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Tyler F. Thigpen

Publications and source records attributed to Tyler F. Thigpen.

2 recordsLinked to original sources

Estimating shorebird populations during spring stopover in rice fields of the Louisiana and Texas Gulf Coastal Plain

Migrating shorebird populations using approximately 2% of Louisiana and Texas Gulf Coastal rice fields were surveyed during spring migration (March–May of 1997 and 1998) using biweekly stratified random surveys conducted at 50 roadside survey points and approximately 30,000 shorebirds were observed. Shorebird counts were extrapolated and almost 1.4 million birds in 1997 and over 1.6 million birds of 31 species in 1998 were estimated to use rice field habitat for stopover sites in Louisiana and Texas. Greater than 50% of the estimated North American populations were estimated to use rice field habitats for five species, including a species of concern, Buff-breasted Sandpiper (Tryngites subruficollis) at 187%. Because of predictability of suitable rice field habitat acreage, timing of field preparation and water availability, coastal rice prairies are identified as critical spring migration stopover sites.

Louisiana;Texas

Efficacy of automatic vocalization recognition software for anuran monitoring

Surveys of vocalizations are a widely used method for monitoring anurans, but it can be difficult to coordinate standardized data collection across a large geographic area. Digital automated recording systems (ARS) offer a low-cost method for obtaining samples of anuran vocalizations, but the number of recordings can easily overwhelm human listeners. We tested Song Scope, an automatic vocalization recognition software program for personal computers to determine if this type of machine learning approach is currently a viable solution for anuran monitoring. For three species, Song Scope scanned more than 200 h of recordings in 3-20 h at the settings we chose. The software misidentified true calls (false positive) at rates of 2.7%-15.8% per species and failed to detect calls (false negative) in 45%-51% of recordings. There exists a tradeoff between false positive and false negative errors, which can be adjusted by setting the minimum criteria for the recognition software. Users of this approach should carefully consider their reasons for monitoring and how they intend to use the data before creating a large monitoring network.

Louisiana