Year of Award

2017

Document Type

Thesis

Degree Type

Master of Science (MS)

Degree Name

Wildlife Biology

Department or School/College

W.A. Franke College of Forestry and Conservation

Committee Chair

Dr. Paul M. Lukacs

Commitee Members

Dr. Michael Mitchell, Dr. Mark Hebblewhite, Dr. Jon Horne

Keywords

abundance, unmarked populations, time-to-event, Poisson process, sampling

Abstract

Abundance estimates are central to the field of ecology and are an important tool for wildlife managers. While many tools are available for estimating abundance from individually identifiable animals, it is much more difficult to estimate abundance of unmarked animals. Most species have no natural markings and capturing them to apply artificial marks is invasive. One step toward noninvasive abundance estimation is the use of passive “traps” such as remote cameras or acoustic recording devices. The continuous-time data from these traps can be used to estimate abundance, although most available methods still require individually identifiable animals. There is a great need for methods to estimate abundance from unmarked populations using these trap data. We developed three methods for estimating abundance of unmarked animals from remote camera trap data. We worked outside the conventional capture-recapture framework to rethink how continuous remote data are handled. In Chapter 1, we developed an Instantaneous Sampling (IS) estimator based in sampling theory that treats remote camera data like point counts. In Chapter 2, we applied a time-to-event framework to develop a Space-to-Event (STE) and Time-to-Event (TTE) model to estimate abundance from trapping rate. We validated these methods on simulated populations with known abundance. All three methods produced unbiased estimates of abundance, regardless of animal movement rate. We performed a case study in which we estimated elk abundance from remote camera trap data in two study areas in Idaho. Estimates in one study area were comparable to an independent estimate of abundance from aerial surveys. In the other study area, other abundance methods are hard to implement, so our three models produced the first elk abundance estimates. The three methods developed here represent new ways of thinking about continuous-time remote camera data. These new methods allow biologists to estimate abundance from unmarked populations without tracking individuals over time. They have wide applications across species; biologists can select the method that best meets their specific circumstances. All three methods greatly reduce the amount of data required for analysis, which makes them practical management tools.

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© Copyright 2017 Anna K. Moeller