Decay is a common defect that occurs in all parts of a tree. Decayed wood has very little strength and often leads to hollows, cavities or both. When decay or a hollow is present in a tree part, its load-bearing capacity is reduced. The amount of reduction depends on the extent of decay – how much is present and where it’s located in the cross section. If we assume the loads on the tree part do not change, reduced load-bearing capacity increases its likelihood of failure. In this article, based on a related research project, we’ll look at how arborists assess decay and review the consistency of their methods.

The left-hand image is the cut stump of a red oak that was removed a year after our study concluded; above right is the electrical resistance tomogram of the stem taken before the study, and below right is the sonic tomogram. Photo and tomograms courtesy of Dr. Nick Brazee, UMass Extension.

Tool of the trade
In general, 1) a greater area of decay or hollow in the cross section leads to a greater reduction in load-bearing capacity, and 2) for the same area of decay or hollow, when it is near the outside of the cross section, there will be a greater reduction in load-bearing capacity.

Because decay is a common defect that often leads to failure, arborists have developed a variety of tools and techniques to determine its extent in a cross section. The simplest approach is a visual assessment, looking for signs of decay such as loose or missing bark, fruiting bodies, cavities and so on. An arborist also can listen for decay by sounding the stem with a plastic mallet. It takes some experience to feel comfortable sounding a stem in this way, but it can be helpful when trying to confirm the presence of a larger hollow than is visible.

Sometimes, more advanced techniques to determine the extent of decay are justified. Two that are more common are drilling into the stem at different locations around the stem’s circumference and using tomography. Drilling to detect decay can be done with a battery-powered hand drill or with a resistance-measuring drill that records the pattern of resistance in the wood as the drill bit cuts into the stem. An arborist can compute the thickness of sound wood at several locations around the stem’s circumference to visualize the extent of decay. They also can compute a ratio of the thickness of sound wood to the stem’s radius: the “t/R” ratio.

Proportions of different likelihood-of-failure ratings within each assessment technique. Graphic courtesy of the author.

Tomograms provide greater detail of the shape of both the cross section and the extent of sound wood. (Tomography uses sonic or electrical waves to create a 2D or 3D image of a tree’s internal structure.) Tomograms are in color, with different colors representing the likely extent of decay.

Both of these sophisticated approaches have limitations and take experience to feel confident when interpreting the output. But with experience, an arborist can be much more confident knowing the extent of decay compared to the simple visual assessment or sounding the stem with a mallet.

Testing the tools
Our team wanted to know how much the sophisticated techniques would influence an arborist’s assessment of likelihood of trunk failure due to decay. We also wanted to see how consistently Tree Risk Assessment Qualified (TRAQ) arborists rated likelihood of failure, and whether things like their experience influenced their ratings, so we designed an experiment to investigate.

We recruited 18 TRAQ arborists to assess the likelihood of trunk failure due to decay in 30 trees, including five white pines and 25 oaks. The arborists visited the UMass – Amherst campus in July 2021, and assigned a likelihood-of-failure rating five times for each tree. The likelihood-of-failure ratings were the same as in the TRAQ Manual: improbable, possible, probable and imminent. The timeframe for assigning a likelihood-of-failure rating was three years.

The arborists assigned the first likelihood-of-failure rating after visually assessing the tree and the surrounding environment. Then, they sounded the trunk with a plastic mallet and assigned a second likelihood-of-failure rating. Next, they looked at a diagram showing the thickness of sound wood at different locations around the stem’s cross section and a table of the t/R ratio at each location, assigning a third likelihood-of-failure rating. Then, they looked at a pair of tomograms and assigned a fourth likelihood-of-failure rating. Finally, they consulted with another arborist and assigned a fifth likelihood-of-failure rating.

We tallied all the ratings and analyzed them in a few different ways. First, we looked at the proportions of each likelihood-of-failure rating for each tree and each assessor to see if there were patterns in how assessors rated trees. Second, if any patterns were obvious, we looked to see if anything about the trees or assessors helped explain the patterns. For example, we checked to see if an assessor’s years of experience made them more (or less) likely to assign higher likelihood-of-failure ratings. Another example is that we checked to see if trunk diameter was related to the ratings. The last analysis we did was to see whether arborists changed their likelihood-of-failure rating with the different assessment tools and techniques. To do this, we compared their second, third, fourth and fifth likelihood-of-failure ratings with their first one, which was based on the initial visual assessment of the tree.

Of nearly 2,300 total likelihood-of-failure ratings, 54% were improbable, 40% were possible and 6% were probable. Since trees on the UMass campus are well maintained and the arborists were experienced TRAQ assessors, there were no imminent likelihood-of-failure ratings. Within four of the five assessment techniques (visual, mallet, resistance drill and consultation), the proportions of improbable, possible and probable ratings were similar to the overall proportions. But that was not true for ratings assigned after arborists viewed the tomograms.

Hard-copy output from resistance drilling into a stem at five different locations around the stem circumference 4.5 feet above the ground; the higher the tracing on the output, the greater resistance in the wood. Photo courtesy of the author.

Assessing the assessments
After viewing tomograms, there were more possible and probable ratings and fewer improbable ratings. This was apparently related to how often arborists used tomography for risk assessments. Arborists who rarely or never used tomography assigned more possible and probable ratings, while arborists who occasionally or frequently used tomography assigned more improbable ratings.

We saw the same pattern when we looked at whether arborists changed their likelihood-of-failure rating from their initial rating after the visual assessment. Following assessments with the mallet, resistance-drilling output and consultation, more than 60% of the likelihood-of-failure ratings were unchanged from the initial rating following the visual assessment, and fewer than 20% of the ratings increased. But following the tomogram assessment, only 50% of the likelihood-of-failure ratings were unchanged from the initial rating following the visual assessment, and 35% of the ratings increased.

Whether arborists changed their likelihood-of-failure ratings from the initial visual assessment also depended on the tree itself. For two trees, more than half the arborists increased their likelihood-of-failure rating after viewing the resistance-drilling output and tomogram. The resistance-drilling output and tomogram both showed a much greater extent of decay than was apparent in the initial visual assessment. For two other trees, more than half the arborists decreased their
likelihood-of-failure rating after viewing the resistance-drilling output and tomogram. The resistance-drilling output and tomogram both showed a much smaller extent of decay than was apparent in the initial visual assessment. Both trees also had a lean, which influenced the initial rating following the visual assessment.

We also found that the 18 arborists did not often assign the same likelihood-of-failure rating. After the initial visual assessment, arborists assigned two different ratings for 23 of 30 trees, and three different ratings for the other seven trees. Even after gaining more information about decay with the four other assessment techniques, arborists assigned the same likelihood-of-failure rating to, at most, five of 30 trees. We also found that the odds of an arborist changing their likelihood-of-failure rating were very different for different arborists. For example, one arborist changed their likelihood-of-failure rating from the initial rating following the visual assessment on only three trees. In contrast, one arborist changed their rating on 15 trees.

Our findings highlight that assessing likelihood of failure remains an inexact science, even with sophisticated tools. Photo courtesy of the author.

Findings
Our experiment provided some interesting insights into how consistently arborists assess likelihood of trunk failure due to decay. For some arborists and trees, the additional information about stem decay caused the likelihood-of-failure ratings to change, but for others, that was not true. The inconsistency of ratings on most trees is also important to understand.

Our findings highlight that assessing likelihood of failure remains an inexact science, even with sophisticated tools. Our findings also remind arborists to consider the subjective nature of assessing likelihood of failure, and to be cautious when interpreting tomograms if they do not regularly use tomography.

Resources
You can read the original scientific paper online at mdpi.com/1999-4907/14/5/1043.

I was very fortunate to have worked with a fine team of co-authors: Ari Okun and Dr. Nick Brazee, both with UMass – Amherst; Dr. Jim Clark, HortScience|Bartlett Consulting; Dr. Mike Cunningham-Minnick, Hiram College; and Dr. Dan Burcham, Colorado State University. And we are deeply grateful for the 18 TRAQ arborists who traveled to UMass – Amherst during the busy growing season to participate in the research.

Brian Kane, Ph.D., is the Massachusetts Arborists Association Professor at the University of Massachusetts – Amherst, Amherst, Mass.