USGS Science⌕ Search

USGS · 70214033

Reliability of external characteristics to age Barrow’s goldeneye

Abstract

Accurate assignment of age class is critical for understanding most demographic processes. For waterfowl, most techniques for determining age class require birds in hand, reducing utility for quickly and efficiently sampling a large portion of the population. As an alternative, we sought to establish an observation‐based methodology, achievable in the field with standard optics, for determining age class of Barrow's goldeneyes ( Bucephala islandica ). We photographed heads, wings, and bellies of 232 Barrow's goldeneyes captured during late winter (February–April) of 2007–2015 along the north Pacific Coast. From these photographs, we focused on 5 external characteristics for both males and females, with binary states that putatively corresponded to 2 age classes—first‐year birds (<1 yr) and adults (>1 yr). For males, all 5 external traits (belly color, head color, eye color, facial crescent, median secondary coverts color) had binary states that were reliably distinguishable by observers. Moreover, all 5 external traits were highly predictive of age class (≥96% concordance between external vs. bursal‐derived ages), and novice observers, after receiving training, were able to accurately age 96% of first‐year and 99% of adult males. In contrast, patterns were weaker for females; putative external characteristics of female age class (belly color, bill radiance, bill blackness, eye color, median secondary coverts color) had 77–91% concordance with bursal‐derived age, compared with 96–100% for males, and observers misidentified age classes of 15% of females, compared with only 2% of males. Overall, age classes of male Barrow's goldeneyes were accurately and reliably distinguishable during winter based on several external characteristics, whereas those of females were not. Our technique may be used to estimate age composition of male Barrow's goldeneyes during winter, providing a useful metric for monitoring annual changes in adult‐to‐juvenile ratios and other important demographic parameters.

Explore related subjects

90° N90° S · 180° W ← longitude → 180° E
Source-reported bounding extent: 48.86471476180277° to 59.93300042374631° latitude; -139.5703125° to -123.04687499999999° longitude. This indicates report coverage, not an exact sampling location. View area on OpenStreetMap.

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Tyler L. Lewis, Daniel Esler, Danica H. Hogan, W. Sean Boyd, Timothy D. Bowman, Jonathan Thompson. 2020-09-18. Reliability of external characteristics to age Barrow’s goldeneye. https://doi.org/10.1002/wsb.1123

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related USGS reports

Considerations for using autonomous recording units to monitor northern bobwhite occupancy at large spatial scales

Northern bobwhites ( Colinus virginianus ) have experienced widespread declines across their range, increasing the need for population monitoring across broad spatial and temporal scales. Yet large-scale monitoring of post-breeding bobwhites remains limited by the time-intensive nature of fall (i.e., autumn) covey counts. Passive acoustic monitoring with autonomous recording units (ARUs) and automatic detection software may help overcome these constraints but requires further study. From 2017 to 2021, we paired in-person observer point counts with ARU surveys during the fall in southern Iowa, USA to 1) evaluate how ARU detections compare to observer detections, 2) assess the accuracy of automatic classification software for covey calls, 3) gauge the influence of weather and ordinal date on detection, and 4) offer best practices for processing ARU acoustic data. We found high correspondence (89.2%) in detection and non-detection outcomes between paired ARU and observer surveys. Across manually processed recordings, 95.7% of covey call detections occurred within a 35-min window (50–15 min before sunrise). Increasing wind speed and cloud cover were associated with reduced detection probabilities. Wind speeds ≥5 m/s resulted in detection probabilities <0.5. The cumulative probability of detecting bobwhites at a site exceeded 0.995 (95% Credible Interval [CrI] = 0.987, 0.999) after 4 survey days when technicians processed the files. Manual processing had the highest mean detection probability (0.75, 95% CrI = 0.66, 0.82) compared with automatic detection software with 2 different confidence thresholds (0.46 [95% CrI = 0.39, 0.53] and 0.45 [95% CrI = 0.36, 0.55]). Automatic detection software allowed for substantially faster processing with relatively few false positives (false positive rate of 0.014 to 0.038). Autonomous recording units coupled with automatic detection software provided a promising alternative to in-person observer point counts of bobwhite covey calls for determining site occupancy and have the potential to greatly increase capacity for landscape-level bobwhite monitoring.

Iowa↗

Developing a low-cost drone-based method for deploying and retrieving autonomous recording units in inaccessible areas

Autonomous Recording Units (ARUs) are increasingly used in research to support passive acoustic monitoring, but deployment in remote or inaccessible locations remains logistically challenging. Traditional manual placement is labor-intensive, time-consuming, potentially hazardous, and often limited to habitat edges, creating sampling biases and restricting spatial coverage. To address placement limitations in wetland systems, we developed and field-tested a low-cost, lightweight floating platform paired with a drone-based deployment and retrieval system. The drone-deployable ARU platform, constructed from inexpensive, off-the-shelf materials (~US $21 per unit), weighed ~560 g and was mounted with an ARU (AudioMoth) at ~1.2 m above water. The platform was designed for stability, portability, and compliance with Federal Aviation Administration Part 107 regulations. Field trials were conducted across 50 operations in various types of historical rice-field impoundments along the South Carolina coast, which provide critical habitat for secretive marsh birds such as rails (e.g ., Rallus, Laterallus , and Porzana ) and bitterns (e.g ., Botaurus and Ixobrychus ). All 50 deployments and retrievals were successful, demonstrating the robustness of the method under diverse hydrologic and vegetative conditions. Deployment times were significantly shorter than retrieval times (median 6 vs. 9 min), with retrieval requiring greater precision for hook engagement. Operation times scaled predictably with distance: deployment increased by 0.93 min/100 m (R 2 = 0.94) and retrieval by 1.17 min/100 m (R 2 = 0.95). No platform damage or displacement was observed, as emergent vegetation was likely providing natural anchoring. Our approach offers an affordable and effective solution for expanding ARU coverage in large, inaccessible wetlands, reducing sampling bias and enhancing biodiversity monitoring. With continued advances in drone payload capacity, battery endurance, and beyond-visual-line-of-sight flight regulations, our workflow could be adapted for deploying multi-sensor platforms (e.g., ARUs, eDNA samplers, water and air quality sensors, plant sample collectors, insect traps, and trail cameras) to support integrated biodiversity monitoring in challenging landscapes worldwide.

South Carollina↗