2020 journal article
A comparison of cost and quality of three methods for estimating density for wild pig (Sus scrofa)
Scientific Reports, 10(1).
MeSH headings : Animals; Animals, Wild / physiology; DNA / isolation & purification; Ecological Parameter Monitoring / economics; Ecological Parameter Monitoring / methods; Feces / chemistry; Introduced Species / statistics & numerical data; Population Density; Real-Time Polymerase Chain Reaction / economics; South Carolina; Sus scrofa / physiology; Video Recording / economics
TL;DR:
A decision tree is provided for researchers to help make monitoring decisions by comparing cost effectiveness of three common population estimation methods to wild pigs across three habitats in South Carolina, U.S.A.
(via Semantic Scholar)
open access via www.nature.com (publisher PDF)
UN Sustainable Development Goal Categories
15. Life on Land
(OpenAlex)
Source: Crossref
Added: April 16, 2021
AbstractA critical element in effective wildlife management is monitoring the status of wildlife populations; however, resources to monitor wildlife populations are typically limited. We compared cost effectiveness of three common population estimation methods (i.e. non-invasive DNA sampling, camera sampling, and sampling from trapping) by applying them to wild pigs (Sus scrofa) across three habitats in South Carolina, U.S.A where they are invasive. We used mark-recapture analyses for fecal DNA sampling data, spatially-explicit capture-recapture analyses for camera sampling data, and a removal analysis for removal sampling from trap data. Density estimates were similar across methods. Camera sampling was the least expensive, but had large variances. Fecal DNA sampling was the most expensive, although this technique generally performed well. We examined how reductions in effort by method related to increases in relative bias or imprecision. For removal sampling, the largest cost savings while maintaining unbiased density estimates was from reducing the number of traps. For fecal DNA sampling, a reduction in effort only minimally reduced costs due to the need for increased lab replicates while maintaining high quality estimates. For camera sampling, effort could only be marginally reduced before inducing bias. We provide a decision tree for researchers to help make monitoring decisions.