Data from: Acoustic indices estimate breeding bird species richness with daily and seasonally variable effectiveness in lowland temperate Białowieża forest

Biodiversity monitoring is important to follow temporal changes of the environment. We examined whether acoustic indices can be used as a rapid and easy-to-apply tool for bird biodiversity estimation in one of the least changed European lowland forests – the Białowieża Forest. We collected soundscape recordings in early and late spring at 84 randomly chosen recording points. At each recording point, we analysed 72 1-min sound samples to evaluate how well acoustic indices predict bird species richness from the perspective of a single sound sample, single survey, and recording point, and how they follow the daily pattern of singing activity. For each 1-min sound sample, we prepared a list of vocalizing bird species and calculated three acoustic indices: Bioacoustic Index (BI), Acoustic Complexity Index (ACI), and Acoustic Diversity Index (ADI). We found that from the perspective of a single 1-min sound sample, BI best predicts the bird species richness, independently of time in the season but variably across the day, while ACI and ADI showed weaker and seasonally and daily variable dependency. The correlation between each index and the number of bird species was stronger in the early survey than in the late survey. All acoustic indices followed daily bird activity patterns, yet they provided greater values before the peak of the species richness estimated by manual spectrogram scanning and listening to recordings. We showed that acoustic indices correlate moderately to strongly with the bird species richness obtained by manual spectrogram scanning and listening to recordings by humans. Therefore, acoustic indices can be used as a tool for rapid estimation of bird biodiversity in temperate forests. However, daily and seasonal variation in effectiveness of acoustic indices should be taken into account in the analysis.The S1Dataset.xlsx file contains: Recording points distribution: ID of recording points, coordinates of each recording point, altitude, date of recording (one morning at each point during early and late survey); List of detected bird species: list of bird species detected during the study; 1-min sound sample: recording point ID, 1-min sound sample corresponding to wav file name, survey (early or late), minute in survey (Ordinal variable describing the order of 1-min sound sample at point during a day), number of bird species detected in 1-min sound sample by manual spectrogram scanning and listening to recordings, values of Bioacoustic Index (BI), Acoustic Complexity Index (ACI) and Acoustic Diversity Index (ADI) calculated for each 1-min sound sample; Survey: recording point ID, survey (early or late), total number of bird species detected manually during single survey at recording point, average number of bird species detected manually during survey at recording point (sum of bird species detected in each 1-min sound sample divided by the number of 1-min sound samples analysed during single survey equal 36); mean BI, ACI, ADI for a single survey (average values for 36 1-min sound samples analysed during single survey per recording point); Recording point: recording point ID, total number of bird species detected manually during early and late survey at recording point, average number of bird species detected manually during early and late survey at recording point (sum of bird species detected in each 1-min sound sample divided by the number of 1-min sound samples analysed during early and late survey equal 72); mean BI, ACI, ADI for for recording point (average values for 72 1-min sound samples analysed during early and late survey at recording point). Funding provided by: Narodowe Centrum NaukiCrossref Funder Registry ID: http://dx.doi.org/10.13039/501100004281Award Number: 2019/35/D/NZ8/0441684 recording points were randomly selected in Białowieża Forest District. Points were located both in protected and unprotected areas. The distance between neighbouring recording points ranged from 470 to 1,180m. At each recording point, we recorded a soundscape twice in the breeding season: once between April 20 and May 02, 2021 (early survey) and once between May 18 and May 26, 2021 (late survey). During each survey, we recorded a soundscape from two hours before sunrise to four hours after sunrise. We used 10 Song Meter SM3 acoustic recorders (Wildlife Acoustics) equipped with one omnidirectional microphone SMM-A1 (sensitivity -11 +/- 4 dB; signal-to-noise ratio > 68 dB). The recording microphones were checked by using VOLTCRAFT SLC-100 sound level calibrator. We recorded mono .wav files (1-hour duration, 48kHz/16 bit sampling rate, low and high frequency filters off, gain 24 dB). From the soundscape recordings, we analysed 1-min every 10 minutes (6 minutes per hour, 36 minutes per survey per recording point). 1-min sound samples were analysed by manual scanning and listening to recordings, in Raven Pro 1.6.1 software (Cornell Lab of Ornithology) with the following settings: Window = Hamming, window size = 23.1 ms, Overlap = 75%. We compiled a list of bird species recorded in each 1-min sound sample. For each 1-min sound sample we calculated acoustic indices using Kaleidoscope Pro 5.4.7 software (Wildlife Acoustics): Bioacoustic Index (BI) – settings: minimal frequency Fmin=500 Hz, Fmax=10 000 Hz, FFT=1,024 samples; Acoustic Complexity Index (ACI) – settings: minimal frequency Fmin=500 Hz, Fmax=10 000 Hz, J=5s; Acoustic Diversity Index (ADI) – settings: minimal frequency Fmin=500 Hz, Fmax=10 000 Hz, frequency step=500 Hz, threshold=-50 dB