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Results for “Journal of Field Ornithology”

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USGS2015

Testing the effectiveness of automated acoustic sensors for monitoring vocal activity of Marbled Murrelets Brachyramphus marmoratus

Cryptic nest sites and secretive breeding behavior make population estimates and monitoring of Marbled Murrelets Brachyramphus marmoratus difficult and expensive. Standard audio-visual and radar protocols have been refined but require intensive field time by trained personnel. We examined the detection range of automated sound recorders (Song Meters; Wildlife Acoustics Inc.) and the reliability of automated recognition models (“recognizers”) for identifying and quantifying Marbled Murrelet vocalizations during the 2011 and 2012 breeding seasons at Kodiak Island, Alaska. The detection range of murrelet calls by Song Meters was estimated to be 60 m. Recognizers detected 20 632 murrelet calls (keer and keheer) from a sample of 268 h of recordings, yielding 5 870 call series, which compared favorably with human scanning of spectrograms (on average detecting 95% of the number of call series identified by a human observer, but not necessarily the same call series). The false-negative rate (percentage of murrelet call series that the recognizers failed to detect) was 32%, mainly involving weak calls and short call series. False-positives (other sounds included by recognizers as murrelet calls) were primarily due to complex songs of other bird species, wind and rain. False-positives were lower in forest nesting habitat (48%) and highest in shrubby vegetation where calls of other birds were common (97%–99%). Acoustic recorders tracked spatial and seasonal trends in vocal activity, with higher call detections in high-quality forested habitat and during late July/early August. Automated acoustic monitoring of Marbled Murrelet calls could provide cost-effective, valuable information for assessing habitat use and temporal and spatial trends in nesting activity; reliability is dependent on careful placement of sensors to minimize false-positives and on prudent application of digital recognizers with visual checking of spectrograms.

Alaska↗

USGS2024

Using remote sensing to identify habitat for wintering Henslow's Sparrows (Centronyx henslowii)

The Henslow's Sparrow ( Centronyx henslowii ) is a grassland bird species that overwinters in the southeastern United States and is a species of conservation concern due to population declines primarily caused by habitat loss. Henslow's Sparrows often overwinter in marginal habitats, such as powerline rights-of-way (ROWs), clear cuts, and field edges that provide some of their desired habitat characteristics, such as low-to-no tree cover and a diverse herbaceous understory. Using remote sensing methods, we evaluated the habitat characteristics of Henslow's Sparrow–occupied ROWs in southeastern Georgia. We calculated 22 satellite imagery metrics from Sentinel 2-L2 10 m resolution imagery, including single-pixel variables (e.g., Enhanced Vegetation Index, EVI) as well as “image texture” metrics that represent spatial heterogeneity. Using Random Forest models, we evaluated whether satellite imagery metrics could be used to discriminate between Henslow's Sparrow used areas (delineated from telemetry data) and surrounding available areas. Satellite imagery metrics were successful in predicting habitat characteristics in the ROWs (as evaluated by out-of-bag error and 3 goodness-of-fit tests), with image texture metrics performing better than single-pixel metrics. Image texture metrics were 9 of the top 10 most important predictors of habitat use in the best performing model that had a 500 m available buffer around use areas (out-of-bag error rate 21.21%). The most important image texture metric, cluster shade, was positively correlated with tree cover; Henslow's Sparrows were more likely to use areas with intermediate levels of cluster shade. From our results, we concluded that image texture metrics derived from 10 m satellite imagery could be used to predict sites that have suitable overwintering habitat for Henslow's Sparrows, but only at coarse resolutions and broader extents (hundreds of meters to kilometers). Therefore, this tool could be used to identify other ROWs (and possibly non-ROWs) with habitat that may support this and other declining grassland species.

Georgia↗