Variable-radius sampling techniques are commonly used during forest inventories. For each sample tree at a particular sampling point, diameter and height(s) are measured and then weight is estimated using established equations.  Heights can require a fair amount of time to measure in the field.  Separating the weight per acre estimate into two components; average basal area per acre and WBAR (individual tree weight-basal area ratio) across all points, can often lead to more efficient sampling schemes. Variable-radius sampling allows for a quick estimate of basal area per acre at a point since no individual tree measurements are needed.  If there is a strong relationship between weight and basal area, then by knowing basal area you essentially know weight.  Separation into two components is advantageous because in most cases there is more variability among basal area estimates per point then there is in WBAR. Hence, you can spend more resources establishing many points that only estimate basal area – often called “Count” points. “Full” points are those where individual tree measurements are also conducted. There is little published information quantifying the impacts on basal area, weight, etc., estimates among different “Full/Count” sample size ratios at the same site. Inventories were examined to determine this method’s applicability to loblolly pine plantations in southern Arkansas and northern Louisiana. Results show there is more variability among basal area estimates than WBAR and that the amount of trees being “intensively” measured is excessive.  Based on these four plantations, a “Full” point could be installed ranging from every other point to every fifth point depending on site conditions and the desired variable.

When it comes to testing for differences in seedling survival, researchers sometimes make a Type II statistical error (i.e. failure to reject a false null hypothesis) due to the inherent variability associated with survival in tree planting studies. For example, in one trial (with five replications) first-year survival of seedlings planted in October (42%) was not significantly different (alpha = 0.05) from those planted in December (69%). Did planting in a dry October truly have no effect on survival? Authors who make a Type II error might not be aware that as seedling survival decreases (down to an overall average of 50% survival), statistical power declines. As a result, the ability to declare an 8% difference as “significant” is very difficult when survival averages 90% or less.  We estimate that about half of regeneration trials (average survival of pines <90%) cannot declare a 12% difference as statistically significant (alpha = 0.05).  When researchers realize their tree planting trials have low statistical power, they should consider using more replications.  Other ways to increase power include: (1) use a one-tailed test (2) use a potentially more powerful contrast test (instead of an overall treatment F-test) and (3) conduct survival trials under a roof.