Geology topics

Alan E. Burger

Publications and source records attributed to Alan E. Burger.

2 recordsLinked to original sources

USGS2016

Techniques for monitoring Brachyramphus murrelets: A comparison of radar, autonomous acoustic recording and audio‐visual surveys

Conditions in Alaska, USA, pose a challenge for monitoring populations of Brachyramphus murrelets using standard survey methods, because of strong winds, 2 sympatric species, short nights, and variable nesting habitat. We tested 3 methods for monitoring Brachyramphus murrelets breeding in the Kodiak Archipelago, Alaska, in 2010–2012. In addition to standard audio‐visual and radar methods, we tested—for the first time with murrelets in Alaska—the application of autonomous acoustic recorders for monitoring vocal activity. We completed 74 radar, 124 audio‐visual, and 134 autonomous acoustic surveys, focused on presunrise activity peaks; this yielded 26,375 murrelet detections. Marbled ( B. marmoratus ) and Kittlitz's murrelets ( B. brevirostris ) could not be distinguished using combinations of radar and acoustic recordings; therefore, at‐sea surveys will be required to determine localized species proportions. Of the 3 methods, radar sampled the largest area and detected silently flying murrelets, providing the most reliable data on local populations; however, radar identification of murrelets was unreliable in winds exceeding 18 km/hr. Audio‐visual surveys were useful for species identification and to document behaviors associated with local nesting, whereas autonomous acoustic recorders allowed season‐long monitoring of murrelet vocal activity. Within potential forest‐nesting habitat of marbled murrelets, all 3 methods gave similar measures of presunrise murrelet activity, but only radar reliably sampled murrelets commuting between nest and ocean. Because of their low cost and flexible programming, automated sound recorders offer an affordable way to sample vocal activity prior to more intensive or expensive radar and audio‐visual surveys. We recommend that population monitoring and habitat studies of Brachyramphus murrelets in Alaska include combinations of all 3 methods.

Alaska↗

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↗