Search USGS⌕ Search

USGS · 70176948

Long-term population monitoring: Lessons learned from an endangered passerine in Hawai‘i

Abstract

Obtaining reliable population estimates is crucial to monitoring endangered species and developing recovery strategies. The palila ( Loxioides bailleui ) is an endangered seed-eating Hawaiian honeycreeper restricted to the subalpine forests of Mauna Kea, a volcano on the island of Hawai‘i, USA. The species is vulnerable to extinction primarily because >90% of the population is concentrated in <30 km 2 of habitat on the western slope of this high, dormant volcano. Annual surveys of the palila population have been conducted for ecological, legal, and other purposes since 1980. Because refinements to sampling protocols and analytical methods have evolved, we examined means of adapting the monitoring program to produce comparable estimates of abundance over the past 25-year period and into the future. We conducted variable circular plot surveys during the nonbreeding season (Jan–Mar) and this used data to obtain estimates of effective detection radius and annual density with Distance 4.0, Release 2. For comparability over the time-series, we excluded from analysis the data from new transects. We partitioned the 25-year data set (1980–1996 and 1997–2004) into 2 separate analyses because, beginning in 1997, observers received more training to reduce their tendency to estimate distances to 5-m intervals. We used geographic strata in the analysis of recent surveys because changes in habitat may have invalidated the density-based strata used previously. By adding observer and year and observer and time of day as co-variables, we improved the model fit to the 2 data sets, respectively. Annual estimates were confounded by changes in sampling methodology and analytical procedures over time. However, the addition of new transects, increased training for observers, and use of exact distance estimates instead of rounding also improved model fit. Habitat characteristics and behavior of palila that potentially influenced detection probability, sampling, analysis, and interpretation were regeneration of trees in response to reduced numbers of introduced browsing mammals, seasonally variable rates of vocalization, non-territoriality, and resource-tracking along an elevation gradient. We believe our adaptive approach to analysis and interpretation of 25 years of annual variable circular plot data could help guide similar long-term monitoring efforts.

Explore related subjects

90° N90° S · 180° W ← longitude → 180° E
Source-reported bounding extent: 18.917466° to 22.23317° latitude; -160.24961° to -154.809379° longitude. This indicates report coverage, not an exact sampling location. View area on OpenStreetMap.

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Luanne Johnson, Richard J. Camp, Kevin W. Brinck, Paul C. Banko. 2006. Long-term population monitoring: Lessons learned from an endangered passerine in Hawai‘i. https://doi.org/10.2193/0091-7648(2006)34%5B1055%3Alpmllf%5D2.0.co%3B2

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↗