Evaluating the effects of wolf culling on livestock predation when considering wolf population dynamics in an individual-based model

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..., , , # 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...