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

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

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

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

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

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

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

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

Le Conte’s Sparrow

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

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

Acknowledgements Funding: