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Detecting change in advance tree regeneration using forest inventory data: the implications of type II error
James A Westfall1, William H McWilliams
1U.S. Forest Service, Northern Research Station, 11 Campus Boulevard, Suite 200, Newtown Square, PA 19073, USA. jameswestfall@fs.fed.us
Environmental Monitoring and Assessment
|September 20, 2011
Summary
Effective forest regeneration monitoring requires balancing statistical errors. High type II error rates can mask real improvements in advance tree regeneration, hindering the identification of successful management strategies.
Area of Science:
- Forestry
- Ecology
- Statistical analysis in natural resources
Background:
- Maintaining native tree species and forest composition relies on adequate advance tree seedling and sapling regeneration.
- Advance regeneration indicates future forest stand composition and is crucial for ecological stability.
- The Pennsylvania Regeneration Study monitors forest regeneration across statewide Forest Inventory and Analysis plots.
Purpose of the Study:
- To assess the impact of management techniques on advance forest regeneration.
- To evaluate the statistical power of hypothesis tests in detecting changes in forest regeneration.
- To highlight the challenges of identifying effective regeneration strategies due to statistical error rates.
Main Methods:
- Utilizing data from the Pennsylvania Regeneration Study, a statewide monitoring initiative.
- Applying hypothesis testing frameworks to analyze changes in the proportion of area with adequate advance regeneration.
- Examining statistical power (1-β) and type I (α) and type II (β) error rates in regeneration assessments.
Main Results:
- Statewide assessments show weak power (≤0.5) to detect changes smaller than 0.05 in adequate advance regeneration.
- Smaller spatial scales (e.g., wildlife management units) exhibit marginal power even for larger changes (≥0.20) due to reduced sample sizes.
- Prioritizing small type I error rates often leads to high type II error rates, obscuring real regeneration improvements.
Conclusions:
- Current statistical approaches may fail to detect positive impacts of management on forest regeneration.
- Adjusting statistical methods, such as accepting larger type I error rates, can increase the probability of detecting genuine regeneration improvements.
- Improved statistical power is essential for accurately evaluating and identifying effective forest regeneration management techniques.
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