AUTHOR=Camp Richard J. , Asing Chauncey K. , Hunt Noah , Wang Alex , Farmer Chris , Neitmann Lindsey , Banko Paul C. 

TITLE=Fine-grained temporal population monitoring of a declining, critically endangered Hawaiian honeycreeper

JOURNAL=Frontiers in Conservation Science

VOLUME=Volume 6 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/conservation-science/articles/10.3389/fcosc.2025.1564661

DOI=10.3389/fcosc.2025.1564661

ISSN=2673-611X

ABSTRACT=Annual point counts are commonly used to monitor birds to track population densities across space and time. Palila (Loxioides bailleui) are surveyed annually in the first quarter, but we recently instituted quarterly sampling that offers a unique opportunity to improve estimator precision. We conducted point-transect distance sampling point counts during the first quarter of 2020 through 2024, and the second through fourth quarters in 2022 and 2023, and the second quarter in 2024. The reduced sampling intensity during the quarterly counts, however, requires model-based methods to estimate abundance to the entire sampling frame. We modeled spatial and temporal correlation using a soap film smoother within a generalized additive modeling framework, a density surface model, fitted to palila counts each quarter for the five-year timeseries to track changes in population abundances. Our results indicate that palila maintained a high-density hotspot throughout the five-year timeseries; however, the extent of the hotspot declined substantially over the timeseries while densities within the hotspot declined from about 3 birds/ha in 2020 to about 1 bird/ha in 2024, which resulted in a 66% decline in palila abundances over 5 years. Density surface model estimates give on average a confidence interval width that was 74.7% shorter than the associated distance sampling confidence interval widths. Our results indicate that palila may benefit most if management actions were applied within the remaining hotspot. Additionally, this temporally fine-grained sampling provides information on seasonal movement patterns and resource tracking, and population response to management and conservation actions. Our spatially explicit, model-based approach is applicable to a wide range of monitoring programs, particularly those with inconsistent, opportunistic spatial coverage.