DOI: 10.5061/dryad.p2ngf1w1c
title: Improving bird abundance estimates in harvested forests with retention by limiting detection radius through sound truncation

description: An inherent challenge with acoustically surveying birds is that the
 distance at which they can be detected depends on how far their song can
 be heard. We developed a distance-based sound detection space truncation
 method to correct for variable sampling radii due to surveying in forested
 or open conditions. The method was pivotal in evaluating bird responses to
 retention patches; without this methodological advancement, the impact of
 retention patches on songbird abundance was vastly underestimated. In the
 boreal forest, these patches of live trees are retained in regenerating
 harvested forests to provide ecological services for species adapted to
 natural disturbances. Although we did not verify our a priori assumption
 with ground observations, our findings suggest that limited-distance
 sampling better captures the effects of retention patches on bird use of
 harvested forests. When evaluated using unlimited distance surveys,
 retained trees had a negligible effect on bird abundance, whereas applying
 detection distance truncation highlighted the importance of retention on
 forest birds. We found that early to mid-seral forest songbirds benefited
 from retention patches, with notable increases in abundance after 10 years
 of regeneration. The size of retention patches, ranging from 0.1 to 1.2
 hectares, did not have a linear relationship with bird abundance. Instead,
 edge effects stemming from the configuration of these patches emerged as
 key determinants of abundance for the majority of the species studied.
 Retention patches that were nearest to unharvested forests were used the
 most, compared to further into harvest areas. Our research not only
 highlights the underestimated impact of small-scale live tree retention on
 forest songbirds but also introduces a significant methodological
 innovation in the field of acoustic monitoring.
description: Study Area & Site Selection We
 conducted the study in harvested areas in the boreal and foothills forests
 of Alberta (Figure 1). Dominant vegetation in and surrounding harvest
 areas consist of stands of jack pine (<em>Pinus
 banksiana</em>) or lodgepole pine (<em>P.
 contorta</em>), mixed-wood stands of upland spruce and deciduous
 trees, conifer stands of white spruce (<em>Picea glauca</em>)
 or Engelmann spruce (<em>P. engelmannii</em>), and deciduous
 stands of trembling aspen (<em>Populous tremuloides</em>), and
 to a lesser extent balsam poplar (<em>P. balsamifera</em>) and
 white birch (<em>Betula papyrifera</em>).
 Using the ABMI Wall-to-Wall Human Footprint Inventory (ABMI
 2021), we identified harvest areas. The year of harvest in these areas was
 determined by detecting the inter-annual drop in Normalised Burn Ratio
 (Hird et al. 2021), utilizing the Landtrendr Google Earth Engine pixel
 time-series tool (Kennedy et al. 2018). Tree stand types within 300m of
 the survey coordinates were extracted using the ABMI Alberta Wall-to-Wall
 Land Cover Map, and summarized by dominant stand if a type occupied
 &gt;=70% of the area, categorizing them as deciduous, spruce, pine, or
 mixed-wood stands. A selection of retention patches ranging in size from
 0.1-12,000 m<sup>2</sup> was made evenly across harvest areas
 aged between 1-22 years old, ensuring a proportional representation of
 harvested forest types. Out of a total of 392 sites visited, 246 contained
 retention patches, and 146 were in harvest areas with no residual trees
 within 150 m. Retention patches and areas without retention were mostly
 identified using satellite imagery. Acoustic Surveys
 & Processing Acoustic surveys were conducted
 by affixing a single SM2, SM3, or SM4 ARU (Wildlife Acoustics Inc.) at
 least 150 m from the harvest edge. ARUs recorded 1-minute segments
 passively every 20 minutes from 1 hour before to 4 hours after dawn over a
 minimum of 3 good weather days during the migratory bird breeding season
 (May 25<sup>th</sup> to July 6<sup>th</sup>).
 Sites were mostly visited over the course of 3 years, from
 2021-2023. Recordings in which loud background noise
 due to inclement weather or industrial activity were identified visually
 by an observer and discarded to ensure bird songs were not drowned out by
 ambient noise and their amplitudes could be calculated. After filtering
 out noisy recordings, 10 random 1-minute recordings were selected from
 each study site, and their spectrograms were processed by expert
 transcribers using the WildTrax platform (https://wildtrax.ca).
 In a multi-visit framework, 3-5 surveys per site are more typical, but we
 opted to process more recordings per site to test our new distance
 truncation method. From 6,250 recordings, we identified every singing
 individual for each of the 6 focal species (Drake et al. 2016) and tagged
 the song of each individual in that minute that was the least masked by
 other birds or noises. This ensured that the tag's amplitude was
 related to the individual of interest. For each song,
 signal strength (S<sub>μ</sub>) was calculated with the SoX
 audio processing software (https://sox.sourceforge.net) via the
 bioacoustics platform Wildtrax (Wildtrax, Edmonton, AB, Canada). Signal
 strength, which can be understood as the volume at which a bird song is
 recorded by an ARU, is extracted from a SoX analysis of the temporal and
 frequency bounded song spectrogram tag as the root mean square (RMS) of
 the decibel relative to full scale (dBFS), averaged between the
 directional left and right microphones. RMS dBFS refers to the average
 power of the audio signal relative to the maximum possible level in the
 digital system, which is set at 0 dBFS to prevent digital clipping and
 ensure optimal audio quality. For example, -3 dBFS would indicate that the
 signal is 3 decibels lower than the maximum possible level.
description: # Code and Data for Improving Bird Abundance Estimates in Harvested
 Forests with Retention by Limiting Detection Radius through Sound
 Truncation ## Description Coda and data for a live-tree retention for
 songbirds recovery study, including distance truncation. ## Authors
 Isabelle Lebeuf-Taylor, Elly Knight, Erin Bayne ## Date September 2024 ##
 Experimental Methods ### Retention in regenerating harvests This study was
 conducted in harvested areas of boreal and foothills forests in Alberta,
 Canada. We selected 392 sites, including 246 with retention patches and
 146 without residual trees within 150 m. Acoustic surveys were performed
 using SM2, SM3, or SM4 Autonomous Recording Units (ARUs) from Wildlife
 Acoustics Inc., placed at least 150 m from the harvest edge. ARUs recorded
 1-minute segments every 20 minutes from 1 hour before to 4 hours after
 dawn, for a minimum of 3 good weather days during the migratory bird
 breeding season (May 25th to July 6th), primarily from 2021-2023. After
 filtering out recordings with excessive background noise, 10 random
 1-minute recordings per site were selected for analysis. Expert
 transcribers processed these recordings using the
 [WildTrax](https://wildtrax.ca) platform, identifying and tagging songs of
 six focal migratory songbird species. Signal strength (Sμ) for each tagged
 song was calculated using SoX audio processing software via
 [WildTrax](https://wildtrax.ca), expressed as the root mean square (RMS)
 of the decibel relative to full scale (dBFS), averaged between the left
 and right microphones. ### Sound attenuation predictive dataset To
 establish the relationship between song amplitude and distance, we
 conducted a playback experiment using 15 indicator bird species selected
 to represent acoustically similar groups. These species were chosen based
 on a cluster analysis of song characteristics (mean peak frequency,
 duration, bandwidth, and spectral entropy) extracted from 3951 songs of 79
 species. Playbacks were performed during the 2023 field season, using a
 FoxPro speaker broadcasting at 90 dB sound pressure level. At each site,
 songs were played at three randomly selected distances between 20 m and
 150 m along a homogeneous forest transect, with the speaker positioned at
 an average height of 1.75 m. ARUs (SM2, SM3, or SM4) continuously recorded
 the playbacks. A total of 2250 songs were broadcast across various sites.
 The recordings were then processed in WildTrax, where each detected song
 was tagged and its signal strength (Sμ) was extracted, excluding
 recordings with excessive background noise. ## Contents 1. Data Files 2. R
 Scripts 3. R Markdown Document 4. Output Files 5. Software Requirements 6.
 Data Dictionary 7. Analysis Workflow 8. Additional Notes ## 1. Data Files
 ### Abundance Data Abundance files contain counts for the Olive-sided
 Flycatcher (OSFL), Red-eyed Vireo (REVI), White-throated Sparrow (WTSP),
 Ruby-crowned Kinglet (RCKI), Yellow-rumped Warbler (YRWA), and Tennessee
 Warbler (TEWA). Other columns are the location and visit datatime. *
 `Abundance within 150m.csv`: Counts are within 150m of ARU. * `Abundance
 within 250m.csv`: Counts are within 250m of ARU. * `Abundance untruncated
 distance.csv`: Counts are not distance truncated. ### Covariates.csv *
 *location*: Site name * *Year_since_logging*: years between sampling and
 logging event. Determined with provincial harvest polygons, and verified
 with Landtrendr's NBR spectral analysis. * *latitude*: latitude of
 site * *longitude*: longitude of site * *RETN_m2*: measured area of
 retention patch in *m^2* * *RETN_perimeter*: measured perimeter of patch
 in *m* * *Veg_cat*: Majority vegetation composition of tree stand within
 150m of sampling unit, defined as &gt;= 70% area occupied by a tree group.
 Categories are Pine (jack pine or lodgepole pine), deciduous (white birch,
 aspen, poplar), spruce (white spruce or Engelmann spruce), mixedwood
 (spruce understory, deciduous overstory). * *Dist_near_forest*: the
 distance form the edge of the patch to the nearest forest of a minimum 80
 years of age, in *m*. ### ampl\_dist\_spp.csv Contains amplitude
 measurements at various distances for different bird species. *
 *location*: Site name * *species_code*: 4-letter code of the species whose
 song was broadcast toward the recording unit * *forest*: Forest
 composition type. Options are AS (aspen), BL (black spruce), MI
 (mixedwood), OP (open), PI (pine), SP (upland spruce). * *distance*:
 distance at which song was ploayed, in *m*. * *left_amplitude*: detected
 amplitude by the left mic, in *dBFS RMS* * *right_amplitude*: detected
 amplitude by the right mic, in *dBFS RMS* ### Counts to truncate.csv Long
 format data of mean amplitude of detected songs in harvests, including
 traunction model parameters. Truncation model can be applied to this data.
 * *location*: site name * *recording_date_time*: datae and time of site
 visit * *species_code*: 4-letter code of detetected species. Options are
 TEWA, OSFL, REVI, WTSP, RCKI, YRWA * *Year_since_logging*: years between
 sampling and logging event. * *mean_amp*: amplitude avergaed between left
 and right mic as *dBFS RMS*. * *SM2*: whether ARU unit was an SM2 (1) or
 not (0). ### locations\_sm2\_status.csv Information about recording
 locations and SM2 recorder status. * *location*: site name * *SM2*:
 whether ARU unit was an SM2 (1) or not (0). ###
 number\_of\_transcribed\_recordings\_per\_visit.csv * *location*: site
 name * *number_of_visits*: number of processed visits per site ###
 predicted\_distance\_amplitudes.csv Output of playback experiment models.
 * *distance*: known distance in *m* of played song * \**species_code*:
 4-letter code of the species whose song was broadcast toward the recording
 unit * *BinForest*: binary forest presence (FO) or absence (OP) * *SM2*:
 whether ARU unit was an SM2 (1) or not (0). * *lwr*: 2.5% of predicted
 amplitude in *dBFS RMS* * *upr*: 97.5% of predicted amplitude in *dBFS
 RMS* * *predicted*: predicted amplitude, given model covariate of species
 at specific distance in *dBFS RMS*. ### transcribed\_tasks\_all.csv
 Recordings per site that are transcribed * *location*: site name *
 *recording_date_time*: ID of transcribed recordings ## 2. Scripts (Zenodo)
 * `sound_attenuation_analysis.Rmd`: R Markdown document to model sound
 attenuation. * `apply_perceptibility_truncation.ipynb`: Jupyter notebook
 for applying perceptibility truncation to acoustic survey data. *
 `negBinom_models.ipynb`: Jupyter notebook for modelling species-level
 relative abundance response to variable retention, after applying distance
 truncation. ## 3. Software Requirements * R (version 4.0.0 or later) *
 Python (version 3.7 or later) * Jupyter Notebook * cmdstan (version
 2.32.1) ### R Packages: * tidyverse * glmmTMB * ggplot2 * gridExtra *
 DHARMa * MuMIn * caret ### Python Packages: * pandas * numpy * matplotlib
 * seaborn * scikit-learn * arviz * cmdstanpy ## 4. Analysis Workflow 1.
 Data Preparation: * Load and merge datasets * Calculate mean amplitude *
 Create binary forest variable * Convert variables to appropriate types 2.
 Model Fitting: * Fit several glmmTMB models with different predictors *
 Compare models using AICc 3. Model Diagnostics: * Check residuals using
 DHARMa * Calculate R-squared values 4. Predictions and Visualization: *
 Generate predictions for open and forested habitats * Create plots showing
 attenuation patterns 5. Cross-validation: * Perform 10-fold
 cross-validation * Calculate RMSE and MAE 6. Perceptibility Truncation: *
 Apply distance-based truncation to abundance data * Compare results at
 different truncation distances 7. Negative Binomial Models: * Fit negative
 binomial models for species abundance * Compare models using WAIC *
 Evaluate model fit using diffenret truncation distances using loo and
 plots * Model evaluation with R-hat & trace plots