ModelSet1_SurvivalData_RecoveryZone_ResidentsNonResidents.csv
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ModelSet2_SurvivalData_APP_ResidentsNonResidents.csv
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ModelSet3_SurvivalData_OutsideAPP_ResidentsNonResidents.csv
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ModelSet4_SurvivalData_RecoveryZone_ResidentsOnly.csv
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ModelSet5_SurvivalData_RecoveryZone_GPSonly.csv
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ModelSet6_SurvivalData_OutsideAPP_GPSonly.csv
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README.md
Abstract
- 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.
README: Humans drive spatial variation in mortality risk for a threatened wolf population in a Canis hybrid zone
https://doi.org/10.5061/dryad.brv15dvgm
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