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 management and assessment 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 level 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, Medium, and Low 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 country 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.
"The implications of recent English legal judgments, inquest verdicts, and ash dieback disease for the defensibility of tree risk management regimes"
We've had several requests for a better quality image that's part of a discussion about this article on the UKTC (attachments on this group have to be below 180kb). Click the image to enlarge it.
You can download Jeremy Barrell's tree risk management article here.
Since then, we've had further calls to set out the points in this big canvas with a step-by-step guide to make it easy to follow.
We're genuinely surprised the article has been peer-reviewed, let alone published in a journal. It's not research. Some obvious key points of fact don't make much sense, even within the questionable logic of its own risk ecosystem. We've sketched them out in the above image so you can see the whole picture, and described them below. We're baffled how they weren't picked up during the peer review.
The matrix has High Risk, Low Risk, and Medium Risk outputs.
No Likelihood of Occupancy
So, we've got a Tree Risk Matrix
High × High = High Risk
High × Low = Medium Risk
Low × High = Medium Risk
Low × Low = Low Risk
High × Low × High = Low/Acceptable Risk
Somehow, we've gone from a Tree Risk Matrix world where:
High × Low = Medium Risk
High× Low = Medium Risk × High = Low/Acceptable Risk
And that's before we consider the really important stuff, like what does High, Medium, and Low actually mean, and how do you go about measuring them?
Unless clearly defined, words like High, Medium, and Low are what Philip Tetlock calls 'vague verbiage'. They're illusions of communication. Or tree risk 'bafflegab', as we call it. Further still, you can't reasonably model tree risk by applying mathematical rules to vague words and then multiplying (or adding) them, or by painting ill-defined words with traffic light colours.
Exploring the low occupancy = acceptable risk statement further.
As we don't know what a duty holder will think low occupancy means, and there's no guidance about what low occupancy means in the article, how do we know the risk is then low enough that it's acceptable no matter how high the likelihood of failure or how high the consequences?
That low occupancy has no clear definition or meaning in Jeremy's Tree Risk Management Frameworks should be worrying for a duty holder.
In VALID, low occupancy is clearly defined and there's no ambiguity. We don't burden the duty holder with trying to second guess what we mean by low occupancy. The reason why low occupancy = Acceptable Risk should be worrying for a duty holder following Jeremy's advice is that in VALID we have several scenarios where low occupancy has risks that are Not Acceptable or Not Tolerable.
Infrequent or very low use is a higher level of occupancy than low
To make matters worse. In Jeremy Barrell's 1:10,000 Time Bomb piece he describes this footpath (below) has having infrequent or very low use. He outlines that every year the path is walked by a person with a working knowledge of trees who gives them a quick visual check. Because these trees are being checked annually that means in Jeremy's tree risk management vocabulary, infrequent use or very low use is a HIGHER level of occupancy than low occupancy - remember, trees in low occupancy don't need checking at all.
Clearly, any duty holders following the guidance in Jeremy Barrell's Tree Risk Management Frameworks could quite reasonably classify the infrequent or very low use of this footpath as low occupancy and not check the trees.
This could be a substantial vulnerability for duty holders because in his 1:10,000 time bomb presentation, Jeremy makes a case for a claim being made against them if a small diameter deadwood branch from an Ash tree falls and causes significant head injuries to someone walking along this path. Even though he describes the risk as being at the lower end of his risk spectrum, the duty holder is expected to have removed the deadwood because it wouldn't have cost that much to do it.
These are just some of the more obvious concerns we have with Jeremy's take on tree risk management in his article.
There are some more insights into the critical problems with a binary take on High v Low Occupancy in the Jeremy Barrell | Tree Risk Management - Likelihood of Occupancy post.
Passive Assessment | The invisible gorilla in the room
There's a famous psychological experiment called the invisible gorilla. In it, you're asked to watch a short video of six people passing a basketball. Three of them are wearing white shirts and three of them black shirts. You're asked to count how many passes are made by the white shirts. Most people get the number of passes right. Because they're focused on this, what half the people don't see is a gorilla walk amongst the players, stop, face the camera, thump their chest, and walk off.
To half the people, this very obvious gorilla is invisible.
I recently found one of my invisible gorillas. Whilst putting a flowchart together for VALID's Tree Risk-Benefit Management Strategies, I realised my invisible gorilla was Passive Assessment.
Passive Assessment, and not Active Assessment, is a duty holder's most valuable tree risk-benefit management asset because;
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