DOI: 10.5061/dryad.83bk3j9zf
title: Predation risk drives long-term shifts in migratory behavior and demography in a large herbivore population

description: Migration is an adaptive life-history strategy that helps individuals
 across taxa maximize fitness by obtaining forage and avoiding predation
 risk. The mechanisms driving migratory changes are poorly understood, and
 links between migratory behavior, space use, and demographic consequences
 are rare. Here, we use a nearly 20-year record of individual-based
 monitoring of elk (Cervus canadensis) to test hypotheses for changing
 patterns of migration in a large herbivore, elk (Cervus canadensis), in
 and adjacent to a large protected area in Banff National Park (BNP),
 Canada. We test whether bottom-up (forage quality) or top-down (predation
 risk) factors explained trends in i) the proportion of individuals using 5
 different migratory tactics, ii) differences in survival rates of
 migratory tactics during migration and whilst on summer ranges, iii)
 cause-specific mortality by wolves and grizzly bears, and iv) population
 abundance. We found dramatic shifts in migration consistent with
 behavioral plasticity in individual choice of annual migratory routes.
 Shifts were inconsistent with exposure to the bottom-up benefits of
 migration. Instead, exposure to landscape gradients in predation risk
 caused by exploitation outside the protected area drove migratory shifts.
 Carnivore exploitation outside the protected area led to higher survival
 rates for female elk remaining resident or migrating outside the protected
 area. Cause-specific mortality aligned with exposure to predation risk
 along migratory routes and summer ranges. Wolf predation risk was higher
 on migratory routes than summer ranges of montane-migrant tactics, but
 wolf predation risk traded-off with heightened risk from grizzly bears on
 summer ranges. A novel eastern migrant tactic emerged following a large
 forest fire that enhanced forage in an area with lower predation risk
 outside of the protected area. The changes in migratory behavior
 translated to population abundance, where abundance of the
 montane-migratory tactics declined over time. The presence of diverse
 migratory life histories maintained a higher total population abundance
 than would have been the case with one migratory tactic in the population.
 Our study demonstrates the complex ways in which migratory populations
 change over time through behavioral plasticity and associated demographic
 consequences because of individuals balancing predation risk and forage
 trade-offs.
description: See methods described in the accompanying paper. 
description: Data Description and Meta Data   The datasets in this paper are comprised
 of 4 main datasources and associated files. These include; 1.     GPS
 location data from gps-collared adult female elk from biological years
 2001 - 2020. These data are freely available as a Movebank data repository
 described by Hebblewhite et al. (2020). Hebblewhite M, Merrill EH, Martin
 H, Berg JE, Bohm H, Eggeman SL. 2020. Data from: Study "Ya Ha Tinda
 elk project, Banff National Park, 2001-2020 (females)". Movebank Data
 Repository. [https://doi.org/10.5441/001/1.5g4h5t6c](https://doi.org/10.5441/001/1.5g4h5t6c) 2.     Migratory classification data based on net-squared displacement methods (e.g., Bunnefeld et al. 2010) derived from GPS location data. 3.     Adult female survival data and 4.     Cause-specific mortality data 5.     Analysis of exposure of individual elk to forage, wolf and grizzly bear predation risk metrics. This data frame (R object) 'elk\_surv\_dat.Rdata' contains survival and migration classification data processed for both migration and survival analysis.  It is a combined GPS / VHF radiotelemetry data frame (from the Movebank dataset above) and survival object consistent with the R package 'Survival'.  It has 56,988 rows (i.e., telemetry locations) with 42 variables, only some of which are relevant to these analyses.   Individual elk (field:elkID) were classified as either migratory or resident (field:MigrationSegment) one of 5 migratory routes (field: MigrationRoute) following the Bunnefeld et al. (2010) methods described in the paper and by Eggeman et al. (2016).  The 5 migratory routes were resident, north, east, south, west and unknown. Other relevant fields unrelated to include: elkid\_yr (a concatenation of elkID and biological year), Date (YYYY/MM/DD format), Human\_year (Calendar year), Easting and Northing (in UTM NAD 83 projected format), and n\_obs (number of telemetry observations for each individual elkid\_yr).  Other unrelated fields are related to the estimated dates of spring and fall migration (i.e., DateSprMig0, etc) which are unused in the present paper.   Survival analysis using the functions survfit(), Surv() and coxph() from the 'Surival' package were both completed using the processed survival data ("elk\_surv\_dat.Rdata") prepared in the included script "prep\_survival\_data.R".  Survival curves and Cox proportional hazards were assessed for a variety of models: (1) season, (2) migration tactic (resident or migrant), (3) migration tactic and aggregated migration route (resident, eastern migrant and western migrant) and (4) migration tactic and all routes separated (resident, eastern migrant, western migrant, southern migrant and northern migrant). This analysis is recorded in the included script "surv\_analysis.R". Cause-specific mortality analysis using the user-defined functions cause.survival() (included in the script "cause\_survival.R") was also completed using the processed survival data ("elk\_surv\_dat.Rdata"). In this data set, the variable "mortality" is renamed "event" and equals 1 when a mortality event is recorded. The variable "Cause" is renamed "cause" and is as follows: 1: live observation, 2: unknown death, 3: wolf-caused mortality, 4: grizzly-caused mortality, 5: cougar-cause mortality and 6: human hunter-caused mortality. For this analysis, the data were separated into groups of each migration tactic and aggregated route (resident, eastern migrant, western migrant) and the migration and summer periods. This analysis is recorded in the included script "cause\_specific\_mort\_3mig.R"   Finally, analysis of differences between elk using different migratory routes is conducted based on the processes elk exposure data in the R data frame "locs\_covs\_raw\_and\_scaled01.Rdata".  This data frame consists of 1274 observations of 26 variables, summarizing exposure at GPS and VHF radiolocations to different calculations of forage biomass, wolf and grizzly bear predation risk averaged for each individual elkID\_year. That is, each row is an average across each individual elk for each biological year and season.  Relevant fields include; elkID (same as above), year (biological year), elkid\_yr (concatenation of elkid and year), season (defined as the Migratory period or Summer), class (resident or migrant), and route (the 5 migratory routes defined as above).  Forage is called herb, wolf daytime predation risk is called wolfd, and wolf nighttime predation risk is called wolfn, and then grizzly bear predation risk in bear risk season 1 and season 2 are reported (see paper for details about day/night and season 1/2 wolf and bear predation risk). The mean values are reported for all 5 of these metrics (called field post-script 'mu', e.g., herb\_mu); median values are reported as med (e.g., herb\_med), and scaled (e.g., centered and scaled/standardized covarates) are followed by the postscript \_01. For example, herb\_mu, herb\_med, herb\_mu01 herb\_med01 correspond to the mean, median, and standardized mean and median calculations of forage biomass that individual elk were exposed to.   To repeat the analysis of differences between migratory routes in exposure to these 5 metrics of forage and wolf/bear predation risk, we used the R script 'elk\_exposure\_analysis.R' to calculate mean wolf and bear risk values, and then conduct linear models (using the R package lme4) by season for the different mirgatory routes.