The diet of Canis lupus in Latvia was studied from December 2001 to April 2008 based on analyses of 165 stomachs. Wild ungulates (cervids and wild boar) were the main food of the wolves. Cervids were found in 64.7% of the samples (69.7% of the biomass), wild boar in 25.9% of the samples (22.6% of the biomass), and beavers in 8.6% of the samples (6.4% of the biomass). The average mass of stomach contents was 824.1 g. Empty stomachs made up 26.7% of all stomachs. Statistically significant differences were found comparing variances of stomach content biomass between 1–2-year-old and adult animals and also in beaver remains in the diet of male and female wolves (12.9% and 3.2% of the stomach content biomass, respectively). There were no other significant differences in the diet composition, stomach content biomass, and percentage of empty stomachs among age groups, between sexes, and between eastern and western parts of Latvia. Interpretation and implications to wolf conservation policy based on the given results are suggested.

In Latvia, livestock depredation by wolves has increased during the last two decades. Most of the attacks occur in summer and autumn during wolf hunting season. Use of effective preventive measures in Latvia is low, and farmers primarily rely on wolf hunting as a depredation reduction measure. The total numbers of wolf attacks and number of affected sheep per year in regional forest management units were analyzed in relation to the estimated wolf density, extent of culling, and proportion of juveniles, as well as the sheep density and estimated number of wild prey animals. The response variables (number of attacks and affected sheep per year) were modelled using a negative binomial regression, testing the effects of every covariate separately and building models from the significant covariates. The depredation level was related to sheep density and estimated wolf population size. No reducing effect was found for culling, and an even greater depredation rate was expected when the proportion of culled wolves increased. In addition, no significant effect was associated with the other covariates. However, greater numbers of affected sheep were expected at higher red deer density, suggesting increased opportunistic livestock depredation when red deer locally outcompete roe deer, the preferred wolf prey in Latvia.

Large carnivores are essential components of natural ecosystems. In populated areas, their conservation depends on preserving a favorable status in coexistence with humans, which may require the elimination of excess carnivores to minimize public concerns. As the Baltic region currently hosts a thriving wolf population, locally sustainable management of wolves is important for preserving biodiversity at a European scale. In this paper, we provide a dynamic assessment of the Latvian wolf subpopulation from 1998 until 2020. This study is based on age composition and fecundity data from teeth, uteri, and ovaria inspections obtained from samples of legally culled or accidentally killed individuals. The abundance estimates indicated population growth that exceeded the previously predicted carrying capacity. The proportion of juveniles among the culled individuals increased in recent years, but the mean age of culled adults exhibited a stable trend. In presumably nonselective hunting, the juveniles and individuals older than 3 years had greater culling mortality estimates in comparison with other age classes, and the culling rates for adult females of particular age classes were higher than for males of the same age. While creating significant hunting pressure, wolf management in Latvia may have contributed to the population growth by affecting its demographic processes.

Passive acoustic sensors have the potential to become a valuable complementary component in red deer Cervus elaphus monitoring providing deeper insight into the behavior of stags during the rutting period. Automation of data acquisition and processing is crucial for adaptation and wider uptake of acoustic monitoring. Therefore, an automated data processing workflow concept for red deer call detection and localization was proposed and demonstrated. The unique dataset of red deer calls during the rut in September 2021 was collected with four GPS time-synchronized microphones. Five supervised machine learning algorithms were tested and compared for the detection of red deer rutting calls where the support-vector-machine-based approach demonstrated the best performance of −96.46% detection accuracy. For sound source location, a hyperbolic localization approach was applied. A novel approach based on cross-correlation and spectral feature similarity was proposed for sound delay assessment in multiple microphones resulting in the median localization error of 16 m, thus providing a solution for automated sound source localization—the main challenge in the automation of the data processing workflow. The automated approach outperformed manual sound delay assessment by a human expert where the median localization error was 43 m. Artificial sound records with a known location in the pilot territory were used for localization performance testing.