Ecosystems (2010) 13: 917–931 DOI: 10.1007/s10021-010-9364-z Ó 2010 UKCrown: Natural Resources , Government of Canada

Future Spruce Budworm Outbreak May Create a Carbon Source in Eastern Canadian Forests

Caren C. Dymond,1,2* Eric T. Neilson,1 Graham Stinson,1 Kevin Porter,3 David A. MacLean,4 David R. Gray,3 Michel Campagna,5 and Werner A. Kurz1

1Natural Resources Canada, Canadian Forest Service, 506 West Burnside Road, Victoria, British Columbia V8Z 1M5, Canada; 2Ministry of Forests and Range, Government of British Columbia, P.O. Box 9504, Stn Prov Govt, Victoria, British Columbia V8W 9C1, Canada; 3Natural Resources Canada, Canadian Forest Service, P.O. Box 4000, 1350 Regent Street South, Fredericton, E3B 5P7, Canada; 4Faculty of Forestry and Environmental Management, University of New Brunswick, P.O. Box 4400, Fredericton, New Bruns- wick E3B 5A3, Canada; 5Ressources Naturelles et faune Que´bec, 880, chemin Sainte-Foy, 10e e´tage, Que´bec, G1S 4X4, Canada

ABSTRACT Spruce budworm (Choristoneura fumiferana Clem.) is adding spruce budworm significantly reduced an important and recurrent disturbance throughout ecosystem C stock change for the landscape from a spruce (Picea sp.) and balsam fir ( L.) sink (4.6 ± 2.7 g C m-2 y-1 in 2018) to a source dominated forests of North America. Forest carbon (-16.8 ± 3.0 g C m-2 y-1 in 2018). This result was (C) dynamics in these ecosystems are affected during mostly due to reduced net primary production. The insect outbreaks because millions of square kilome- ecosystem stock change was reduced on average by ters of forest suffer growth loss and mortality. We 2TgCy-1 for the entire simulated area. This study tested the hypothesis that a spruce budworm out- provides the first estimate that spruce budworm can break similar to those in the past could switch a forest significantly affect the C sink or source status of a from a C sink to a source in the near future. We used a large landscape. These results indicate that reducing model of ecosystem C to integrate past spruce bud- spruce budworm impacts on timber may also pro- worm impact sequences with current forest man- vide an opportunity to mitigate a C source. agement data on 106,000 km2 of forest in eastern Que´bec. Spruce budworm-caused mortality decreased Key words: carbon cycle; net primary produc- stand-level merchantable C stocks by 11–90% and tion; net biome production; boreal forest; defolia- decreased ecosystem C stocks by 2–10% by the end tion; ecosystem carbon; CBM-CFS3; ecosystem of the simulation. For the first 13 years (2011–2024), stock change.

Received 30 November 2009; accepted 23 June 2010; published online 23 July 2010 INTRODUCTION Electronic supplementary material: The online version of this article (doi:10.1007/s10021-010-9364-z) contains supplementary material, Spruce budworm (Choristoneura fumiferana (Clem.), which is available to authorized users. Lepidoptera: Tortricidae) is a native insect in spruce Author Contributions: CD led writing of the article and performed research. EN performed research and contributed to the article. GS per- (Picea sp.) and balsam fir (Abies balsamea L.) forests of formed research, contributed to the study design study, and contributed eastern North America. Spruce budworm outbreaks to the article. KP, DM, and DG contributed to the study design, models, have occurred periodically in these forests for cen- and contributed to the article. MC analyzed data, and WK conceived of the study and contributed to the article. turies (Blais 1983; Royama 1984). These outbreaks *Corresponding author; e-mail: [email protected] play an important role in the natural history of these

917 918 C. C. Dymond and others forest ecosystems, and have strong influence on severe because it kills host trees in a single year, has stand succession and landscape vegetation dynamics been forecast to reduce forest C stocks by 270 Tg C (Baskerville 1975; MacLean 1984). Repeated defo- over 20 years as a result of decreased uptake and liation of host trees during budworm outbreak increased decomposition from beetle-killed trees periods results in growth loss and mortality of (Kurz and others 2008a). We anticipate that a severely defoliated trees. Mortality of dominant or spruce budworm outbreak of similar magnitude co-dominant canopy trees creates openings for to those that have occurred in the past will also succession by the younger trees. Spruce budworm have significant impacts on the forest C budget in outbreaks have occurred across tens of millions of affected regions. square kilometers in eastern North America several Large insect outbreaks can have considerable times during the twentieth century (Williams and impact not just regionally, but also at the scale of Birdsey 2003), and are anticipated to do so again in Canada’s National Forest Carbon Budget (Kurz and the future (Candau and Fleming 2005; Gray 2008). Apps 1999; Kurz and others 2008b). These previous The spruce budworm primarily attacks balsam fir, analyses took into account the impact of spruce white spruce ( Moench. Voss.), and red budworm outbreaks as one of several processes spruce (Picea rubens Sarg.). Budworm larvae feed ongoing simultaneously on the landscape. We predominantly on current-year foliage, so the loss in tested the hypothesis that a spruce budworm out- leaf area only becomes severe after several consec- break similar to those in the past could switch utive years of defoliation (MacLean and Ostaff affected forests from a C sink to a source in the near 1989). Outbreaks generally end while there is still future. Our objectives were to (1) use historically mature spruce and fir foliage available in the land- derived, spatially explicit defoliation sequences and scape (Royama 1984). the SBWDSS to generate estimates of budworm Growth loss and tree mortality during past out- impacts; (2) apply these in the Carbon Budget breaks have resulted in significant reductions in Model of the Canadian Forest Sector (CBM-CFS3) timber supply (Sterner and Davidson 1982). It was (Kurz and others 2009) to estimate C dynamics in estimated that Canada lost 44 Mm3 of timber in the boreal forest of from 2000 to just 5 years (1977–1981) at the peak of the last 2024; and (3) compare both stand- and landscape- budworm outbreak, which affected as much as level model outputs to independent data from the 200,000 to 500,000 km2 y-1 (Kettela 1983; Simp- literature. Our study was relatively short-term and son and Coy 1999; NFDP 2008). As a consequence, therefore did not explicitly take into account global considerable investment has since been made in change factors on forest growth, decomposition, or scientific research to develop a sound biological and disturbances. Given the uncertainty of post-out- ecological understanding of spruce budworm pop- break dynamics, we terminated the landscape-scale ulation dynamics and associated impacts on forest simulations in 2024, even though impacts were dynamics. Tools such as the Spruce Budworm projected to continue somewhat beyond this time. Decision Support System (SBWDSS) were devel- We also did not assess the probability of a spruce oped to synthesize and make scientific information budworm outbreak although the insect tends to accessible to forest managers, helping them inte- display cyclical population dynamics (for example, grate budworm considerations directly into forest Boulanger and Arsenault 2004) and budworm management planning processes (MacLean 1996; defoliation in Que´bec has increased from 3000 ha MacLean and others 2001). in 2003 to 338,000 ha in 2009 (QMRNF 2009). Insect disturbance agents that have eruptive outbreak dynamics and widespread impacts in terms of reduced growth and increased mortality MATERIALS AND METHODS may exert considerable influence on the forest Study Area carbon (C) and other nutrient cycles (see overview in Lovett and others 2006). One recent study The study focused on 106,000 km2 within a investigated the impact of a defoliator (gypsy moth, 146,000 km2 region of eastern Que´bec, Canada Lymantria dispar L.) on net ecosystem exchange. (Figure 1). The forest was composed of about Clark and others (2009) found stands that were net 60,000 km2 of spruce-fir dominated stand types, C sinks without the defoliator became net sources 35,000 km2 of hardwoods, mostly sugar maple when there was severe defoliation of the canopy (Acer saccharum Marsh.), with the balance com- trees. Across the 1600 km2 landscape, they esti- posed of jack pine (Pinus banksiana Lamb.), or a mated a reduction in C uptake of 41%. Mountain mixture of hardwood and softwood species. White pine beetle (Dendroctonus ponderosae Hopk.), more birch (Betula papyrifera Ehrh.), larch (Larix laricina Spruce Budworm Impact on Forest Carbon 919

Figure 1. The study area includes 106,000 km2 of forest within a 146,000 km2 landscape in Canada’s eastern boreal forest.

Kock.), and poplar (Populus sp.) are also important that allow the model to represent key ecological species in the region. The area is characterized by processes and allow users to compare estimates of rolling hills with hardwoods on the more well- stocks with field measurements. CBM-CFS3 simu- drained sites. Black spruce ( Mill. BSP) lates annual changes in C stocks of each pool due to and eastern white cedar (Thuja occidentalis L.) tend growth, litter fall, turnover, decomposition, natural to grow in areas of organic soils and poorly drained disturbances, and forest management (Figure 2). sites (Boulanger and Arsenault 2004), whereas Where a direct comparison has been done, the balsam fir is often found with yellow birch (Betula basic model outputs of net primary productivity alleghaniensis Britton) on the more mesic sites. The (NPP) and heterotrophic respiration (Rh) were anthropological influence on forest structure found to be generally similar to those derived from through wood harvesting began in the early nine- flux tower estimates (Trofymow and others 2008). teenth century and has continued ever since Forest inventory data for the region were (Boulanger and Arsenault 2004). The region suf- extracted from Sylva II, the Que´bec forest man- fered a severe spruce budworm outbreak from 1970 agement planning system (Lessard and others to 1981 (Gray and others 2000), and it is generally 2000). These data included a complete circa 2000 assumed that another outbreak in eastern Canada, inventory of all forests in the study area and asso- including this study area, will occur (Candau and ciated merchantable volume yield tables. For Fleming 2005; Gray 2008). The maximum stand incorporation into the CBM-CFS3 modeling age in the forest inventory was 175 years. The framework, the study area was stratified into 50 distribution was bi-modal with about 40% of the spatial analysis units defined by Que´bec’s forest stands between 41 and 80 years of age and about management unit boundaries (Common Areas) 30% of stands between 140 and 160 years old. that were in use at the time of the inventory compilation. All data loaded into the CBM-CFS3 Modeling were spatially referenced to these units. The 8919 inventory records, called stands for convenience, The CBM-CFS3 is an empirically driven, stand-, averaged 5.2 km2. Each one was associated with a and landscape-level model for the simulation of merchantable volume yield table extracted from forest C dynamics (Kull and others 2006; Kurz and Sylva II. In total, the inventory was stratified into others 2009). Stand-level C dynamics are repre- about 5600 different growth strata. Yield tables are sented in the CBM-CFS3 using a system of pools used in CBM-CFS3 to drive tree productivity at the 920 C. C. Dymond and others

Figure 2. Simple schematic of CBM-CFS3. Simulation of growth causes C to enter the forest ecosystem as living biomass. Simulation of turnover and disturbance processes causes the transfers of C from biomass to woody debris, litter, and soil pools. Natural disturbances can also cause the loss of C from the ecosystem as gaseous emissions (for example, combustion during wildfire). Harvesting causes the loss of C from the ecosystem and the transfer to the forest product sector. C is also lost from the ecosystem due to decay of the DOM and soil (Kurz and others 2009, ÓHer Majesty the Queen in right of Canada, Natural Resources Canada, Canadian Forest Service as originally published in Ecological Modelling).

stand level, as a function of stand age and stand growth followed by a stand-replacing fire until the type (growth stratum), as described by Kurz and sum of the above- and belowground slow C pools at others (2009). The model was run with 8919 re- the end of two successive rotations met a difference cords assigned to 50 spatial analysis units. Model tolerance of less than 1.00%. Once the quasi-steady output reported on 417 aggregated records (sum- state was reached, the model simulated one more med) that did not contain age or growth strata rotation terminated by a clear-cut. The model then information but did vary by spatial analysis unit grew each stand to its current age as defined in the and forest type. We used these aggregated records inventory. More information about initialization is to calculate landscape averages and standard errors, available in Kurz and others (2009). and to conduct t-tests comparing a simulation with Spruce budworm host species were defined for spruce budworm to one without spruce budworm. the purposes of this study as any forest stand Simulation initialization of the model’s dead or- greater than 4 years old and composed predomi- ganic matter (DOM) and soil pools was based on a nantly of spruce, balsam fir, or a spruce–fir mix- historic natural disturbance regime of stand- ture. In past outbreaks, the age and species of trees replacing disturbances every 125 years, with fire infested dictated the intensity of mortality and used for simplicity (Blais 1983; Bergeron and others growth loss at the stand and landscape levels (Erdle 2004). The model started the initialization process and MacLean 1999; Bouchard and others 2007). with all pools containing zero C stocks. The model Stand types composed mainly of fir typically have simulated each stand through repeated iterations of more defoliation than spruce stand types (MacLean Spruce Budworm Impact on Forest Carbon 921 and MacKinnon 1997; Hennigar and others 2008). percent growth loss and mortality in affected In addition, mature stands have sustained sub- stands. Impact intensities varied, with some defo- stantially higher budworm-caused mortality than liation patterns resulting in only 7 years of growth immature stands, on average 85% versus 35–40% loss, whereas others had up to 24 years of growth (MacLean 1980). Therefore, eligible host was par- loss and elevated mortality rates. titioned into four types: young spruce, old spruce, The spruce budworm impact sequences gener- young fir, and old fir. Young host included stands ated by the SBWDSS were used in the CBM-CFS3 between 5 and 60 years of age, whereas old host to simulate C impacts within the forest ecosystem included stands greater than 60 years old. We during the outbreak period (2011–2024). Impact identified 58,000 km2 of host stands in the study sequences were structured in the CBM-CFS3 as area, including 15,000 km2 of young spruce, multi-year sequences of growth reduction multi- 20,000 km2 of old spruce, 14,000 km2 of young fir, pliers and mortality disturbance matrices (see Kurz and 9,000 km2 of old fir. Recent cutovers and tol- and others 1992 for a complete description of dis- erant or intolerant hardwood stand types were turbance matrices). The amount of area affected by ineligible for budworm infestation in the model a particular impact sequence depended on the simulations. During the 1970–1981 outbreak in the amount of different types of stands within the area region, 95% of the host area experienced some (details in Supplementary material). The model level of defoliation (Gray and others 2000). We was provided areas of each host type, impact se- therefore simulated impacts in 95% of the host area quence, and spatial analysis unit for each year of for the future outbreak scenario. the simulation. If required, the model would split The budworm outbreak was simulated at the an inventory record into smaller areas to meet the landscape scale because population dynamics in required host type areas. Overall, area affected by large regions tend to cycle in synchrony (for mortality peaked in 2016 (Figure 3). Area affected example, Williams and Liebhold 2000). We simu- by growth reduction increased until 2015 and re- lated an outbreak start year of 2011 by applying an mained at 55,500 km2 through the rest of the assumed interval of 41 years to the last outbreak simulation. We terminated the landscape-scale start date of 1970. Boulanger and Arsenault (2004) simulations in 2024, even though impacts were found the mean interval of outbreaks in Que´bec to projected to continue somewhat beyond this be 40 years, whereas others have found shorter time, and thus did not fully estimate impacts of the outbreak intervals between 30 and 36 years (Can- entire outbreak cycle. The longer simulations are dau and others 1998; Jardon and others 2003). At extended in the future, the more sensitive esti- the time that this manuscript was written, the mates become to assumptions about post-outbreak anticipated severe outbreak had not yet begun stand dynamics, including succession and compet- (39 years after the 1970 outbreak started) but, itive release in severely defoliated stands. the area defoliated by budworm in Que´bec has The model applied the estimated growth loss and increased from near zero in 2003 to 338,000 ha in mortality rates to infested stands as a function of 2009 (QMRNF 2009). Within the study area, the host type and year of outbreak. Stands remained on outbreak start years for individual spatial analysis their assigned mortality and growth loss sequence units were assumed to follow dynamics of the for the duration of that outbreak pattern unless previous 1970–1981 epidemic, because those were interrupted by another disturbance, such as fire or the only spatial data available (Gray and others harvesting. Mortality was simulated as percentage 2000) (Figure 1). decrease in biomass pools, such as stemwood or Gray and MacKinnon (2006) analyzed spruce foliage, with the C transferred to one or more of the budworm defoliation from 1941 to 1998 in Canada, DOM pools in each simulated year. The disturbance and described 27 patterns representing unique matrices simply move C from one pool within the temporal sequences of annual percentage defolia- CBM-CFS3 to another. In subsequent years, the tion per year. Eighteen of these defoliation patterns added DOM decays, as defined by decay parameters occurred in Que´bec; spatial locations of these associated with each pool (Kurz and others 2009). defoliation patterns were used in the SBWDSS to Growth reduction multipliers resulted in decreased estimate impact sequences (annual percent growth accumulation rates of biomass within stands and loss and percent mortality) for each of the four host did not result in additional input to the DOM pools. stand types (Figure 3). (Details on the impact se- During the simulated outbreak, infested stands quences are provided in Supplementary material). remained eligible for other disturbances in the MacLean and others (2001) described how the model. In the event of stand-replacing disturbance SBWDSS determines annual impacts expressed as (that is, clear-cut harvesting), the spruce budworm 922 C. C. Dymond and others

Figure 3. Area affected by each level of percent defoliation-caused mortality per year for each host type: A old fir, B young fir, C old spruce, D young spruce. Levels of mortality were determined by the SBWDSS as a function of multi- year defoliation sequences. E Area affected by each level of percent growth loss per year. outbreak was terminated in the model and the in accordance with current international C regenerating stand was not re-infested by bud- accounting guidelines and good practice guidance worm. Furthermore, as stands aged, some crossed provided by the Intergovernmental Panel on Cli- the threshold used to distinguish between young mate Change (IPCC 2006). Disturbances can affect and old budworm hosts (that is, age = 60 years); stand age and subsequent biomass and DOM C however, they remained on their original impact dynamics in the disturbed stand. Following stand- sequence. replacing disturbance events, disturbed stands were In the CBM-CFS3, disturbances and forest man- regrown from age zero on the original yield curve. agement activities cause transfers of C between The simulation period of forest C dynamics was pools and removals from the ecosystem. Based on from 2000 to 2024, with the spruce budworm input from Que´bec forestry experts, clear-cut har- outbreak occurring from 2011 to 2024. vest events were simulated with 97% of mer- Harvest statistics for 2000–2005 and harvest chantable stemwood and 50% of snag stemwood C projections for 2006–2024 were provided by the transferred out of the ecosystem to the forest Government of Que´bec. No changes in harvest products sector. Commercial thinning activities rates were simulated in response to the spruce were also simulated, with stemwood mortality due budworm outbreak. Clear-cut harvesting was sim- to thinning ranging from 20 to 40%. Transfers of C ulated, as were selection cutting, eight different from the ecosystem to forest products were re- intensities of commercial thinning, and silvicultural ported by the model as losses from the ecosystem, treatments such as pre-commercial thinning. Har- Spruce Budworm Impact on Forest Carbon 923 vest projections were generated using Sylva II, biomass (merchantable stemwood plus merchant- which calculates sustainable harvest rates for 5- able stem bark) by the inside-bark biomass (mer- year periods. These were converted into annual chantable stemwood only), both estimated using harvest instructions and formatted for use in the models from Boudewyn and others (2007), for all CBM-CFS3 over the 2000–2024 simulation. Fur- stands in the forest inventory, and calculating an thermore, we instructed the model to achieve the area-weighted mean ratio for the study area. greatest possible proportion of total harvest by Forest fire is a naturally occurring disturbance salvaging standing dead trees rather than cutting agent in the eastern Boreal Shield ecozone. In the live trees. This was accomplished by assigning study area, however, fire has been far less domi- preference to stands with higher quantities of sal- nant than in other boreal regions. From 1959 to vageable standing dead timber when selecting 1999 the average area burned was 1.1 km2 y-1, stands for harvest during model simulations. with the maximum of 530 km2 in 1962 (Stocks and Sylva II harvest projections were expressed in others 2002). Given the relatively small area and C terms of both areas and merchantable volumes to impacts and to simplify the modeling, we excluded be harvested. Area targets can be used directly in fire from the simulations of the study area. the CBM-CFS3, but volume targets must be con- verted from cubic m to tonnes of C. We converted the volume targets into merchantable biomass C RESULTS targets, multiplying volume by three terms: (i) Stand-Level C Impacts wood density, (ii) C density, and (iii) bark adjust- ment factor. We assumed an average wood density At the stand level, simulated C impacts of the in the study area of 0.5 t m-3 for hardwood and spruce budworm outbreak varied from mild to very 0.45 t m-3 for softwood species (the weighted severe. The C consequences of the range of impact average wood density of leading hardwood and sequences (based on defoliation patterns from Gray softwood species in the study area, respectively) and MacKinnon 2006) simulated are illustrated in and a C density of 0.5 t C t-1 biomass. A bark Figure 4. This graph shows stand-level C dynamics adjustment factor of 1.19 was applied to account in an example young balsam fir stand simulated for the fact that volumes in Sylva II represent only with four contrasting intensities of budworm im- the merchantable stemwood (that is, inside-bark pact sequences, which resulted in 3, 29, 66, and volume), whereas the CBM-CFS3 harvests mer- 93% cumulative merchantable softwood mortality chantable stems including both wood and bark. The over the course of the outbreak. The spruce bud- bark factor was calculated by dividing outside-bark worm outbreaks were simulated from year 2011 to

Figure 4. C stocks in a young balsam fir stand with example spruce budworm scenarios relative to a control (no- outbreak) scenario. The four spruce budworm impact sequences resulted in 3, 29, 66, and 93% cumulative mortality. A Wood and bark in the merchantable portion of stems of softwood trees, B wood and bark in standing dead softwood trees, C all DOM and soil C, and D all tree biomass, DOM and soil C. Note y-axes scales vary. 924 C. C. Dymond and others

2024 (stand age 20–33). Merchantable softwood C Our simulations estimated that, pre-budworm, stocks were reduced during the outbreak relative to from 2000 to 2010, forests in the study area acted the no-outbreak scenario, as tree growth was re- as a net sink of 13.0 ± 3gCm-2 y-1 (ecosystem duced and trees were killed (Figure 4A). The C in stock change—ESC—also sometimes referred to as killed trees was transferred by the CBM-CFS3 to net biome production, Chapin and others 2006) the DOM pools, for example, merchantable stem- (Figure 6A). As the simulated spruce budworm wood is transferred to the standing dead tree outbreak started in 2011, C uptake began to decline, (snags) pools. This resulted in a large increase in emissions increased, and the landscape transitioned standing dead wood C stocks relative to the control, from a net C sink to a net C source by 2014. In particularly in the most severely affected stands 2018, the source was estimated at -16.8 ± 3.0 g (Figure 4B). Gradually, snag C stocks declined as Cm-2 y-1. This was significantly different from the standing dead trees were modeled to fall over and simulation without spruce budworm, in which the decay, transferring C into other DOM and soil study landscape was a sink in 2018 (ESC of pools. As expected, greater transfers of C from 4.6 + 2.7 g C m-2 y-1; two sample t-tests, P = 0.0, biomass to snags occurred in stands suffering df = 940). The ESC from the two simulations re- higher mortality. Total DOM (including snags) and mained significantly different in 2024 (two sample soil C pools were higher than the control for all but t-tests, P = 0.0, df = 940). In the absence of a spruce the mildest impact simulated (Figure 4C). The net budworm outbreak, the study area remained a net impact on total ecosystem C at the stand level was a C sink throughout the 2000–2024 simulation. From gradual loss of C relative to the no-outbreak sce- this comparison, we observed that the simulated nario, as trees were killed and the C released spruce budworm outbreak was responsible for the gradually to the atmosphere via decomposition in landscape switching from a net C sink to a net C the model (Figure 4D). source to the atmosphere. The average NPP values for the two simulations Landscape-Level C Impacts were also significantly different in 2018 and 2024 (two sample t-tests, t = -2.593, t = -2.511 At the landscape scale, there was no apparent trend 2018 2024 P = 0.01, df = 940) (Figure 6B). Rh in the study in C stocks before the onset of the outbreak (Fig- area increased due to mortality caused by the ure 5). The simulated impacts of spruce budworm spruce budworm (Figure 6C). Mortality increased caused biomass C stocks to start declining about the mass of snags, coarse woody debris, and other 4 years after the outbreak started in 2011. At the DOM C in the landscape. However, the landscape same time, DOM and soil C stocks started increas- average Rh values with and without spruce bud- ing as the repeated defoliations caused trees to die. worm were not significantly different (two sample The simulated total ecosystem C stocks remained t-tests, t = 1.449, P = 0.15 in 2018, df = 940). Net fairly constant until declining later during the growth (NPP minus litterfall) was reduced signifi- 2011–2024 simulated outbreak, namely after 2018. cantly in 2018 (two sample t-tests, t = -2.261, P = 0.02, df = 940) but not in 2024 (two sample t-tests, t = -0.153, P = 0.88, df = 940). These results indicate that the difference in ESC was lar- gely due to the decrease in NPP. A smaller con- tributing factor was the shift of about 10% of the logging from living biomass to dead trees. Spruce budworm was estimated to reduce annual change in ecosystem C stock integrated over the entire study area by up to 3.4 Tg y-1 and it continued to be reduced for the duration of the simulation period (Figure 6E). The reduction in NPP over the entire area reached a maximum of 2.5 Tg C in 2018 due to the combined impact of growth loss and mortality of hosts, and remained Figure 5. C stocks in the 106,000 km2 forest area with a below the no-outbreak levels throughout the spruce budworm outbreak simulated starting in 2011. simulation period (Figure 6F). Net growth fol- ‘Biomass’ includes all tree biomass C (above- and lowed a similar pattern although reaching nadir belowground), ‘DOM’ includes all DOM and soil C, and earlier in 2016 at -1.2 Tg C and with a stronger ‘Total’ is the sum of biomass, DOM and soil C. recovery. Spruce Budworm Impact on Forest Carbon 925

Figure 6. Simulated average landscape fluxes with (circle) and without (x) a possible spruce budworm outbreak starting in 2011, for A ESC, B NPP, C Rh, and D net growth (Net Grow). The difference in total landscape fluxes between simulations with or without spruce budworm, for E ESC F NPP, G Rh, and H net growth.

Sensitivity of ecosystem C fluxes to harvest rates study as a loss from the ecosystem (reduction in was evident in the step changes in 2005 and 2010 ESC), in accordance with current international (Figure 6A). In the simulations, projected harvest accounting rules (IPCC 2006). levels change every 5 years because these projec- tions were obtained from Sylva II, which uses Validation 5-year time steps. Projected harvest levels from Sylva’s second simulation period (2005–2009) were We have no direct way to verify the C fluxes esti- 10% lower than harvest levels during 2000–2004, mated by CBM-CFS3 over 23 years and large areas resulting in an 18% increase in the total landscape of forest. However, the literature provides an sink of C. Removals of C from the ecosystem during opportunity for cross-model validation of NPP and harvesting averaged about 4.4 Tg C y-1 over the net ecosystem productivity (NEP). The average NPP simulation period; these were accounted for in this of trees in the CBM-CFS3 simulations from 2000 to 926 C. C. Dymond and others

2010 was 434 ± 4.3 g C m-2 y-1. Jenkins and sequences resulting in the wide range of 3, 29, 66, others (1999) used the TEM and PnET models to and 93% cumulative tree mortality over the course estimate regional NPP in the north-eastern US. of the outbreak. In the 1970s outbreak in the study Their modeling estimated 323 or 463 g C m-2 y-1 area, cumulative mortality rates were measured in for the spruce–fir forests most similar to our study 1979 at an average of 91% for balsam fir and 52% area. Although the three models are quite different for white spruce stands (Blais 1981). in how they estimate growth, the NPP estimates At the landscape scale, Blais (1964) observed were of similar scale. A more recent study esti- impacts amounting to 7.6 m3 ha-1 y-1 killed by mated NPP for spruce–fir forests in the north-east- budworm during the period 1950–1960 in an area ern US at approximately 717 ± 200 g C m-2 y-1 occupying about 25% of the study area. In a from FIA plots, approximately 750 g C m-2 y-1 province-wide 1981 survey, Sterner and Davidson using the PnET model and approximately (1982) estimated that volume killed from 1977 to 800 ± 200 g C m-2 y-1 using MODIS (Moderate 1981 was 4.5 m3 ha-1 y-1. Our simulation had an Resolution Imaging Spectroradiometer) to drive a overall volume killed of 2.9 m3 ha-1 y-1 from model (Pan and others 2006). Note however that 2012 to 2024; the peak mortality rate during our spruce–fir forest comprised only 1% of their study simulation period was 4.9 m3 ha-1 y-1 in 2016 area in a climate considerably warmer than our (Figure 7). Individual disturbance events had study area, so the higher NPP rates are not sur- mortality rates as high as 32 m3 ha-1 y-1 (Fig- prising. An estimate of 4 months of NEP in a black ure 3). All of these per hectare values are based spruce site using the TRIPLEX model is approxi- only on the forest area moderately to severely mately 180 g C m-2 (Sun and others 2008). These defoliated by spruce budworm. These comparisons results were somewhat higher than our study suggest that our results may be conservative, indi- where the CBM-CFS3 estimated the mean NEP of cating that the true C impacts of a future spruce trees from 2000 to 2010 was 56.3 ± 51 g C m-2 y-1. budworm outbreak could exceed those estimated The considerable variability in the standard error here. was due to the diversity of forest types across the landscape. The NEP is higher than the ESC DISCUSSION reported above (13.0 ± 3gCm-2 y-1) because it does not include disturbance emissions and har- Our model projections indicate that a spruce bud- vesting losses from the ecosystem. worm outbreak similar to one in the past could The FLUXNET network includes one eddy cause the forest, which has been a net C sink, to covariance flux tower located in a black spruce become a net C source for over 10 years. The stand in the boreal forest of Que´bec. The published magnitude of these impacts suggests that a spruce data from that tower reported for a single year an budworm outbreak represents a significant risk NEP of 37 g C m-2 y-1 observed with data gaps factor for forest C management, although some of filled, and 77 g C m-2 y-1 modeled with CN- CLASS (Yuan and others 2008). Two other flux towers that are also located in Canadian black spruce stands estimated NEP values from 32 to 81 g Cm-2 y-1(Yuan and others 2008). Our estimates of NEP values (56.3 ± 51 g C m-2 y-1) were of a similar magnitude to those estimated by flux tow- ers in similar forests and substantially lower than temperate forest estimates from flux towers (Yuan and others 2008). Somewhat south of the study area but still in a similar spruce, fir and eastern hemlock forest is the Howland Ameriflux site (Hollinger and others 2004). That site reported a 7- year average NEP of 174 + 46 g C m-2 y-1. To validate our modeled impacts of spruce bud- worm we compared results with field and aerial survey-based impact studies from outbreaks in the 1950s and 1970s. These data were not used in Figure 7. Landscape average of volume per hectare calibrating the SBWDSS or the CBM-CFS3. At the killed per year by moderate or severe defoliation during stand level, we selected four example impact the simulation. Spruce Budworm Impact on Forest Carbon 927 the loss in net uptake could be mitigated by spatial synchrony across large areas (for example, enhanced C-uptake by non-tree species in the Peltonen and others 2002). However, the degree of ecosystem. If an outbreak of this magnitude does spatial synchrony may not be constant over mul- take place, then it will become difficult, if not tiple outbreaks. Moreover, periodicity of outbreaks impossible, to maintain a net C sink in the forests of in Que´bec has been suggested to have a stable the study area and the larger outbreak area not oscillation of about 39 years (Royama 1984)orto included here. Quantifying the impacts of a future be unstable, ranging from 26 to 79 years within the outbreak would require ongoing monitoring pro- study area (Blais 1983). We assumed an interval of grams of defoliation level and tree mortality. 41 years based on information provided in these Comparisons of spruce budworm impacts esti- references and current larval sampling results mated in this study against those observed during indicating that an outbreak had not started as of previous outbreaks suggest that our impact pro- 2009. Finally, the actual start of the outbreak may jections may be conservative (Blais 1964, 1981; be later than predicted. The main effect of this Sterner and Davidson 1982). Our estimates of would be a shift in the timing of outbreak effects, volume killed per hectare were somewhat lower not necessarily in their magnitude. than estimates reported in the literature. There are In our simulations, the ESC did not recover from two possible reasons for this: first, our estimates the outbreak relative to the no-outbreak scenario include the beginning and end of the outbreak, during the simulation period. This result is some- whereas the literature values focused on the out- what uncertain because of assumptions about non- break peak; and second, species composition of the host and post-outbreak stand dynamics. We did not forest may have changed since the 1970–1981 explicitly model competitive release following se- outbreak, resulting in less vulnerable stands vere defoliation; the hardwoods in those stands becoming a larger component of the forest land- remained on their pre-infestation yield expecta- scape. Less vulnerable stands include younger tions and softwoods continued to have reduced stands and those with a smaller fir component growth rates throughout the simulation. Our lim- (MacLean 1980; MacLean and MacKinnon 1997; ited ability to model non-host and post-outbreak Hennigar and others 2008). We made considerable dynamics was the key factor in ending the simu- effort in simulating the outbreak in the context of lations in 2024—the highest impact of the spruce the current forest condition because the extent of budworm would be included but the simulation the most susceptible forest types is likely lower ended while growth reduction data were still today than it was prior to the previous outbreak available from the SBWDSS. If competitive release (Bouchard and others 2007). of non-host trees results in greater productivity, The maximum impact on simulations of ESC was such as in aspen (Nealis and Re´gnie`re 2004), then less from spruce budworm (-21.4 g C m-2 y-1) actual future species composition will differ from compared to mountain pine beetle (-53 g C m-2 y-1) the simulated condition, and growth increments as reported by Kurz and others (2008a). The impact provided in the yield tables would most likely be of the insects on NPP was part of the difference poor estimators of post-outbreak growth. However, (-3.5% for spruce budworm and -10% for moun- such release is probably limited in most spruce–fir tain pine beetle). However, a greater difference was stands, based on long-term stand recovery results of due to Rh (+0.5% for spruce budworm and +6% for Baskerville and MacLean (1979) and MacLean and mountain pine beetle). These differences can be Andersen (2008). Hennigar and others (2007) explained by the interaction between the insect and found in simulations that non-host volume in- its host, in that mountain pine beetle kills the host creases in response to budworm-caused mortality within a year, whereas spruce budworm kills the averaged 14 ± 2.7 and 20 ± 4.2% for 53 stand host only after severe defoliation over multiple types for moderate and severe outbreaks, versus years. Our results were similar, although somewhat 5 ± 1% when there was 60% foliage protection. more severe than a study of gypsy moth impacts Although a regeneration pulse likely occurs in on oak–pine forests (Clark and others 2009). Our response to substantial mortality, the regeneration simulation showed a reduction in net ecosystem is small enough for the first 10 years or so that it exchange of up to 48% (in 2018), whereas Clark and would have limited biomass and C in the 13-year others reported a reduction of 41%. period of our simulated outbreaks. There are a number of important sources of The simulation modeling approach used in this uncertainty in our simulations. One is the timing of study only provides the capability to project the start of the next spruce budworm outbreak. ESC for a limited period in the future. The longer There is evidence described in the literature of simulations are extended in the future, the more 928 C. C. Dymond and others sensitive estimates become to assumptions about climate, as well as other factors including distur- post-outbreak stand dynamics, including succes- bances, ozone concentrations, acid and nitrogen sion and competitive release in severely defoliated deposition, and CO2 concentration effects on stands. We terminated landscape-scale simulations growth rates. Recent reviews by Mohan and others in 2024, even though impacts were projected to (2009) and Campbell and others’ (2009) report on continue somewhat beyond this time, and thus did the known complexity and remaining data gaps for not fully estimate impacts over the entire outbreak northeastern North America. Campbell and others’ cycle. Longer-term simulations would be further (2009) projections show a range of potential out- complicated by changes in forest management re- comes including both increasing and decreasing sponse and by impacts of global change, such as NPP. In some parts of the boreal forest, even effects of elevated atmospheric CO2, increased increasing NPP may be overwhelmed by increases atmospheric N deposition, and climate warming on in burned area, resulting in a net C source (Kurz tree productivity, insect lifecycle development, and others 2007). However, fire plays a smaller role natural enemies, or host–insect interactions. All of in our study area and is expected to decrease (Ber- these factors were beyond the scope of this study geron and others 2004). and represent sources of uncertainty. Management response to budworm outbreak An abundance of literature highlights the effect of also represents a source of uncertainty for this temperature and/or precipitation on factors that study. Several management options are available to influence spruce budworm outbreak dynamics: mitigate the impacts of spruce budworm outbreak developmental rates (Re´gnie`re and You 1991; We- on forest C stocks and timber supply (MacLean ber and others 1999), dispersal (Greenbank and 1996; MacLean and others 2001; Hennigar and others 1980), feeding (Re´gnie`re and You 1991), MacLean 2010). During the outbreak, insecticides fecundity (Sanders and others 1978; Harvey 1983), can and have been used to reduce foliage loss in and survival (Re´gnie`re and Duval 1998); micro- infested stands. Our simulations were conducted sporidian parasite development (Wilson 1974); with the assumption that no insecticide-treatment parasitoid developmental rates (Lysyk and Nealis efforts would be applied: they represent a baseline 1988; Nealis and Fraser 1988; Thireau and Re´gnie`re without any spruce budworm suppression. During 1995), flight activity (and presumably search rates) and after an outbreak for a limited time, harvesting (Elliott and others 1986; Nyrop and Simmons can be redirected toward salvage of spruce and fir 1986), longevity (Nealis and Fraser 1988), and killed by the budworm as was assumed in our oviposition rate (Nealis 1988); host abundance study. Long-term silvicultural strategies can also phenology and growth (Lekas and others 1990; reduce landscape susceptibility to large-scale out- Deslauriers and others 2003); and epizootiology of breaks by managing for a broader mix of forest forest insect pathogens (Smitley and others 1995). types and wider range of stand ages on the land- Unfortunately, it is not known how these factors scape, much of which has already taken place in will interact in an altered climatic environment. The New Brunswick (MacLean 1996). analysis of Candau and Fleming (2005) suggests In this study, C removed from the forest during that defoliation frequency in Ontario will increase harvesting is accounted for as a direct emission to with rising winter minimum and maximum tem- the atmosphere. Although the international peratures, lower spring minimum, May maximum accounting rules treat removals of C from forests as and August minimum temperatures, and lower direct losses to the atmosphere (IPCC 2006), some June precipitation. They did not estimate the net of the harvested C accumulates in harvested wood effect under any climate change scenario. Gray products and landfills (Apps and others 1999). (2008) predicted an average increase in outbreak Therefore, although harvest removals reduce eco- duration of approximately 6 years, and an average system C stocks, they do not necessarily result in increase in approximately 15% in defoliation levels increased greenhouse gas emissions to the atmo- in eastern Canada under the IPCC SRES-B1 climate sphere. Moreover, in addition to the C storage, the scenario. Neither Candau and Fleming (2005) nor impacts of substitution effects (that is, reduced Gray (2008) estimated how climate change would fossil fuel emissions from the use of wood products affect forest composition, a significant factor in instead of more energy-intensive products) should outbreak dynamics. be considered when comparing alternative man- The future forest composition and C balance agement scenarios (Stinson and Freedman 2001; will depend on growth, mortality, decomposition, Neilson and others 2008). regeneration, competition (succession), and migra- In regions suffering high mortality from insect tion. Each of these processes is affected by change in outbreaks, a common policy response is to increase Spruce Budworm Impact on Forest Carbon 929 harvest rates to provide the forest industry with including biodiversity, habitat, water quality, and more latitude to recover standing dead timber. The reforestation. provincial governments may choose to raise har- vest levels in the event of a major budworm out- ACKNOWLEDGEMENTS break. This strategy would not provide any C We would like to acknowledge the contributions of benefits under the current international accounting Greg Rampley for CBM-CFS3 model code devel- rules, but could provide benefits to the atmosphere opment, Mark Budd for SBWDSS model parame- if forest products are used to substitute for more terization, Kathy Beaton for SBWDSS model GHG-intensive products. For example, wood parameterization and expertise, and Juha Metsa- products in the construction industry have been ranta and Kevin Belanger for assistance structuring shown to reduce CO emissions by 93–1062 kg CO 2 2 SBWDSS output for input into the CBM-CFS3. We equivalent per m3 when replacing concrete and would also like to thank three anonymous 36–442 kg CO equivalent per m3 when substi- 2 reviewers and editor Dr. Gary Lovett for their tuted for steel (Petersen and Solberg 2005). helpful suggestions and revisions on a previous version of the manuscript.

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