Search USGS⌕ Search

USGS · 1003872

Avian cholera mortality in lesser snow geese nesting on Banks Island, Northwest Territories

Abstract

Avian cholera is one of the most important diseases affecting waterfowl in North America, but little is known about its ecology and its impact on waterfowl populations. We documented avian cholera mortality in breeding lesser snow geese (Chen c. caerulescens) at the Egg River colony on Banks Island, Northwest Territories, Canada, in 1995 and 1996. Area of the breeding colony, core nesting area, and number of nesting geese were greater in 1996 (colony=7,537 ha, core area=1,581 ha, 401,000 nesting geese) than in 1995 (colony=6,637 ha, core area=996 ha, 318,000 nesting geese). Density of nesting geese also was greater in the core area during 1995 (120 geese/ha) than in 1996 (90 geese/ha). Pasteurella multocida (serotype 1) was cultured from the leg bones of adult snow goose carcasses collected after outbreaks. Mortality from avian cholera began during nesting and continued until birds dispersed at hatch. Mortality appeared to be in foci scattered throughout the nesting colony, but generally was greater where there were greater densities of nesting geese. We estimated that 30,000 and 20,000 geese died in 1995 and 1996, respectively, about 5-9% of the nesting colony. Between 1991 and 1997, at least 4 avian cholera outbreaks occurred at Banks Island. It appears that avian cholera has become endemic in this population of snow geese, and these birds have the potential to transmit the disease to other waterfowl, especially on wintering areas where waterfowl are very concentrated.

Explore related subjects

90° N90° S · 180° W ← longitude → 180° E
Source-reported bounding extent: 70.93100353239014° to 74.66001636880338° latitude; -126.298828125° to -114.9609375° longitude. This indicates report coverage, not an exact sampling location. View area on OpenStreetMap.

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

M.D. Samuel, John Y. Takekawa, G. Samelius, Diana R. Goldberg. 1999. Avian cholera mortality in lesser snow geese nesting on Banks Island, Northwest Territories. https://pubs.usgs.gov/publication/1003872

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↗