Detecting change in advance tree regeneration using forest inventory data: the implications of type II error
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Environmental Monitoring and Assessment
Achieving adequate and desirable forest regeneration is necessary for maintaining native tree species and forest composition. Advance tree seedling and sapling regeneration is the basis of the next stand and serves as an indicator of future composition. The Pennsylvania Regeneration Study was implemented statewide to monitor regeneration on a subset of Forest Inventory and Analysis plots measured by the U.S. Forest Service. As management techniques are implemented to improve advance regeneration, assessments of the change in the forest resource are needed. When the primary focus is on detecting change, hypothesis tests should have small type II (β) error rates. However, most analyses are based on minimizing type I (α) error rates and type II error rates can be quite large. When type II error rates are high, actual improvements in regeneration can remain undetected and the methods that brought these improvements may be deemed ineffective. The difficulty in detecting significant change in advance regeneration when small type I error rates are given priority is illustrated. For statewide assessments, power (1-β) to detect changes in proportion of area having adequate advance regeneration is relatively weak (≤0.5) when the change is smaller than 0.05. For evaluations conducted at smaller spatial scales, such as wildlife management units, the reduced sample size results in only marginal power even when relatively large changes (≥0.20) in area proportion occur. For fixed sample sizes, analysts can consider accepting larger type I error rates to increase the probability of detecting change (smaller type II error rates) when it occurs, such that management methods that positively affect regeneration can be identified.
Westfall, James A.; McWilliams, William H. 2012. Detecting change in advance tree regeneration using forest inventory data: the implications of type II error. Environmental Monitoring and Assessment. 184(9): 5601-5611. https://doi.org/10.1007/s10661-011-2365-3.