1. Luke Smith
Emily Cordy
Lucas Bluff
Reducing the effect of planned burns on hollow-bearing trees

2. • Anecdotal instances of hollow tree collapse
• Few published studies, with limited designs
• No replicated, before/after control/impact study
Photo: Rohan Bilney
Context for study
Reference State Collapse Rate N burns Control? Pre/Post
Inions et al. 1989 WA 38% 1 Yes Yes
Parnaby et al. 2010 NSW 14 - 26% 3 No No
Gibbons et al. 2000* VIC 14 - 37% 57 No No

3. Overview and Aims
1. Measuring the collapse rate of hollow-bearing trees (HBTs) in planned
burns (Luke)
2. Mitigating hollow-bearing tree collapse: an experiment (Emily)
3. Management options to reduce hollow-bearing tree collapse (Luke)

4. Design Intent
• Representative sample of planned burns (n = 30) and non-burn controls (n = 6)
• Spatial spread across multiple sites in Eastern Victoria.
• One forest type; Lowland Forest
• Design target: >90% power to deliver a collapse rate estimate ±5% of actual
• But deliberately ‘overbuilt’: 150 x 0.5 ha plots and 1575 individual HBTs
Bairnsdale 50 km
Sale
Orbost
Measuring the collapse rate of HBTs in planned burns

5. Methods: Pre-burn
• Sampling unit is a single HBT
(not a single hollow)
• ~25 pre-fire variables/tree, e.g.
DBH
Crown
Score
Living
status
Intact
base %
Tree
species
Position
of
hollow
Fuel
hazard
Hollow
size
Number
of
hollows

7. Methods: Post-burn
Collapsed
Damaged
UndamagedNot collapsed
(9 variables)*
(~22 variables)*

8. Results: Individual HBT outcomes

9. Mitigating hollow-bearing tree collapse: an experiment
•Is manual rakehoe treatment effective for
reducing HBT collapse rate?
•If so, what is the most efficient distance to
rakehoe?
•Which HBTs should we prioritise for
treatment?
Research Qs?

10. Experimental design
Stratified random allocation of HBTs to
four treatment groups:
• Control (no rakehoe)
• Clearing to 0.5m, 1m or 2m (using
rakehoe)

11. Locate and assess
HBTs
Randomly assign
treatments
Deliver treatment
Conducting the
planned burn
Reassessing each
HBT
Conducting
statistical analysis
Method

13. Is rakehoe treatment effective at reducing HBT collapse?
Rakehoe treatment
halved the rate of
HBT collapse
0%
20%
40%
60%
80%
100%
Control Rakehoe
%ofHBTs
Not collapsed
Collapsed

14. What is the most efficient distance to rakehoe?
0
2
4
6
8
10
12
14
16
18
0.5 m 1.0 m 2.0m
Rakehoe distance
Expected number of
HBT collapses
prevented
(per team member per
day)

15. Which HBTs should we prioritise for treatment?
Treat HBTs most likely to collapse:
•Dead HBTs
•HBTs with basal damage (hollowbutt)
•HBTs with large hollows
•Also high fire severity increases collapse probability

16. Proposed risk reduction measures
Management options to reduce HBT collapse

17. Trading off HBT habitat value with human risk reduction
Human risk
from bushfire
Hollow tree
habitat value
Approach Burn planning action
High Low Treat human risk • Business as normal
Low High Prioritise hollow values • Reduce burn cover, severity
• Reduce burn frequency
• Consider nominating areas for
extremely long rotations
• Big benefit for little $$$
High High Treat human risk but
mitigate impact on
values
• Focus on burn prep and delivery of
fire
• (Rakehoeing, exclusion from HBT
stands/patches)

18. Key messages
• On average 25% of hollow-bearing trees directly exposed to
single planned burns in Gippsland dry/mixed forest will collapse.
• 1m rakehoe line around a HBT reduces their probability of
collapse – approx. halves it
• Strategic approach is needed; DELWP are investigating

19. Acknowledgements
Project leads:
Dr Lucas Bluff – Senior Biodiversity Officer – DELWP
Assoc. Prof. Wendy Wright – Federation University
Field delivery:
Emily Cordy
Luke Smith
Ecosystems Management Australia
Tambo District field staff (DELWP)
Thanks to:
Fiona Hamilton, Stephen Platt, Nevil Amos, Michelle Ibbett,
Natasha Schedvin, Penny Gay, Sarah Chubb, Amelia Koch

20. Thankyou and Questions
PDF online
Google title or
ISBN 978-1-76047-032-6
Photo: David Hollingsworth

21. Methods: Plot level
150 half-hectare plots
126 plots across 30 burns scheduled on Autumn 2013 FOP
24 plots across 6 pseudo-burn controls
Audits (replicate assessments) at 18 plots
Pre-treatment surveys: 1575 individual HBTs ~ 25
variables/tree
Burn  SFRI stands 1 SFRI stand  1 Plot

22. Burns differed in % cover and severity

23. Probability of collapse formula
𝑷𝒓𝒐𝒃𝒂𝒃𝒊𝒍𝒊𝒕𝒚 𝒐𝒇 𝒄𝒐𝒍𝒍𝒂𝒑𝒔𝒆 =
𝟏. 𝟒 ∗ #𝒔𝒎𝒂𝒍𝒍 𝒉𝒐𝒍𝒍𝒐𝒘𝒔 + 𝟒. 𝟔 ∗ #𝒎𝒆𝒅𝒊𝒖𝒎 𝒉𝒐𝒍𝒍𝒐𝒘𝒔 + 𝟏𝟒. 𝟔 ∗ #𝒍𝒂𝒓𝒈𝒆 𝒉𝒐𝒍𝒍𝒐𝒘𝒔 +
𝑳𝒊𝒗𝒊𝒏𝒈 𝒔𝒕𝒂𝒕𝒖𝒔 + 𝑳𝒐𝒈 𝟏𝟎𝟏 − % 𝒅𝒆𝒈𝒓𝒆𝒆 𝒐𝒇 𝒃𝒂𝒔𝒂𝒍 𝒅𝒂𝒎𝒂𝒈𝒆 + 𝒅𝒓𝒚𝒘𝒐𝒐𝒅
All trees ranked in probability of collapse
Split into four groups
Treatments allocated across the four groups, accounting for probability of collapse

24. Results: Tree-level variables associated with HBT collapse
Variable Importance Coef. p
Dry wood (Yes) 1.00 0.787 0.037
Habitat Index 0.96 0.370 0.028
Living (Dead) 0.84 1.528 <0.001
ln(Base Damage) 0.78 0.298 0.018
Binomial (logit) GLM; 235 HBTs exposed to fire, model-averaged coefficients from models
with AICc<3; global model was an a priori subset of 12 variables; one-sided p

25. Binomial regression – Tyers W4 and
Carrajung Mill track
B S.E. Significance Exp(B) 95% C.I.for EXP(B)
Lower Upper
Onsite fire severity .902 .305 .003 2.465 1.356 4.481
Treatment: 0.5m clearing -.671 .591 .256 .511 .160 1.628
Treatment: 1m clearing -1.072 .651 .100 .342 .095 1.227
Treatment: 2m clearing -1.421 .670 .034 .241 .065 .897
Generated predictor of
collapse
4.035 .921 .000 56.563 9.302 343.957
Site: Tyers W4 or Carrajung
Mill Track
.758 .536 .157 2.135 .746 6.109
Constant -4.225

26. Binomial regression – Tyers W4, Carrajung Mill
Track, Nowa Nowa Gorge road, and Lakes Entrance
– Colquhoun
B S.E. Significance Exp(B) 95% C.I.for
EXP(B)
Lower Upper
Predictor of collapse 4.379 .820 .000 79.767 15.985 398.053
Treatment: 0.5m clearing -.409 .488 .402 .664 .255 1.730
Treatment: 1m clearing -1.478 .609 .015 .228 .069 .752
Treatment: 2m clearing -1.438 .586 .014 .237 .075 .749
Tyers W4 .589 .857 .492 1.801 .336 9.658
Carrajung Mill Track 1.542 .816 .059 4.674 .945 23.117
Lakes Entrance – Colquhoun .017 .875 .984 1.018 .183 5.652
Aerial severity .865 .314 .006 2.374 1.282 4.397
Constant -4.966