Abstract
Suitable habitat is a key factor in a species' distribution and is essential for management and conservation assessment. The rock ptarmigan (Lagopus muta) inhabits mountain ranges across the northern hemisphere but is poorly investigated in Fennoscandia. With the rise in temperature and other climate changes influencing the environment, it is essential to understand rock ptarmigans’ niche in the fauna.
The data for this study was collected over a period of two years, from 2022 to 2024, from 34 rock ptarmigans that were tagged with a GPS transmitter in the southern Sweden mountain range. A combination of six parameters was used to investigate the environmental factors that influence the spatial pattern of rock ptarmigans during the autumn. This included the creation of a vegetation map using remote sensing and machine learning methods and applying a generalised linear mixed model (GLMM) to describe rock ptarmigans’ selection of vegetation and topographic features. This was applied to a resource selection function (RSF) by comparing used vs. available in a general linear model (GLM). The results revealed that the most selected topographic features were steep north-facing slopes (34° degrees) with sparse vegetation based on the Normalized difference vegetation index (NDVI) score (0.2), often consisting of a rock/heather combo or non-vegetation with an altitude ranging from 900-1200 meters above sea level. Altitude was found to be a critical factor in rock ptarmigan distribution, as highlighted in the RSF. They are limited to the mountain peaks or surrounding areas. Additionally, a topographic position index (TPI) was applied to gain a more in-depth understanding of rock ptarmigans' use of the terrain, indicating a selection of beneath the peaks.
These findings highlight the rising concern about future suitable habitats for rock ptarmigans in the southern Sweden mountain ranges and the importance of carefully planned management and conservation efforts.
Keywords: Habitat, rock ptarmigan, Lagopus muta, resource selection function, remote sensing