Abstract
This study investigates a regional population trend of willow ptarmigan in Jämtland County, Sweden. This was done by using hierarchical distance sampling and Bayesian inference to estimate population densities from line transect surveys conducted for 29 consecutive years. The ptarmigan exhibits fascinating population dynamics, described to be influenced by habitat characteristics, predation, and climatic factors. The primary goal was to develop a robust model that captures annual density trends, enabling a broader understanding of population variability over time.
The methodology employs hierarchical distance sampling models to analyze survey data, incorporating random effects for year, area, and transect to account for temporal and spatial variability. Field data were collected along systematically spaced line transects across varied alpine habitats using trained personnel with pointing dogs.
Results show a generally stable population density over the last two decades, influenced by periodic fluctuations aligning with the species’ underlying cyclic patterns in population dynamics. The best-fitting model incorporated random effects for year, area, and transect, which provided the highest accuracy in capturing density patterns. Residual analyses revealed that while the model captures general density trends, experiences some limitations regarding variability.
By refining the model's spatial resolution and incorporating ecologically relevant covariates, managers could detect trends on a smaller scale within the region, facilitating more adaptive, area-specific management practices.