A novel sampling approach to estimating abundance of low-density and observable species

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.

Automated summary

This article discusses the challenges of estimating abundance for low-density, unmarked species and introduces a method that combines a probability-based survey design and statistical models. By using an N-mixture model with spatially balanced random sampling and double-observer data collection, the approach directly addresses the problem of approximating statistical assumptions and provides unbiased estimates.