DOI: 10.5281/zenodo.15835877
title: BETA-FOR_SP9_Birds_Soundboxes_2022/2023

description: one-time sampling + rapid assessment 2023

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BETA-FOR 2022/2023 Sound data - Protocol

 

* Sources: 

** Bavarian Forest (2022,2023)

** University Forest (2022, 2023)

** Hunsrueck (2023)

** Luebeck (2023)

** Saarland (2023)

 

* Data Cleaning

 

* Analyzing

** birdNET V2.4

** custom model: beta-for model (_bf) (retrained birdNET V2.4)

*** Params:

Hidden units,Dropout,Batchsize,Learning rate,Crop mode,Crop overlap,Upsamling mode,Upsamling ratio,use mixup,use label smoothing

1024,0.5,8,0.001,center,0,mean,0.25,True,False

** general confidence threshold: 0.5

 

* Data Filtering

** research period (based on expert knowledge): 24.03. - 15.06.

** target species (based on expert selection, s. 'IndenGebietenmoeglicheArten.csv')

** species-specific confidence thresholds (based on https://doi.org/10.1007/s10336-024-02144-5 / expert knowledge, s. 'birdNET_validation_thresholds_logit_JM.csv')

** replace Picoides_tridactylus by Picoides_tridactylus_bf (based on expert selection)

** consider only one species per 2min-file as (1) detection

** pivot -> species incidences per file; including files without detections 

 

* Aggregation

** group by plot_year (sum incidences, count files)

 

 

 

 

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We greatly acknowledge the help of:

Michael Junginger, Sophia Hochrein, Ruth Pickert, Katharina Kallnik, Manuel Mauermann, Tim Becker, Tamara Rosch, Ralph Martin, Interns of the Bavarian Forest Nationalpark and all other assistants
description:  

Variables Description (betafor_individualThresholds_targetSpecies_researchPeriod_pivot_allFiles_corrected)

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*file: filename

*plot: unique BETA-FOR patch label

*year: year when the sound data was recorded (2022 or 2023)

*plot_year: combination of plot and year

*date: date when the sound data was recorded

*time: time when the sound data was recorded

* [Birds]: presence/absence (1/0)
description: Variables Description (birdNET_validation_thresholds_logit_JM.csv)

The species-specific thresholds were calculated based on sound recordings from the Bavarian Forest, Berchtesgaden and the University Forest, labelled by Ralph Martin and following Wood, C.M., Kahl, S. Guidelines for appropriate use of BirdNET scores and other detector outputs. J Ornithol 165, 777–782 (2024). https://doi.org/10.1007/s10336-024-02144-5.

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*scientificName: The taxonomic name of the bird species being analyzed.

*true_positives: The count of detections manually verified as the correct target species within the validation dataset.

*false_positives: The count of detections manually verified as noise, background sounds, or non-target species (misidentifications).

*intercept: The constant in the logistic regression model; it represents the log-odds of a detection being a true positive when the BirdNET confidence score is zero.

*beta: The regression coefficient for the BirdNET confidence score, indicating the rate of change in the log-odds of a true detection as the BirdNET score increases.

*threshold85 (to 99): The BirdNET confidence score (ranging from 0 to 1) required to reach a specific precision (e.g., 85% of detections are expected to be True Positives).

*Aktueller Threshold: The final confidence threshold selected for further analyses (selected by Jörg Müller). 

*AlternativModell: note indicating if further analyses should build on the results of the standard BirdNET global classifier or a custom model/classifier (beta-for model (_bf)) specifically trained or adjusted for that species.