Estimating Raptor Nesting Success: Old and New Approaches

Area of Science:

• Ornithology
• Wildlife population monitoring
• Ecological statistics

Background:

• Nesting success studies are vital for assessing raptor population health.
• Inconsistent monitoring protocols complicate cross-species and temporal comparisons.
• Raptor species like golden eagles, prairie falcons, and American kestrels exhibit diverse breeding biology.

Purpose of the Study:

• To compare the accuracy and precision of three nest success estimators: apparent nest success (ANS), Mayfield nesting success, and the logistic-exposure model.
• To evaluate the suitability of these estimators for diverse raptor species and monitoring scenarios.

Main Methods:

• Utilized large, long-term datasets from studies of three distinct raptor species.
• Applied Mayfield and logistic-exposure models to estimate nest survival.
• Calculated apparent nest success (ANS) as the ratio of successful to total nesting attempts.

Main Results:

• Mayfield and logistic-exposure models yielded comparable nesting success estimates and precision.
• Apparent nest success (ANS) tended to overestimate nesting success.
• ANS was highly sensitive to the inclusion of nesting attempts discovered late in the season.

Conclusions:

• The logistic-exposure and Mayfield models are more appropriate for precise, quantitative estimates of raptor nesting success.
• Apparent nest success (ANS) is unreliable for exact estimates, particularly when early nest detection is challenging.
• ANS may suffice for long-term trend analysis in species with highly detectable nests.