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Chapter 7

Multiaged Stocking Control

7.1 Introduction

Stocking control or stocking regulation of multiaged stands is the process of controlling the density, species composition, and sizes of trees through periodic harvest treatments, thinnings, and regeneration treatments. These treatments or operations serve to reallocate growing space within the stand. Stocking control is an integral part of multiaged silviculture because it provides the means for harvest treatments, consistent target stand structures, and provide for long-term sustainability. Alternatively, stocking control is the primary means to transform one structure to another. It also provides the guidance on other structural elements that contribute to stand complexity such as snags, down woody debris, or species composition. Management objectives are an overriding concern in development of strategies for regulation of multiaged stands. Stand structures fluctuate through a range of conditions between treatments as new trees are recruited and as trees expand into available growing space. There is an implied assumption that stocking control should achieve a relatively constant stand structure over time. Multiaged stocking control therefore attempts to develop a strategy for maintaining a range of stand structural conditions that can be maintained over time and meet management objectives. If stands are overcut, the reduced residual growing stock may be less productive yielding less volume over time. If stands are undercut, there is a risk of overstocking, inhibition of regeneration, or encouraging features of a more uniform structure. An additional risk with multiaged stand control is the system may favor less desirable species. For example, a system which maintained a high level of stocking might achieve growth targets but might encourage more shade tolerant species. This chapter presents basic concepts related to multiaged stocking control and provides examples of different types of stocking control tools. 7.2 Stocking Control Concepts

A central goal in multiaged stand management or in the development of a stocking control procedure is to achieve sustainability with the management system. Sustainability in multiaged stands is generally a function of three indicators: 1) maintenance of a continuous production of wood volume or biomass from one cutting cycle to the next; 2) maintenance of a consistent stand structure from one cutting cycle to the next; and 3) development of regeneration to serve as replacement trees for harvested trees or trees lost to mortality (Figure 7.1; O’Hara and Gersonde 2004).

Multiaged stocking control involves allocating growing space to different stand components which can be age classes or cohorts, canopy strata, or species groups (O’Hara and Valappil 1999). It can therefore be viewed as a process of establishing tradeoffs between these different components (Figure 4.3; O’Hara 1998). If an older age class is allocated more growing space, then this would imply a younger cohort has less. Likewise, if a lower canopy stratum is allocated more growing space, then less growing space is available to higher strata. The correct allocation is one that meets objectives and is sustainable. The tradeoffs can be viewed as a series of two-dimensional graphs for stands with two canopy strata in Figure 7.2 (Oliver and O’Hara 2005). Alternatively,

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Martin et al. (2005) developed a system that assessed understory stocking in relation to overstory density, a procedure that quantifies these tradeoffs for partially cut stands. In designing multiaged stands, three critical points include: 1) stand components compete with each other, 2) younger components are necessary to replace older ones, and 3) there is an upper limit on how much growing space is available for allocation to the total stand.

7.2.1 Stocking and growth For even-aged stands, there has been considerable research on the relationship

between periodic increment and stocking. Although these studies typically ignore variations in stand structure that can also affect increment for a given level of stocking (e.g., O’Hara 1989), increment generally increases with stocking. For example, Curtis et al. (1997) found increasing periodic increment in coast Douglas-fir through the entire range of stocking (Figure 7.3). Likewise, Pretzsch (2005), found a similar relationship in European beech (Figure 7.3). The implication from these studies of managed even-aged stands is that greater stocking corresponds to greater leaf area and greater photosynthetic potential. In the studies shown by Curtis et al. (1997), density was held constant to attempt to isolate density effects. The European beech and Norway spruce results shown by Pretzsch (2005) received a variety of thinning treatments. Hence volume growth tends to increase with stand density, but thinning to control density introduces other variables that confound this relationship. Whether similar patterns occur in multiaged stands is uncertain, but, at present, there is no evidence to indicate otherwise. One exception found similar levels of production from different stocking levels in a 57-year study in northern hardwoods in Michigan, USA (Gronewold et al. 2012). An alternate way to view stand productivity is through the relative growth

efficiency or growing space efficiency of trees, stand components, and whole stands. Efficiency is usually expressed as a ratio of increment to a measure of occupied growing space such as crown length, crown projection area, or leaf area. If a stand component is efficient, then it is producing volume increment at a higher level per unit of occupied space. For individual trees in even-aged stands, some studies have found dominant trees were most efficient (e.g., O’Hara 1988) and some have found suppressed trees were more efficient (e.g., Reid et al. 2003). Some of these differences are simply the result of differences in species. In either case, these individual tree relationships suggest that organizing individual trees into stand structures can have a large effect on stand production. However, there is no evidence that these efficiency relationships provide new insights into the basic stand production/stocking relationships shown in Figure 7.3. Growing space efficiency in mixed species stands is more difficult to interpret because species occupy growing space in different ways. Many of these differences relate to shade tolerance or in the way species allocate carbon to different needs. A shade tolerant species will have a greater ratio of leaf area to crown projection area than a less tolerant species. However, shade tolerant species are generally less efficient at converting foliage area to volume or biomass. Efficiencies are typically less for shade tolerant species although their actual growth rates may be similar to less tolerant species. Gersonde and O’Hara (2005) compared tree growing space efficiencies of five species in mixed-conifer forests of California over a range of projected leaf area (Figure 7.4). Species ranking by efficiency corresponded to shade tolerance ranking with ponderosa pine being most efficient. There was some change in ranking at large and small leaf areas

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that may have been due to model fitting. Directly comparing growing space efficiencies for different species is difficult at best, even when they are from the same stand. For multiaged stands, there are also some confounding results related to efficiency. O’Hara (1996) found trends of increasing growing space efficiency with increasing age of ponderosa pine age classes. This implies increasing the proportion of older trees in a multiaged structure would increase stand productivity. In contrast, in multiaged eastern hemlock/red spruce stands, Seymour and Kenefic (2002) found greater efficiency in upper canopy trees with moderate-sized crowns, but efficiency declined with age when crown size (leaf area) was held constant. They attributed the decline in efficiency, in part, to periods of suppression that in theory were experienced at one time by all trees in multiaged stands. Although this suggests structures with a greater allocation of growing space to upper crown class trees will have greater volume production, it also suggests an age-related decline that O’Hara (1996) did not see in ponderosa pine. Both of these studies indicate there are ways to affect stand volume production through the design or allocation of trees within stand structures. In the context of how stocking affects volume production, the ponderosa pine study demonstrated how production increases with increasing stocking but that the stand structure – or the relative sizes of trees and the arrangement of leaf area – was also of great importance. 7.2.2 Stocking and regeneration

Regeneration is a major consideration in multiaged systems. Without consistent, timely and well-placed regeneration, the multiaged system cannot achieve the overriding goal of sustainability. Obtaining timely natural regeneration is somewhat due to chance because natural regeneration may be dependent on inconsistent seed crops, unpredictable weather, or predation by animals and insects. Regeneration will generally occur more commonly with lower stocking because of greater growing space availability (see chapter 8). Additionally, this regeneration will generally grow more rapidly with greater growing space availability. This represents a tradeoff in multiaged stocking control: lower stocking favors regeneration in terms of numbers and growth, but reduces stand volume production.

Artificial regeneration is an important means of supplementing natural regeneration in multiaged stands. Although multiaged silviculture often carries a label of being natural and relying on natural regeneration only, there is no reason to preclude artificial regeneration. Seedlings can be planted to accelerate the process of developing a new cohort and thereby shortening cutting cycle lengths. Seedlings can also be used to increase certain species, genotypes, or provide trees in specific locations in a stand.

Cumulative stocking affects the light environment of the stand and therefore can have a profound effect on the understory environment. Greater stocking will then result in reduced light availability in the understory. For many forest types, this will determine whether regeneration will occur or not. It may also affect which species regenerates, or which species regenerate most successfully, in the understory. Tolerance to shade may be an important determinant of which species are successful in this environment. 7.2.3 Stocking and stand structure Stocking control has traditionally focused on representing stand structure through “horizontal” measures such as through diameter distributions, or stand density measures

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that are usually based on tree diameters (Curtis 1970, West 1983, Ernst and Knapp 1985). Stand structure is more than the cumulative diameters or basal area on a horizontal plane at breast height. It is a three-dimensional system with a physical structure and a variety of ecological functions (Spies 1999). These include the distribution of live tree sizes, as well as the vertical distribution of foliage, horizontal patterns of trees and shrubs, and the presence of standing and down wood. Stand structure also affects volume production through location and efficiency of tree crowns in even-aged (O’Hara 1988, 1989, Long and Smith 1990) and multiaged stands (O’Hara 1996, Kollenberg and O’Hara 1999, Seymour and Kenefic 2002). There have been many approaches to describe measure stand structure beyond traditional stand density measures. These approaches attempt to distinguish between stands of different structure or to describe differences in structural attributes with stands (Pommerening 2002). Many have attempted to subjectively describe stand structure based on stand development processes (Oliver (1981, Oliver and Larson 1996, Carey and Curtis 1996), and others describe the stand structure based on the predominant size of the trees (e.g., seedlings, saplings, etc.; Thomas 1979). Diversity indices (e.g., Magurran 1988) are used to describe size class or species distributions (Sterba and Zingg 2006), but without a spatial component. There have also been many efforts to quantify horizontal and vertical spatial patterns and develop indices to represent structure (Pommerening 2002, Sterba and Zingg 2006, Zenner et al. 2012, Gadow et al. 2012). Although there have been major advances in describing stand structure, there is not yet a method that links stand structure to management beyond simple diameter distributions. 7.2.4 Measuring stand density

Stand density is typically measured in an attempt to quantify the amount of competition or level of growing space occupancy in a stand. A long history of work has advanced stand density measurement in even-aged stands. Stand density is usually expressed either in absolute or in relative terms. Absolute stand density measures are a simple count or sum per unit area. For example, trees per hectare, basal area per acre, volume per hectare. Relative stand density measures are expressions of the ratio, proportion, or percent of absolute stand density to a reference level defined by a standard level of competition (Helms 1998). Relative density measures take an absolute density measure and make it comparable across different stages of stand development. For example, relative stand density measures provide a common scale to compare the competition level of a young radiata pine stand with an older radiata pine. In addition to providing a measure of growing space occupancy that can be compared for stands in different stages of development, relative density measures also provide an index to the maximum potential growing space occupancy. This provides simple measures of relative competition that are usually assumed to be independent of site quality.

Absolute stand density measures are easy to measure, commonly used, and easy to conceptualize. Relative density measures are more difficult to calculate and conceptualize, and, as a result, are used less commonly. The most common relative stand density measures use average tree diameter or basal area as a reference level. For example, stand density index (Reineke 1933), tree area ratio (Chisman and Schumacher 1940), crown competition factor (Krajicek et al. 1961), the central hardwood stock guide (Gingrich 1967), relative density index (Curtis 1982), and others. Curtis (1971) and West

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(1983) found these measures behaved somewhat differently than relative density measures that used volume (e.g., Drew and Flewelling 1977, 1979), or tree height (e.g., Wilson 1979) as a reference level. Overviews of stand density measurement in even-aged stands can be found in Bickford et al. (1957), Ernst and Knapp (1985), Long (1985), and in chapter 7 of Tappeiner et al. (2007). For multiaged stands, both absolute and relative stand density are used to describe competition or total growing space occupancy. Unlike even-aged stands, multiaged stands also need a division of growing space among stand components. This is where stocking control becomes more complicated for multiaged than even-aged stands. Instead of determining an appropriate level of growing space occupancy or a range of competition as in even-aged stands, this growing space must also be divided among multiaged stand components, such as age classes or species. 7.2.5 Density management zones Silviculturists have long sought an optimal stand density for managed stands. An optimal stand density may maximize wood volume production, or another may achieve the best habitat for a particular species, or yet another may provide the most aesthetic stand. Although there are certainly optimal stocking levels or stand densities for many resource objectives, they will generally not be the same for different objectives. Stand density also represents only one aspect of a prescription: another is the stand structure which can vary independent of stand density. For example, a level of basal area could describe a stand with a great diversity or little diversity in trees sizes, or a stand with a few large trees or a stand with many. Regardless of the management objective, optimal stand densities or stand structures change over time as a stand develops. It is impractical, or even unrealistic, to assume a stand could continuously exist at its optimal stocking. Hence stands are managed to stay within a range of conditions (density or structure) that encompasses the optimal condition. For stand density, this is referred to as the density

management zone (DMZ). In addition to encompassing the range of desirable density over time, the size of the DMZ also integrates the time period between treatments. For example, a wider DMZ would correspond to greater time between density adjustments.

A DMZ defines the prescribed range in density within a silvicultural prescription (Figure 7.5). For even-aged stands, it represents the density between the minimum density that might occur after a thinning and the maximum that occurs before a thinning or final harvest. This DMZ is prescribed to meet some management objective within operational constraints. For example, a narrow DMZ would require either very frequent, light thinnings or very slow growth rates Likewise, heavy, infrequent thinnings would require a large DMZ, and a stand with a fast growth rate would avoid frequent thinnings if the DMZ was large. In multiaged stands, the DMZ is defined by the range of stocking between the beginning and end of the cutting cycle for the entire stand (Figure 7.6). At the beginning of the cycle, or immediately after a harvest treatment, stocking is low. It then increases to a maximum immediately before harvest. This pattern would be repeated over each cycle under an idealized system. If competition was causing mortality, stocking might exhibit the opposite pattern if it was measured in trees per unit area rather than using basal area, volume, or a relative stand density measures. Intermediate thinnings between harvest treatments might alter the amount of stocking as would any episodic mortality events

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such as windthrow or insect damage. 7.2.6 Cutting cycle length and cutting severity

The cutting cycle is the time interval between harvest treatments in multiaged stands. It is an important concept with regard to stocking control because a direct relationship exists between cutting cycle length and stocking removals during harvest treatments (Figure 7.7A and B). If a longer period between harvest entries is desired, then the stocking removals at harvest will have to be severe to maintain the stand in the DMZ. Likewise, a short cutting cycle requires more frequent, light volume harvests (Hansen and Nyland 1987, O’Hara and Valappil 1999).

The density management zone (DMZ) also describes an important aspect of the multiaged stocking control regime. The DMZ should encompass the range of density of the entire cutting cycle. However, this can be higher or lower depending on management objectives (Figure 7.7C and D). For example, on a dry site prone to bark beetle problems, a lower DMZ may be more suitable than on a site without these potential problems (Figure 7.7d). Or on a site where regeneration is not problematic, stocking could be maintained in a higher DMZ (Figure 7.7c). In both situations, stocking might fluctuate across similar ranges within a DMZ but the minimum and maximum level of the DMZ may be dramatically different. 7.2.7 Balanced stands and sustainability

A central concept in multiaged silviculture is the balanced stand. The concept of the “balanced stand” appears to have originated with Meyer (1943) and Meyer and Stevenson (1943). They equated a negative exponential diameter distribution to a natural or “virgin” structure capable of maintaining a constant volume over time. Meyer was apparently looking for a standard to insure sustainability and believed this was achieved through maintenance of a constant structure over time and a constant level of cutting that maintained the natural distribution in some sort of equilibrium. Building on de Liocourt’s (1898) work in Europe, Meyer (1943) published diameter distributions of forest-level studies in several locations that followed a negative exponential distribution. He then assumed this forest-level pattern was a natural uneven-aged structure and an appropriate model to guide a sustainable stand structure (O’Hara 1996). Another view of a balanced uneven-aged stand was provided by Smith (1962) who defined the balance as being achieved with each size or age class occupying equal area (Figure 7.8). This interpretation is analogous to area-control forest regulation applied to individual stands (O’Hara 1996). Equal amounts of growing space are allocated to each age class and each age class is assumed to be harvested sequentially thereby maintaining a stable stand structure and stable production of wood over time. These justifications for “balanced” stands were attempts to find a sustainable basis for management. Meyer was trying to justify a sustainable system of regulation for uneven-aged stands and Smith was trying to justify why a negative exponential diameter distribution might be sustainable (O’Hara 1996). Although the intent was to insure sustainability, there have been few examinations of these assumptions. By providing for regeneration and maintaining a target structure over time, many methods meet these basic criteria for sustainability. However, merely being sustainable does not assure that a stocking method provides anything approaching potential productivity or meets other

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objectives. In a simulation analysis of multiaged ponderosa pine, O’Hara (1996) found the negative exponential diameter distributions produced considerably less volume than other structures because so much growing space was allocated to small trees. Stand structures with greater allocations of growing space to larger trees were more productive while also being sustainable. In contrast, Donoso (2005), working with mixed forests in Chile, developed a crown index that suggested balanced structures were productive and provided sufficient regeneration. However, this latter work does not demonstrate that the artificial construct of a balanced structure meets objectives better than other options.

A similar concept is the “equilibrium” stand structure. This equilibrium has been used to describe both a stand where structure and process become relatively constant (e.g., Bormann and Likens 1979), and a target multiaged stand structure that meets some predefined objective (Schütz 2001a). For example, systems in central Europe have used equilibrium diameter distributions where higher stockings will result in reduced regeneration and lower stockings will result in reduced increment (Schütz 2001a). These multiple states of equilibrium would vary by forest type. The equilibrium curve used in European beech dominated sites would have lower density and standing volume than a stand dominated by central European conifers (Schütz 2006).

7.2.8 Diameter distributions

Frequency distributions of tree diameters have a long history of use for multiaged stocking control. These distributions often serve as target structures for multiaged systems. An important early publication was the work of de Liocourt (1898) who documented a general “reverse-J” distribution for forests in France (Kerr 2014). Examples of stocking control tools based either rigidly or with more flexibility, on diameter distributions include Arbogast (1957), Alexander and Edminster (1977), Guldin (1991), Baker et al. (1996), Schütz (2006), and many others. A reverse-J diameter distribution has declining numbers of trees with increasing diameter and may not necessarily follow the more precise negative exponential form. Meyer (1943, 1952) found the negative exponential form in a variety of forest-level studies and promoted it as a target for multiaged structures. It was believed to be a natural structure and, because Meyer observed it in somewhat natural structures, he believed it demonstrated a sustainable structure. The negative exponential diameter distribution has since become synonymous with uneven-aged structures in many circles and the more general reverse-J form is often used as diagnostic criteria for determining age structure (Leak 1964). Although even-aged stands often have normal distributions and multiaged stands have distributions that are reverse-J (see Ford 1975, Harcombe and Marks 1978, Kunisaki and Imada 1996, Leak 2002), there are exceptions. For example, even-aged stands can also form reverse-J diameter distributions (Oliver and Larson 1996).

Stand-level analyses of diameter distributions have revealed great diversity in multiaged stand structures. For example, Janowiak et al. (2008) found a variety of diameter frequency distributions in multiaged northern hardwood stands in Michigan (Figure 7.9). In theory, any of these could be used to develop target stocking control regimes for multiaged stands. Goff and West (1975) reported that mixed northern hardwood/conifer stands formed steady-state diameter distributions with a rotated sigmoid form (Figure 7.9). This was assumed to be due to reduced mortality and greater growth in the middle strata. Similar diameter distributions were seen in uncut beech

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forests in central Europe where a rotated sigmoid form was also common (Westphal et al. 2006). Likewise, others have found the rotated sigmoid form in unmanaged stands (Lorimer and Frelich 1984, Parker et al. 1985, Goodburn and Lorimer 1999). These distributions change over time and are affected by management. For coast Douglas-fir, Zenner (2005) found stands with infrequent disturbances developed rotated sigmoid diameter and then reverse-J distributions as they developed into “old-growth” structures. Stands that experienced a more frequent history of minor disturbance formed reverse-J distributions. Leak (1996) found northern hardwood stands had negative exponential distributions that became rotated sigmoid after partial cutting. In managed northern hardwood stands, Janowiak et al. (2008) found a variety of forms but they tended toward an “increasing-q” form (Figure 7.9). There are also issues of sampling and scale which potentially affect the shape of the diameter distribution (Rubin et al. 2006, Janowiak et al. 2008).

Although there are general trends in the form of diameter distributions of unmanaged stands, a valid question is whether these diameter distribution forms should be the target structures for managed multiaged stands. Managed stands are typically maintained at substantially lower densities than those of unmanaged stands. For example, the upper limit of the density management zone for even-aged stands is often only 50 to 75 percent of the maximum (Drew and Flewelling 1979, Long 1985, Cochran et al. 1994). Multiaged stands would also be managed at similar ranges of density (O’Hara and Valappil 1999). Since natural stands are assumed to be equilibrium structures, it is also assumed that managed stands with similarly shaped diameter distributions are also sustainable although they may be maintained at a substantially lower range of density. The primary evidence to support this is the presence of abundant numbers of small trees in these various reverse-J diameter distributions and operational experience with multiaged stands maintained with these distributions. However, the sustainability of one structure does not, by itself, indicate another structure is unsustainable. Many structures can meet the criteria for sustainability, but may vary greatly in structure, productivity, or appearance (O’Hara 1998).

7.3 Multiaged Stocking Control

Forest managers attempting to manage a multiaged stand without any previous experience and guidance might struggle with the complexities of this difficult management system. Decisions include determining which trees to remove and which to leave, what are the appropriate numbers of trees of each species, age class, size class, or canopy strata to leave, and the length of cutting cycles. As foresters attempted to manage multiaged stands they have looked to a variety of examples for inspiration and guidance. Foremost among these has been the expectation that the multiaged stand – or usually what were referred to as uneven-aged or all-aged stands – occurred commonly in nature and were therefore a logical model for emulation. This is seen in both the description and the theory behind the ecological climax model where by its name it is assumed to be the desirable endpoint of plant community development. Additionally, self-perpetuating tree replacement was assumed to occur continuously thereby forming all-aged stands (Oliver and Larson 1996). As a result, the assumption of all-aged forests with individual tree replacement is an underlying concept in the formation of many early stocking control procedures for multiaged stands.

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A variety of multiaged stocking control systems have been developed and these tend to be representative of the current ecological knowledge at the time of their development and also on the local features of the forests. These stocking control systems sometimes may also include an element of economic justification that provides for sufficient volume removals to cover harvesting costs. But many others are simply attempts to meet objectives of providing for regeneration and a sustainable structure. Other systems exist that have not been documented and are based on local knowledge and experience. 7.3.1 Diameter-limit cutting A common intervention related to multiaged stand culture has been various forms of diameter-limit cutting. In these systems, trees above a given tree diameter at breast height are removed and the residual trees generally unmanaged (Figure 7.10). These diameter-limit treatments were often assumed to be removing older trees in stands where younger trees would then grow into the canopy thereby perpetuating an uneven-aged structure. However, stands were often even-aged and more dominant trees were simply better genotypes or faster-growing species. This removal of bigger trees was also driven by economic justifications rather than purposeful, if misguided, emulation of ecological systems. Hence the determination of what trees to remove, or the diameter limit, was largely based on merchantability. The best trees – or the high-grade trees – that were cut gave rise to the term high grading. As a result, diameter-limit cutting has a poor reputation because it reduces timber quality, and is possibly dysgenic. Kenefic and Nyland (2005) provide additional information on diameter-limit cutting in forests in northeastern North America. In a multiaged stand, diameter-limit cutting that repeatedly removed trees above a given tree diameter or where this diameter limit might be adjusted at each entry could be effective. For example, in a single species stand where distinct age classes are formed and occupy distinct canopy strata, the diameter-limit cutting might closely resemble an individual tree selection cut. Diameter-limit cuttings may also be successful if sufficient quality growing stock was left, but long-term studies are still needed (Sendak et al. 2003). However, by definition diameter-limit cutting does not treat the size classes lower than the diameter limit. Alternatively, in a multiaged stand with many species of variable shade tolerance, the removal of larger trees may discourage shade intolerant species and dramatically alter species composition. Or a diameter-limit cut that was too severe or too frequent might also have undesirable effects on future stand development and structure. In a long-term study on the Penobscot Experimental Forest in Maine, USA, diameter-limit cutting was compared to selection cutting (Kenefic et al. 2005). Both treatments involved three harvest entries over several decades. The appeal of the diameter-limit treatments was apparent through greater volume removals and therefore greater initial economic returns to the landowner. By the third harvest treatment, the value of removed trees was several times greater in the selection treatment, and the diameter-limit treatments had favored the less desirable balsam fir over red spruce. These findings demonstrate both the initial attraction of diameter-limit cutting and how the real effects of these treatments become more apparent over time. Differences in treatments were increasingly apparent resulting in fewer large trees and greater defect in the diameter-limit treatment.

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An alternate form of diameter-limit cutting is to remove trees below a diameter limit. These upper diameter-limit treatments have been used on forests in the western USA on public ownerships where restoration is an objective and where maintaining sufficient numbers of large trees are a dominant management issue. The effect, however, is often to make any form of management infeasible because only small trees can be removed (Stine et al. 2014). 7.3.2 Plenter system In central Europe, The Plenter system has developed since the 1800s to control stocking in multiaged stands. This system attempts to achieve sustainability through development of an equilibrium diameter frequency distribution. This has also been described as a form of “demographic steady state” (Schütz 2006) where ingrowth equals the volume removed by harvesting and mortality. Sustainability is achieved by maintaining a constant stand structure from one cutting cycle to the next so that harvest volume equals increment. The Plenter system may be the oldest and most continuously used system for multiaged stocking control. The system originated in silver fir, Norway spruce, European beech forests of central Europe (Figure 7.11). In theory, the Plenter system uses a negative exponential diameter distribution to develop the desired equilibrium structure. An equilibrium is achieved when standing volume remains constant from one cutting cycle to the next and when growth equals harvest, thus providing for long-term sustainability. In reality, however, countless variations on the Plenter structure may exist in which single tree selection is used for stocking control (Burschel and Huss, 1987). The stand structure objective of a Plenter system consists of trees of all sizes and ages. However, tree age is not considered an important variable because many central European tree species can remain in a state of understory suppression for many years. Early Plenter systems used detailed inventory methods – often a 100% sample – to determine cutting levels and specific trees for cutting. These control or check methods were systems which date back to Gurnaud and Biolley in France and Switzerland (Schütz et al. 2012). An inventory of trees by size classes provided data which were compared to the desired structure to determine surplus trees for cutting. Short cutting cycles were used with a repeat full measurement of the stand at each cutting cycle.

Schütz (2001a) described the Plenter structure as being maintained through continuous control of the growing stock (standing volume) and that a growing stock in excess of the equilibrium will lead to reduced regeneration and recruitment into smaller diameter classes. Levels of growing stock below the equilibrium would reduce total increment and reduce the quality of trees. Schütz (1975) developed an equilibrium stocking relationship for Norway spruce/silver fir that varies from the negative exponential diameter distribution. On linear scales, the equilibrium relationship resembles a negative exponential form, but on logarithmic scales more trees in larger diameter classes are apparent than a negative exponential diameter distribution (Figure 7.12).

Schütz (1997) justified the sigmoid shape (Figure 7.12) as resulting from a non-linear increase in periodic increment with tree size and the disproportionally greater effect on the remaining stock when large diameter trees are removed. Increment in small diameter classes is relatively low and, therefore, is assumed to require a high number of

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stems to produce enough diameter class advancement, causing a steep decline in the diameter distribution in these size classes. As trees grow into larger diameter classes, mortality declines and diameter class advancement increases causing a slower decline in stem number. Increased tree harvest in the larger diameter classes causes a more rapid decline in the distribution. The equilibrium diameter distribution can be calculated from empirical values of diameter growth and harvesting of trees and tree mortality. The equilibrium stand basal area, or the total growing space occupancy, can be derived from growth and survival of regeneration depending on stand density. A family of diameter distributions can be constructed with estimates of corresponding standing volume. However, only a few curves will produce an equilibrium because of the negative effect of increased growing stock on regeneration and ingrowth. Realistic values for stem numbers in the lowest diameter classes are taken from observations in the field together with standing volume and ingrowth. Finally, the form of the diameter distribution is also a function of the management objective in the Plenter forest. Although different amounts of growing stock can be held at equilibrium through periodic inventory and stocking control, they result in varying yields of small, medium and large timber which will influence management decisions by the forest owner (O’Hara and Gersonde 2004).

The development of alternative equilibrium diameter distributions can be applied to other forest types or sites. These alternative diameter distributions may vary in shape due to differences in diameter increment and rates of removal. In silver fir-dominated forests at lower elevations in central Europe, the equilibrium distribution may have a more pronounced sigmoid shape, whereas a more consistently negative slope might be found in high elevation forests because of more uniform diameter increment across all size classes. These differences represent the relative diameter increment across the diameter distribution: equal diameter growth would result in a negative exponential relationship, whereas unequal diameter increment results in the rotated sigmoid form (assuming more rapid diameter increment in larger classes) (Schütz, 1997). This resembles the ‘rotated sigmoid’ curve (see Figure 7.9) described by Goff and West (1975) with a greater number of larger trees than indicated for a negative exponential curve.

The advantage of this stocking control approach is that it has some flexibility in developing target diameter distributions. Removal rates are dependent on growth rates and competition, thereby providing some assurances of sustainability. There is also a long history of its use in central Europe and considerable experience with application of the system. However, there are relatively few examples of its application outside central Europe. As with many stocking approaches for multiaged stands, the target structure or, in this case, the equilibrium diameter distribution, is a compromise or tradeoff between getting regeneration and maximizing wood production. In practice, small-scale variability creates opportunities to meet multiple objectives. In the central European forests where this method was developed, the three primary species – silver fir, European beech, and Norway spruce – are all relatively shade tolerant and can regenerate in somewhat poor light environments (Stancioiu and O’Hara 2006c). In other forest types where desired species include a greater range of shade tolerance, maintaining this equilibrium or balance is more difficult because tolerant species will be favored by the light, frequent cutting treatments. An analysis of long-term plot data from Switzerland for stands managed with the Plenter system concluded the system is still evolving because long-

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term trends in species composition, stocking, and increment were not constant (O’Hara et al. 2007a). The emphasis on a diameter distribution in any stocking control system also presents some limitations: although the target diameter distributions may show some flexibility, in common usage they are continuous distributions with a negative slope. They would be more difficult to apply in stand structures with discontinuous diameter distributions or with distributions that have non-negative slopes between some diameter classes as you might find in a two-aged stand or a stand with many reserve trees (Figure 7.13). 7.3.3 BDq system

The BDq approach is a diameter distribution-based stocking control tool in common usage in North America. Its development originates primarily with Meyer (1943) who brought concepts from Europe related to using negative exponential or reverse-J diameter distributions as target structures for uneven-aged stands. BDq refers to the three parameters of this stocking tool: B for basal area, D for maximum diameter, and q for the q-factor. The q-factor is the diminution quotient for the negative exponential distribution (Figure 7.13). Because of its importance to the BDq approach to stocking control, the method is also known as the q-factor approach (O’Hara and Gersone 2004). The q-factor is the defined as:

1

i

i

n

nq

Where: ni is the number of trees in the ith diameter class and ni+1 is the number of trees in the next larger class. Using 5 cm diameter classes, a q-factor of 1.5 would imply that if one diameter class had 12 trees, the next smaller diameter class would have 18, and the next 27, etc. The q-factor defines the negative slope of the diameter distribution, but because it is based on diameter classes the slope is dependent on the size of the diameter classes. If the diameter class size is increased then the q-factor also must increase to represent an equivalent diameter distribution. This change can be described by a relationship where an increase in the diameter class size by a factor of 2.0 requires the q-factor increase to that power or 2.0. Hence if the q-factor was 1.3 with 5 cm diameter classes, the equivalent q becomes 1.69 with 10 cm diameter classes. This requires that a silvicultural prescription specifies the q-factor and the size of the diameter classes to provide a complete description of the desired stand structure. Q-factors typically vary between 1.0 and 2.0. A q-factor of 1.0 defines a flat diameter distribution with the same number of trees in all diameter classes. A q-factor of 2.0 defines a steep negative exponential distribution with many small trees. The number of trees in a smaller diameter class can be calculated using

)1( j

ji qnn Where: ni is the number of trees in a smaller diameter class, nj is the number of trees in a larger diameter class, and q is the q-factor. For example, a diameter distribution with 12 diameter classes, a q-factor of 1.4, and 10 trees in the largest class, the number of trees in the smallest class (ni) would be:

13

)112(4.110405

For the same situation but a q-factor of 1.2 the number of trees in the smaller class would only be 74 but with a q-factor of 1.6 it would be 1759 trees. This demonstrates the sensitivity of the diameter distribution – and particularly the number of small trees – to the q-factor.

On semi-logarithmic scales, the q-factor defines a straight line. The basal area (B) defines the area under the curve and the maximum diameter (D) defines the intercept on the diameter axis. Mathematically, both of these last two parameters are not necessary, but they make implementation easier. Both the B and D parameters can vary for a given q-factor. For example, a given q-factor can describe stands with a range of basal area levels or a range of different maximum diameters.

For application, the BDq system requires development of a target diameter distribution and an inventory of current stocking. As with the plenter system, the target and actual diameter distributions are compared to identify diameter classes with too many or too few trees. This guides marking of trees for harvest where trees are cut from diameter classes with surplus trees or possibly left to compensate for other diameter classes where tree numbers are deficient (Figure 7.13). Ideally, the diameter distribution will conform to the target negative exponential distribution after cutting. Once a stand is regulated – or conforming to the target negative exponential distribution – the surplus trees in each diameter class would be cut at each cutting cycle. Because they represent the cutting cycle increment, cutting these trees and returning the stand to the target diameter distribution at the end of each cutting cycle assures sustainability. The foundation of the BDq system is the q-factor or negative exponential diameter distribution that defines the stand structure. Unless the q-factor is close to 1.0, it will include many more small than large trees. For this reason, many recent applications of the BDq system have used low q-factors or a segmented diameter distribution where a two or more q-factors are used to describe the structure. For example, one q-factor might apply to the commercial-sized trees and another to smaller, non-commercial trees. 7.3.4 Stand density index allocation

An alternative approach is to allocate relative density values from an even-aged relative density measure to diameter classes in a multiaged stand. The relative density measure serves as the measure of growing space occupancy with this approach. This represents a more complex approach to stocking control than the Plenter system or the BDq system because the density measure, rather than number of trees, is being allocated to diameter classes (O’Hara and Gersonde 2004) .

The central question with this approach is whether the distribution of a relative stand density index by diameter classes provides a better means of allocating growing space than allocating number of trees. This is essentially a comparison of whether a relative density measure is more effective than an absolute density measure for representing competition or growing space occupancy. In principle, the use of a relative density measure would appear to provide the same benefits over an absolute density measure as with even-aged stands. Relative density measures are weighted per unit of tree size and thereby provide an index of density that is more independent of tree size or

14

stage of stand development. For even-aged stands a relative density measure can be used to compare the competition in two stands that might be at very different stages of development. If stocking allocations are being made for multiaged stands by diameter class, then it probably makes little difference whether a relative or absolute density measure is used. But for larger diameter class groups, or alternate separations of stand components, the relative density measure may have advantages.

How much of the relative density should be allocated to each diameter class? Long and Daniel (1990) allocated stand density index (SDI) to groups of diameter classes for ponderosa pine. Their examples showed how a diameter distribution defined with a q-factor resulted in unequal allocation of SDI and basal area (Figure 7.14). This has implications for how we perceive stands to be balanced (Chapter 7.2.7, O’Hara 1996) since, in this example, a stand structure designed with the q-factor and assumed to be balanced produces unequal distributions of growing space occupancy based on basal area and SDI (O’Hara and Gersonde 2004). In even-aged stands SDI is calculated as:

6.1

25

qDNSDI

Where Dq is the quadratic mean diameter of the stand in cm, and N is the number of trees per hectare. However, in multiaged stands, or any stand with a non-normal diameter distribution, an additive equation for individual stand components is recommended. Long and Daniel (1990) proposed:

6.1

25

i

iC

DNSDI

Where SDIC is the stand density index of a single component, Ni is the number of trees in the component, and Di is the midpoint of the ith tree in the component in cm. In current usage, diameter classes, or groups of diameter classes, are the most common component. However, similar methods can be applied to other stand components. There is some question about the importance of the additive approach to SDI calculation. Ducey (2009) found that the equations produced divergent values in some structures or when the diameter distribution was truncated to exclude smaller trees. Other references on using SDI in complex stands include Ducey and Larson (2003), Woodall et al. (2003), Zeide (2005), and Ducey and Valentine (2008). 7.3.5 Leaf area allocation Leaf area is another representation of occupied growing space that can be used to guide stocking in multiaged stands (O’Hara and Gersonde 2004). Leaf area is the surface area of a tree or plant’s foliage and is expressed either as the amount of one-sided or projected surface area, or as all-sided surface area. Leaf area index (LAI) is the sum of all the upper or all-side leaf surface areas per unit of ground area below the canopy. Because it is normally expressed as m2 leaf area per unit of ground area in m2, it is unitless.

15

LAI is a useful measure of growing space occupancy because it generally increases during early stand development in even-aged stands and attains a maximum that is related to site quality (Vose et al. 1994, Margolis et al. 1995). For example, on a high productivity site, a higher LAI would be attained than on a low quality site. Similar limits apply to the maximum attainable LAI in multiaged stands (O’Hara and Gersonde 2004).

This approach has been described as a “first principles” approach to stocking control (O’Hara and Valappil 1999) because LAI involves a more direct representation of growing space occupancy. LAI could represent the opportunity to increase stocking on higher quality sites that can support greater LAI, or the need to adjust opening size due to higher light quality on poorer sites that support less LAI. This approach also allocates growing space to canopy strata, age classes, or species rather than diameter classes. For example, a ponderosa pine guide uses age classes or cohorts (Figure 7.15) and a Norway spruce/ Scots pine guide uses canopy strata and species (Figure 7.16). When growing space is allocated to a stand component (cohort, strata, species, etc), it affects both the amount of growing space left for other components and also their increment (e.g. Figures 4.3 and 7.2). Leaf area is also a relatively efficient variable for predicting tree growth. The leaf area allocation approach therefore provides some predictions of these tradeoffs.

The user designs the desired stand structure by setting a maximum total level of growing space occupancy or LAI, the number of components, and how the LAI is allocated among the components. Maximum LAI can be estimated through plot data, previous research studies, or remote sensing. A spreadsheet model called the “Multiaged Stocking Assessment Model (MASAM) was used to assess different stocking allocations. In the ponderosa pine example in Figure 7.15, LAI was set at 6.0, and four cohorts were assigned variable amounts of growing space and numbers of trees. The model does not provide the user with a diameter frequency distribution; instead estimates of a variety of details about the structure being designed under ‘Diagnostic Information’. This information is based on age classes or canopy strata so the number of these components varies with the target stand structure. The diagnostic information includes information at the beginning (BCC) and end of each cutting cycle (ECC) for LAI, basal area and SDI. End-of-cutting cycle values are provided for stand increment and average tree vigor.

The model is constructed so that the oldest age class, or cohort, is removed at the end of each cutting cycle. The second oldest cohort becomes the oldest and a new cohort is regenerated. If canopy strata are used instead of cohorts, the model assumes the tallest stratum is removed at the end of the cutting cycle and the second-tallest becomes the tallest stratum during the next cutting cycle. Cutting cycles are assumed to be repeated over time; however, the user can change the stocking parameters to design a series of consecutive structures that might be useful for transforming a stand from even-aged to a stand with two or more age classes.

An advantage of this approach is that it can easily be used to design a diversity of stand structures. For example, a user is not restricted to a particular shape of diameter distribution. Figure 7.17 shows a two-cohort ponderosa pine stand that is similar to the example in Figure 7.15 except that fewer cohorts are retained. The model can also be adjusted to retain some trees as reserves or to guide restoration treatments (O’Hara et al. 2003). In this case, the normal assumption that the oldest cohort/largest canopy strata is removed at the end of each cutting cycle is modified to retain these trees.

16

7.3.6 Stocking control and group selection Similar stocking control concepts also apply to stands managed with group selection methods (Chapter 6), but with certain qualifications. Group selection essentially expands the spatial scale of removals so that rather than leaving individual tree opening, larger groups of trees are removed (Figure 7.18). Single tree and group selection blend together at intermediate opening sizes. Group selection stands are typically controlled using area-control forest regulation concepts from even-aged forest management. Within a stand, the area treated is the same for each cutting cycle. Each group is typically managed over a rotation as though it were an even-aged stand. The stand size divided by the number of cutting cycles in a rotation to get the area treated each cutting cycle. Or conversely, the cutting cycle can be determined by dividing the rotation by the number of cutting cycles. The diameter distribution of a stand managed under the group selection system may follow a reverse-J diameter distribution because of decreasing numbers of trees in older groups. Group selection units may have more constant levels of a relative density index or similar amounts of leaf area index, regardless of group age. However, the density of trees in groups can be managed to follow any trajectory as they develop. For example, a well-managed stand might have a diameter distribution that approaches a flat, straight line if density were closely controlled and mortality factors were minimal. Likewise, relative density or LAI could be distributed in different patterns depending on the density management limitations and other objectives. Flexibility is important in stocking regimes for group selection systems as it is for single tree selection systems.

7.3.7 “Free” and other alternative approaches to stocking control Silviculture is evolving to accommodate an increasingly diverse set of

objectives, including many that attempt to increase stand and broad-scale diversity in stand structure. Many current stocking tools assume a traditional approach of being focused on stand averages, thereby ignoring variability in stand structure. It is also difficult to integrate the diversity of stand level elements of structure into a single stocking tool. For example, a multiaged stocking prescription may be concerned with species composition, standing and down wood, spatial arrangements of elements, as well as the traditional stocking parameters of numbers of trees and size classes. This is a difficult set of parameters to include in a general stocking guide, as well as a specific stand-level prescription. An alternative approach is to give flexibility to the manager to develop a prescription or mark a stand based on the structural features of that stand. A very general stand structure may be recommended within broad guidelines or constraints, but that does not have specific structural requirements. The manager could then integrate existing structural features, such as the presence of large trees, or site variation into design of the stand structure. These “free approaches” are therefore an alternative that do not utilize the specific stocking parameters of many current approaches. Examples include the free selection approaches of Graham and Jain (2005), Mount (2010), and Bončina (2011).

There are many other stocking control approaches for multiaged stands beyond those described here. Some of these have been discussed in the scientific literature, others have not and may only be known regionally. Examples of more formal approaches

17

include the ‘volume/guiding diameter limit’ (VGDL) approach that was used for decades on the Crossett study in southern pines (Reynolds et al. 1984; Baker et al. 1996, Guldin 2011). VGDL sets harvest volume equal to growth over a cutting cycle and uses a flexible upper diameter limit to guide tree selections towards minimum residual stand volume. Hallin’s (1959) ‘unit area control’ treated homogeneous areas within a stand as harvest areas thereby perpetuating an uneven-aged structure through a form of group selection Box 6.1). Seydack’s (1995, 2012The ‘maturity selection’ and “improvement selection systems for ponderosa pine blended financial and biological maturity of individual trees to make tree selections (Munger et al., 1936, Pearson 1942, O’Hara et al. 2010). Two other regional approaches in North America include the long-term selection work in Pioneer Forest (Box 7.1) and the Stoddard-Neel approach (Box 7.2) in the midwest, and southeastern USA. 7.4 Synthesis

Stocking control in multiaged stands is an exercise in allocation of growing space to a finite number of stand components. The basis for dividing growing space among stand components can be to achieve objectives related to sustainability, maximization of timber production, creation of wildlife habitat, or many others. How this growing space is allocated will vary with the objective, the forest type, the site characteristics, and the existing stand conditions. As a result, many different procedures have been developed. Some are commonly known and are described in the scientific literature. Others are local stocking control procedures that are less known. Because stocking control is such an important part of multiaged silviculture, these tools occupy a central place in the management of multiaged stands.

In this current era of managing for multiple resource values over large scales, there are advantages to stocking control tools that are flexible to a range of objectives and that are meaningful at multiple spatial scales. But there are justifiable reasons to use any method. The Plenter and BDq methods have a long history of use and considerable experience in Europe and North America. Diameter-limit cutting is a means of maximizing short-term benefits but with risky long-term effects. SDI builds on a strong foundation of even-aged density management theory. The leaf area allocation approach provides strong links to other ecosystem values and flexibility to a range of target stand structures. Many other systems are based on local experience that are far less well-known, but meet objectives and may have been in use long enough to demonstrate sustainability. Approaches termed “free” provide broad guidelines or constraints for managers to design and implement stand structures that are unique to the local conditions and that integrate existing structural feature in the prescription or marking. Ultimately, the question of which stocking control system to use depends on objectives, ease-of use, availability of developed systems, and local experience. In an emerging era when structural heterogeneity is sought at multiple scales for a diverse suite of objectives, stocking control in multiaged stands may be evolving to provide a much greater degree of discretion to the manager.

18

Figure 7.1. Harvest treatment in plenter forest in the Black Forest of Germany.

19

Figure 7.2. Multiple possible tradeoffs between simple two-aged or two-strata stands. Where the stand structure produces greater volume, the upper line shows a peak at an intermediate level (figure A) , and where total volume is reduced, the total volume is lowest at an intermediate level (figure I). All tradeoffs are possible, but these only represent those from two-aged stands: with additional age classes, the possible effects on volume production are much greater (from Oliver and O’Hara 2005).

20

Figure 7.3. Volume increment-density relationships. The Douglas-fir diagram (left) shows the effects of controlling density on volume increment (from Curtis et al. 1997). The European beech figure (right) shows periodic annual increment (RPAI) and stand density (SSDI) for different site indices (SI) in a series of thinning trials (from Pretzsch 2005).

21

Figure 7.4. Patterns of growth efficiency with increasing projected leaf area in five conifer species in California, USA. PP=ponderosa pine, SP=sugar pine, DF=coast Douglas-fir, WF=white fir, IC=incense-cedar (from Gersonde and O’Hara 2005).

22

Figure 7.5. Density management zones (DMZ) for even-aged and multiaged stands in terms of leaf area index (LAI). In these hypothetical examples, the even-aged DMZ fluctuates from about 30 to 60 percent LAI and the multiaged stand from about 35 to 60%.

23

Figure 7.6. Density management zone (DMZ) for a multiaged stand showing multiple cutting cycles fluctuating between the upper and lower limit of the DMZ.

Time

2.0 2.5 3.0 3.5 4.0

Gro

win

g s

pace o

ccupancy

0.6

0.8

1.0

1.2

1.4

1.6

1.8

2.0

lower limit of DMZ

upper limit of DMZ

24

Figure 7.7. Density management zones (DMZs) and cutting cycles are interrelated. When the cutting cycle is long, the DMZ must be large (B), and a short cutting cycle requires a smaller DMZ (A) (from O’Hara and Valappil 1999).

25

Figure 7.8. Equal growing space for each age class has been one interpretation of a “balanced stand”. In this figure, each age or size occupies equal area which requires that younger/smaller classes have many more trees that older/larger size classes (from Smith et al. 1997).

26

Figure 7.9. Examples of diameter frequency distributions on both linear and semi-logarithmic scales (from Janowiak et al. 2008).

27

Figure 7.10. Diameter distributions showing possible effects of a strict diameter-limit cutting. Diameter-limit cuts have traditional been applied in a wide variety of stand structures including those resembling even-aged stands (left) and more complex multiaged stands (right).

Tree diameter

Num

ber

of tr

ees/a

rea

cut treesuncut trees

Tree diameter

Num

ber

of tr

ees/a

rea

cut treesuncut trees

28

Figure 7.11. Mixed stand of silver fir, Norway spruce, and European beech stand in Switzerland.

29

Diameter (cm)

20 40 60 80

Tre

es/h

a

1

10

100

European beech

Silver fir/Norway spruce

Figure 7.12. Equilibrium stand structures for plenter system European beech stand and a plenter system silver fir/Norway spruce stand (adapted from Schutz 2006).

Diameter (cm)

0 10 20 30 40 50

Num

ber

of tr

ees/a

rea

0

50

100

150

200

250

Figure 7.13. Negative exponential curve shown as “target structure” and the “actual structure” showing a stand that might be treated to conform to the target structure.

30

Figure 7.14. Stand density index (SDI), basal area in m2 (BA), and trees/ha (Diam. Distr.) for a ponderosa pine stand. This structure shows equal SDI for the three larger diameter class groups, but less for the smallest diameter class group. Note that basal area and SDI are not distributed equally. The trees/ha follow a q-factor that ranges from 1.15 to 1.42 (adapted from O’Hara and Gersonde 2004).

DBH Class (cm)

0 10 20 30 40 50 60

Ba

sa

l A

rea

(m

2/h

ecta

re)

0

2

4

6

8

Nu

mb

er

of T

ree

s a

nd

SD

I

0

50

100

150

200

250

SDI

BA

Diam. Distr.

31

Figure 7.15. Example of stocking control in a multiaged ponderosa pine stand with four age classes or cohorts using the leaf area approach. A linear increase in trees per acre with younger cohorts is used, but leaf area is allocated with a linear decline with younger cohorts. Estimated production from this structure is 3.4 m3/ha/yr (from O’Hara et al. 2003).

TOTAL Leaf Area Index (LAI) 6

Cohort 1 Cohort 2 Cohort 3 Cohort 4 TOTAL

Number of Trees/Cohort/Hectare 46 61 76 91 274

Percent of LAI/Cohort 40 30 20 10 100

Cohort 1 Cohort 2 Cohort 3 Cohort 4 TOTAL

Leaf Area Index/Cohort ECC 2.4 1.8 1.2 0.6 6.0

Leaf Area Index/Cohort BCC 1.4 1.0 0.5 2.8

Leaf Area/Tree (m 2̂) ECC 521.7 295.1 157.9 65.9

BA/Cohort (m 2̂/ha) ECC 12.1 8.3 5.1 2.5 28.1

BA/Cohort (m 2̂/ac) BCC 6.3 4.1 2.1 12.5

Avg. Vol. Increment/Tree (m 3̂/yr) ECC 0.05 0.02 0.02 0.00

Avg. Vol. Increment/CC (m 3̂/ha/yr) 1.5 1.0 0.7 0.1 3.4

Quadratic Mean DBH/Cohort (cm) ECC 51.2 37.0 26.0 16.6

Tree Vigor (cm 3̂/m 2̂/yr) 77.4 80.6 71.2 46.4

Stand Density Index ECC 145.0 114.2 80.8 47.4 387.5

Stand Density Index BCC 86.1 64.9 39.6 190.6

Ponderosa pine MASAM - MONTANA

USER-SPECIFIED VARIABLES

DIAGNOSTIC INFORMATION

32

Figure 7.16. Depiction of mixed Norway spruce/Scots pine stand in southern Finland with stocking controlled through allocation of leaf area. Norway spruce are shown with longer crowns than Scots pine. LAI totals 1.9 with 1.2 being Scots pine. Basal area totaled 30.5 m2/ha and periodic annual increment 9.5 m3/ha/yr (adapted from O’Hara et al. 2001).

33

Figure 7.17. Example of ponderosa pine restoration using the leaf area approach. A large proportion of growing space allocated to the oldest age class or stratum helps meet the restoration goal of a presettlement type of multiaged stand structure, but does not follow the conventional definitions of balanced stands (from O’Hara et al. 2003).

TOTAL Leaf Area Index (LAI) 6

Cohort 1 Cohort 2 Cohort 3 Cohort 4 TOTAL

Number of Trees/Cohort/Hectare 50 12 25 40 127

Percent of LAI/Cohort 90 7 2 1 100

Cohort 1 Cohort 2 Cohort 3 Cohort 4 TOTAL

Leaf Area Index/Cohort ECC 5.4 0.4 0.1 0.1 6.0

Leaf Area Index/Cohort BCC 1.8 0.1 0.0 1.8

Leaf Area/Tree (m 2̂) ECC 1000.0 350.0 48.0 15.0

BA/Cohort (m 2̂/ha) ECC 26.9 2.1 0.4 0.1 29.5

BA/Cohort (m 2̂/ac) BCC 8.6 0.2 0.1 8.9

Avg. Vol. Increment/Tree (m 3̂/yr) ECC 0.13 0.02 0.00 0.00

Avg. Vol. Increment/CC (m 3̂/ha/yr) 3.9 0.2 0.1 0.0 4.2

Quadratic Mean DBH/Cohort (cm) ECC 73.4 41.5 12.7 5.7

Tree Vigor (cm 3̂/m 2̂/yr) 99.410 76.925 68.914 48.700

Stand Density Index ECC 280.0 27.0 8.4 3.8 319.3

Stand Density Index BCC 112.5 4.0 2.4 118.9

Ponderosa pine MASAM - MONTANA

USER-SPECIFIED VARIABLES

DIAGNOSTIC INFORMATION

34

Figure 7.18. Group selection in mixed-conifer forests in the Sierra Nevada, California. A new group is established in the foreground and an approximately 15-year-old group in the middle.

35

Box 7.1 Single Tree Selection in North American Central Hardwoods at Pioneer

Forest

The central hardwood region of the United States includes large areas of mixed hardwoods growing on moderate to low productivity sites. Both tree species and biodiversity can be high in these systems. Various oaks, particularly white oak, are often the most valuable or desired species. The Pioneer Forest in southern Missouri is a 65,000 ha forest managed with a single tree selection approach since the 1950s. The history of Pioneer Forest and its management approach have been described by Iffrig et al. (2004), and Guldin et al. (2008).

The Pioneer Forest was acquired in the 1950s in a somewhat degraded state. One objective was therefore to build volume or growing stock, maintain the more valuable oaks, and produce some income. The single tree selection method that evolved over time on Pioneer Forest removed approximately 40% of the volume on cutting cycles that ranged from 15-25 years. Individual tree selections generally removed 30-37 merchantable trees/ha (12-15 trees/ac) based on tree age, species, tree quality, presence of insects or pathogens, and spatial arrangements of trees and tree crowns. These factors may vary in importance depending on local site conditions or management history. Tree markers look at every tree and leave the healthiest trees on the site that have the best potential for future development on that site (Iffrig et al. 2004). Although species vary in value, there are no competitive species that are not valuable.

The stocking control procedure at Pioneer Forest therefore is not well-suited to quantification or development of detailed guidelines because it is sensitive to highly variable site and stand conditions. The marking on one site may favor one set of species while the marking on another may favor a different suite because of site variation, existing tree species composition, or possibly for other objectives such as leaving mast-producing trees for wildlife (Iffrig et al. 2004). Resultant structures also do not form consistent diameter distributions at the stand- or section-level (Loewenstein 2000).

After decades of practicing the same general approach to single tree selection, Pioneer Forest has seen a steady increase in standing commercial volume along with periodic volume removals. Figure 7.19 shows an increase of 184% in commercial volume for all species and positive trends for individual species or species groups. For example, the approach has resulted in an increase in white oak – one of the more important species – of 340% over the 40-year period represented in Figure 7.20. The Pioneer Forest approach has been in place sufficiently long to demonstrate: flexibility to accommodate a variety of site conditions and an increasing inventory over time, a sustainability of species composition, production of wood products, and the capacity to also guide sustainability for longer term, and restoration towards a suite of economic, social and economic benefits.

The Pioneer Forest system is successful without having any standard marking protocol or preset stocking standard. Instead, it relies on the ability of managers to consistently mark trees in sufficient numbers to produce viable sales of timber. It demonstrates the importance of manager experience to understand the local forest type and also how providing managers with the flexibility to make decisions with minimal guidance can succeed. However, this works especially well in this forest type where natural regeneration is reliable and all species have some value.

36

Figure 7.19. Recently marked mixed pine/oak stand on Pioneer Forest. White arrows show trees marked for removal.

37

Year

1960 1970 1980 1990 2000

Com

merc

ial volu

me (

m3/h

a)

0

10

20

30

40

50

total

red oakshortleafpine

white oak

Figure 7.20. Commercial volume from 1957-1997 from Pioneer Forest inventory plots. Board foot volumes/acre were converted to m3/ha using a factor of 1 board foot/ac - 0.0125 m3/ha. Original data from Iffrig et al. (2004) included only broadleaved trees greater than 3.9 cm and pines greater than 3.2 cm dbh.

38

Box 7.2 Stoddard-Neel Approach in Longleaf Pine

Longleaf pine is a very fire-dependent species that occurs in the southeastern United States. It was once common throughout this region, primarily on the coastal plain, but it is currently found only on a small proportion of its original range. Fire return intervals in longleaf pine stands were extremely short, sometimes as low as a year or two. Longleaf pine is fire resistant and also initiates through a “grass stage” that is also very fire dependent. Stands with several age classes present and open stand structures with great sight distances are common when stands are managed to emulate these natural forests (Figure 7.21).

The “Stoddard-Neel Approach” has been developed to maintain these structures and the extremely high plant and animal diversity found in these ecosystems. The application of this approach in southern Georgia has been described by Mitchell et al. (2006), Jack et al. (2006a), Moser (2006), and Neel et al. (2010). A strong impetus for management in this part of Georgia is bobwhite quail which are a desired game species and common in frequently burned longleaf pine landscapes. The Stoddard-Neel approach manages for quail habitat, maintenance of biodiversity, and aesthetic qualities in longleaf pine stands in addition to timber values. Prescribed burning is used very frequently because it encourages the understory flora that the quail require while also discouraging the mid-story broadleaved vegetation. Trees are often removed in patches that favor regeneration. But overstory density of longleaf pine interacts with fire by providing the primary fuel – pine needles – necessary to carry a fire. Hence quail habitat requires the prescribed burning, and the burning requires the longleaf pine in a suitable overstory density. The ecosystem benefits include extremely high native species diversity, regeneration, and control of the broadleaved trees that will invade these sites.

Burning schedules are flexible to accommodate the needs of management which can include maintenance of the understory plants required by the quail, avoiding accumulations of fuels, or controlling the development of broadleaved trees. A short fire return interval of several years will result in cooler fires than a longer interval but each will have different effects that may be desired in some cases and not in others.

The Stoddard-Neel Approach emphasizes the residual structure rather than the trees or volume removed. Neel et al. (2010) describe a strong philosophy towards building growing stock and value in the woods: harvesting may remove as little as 50% or as much as 90% of growth in a cutting cycle (Moser 2006, Neel et al. 2010). Increment may also be low: Jack et al. (2006b) presented an example of 50 years of the Stoddard-Neel Approach that produced only about 0.5 m3/ha/yr. There are no residual basal area targets or targets for diameter distributions. A tree marker has to be capable of evaluating a tree’s current value, potential value, and its contribution to the diversity of the system. Hence some good trees will be left for future value and some poor trees will be left for their aesthetic or diversity value. Regeneration is encouraged by creating gap openings which may be expanded in future markings, and typically these canopy openings are located to release advance regeneration rather than to establish new age classes. This approach demonstrates the importance of frequent burning to provide control of competing broad leaved oaks and other trees. With frequent burning and no measured residual stocking targets, the Stoddard-Neel Approach is flexible to provide regeneration and sustainability through continued development of new age classes. However, with

39

little quantification or “formula” for this method, it is difficult to understand for persons unfamiliar with longleaf pine forests, and very difficult to train new tree markers.

Figure 7.21. Multiaged longleaf pine stand in Georgia, USA managed with frequent prescribed burning.