DOI: 10.5061/dryad.xksn02vs8
title: Data from: Reaching reproduction in a large carnivore: Role of early environment and intrinsic traits

description: To reach reproduction, individuals must survive the juvenile stage, a
 critical period of low survival rates in large carnivores. Early-life
 conditions during this stage can have lasting effects on survival,
 reproductive maturation, growth, physiology, and behaviour. We assessed
 recruitment probability in Scandinavian wolves, i.e., the probability that
 a wolf to reaches the reproductive stage and has pups surviving at least
 five months of age. To unravel human-related and biological factors within
 the natal territory that could affect recruitment probability, we analysed
 life-history data from 582 Scandinavian wolves (Canis lupus) identified by
 DNA as pups or juveniles in their birth territory. Factors considered
 included main prey density, road density, human density, and proximity to
 non-breeding zones, as well as sex, inbreeding level and collaring. Among
 the 582 wolves analysed, 122 produced at least one surviving pup,
 corresponding to a recruitment probability of 0.21. Recruitment
 probability was more than twice as high (0.5) for juvenile wolves fitted
 with GPS collars compared to non-collared individuals (0.22), and was
 positively correlated with human population density in the natal
 territory. We found no significant effects of other biologically or
 human-related predictors. These results suggest that in this large
 carnivore population, managed below carrying capacity, individual
 recruitment probability is primarily influenced by human-related factors,
 potentially reflecting poaching risk.
description: Dataset: reaching_reproduction_frame.csv File Format: ``` Name:
 reaching_reproduction_frame.csv Type: CSV (Comma-Separated Values)
 Delimiter: , ``` Description: The dataset reaching_reproduction_frame.csv
 contains the folowing information about individual wolves: ```
 -Reproduction (categorical): Whether the individual reached reproduction
 (1 = Yes, 0 = No). -Collared (categorical): Whether the individual was
 collared for tracking (Yes, No). -Inbreeding (numeric): Inbreeding
 coefficient of the individual based on the pedigree. -Sex (categorical):
 Sex of the individual (M = Male, F = Female). -Human_density (numeric):
 Density of humans in the individual's natal territory in number of
 inhabitants per km2. -Average_gravel_road (numeric): Average gravel road
 density in the natal territory in km per km2. -Country (categorical):
 Birth country of the individual: location of the centroid of the natal
 territory (S = Sweden, N = Norway) -Wolf_density (numeric): Number of
 bordering neighbouring territories, i.e. the number of territories
 overlapping with the natal territory. -Dist_non_breeding_zone (numeric):
 Distance in m between the centroid of the natal territory and the nearest
 non-breeding zone, i.e. area where the wolves were not allowed to
 establish. -Snow_depth (numeric): Average snow depth in cm in the natal
 territory during cold month: from the 1st of May (average birth of pups)
 to 30th of April next (standardized). -Moose_density (numeric): Hunting
 bag records of moose counted as killed/10 km2 for counties. Data on
 hunting bag records was generated as a weighted average of the moose
 density of the counties overlapping with the natal territory.
 -Legally_killed (binary): Whether the individual was legally killed (1 =
 Yes, 0 = No). -Date of death (numeric): Date of death for the individual,
 if known; otherwise recorded as "null". Dates are stored in
 numeric format, with origin set to "1899-12-30". -Birth_year
 (numeric): Year of birth of the individual. -Parental_pair_id
 (categorical): Identifier for the parental pair. -Age_known (binary):
 Whether the age of the individual is known with certainty or estimated (1
 = Yes, 0 = No). ``` Script: Analysis of Factors Influencing Reproduction
 File Format: ``` Name: Analysis_script.R Type: R ``` Purpose: The R script
 provided analyzes performed in the paper "Reaching reproduction in a
 large carnivore: role of early environment and intrinsic traits". It
 includes: ``` Data Preparation: Standardizing numerical predictors.
 Converting variables to appropriate data types (e.g., factors for
 categorical variables). Filtering data to create subsets for specific
 analyses (e.g., excluding legally killed or collared individuals). Model
 Building: Generalized linear mixed models (GLMMs) using the glmmTMB
 package. Fixed effects include environmental, genetic, and anthropogenic
 variables. Random effect: Parental_pair_id. Diagnostics and Model
 Checking: Checking multicollinearity using check_collinearity (from the
 performance package). Residual diagnostics with the DHARMa package. Model
 Selection and Averaging: Using MuMIn::dredge to identify top models based
 on AICc. Averaging models within a delta AICc of 2. Visualization:
 Correlation matrices visualized with the corrplot package. ```