Wednesday (Group 2) - SERDP-ESTCP - Strategic Environmental ...

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Sustainable Infrastructure (SI) Natural Resources Management — Threatened and Endangered Species Poster Number 88 – Wednesday POPULATION VIABILITY ANALYSIS OF SHORTNOSE STURGEON (ACIPENSER BREVINOSTRUS) IN THE OGEECHEE RIVER, GEORGIA DR. HENRIETTE JAGER Oak Ridge National Laboratory P.O. Box 2008, Mail Stop 6036 Oak Ridge, TN 37922 (865) 574-8143 jagerhi@ornl.gov CO-PERFORMERS: Dr. Mark Bevelhimer (Oak Ridge National Laboratory); Dr. Doug Peterson and Daniel Farrae (University of Georgia); Roy King (Fort Stewart) R ivers and estuaries along the Atlantic coast support both military installations and populations of the federally endangered shortnose sturgeon (Acipenser brevirostrum). This project focuses on the population in the Ogeechee River system, near Fort Stewart. Three goals of this research are: (1) to quantitify and partition the influences on shortnose sturgeon recovery under the control of the military from those that are not; (2) to prioritize recovery efforts; and (3) to quantify population thresholds. This project integrates field and modeling efforts. In the field, river depth and width was characterized for the area of interest. Water quality monitoring began in winter and continued through early fall. Temperature and dissolved oxygen showed seasonal, but not longitudinal variation. Salinity increased downstream, but showed little seasonal variation. These data will be used to calibrate the water quality model, which we will use to simulate episodes of poor water quality in summer. We conducted a survey of shad fishing effort during late winter-early spring, which will help us to quantify the risk of capture as bycatch. A sister telemetry study tagged over 100 individuals, three of which were tagged in the Altamaha River. On the modeling front, an individual-based and spatially explicit population viability model was developed for the shortnose sturgeon. This model will be used to evaluate the number of individuals needed to sustain a viable population. This work is funded by SERDP Project SI-1543. G-37
Sustainable Infrastructure (SI) Natural Resources Management — Threatened and Endangered Species Poster Number 89 – Wednesday AUTOMATED ACOUSTIC MONITORING OF BAT AND BIRD POPULATIONS F DR. JOSEPH M. SZEWCZAK Humboldt State University Department of Biological Sciences 1 Harpst Street Arcata, CA 95521 (707) 826-4132 joe@humboldt.edu CO-PERFORMER: Dr. Stuart Parsons (University of Auckland, New Zealand) ederal regulations require the monitoring and management of sensitive species on public land jurisdictions and for many species this work necessitates specialized and expensive personnel. In addition, the rarity of most sensitive species typically obligates greater survey effort (and higher cost) to acquire reliable data compared to more common species, particularly over the extensive landscapes of U.S. military installations. Fortunately, species that emit acoustic signals facilitate automated monitoring that can provide a cost-effective solution to confidently assess activity and confirm presence or absence. Automated acoustic monitoring can also provide consistency by eliminating variability in the skill level of monitoring personnel, operate continuously to better detect rare species that intermittent survey efforts may miss, and transmit data remotely from personnel-restricted areas. Automated acoustic monitoring entails (1) autonomous field recording hardware, (2) software to automate detection and classification of signals based on, (3) a comprehensive library of reference recordings from target and sympatric species to cover their range of signals under field conditions. Our SERDP supported team (under Project SI-1394) has completed these tasks to deploy a functioning automated monitoring and classification system. Calibrating species classification depended upon acquiring a reliable library of known species recordings from free-flying bats. This presented a considerable challenge because: (1) many sympatric species emit echolocation calls overlapping in many acoustic characteristics; (2) bats exhibit considerable plasticity in their calls; and (3) unlike birds that can be identified at a distance while recording, most bats require capture to determine species. We captured bats and tracked them with light-tags to acquire free-flight recordings. Our field crews acquired in excess of 10,000 recordings from 34 species in 23 states. We then developed robust novel algorithms including an adaptive fast Fourier transform (FFT) method that automatically optimizes FFT parameters to signal characteristics, an innovative method to attenuate echo distortion, and intelligent signal tracing routines. This enabled the extraction of several million data points from the reference library to calibrate and generate regional classification modules to automatically classify unknown bats from field recordings. By using a variety of machine learning methods and ensembles of machine learning methods, we have met or exceeded a target correct classification rate of 90% for most species, including the federally listed Indiana bat (Myotis sodalis) and gray bat (M. grisescens). G-38
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