Avian Conservation and Ecology (Dec 2020)
Integrating wetland bird point count data from humans and acoustic recorders
Abstract
Wetland loss is cause for concern for populations of many wetland bird species throughout North America. The North American Breeding Bird Survey, the primary resource for broad-scale avian population data, does not provide sufficient data for many marsh bird species. Targeted marsh bird monitoring programs have been implemented across the continent in an attempt to fill this gap. Despite these efforts, a number of wetland species are so elusive that they remain an analytical challenge because of small sample sizes and low detectability. Thus, there is need for tools and approaches that will increase sampling efficiency and boost geographic representation. Autonomous recording units (ARUs) have the potential to address some of these challenges, but require the ability to combine in-person survey data with ARU data for collective analysis. Our primary objective was to estimate statistical offsets, or correction factors, to account for systematic differences between in-person and ARU counts of wetland-associated bird species. We found that ARU recordings were generally equivalent to in-person point counts, with bias in a small number of species (2 of 19 for Song MeterTM SM2 and 1 of 16 for Song MeterTM SM4 Acoustic Recorders; Wildlife Acoustics Inc. ©, Maynard, MA). However, bias was removed in all of the species through use of our correction factors. Therefore, our correction factors were effective for integrating in-person and ARU point count data even for species where differences exist. We also found that commercially available SM4 recorders have larger effective detection radii than SM2 recorders. Researchers should consider the microphone sensitivity and signal-to-noise ratios of any recording unit before purchasing, and more sensitive models with lower noise should be used where possible. Our results, and particularly our correction factors, are useful for biologists combining in-person and ARU point count data to achieve larger sample sizes, higher statistical power, and ultimately better information for more effective wetland conservation.
