DOI: 10.5061/dryad.dncjsxm5m
title: Habitat and density effects on the demography of an expanding wolf population in Central Europe

description: Demographic parameters are key to understanding population dynamics. Here,
 we analyse the survival and reproduction of the German wolf population in
 the 20 years following recolonization. Specifically, we analysed the
 effects of environmental, ecological, and individual characteristics on i)
 the survival probability of the population; ii) annual survival rates of
 age classes; iii) reproduction probability; and iv) reproductive output,
 measured as the number of detected pups/juveniles. Using the Cox
 proportional hazards model, we estimated a median survival time of circa 3
 years for wolves. Annual survival probabilities were found to be 0.75 for
 juveniles, 0.75 for subadults, and 0.88 for adults. Survival was lower for
 juveniles in winter and for subadult males in summer, probably associated
 with dispersal events. Low habitat suitability was clearly associated with
 lower survival in juveniles and subadults, but not in adults. Local
 territory density was related to increased survival. Reproduction
 probability within a territory was 0.89, but explanatory variables had no
 effect. Reproductive output was four pups/juveniles on average, positively
 related to habitat suitability and female experience, but negatively
 related to territory density. Survival values were very high for the
 species when compared to other regions. We hypothesize that carrying
 capacity has not been reached in the study area, thus the survival may
 decrease in the future if the landscape becomes saturated. Furthermore,
 our results highlight a spatial pattern in survival and reproduction, with
 areas of better habitat suitability favouring faster population growth.
 Thus, targeting conservation measures to low habitat suitability areas
 will have a strong population effect in the short term by boosting the
 survival and reproduction of the individuals, while long-term viability
 should be carefully planned with high suitability areas in mind, as those
 contain the territories with higher survival and reproduction potential.
description: Wolf individual and territory data for survival and reproduction
 analyses were provided by the Federal Documentation and Consultation
 Centre on Wolves (DBBW, www.dbb-wolf.de) and by the Senckenberg Centre for
 Wildlife Genetics. Information about individuals and territories was
 grouped into monitoring years (from the 1st of May to the 30th of April
 next year), starting in 2000 until 2020 (April 2021). 
 Individuals were identified genetically and for the survival
 analysis, the original dataset was filtered to retain only reliable
 information on the lifespan of the animals. Thus, individuals with NA
 ("not available") in the variables 'sex' or 'date
 of birth' as well as individuals born or died outside the German
 border were removed, as the environmental data included in the demographic
 analyses were only available for Germany. Consequently, the status of the
 individuals (dead, alive) was assessed until April 2021. The age classes
 were defined as juveniles including pups (0-12 months), subadults (13-24
 months), and adults (&gt; 24 months) (Mech and Boitani, 2003). The
 final dataset contained a total of 1054 individuals.
 Reproduction data was analysed at the territory level. The number
 of juvenile counts might be less than the actual number of pups born, thus
 we defined this variable as ‘minimum reproductive output’. Territories
 with more than 10 observed pups/ juveniles were removed from analyses to
 account for the fact that such a high number of pups might stem from a
 double reproduction and thus belong to one or more females (n = 4). In
 addition, territories from the first year of pair formation were removed
 (n = 227), because pairs typically form shortly before or during the
 breeding season (in autumn or winter) and therefore, there is no
 opportunity for reproduction in the months prior to the pair formation,
 which would correspond to the reproduction in the first year in the
 dataset. The final dataset consisted of 723 entries comprising 205
 different territories with data from 1-16 years per territory.
 Explanatory variables We
 analysed the survival and reproduction of wolves in relation to
 environmental and ecological conditions and individual characteristics.
 For the survival analysis, we used as environmental variables the wolf
 habitat suitability (Planillo et al., 2024) in an 8 km radius of the
 territory centroid, wolf local territory density for each year in a 50 km
 radius, season defined as summer (May-Oct) or winter (Nov-Apr), individual
 sex and age, with the latter being classified as age classes: juveniles
 &lt; 12 months, subadults 12 to 24 months, and adults &gt; 24
 months old. For the reproduction analysis,
 environmental and ecological conditions were described by habitat
 suitability values and local territory density around each breeding
 territory. As individual characteristics, we included the experience of
 the reproductive female in the models, measured sequentially as the number
 of years the same breeding female had reproduced, i.e., the first year
 that the female reproduced was considered year 1, the second year 2, and
 so forth. Data Analysis
 <em>Survival analysis: </em>Survival
 analysis was calculated for the whole population and for each of the age
 classes using Cox Proportional Hazards Regression (Therneau and Grambsch
 2000). <em>Reproduction analysis</em>:
 We analyzed reproduction patterns for i) the probability of reproduction
 in a territory and ii) the total number of juveniles per reproductive
 event. In both cases, data were analysed using generalised linear
 mixed-effects models (GLMMs) with binomial error distribution and logit
 link for reproduction probability and Poisson error distribution and log
 link for the reproductive output. Territory identity was included as a
 random effect. The total number of years that a territory was monitored in
 the dataset was included as a weighting variable to avoid an inflated
 effect of the territories observed only for one year. As explanatory
 variables, we included the mean habitat suitability of the territory,
 local territory density in a buffer of 50 km, and the quadratic effect of
 the experience of the female as fixed effects. 
 <em>Population growth</em>: We used the values of survival and
 reproduction to estimate population growth (λ) and contrast it with the
 observed data. We developed a population matrix model using three age
 classes, based on obtained values of reproduction and annual survival for
 the age classes, and used the eigenvalue of the matrix as our λ. We
 explored the observed population growth values with respect to the effects
 of the minimum and maximum values of habitat suitability. To compute the
 lambda for the latter cases, we predicted the survival of juveniles and
 subadults and the number of pups per reproduction in areas with the lowest
 and highest observed values of habitat suitability.
description: # Habitat and density effects on the demography of an expanding wolf
 population in Central Europe
 [https://doi.org/10.5061/dryad.dncjsxm5m](https://doi.org/10.5061/dryad.dncjsxm5m) Tables containing the data used in the demographic analyses of the German wolf population. ## Description of the data and file structure **Two tables** are provided: * *data_wolf_survival_table.csv.* This table contains individual information used in the survival analyses: information on the number of weeks an individual was alive *(weeks_date*) until confirmed death (*status* = 1) or went missing (censored, *status* = 0), sex, the month of the year that the individual was last detected *(death_month),* the season of the last detection, habitat suitability of the natal (*hs_8km_natal*) and final territories (*hs_8km_final*), the information to calculate the territory density in the 50 km buffer in the natal (*_first*) and final (*_last*) territories (*nterr_buffer50* = the number of territories in buffer, *area_buffer50 =* area of buffer within Germany in km2, *nterr_dens_buffer50 =* territory density). * *data_wolf_survival_table.csv.* This table contains the data used in the reproduction analyses. *TerrID*: unique identifier for the territories; *FemID*: unique identifier for the breeding females; *MaleID*: unique identifier for the breeding males; *Repro*: presence/absence reproduction signs; *Npups*: number of pups/juveniles detected in the territory the same year of the reproduction; *hs_8km*: habitat suitability value for the territory; *nterr_50km*: number of territories in 50 km buffer; area_buffer50: area of 50 km buffer inside Germany in km2; densTerr_buffer50: territory density in 50 km buffer; Delta_bfem: experience of breeding female. In both cases, NA values refer to missing data. **Eight R scripts** are included in this project. Working directories to load and save the data should be adjusted to match the location of the data tables. We recommend running them from a Rstudio project that contains the data in a folder named "data_proc". * 1_prepare_data: script to clean and filter the initial data. Includes the code to produce the tables described above. * 2_explore_data: script to run basic summaries and exploration of the data. * 3_survival_general_model: script that runs the overall population survival model. * 4_survival_ageclass_models: script that runs the annual survival probabilities for all individuals, juveniles, subadults, and adults. * 5_reproduction_models: scripts that run the reproduction analysis. * 6_population_growth: script that runs the matrix models to estimate wolf population growth. * source_functions and source_packages: these scripts contain the basic functions and R libraries needed to run the previous scripts.