Soundscapes and airborne laser scanning identify vegetation density and its interaction with elevation as main driver of bird diversity and community composition

Abstract

Aim

Mountain ecosystems are hotspots of biodiversity due to their high variation in climate and habitats. Yet, above average rates of climate change and enhanced forest disturbance regimes alter local climatic conditions and vegetation structure, which should impact biodiversity. We here investigated the impact of vegetation and elevation as well as their interactions on bird communities to improve our ability to predict climate change effects on bird communities.

Location

European Alps, Germany.

Methods

We studied patterns and drivers of bird communities at 213 plots along gradients in vegetation density and elevation using autonomous sound recorders. Bird species were identified from soundscapes by Convolutional Neural Networks (BirdNET) and taxonomists.

Results

Bird diversity and community metrics were moderately to strongly correlated for data based on either identification by BirdNET or taxonomists (Pearson's r = .47–.94), and ecological findings were overall similar for both datasets. Vegetation density 1–2 m and >2 m above ground strongly affected bird diversity and community composition and mediated effects of elevation. Community composition changed with elevation more strongly in habitats with low than high vegetation density >2 m. Species numbers decreased with elevation in habitats with low vegetation density 1–2 m and >2 m above ground, but increased in habitats with high vegetation density. Overall, functional and phylogenetic diversity increased with elevation indicating lower habitat filtering, but patterns were also mediated by vegetation density.

Main Conclusions

Our results indicate that bird communities in the German Alps are determined by strong interactive effects of elevation and vegetation, underlining the importance to consider variation in vegetation in studies of biodiversity patterns along elevational gradients and under climate change. Combining remote sensing data and biodiversity monitoring based on autonomous sampling and AI-based species identification opens new avenues for bird monitoring and research in remote areas.