Chestnut, T. | bioRxiv Science

bioRxiv2025

Passive Acoustic Monitoring within the Northwest Forest Plan Area: 2023 Annual Report

Here we document progress in implementing large-scale passive acoustic monitoring across the Northwest Forest Plan area to track population trends of northern spotted owls (Strix occidentalis caurina), barred owls (S. varia), marbled murrelets (Brachyramphus marmoratus), and a broad array of forest-adapted wildlife. In 2023, we deployed 4,012 autonomous recording units across 1,009 randomly selected 5-km{superscript 2} hexagons, generating nearly 2.2 million hours of recordings, representing approximately 1 petabyte of acoustic data. These data were processed with PNW-Cnet v5, the latest version of our convolutional neural network model, trained on 135 sound classes representing over 80 species and environmental sounds. Model performance demonstrated high precision for focal species and many additional taxa, substantially reducing manual review effort while enabling broad-scale biodiversity assessments. Results confirmed northern spotted owl detections in all 20% sample areas, with occupancy varying geographically and declining notably in the Tyee study area. Barred owls were widely detected, with the highest prevalence in Oregon and Washington and comparatively lower occupancy in California. Marbled murrelets were consistently detected in coastal areas, particularly the Olympic Peninsula and Oregon Coast Range. Beyond these focal species, PAM and PNW-Cnet generated robust datasets for a wide range of birds, mammals, and disturbance indicators, underscoring the value of random-site, multi-species monitoring. The 2023 field season marked the first full implementation of the 2% + 20% NWFP sampling design, expanding monitoring coverage while strengthening collaborations with federal and state partners. These efforts provide the foundation for long-term, cost-effective wildlife monitoring and inform conservation strategies in dynamic forest ecosystems.

ecology↗

bioRxiv2023

Foot orientation and trajectory variability in locomotion: effects of real-world terrain

Capturing human locomotion in nearly any environment or context is becoming increasingly feasible with wearable sensors, giving access to commonly encountered walking conditions. While important in expanding our understanding of locomotor biomechanics, these more variable environments present challenges to identify changes in data due to person-level factors among the varying environment-level factors. Our study examined foot-specific biomechanics while walking on terrain commonly encountered with the goal of understanding the extent to which these variables change due to terrain. We recruited healthy adults to walk at self-selected speeds on stairs, flat ground, and both shallow and steep sloped terrain. A pair of inertial measurement units were embedded in both shoes to capture foot biomechanics while walking. Foot orientation was calculated using a strapdown procedure and foot trajectory was determined by double integrating the linear acceleration. Stance time, swing time, cadence, sagittal and frontal orientations, stride length and width were extracted as discrete variables. These data were compared within-participant and across terrain conditions. The physical constraints of the stairs resulted in shorter stride lengths, less time spent in swing, toe-first foot contact, and higher variability during stair ascent specifically (p<0.05). Stride lengths increased when ascending compared to descending slopes, and the sagittal foot angle at initial contact was greatest in the steep slope descent condition (p<0.05). No differences were found between conditions for horizontal foot angle in midstance (p[≥]0.067). Our results show that walking on slopes creates differential changes in foot biomechanics depending on whether one is descending or ascending, and stairs require different biomechanics and gait timing than slopes or flat ground. This may be an important factor to consider when making comparisons of real-world walking bouts, as greater proportions of one terrain feature in a data set could create bias in the outcomes. Classifying terrain in unsupervised walking datasets would be helpful to avoid comparing metrics from different walking terrain scenarios.

bioengineering↗