Next Article in Journal
The Impact of Resident Participation on Urban Woodland Quality—A Case Study of Sletten, Denmark
Previous Article in Journal
Factors Explaining the Interest of Adult Offspring in Succeeding Their Parents as Forest Owners
Article

Evaluating Inter-Rater Reliability and Statistical Power of Vegetation Measures Assessing Deer Impact

1
Pennsylvania Cooperative Fish and Wildlife Research Unit, Pennsylvania State University, University Park, PA 16802, USA
2
U.S. Geological Survey, Pennsylvania Cooperative Fish and Wildlife Research Unit, Pennsylvania State University, University Park, PA 16802, USA
3
Department of Ecosystem Science and Management, Pennsylvania State University, University Park, PA 16802, USA
4
Pennsylvania Game Commission, Harrisburg, PA 17110, USA
5
Department of Conservation and Natural Resources, Bureau of Forestry, Harrisburg, PA 17105, USA
*
Author to whom correspondence should be addressed.
Forests 2018, 9(11), 669; https://doi.org/10.3390/f9110669
Received: 24 August 2018 / Revised: 12 October 2018 / Accepted: 15 October 2018 / Published: 25 October 2018
(This article belongs to the Section Forest Ecology and Management)
Long-term vegetation monitoring projects are often used to evaluate how plant communities change through time in response to some external influence. Here, we evaluate the efficacy of vegetation monitoring to consistently detect changes in white-tailed deer browsing effects. Specifically, we compared inter-rater reliability (Cohen’s κ and Lin’s concordance correlation coefficient) between two identically trained field crews for several plant metrics used by Pennsylvania state agencies to monitor deer browsing impact. Additionally, we conducted a power analysis to determine the effect of sampling scale (1/2500th or 1/750th ha plots) on the ability to detect changes in tree seedling stem counts over time. Inter-rater reliability across sampling crews was substantial for most metrics based on direct measurements, while the observational based Deer Impact Index (DII) had only moderate inter-rater reliability. The smaller, 1/2500th ha sampling scale resulted in higher statistical power to detect changes in tree seedling stem counts due to reduced observer error. Overall, this study indicates that extensive training on plant identification, project protocols, and consistent data collection methods can result in reliable vegetation metrics useful for tracking understory responses to white-tailed deer browsing. Smaller sampling scales and objective plant measures (i.e., seedling counts, species richness) improve inter-rater reliability over subjective measures of deer impact (i.e., DII). However, considering objective plant measures when making a subjective assessment regarding deer browsing effects may also improve DII inter-rater reliability. View Full-Text
Keywords: vegetation monitoring; observer error; statistical power; white-tailed deer; inter-rater reliability vegetation monitoring; observer error; statistical power; white-tailed deer; inter-rater reliability
Show Figures

Figure 1

MDPI and ACS Style

Begley-Miller, D.R.; Diefenbach, D.R.; McDill, M.E.; Rosenberry, C.S.; Just, E.H. Evaluating Inter-Rater Reliability and Statistical Power of Vegetation Measures Assessing Deer Impact. Forests 2018, 9, 669. https://doi.org/10.3390/f9110669

AMA Style

Begley-Miller DR, Diefenbach DR, McDill ME, Rosenberry CS, Just EH. Evaluating Inter-Rater Reliability and Statistical Power of Vegetation Measures Assessing Deer Impact. Forests. 2018; 9(11):669. https://doi.org/10.3390/f9110669

Chicago/Turabian Style

Begley-Miller, Danielle R., Duane R. Diefenbach, Marc E. McDill, Christopher S. Rosenberry, and Emily H. Just 2018. "Evaluating Inter-Rater Reliability and Statistical Power of Vegetation Measures Assessing Deer Impact" Forests 9, no. 11: 669. https://doi.org/10.3390/f9110669

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop