MONITORING GRASSLAND BIRD POPULATIONS ON FORT CAMPBELL MILITARY RESERVATION, KENTUCKY-TENNESSEE, WITH A SPECIAL EMPHASIS ON BACHMAN’S SPARROW (Peucaea aestivalis)

Grassland birds have declined more in the past four decades than any other group, primarily because of the suppression of ecological disturbance. Fort Campbell Military Reservation (FCMR) has maintained large amounts of grasslands and oak (Quercus spp.) savannas because of military training and prescribed fires, and supports many grassland bird populations. I established a survey route to investigate vegetation influencing occupancy of grassland birds with an emphasis on Bachman’s Sparrows (Peucaea aestivalis), and additionally described habitat selection of Bachman’s Sparrows on FCMR. Bachman’s Sparrow, Eastern Meadowlark (Sturnella magna), Henslow’s Sparrow (Ammodramus henslowii), and Orchard Oriole (Icterus spurius) occupancy were positively related to grass cover (β [beta] = 10.02 ± [plus-minus] 2.80 SE, β = 9.93 ± 2.05 SE, β = 7.09 ± 2.35 SE, β = 17.12 ± 5.81 SE), whereas Blue Grosbeak (Passerina caerulea) and Northern Bobwhite (Colinus virginianus) occupancy were related to grass cohesion (β = 0.08 ± 0.03 SE, β = 0.08 ± 0.02 SE). Blue-winged Warbler (Vermivora cyanoptera) occupancy was positively related to shrub cover (β = 4.90 ± 1.85 SE), Prairie Warbler (Setophaga discolor) occupancy was positively related to interspersion and juxtaposition (β = 0.05 ± 0.02 SE), and Dickcissel (Spiza americana) occupancy was negatively related to tree cover (β = -7.28 ± 0.48 SE). Bachman’s Sparrow territory size averaged 2.66 ha (± 0.57 SE); basal area was 2.25 m2/ha [meters squared per hectacre] (± 0.57 SE). Occupied territories had greater cover of forbs than unoccupied savannas (27% ± 1.55 SE vs 22% ± 1.02 SE, p = 0.0001) and greater variance in litter (0.71 ± 0.03 SE vs 0.6 ± 0.02 SE, p = 0.01). There was less variance between occupied and unoccupied territory points for bareground (0.58 ± 0.02 SE vs 0.66 ±0.03 SE, p = 0.02), forbs (0.47 ± 0.01 SE vs 0.53 ± 0.02 SE, p = 0.02), and woody species (0.85 ± 0.03 SE vs 0.96 ± 0.04, p = 0.03). Our goal is to use these data to develop a conservation strategy to monitor and enhance Bachman’s Sparrows and other high-priority species at FCMR and elsewhere in the region

Acoustic monitoring of wildlife in inaccessible areas and automatic detection of bird songs from continuous recordings

The use of new technology for wildlife monitoring comes with both possible benefits and challenges. Unmanned aerial vehicles (UAVs) and automatic recording units (ARUs) can allow researchers to automatically record videos, photographs, and audio recordings of animals in unusual or inaccessible locations. However, new acoustic monitoring techniques require innovative methods to extract and utilize data from acoustic recordings. In this project we developed novel technology to record bird songs in inaccessible areas and demonstrated a useful method for extracting and classifying songs from continuous recordings. The autonomous aerial acoustic recording system (AAARS) was a UAV developed at the University of Tennessee capable of generating high-quality WAV recordings of bird songs in a variety of landscapes. The AAARS was completely silent in flight controlled by a ground-based computer monitoring station. I developed a model to convert the AAARS GPS-based flight path into a microphone exposure surface to relate species-specific acoustic signals recorded to area of microphone coverage. The vocalizations per unit area per unit time for a given focal species could then be used as an index of relative abundance or as an input in density estimation. Once collected, extraction and classification of birdsongs from acoustic recordings remains a major technological challenge. I used quadratic discrimination analysis to differentiate between inter- and intra-specific bird songs using up to sixteen acoustic measurements on human-extracted signals from audio spectrograms of five focal songbird species. Measurement-based classification was successful at separating the five species apart with only ≤5% classification error. I then used a template-matching model to extract target birdsongs from continuous field recordings and investigated the efficiency of different analytical options for classification of five focal songbird species. Decision trees, neural networks, and quadratic discriminant analysis all produced similar classification results. The means to optimize the analytical approach varied by species. I concluded that a species-specific approach should be used to accurately extract and classify songs from continuous recordings