We're updating all our publications from Tree Risk-Benefit Assessment & Management to Tree Risk-Benefit Management and Assessment.
We thought we'd gotten past looking at tree risk through the eyes of an Arborist, rather than a Duty Holder, with v5.0 of the Strategy. This is where creating a flowchart helped reveal that the most valuable risk management asset is Passive Assessment by anyone. Not Active Assessment by an Arborist.
A 5 yearly Active Assessment isn't being topped up by Passive Assessment, as we used to propose. Passive Assessment is being carried out all of the time, day in day out. It's being topped up by Active Assessment in zones of high confluence every 5 years.
Well, in the process of completing our work helping the Tasmanian Government manage their tree risk, David was asked whether we could produce a summary to explain what VALID is about. It's for the strategic decision-makers, they explained.
"'What is VALID?', does that", David said.
"In 'What is VALID?', the first picture is of a woman looking at a VALID likelihood of failure decision in the Tree Risk App. That's an Active Assessment at a Detailed level", they replied.
"That level of investigation is not the first thing in the minds of Tasmanian Government decision-makers when it comes to adopting VALID's approach to Tree Risk-Benefit Management, is it?"
Hence the upgrade and the new, 'What is VALID?'
Jeremy Barrell’s Arb Magazine version of his journal article has had some Arborists ask us about where his binary, ‘High’ or ‘Low’ likelihood of occupancy approach sits with VALID.
To recap. The heart of the article is three legal cases cherry-picked by Jeremy where Jeremy acted as an expert and are Jeremy’s interpretations of these cases where Jeremy’s evidence was a key factor.
When we first went through the article, we were alarmed to find it appeared to be less about reasonable, proportionate, and reasonably practicable tree risk-benefit management (which is what the Courts and Coroners want) and more about what Jeremy expects to contest as an ‘expert’.
Back to the binary ‘High’ v ‘Low’ likelihood of occupancy. VALID’s likelihood of occupancy categories are based on log base 10, like the Richter scale uses to measure earthquakes. If we show the likelihood of occupancy to scale, and set High at the centre, we can compare VALID’s and Jeremy’s likelihood of occupancy categories. Jeremy’s High occupancy spans four orders of magnitude. Or, another way of looking at it. A Richter scale 4 earthquake is the same as a Richter scale 7 earthquake.
We know from pedestrian data the centre of built-up areas have a likelihood of occupancy that means on average more than one person is exposed to the risk. Occupancy is Very High.
We also know the busiest roads have a likelihood of occupancy that means on average more than one vehicle is exposed to the risk. Occupancy is Very High.
Where we have busy roads next to busy footpaths in towns and cities, we know that the combined occupancy of people and traffic means on average more than one person AND one vehicle is exposed to the risk. Occupancy is Very High.
From the training we’ve delivered to those who have upgraded their approach to tree risk, we know that tree risk assessors have been poorly trained to recognise Very High and High Occupancy.
What all this means is that unless you’re using VALID, and have had likelihood of occupancy training (it’s really easy), you’ll be undervaluing the risk where it matters most.
You’ll most likely be undervaluing the risk by at least a whopping factor of x10 just on the ‘targets’ you’ve chosen.
In the previous post we looked at VALID's Goldilocks Likelihood of Occupancy canvas to explore categories that are not too wide, and not too narrow, that are just right.
This Likelihood of Occupancy canvas is useful to examine tree risk decision-making at the High Court in the UK’s landmark Poll v Bartholomew Judgment.
In Poll, a motorcyclist was seriously injured by a falling Ash stem. The Judge found for the Claimant because the tree was ‘High Risk.’
In their reports, the Claimant’s expert said the tree was ‘High Risk’ and the Defendant’s expert didn’t mention any risk. Yet in their Joint Statement, the experts agreed the tree was a ‘Medium Risk.’
Naturally, the expert’s opinions left the court scratching its head and it had to ask them to produce a Second Joint Statement to define what they meant by high and medium risk.
In the Second Joint Statement, the experts told the court the tree was ‘High Risk’. However, they concluded the risk was high after they'd assessed the Likelihood of Occupancy for a minor road at 50%, when in fact it was 1%. The experts overvalued the occupancy by a whopping factor of 50. This gaffe was so enormous the tree was in fact a ‘Medium Risk’ and not a ‘High Risk.’
The Judge would’ve found for the Defendant if the tree was a ‘Medium Risk.’
In the Scale of the Problem we saw the overall risk from branches and trees falling is so extremely low we need a microscope to see it.
The only sensible way to measure risks that go this low is to use a logarithmic scale. It turns out the Goldilocks logarithmic scale for tree risk that’s not too narrow, and not too wide, that's just right is log base 10. Just like the Richter Scale is for measuring earthquakes.
So far so numberwang. What does that mean for you?
Well, when it comes to Likelihood of Occupancy decision-making, the advantages of log base 10 are obvious when drawn to scale. There’s 5 colour-coded Likelihood of Occupancy categories in VALID. If we centre High, then you can only see a bit of the heel of Very High. Nearly all of it’s off the screen. You can make sense of Moderate but you can barely make out Low. And you can’t see Very Low at all.
So, the first decision a Validator makes with Likelihood of Occupancy is what 3 categories can’t it be? Which 3 make no sense? Once calibrated this is an effortless decision. Then it comes down to one of two. Usually, which one is the most obvious because they're huge canvases. If in doubt you go for the higher one.
We regularly use visuals to convey both the context of tree risk and how you can measure it. Recently, we shared a post with Professor David Ball's quote that the prospects of reducing the risk below the current level were comparable to finding a microscopic needle in a gargantuan haystack.
To help convey that finding a microscopic needle in a gargantuan haystack simile further, we've had a play around with illustrating the tree risk context using coloured spectrums.
In the graphic, the top spectrum shows the reality of tree risk to scale. We know that compared to other everyday risks we readily accept, the overall risk to us from branches or trees falling is extremely low. Our annual risk of being killed or seriously injured is less than one in a million. At this scale, we can't see the amber and red risks. We'd need a microscopic.
If we take the risk spectrum to a scale where we can just make out the risks that we're trying to find and manage, we have to overvalue the base-rate risk by a factor of more than 1000.
Taking the 'defect' out of tree risk-benefit assessment
Has been published in the spring edition of the Arboricultural Association's Arb Magazine. You're welcome to download a pdf copy by clicking the link above or the image below.
Here's the introduction to whet your appetite.
"We’ve grown up being told that when we assess tree risk we should be looking out for tree ‘defects’. The problem with this approach is what are commonly labelled as defects often aren’t defects at all."
Any publication about tree risk management lacks credibility if it neglects the overall risk. That's because the overall risk from branches and trees falling and causing death, injury, or property damage provides the 'Context' (ISO 31000 - Risk Management). It gives us a base rate.
This is the context of the overall risk in VALID's Tree Risk-Benefit Management Strategies.
"Compared to other everyday risks we readily accept, the overall risk to us from branches or trees falling is extremely low. Our annual risk of being killed or seriously injured is less than one in a million. That's so low, we're at greater risk from a 200 miles (320km) round trip drive to visit friends for a weekend than from branches or trees falling for a whole year. Given the number of trees we live with, and how many of us pass them daily, being killed or injured by a tree is a rare event; one that usually happens during severe weather."
Why is establishing context and base rate so important? A risk expert nails it.
VALID has four easy to understand traffic light coloured risk ratings, and this is where they sit in the Tolerability of Risk Framework (ToR).
The Tolerability of Risk Framework is an internationally recognised approach to making risk management decisions where the risk is imposed on the public.
The ToR triangle gets fatter and redder where more attention and resources should be allocated to managing the risk. It gets thinner and greener where less attention and resources should be allocated.
Where ToR is amber the risk is Tolerable if it’s ‘as low as reasonably practicable’ (ALARP) - where the costs of the risk reduction are much greater than the value of the risk reduction.
VALID has applied ToR to tree risk but has removed the numberwang because:
1) Tree risk has too much uncertainty to credibly measure at single figure accuracy with risks like 1/4, 1/300, 1/20 000, or 1/500 000 000.
2) Risk outputs as probabilities create friction in communication because many people struggle with numbers. Research shows that about 25-33% can't rank 1:10, 1:1000, and 1:100 risks from highest to lowest.
3) The risk assessor and duty holder are spared the complexity of numerical cost-benefit analysis in the amber ALARP zone.
Recently, we caught a podcast where a tree was declared 'safe' if it's less than 30% hollow. We think they meant 70% hollow. Either way, this isn't right for several reasons.
We've posted about this before, but as long as this kind of mistake is being broadcast we think it's worth repeating so the message gradually gets home.
The heart of the confusion is the t/R = 0.3 fallacy. t/R = 0.3 is when a residual wall thickness (t) is 30% of the stem radius (R). It's often cited as a failure threshold. It's not. The 'Why t/R Ratios Aren't Very Helpful' pdf explains why in detail.
In short, one reason is because of a geometric property called section modulus. Wind load and material properties remaining equal, if you double the diameter you increase the load bearing capacity of a tree by 8 times.
To add to the confusion, t/R 0.3 is often referred to as 70% hollow. In fact, a 0.3 t/R ratio is only 50% hollow. 70% is the radius, which is one dimension. t/R 0.3 is the area, which is two dimensions.
This graph from Paul Muir shows the relationship of central hollowing on:
A = Cross Sectional Area
Z = Section Modulus
t/R = 0.3
A = 49% loss of cross sectional area
Z = 24% reduction in load bearing capacity
To make matters worse. A tree with a t/R ratio of 0.3 can have a very high likelihood of failure, or it can have a very low likelihood of failure.
If all that wasn't enough, it's seldom that where decay is of concern we're dealing with a cross sectional area of a tree that's a circle.
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