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Featured in the Journal of Wildlife Management
Letter to the Editor
Authored by:
Joshua H Schmidt, U.S. National Park Service, Central Alaska Network, 4175 Geist Road, Fairbanks, AK 99709, USA
Joel H Reynolds, U.S. National Park Service, Climate Change Response Program, 1201 Oakridge Drive, Suite 200, Fort Collins, CO 80525, USA
Kevin S White, Alaska Department of Fish and Game, Division of Wildlife Conservation, P.O. Box 110024, Juneau, AK 99811, USA
Dylan T Schertz, U.S. National Park Service, Arctic Network, 4175 Geist Road, Fairbanks, AK 99709, USA
John M Morton, Alaska Wildlife Alliance, P.O. Box 202022, Anchorage, AK 99520, USA
H. Sharon Kim, U.S. National Park Service, Kenai Fjords National Park, P.O. Box 1727, Seward, AK 99664 USA
You can read the full article below. *Note, you may need to refresh the page to see the PDFs below.
Excerpt: Becker and Herreman (2021) critique the approach of Schmidt et al. (2019), which integrates local minimum counts with landscape‐scale conventional distance sampling (CDS) surveys. They list concerns with model structure, fundamental assumptions, sampling approach, and the application to mountain goats (Oreamnos americanus) on the Kenai Peninsula, Alaska, USA. After careful review, these concerns appear to be largely due to misunderstandings of the intent of the original manuscript and the details of the integrated approach as presented, in addition to a perhaps common confusion over the relationship between the assumption of perfect detection on the transect line (i.e., the g(0)=1 assumption) and estimator bias in CDS applications. We address these points in detail so that practitioners can fully weigh the potential benefits of integrated approaches as illustrated by Schmidt et al. (2019) and better understand the role of estimator bias in CDS applications. Given the numerous challenges and tradeoffs in monitoring and managing wildlife populations, particularly in remote areas, we continue to advocate for the development of reliable survey alternatives that are logistically feasible, cost effective, and relatively unbiased. We maintain that the approach presented by Schmidt et al. (2019) represents an effective tool for addressing management‐relevant monitoring objectives and is primarily limited by the spatial and temporal extent of input data—an issue common to any estimator.
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