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Evaluating the Use of Autonomous Recording Units for Monitoring Yellow Rails and Other Nocturnal Wet Meadow Birds

Evaluating the Use of Autonomous Recording Units for Monitoring Yellow Rails and Other Nocturnal Wet Meadow Birds. Anna M. Sidie-Slettedahl , US Fish and Wildlife Service, South Dakota State University Rex Johnson, US Fish and Wildlife Service Todd Arnold, University of Minnesota

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Evaluating the Use of Autonomous Recording Units for Monitoring Yellow Rails and Other Nocturnal Wet Meadow Birds

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  1. Evaluating the Use of Autonomous Recording Units for Monitoring Yellow Rails and Other Nocturnal Wet Meadow Birds • Anna M. Sidie-Slettedahl, US Fish and Wildlife Service, South Dakota State University • Rex Johnson, US Fish and Wildlife Service • Todd Arnold, University of Minnesota • Jane Austin, USGS Northern Prairie Wildlife Research Center • Joshua Stafford, USGS South Dakota Cooperative Fish & Wildlife Research Unit • KC Jensen, South Dakota State University

  2. Introduction • Limited knowledge of these species: • Yellow Rail • Nelson’s Sparrow • Le Conte’s Sparrow

  3. Conservation • Loss and degradation of wetlands due to human activity • All three = Species of Greatest Conservation Need in MN • Yellow Rail = species of high concern • North American Waterbird Conservation Plan • Northern Prairie and Parkland Waterbird Conservation Plan

  4. Systematic surveys are needed • USFWS & USGS: Standardized North American Marsh Bird Monitoring Protocols (Conway 2009) • However… • Tendencies to call at night = often times missed by surveys • Also… • Secretive habits • Cryptic coloration • Difficult to access habitat Dr. Jim Petranka

  5. Autonomous Recording Units (ARUs) • Good candidates for surveying with ARUs that can be analyzed in a laboratory • Benefits: • Minimize observer biases • Permanent records of surveys • 24 hr/day data collection • Limited numbers of expert field observers • Disadvantages: • No visual detections • Estimating distances and numbers of birds? • Time spent going through recordings

  6. Song Scope Bioacoustics Recognition Software • Ideally, you build “recognizers” to scan recordings

  7. Main Objectives • Compare the detection probabilities of these three species using ARUs versus the standard marsh bird monitoring protocol. • Use ARU recordings to determine temporal (daily and seasonal) changes in species calling and environmental factors affecting detection, in order to improve survey efforts

  8. Field Methods • 16 survey routes, 10 stations • 22 ARUs (per season; 1-4 ARUs/route) • SM1 Song Meter, Wildlife Acoustics • 10 min every 15 min, from 20:00 until 08:00

  9. Field Methods • Standard Marsh Bird Protocol • Call-broadcast surveys--Yellow Rail call only • Start 1 hour after sunset • May-June = survey season • 4 times/season

  10. Objective 1: Comparing the Two Survey Methods Methods: • Isolated all 6 minute recorded standard surveys (172 in total) • Use “recognizers” to automatically detect and identify target species calls on recordings • NO!! Ran into problems: missed detections and too many false positives

  11. Manual Scan Method • Resorted to the “Manual Scan” Method • Quickly visually and aurally scan through recording to detect target species • Robust design occupancy model in Program MARK

  12. Probability of Detection -Each species -Each survey repetition -Each survey method

  13. Why? • Most calls not detected on ARU recording, but that were detected during Standard Survey, were too faint or not “strong” enough to be recorded by ARU • Reduced detection by ARUs was likely due to human observers being able to detect birds at greater distances

  14. How many 6 minute ARU recordings to be at least 95% sure of detection?

  15. However • Because ARUs are in the field for longer periods than human observers, there are more cumulative opportunities for detection

  16. Objective 2: Factors affecting detection • Looking at temporal and environmental variables that may affect calling and/or detection of these species • Generalized linear mixed models in R • Presence/absence from 3035 three minute recordings, from 43 ARU stations • Hourly weather data

  17. Variables of Interest • Random effect = Survey Site • Fixed effects = • Year • Julian day • Precipitation (yes or no) • Temperature • Wind speed • Atmospheric pressure • Moonlight • Hours after sunset

  18. Yellow Rail • Precipitation No precip. = 0.63(95%CI = 0.55 and 0.71) Precip. = 0.47(95%CI = 0.36 and 0.59)

  19. Le Conte’s Sparrow

  20. Nelson’s Sparrow • Precipitation • No precip. = 0.22(95%CI = 0.16 and 0.30) • Precip. = 0.08(95%CI = 0.03 and 0.16

  21. Management Implications • Incorporate these factors into existing survey protocols to improve survey efforts • Standard surveys • Use of ARUs • Improvement of systematic surveys

  22. Acknowledgements Funding: • HAPET – US Fish and Wildlife Service • Agassiz National Wildlife Refuge • South Dakota State University

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