Simulation Model

# Spruce Budworm (SBW) Disturbance Extension

This extension was coded by Dr. Chris Hennigar in 2020 and derived in principle from SBW DSS methods (Erdle and MacLean 1999; MacLean et al. 2001) with notable upgrades over the past 20 years described below. This extension is currently accessible only from the Acadian variant.

Properties

Examples

Background and Description

This extension provides means to:

1) Submit and remember spruce budworm historical observations and/or future projections of either:

a. annual current-shoot defoliation

OR

b. annual L2 populations.

2) Estimate host annual current-shoot defoliation from annual L2 levels, with consideration of:

a. stand hardwood content,

b. natural enemy buildup during the outbreak,

c. foliage protection efficacy and frequency,

d. white spruce foliage endophyte efficacy, and

e. host susceptibility differences.

3) Translate annual current-shoot defoliation to whole-tree defoliation according to the SBW DSS (MacLean et al. 2001).

4) Predict SBW DSS impact rates as a function of whole-tree defoliation, host species, and tree size, including:

a. Tree diameter growth reduction multipliers, and

b. Tree additive mortality rates (probability of tree death).

5) Convert periodic defoliation and impact relationships to annual rates for use in OSM annual or periodic simulation cycles.

6) And finally, modify OSM host-tree base growth and mortality rates with these predictions during simulation.

## Properties and Methods

These properties and methods are accessible using the command SIMULATION.MODEL.SBW

Property | Method | Data Type | Constraint | Default | Description
Outbreak Definition
SetPopulationPattern | Text | ≥ 0 | None | Pre-coded outbreak patterns (MacLean et al. 2001): · Moderate (based on 1980s NB ‘Normal’ Outbreak) · Severe (based on 1970s Cape Breton, NS Outbreak) · None (no outbreak) Defoliation is calculated as a function of L2 values, host susceptibility, foliage protection properties. · See SbwOutbreakAssumptions.xlsx
SetPopulation | Float Array | ≥ 0 | 0 | Sets a user-defined annual population (L2/m2) sequence for an outbreak. Defoliation is later calculated as a function of L2, host susceptibility, foliage protection, etc. · See SbwOutbreakAssumptions.xlsx
SetDefoliation | Float Array | 0 – 100 | 0 | Sets a user-defined annual current-shoot defoliation (%) sequence for an outbreak. Annual defoliation levels are influenced by foliage protection. Defoliation is assumed to be based on actual observed levels. Levels should already be adjusted by the user to account for past or future protection, if any. If set, then any previously set population pattern is reset to 0.
OutbreakOffset | Int32 | -5 – 100 | 0 | Number of years to offset the start of the outbreak; e.g., a value of -5 causes the outbreak pattern to shift backward five years from the start year of the simulation, and likewise end five years sooner.
Foliage protection
ProtectFoliage | Boolean | TRUE|FALSE | FALSE | Turn foliage protection on (TRUE) or off (FALSE).
ProtectEfficacy | Float | 0 – 1 | 0.5 (50%) | Ratio of L2 that will die as a result of foliage protection. 1 = 100% effective and 100% budworm mortality. Single applications of Btk and Mimic efficacies vary in the range of 40-80%. Double or triple applications may assume slightly higher protection levels.
ProtectionThreshold | Float | ≥ 0 | 40 (40%) | Threshold percent annual current-year shoot defoliation on balsam fir that would trigger a foliage protection treatment.
ProtectionDelay | Byte | ≥ 0 | 0 (none) | Number of years to delay protection after first eligible year (when protection threshold is first achieved); default = 1 (skip first eligible year). If = 0, protection will occur in the first eligible year.
ProtectionLock | Byte | ≥ 0 | 0 (none) | Number of years to skip between annual protection treatments; default = 0 (all years). If = 1, protection will occur in every second year.
EndophyteEfficacy_WS | Float | 0 – 1 | 0 (none) | Assume planted white spruce is inoculated with endophytes that reduce budworm survival; e.g., 0.3 = 30% mortality in every outbreak year. This effect is multiplicative to effects of foliage protection.

## Examples

### Moderate outbreak

SIMULATION.MODEL.SBW.SetPopulationPattern Moderate

### Severe outbreak

SIMULATION.MODEL.SBW.SetPopulationPattern Severe

### Severe outbreak beginning 5 years before start of simulation

SIMULATION.MODEL.SBW.SetPopulationPattern Severe

SIMULATION.MODEL.SBW.OutbreakOffset -5

### Deactivate outbreak

SIMULATION.MODEL.SBW.SetPopulationPattern None

### User-defined outbreak with annual population (L2/m2) series (16 years, starting in year 1 of simulation)

SIMULATION.MODEL.SBW.SetPopulation 20.00 20.51 21.05 21.62 22.22 80.00 105.88 225.00 112.50 103.23 106.67 96.55 57.14 14.81 15.38 16.00

### Severe outbreak with foliage protection

SIMULATION

MODEL

SBW

SetPopulationPattern Severe

ProtectFoliage TRUE

### Severe outbreak with foliage protection assumption adjustments

SIMULATION

MODEL

SBW

SetPopulationPattern Severe

ProtectFoliage TRUE

ProtectionEfficacy 0.6

ProtectionThreshold 50

ProtectionDelay 2

ProtectionLock 1

## Description

The SIMULATION.MODEL.SBW object provides stand-impact routines derived from the Spruce Budworm Decision Support System (Erdle and MacLean 1999, MacLean et al. 2001) with some significant method upgrades over the past 20 years, including:

1) Reduced defoliation on spruce, especially red and black, relative to fir according to Hennigar et al. (2008).

2) Reduced defoliation with increasing stand hardwood content (Su et al. 1996, Zhang et al. 2018).

3) Ability to input annual SBW population levels [larval 2nd instar (L2)/m2], with internal functions to predict defoliation on respective host species as a function of defoliation: population relationships, stand hardwood basal area ratio, and foliage protection assumptions.

4) Ability to use annual stand-level defoliation levels (as in the original SBW DSS), but with auto-prorating of stand defoliation onto hosts (balsam fir and white, red, and black spruce) as a function of their basal area ratio and relative susceptibility to SBW defoliation; i.e., if the stand is composed half of fir and half of black spruce, then the fir will sustain more relative defoliation compared to the black spruce, but basal area weighted stand defoliation will still match aerially observed or projected input levels.

5) Preprogrammed ‘Moderate’ and ‘Severe’ SBW outbreak scenarios expressed in annual population levels. These population patterns were estimated from the original SBW DSS defoliation-based outbreak scenarios using the same defoliation: population relationships as discussed above, so outcomes should be similar.

6) Foliage protection efficacy assumptions and treatment options that can be modified easily.

Technical details of these assumptions and calculations are provided in SbwOutbreakAssumptions.xlsx. This Excel file demonstrates the effect of modifying some of the foliage protection assumptions on resulting host defoliation and calculation of periodic average whole-tree defoliation. These calculations are nearly identical to the calculation performed by the SBW extension. Growth multipliers & additive mortality rates are also provided as a function of host, tree size, and periodic average whole-tree defoliation (aka cumulative defoliation in MacLean et al. 2001). These tree impact: defoliation relationships have been modified slightly from the original SBW DSS to 1) use tree size (DBH class) as a surrogate for tree age (age is not a readily available attribute on all trees in most inventories), and 2) predict impact rates with equations rather than a lookup table for application coding/performance reasons.

### How is whole-tree defoliation calculated?

#### Population survival adjustments and annual defoliation calculations

Before translating population levels to defoliation levels, annual populations (L2/m2) are adjusted for multiplicative effects of stand hardwood content, natural enemy buildup during the outbreak, foliage protection, white spruce plantation endophyte efficacy, and host susceptibility to defoliation, in that order. These adjustments are provided in SbwOutbreakAssumptions.xlsx, including assumptions and cell comments regarding the conversion of the SBW DSS Moderate and Severe outbreak patterns from annual fir defoliation to annual L2 density. Dr. David MacLean provided technical review of this work as part of Eric Ye Liu’s masters project at UNB in 2019 studying the effects of SBW on the NB forest sector and economy.

Increased population density is assumed here to cause linear increases in annual current-shoot defoliation for fir, up to 100% defoliation. Likewise, we assume studies that predict relative defoliation reductions from increased hardwood content or reduced host susceptibility, can be interpreted as equivalent reductions to larva survival in most cases.

Here, annual population is converted to annual host current-shoot % defoliation (AD) using: AD = L2 * HW * NE * FP * wsENDO * HOST, where AD is capped at 100 (100% defoliation); HW is the stand hardwood effect = 1 – hardwood basal area ratio; NE is outbreak natural enemy buildup factor = Max (1 – 0.035 * outbreak_year, 0.6); FP = 1 – the foliage protection efficacy ratio; wsENDO = 1 – efficacy ratio of planted white spruce inoculated with endophytes; and Host = 1.00 for fir, 0.72 for wS, 0.42 for rS, and 0.28 for bS. This assumes that 50 L2/m2 will result in 50% defoliation on fir, assuming: i) the stand has 0% hardwood, ii) the outbreak is in year 1 (no buildup of natural enemies), and iii) no foliage protection occurs in that year. For white, red, and black spruce, if L2 density post HW, NE, FP, and wsENDO adjustments is > 70/m2, defoliation is predicted from adjusted L2 levels using a logistic relationship (see worksheet ‘Host L2 à DEFOL relationship’) to yield higher spruce defoliation levels under severe populations and to cap red and black spruce defoliation at 90 and 80%, respectively.

This population-based input approach is useful for simulating severe population years where budworm densities are far above levels needed to cause 100% fir defoliation and where no amount of foliage protection would be sufficient to avoid significant stand defoliation. In past SBW DSS work, protection was assumed to always reduce defoliation to 40% regardless of L2 density. Here, if populations are protected at 200 L2/m2 and we assume 50% protection efficacy (50% more L5-L6 die), the surviving budworm, despite protection, would remain high enough to cause moderate-severe defoliation; and conversely, if there is only enough budworm to yield 40% defoliation, then we would expect foliage protection to push defoliation much lower than 40%. Alternatively, from a host susceptibility perspective, if populations substantially exceed levels to cause 100% fir defoliation, we would expect higher than 28% defoliation on black spruce (following from Hennigar et al. 2008) from droves of starving budworm dispersing to less-desirable hosts like black spruce in search of food. Using these equations, we assume 200 L2/m2 would result in 78% defoliation on black spruce.

#### Whole-tree defoliation calculation

Whole-tree defoliation each year is calculated using MacLean et al. (2001) as the weighted average of current-shoot foliage loss over the past six years: TDt = 0.28Ct + 0.26Ct–1 + 0.22Ct–2 + 0.13Ct–3 + 0.08Ct–4 + 0.03Ct–5 where t is year, TD is whole-tree defoliation (%), and C is current-shoot defoliation (%). Coefficients for each term in the equation equal the proportion of total foliage mass by age-class on a healthy balsam fir crown, derived from Baskerville and Kleinschmidt (1981).

In the SBW DSS, whole-tree defoliation was averaged over 5-years and called periodic cumulative-defoliation by MacLean et al. (2001). Periodic cumulative defoliation was used to build defoliation impact relationships used in the SBW DSS and here. However, as discussed in following sections, to permit annual simulation cycles, this extension departs from this periodic convention by instead using annual whole-tree defoliation to predict annualized versions of the SBW DSS growth and mortality rates with minor bias correction scaling.

### How is tree growth and survival adjusted?

See SbwOutbreakAssumptions.xlsx worksheet ‘Host DEFOL --> Impact’ for growth and mortality relationships used in the SBW DSS. Note that this impact table has been adjusted to use DBH ranges as a surrogate for age ranges in the original SBW DSS calibration (Erdle and MacLean 1999) by using DBH: Age relationships developed from New Brunswick’s extensive network of timber cruises collected in the 1990s and 2000s.

#### Growth reduction

Base growth rates are reduced proportional to the % of whole-tree foliage remaining. Technically, the extension uses a simple linear equation (growth rate multiplier = -0.0099 * defoliation + 1.0182; bounded between 0 and 1; r2 = 0.9984) to closely match the SBW DSS impact table in SbwOutbreakAssumptions.xlsx. If the simulation cycle is between 2 and 5 years, these growth rate multipliers are averaged over the cycle. SBW growth reduction multipliers are applied to both DBH and height increments.

#### Additive mortality

Periodic additive mortality rates are calculated with a 3rd order polynomial regression equation for each host in the form of: Intercept + DBH + DBH*D + D2 + D3, where D is whole-tree defoliation. The SBW extension equation closely matches the periodic mortality rates in the SBW DSS impact table in SbwOutbreakAssumptions.xlsx, when predicted rates are bounded between 0 and 1 and DBH is bounded to the maximum upper range of DBH in the impact table. Predicted periodic rates are then annualized using the inverse of the compound interest formula. Annual rates are then scaled down by 6% (rate * 0.94) for reasons discussed in the following section. If the simulation cycle is between 2 and 5 years, these scaled annual mortality rates are averaged over the cycle. The cycle average annual tree mortality rate is added to the base tree annual mortality rate.

Secondary additive mortality post-outbreak from wind, beetles, and rot, while not accounted for in the SBW extension, can be introduced manually with Amend commands.

### Can annual cycles be used?

The extension supports 1 – 5-year cycle lengths.

In the original SBW DSS, the start date of the annual defoliation pattern within the 5-year periods windows had an unintended influence on the magnitude of stand mortality by the end of the outbreak. This was because mortality risk increases exponentially with defoliation, so if all severe years by chance occurred in one period, stand impacts by the end of the outbreak would be predicted higher, compared to distributing the same number of severe years over two or more periods. In other words, when severe defoliation is spread across multiple periodic windows, it results in less cumulative mortality, compared to one period with very high average defoliation. This is not an issue for growth multipliers, as growth linearly declines with defoliation and linear calculations are not sensitive to cycle duration or defoliation timing.

Here, we attempt to avoid major differences in stand impact results due to 1) the number of years in a simulation cycle (1-5), and 2) the timing of defoliation (e.g., outbreak start offset between -2 and 2 years) within fixed 5-year cycle windows. Following many different approaches explored, the simplest and most reliable solution was to avoid periodic defoliation calculations entirely and instead sum annualized mortality rates predicted from annual whole-tree defoliation. A 6% reduction factor was required on annual whole-tree defoliation to avoid mortality overestimation, compared to the original SBW DSS, which relied on average whole-tree defoliation rates over 5-years. These annualized defoliation-mortality calculations and scaling reduced variation in stand volume impacts to +/-3% from effects of cycle length changes (1-5 years) and resulted in acceptable alignment with average impacts predicted from the original SBW DSS when outbreak start year was varied between -2 and 2 years for a wide range of outbreak patterns.

Whenever possible, it is recommended to run OSM with 5-year cycles to align with data collection periods used to measure and model tree growth and mortality.

See also: SIMULATION.YPC

### When does tree growth and survival get adjusted?

Adjustments can occur once in each simulation cycle when OSM calls the method ‘Predict Stand Dynamics’. During this call, variant tree growth (DBH and Height) increments, tree mortality rate, and ingrowth recruits are predicted by the variant model. Before returning from the call, the Acadian-Variant version of this method checks to see if the SBW extension is active (true if a population or defoliation sequence was assigned). If the SNW extension is active, then the trees, along with their baseline predicted increments and survival rates, are passed to the SBW disturbance extension for adjustment.

Additional user-defined growth and survival amendments, if any, occur after the SBW disturbance extension adjustments.

### Real-world use examples

· Recent Estimates and Future Projections of Maine’s Timber Inventory, Forest Carbon Stocks, and Sequestration Rates: 2003 -2048, Executive Summary. Maine Forest Service, Department of Agriculture, Conservation and Forestry, Augusta. Pre-release draft version Dec 20th, 2021.

· Maine Wood Volume and Projection Study 2021 Update. Sewall Forestry & Natural Resource Consulting. Sept. 20, 2021.

· Projections of Spruce Budworm Impact on Potential Wood Supply in Nova Scotia from 2020-2060. Report prepared by FORUS Research, Fredericton for the Nova Scotia Department of Natural Resources and Renewables. Oct 4th, 2021. 20 p.

· Liu EY, Lantz VA, MacLean DA, Hennigar C. 2019. Economics of early intervention to suppress a potential spruce budworm outbreak on Crown land in New Brunswick, Canada. Forests: 10: 481. https://doi.org/10.3390/f10060481

### Limitations

These limitations below all result in underestimated stand impacts, and therefore should be considered when using this extension, and where possible, factored into results before decision making.

· Post-outbreak secondary disturbances (wind, beetle, rot) are not considered here. In pure fir-spruce stands that have experienced prolonged moderate-severe defoliation, these secondary disturbances can result in complete stand replacement in 10-15 years post outbreak. Taylor and MacLean (2009) showed rate of wind-related mortality peaked at 11 m3/ha/yr 11–15 years after defoliation ceased in mature-old stands dominated by fir and spruce. In forest-level modeling, where outbreak is severe enough and the stand vulnerable enough to expect complete stand replacement, it is recommended that these areas be transitioned to an entirely new regenerating state (e.g., one or more regenerating yield curves). For example, in forest SBW-impact modelling, if a stand’s volume declines more than about 40% during outbreak, then one may assume eventual stand replacement with additional secondary disturbance and immediately transition the stand to a new regenerating condition post-outbreak. While this additional secondary mortality and stand regeneration can be accomplished in OSM through user-defined amendments of mortality and recruitment, the outcome is generally similar and can be simpler to accomplish in the forest model.

· Top kill is a likely outcome during severe outbreaks, especially for fir, but is excluded in this extension. While some trees may live on with top kill, their growth becomes stunted with multiple unmerchantable tops growing above the point of top kill. Saw material downgrades can be very significant if the point of top kill is low on the bole. In the future, we hope to introduce a top kill model presented by Virgin et al. (2017) that documents top kill on 40-88% of trees, with top kill height at 32-88% of bole length, after 4-6 years of severe defoliation in Cape Breton, NS. This study suggest that large saw material and volume downgrades should be considered on surviving trees that have been severely defoliated.

· Back-feeding on previous years foliage is not simulated here but should be considered under very severe populations such as in Cape Breton, NS during the 1980s outbreak. Ostaff and MacLean (1989) reported 20 plots in Cape Breton had an average L2/m2 of 690 in 1977 and 540 in 1980, with as high as 1340-1570 in individual plots! Piene (1989) in the same study area observed trees stripped of all age classes by very high populations in 1977 and 1978. In the future, we hope to introduce a threshold assumption in the population-defoliation model that removes all tree foliage if L2 densities are projected to be this extreme. The current ‘Severe’ outbreak pattern peaks slightly over 300 L2/m2, but this by no means represents the most extreme population levels observed in recent history.

· Stand mortality during outbreak can be clumpy, resulting in regeneration gaps where clumps of mature fir have died. As OSM is non-spatial, and because SBW mortality rates are based on average impacts over many stands, even during severe outbreak, the simulated post-disturbance condition tends to retain some trees and grow like a well-spaced naturally thinned stand. Actual SBW-caused tree mortality patterns are often spatially complex and stochastic (MacLean 1980). Some fir stands, or parts of stands, will undergo complete stand replacement while others will be relatively unscathed, and yet others will have well-spaced mortality. It is unclear whether the extra effort to model this stochastic spatial mortality in a spatially explicit individual-tree environment would make a meaningful difference in average stand impact results, but some additional impact is likely due to reduced full site occupancy. Increased difficulty scheduling salvage and future harvest operations in these clumped post-outbreak forest conditions (regenerating/mature) is another consideration.

### References

Baskerville, G., and Kleinschmidt, S. 1981. A dynamic model of growth in defoliated fir stands. Can. J. For. Res. 11: 206–214

Erdle, T.A., and MacLean, D.A. 1999. Stand growth model calibration for use in forest pest impact assessment. For. Chron. 75: 141-152.

Hennigar, C.R., MacLean, D.A., Porter, K.B., and Quiring D.T. 2007. Optimized insecticide application and harvest planning to reduce volume losses to spruce budworm. Can. J. For. Res. 37: 1755-1769.

Hennigar, C.R., MacLean, D.A., Quiring D.T., and Kershaw J.A. Jr. 2008. Differences in spruce budworm defoliation among balsam fir and white, red, and black spruce. For. Sci. 54: 158-166.

MacLean, D.A. 1980. Vulnerability of fir-spruce stands during uncontrolled spruce budworm outbreaks: a review and discussion. For. Chron. 56: 213-221.

MacLean, D.A., Erdle, T.A., MacKinnon, W.E., Porter, K.B., Beaton, K.P., Cormier, G., Morehouse, S., and Budd, M. 2001. The Spruce Budworm Decision Support System: forest protection planning to sustain long-term wood supplies. Can. J. For. Res. 31: 1742-1757.

MacLean, D.A., and Ostaff, D.P. 1989. Patterns of balsam fir mortality caused by an uncontrolled spruce budworm outbreak. Can. J. For. Res. 19: 1087-1095.

Ostaff, D.P., and MacLean, D.A. 1989. Spruce budworm populations, defoliation, and changes in stand condition during an uncontrolled spruce budworm outbreak on Cape Breton Island, Nova Scotia. Can. J. For. Res. 19: 1077-1086. https://doi.org/10.1139/x89-164

Su, Q., Needham, T.D., MacLean, D.A., 1996. The influence of hardwood content on balsam fir defoliation by spruce budworm. Can. J. For. Res. 26: 1620-1628.

Taylor, S.L., and MacLean, D.A. 2009. Legacy of insect defoliators: increased wind-related mortality two decades after a spruce budworm outbreak. Forest Science 55: 256-267.

Virgin, G.V.J., MacLean, D.A., Kershaw, J.A,. Jr. 2018. Topkill and stem defects initiated during an uncontrolled spruce budworm outbreak on Cape Breton Island, Nova Scotia. Forestry: An International J. For. Res.: 91: 63-72. https://doi.org/10.1093/forestry/cpx035

Zhang, B., MacLean, D., Johns R., Eveleigh E. 2018. Effects of hardwood content on balsam fir defoliation during the building phase of a spruce budworm outbreak. Forests 9: 530. https://doi.org/10.3390/f9090530