Improving bird abundance estimates in harvested forests with retention by limiting detection radius through sound truncation

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.

Methods
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 (Pinus banksiana) or lodgepole pine (P. contorta), mixed-wood stands of upland spruce and deciduous trees, conifer stands of white spruce (Picea glauca) or Engelmann spruce (P. engelmannii), and deciduous stands of trembling aspen (Populous tremuloides), and to a lesser extent balsam poplar (P. balsamifera) and white birch (Betula papyrifera).

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 >=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 m2 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 25th to July 6th). 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 (Sμ) 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.