Abigail Thiemkey, University of Maryland College Park

Estimates of wildlife populations are foundational to management decision-making. Sound science enables wildlife managers to develop rules and regulations that inform harvest recommendations and ensure population sustainability. American black bears (Ursus americanus) are game mammals that state wildlife agencies monitor over time to assess population status. Since the 1990s, researchers have used hair snares to estimate bear populations. This method involves wrapping barbed wire around trees and placing baited attractants, with the expectation that bears will leave hair samples on the wire. These samples are then analyzed to identify individual bears, determine sex, and relatedness. Data from these samples are often analyzed using spatially explicit capture–recapture (SECR) models, which help estimate population size and density, detectability, and how bears use their space. Despite the widespread use of hair snares, less attention has been paid to how placement and sampling design influence population estimates. Specifically, systematic random and clustered sampling designs differ in their assumptions, logistical constraints, and potential biases related to movement and detection. This presentation reviews current applications of these two study designs within SECR frameworks and discusses their advantages and limitations when estimating bear abundance. The goal is to provide practical recommendations for wildlife managers to consider when designing hair snare studies, helping ensure reliable population estimates that support sound management decisions.