DOI: 10.5061/dryad.0000000br
title: Soundscapes and airborne laser scanning identify vegetation density and its interaction with elevation as main driver of bird diversity and community composition

description: 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 &gt;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 &gt;2 m. Species numbers
 decreased with elevation in habitats with low vegetation density 1-2 m and
 &gt;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.
description: <strong>Bird sampling</strong> We
 used bioacoustic audio recorders (BAR, Frontier Labs, Salisbury,
 Australia; standard settings) to capture soundscapes in 2021. Recorders
 had to be moved between plots and could not be installed permanently due
 to the limited availability of recorders. On each plot, recording took
 place on four to five days distributed evenly between late winter (mid
 March) and late summer (mid August) in the submontane, montane, and
 subalpine zone. In the subalpine and alpine zone, only three to four
 recordings were conducted between late April and mid August due to snow
 cover restricting access in spring. Recording was limited to days with no
 or negligible rain and low wind speed. Recorders were placed at
 approximately 1.8 m height close to the plot centre, either attached to a
 tree or wooden pole. Recorders were programmed to record for two minutes
 every twelve minutes from two hours before to four hours after sunrise and
 from three hours before sunset to three hours after sunset.
 <strong>Bird identification</strong>
 For species identification by taxonomists, we selected the first
 2 min of every hour of the morning recording, that is, 12 min per plot and
 sampling day. However, since owls typically sing early in the season
 (Südbeck et al., 2005), we omitted the recording from 2 h before sunrise
 from the second sampling on and only used the subsequent five recordings,
 that is, 10 min per plot and sampling day. Taxonomists (R.M., Lu.G., and
 others (see acknowledgments)) identified vocalizing species and documented
 each species as presence/ and absence for each recording. For further
 analyses, we excluded all species which are not breeding bird species of
 225 terrestrial habitats of the region to avoid spurious results due to
 species not associated with the environmental conditions of our
 plots. For species identification with BirdNET (version
 2.4), a Convolutional Neural Network (Kahl et al., 2021), we used all
 recordings, that is, 60 min around sunrise and sunset. All species that
 are not breeding bird species of terrestrial habitats of the region were
 then excluded. We validated 7399 classifications across 89 (out of 98
 species identified by CNN) in order to identify species specific
 confidence thresholds that maximize the separation between correct and
 incorrect identifications. R.M. reviewed 5527 3-s segments and categorized
 the BirdNET classifications either as true or false positive. We further
 used annotations of our recordings done by Lu.G. at the Bird Sounds Global
 platform (https://bsg.laji.fi/) of the LIFEPLAN research programme
 (https://www.helsinki.fi/en/projects/lifeplan). The annotations were
 provided with a timestamp which allowed us to match and categorize 1874
 additional classifications. For all species with more than 30 true
 positive classifications, we fitted Conditional Inference Trees to
 identify species-specific confidence thresholds. For species with 5 to 30
 true positive classifications, we visually inspected the distribution of
 true and false positives along the confidence axis. If the distribution of
 true and false positives showed a discriminable pattern, we assigned them
 to one of three threshold classes (0.3, 0.5 and 0.8). If true and false
 positives were similarly distributed along the confidence axis or if less
 than 5 true positive classifications were available, we used the highest
 threshold class (0.8). <strong>Trait data and
 phylogeny</strong> We downloaded the bird
 megatree by Jin and Qian (2023) based on Jetz et al. (2012), which was
 pruned to the species identified by one of the two methods applied in our
 study. Moreover, we compiled information on 11 morphometric traits, two
 habitat-related traits, migratory behaviour and trophic level from the
 AVONET database (Tobias et al., 2022). Morphometric traits were corrected
 for their relationship with body size by taking residuals from linear
 models with respective traits as response (log-scale) and body mass
 (log-scale) as predictor (Hagge et al., 2021). Based on correlations among
 morphometric traits we selected the continuous traits body mass, hand wing
 index, beak length, beak width, tail length and tarsus length for further
 analyses. In addition, analyses included preferred habitat (ordinal: 1 =
 dense, 2 = semi-open, 3 = open), migratory behaviour (ordinal: 1 =
 sedentary, 2 = partially migratory, 3 = migratory) and trophic 260 level
 (categorical: herbivore, carnivore, and omnivore). The trait trophic level
 was converted into two binary traits herbivore (0/1) and carnivore (0/1),
 whereas omnivores were binned 1 in both binary traits.
 <strong>Environmental data</strong>
 We measured the coordinates and elevation of each plot centre
 using a Trimble r12i GNSS receiver. To characterize vegetation at each
 plot, we used a high-resolution LiDAR dataset acquired in September 2021
 during leaf-on conditions using a helicopter-mounted Riegel VQ-780i sensor
 with average point density of ~50 points m2 (Mandl et al., 2023).
 Vegetation parameters were calculated within a 25 m radius around the
 plot. Vegetation density &gt;2 m above ground and 1-2 m above ground
 were calculated as the proportion of returns within these height layers,
 and the variation in vegetation height was characterized as the standard
 deviation of LiDAR returns. In addition, we used data on herb layer cover
 (&lt;1 m above ground) and shrub layer cover (1-5 m above ground) from
 ground-based vegetation surveys conducted on one 4 m x 4 m vegetation
 survey area per plot (Braziunas et al., 2024). We tested for
 collinearities between vegetation characteristics calculating pairwise
 Pearson'´s correlation coefficients and by conducting a principal
 component analysis. Based on these results (Figure S3), we selected
 LiDAR-based vegetation density &gt;2 m above ground and vegetation
 density 1-2 m above ground as predictors for bird analyses since they
 represent different vegetation layers, were not correlated strongly and
 reflected a larger area around the plot centre than the parameters derived
 from the vegetation surveys.
description: # Soundscapes and airborne laser scanning identify vegetation density and
 its interaction with elevation as main driver of bird diversity and
 community composition [https://doi.org/10.5061/dryad.0000000br
 ](https://doi.org/10.5061/dryad.0000000br) The dataset contains the audio
 files that were used for identification by taxonomists and the processed
 bird and environmental data at the level of sampling plots (number of rows
 = 213). Bird community metrics are based on the final list of species
 after excluding water-associated and wintering species as well as species
 detections by BirdNET with confidence levels smaller than the
 species-specific thresholds. ## Description of the data and file structure
 The zip file of the audios contains a folder for each plot, each of which
 contains a folder for each sampling where the files are stored. The csv
 file of the processed bird and environmental data contains: * plot: unique
 identifier for each sampling plot Environmental data: * pzabove2
 (vegetation density taller than 2m measured by LiDAR); * pzabove1_2
 (vegetation density 1m to 2m above ground measured by LiDAR); * elevation
 (elevation [m] at the plot center); * elev_class (elevation zone:
 submontan, montan, subalpin), * type_eng (detailed habitat type: pasture,
 herb-, rock-, shrub dominated open land, five forest development stages
 (gap, establishment, plenter, optimum, terminal) * habitat (coarse habitat
 type: open & forest) * tour: 20 specifies plot clusters along main
 access routes; included as random effect to account for spatial
 distribution Bird community metrics based on identification by
 taxonomists: * MDS1tax (NMDS axis 1), * MDS2tax (NMDS axis 2), * SC
 (sample coverage), * qD0_tax (species diversity for q = 0), * qD1_tax
 (species diversity for q = 1), * qD2_tax (species diversity for q = 2), *
 ntaxa (number of species), * PDobsz (ses of phylogenetic diversity based
 on abundance-weighted data), * PDobszPA (ses of phylogenetic diversity
 based on presence-absence data), * FDobsz (ses of functional diversity
 based on abundance-weighted data), * FDobszPA (ses of functional diversity
 based on presence-absence data) Bird community metrics based on
 identification by BirdNET: * MDS1BN (NMDS axis 1), * MDS2BN (NMDS axis 2),
 * SCBN (sample coverage), * qD0_BN (species diversity for q = 0), * qD1_BN
 (species diversity for q = 1), * qD2_BN (species diversity for q = 2), *
 ntaxaBN (number of species), * PDobszBN (ses of phylogenetic diversity
 based on abundance-weighted data), * PDobszBN_PA (ses of phylogenetic
 diversity based on presence-absence data), * FDobszBN (ses of functional
 diversity based on abundance-weighted data), * FDobszBN_PA (ses of
 functional diversity based on presence-absence data)