Simple SummaryWildlife conservation effectiveness relies on the accuracy of population data used to build management plans. Estimating population size and dynamics of large vertebrates living on wide areas is a challenging task and results can vary depending on the methods adopted. In the present study, the size of a wolf population was estimated both by field monitoring and statistical modeling on demographic data. Results showed discrepancies with respect to previous bibliographic estimates which resulted significantly lower. Moreover, statistical techniques on demographic data allowed a deeper knowledge of population’s trend, pointing out the importance of mortality rates in driving population growth. We suggest combining statistical modeling and field monitoring to achieve a satisfactory knowledge of population size and dynamics useful to foster reliable management and conservation plans.AbstractWe estimated the current size and dynamics of the wolf population in Tuscany and investigated the trends and demographic drivers of population changes. Estimates were obtained by two different approaches: (i) mixed-technique field monitoring (from 2014 to 2016) that found the minimum observed pack number and estimated population size, and (ii) an individual-based model (run by Vortex software v. 10.3.8.0) with demographic inputs derived from a local intensive study area and historic data on population size. Field monitoring showed a minimum population size of 558 wolves (SE = 12.005) in 2016, with a density of 2.74 individuals/100 km2. The population model described an increasing trend with an average annual rate of increase λ = 1.075 (SE = 0.014), an estimated population size of about 882 individuals (SE = 9.397) in 2016, and a density of 4.29 wolves/100 km2. Previously published estimates of wolf population were as low as 56.2% compared to our field monitoring estimation and 34.6% in comparison to our model estimation. We conducted sensitivity tests to analyze the key parameters driving population changes based on juvenile and adult mortality rates, female breeding success, and litter size. Mortality rates played a major role in determining intrinsic growth rate changes, with adult mortality accounting for 62.5% of the total variance explained by the four parameters. Juvenile mortality was responsible for 35.8% of the variance, while female breeding success and litter size had weak or negligible effects. We concluded that reliable estimates of population abundance and a deeper understanding of the role of different demographic parameters in determining population dynamics are crucial to define and carry out appropriate conservation and management strategies to address human–wildlife conflicts.