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

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.
Here, we investigated the impact of vegetation and climate 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, 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 elevation 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.