A novel sampling approach to estimating abundance of low‐density and observable species. Issue 11 (11th November 2021)

• Record Type: Journal Article
• Authors: McDevitt, Molly C.; Cassirer, E. Frances; Roberts, Shane B.; Lukacs, Paul M.
• Is Part Of: Ecosphere. Volume 12:Issue 11(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-11-11
• Subjects: assumptions -- Idaho -- monitoring -- mountain goats -- multiple observer -- Oreamnos americanus -- sampling -- study design -- unmarked population; Ecology -- Periodicals; Ecology; Periodicals; 577.05
• DOI: 10.1002/ecs2.3815
• Languages: English
• ISSNs: 2150-8925

Abstract

Abstract: Informative species abundance estimates are critical for guiding decisions around the conservation and management of ecological systems. There exist many methods for estimating abundance of frequently encountered species and populations with uniquely identifiable individuals. However, for wildlife populations with unmarked individuals that occur at low densities, there exist a variety of behaviors and characteristics that make effectively surveying and sampling challenging or uninformative. Examples of challenging characteristics include the elusive behaviors of low‐density species that occur in complex and rugged terrain. Such characteristics make detection difficult and surveys expensive, dangerous, and potentially biased. To address these challenges, we used a common, non‐invasive field survey method combined with a probability‐based study design and frequently utilized statistical model to estimate abundance of an unmarked mountain goat population in eastern Idaho. We developed a novel data analysis approach using an N ‐mixture model that, together with spatially balanced random sampling and a double‐observer field data collection method, directly solves the problem of approximating statistical assumptions, including population closure. We demonstrate that a probability‐based sampling design not only is feasible, but also is important for estimating population parameters for unmarked and low‐density species. With this approach, we present a procedure that offers unbiased abundance estimates, empowering managers to track low‐density species' population trends across time.