forest health and tsr: how do pests “move the needle”?
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Stefan Zeglen, Forest Pathologist, West Coast Region Jim Brown, Senior Analyst, Forest Analysis and Inventory Branch. Forest Health and TSR: how do pests “move the needle”?. Scale matters. CSC Winter Workshop, Nanaimo, BCFebruary 27, 2014. The Mythology of Pests. - PowerPoint PPT PresentationTRANSCRIPT
Forest Health and TSR: how do pests “move the needle”?
Stefan Zeglen, Forest Pathologist, West Coast RegionJim Brown, Senior Analyst, Forest Analysis and Inventory Branch
CSC Winter Workshop, Nanaimo, BC February 27, 2014
Scale matters
CSC Winter Workshop, Nanaimo, BC February 27, 2014
The Mythology of Pests
Myth 1: Pests don’t matter on the Coast like they do in the Interior.
Myth 2: Even if pests are present they never do enough damage to worry about.
Myth 3: Pest losses are already accounted for in growth & yield models.CSC Winter Workshop, Nanaimo, BC February 27, 2014
Accounting for pest impacts Most basic:
Unsalvaged loss estimates or non-recoverable loss estimates (NRL)
More refined: Operational Adjustment Factors (OAF) for
model estimates Catastrophic loss estimates:
One-off calculations for large scale events
CSC Winter Workshop, Nanaimo, BC February 27, 2014
Unsalvaged loss estimates Required component of TSR data packages. Usually include losses to wind, wildfire,
landslides, insects and diseases. Data used comes from wildfire records,
aerial overview surveys (AOS) and other reports.
Usually provided as a m³/year adjustment. Tempered by district knowledge of salvage
rates for various events. Updated each TSR cycle.
CSC Winter Workshop, Nanaimo, BC February 27, 2014
Operational Adjustment Factors
Used to adjust model output (e.g., TASS/TIPSY) of stand volume projections.
Restricted to a set of insects or diseases in a defined area (e.g., root disease in the CDFmm and CWHxm1)
Data is usually sourced from fixed plots (e.g., G&Y PSP, research trials) measured over time.
Updated as new information becomes available.
CSC Winter Workshop, Nanaimo, BC February 27, 2014
Catastrophic loss estimates
Rarely necessary. Attempts to predict impact on a large
scale from biological spread and mortality factors.
Most recent example is the Provincial Level Projection of the Current Mountain Pine Beetle Outbreak (M Eng and A Walton).
Intended to drive decision-making and mitigation response over a large area.
CSC Winter Workshop, Nanaimo, BC February 27, 2014
CSC Winter Workshop, Nanaimo, BC
Factoring Forest Health into Timber Supply ForecastsSome Considerations What are the forest health factors influencing the
forest What stand types / tree species / ages are affected What are the ST/ LT influences of these factors:
timber yield (recoverable volume) timber grades and species mix stand development, growth and future yields
Are losses constant over time (endemic) or periodic (epidemic)
To what extent is it possible to salvage damaged timber (considering access, economics, shelf life)
Unsalvaged loss estimates
Often applied as a constant reduction in the periodic supply, though estimates could vary over time or by forest type if information supports doing so.
Estimates are based on observed losses, taking into account salvage and recovery, ideally over the previous 10 years or more.
Recognized uncertainty that historic losses = future losses (given changing economics, management practices and climate)
CSC Winter Workshop, Nanaimo, BC February 27, 2014
Unsalvaged loss estimates
Cause of loss
Annual loss within the
THLB (m³/year)
Salvage rate (%)
Annual unsalvaged
losses within the THLB (m³/year)
Wind 3100 25 2340Fire 16500 0 16500Mountain Pine Beetle 400 0 400Douglas-fir Bark Beetle
8800 50 4400
Spruce Beetle 200 0 200Western Balsam BB 3100 0 3100Total 33120 26940
CSC Winter Workshop, Nanaimo, BC February 27, 2014
Example, Fraser TSA, 2013 TSR
Operational Adjustment FactorsIn Timber Supply Analysis Natural stands are modelled using VDYP7, often with the
assumption that endemic losses are reflected in the net volume estimates.
Managed stands are modelled using TASS/TIPSY. Since TASS models fully stocked stands free of significant health factors, OAFs are necessary.
A standard OAF1 of 15% was developed from research trials to account for stands gaps and losses that act over the life of the stand.
Locally defined OAFs are needed to account for accute outbreaks or other endemic factors not accounted in the standard OAF1
Ideally local defined OAFs are based on stand monitoring data (e.g. a YSM/CMI) including field derived volume estimates.
CSC Winter Workshop, Nanaimo, BC February 27, 2014
CSC Winter Workshop, Nanaimo, BC
Operational Adjustment Factors A YSM project in the Morice TSA targeted stands
between 15 and 50 years old Initial results suggested that TIPSY volumes are
overestimated and inventory heights are underestimated.
The project also provided information to assess the incremental forest health impact of pine stem rusts
There are a number of approaches that could be used to adjust MSYT for forest health factors using YSM results
CSC Winter Workshop, Nanaimo, BC
Operational Adjustment Factors
0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 951000
500
1000
1500
2000
2500harvest ('000s m3/year)
years from 2014
OAF1 (10%) for stem rusts
A worst-case scenario assumes affected trees will die at rotation and applies an incremental OAF1 to account for losses. This is similar to how we typically adjust for root disease on the coast
Operational Adjustment Factors
CSC Winter Workshop, Nanaimo, BC
0 10 20 30 40 50 60 70 80 90 1000
500
1000
1500
2000
2500
3000
3500
4000
None1000/ha 2000/ha
Stand age
Live
tre
es p
er h
a NaturalRegen.
0 10 20 30 40 50 60 70 80 90 1000
500
1000
1500
2000
2500
3000
3500
4000
None - no rustNone - 25% rust
Stand age
Live
tre
es p
er h
a
Natural earlyRegen. rust
Alternatively, TASS can be used to simulate the change in stand dynamics from a (e.g. western gull rust)
Early rust event
adapted from J. Goudie & I Cameron 2014
CSC Winter Workshop, Nanaimo, BC
Operational Adjustment Factors TASS allow for a more complex simulation of stand
response to early mortality and growth losses.
0 10 20 30 40 50 60 70 80 90 1000
500
1000
1500
2000
2500
3000
3500
4000
None - none - noneNone - 25% - noneNone - 25% - 2%
Stand age
Live
tre
es p
er h
a
Natural rustRegen. early late
Early rust event
0 10 20 30 40 50 60 70 80 90 1000
50
100
150
200
250
300
350
400
450
500
None - none - noneNone - 25% - noneNone - 25% - 2%1000/ha - 25% - 2%2000/ha - 25% - 2%
Stand age
Mer
chan
tabl
e vo
lum
e (m
3/ha
-6%-15%
Natural rustRegen. early late
adapted from J. Goudie & I Cameron 2014
CSC Winter Workshop, Nanaimo, BC
Operational Adjustment Factors YSM provides a means of collecting data on the
effects of forest health factors and developing assumptions to account for them in T.S.A.
When using OAFs one must understand how the factor alters the dynamics and conditions of a stand and how well the model effectively simulates those dynamics.
TASS modelling and/or custom OAFs may be the most appropriate means of adjusting for both tree mortality and live-stem volume losses.