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.

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 (> 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 < 12 months, subadults 12 to 24 months, and adults > 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

• Survival analysis: Survival analysis was calculated for the whole population and for each of the age classes using Cox Proportional Hazards Regression (Therneau and Grambsch 2000).
• Reproduction analysis: 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.
• Population growth: 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.