DOI: 10.5061/dryad.v15dv4243
title: Data from: Evaluating the effects of wolf culling on livestock predation when considering wolf population dynamics in an individual-based model

description: The efficiency of the management of predations on livestock by gray wolves
 (Canis lupus) through culling is under debate. Evaluating wolf culling
 efficiency requires to simultaneously analyze the effects of culling on
 the wolf population and the repercussions of these population changes on
 livestock predation. This protocol is technically difficult to implement
 in the field. To properly assess culling efficiency, we provided an
 integrated and flexible individual-based model that simulated interactions
 between wolf population dynamics, predation behavior and culling
 management. We considered many social processes in wolves. We calibrated
 the model to match the Western Alps as a case study. By considering the
 prey community in this area and the opportunistic nature of wolf
 predation, we assumed that predation on livestock at the wolf territory
 level increased with pack’s food needs. Under this assumption and
 considering livestock availability as high and livestock vulnerability as
 uniform in space and time, culling maintained wolf population size and
 predation risks at low levels. Contrary to what was expected, culling
 decreased the mean annual proportions of dispersing wolves in our
 simulations, by speeding settlement. This population-level mechanism
 compensated for the high mortality and the pack instability caused by
 culling. Compensation was however dependent on the selectivity and the
 timing of culling. When executed before the natural mortality module in
 our model, the selective culling could undermine replacement of lost
 breeders and therefore decrease wolf population resilience to culling. Our
 model gives insights about culling effects in one specific simulated
 context, but we do not expect that our assumption about predation behavior
 necessarily holds in other ecological contexts and we therefore encourage
 further explorations of the model.
description: # Data from: Evaluating the effects of wolf culling on livestock predation
 when considering wolf population dynamics in an individual-based model
 [https://doi.org/10.5061/dryad.v15dv4243](https://doi.org/10.5061/dryad.v15dv4243) ## Description of the data and file structure You can find the three R scripts required to run the model: * The script describing each of the modules of the model (**submodels.R**): Reproduction, Aging, Natural mortality, Pack dissolution, Breeding access to female subordinates, Dispersal, Migration (Immigration, Emigration), Adoption, Breeding access to dispersing wolves, Pairing, Budding, Establishment alone, Breeding access to male subordinates, Pack Needs, Culling. * The script giving all the parameters used to parameterize the model (**initParam.R**), see below for a detailed description. * The script running the model, that uses the two previous scripts. It defines and runs the cycle of the model, by giving the order of the modules (**run.R**). The raw data (see below) are recorded following the run of this script. The numbers of the scenarios that are recorded in the raw data are attributed in this script (L.24): * scenario 1 (null scenario, called S0 in the manuscript), * scenario 2 (selective culling applied after the natural mortality module, S2A), * scenario 3 (random culling applied after the natural mortality module, S1A), * scenario 4 (selective culling applied before the natural mortality module, S2B), * scenario 5 (random culling applied before the natural mortality module, S1B). We provide the raw simulated data presented in the manuscript: * The data table **wolfSimRes_pop.RData** provides the census of the population (one row per individual) for each simulated year (100) of each replicate (150) of each scenario (5). * The data table **wolfSimRes_pop_removals.RData** provides the information about the culled individuals (one row per individual) for each simulated year (100) of each replicate (150) of each scenario with culling (4). Years without rows means that no individual was culled during these years. * The data table **wolfSimRes_pop_nat_mort.RData** provides the information about the individuals that died from natural causes (one row per individual) for each simulated year (100) of each replicate (150) of each scenario (5). Years without rows means that no individual died from natural causes during these years. * The data table **wolfSimRes_pop_bef_mort.RData** provides the information about the individuals after the reproduction module and before the natural mortality module (one row per individual) for each simulated year (100) of each replicate (150) of each scenario (5). * The data table **wolfSimRes_pack_dyn.RData** provides 6 metrics relative to pack dynamics for each simulated year (100) of each replicate (150) of each scenario (5). See below for a detailed description. You can find the R script in which we format the raw data to produce the figures 2 to 5 from the manuscript (**figures.R**). We provide here these formatted data: * The data_table **Ind_extr_dt.RData** provides the total of individuals per year (100), per replicate (150) and per scenario (5). * The data table **Results_SY_dt.RData** provides the mean and the standard deviation of the population size per year (100) and per scenario (5), summarized across the 150 replicates. * The data table **Results_SR_dt.RData** provides 7 metrics per replicate (150) and per scenario (5), summarized across the 100 years. See below for a detailed description. ## Description of the variables ### initParam * *meanPups*: mean litter size * *mortalityPup*: mean mortality for non dispersing pups * *mortalityPupSD*: sd mortality for non dispersing pups * *mortalityYearling*: mean mortality for non dispersing yearling * *mortalityYearlingSD*: sd mortality for non dispersing yearling * *mortalityAdult*: mean (non density-dependent) mortality for non dispersing adult * *mortalityAdultSD*: sd (non density-dependent) mortality for non dispersing adult * *equilibriumDens*: pack carrying capacity * *indCC*: individual carrying capacity * *terrSize*: territory size (used for density-dependent mortality) * *mortalityDispPup*: mean mortality for dispersing pups * *mortalityDispPupSD*: sd mortality for dispersing pups * *mortalityDisp*: mean mortality for dispersing individuals * *mortalityDispSD*: sd mortality for dispersing individuals * *thresholdRelatedness*: relatedness threshold * *probBudd*: probability of budding * *meanPackSize*: mean pack size * *sdPackSize*: sd pack size * *probDispPup*: pup dispersal probability * *probDispYearling*: yearling dispersal probability * *probDispAdult*: adult dispersal probability * *nImmigrants*: number of immigrants entering the study area * *pEmigr*: proportion of dispersings individuals emigrating outside of the study area * *probAdopt*: probability of adoption * *Year_burninend*: first year where legal removals can occur * *viabilityThresh*: proportion of the individual carrying capacity when the population is considered viable * *removalQuota*: maximum proportion of the wolf population that can be legally killed * *sensibility*: percentage of changes in coefficients of the pack dissolution function in the context of the sensitivity analysis ### wolfSimRes\_pop, wolfSimRes\_pop\_removals, wolfSimRes\_pop\_nat\_mort and wolfSimRes\_pop\_bef\_mort * *who*: unique number attributed to an individual * *sex*: sex of the individual (F for females, M for males) * *age*: age of the individual (e.g. 1 = individuals in their first year of life) * *alpha*: breeding status (0 for subordinates, 1 for breeders) * *packID*: the pack number in which the individual is during the record * *disp*: the dispersing status (0 for resident individuals, 1 for dispersing individuals) * *motherID*: the number of the mother of the individual (used for relatedness caculations) * *fatherID*: the number of the father of the individual (used for relatedness caculations) * *cohort*: year of birth of the individual * *EN*: energetic needs of the pack of the individual (total of pack size, with pups counting for half), relative to "Pack Needs" (PN) in the manuscript. ### wolfSimRes\_pack\_dyn * *numPack*: number of packs at the beginning of the cycle * *lostPackMort*: number of packs that disappeared during the cycle because all the individuals in the packs died * *packDiss*: number of packs that dissolved during the year * *newPackPair*: number of packs created during the year by the pairing of two dispersing individuals * *newPackBud*: number of packs created during the year by "budding" * *newPackSingle*: number of packs created during the year by individuals alone ### Ind\_extr\_dt * *Ind_count*: total count of individuals ### Results\_SY\_dt * *Ind*: mean of the population size * *Ind_sd*: standard deviation of the population size ### Results\_SR\_dt * *Packduration*: mean pack duration in years summarized across all simulated years * *Disp*: mean ratio of dispersing individuals at the end of each year, in %, summarized across all simulated years * *DI*: mean annual sum of Pack Needs (PN) of all individuals at the end of each year (PN is updated after the modules of natural mortality and culling), summarized across all simulated years * *Cull_tot*: total of culled individuals across all simulated years * *Disp_tot*: total of culled dispersing wolves across all simulated years * *Alpha_tot*: total of culled breeding wolves across all simulated years * *Subord_tot*: total of culled subordinate wolves across all simulated years ## Sharing/Access information The three R scripts for analysis (**run.R**, **submodels.R**, **initParam.R**) were based on our previous work, published in Ecological Modelling in 2020: * Bauduin, S., Grente, O., Santostasi, N. L., Ciucci, P., Duchamp, C. and Gimenez, O. 2020. An individual-based model to explore the impacts of lesser-known social dynamics on wolf populations. - *Ecol. Model.* 433 ## Code/Software We used the R package NetLogoR v0.3.11 (Bauduin et al. 2019) to run the model in R 4.2.3. We provided comments throughout the four scripts.