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Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.


Common raven (Corvus corax; raven) populations have increased over the past 5 decades within the western United States. Raven population increases have been largely attributed to growing resource subsidies from expansion of human enterprise. Concomitantly, managers are becoming increasingly concerned about elevated adverse effects on multiple sensitive prey species, damage to livestock and agriculture, and human safety. Managers could benefit from a rapid but reliable method to estimate raven densities across spatiotemporal scales to monitor raven populations more efficiently and inform targeted and adaptive management frameworks. However, obtaining estimates of raven density is data- and resource-intensive, which renders monitoring within an adaptive framework unrealistic. To address this need, we developed a rapid survey protocol for resource managers to estimate site-level density based on the average number of ravens per survey. Specifically, we first estimated raven densities at numerous field sites with robust distance sampling procedures and then used regression to investigate the relationship between those density estimates and the number of ravens per survey, which revealed a strong correlation (R2 = 0.86). For management application, we provide access to R function software through a web-based interface to estimate density using number of ravens per survey, which we refer to as a Rapid Assessment Function (RAF). Then, using a simulation analysis of data from sites with abundant surveys and the RAF, we estimated raven density based on different numbers of surveys to help inform how many surveys are needed to achieve reliable estimates within this rapid assessment. While more robust procedures of distance sampling are the preferred methods for estimating raven densities from count surveys, the RAF tool presented herein provides a reliable approximation for informing management decisions when managers are faced with resource and small sample size constraints.

Additional Files

Brussee et al. Appendices.pdf (803 kB)
Supplemental material