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Chapter 8

A process approach to understanding disturbanceand forest dynamics for sustainable forestry

E.A. Johnson, H. Morin, K. Miyanishi, R. Gagnon, and D.F. Greene

The ideas of community structure and the expression of dominance, that of bio-logical succession, and finally that of climax, are based largely upon theassumption of long-term stability in the physical habitat. Remove this assump-tion and the entire theoretical structure becomes a shambles.

. . . Disturbances have been so frequent and so generally effective that theexpected “climaxes,” or “equilibria,” recede into pure speculation. Naturalsuccessions either do not occur at all or are limited to such incomplete frag-ments as can be accomplished between upheavals.

Hugh Raup (1956, p. 45)

Introduction

There has been a change in boreal forest management and silviculture from a focus onwood products to include other products and services of the forest (see Chap. 2). Thishas required a rethinking of forest management from only sustained-yield and regulatedforests to the more inclusive ecosystem-based management that considers managementfor nontimber values as well (Slocombe 1993; Kaufmann et al. 1994). One of the sev-eral goals in ecosystem management has been to incorporate the understanding of nat-ural disturbance that has been gained in the last 30 years (Pickett and White 1985;Johnson 1992; Coutts and Grace 1995).

1

E.A. Johnson. Department of Biological Sciences, University of Calgary, Calgary, Alberta,Canada.H. Morin and R. Gagnon. Départemente des Sciences fondamentales, Université duQuébec à Chicoutimi, Chicoutimi, Quebec, Canada.K. Miyanishi.1 Department of Geography, University of Guelph, Guelph, OntarioN1G 2W1, Canada.D.F. Greene. Department of Geography, Concordia University, Montreal, Quebec, Canada.1Author for correspondence. Telephone: 519-824-4120, ext. 6720. email:[email protected] citation: Johnson, E.A., Morin, H., Miyanishi, K., Gagnon, R., and Greene, D.F.2003. A process approach to understanding disturbance and forest dynamics for sustainableforestry. Chapter 8. In Towards Sustainable Management of the Boreal Forest. Edited byP.J. Burton, C. Messier, D.W. Smith, and W.L. Adamowicz. NRC Research Press, Ottawa,Ontario, Canada. pp.

If there is one message from disturbance studies, it is that the dynamics of ecosys-tems cannot be understood without considering natural disturbances at different scalesof time and space. As introduced in Chap. 2 (see Box 2.8) and discussed further in Chap.9, one widely proposed approach to ecosystem management is to attempt to have man-agement practices emulate natural disturbances, thereby retaining the range of naturalvariation in ecosystems in order to maintain resilience and sustainability (Hunter 1993;Swanson et al. 1994; Bunnell 1995; DeLong and Tanner 1996; Holling and Meffe 1996;Cissel et al. 1999; Landres et al. 1999). The objective in this approach is to determinethe natural variation of the disturbance regime (Johnson and Van Wagner 1985) andthen to maintain this variation through management practices. Swanson et al. (1994)suggested an example of a three-variable regime (Fig. 8.1) in which disturbance fre-quency is emulated by forest rotation age, disturbance size by cut-block size, and dis-turbance intensity by utilization standards. Certainly other variables can be imagined,but these descriptors are illustrative of the “regime approach” to describing regional dis-turbance ecology.

One can think of many potential problems with emulating the range of natural vari-ation in disturbance (Parsons et al. 1999); we will discuss two that will help in under-standing the process approach to disturbance ecology used in this chapter. The first andperhaps most obvious problem is that natural disturbances (e.g., wildfires) are seen, atleast implicitly, as eventually being replaced by the emulated disturbance of manage-ment (Chap. 9). Usually little or no consideration is given of the fact that completeexclusion of natural disturbance may be impossible and that the landscape pattern willalways be a product of both natural disturbance and forest management practices.Despite the technological advances made in firefighting, the boreal forest has experi-enced extremely large fires in the past two decades. Thus, despite the optimisticapproach of managing biophysical risk through “early detection and rapid suppression

2 Towards Sustainable Management of the Boreal Forest

Fig. 8.1. Three-variable (frequency, size, and intensity) disturbance regime proposed by Swan-son et al. (1994), showing the range of variation produced by natural disturbances and byforestry practices.

of wildfires” (Chap. 2), there is little evidence that such activities have a significantimpact on the landscape pattern (Bridge 2001).

A second and more fundamental problem is the descriptive nature of the goal in thisapproach, typically to replicate the variation in pattern of the disturbance (e.g., Berg-eron and Harvey 1997; Cissel et al. 1999) or the remnant structures left by the distur-bance (Hansen et al. 1991). Such templates for management do not consider how thedisturbances work (i.e., the disturbance processes involved) and in any precise way whattheir effect is on ecosystem components and processes. However, without an under-standing of the natural disturbance processes (e.g., smoldering combustion in forestfires), and the ecosystem processes (e.g., tree population dynamics) as well as how thedisturbance causes the ecosystem effects, we do not know what disturbance variables toemulate and if those chosen will produce the ecosystem effects desired. This has been achronic problem in prescribed burns that often use simplistic descriptions (e.g., hot orcool fires) of the natural fire regime for guidance (Johnson and Miyanishi 1996).

The problem in choice of variables when no processes are understood can be seen inthe Swanson et al. (1994) model (Fig. 8.1) and in later modifications of this model(Chap. 9, Fig. 9.1). Disturbance frequency and size describe characteristics of the dis-turbance but not of specific ecosystem effects. Intensity has been used to characterizeeither the disturbance process or an ecosystem effect and has been used almost inter-changeably with severity. Furthermore, although severity is generally defined in termsof ecosystem effects or responses (e.g., tree mortality, duff consumption, soil alteration,etc.), it is often given as a characteristic of the disturbance itself (e.g., “severe firebehaviour” in Chap. 9). One must start by indicating what ecosystem process onewishes to affect and work back to the specifics of the disturbance processes. The regimeapproach, as it is presently formulated, cannot lead to this. A more detailed discussionof the process–response approach can be found in Johnson (1985) and Johnson andMiyanishi (2001).

Underlying the approach of emulating natural disturbance may be the belief that, bymimicking the pattern, one can escape the necessity of understanding the disturbanceprocesses and ecosystem responses involved (the coarse filter approach). This explainsthe use of largely descriptive indexes of patterns (Turner et al. 1989; Ripple et al. 1991;McGarigal and Marks 1995; Gustafson 1998). Unfortunately, different parameters ofspecies populations respond differently to different processes in disturbances, and mayor may not show any response to landscape pattern. The different scale-dependence ofspecies (Chap. 9) is in part related to this different process–response relationship. Astrictly empirical and clinical approach (rather than the process–response approach) toresearch in disturbance ecology does not provide any scientific basis for selection of theappropriate disturbance variables to be emulated or the ecosystem responses to be sub-sequently monitored. Accepting the belief of not needing to understand the disturbanceprocesses and ecosystem responses reduces natural resource management to determin-ing what natural disturbance variation is to be measured. Thus the emulation idea isbeing implemented in management policy (e.g., in British Columbia, Alberta, Ontario,etc.) before the hypothesis has been tested or a well-defined model has been developed.Even in the context of adaptive management (Chap. 21), there is a requirement for fore-casting the results of management practices through the use of quantitative models and

3Chapter 8. A process approach to understanding disturbance and forest dynamics

evaluating the forecasts through monitoring. Process-based models will lead to betterpredictions than strictly empirical models that may, at times, be based on spurious cor-relations between arbitrarily selected variables.

Perhaps part of the reason for this reversal of the hypothesis testing-applicationsequence is the emotive nature of emulating, mimicking, or faking the variation of nat-ural disturbance. The unquestioning acceptance of the hypothesis and its implementa-tion in policy made it seem unnecessary for researchers to question the underlyingassumptions and to conduct research that would lead to a better understanding of theprocesses involved in natural disturbances as well as the connections between theseprocesses and the responses of the various ecosystem components. Thus, from thebeginning, the goals of the disturbance group in the Sustainable Forest ManagementNetwork included testing the hypothesis of emulating natural disturbance and buildinga better process–response understanding for the boreal forest ecosystem.

In this chapter, we will consider two important natural disturbances on tree popula-tions in the North American boreal forest: wildfire; and outbreaks of the eastern sprucebudworm (Choristoneura fumiferana). The discussion will use tree population dynam-ics as the ecosystem process of interest and explain disturbance processes as they affectrecruitment and mortality. Foresters should be interested in understanding tree popula-tion dynamics (Box 8.1), even if they presently have solutions (e.g., using mechanicalsite preparation to address problems of regeneration). Such understanding would lead tomore reasoned solutions to future problems that may arise (and less need for a trial-and-error approach to finding such solutions). While we have not used data or examplesfrom outside North America, the approach taken here to study disturbance ecology isgeneralizable.

Forest tree populations and disturbance

A forested landscape consists of a hierarchy, starting at the bottom with individual trees,these trees being grouped together into local populations, and the local populations intoregional populations. Most local tree populations have cohort structure; i.e., they aredivided into groups of individuals born at more or less the same time and having simi-lar mortality histories. Note that cohorts are not subpopulations because they are definedonly by their birth and mortality patterns. Their offspring cannot be recruited into theircohort; instead, the offspring form new cohorts.


Box 8.1. Challenges to sustainable forest management:

• To understand disturbance processes, not just emulate disturbance patterns;• To sustain important ecosystem processes, including tree population dynamics;• To characterize regional differences in disturbance regimes in a meaningful way; • To recognize the constraints to forest management imposed by regional disturbance

processes; and• To consider the forest management opportunities offered by regional disturbance

processes.

Within the regional population, the local populations or their cohorts act as individ-uals in the sense that whole local populations or certain cohorts within local populationscan be exterminated. In this sense, the regional population has a regional mortality rate.An example of local population mortality is the occurrence of a large high-intensitywildfire that may destroy a number of local populations by killing all trees within thesepopulations. Examples of cohort mortality in regional populations include:• low-intensity surface fires that may kill only the younger age classes (cohorts) in

one or more local populations, leaving the older age classes (cohorts) intact; and• insect outbreaks such as spruce budworm that may kill only the older cohorts, leav-

ing the younger cohorts unaffected.Local populations and their cohorts also act as individuals within regional popula-

tions in the sense that they are recruited into the regional population. For example, fol-lowing a high-intensity wildfire that destroys one or more local populations, new localpopulations will arise to replace those destroyed. These new local populations (i.e.,recruits) arise from seeds dispersed from adjacent surviving local populations, exceptwhen the species has serotinous cones (e.g., jack pine, Pinus banksiana) or can resprout(e.g., trembling aspen, Populus tremuloides). In these latter cases, the new local popu-lations will primarily arise from seeds or sprouts produced in situ by the previous localpopulation. In the case of cohort (rather than local population) mortality due to distur-bances such as low-intensity surface fires or spruce budworm outbreaks, the recruitmentof new cohorts (even of nonserotinous and nonsprouting species) will be primarily fromoffspring of the surviving cohorts; thus, dispersal from adjacent local populationsbecomes less important. As a result, long-distance dispersal plays a significant role onlywhen the whole local population (not just a cohort within the local population) is killedand when there are no in situ sources of propagules (seeds or sprouts). This means that,because of its serotinous cones, jack pine doesn’t generally have a regional populationstructure, since after a fire it is not dependent on other local populations for its recruit-ment. The exception to this is when the local population is destroyed before its oldestcohort has reached reproductive maturity. On the other hand, white spruce (Piceaglauca) does have a regional population structure, since, after being destroyed by a fire,a local population is dependent on dispersal from surviving adjacent populations for itsrecruitment.

Local populations can be subdivided into cohorts of individuals that recruit at simi-lar times and have similar mortality schedules. Cohorts are recognized because differ-ent age classes (cohorts) of the population recruit in different numbers at different timesand can find themselves in different mortality environments (e.g., the tree cohort start-ing soon after a fire lives its life in a different environment than subsequent cohortswhich start in the shade of an established canopy).

Recruitment into a local population will come from individuals within that local pop-ulation and also from individuals in adjacent local populations. If the local populationis completely extirpated by a disturbance, then seeds will have to disperse from surviv-ing adjacent populations, unless, of course, the species has serotinous cones or canresprout. Recruitment varies significantly between cohorts as a result of temporal vari-ability in the availability of seeds and suitable seedbeds.


Mortality in the local population also varies between cohorts. For example, thecohort which recruits shortly after extermination of the whole local population (e.g., bya crown fire) will have a better survivorship in most cases than subsequent cohortsrecruiting in the shade of the initial post-disturbance cohort (Johnson and Fryer 1989).If only some individuals or certain cohorts within a local population are killed by dis-turbance (e.g., by insects), the survivorship of other individuals or cohorts will beimproved. For example, if the cohort comprising the canopy trees are killed, the sup-pressed seedlings and saplings in the understory (called the seedling bank or advancedregeneration) are released, improving both their growth and survivorship.

A stable population requires that each cohort have the same recruitment and mortal-ity; i.e., all cohorts are really the same as every other. From the above discussion, it isobvious that local populations of most boreal forest trees are not stable, since the dif-ferent cohorts within any local population have different recruitment and mortalityrates. Disturbances may cause mortality of certain cohorts within a local population(e.g., surface fire or insect outbreaks) or mortality of the whole local population (e.g.,crown fire). The dynamics of these local populations subject to such disturbances canonly be understood by seeing the forested landscape as being made up of many localpopulations that are connected to each other through seed and pollen dispersal. Recentstudies of such metapopulations (Pulliam 1988; Kareiva 1990; Hanski and Gilpin 1991)and conservation biology (Hunter 1996) have shown that examining only stand-levelprocesses (stand development or forest succession) does not provide an adequate under-standing of forest dynamics.

Wildfires and tree population dynamics

Wildfires consist of a large number of processes (see Johnson and Miyanishi 2001) ofwhich only those tied to population processes of birth, mortality, dispersal, and regionalpopulation mortality are discussed here.

Regional population mortality

Within local populations, mortality is described by death of a few individuals at a time;on the other hand, regional population mortality involves death of large numbers of indi-viduals (sometimes the whole local population) and also large numbers in adjacent pop-ulations. This section will explain the role of wildfires in determining regionalpopulation mortality of forested landscapes by causing catastrophic mortality of localtree populations over large areas. First, we will explain why wildfires cause catastrophicmortality in the boreal forest, and second, we will address how many local populationswould be affected by an individual wildfire.

In the North American boreal forest, crown fires account for most of the area burned(Johnson 1992; Fig. 8.2). Crown fires occur when the surface fire intensity (heat output)in kW m–1 produces enough heat to ignite the tree crowns. In order for the fire to stayin the crowns two criteria must be met: (1) the surface fire intensity must be above athreshold value, and (2) the crown fire intensity and rate of spread must be sufficient totransfer enough heat forward to maintain flaming combustion within the forest canopy


(Van Wagner 1977). If these criteria are not met, the crown fire may be sporadic, burn-ing individual trees or groups of trees while burning only on the surface in other areas.

Crown fires are predominant in the boreal forest for two general reasons: (1) theoccurrence of certain mid-tropospheric weather conditions that cause both extreme fueldrying and convective storms that produce lightning, and (2) a fuel structure andarrangement (a continuous canopy of predominantly needle-leaves arranged on numer-ous small branches) that promote flame laddering and active crown-to-crown spread.

The mid-latitude atmosphere is dominated by the westerly flow of air. Occasionallythis flow is deflected by high-pressure systems which persist for more than 8 days.These mid-tropospheric patterns are often called blocking systems because they blockthe normal alternation of west–east movement of high- and low-pressure cells over aregion, diverting the flow north or south. Because the persistent highs result in clearweather and high temperatures in spring and summer, they are associated with rapid fueldrying. These blocking highs are the result of incompletely understood atmosphericprocesses, but some have very characteristic patterns. Two of these patterns, which areknown to be associated with years in which large areas of forest are burned, are thePacific North America (PNA) pattern (Johnson and Wowchuk 1993; Flannigan andWotton 2001) and the Hudson Bay High (Schroeder 1950). The PNA pattern consistsof a persistent high over western Canada and the U.S.A., a low in the Gulf of Alaskaand another low over Ontario and adjacent U.S.A. The Hudson Bay High occurs overManitoba, Ontario, and adjacent U.S.A. These highs often persist for several weeks and,at times, have convective thunderstorms that produce the lightning for wildfire ignition.While past occurrences of atmospheric patterns such as the El Niño – Southern Oscilla-tion (ENSO) have been well studied (e.g., Horel and Wallace 1981; Rasmussen andWallace 1983) and future occurrences can be reasonably predicted, at the present timeit is not possible to determine past occurrences of the PNA pattern in the same way. Ifthis were possible, we might be able to determine not only a pattern in large-fire years


Fig. 8.2. Active crown fire in the Northwest Territories, Canada. Note that the flaming front isa wall of fire from the surface into the crown. Also evident in this photograph is the atmos-pheric convection generated by the heat, resulting in turbulent coherent structure within thefire.

in the boreal forest but also tie them more closely to the characteristic weather patternswhich cause them. The ability to forecast these patterns could lead to better predictionsof large wildfire burn years in the boreal forest (Johnson and Wowchuk 1993; Skinneret al. 1999; Flannigan et al. 2000). Some progress has been made in using the surfacewater temperature anomalies in the North Pacific Ocean to predict the PNA pattern(Walsh and Richman 1981).

In order to judge the number of local tree populations affected by an individual firewe require an understanding of (1) the size distribution of fires, and (2) the distributionof burned local populations.

Size distributions of fires can be presented in two ways, one giving the probabilitydistribution of fire sizes on the landscape and the other giving the proportion of totalarea burned by each fire size. The probability density distribution (frequency histogram)is usually heavily skewed (Fig. 8.3). The data are always truncated at smaller areasbecause of the difficulty and economics of finding small fires, particularly in regionswith little or no fire suppression. However, it does seem that the distribution increasesto a peak in the lower size classes and then decreases with a very long tail if the recordis carefully constructed. Surprisingly few attempts have been made to fit a function tothe fire size distributions. Reed and McKelvey (2002) have shown for a diversity ofregions that the size distribution of fires does not fit the power law relationships pro-posed by Malamud et al. (1998), Ricotta et al. (1999), and Cumming (2001); instead,they obtained the best fit with a competing hazard model of extinguishment. The sizedistribution of fires raises the question of the peak (Fig. 8.3); i.e., why do a large num-ber of fires only grow to a certain size before going out while fewer fires remain smallerand few fires get very large? This must tell us something about ignition, extinction, andspread of fires, but exactly what is not yet clear. It may be related to the fact that the dis-tribution of persistence of pressure systems has a peak at about 5 days (see fig. 2.10 inJohnson 1992).

One must be careful not to interpret this peak in the distribution of fire sizes as anindication of which fires have the most impact on the landscape. This latter issue is bet-ter shown by a Lorenz graph (Fig. 8.4). These graphs show what proportion of the totalarea is burned by fires in different size classes. When presented as a cumulative distri-bution, it shows how the distributions deviate from an equitable distribution of the total


Fig. 8.3. Density distributions of fire sizes for (a) northeastern Alberta and (b) Northwest Ter-ritories (Reed and McKelvey 2002).

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log(Area) log(Area)

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(a) (b)

area around fires of different sizes (i.e., an equitable distribution would result in astraight diagonal line). In fact, the Lorenz graphs of fire distributions are all right-skewed, showing that large fires (although few in number) account for the majority ofthe total area burned.

The size distribution of fires gives the area covered by individual fires but not thenumber of local tree populations affected by fires. The ability to estimate the actualnumber of specific local populations (e.g., populations of white spruce) affecteddepends on an understanding of the structure of the landscape on which the species isdistributed. All landscapes are made up of drainage channels and ridgelines betweenwhich hillslopes are hung. Hillslopes form the fundamental upland hydrological gradi-ents and stream courses form the lowland hydrological gradients. Because the down-ward flow of water from the ridgelines to the hillslope bottoms also results in thedownward movement of nutrients, hillslopes create the moisture and nutrient gradientsthat explain the important compositional gradients in vegetation (Bridge and Johnson2000). Figure 8.5 gives an example of two such hillslope gradients in the mixedwoodboreal forest on glaciofluvial and glacial till substrate. Note that both the steepness ofthe hillslope and the moisture and nutrient values depend in part on the surficial sub-strate.

Consequently, if there is a general pattern in hillslopes and stream courses, oneshould be able to predict some generalized distributions of species on the landscape.One method for doing this is to use digital elevation model (DEM) and surficial geol-ogy data in a hydrologic model to calculate a wetness index. The results of such studies(e.g., Sivapalan et al. 1987) suggest that most landscapes have distributions of wetnessvalues similar to those in Fig. 8.6. Relatively dry parts of the landscape (ridge lines) arenot very abundant and neither are very wet areas along stream courses and hillslope bot-toms. The most abundant wetness values are those occurring just below the ridge line.This follows from the allometric relationship (Hack 1957) of basin length equals basinarea0.6. This relationship indicates that basins tend to get longer and narrower as theyget larger and that the average distance from ridgeline to channel tends to remain rela-tively constant or to increase very slowly. Thus species populations which tolerate (andare more competitive at) the wetness values that are most abundant on the landscape


Fig. 8.4. Lorenz graphs for the fire size distributions from (a) northern Alberta and (b) north-western Ontario.

(a) (b)


Fig. 8.5. Landscape patterns of canopy tree species composition in the mixedwood boreal for-est in central Saskatchewan, based on the relationship between the stand positions on the mois-ture and nutrient gradients and the stand distances from the ridgeline (Bridge and Johnson2000).

Fig. 8.6. Distribution of wetness values (topographic index) for a study site in centralSaskatchewan (Tchir 2000).

will be more widespread. Remember that wetness values depend on surficial material,slope angle, catchment area, and the regional climate.

With the exception of stream courses and wetlands, fires burn landscapes with noapparent consistent reference to hillslopes. Although one can see specific parts of hill-slopes not burning in a fire, rarely do we see a statistically consistent pattern. This tendsto go against what most of us intuitively think. However, the conditions under whichlarge fires occur in the boreal forest are more conducive to remnant unburned patchesbeing explained by turbulent coherent structure in the fire (Jenkins et al. 2001) ratherthan by site factors. Thus it may be that the areas burned by fires above a certain sizesimply represent a sampling of the landscape wetness distribution (and hence the forestcomposition) as described above.

Now that we have some understanding of the number of local populations affectedby individual fires, we can further ask what the catastrophic mortality is of these localpopulations. Interestingly, we know this catastrophic mortality because this is what firefrequency studies give (Johnson and Gutsell 1994). The time-since-last-fire distributionis, in fact, the survivorship of the regional population subject to crown fires. In theboreal forest, time-since-fire distributions are mixed negative exponentials (e.g., Fig.8.7). The distributions are mixed because the fire frequency has changed more than oncein the past 300 years. These changes were often at similar times over large areas (John-son 1992), suggesting a large-scale climate mechanism. For example, changes in themid-1700s and late 1800s are often associated with the Little Ice Age (Johnson andLarsen 1991; Bergeron and Archambault 1993). Between the changes in frequency, thedistributions appear to be negative exponentials. This distribution suggests that there isno aging effect; i.e., as a forest gets older, there is no change in the instantaneous mor-tality (hazard) rate. Thus, old forests are not more subject to fire than younger ones, ashas sometimes been suggested (Tymstra et al. 1998) although with little or no evidencepresented.


Fig. 8.7. Time-since-fire mixed negative exponential distribution for forest stands in PrinceAlbert National Park (Weir et al. 2000).

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There is some evidence that changes in fire frequency change the spatial mosaic oftime since last fire. As currently understood in the mixedwood boreal forest, short firecycles lead to younger, larger oblong-shaped polygons with irregular edges, whilelonger fire cycles lead to older, smaller, more circular polygons (Weir et al. 2000). Thisis because a short fire cycle results in most of the remnants of past burns (that are small,old, compact and circular polygons) being overburned; thus the polygons reflect thelarge oblong irregular shapes of these recent burns.

Post-fire recruitment

In the boreal forest where stand-replacing crown fire is the principal disturbance, mosttree regeneration generally occurs within the first six years after a fire (St-Pierre et al.1992; Charron and Greene 2002; Greene et al. 1999; Gutsell and Johnson 2002). Thissection will address the two factors determining tree recruitment in burned areas: avail-ability of seed (source strength and distance) and availability of appropriate seedbedsfor germination and seedling establishment. The spatial pattern of individual fires is ofinterest because dispersal and colonization are, in part, dependent on distance from sur-viving vegetation. Distance is particularly important for nonserotinous and nonsprout-ing species such as white spruce, balsam fir (Abies balsamea), and tamarack (Larixlaricina) that require wind dispersal of their seeds from surviving individuals for recol-onization of burned areas. Paper birch (Betula papyrifera), although capable of basalsprouting, cannot adequately colonize areas by vegetative means like trembling aspenor balsam poplar (Populus balsamifera) which can reproduce by root suckering.

Ignoring for the moment the surviving remnants within burns, the relationshipbetween the perimeter of burns and burn area reveals that fires tend to become narroweras they get larger. This somewhat oblong shape results from wind-driven fires spread-ing faster in the direction of the wind than perpendicular to the wind. Therefore, the dis-tance from the edge to any point within the burn will not increase greatly as the fire sizeincreases. Interestingly, this allometric relationship is similar to that found for streambasins as discussed in the previous section. McAlpine and Wotton (1993) have alsoshown that the convolution of the burn perimeter is similar across several magnitudesof size. Thus, the mean dispersal distance into the burn for plants near the burn marginincreases slowly with burn size (Fig. 8.8), primarily as a function of the relationshipbetween burn area and burn length.

Besides seed sources from outside the burn, there are also sources within burns dueto the areas of forest that escape burning: the residual stands. As mentioned in the pre-vious section, these unburned patches within burns may be explained by turbulentcoherent structure in the fire (Jenkins et al. 2001). Unlike what was originally believed,atmospheric physicists and fluid mechanicists have shown that turbulence is not with-out organization and that this organization forms “coherent structures” which are some-times called hairpin or horseshoe vortices. The pattern of air movement associated withthe development of these vortices seems to have something to do with survival of for-est patches within fires but the process is still not clearly understood. The unburnedresiduals show similarities between different size burns. The amount of surviving forestper unit area of burn remains constant with increasing fire size, and the number of sur-


viving forest polygons per unit area of burn decreases with burn area (Fig. 8.9). Thesetwo results suggest that, as the burns increase in size, the surviving polygons get bigger.The frequency distribution of sizes of surviving forest polygons in a burn fits an extremevalue distribution with larger burns having larger modal remnant sizes than smallerburns.

Thus, despite differences in fire size, the maximum dispersal distance either from theburn perimeter or from surviving polygons typically is not greater than about 150 m(Greene and Johnson 2000) because of the oblong shape of fires, the convolution of thefire margins, and the ubiquity of residual stands in large fires. Not surprisingly, the dis-tribution of distances of any point in a burn to the nearest source of surviving trees fitsan extreme value distribution.

The seemingly random dispersal of tree seeds and establishment of tree seedlingsare, in fact, quite tractable. Most of these principles apply to natural regeneration inrecently harvested cutblocks as well as in recent burns (Greene et al. 2002a). Seed dis-persal of boreal trees is by wind; consequently, dispersal curves can be developed froma micrometeorological model of the flight of the seeds. Models based on this mechanismhave been developed by Greene and Johnson (1989; 1992a, b; 1995; 1996) usingheights at which seeds are released, terminal velocity (rate at which seeds fall in stillair), and wind velocity (see the references listed above for details on specific valuesused in the discussion below). Below we give four examples of models. The first twoare spatially explicit: a point source (single tree) model and an area source (collectionof conspecific trees) model. The final two models are merely stand-scale mean recruit-ment densities for aerial seedbank species (e.g., jack pine and black spruce, Picea mar-iana) and for asexual recruitment of suckers (e.g., trembling aspen, balsam poplar).

The point source model results in a log normal distribution of recruited stems (FDx)as a function of distance (x) from an individual tree:

[1] FDx = (F/((2π)1.5σx x2)) exp[(–0.5/σx

2)(ln(x/x0.5))2]


1 1010

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Nominal burn area (ha)

Fig. 8.8. Mean distance to nearest edge as a function of nominal burn area, i.e., the total areainside the perimeter of the burn including any unburned remnants within the burn.

The cumulative source strength F (seedlings produced per tree per T years) is givenas F = QSJT where Q is the mean annual filled seed production, SJ is the juvenile sur-vivorship of a cohort, and T is the number of years in which colonization can occurbefore the seed bed becomes so poor that colonization effectively ceases, here assumedto be T = 4. (One notes that masting is such a strong source of temporal variation thateven 4-year sums will not render it unimportant.) According to Greene and Johnson(1994), Q is a function of seed mass (m in g) and basal area (B in m2) of the seed-producing tree,

[2] Q = 3067m–0.58B0.92

and x0.5 is the median distance travelled by seeds of mass m:

[3] x0.5 = zau0.5 / f


Fig. 8.9. (a) Amount of surviving forest per unit area of burn, and (b) number of survivingforest polygons per unit area of burn.

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tal

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aof

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ofr

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dualst

ands



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(b)

where f is the mean terminal velocity (m s–1), za = 0.75 zh (zh is forest height and za isthe abscission height), and u0.5 is the median wind speed (around 2.5 m s–1 in recentburns and 0.7 m s–1 in intact forests; Greene and Johnson 1996).

The standard deviation of the logarithms of the horizontal distances travelled (σx) isa function of terminal velocity, and the vertical and horizontal turbulence. These valuesare typically around σx = 1.0 (Greene and Johnson 2000). From empirical evidence(Greene and Johnson 1998), juvenile survivorship (SJ) is proportionate to germinant sizeand seedbed type. Following the empirical literature, Greene and Johnson (1998)dichotomized upland seedbed types into “optimal” (mineral soil, thin humus, rottedlogs) and “poor” (all others). The areally averaged survivorship will, of course, dependon the percentage allocated to these two seedbed categories (see below). If the optimalseedbeds comprise about 17% coverage (this value is typical of site-prepared clearcuts)then:

[4] SJ = 0.28m0.52

for each cohort.By rewriting equation [1] with the values given above, we get:

[5] F = 3312B0.92

(The exponent on m becomes virtually zero so seed mass can be ignored.) Thus, essen-tially, source strength is a function of tree size. See Greene et al. (2002b) for a morethorough treatment of the relationship between light availability, tree size, and seed cropsize.

Area sources are collections of contiguous point sources, and these are, familiarly,forests abutting clearcuts or burns, or perhaps trees adhering to the contours of hill-slopes. The area source recruitment curve is simply the sum of individual recruitmentcurves of equation [1] assuming trees are more or less uniformly distributed within thearea source. Further, where x = 0 is regarded as the edge of the area source. The densityof recruits (FDx = number per m2) is related to distance x from the edge as:

[6] FDx = (FD/2) exp(–0.35(x f )0.4)

The potential source strength is a density (FD = QDSJT) where the seed density (QD =seeds m–2) is estimated well within the source area as QBD (and BD is the basal area perarea). Meanwhile, SJ, juvenile survivorship, is based on the area of suitable seedbedsoutside the area source (i.e., clearcut, burn, etc.).

Singly or in combination, the point source and area source models can be used to pre-dict the sexual recruitment in burns, clearcuts, blowdowns, and other sorts of distur-bances given any spatial configuration of sources. Greater realism can, of course, begained by accounting for differences in prevailing winds (e.g., Greene and Johnson1999; Greene and Johnson 1996), non-random distribution of seedbed types in Carte-sian space, etc.

The foregoing models apply especially to species such as white spruce and balsamfir that are dependent upon sexual recruitment from a living source. These two commonspecies do not seem to be well adapted to regenerating after fire because they lack


serotinous cones and any vegetative form of regeneration (the occasional layering canbe ignored). The typical distance from burn edges or unburned patches is generallygreater than their median dispersal distance (150 m vs. around 30–50 m, depending onwhether the stand is open or closed), and consequently, early colonization tends to bepoor. Indeed, Greene and Johnson (2000) showed that these species seldom have ade-quate stocking beyond approximately 50 m from an area source in burns. Thus, thesespecies are dispersal limited; their recolonization of even the sites in which they hadbeen previously present may be questionable if no surviving individuals are nearby. Bal-sam fir, at least in the mixedwood boreal forest, has another problem in that the bottomhillslope position where it grows best is also limited in area, as previously pointed out.How then does it persist in the forest? One might speculate that it is the combination ofits distribution on the landscape and the disturbance frequency that allows such persist-ence.

The ready supply of seeds in the aerial seedbanks of jack pine and black spruce, cou-pled with their tendency to form near-monocultures, lead to a simpler recruitmentmodel. For a fire, their expected recruitment density becomes:

[7] FD = QDSJSA

where QD is proportional to basal area/area (BD); Greene and Johnson (1999) havefound QD = 35 097BD

0.86 for jack pine and QD = 163 400BD0.95 for black spruce. As

above, SJ, denotes juvenile survivorship, and depends on the area of suitable seedbedsafter a fire. Finally, SA, the pre-abscission survivorship of the seeds during the passageof the flaming front, has been estimated as 1.0 for jack pine and 0.58 for black spruce.

The foregoing has made clear that a principal factor determining post-fire recruit-ment is the proportional coverage of seedbed types. Seedlings of boreal forest treespecies generally have very poor juvenile survivorship on litter or on the thick organiclayer overlaying the mineral soil (Chrosciewicz 1974, 1976; Zasada et al. 1983; Thomasand Wein 1985; Weber et al. 1987; Greene et al. 1999), especially at the germinationstage, but also for the subsequent 2 years for a cohort (Charron and Greene 2002). Fireremoves litter and the organic layer to varying degrees, exposing suitable seedbed (Fig.8.10a). The area of the forest floor from which the F layer is removed thus determinesthe minimum area available for post-fire recruitment of tree seedlings. Obviously, inorder to understand recruitment in burns, we require an understanding of the process bywhich the organic layer is removed by fire.

While the litter layer is largely removed by flaming combustion during passage ofthe fire front, the partially decomposed F and H organic layers, together known as duff,are generally consumed by smoldering combustion after the flaming front has passed(Wein 1981, 1983; Frandsen 1991; Johnson 1992; Hungerford et al. 1995; Fig. 8.10b).The duff is not consumed by flaming combustion because of its physical and chemicalproperties (see Miyanishi 2001). The decomposition of forest litter results in organicmatter that is more compacted (with a higher packing ratio) and that has a lower volatilecontent and a higher lignin:cellulose ratio than the original litter. When heated, thiscompacted chemically altered organic matter pyrolyzes, producing char which oxidizesslowly as a solid rather than releasing sufficient combustible volatiles to sustain flam-ing combustion.


Smoldering differs from flaming combustion (rapid oxidation of gases) in being amuch slower oxidation of a porous char-forming solid (Drysdale 1985). Smolderinginvolves the endothermic process of pyrolysis (thermal degradation), which releasessome volatiles and forms char; the char then oxidizes exothermically. Smoldering stopswhen the heat generated by oxidation of the char or the transfer of heat to the virgin duffis too low to cause pyrolysis. The principal variables that influence this extinctionprocess are duff moisture, density, and depth (Miyanishi and Johnson 2002). Moistureis important because latent heat of vaporization is a significant heat sink. Heat transferbetween the oxidation zone and pyrolysis zone by conduction is determined by the tem-perature difference and thermal diffusivity of the duff. Thermal diffusivity is in partdetermined by density. Duff depth is important because it influences the heat transferbetween the oxidation and pyrolysis zones. In thicker duff layers, the surface area overwhich oxidation is occurring is larger, thus generating more heat. Furthermore, thickerduff traps more of the heat and a lower proportion of the heat generated by oxidation islost by convection from the bed. As the duff layer becomes thinner, not enough heat istransferred to the unaltered duff to sustain smoldering (Palmer 1957; Jones et al. 1994;Miyanishi and Johnson 2002).

Smoldering is patchy at different spatial scales (Dyrness and Norum 1983; St-Pierreet al. 1991; Miyanishi and Johnson 2002) because moisture, depth, and density of duffvary between canopy and gap locations within stands, down hillslopes, and regionally.Although different tree species intercept precipitation with different efficiencies, thecanopies of all trees intercept to some degree, resulting in differences in moisture inputsto duff at the scale of metres or less. The tree canopy also intercepts both incoming solarand outgoing terrestrial radiation. The interception of solar radiation would decreasesurface evaporation while interception of terrestrial radiation would decrease surfacecooling at night, inhibiting dew formation. These opposing effects may tend to canceleach other’s impacts on duff moisture. Greater litter accumulation beneath trees than


Fig. 8.10. (a) Patchiness and patterning of duff consumption within burned stands following astand- replacing crown fire in the boreal forest of central Saskatchewan. (b) The duff is con-sumed by smoldering combustion which occurs after passage of the flaming front. Note in(a) that regeneration of both herbaceous plants and trees occurs only within the burned outpatches.

(a) (b)

Photo by E. JohnsonPhoto by K. Miyanishi

between crowns would lead to differences in duff depths at this scale. Furthermore, dif-ferences in the chemical composition of litter from tree species and from herb and shrubspecies in gaps, as well as differences in moisture and radiation balances beneath treecrowns and in gaps, would affect decomposition rates. Thus, duff depths and densitieswould also vary at this scale. As a result, the patchiness of duff consumption withinstands shows a pattern related to the location of fire-killed trees; i.e., the holes in theduff layer resulting from smoldering are spatially correlated with the trees (Miyanishiand Johnson 2002). As we have discussed before, moisture and nutrients change downhillslopes; this affects both duff moisture and canopy tree species composition. Both ofthese factors will also influence duff depth and density and hence smoldering propaga-tion and extinction. Since precipitation and temperature affect the drying rate of duff,variation in these variables at the regional scale will influence the patterns of duff con-sumption. This very complex variation in duff consumption pattern is important, sincealmost all boreal tree recruitment from seeds after fire occurs only in patches in whichmost of the duff has been consumed (Charron 1998; Greene et al. 1999; Fig. 8.10a).

Above, we assumed that mineral soil and thin humus comprised about 20% of theimmediate post-fire surface. Until recently, there were very few studies available thatquantified this percentage. Miyanishi and Johnson (2002) reported about 40% coveragefor the optimal seedbeds in two Saskatchewan fires, with D.F. Greene (unpublisheddata) finding a similar value for the 2000 Chisholm burn in Alberta. Charron and Greene(2002) found somewhat lower values in Saskatchewan, while Greene et al.2 found muchlower values (as low as 5% in lowland black spruce stands) for a Quebec fire. Clearly,there is still much to be learned about the combustion effects within and among fires.

Emulating the dynamics of fire?

Our present understanding of both wildfire and forest population dynamics is still rela-tively modest. Given our confusion and misunderstandings in the past of the role of dis-turbance in ecosystems (Raup 1956), it is not responsible to suggest applications thatcould be considered professional practice. However, we will try at this point to considera selection of issues related to emulating wildfire effects.

Foresters have “learned by doing” what silviculture works in the boreal forest (seeChap. 13). Not surprisingly, many of these practices can be explained in terms of thetree population dynamics. In the boreal forest, as we have seen, there are two generaltypes of tree dynamics: that of dispersal-limited species for which the regional disper-sal and mortality (wildfire) are both important in local dynamics, and that of specieswith good on-site seed availability for which only regional mortality (wildfire) is ofmajor importance.

Trees with serotinous cones (jack pine and black spruce) or vegetative reproduction(trembling aspen) do not, in general, depend on dispersal from adjacent populations forregeneration after fire. There are generally enough seeds or root suckers available togive adequate stocking if the local pre-fire population was dense enough and of repro-


2Greene, D.F., Noël, J., Bergeron, Y., Rousseau, M., and Gauthier, S. Tree recruitment across a burnseverity gradient following wildfire in the southern boreal forest of Quebec. Submitted to CanadianJournal of Forest Research for review.

ductive age. Local population extinction would occur, on average, in 19% of the land-scape if the fire cycle is 100 years and the age of first reproduction is 20 years (Fig.8.11). Dispersal for serotinous populations is important only in this small part of thelandscape. Aspen is more complicated, since its mortality will depend on killing notonly the aboveground stems (fire intensity) but also the underground roots (smolderingcombustion). Consequently, the regional mortality (wildfire) and pattern of duff con-sumption by smoldering combustion are the regional processes that are dynamicallyimportant in the local population.

Tree harvesting by partial cutting (e.g., commercial thinning, shelterwood, or selec-tion harvesting) is like a crown fire in that both processes generally kill most of the treepopulations while leaving an in situ seed source. The size and shape of the cut is lessimportant to serotinous species, since dispersal from outside the burn or cutblock is notas important. However, site preparation by prescribed burning or surface disturbance(scarification) is important (Chap. 13). Few comparisons have been made between thepatterns created by smoldering in wildfires and the often more complete duff removalin prescribed slash burns or the pattern created by harvesting activities or mechanicalsite preparation methods such as disk trenching. Generally, site preparation has beensuccessful at getting suitable densities and quality of recruits.

Trees with nonserotinous cones (white spruce and balsam fir) depend on both dis-persal and regional mortality. The regional mortality issues are similar to those forserotinous species except that the age of first reproduction is typically older (30 yearsfor white spruce and 35 years for balsam fir). This older age of first reproduction meansthat a slightly greater proportion of the landscape (20%) could potentially burn beforebecoming reproductively mature and sources of seeds for dispersal (Fig. 8.11). On theother hand, local populations of nonserotinous species also may become extinct on pre-viously occupied sites if the sites are beyond the dispersal distance of surviving seedtrees. Thus, in these species, fire and cutblock size and shape as they affect dispersal areimportant if natural regeneration is expected (Greene et al. 1999). However, if seedlings


Fig. 8.11. The probability of regional mortality of a local tree population given the fire cycleand the age of first reproduction.

are planted, as is often done for white spruce, dispersal is not a problem. The success-ful establishment of white spruce plantations often depends on site preparation (to cre-ate plantable spots in deep duff, or to depress competition from shrubs and herbs; seeChap. 13), but as noted before, comparisons of forest floor patterns created by sitepreparation with those made by smoldering combustion remain unexplored at present.

Up to now, we have seen the effect of the wildfire processes primarily on the cohortsrecruited immediately after and as a result of the fire-caused mortality and duff con-sumption. The mortality schedules of these trees seem to fall into roughly two types:those for species such as jack pine and aspen with rapid individual height growth ratesin the first 10 years, little shade tolerance and hence higher density-dependent mortal-ity, and those for species such as white spruce, black spruce, and balsam fir with slowergrowth rates in the first 10 years, shade tolerance and hence lower density-dependentmortality. One result of the different growth rates in the first years after germination iswidespread error in age determination at the base of the tree (as described in followingsections of this chapter) and difference in tree height. Error in age determination is theresult of not aging at the root collar but only near the base of the tree. If seedlings arefast growing, the error between root collar and tree basal age is small, but if seedlingsare slow growing the error will be as much as 15 or more years (Gutsell and Johnson2002). The result is that white spruce will appear to be coming in a decade or more afteraspen and black spruce, often up to 20 years after jack pine. This apparent lag in recruit-ment suggests that the slower growing species were born into the stand some time afterthe fire when, in fact, they were born at the same time as the faster growing species. Fur-ther, because of the initial growth rate differences, the slower and faster species con-tinue to be different in height until maturity (Gutsell and Johnson 2002; Fig. 8.12). Theage error and height difference has led to the belief that one is seeing one species suc-


Fig. 8.12. Jack pine canopy and black spruce understory in the mixedwood boreal forest ofcentral Saskatchewan. When accurately aged, these trees are found to be the same age. Thedifferences in size are due to differences in growth rates between these two species and do notreflect differences in time of establishment.

Photo by E. Johnson

ceeding the next; however, they are often cohorts born at approximately the same time(Gutsell and Johnson 2002). This has implications for ecologically sustainable mixed-wood management (Chap. 13).

What of the cohorts that are truly born after the fire cohort (i.e., not incorrectly inter-preted as such because of aging error)? In most situations, these consist of slower grow-ing, shade-tolerant species (e.g., white spruce, black spruce, balsam fir; Gutsell andJohnson 2002). They start in a shaded environment and generally on the rotted logsderived from the canopy cohort that burned decades before or, much later, canopy treesfelled by more minor disturbances (wind, insects). These cohorts generally exhibitlower birth rates and higher mortality rates. Note that layering in black spruce and bal-sam fir is not included here; we are only talking about seed germination. What is thechance of these later cohorts reaching the canopy and becoming reproductive? Figure8.13 shows the probability of reaching the canopy for a series of growth rates and firecycles. The conclusion from this graph is that, even with growth rates that might occurin a gap caused by death of canopy tree(s), at the rate at which fires occur in the west-ern boreal forest, there is little chance of understory trees reaching the canopy before thenext fire occurs. This last conclusion has some significance to the forest if all wildfirescannot be suppressed.

It is often assumed that emulating the effects of fire through forest practices willreplace wildfire (Chap. 9). If this does not occur, then what will happen to the age dis-tribution of the forest when both cutting and wildfire compete for the wood supply?Several theoretical studies have been carried out (Martell 1980; Routledge 1980; VanWagner 1983; Reed 1984; Reed and Errico 1985, 1986). In general, these studies showthat if the goal of forest management is a regulated forest and if fire follows an expo-nential time-since-fire distribution, then the resulting forest age distribution will be adecreasing geometric distribution (Fig. 8.14). The actual rotation age and long-termyield will be reduced from that for a regulated forest alone. Interestingly, the shape of


Fig. 8.13. Probability of reaching the canopy for different growth rates and fire cycles. Curveswere calculated using growth rates of Picea glauca trees in the canopy and understory obtainedfrom a 111-year-old stand in central Saskatchewan. The numbers 1, 3, and 5 indicate initialtree heights in metres. For example, a 1-m-tall tree growing in understory conditions has a0.65 probability of being burned before reaching the canopy if the fire cycle is 100 years (datafrom Gutsell 2001).

the forest age-class distribution is similar to that for fire alone although the age scalingis different; i.e., the old age tail isn’t very old. The reason for this is that fire is contin-uing to burn all ages of the forest, while harvesting only removes older stands (abovethe rotation age).

Of course, one could ask what the forest age-class distribution actually produced bycutting and wildfire after 75 or more years is. Unfortunately, there are almost no docu-mented examples of such forest age-class distributions in the literature. One example ineastern Ontario has a decreasing distribution not unlike the mixed time-since-fire distri-bution expected for this region (see Bridge 2001). The reason for this is not only con-tinuing fire but also the fact that forest management areas are not completely productive(i.e., non-productive areas are not harvested). Thus comparing the age distribution of acompletely regulated forest to that of the whole landscape is not appropriate. Further-more, changing ideas of forest practices, technological changes, and new and differentmarkets have changed harvesting practices over time, and access to all parts of the man-agement areas is not equal. Therefore, even if the whole landscape were productive, weshould not expect to find a uniform age distribution after a century of harvesting.

Natural variation

Natural variation in a forested landscape (site differences aside) will here be consideredin terms of the statistical distribution of regional and local tree mortality. Mortality dis-tributions can be looked at in several ways, all of which are interrelated in a logical man-ner. F(t) is the inverse of cumulative survival, usually called survivorship, S(t); i.e., F(t)= 1 – S(t). The cumulative survival is the chance of surviving (avoiding mortality) up totime t. Obviously, survival must start at 1.0 at t = 0 and decrease afterwards. Another wayof looking at natural variation in mortality is the probability density distribution f(t), thatis, the probability of mortality in a time interval. Survival, S(t), and the probability den-sity distribution are related by f(t) = 1 – S(t)dt, which is to say that 1 – S(t) is the accu-mulation of f(t). More importantly for our interest in natural variation, the probabilitydensity distribution f(t) explains how mortality comes about, since f(t) = q(t)•S(t) wheremortality f(t) in the time interval dt is a result of the probability of surviving, S(t), to thebeginning of the interval t and then the instantaneous mortality q(t) in the interval.


Fig. 8.14. Forest age distribution (decreasing geometric) resulting from logging and fire (Reedand Errico 1985).

Now with this viewpoint, the distribution of instantaneous mortality of the regionaland local population gives us the natural variation. For example, Fig. 8.15a gives thenatural variation in instantaneous mortality q(t) in a local population. The exact valuesand shape of this distribution will vary some by species, density, site, seedbed, and localweather, etc., as previously discussed. The natural variation in instantaneous mortality,h(t), in regional populations (Fig. 8.15b) follows a mixed negative exponential orWeibull distribution (Johnson and Gutsell 1994). The distribution is constant betweenchanges in the fire frequency (see previous sections).

Natural variation in the instantaneous mortality of both the local and regional mor-tality would have to incorporate fire (disturbance) into the local mortality. Local causesof mortality are important during the first 15–20 years, and fire becomes the mostimportant cause of mortality after this. Silviculture is primarily used to manipulate localcauses of mortality. However, if wildfires are still occurring (Johnson et al. 2001), thenin later years this would be an important source of mortality.

Insect outbreaks and tree population dynamics

Spatial and temporal dynamics of outbreaks

Spruce budworm, Choristoneura fumiferana (Clem.), is the insect that causes the mostdisturbance to coniferous tree populations in eastern North America (Fig. 8.16). The lastoutbreak, which took place between 1974 and 1988, devastated more than 55 million haof forest and destroyed between 139 and 238 million m3 of spruce and fir, the equiva-lent of 10 years of intensive logging (QMER 1985).

Dendroecological analysis of growth reductions caused by budworm defoliationindicates that budworm populations have reached epidemic levels regularly over thepast 300 years (Morin and Laprise 1990; Krause 1997; Fig. 8.17). Reliable defoliationsurveys are available for the last outbreak and for part of the mid-20th century outbreak(Kettella 1983; Hardy et al. 1986; Boulet 1996; Candau et al. 1998). They have beenused to develop models of the spatial and temporal dynamics of the budworm in orderto predict the dynamics of the next outbreak (Candau et al. 1998; Gray et al. 1998, 2000;Williams and Liebhold 2000). However, even if these models are extremely well done,


Fig. 8.15. (a) Instantaneous mortality, q(t), in a local population, and (b) instantaneous mortal-ity, h(t), in a regional population (fire frequency).

(a) (b)

they can only reproduce the spatial and temporal patterns of the last outbreak. Theadvantage of dendroecological studies is that they can provide reliable information onpast defoliations over periods up to 300 years, since trees maintain a record of defolia-tions in their ring width patterns (Fig. 8.17).

A recent detailed study of the Quebec vegetation zones south of the 50th parallelshows a very regular periodicity of outbreaks at the supra-regional scale. As indicatedby O1–O6 in Fig. 8.18, there were three outbreaks per century during the 19th and 20th


Fig. 8.17. Chronology of spruce budworm outbreaks in the boreal forest of Québec derivedfrom dendroecological analysis from (a) Morin and Laprise (1990) and (b) Krause (1997).

Fig. 8.16. Trees killed by spruce budworm in a balsam fir forest in Quebec. Note that thecanopy trees are killed while the understory trees survive.

Photo by Natalie Perron

centuries (Jardon 2001). Based on historical data, it has been suggested that outbreaksduring the 20th century became increasingly important and devastated larger areas(Blais 1983, 1984). This change has been attributed to human activities such as loggingand fire suppression. However, until recently, data were scarce across eastern NorthAmerica, making it difficult to test this hypothesis (Morin 1998).

The first attempt to compare histories of spruce budworm outbreaks in different loca-tions across North America using published dendrochronological data showed that out-breaks in the 20th century occurred at a very regular interval (Morin 1998). Severeoutbreaks, such as the ones that occurred at the beginning (O3) and the end (O1) of thecentury, were synchronized across locations, as indicated by the date of occurrence ofthe smallest ring produced by defoliation. However, such synchronicity was not evidentfor the outbreak (O2) that occurred in the middle of the 20th century (Morin 1998). Evi-dence now suggests that there was a noticeable shift in outbreak patterns at the turn ofthe century. Even if the 19th century outbreaks covered the entire territory, these out-breaks showed a slow patchy pattern of dispersal within a stand as well as across theregion. On the contrary, the severe 20th century outbreaks (O1 and O3) showed a rapidand synchronous dispersal pattern both within stands and across the entire area (Fig.8.18). It is unlikely that human activities are the only cause of this shift because loggingactivities and fire suppression programs were not active in most of the area at the begin-ning of the 20th century when one of the most severe outbreaks (O3) occurred at almostevery location at the same time, showing an explosive diffusion pattern (Jardon 2001).A change in forest age structure across the area from younger to older forests, contain-ing more susceptible species such as balsam fir, may have contributed to this shift. This


Fig. 8.18. Relative frequencies of sampled sites affected by the 6 last outbreaks (O1–O6)south of the 50th parallel in the province of Quebec (from Jardon 2001).

1800 1820 1840 1860 1880 1900 1920 1940 1960 19800

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change to older forests may be linked to a lengthening of the fire cycle that occurred atthe end of the Little Ice Age (Bergeron et al. 1995; Jardon 2001).

The spruce budworm diffusion pattern detected from dendroecological data for thelast outbreak of the 20th century is in general agreement with the well-known diffusionpattern described by defoliation surveys for the same area (Kettela 1983; Hardy et al.1986; Boulet 1996; Gray et al. 1998, 2000). However, dendroecological data based onfour outbreaks showed that each outbreak had its own distinctive pattern of spread,some being detected first in the southwest and expanding toward the northeast (e.g., O1)and some showing the reverse pattern from the northeast to the southwest (e.g., O4)(Jardon 2001). The mechanisms by which the insects cover such large areas in such dif-ferent patterns are not fully understood. The explosive pattern described for the O3 out-break suggests that the insect was present everywhere at an endemic level and that alarge-scale variable such as climate may have acted as a synchronizing agent; thismechanism has been described as the Moran effect (Williams and Liebhold 2000).However, a diffusion pattern such as the one described for the O1 outbreak suggests thatother variables (e.g., the dispersal of the insect from very dense populations) could haveplayed a role in synchronization of the outbreak (Régnière and Lysk 1995; Jardon2001).

Structure of mature boreal balsam fir populations

In the interval between destructive fires, the dynamics of natural boreal balsam fir pop-ulations are controlled by canopy openings caused by tree mortality. This mortality isoften associated with spruce budworm outbreaks that sculpt the landscape in a patchymosaic of variable age (Morin 1994).

A study of a boreal fir population situated at the 50th parallel north of Lac St.-Jean,Quebec, that was exposed to a major disturbance (a wind storm that resulted in largeareas of blowdown) has been used to put forward a model for understanding the mech-anisms involved in regeneration of the population (Morin 1990). The defoliation andopening of the site by the blowdown resulted in growth release of the seedlings alreadyestablished on the forest floor (the seedling bank; Fig. 8.19). Thus, the original popula-tion was replaced by a population of the same species with an apparent even-aged struc-ture. Such regeneration from a seedling bank (Thompson and Grime 1979; Morin 1990)is also representative of even-aged stands originating solely from a budworm outbreak(Morin 1990; Fig. 8.20). Using the dendroecological model presented for the blowdown(Morin 1990) and the chronology of spruce budworm outbreaks for the region (Morinand Laprise 1990), Morin (1994) evaluated the importance of recurrent spruce budwormoutbreaks in the dynamics of these balsam fir forests.

Most of the studied populations showed the same dynamics as the blowdown site andcould be classified as budworm-origin even-aged stands. Characteristic growth releasesin the sampled trees generally occurred after exceptionally narrow rings around 1952(Group 1), 1914 (Group 2), and 1860–1890 (Group 3). A characteristic example of theage structures and growth curves obtained for each of the groups is presented in Fig.8.20. In general, this pattern of age structure and rapid growth release of seedlings sug-gests a rapid mortality of the canopy trees, possibly associated with a partial or total


blowdown. This situation is in good agreement with spruce budworm dynamics, whichhave been very consistent in different regions and across different outbreaks (MacLean1980). This is a good example of a cyclic change that was proposed to explain recentdynamics of the fir forests of New Brunswick (Morris 1963; Baskerville 1975, 1986;MacLean 1984), Nova Scotia (MacLean 1988), Quebec (Ghent et al. 1957), and Ontario(Blais 1954; Ghent et al. 1957).

The seedling bank

During an outbreak, the seed supply is limited because of the feeding of larvae on thestaminate cones (Blais 1952; Powell 1973). Therefore, in order to achieve replacementof the canopy by trees of the same species, a well-established seedling bank of thisspecies should be present in the stand when a major disturbance such as a budworm out-break, blowdown, or clearcut occurs. In this part of the chapter, we describe the struc-ture of the seedling bank and show how determining the age structure of the seedlingsby ring counts at the presumed collar level can lead to erroneous interpretation of themechanisms that permit regeneration of the fir population. Detailed dendroecologicalanalysis of seedlings allows us to describe the forest dynamics that permit the seedlingbank to stay alive under the canopy while maintaining the same age and size structurefor decades.

Structure of the seedling bank

Seedlings from the seedling bank were sampled within the same populations used forthe age structure analysis of mature populations. Ages were determined at the presumedcollar level, i.e., the location in the stem where the maximum number of growth ringswas found under a microscope using fine cross sections. This count was compared tothe bud scar count down to this location. The most striking result was that the seedlingbanks had very similar age and height structures despite the various ages of the mature


Fig. 8.19. The permanent seedling bank under a closed balsam fir canopy.

Photo by H. Morin

tree populations (Morin and Laprise 1997). Seedling age structures had normal distri-butions centred on 20 years (Fig. 8.21). Two hypotheses were proposed to explain thesimilarity of the age and height structures of the seedling banks: (1) the seedling bankis always being renewed by a turnover process; or (2) the bank establishes and grows toa certain point corresponding generally to 20 growth rings or 20 terminal bud scars, thenit stops growing. If the turnover process is the dominant one, then the age structure ofthe seedlings should fit a “reverse J” shaped distribution characteristic of a stable pop-ulation. Additionally, older seedlings would be expected in older stands and some mor-tality should be observed among those old seedlings. As shown in Fig. 8.21, the agestructures of the seedling bank fit a normal distribution, not a reverse J. Also, relativelyold dead seedlings were virtually absent. The lack of young seedlings in the seedlingbank was probably due to the lack of seed production at the end of the last budwormoutbreak, a consequence of the budworm feeding at the time of sampling (1985–1986).Results from this study and field observations did not support the turnover hypothesisand suggested that the seedling bank dynamics could, at least in part, be based on a strat-egy that enables the fir to survive under the canopy for long periods with a completelack of detectable radial and apical growth.


Fig. 8.20. Age structure (5-year age classes) and mean growth curves from three typicalmature balsam fir stands in the boreal zone of Quebec (from Morin 1994).

1706-10 1746-50 1946-501786-90 1986-901826-30 1866-70 1906-10

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Stand 342 living28 dead



1910 1930 1950 1970 1990

2

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(22 radii)

1870 1890 1910 1930 1950 1970 1990

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er

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er

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er

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(12 radii)

n = 8 trees

(16 radii)

O3 O2 O1

O3 O2 O1

O3 O2 O1O4

2


Demography of the seedling bank

The best way to test the two hypotheses of seedling bank dynamics was to establish per-manent plots in which we could follow all the steps of the regeneration process from theformation of the seeds to their dispersal, germination, seedling growth, and mortality.Four permanent plots were established in the autumn of 1994 in representative sitesbased on age structure analysis (Morin 1994). Duchesneau and Morin (1999) conducteddetailed seedling demography surveys in order to characterize the ecological factorsaffecting germination and early establishment in understory balsam fir seedling banksand to determine if continued recruitment can potentially renew the seedling bank.

Seven years after the 1994–1995 mast year, very few seedlings from this cohortremained, and very few seedlings from subsequent years of seed production survived.On the other hand, the seedlings that were present in the bank at the beginning of thestudy were still present, had an unchanged height structure, and had experienced very

Fig. 8.21. Age structure of balsam fir seedlings from typical mature balsam fir stands of vari-ous ages (from Morin and Laprise 1997).

0

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1915 19751925 1935 1945 1955 1965 1985

n = 382

n = 820

n = 1483

n = 169

n = 219

Sampling area = 20 m2





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1915 19751925 1935 1945 1955 1965 1985

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ings

little, if any, mortality. These results further supported our second hypothesis that a stag-nation process played an important role in maintaining the seedling bank. Stagnationwould imply that apical and radial growth must cease after a certain point (Morin andLaprise 1997). However, careful follow-up of the seedlings in the bank since 1994demonstrated that more than 80% of the seedlings showed yearly apical growth, sug-gesting that the stagnation hypothesis was not supported. So, how can seedlings keepapproximately the same age and height from year to year if they continue to grow everyyear?

Growth strategy of the seedling bank

As a balsam fir seedling grows in the forest understory, its stem is often buried in thelitter and is sometimes characterized by prostrate growth (Fig. 8.22). These situationspromote the formation of adventitious roots and reverse taper as the stem is buried, lead-ing to a loss of growth rings (DesRochers and Gagnon 1997; Parent et al. 2000). Thus,measuring the aboveground portion of the seedling and calculating the age from a ringcount at the presumed collar level (in many studies, at the level of the first adventitiousroot) leads to the erroneous age and height structure depicted in most studies (Ghent1958; Batzer and Popp 1985; Côté and Bélanger 1991; Bélanger et al. 1993; Bergeronand Charron 1994; Osawa 1994; McLaren and Janke 1996; Galipeau et al. 1997; Morinand Laprise 1997; Sirois 1997; Kneeshaw and Bergeron 1998).

The formation of adventitious roots on a gradually buried stem represents an effi-cient mechanism that permits seedlings to maintain a balance between the photosyn-thetic and non-photosynthetic parts and to stay alive for long periods in a low-lightenvironment (Parent et al. 2002). This is a good example of how an understanding of


Fig. 8.22. Typical balsam fir seedling from the seedling bank showing belowground portionwith adventitious roots, terminal bud scars, hypocotyl region, and primary roots (from Parentet al. 2000).

the mechanism involved allows us to understand the dynamics of regeneration, a phe-nomenon which previously had been misinterpreted in most cases.

From these results, it also becomes clear that age structures reported for mature bal-sam fir populations (Morin 1994) are not reliable but are artefacts of the aging method.These age estimates reflect the number of growth rings that we can count at the base ofthe trees after cross-dating, i.e., mainly the number of growth rings set down after thegrowth release, not the true ages of the trees. Although such age structures may allowus to study the importance of budworm outbreaks in the dynamics of balsam fir popu-lations, they cannot be used to make any inferences about the regeneration processes.

Post-outbreak regeneration of balsam fir forest

Regeneration following spruce budworm outbreaks has not been studied as intensivelyas post-fire regeneration in the boreal forest. Although both fire and insect outbreaksoccur at the landscape scale and affect tree population structures, there are severalimportant differences between these two catastrophic disturbances. For example, thereare no time-since-last-outbreak maps for any large areas as there are time-since-firemaps and, therefore, no good estimates with confidence intervals of the disturbancecycle for budworm outbreaks. What we know is that, despite the 33-year recurrence ofoutbreaks at the landscape level, a particular stand will generally not be severelyaffected by two consecutive outbreaks. Unlike disturbance by fire which does notappear to be age-dependent (Johnson 1992), the impact of a budworm outbreak has beenshown to be related to stand age (MacLean 1980). In general, spruce budworm willmostly attack mature trees (regardless of their size) except in the most severe years (Fig.8.16). Stand structure and composition have also been recognized to be important forthe occurrence of outbreaks as well as the impact of defoliation. Although outbreakscover large areas and are relatively predictable at the landscape scale, we don’t fullyunderstand why a given mature stand will be more affected than a neighbouring one.The proportion of balsam fir, the preferred host of the budworm, in the stand is the onlyvariable that has been found to be significantly related to the impact of budworm defo-liation (Bergeron et al. 1995; MacLean and MacKinnon 1997).

The model of balsam fir populations affected by recurrent spruce budworm out-breaks that we present here differs from the classical successional model. The main rea-son for this difference is the dating technique we used that enabled us to show missingcohorts in the canopy and in the understory. Successional models based on static agestructures (e.g., Morin 1994; Morin and Laprise 1997), should be re-examined in lightof these new findings. We now know that it is probably impossible to date mature bal-sam fir exactly because of the difficulty of finding missing rings below the conventionalshoot–root interface. However, new findings on the possibility of dating seedlings (asdescribed above), as well as permanent plot monitoring, suggest a new approach tounderstanding the mechanisms involved in regeneration of balsam fir populations sub-jected to recurrent insect outbreaks.


Germination

Mature balsam fir trees produce seed almost every year, but with mast years of produc-tion every 2–8 years. The main requirement for seed germination is any seedbed withsufficient moisture. In natural mature fir populations, this type of seedbed is generallyabundant and associated with mosses and fallen logs (Simard et al. 1998; Duchesneauand Morin 1999). Germination is thus a continuous process in mature fir populations.This situation is different during budworm outbreaks when there are no or few seedsproduced because of larval feeding on the staminate cones (Blais 1952; Powell 1973).If the majority of canopy trees in a stand are killed during an outbreak, there will besome seed dispersal from nearby less affected stands as well as from the few survivors.This represents approximately 25% of the normal seed rain in a mature stand. If we con-sider that balsam fir starts to produce seeds 15 years after growth release and more effi-ciently after 20–30 years, and if there is no mortality among the germinants and nolimitation in the seedbed availability, then the theoretical seedling bank age structurewithout mortality would reflect the pattern of viable seed dispersal in the stand (Fig.8.21). Figure 8.21 is based on data from yearly monitoring of seed germination withinthirty-two 60 × 60 cm permanent plots established in 1994 in four balsam fir stands ofdifferent ages (Duchesneau and Morin 1999). When an accurate dating of seedlings wasdone, we could detect in the seedling bank age structure the mast years of seed produc-tion. Of course, mortality also alters the age structure (Parent et al. 2002).

This theoretical pattern of seedling recruitment cannot always be seen in a studyusing conventional dating techniques such as ring counts at ground level or bud scalecounting from ground level to the apex. One reason for this is, of course, mortality (afactor considered in more detail in the next sub-section), but a second reason is the dat-ing technique. Using conventional techniques, the best figure one can obtain is areverse-J age-class distribution, sometimes showing peaks corresponding to mast yearsof production (Morin and Laprise 1997). If the stand has been affected recently by anoutbreak, as was the case with many stands we sampled, there will be a lack of recruitsin the youngest ages. But sometimes the dating is simply not accurate enough to showmast years. Inaccurate dating techniques lead to missing cohorts in the age distribution,giving the impression that there are no or very few old seedlings, and that there are somerecruits even during outbreak periods.

Mortality

Two conditions are required for seedlings to survive: a suitable seedbed (mainly mosseson rotten logs; Simard et al. 1998; Duchesneau and Morin 1999) and a minimum levelof light to permit net photosynthesis. Within a mature fir stand with an establishedseedling bank, the principal limitation is the lack of available seedbeds. Thus, even dur-ing years of high seed production (e.g., 1994–1995 in the permanent plots describedabove), seedling establishment is limited. As shown in Fig. 8.23, initial seedling mor-tality within the first year is very high. Although mortality decreases exponentially dur-ing the first few years, after several seasons there are few, if any, seedlings left from thiscohort (Morin and Laprise 1997). Thus these seedlings constitute a transient seedlingbank whose relative cohort numbers reflect seed production.


When the two conditions of available seedbed and good light levels are available(e.g., following a budworm outbreak and (or) a blowdown event), higher seedling num-bers establishing would result in a greater number of seedlings persisting beyond thefirst few years. Once well established, such seedlings experience very low mortality(Box 8.2a) and can thus survive a long time. The survivors of such cohorts form a per-sistent seedling bank. Consequently, balsam fir stands would simultaneously have twodistinct seedling banks:• a persistent one generally establishing shortly after an insect outbreak when seedbed

and light availability are high; and• a transient one establishing in all subsequent years between outbreaks when both

conditions (and particularly seedbed availability) are in low supply.Seedlings of the transient seedling bank will be younger and smaller and therefore

subject to higher mortality than those of the persistent seedling bank. The seedlings ofthe persistent seedling bank that were 20–30 cm in height when the 1994–1995 mastyear seedlings established are still alive in the permanent plots, while most of theseedlings born in 1994–1995 have since died.

For the transient seedling bank, the age-specific mortality rate (qx sensu Johnson andFryer 1989) is very high at germination and within the first year. This event can occurany time during the life span of the stand. Mortality stabilizes at a low level thereafterfor the few remaining seedlings (Box 8.2a). At the time of site opening after an outbreakthat kills the mature canopy trees, the qx rises again during growth release of the olderseedlings in the persistent seedling bank. Thus, the probability of seedlings from thetransient seedling bank making it to the canopy during this period of canopy openingand growth release is very low. In contrast, the persistent seedling bank has a lower ger-minant mortality because of the availability of seedbed. Thus, a greater number ofseedlings survive the initial high seedling mortality and the subsequent low mortality


Fig. 8.23. Survival curves of the 1995 balsam fir seedling cohorts from 4 permanent plots.Relative percentage of seedling survival is defined as the number of seedlings found at thetime (weeks post-germination)/(total number of seedlings) × 100 (from Duchesneau andMorin 1999).

over the long period of suppression (Box 8.2b). These seedlings will be released by thecanopy mortality due to a budworm outbreak. However, some thinning will graduallyoccur so that the qx gradually rises, and the cycle starts again.

The three conditions necessary for the buildup of the persistent seedling bank (seeds,suitable seedbed, and sufficient light availability) are not always met very early afterstand opening. For example, mature trees in one permanent plot were almost all killedby the last spruce budworm outbreak at the end of the 1980s. Fifteen years after theopening of the site, the persistent seedling bank had not yet established, as indicated bythe high mortality of the small seedlings originating from nearby seed sources. Anotherpermanent plot in which the release dates back to the 1952 outbreak still has only a tran-sient seedling bank and a high mortality of the small seedlings because of a very lowlight environment under a very dense stand. Since we don’t know exactly when the per-sistent seedling bank is building up, we don’t know how long it takes to fully occupythe good seedbeds and exactly what the real range of ages in this bank would be. We


Box 8.2. Mortality patterns of balsam fir seedlings.

Fig. 8.24. The age-specific mortality rates, qx, for balsam fir from (a) the transient seedlingbank, and (b) the persistent seedling bank after the release of the stand by a spruce bud-worm outbreak.

Closed circles indicate computed qx from four permanent plots in mature balsam fir stands forboth permanent and transient seedling banks (Morin 1994). The qx pattern from the transientseedling bank represents any cohorts establishing in the understory anytime during the lifes-pan of the stand (100 years), whereas the qx pattern for the persistent seedling bank representsthe cohorts that establish when there are adequate seedbeds and light conditions at the begin-ning of the development of the stand.

can, however, presume that the window of opportunity during which the good condi-tions are met is quite narrow and that this event is crucial for the future of the stand. Pre-cise counting of above- and below-ground bud scars indicates that seedlings can be asold as 60 years or more (Parent et al. 2001). Nevertheless, the persistent seedling bankconstitutes a very discrete entity with a very low mortality. The theoretical figures of theage-specific mortality rate (qx) given in Box 8.2 are based on data compiled from per-manent plot monitoring for an understory cohort originating from a mast year in amature stand (Box 8.2a) and from the persistent seedling bank (Box 8.2b). The rate cor-responding to the constitution of the persistent seedling bank is based on the assumptionthat seeds germinated on a suitable seedbed in a favorable light environment.

Why is this regeneration phenomenon not detected in studies of static stand agestructures? Without precise dating of the seedlings, no distinction can be made betweenthe persistent and transient seedling banks. Inaccurate dating can lead to the impressionthat the age structure shows a typical reverse J shaped curve with a decreasing mortal-ity rate, leading to the presence of one type of seedling bank, the transient seedling bank,and that there are no old seedlings. Permanent plot monitoring of the seedlings permitsus to demonstrate that the persistent seedling bank has very low mortality in compari-son to the transient seedling bank (Box 8.2).

Putting birth and death together

Putting together the birth and mortality models presented above, we can reconstruct thetheoretical age distribution of a balsam fir stand after a spruce budworm outbreak (Box8.3). The persistent seedling bank will rapidly start to grow following canopy mortality.Let us assume that the seedlings have an apparent 20 year range in ages and a structurelike those suggested by the seedling bank age structures already presented (Morin andLaprise 1997). As these seedlings grow, seeds from nearby less-affected stands or sur-vivors within the stand provide a continuous seed supply. Then, one of two scenarioscan occur: (1) the density of the growing regeneration is very high; or (2) there are someopen patches in the regeneration.

In the first scenario, there are a large number of very young seedlings but very fewsurvive under the dense canopy of the growing seedlings. The seedlings grow up andsome 15–20 years later they start to produce seeds themselves. There are still a few sur-vivors, and this situation prevails until self-thinning of the stand (that can be triggeredby another budworm outbreak some 30–40 years later) permits some seedlings to sur-vive and occupy the best seedbeds left by the previous outbreaks, namely moss mats onold rotten logs.

If the second scenario prevails, the best seedbeds could be occupied earlier in thedevelopment of the stand. Since the stand is relatively young after release, it will be lessaffected than a mature one and the outbreak will have a thinning effect rather than caus-ing extensive canopy death. The seedlings present at this time will constitute the per-sistent seedling bank. Mortality which was high at the beginning will stabilize and thefew survivors will persist during the lifespan of the stand. During this life span, numer-ous seedlings will germinate during good seed years but very few will survive. If an out-break kills the majority of the mature trees 30–40 years later, or, as is often the case,after two outbreaks 60–80 years later, the persistent seedling bank will take over the



Box 8.3. Balsam fir stand development under the influence of periodic sprucebudworm outbreaks.

Fig. 8.25. Reconstructed age distributions for a typical balsam fir stand after the growthrelease caused by a spruce budworm outbreak.

Year 0 seedling age structure is based onthe apparent age structure of a typicalbalsam fir seedling bank (Morin andLaprise 1997).

The decreasing number of stems in thiscohort is based on typical apparent agestructure from stands 10, 20, 40, 50, 70,and 80 years after release (Morin 1994).

Age of the recruits is real age based onthe recruitment pattern from permanentplot monitoring (Box 8.2).

Spruce budworm outbreaks occurred inthis stand between 21–30, 51–60, and81–90 years.

Age-specific mortality rates, as definedin Box 8.2, have been applied to perma-nent and transient seedling banks overthe lifespan of the stand.

A high mortality rate was applied to thefirst cohorts of seedlings germinatingunder the dense seedling bank (year0–20 years) until a spruce budworm out-break thinned the stand between 21–30years.

A lower mortality rate typical of the per-sistent seedling bank was then applied tothese seedlings, and a high mortality ratetypical of the transient seedling bankwas applied to the other seedlings grow-ing under the canopy on less favourableseedbeds.

The last outbreak killed the majority ofthe trees in the overstory.

canopy after some 60–120 years of growth. This persistent seedling bank will play acrucial role in the dynamics of balsam fir stands. Its build-up determines the regenera-tive capacity of a stand subjected to spruce budworm outbreaks. Indeed, the transientseedling bank is very fragile, as it depends largely on mast years of seed production, andit does not persist long enough to permit adequate stand regeneration.

The theoretical figure in Box 8.3 is based on:(1) data from permanent plots for the age structure of the growing seedlings 20–30,

50–60, and 80–90 years after release of the stand; they represent apparent ages andage structures because determining the exact age of mature balsam fir is not possi-ble; however, the number of trees is realistic;

(2) the same distribution of seedlings/m2 that was proposed for the recruits (Fig. 8.26);these are exact ages, since they come from permanent plot monitoring;

(3) the age-specific mortality rate applied to these recruits over time as determined frompermanent plot data (Box 8.2); and

(4) the hypothesis that outbreaks occurred between 21 and 30 years, 51 and 60, and 81and 90 years after the first opening, a typical cycle for the budworm (Jardon 2001).

We think that this model of balsam fir regeneration following a budworm outbreakis a more realistic representation of the dynamics of balsam fir populations. If we coulddate precisely all the cohorts in the population, we would expect this to be the result. Itshould be noted that the persistent seedling bank in this model is the result of the firstcohorts to survive after the opening of the stand. These seedlings can be very old, almostas old as the developing stand itself. It must also be emphasized that this persistentseedling bank is the future of the stand and that it will form the next stand after the open-ing of the canopy. This interpretation is in contrast to many other studies that suggestthat younger seedlings, and even seedlings that germinate during or after the outbreak,will form the next stand. In this model, younger seedlings are always smaller than thepersistent seedlings and they have little chance, if any, to be dominant or even to reachthe canopy.

Some management implications

It is usually thought that balsam fir regeneration is widespread and that it occurs read-ily after natural or anthropogenic disturbances. This is based on the assumption thatthere is a continuous supply of seedlings to the seedling bank and that all those seedlingscan participate in the regeneration of the stand and reach the canopy. The model pre-sented here is not in accordance with this assumption. Rather, regeneration of balsam firstands relies heavily on a persistent seedling bank. If the persistent seedling bank is notpresent, the transient seedling bank is a very fragile one on which to count if a distur-bance affects the stand. In this case, if there was a run of poor seed production years orif there was a budworm outbreak that stopped seed production for 10–15 years, the sur-vivors of the transient seedling bank would be insufficient to adequately regenerate thestand. A good example of a situation where the persistent seedling bank is not presentand where there are problems with regeneration is that of dense balsam fir populationsmanaged on short 40-year rotations (Côté and Bélanger 1991). In these heavily stockedstands, there is not enough light on the ground to permit seedlings to survive. Also, these


intensively managed stands have few logs (the best seedbeds for survival of balsam firseedlings) on the ground.

Conclusions

The importance of natural disturbance to the dynamics of forest trees is well recognized.Also, forest dynamics are now recognized as occurring at the scale of both local andregional tree populations. Unfortunately, a precise understanding of how disturbancesactually affect the dynamics of both regional and local tree populations is far fromunderstood. Past disturbance studies have generally not attempted to develop this under-standing but have focused mostly on simply describing patterns of disturbances andtheir effects. This descriptive approach does not lead to an understanding of the dynam-ics (i.e., dispersal, birth, recruitment, and mortality rates) of these tree populations, norof how different disturbances affect these rates. We hope that some of the discussion in


Fig. 8.26. Theoretical seedling recruitment (without mortality) in a balsam fir stand aftergrowth release of the seedling bank. The figure illustrates the lower recruitment at the begin-ning of the development of the stand because of seeding in from outside the stand, and the typ-ical variability in seedling recruitment caused by recorded mast years. The estimates are madefrom permanent plot monitoring in stands after 10, 40, and 70 years of release.

Years

0,1

1

10

100

1000

Growth releaseafter theoutbreak

outbreakperiods

Num

ber

of r

ecru

its /

m2

this chapter has revealed the need for a more precise understanding of the connectionbetween the disturbance processes and population dynamics processes, a connectionthat is essential for sustainable management of forests (Box 8.4).

The studies of disturbances caused by wildfires and insect outbreaks discussed inthis chapter have shown the importance of studying the cohort structure of local treepopulations. They have also shown that only certain cohorts are responsible for regen-eration of the canopy following disturbance, despite relatively continuous germinationand seedling establishment. Following either wildfire or insect outbreaks, the cohort thatwill replace the dead canopy trees establishes under conditions of abundantly availableseedbed and relatively high light conditions. All subsequent seedlings establishingunder less optimal conditions have lower recruitment and higher mortality rates suchthat their chances of surviving long enough to grow into the canopy are virtually zero.

The situation described above differs from the traditional idea of succession in whichthe forest is viewed as a series of cohorts replacing each other. One of the aspects of thetraditional notion of succession has been a rather linear view of how forest dynamicswork. Even the more recent ideas of succession consisting of multiple pathways (Fastie1995) do not seem to completely encompass the incredible flexibility of tree popula-tions. Despite the constraints of the life history strategies of each species population(e.g., serotinous and nonserotinous cones, shade tolerance, growth rate, etc.), thesespecies are capable of using these strategies in a very diverse manner, depending on theparticular way disturbances affect their populations. The only way to understand thisflexibility is to understand the disturbances and their effects on tree population dynam-ics (i.e., birth and death rates). Careful studies of these disturbances and the ways inwhich they affect population dynamics at both the local and regional scale may lead tosome novel approaches to forest management.


Box 8.4. Options for improving sustainable forest management:

• Recognize that fire and insect disturbances in the boreal forest are part of the ecosystemand cannot and should not be completely excluded. Therefore, these disturbances andtheir effects on tree population dynamics (age-specific mortality, recruitment, cohortstructure) should be taken into consideration in any forest management plans (e.g., in set-ting sustainable harvest levels).

• Recognize that, as a result of such disturbances, many tree populations operate on aregional scale with dispersal and local population extinctions as important processes.Thus, landscape-scale forest management should incorporate these regional-scale popula-tion processes and not focus solely on stand-level stages such as stand initiation, stemexclusion, understory regeneration, and old growth.

• Finally, recognize that an understanding of tree population dynamics at both the local andregional scales indicates that traditional succession arguments do not adequately explainthe forest dynamics. Changes in forest composition and structure over time are a result ofthese population dynamic processes.

Acknowledgements

We gratefully acknowledge funding support to all of the authors from the SustainableForest Management Network and the Natural Sciences and Engineering Research Coun-cil of Canada Discovery Grants Program, and to H.M. and R.J. from the Consortium derecherche sur la forêt boréale commerciale. Many thanks to François Gionest, Pierre-Yves Plourde, and Danielle Laprise for their help with field work and data processing,and Marie Puddister for producing all of the final figures for the chapter.

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