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Modeling the Effects of Fire on the Long-Term Dynamics and
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Restoration of Yellow Pine and Oak Forests in the Southern
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Appalachian Mountains
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Charles W. Lafon1, John D. Waldron2, David M. Cairns1, Maria D. Tchakerian3,
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Robert N. Coulson3, Kier D. Klepzig4
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77843, USA
Department of Geography, Texas A&M University, 3147 TAMU, College Station, TX
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FL 32547, USA
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University, 2475 TAMU, College Station, TX 77843, USA
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71360, USA
Department of Environmental Studies, University of West Florida, Fort Walton Beach,
Knowledge Engineering Laboratory, Department of Entomology, Texas A&M
USDA Forest Service, Southern Research Station, 2500 Shreveport Hwy., Pineville, LA
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Abstract
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We use LANDIS, a model of forest disturbance and succession, to simulate
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successional dynamics and restoration of forests in the southern Appalachian Mountains.
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In particular, we focus on the consequences of two contrasting disturbance regimes – fire
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exclusion versus frequent burning – for the yellow pine and oak forests that occupy dry
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mountain slopes and ridgetops. These ecosystems are a conservation priority, and
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declines in their abundance have stimulated considerable interest in the use of fire for
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ecosystem restoration.
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Under fire exclusion, the abundance of yellow pines is projected to decrease, even
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on the driest sites (ridgetops, south- and west-facing slopes). Hardwoods and white pine
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replace the yellow pines. In contrast, frequent burning promotes high levels of Table
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Mountain pine and pitch pine on the driest sites, and reduces the abundance of less fire-
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tolerant species. Our simulations also imply that fire maintains open woodland
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conditions, rather than closed-canopy forest. With respect to oaks, fire exclusion is
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beneficial on the driest sites because it permits oaks to replace the pines. On moister sites
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(north- and east-facing slopes), however, fire exclusion leads to a diverse mix of oaks and
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other species, whereas frequent burning favors chestnut oak and white oak dominance.
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Our results suggest that reintroducing fire may help restore decadent pine and oak stands
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in the southern Appalachian Mountains.
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Key words: disturbance, fire, forest restoration, simulation, succession
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Introduction
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Historic changes in the disturbance regimes of eastern North American landscapes
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have greatly modified the composition and structure of forest ecosystems. Cultural
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disturbances associated with forestry, agriculture, and urbanization have created forest
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landscapes that differ strongly from conditions prior to European settlement (Foster et al.,
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1998; Abrams, 2003). At the same time, suppression activities have greatly reduced the
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frequency of fire, which formerly was a pervasive disturbance integral to the functioning
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of many ecosystems (Pyne, 1982; Abrams, 1992). The removal of fire permitted the
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successional replacement of fire-dependent vegetation by species intolerant of fire, and
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also favored the development of dense stands of stressed trees that are vulnerable to
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insect infestation and disease (Schowalter et al., 1981; Coulson and Wunneburger, 2000).
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The impacts (ecological, economic, and social) of these changes have served as the
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impetus for research on forest restoration approaches that foster conditions in which the
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disturbances operate within the historic range of amplitude, frequency, and duration
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(Frelich, 2002; Mitchell et al., 2002; Palik et al., 2002).
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Of particular interest to many resource managers is the use of fire as a restoration
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tool, especially in forests dominated by Pinus L., subgenus Diploxylon Koehne (yellow
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pine) and Quercus L. (oak) (Pyne 1982; Haines and Busby 2001; Palik et al. 2002; van
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Lear and Brose 2002). These forests are hypothesized to depend on periodic burning for
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their long-term maintenance (Abrams 1992; Agee 1998; Williams 1998; Wade et al.
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2000; Abrams 2003). Most pine and oak species are intolerant of shade and appear to
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thrive best in open stands maintained by fire. They also are more fire-tolerant than their
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associates, and were favored in the regime of frequent surface fires that historically
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characterized many landscapes in eastern North America. Fire exclusion, in concert with
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insects, disease, and other natural disturbances, has contributed to recent, widespread
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declines in the abundance of yellow pine and oak. The declines have prompted concern
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about the long-term maintenance of these species, because they are among the most
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valuable trees in North America for wildlife habitat, timber production, and biodiversity
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conservation. Reversing these declines may require the reintroduction of frequent
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burning similar to the pre-suppression fire regime (SAMAB 1996; Harrod et al. 1998;
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Williams 1998; Dey 2002; Palik et al. 2002).
In the southern Appalachian Mountains, a considerable proportion of the
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landscape is under federal ownership, and resource managers are using fire to restore
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yellow pine and oak forests on these lands (SAMAB 1996; Elliott et al. 1999; Waldrop
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and Brose 1999; Welch et al. 2000; Hubbard et al. 2004). Oak forests are the
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predominant land cover type, occupying xeric, subxeric, and submesic sites on ridgetops
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and dry slopes (Stephenson et al., 1993; SAMAB, 1996). These are among the most
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extensive oak forests in North America (McWilliams et al., 2002). Yellow pine stands
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are less extensive but nonetheless comprise the second most widely distributed forest
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type in the region (approximately 15% of the forest cover) (SAMAB, 1996). They
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generally are confined to ridgetops and southwest-facing slopes, the driest sites on the
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landscape (Whittaker, 1956; Stephenson et al., 1993). One species, Pinus pungens Lamb.
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(Table Mountain pine), is endemic to the Appalachian Mountains and is a species of
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concern for land managers (SAMAB 1996; Williams 1998).
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In the past, burning by Native Americans, European settlers, and lightning-set
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fires was widespread in the Appalachian Mountains and likely promoted oak and pine
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(Harmon et al. 1983; van Lear and Waldrop 1989; Delcourt and Delcourt 1997; Delcourt
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and Delcourt 1998). Paleoecological analyses of sediment charcoal and pollen reveal that
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fires were common on southern Appalachian landscapes during the last 3000–4000 years,
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and that oak, chestnut, and pine were the dominant tree species (Delcourt and Delcourt
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1997; Delcourt and Delcourt 1998). Delcourt and Delcourt (1997, 1998, 2000) argued
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that burning, particularly on dry upper slopes and ridgetops, was a major factor
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contributing to the dominance of these species. More detailed records of fire history have
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been constructed for the past 150–400 years using dendroecological techniques (Harmon
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1982; Sutherland et al. 1995; Shumway et al. 2001; Armbrister 2002; Shuler and
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McClain 2003). These studies suggest that surface fires burned at intervals of about 5–15
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years in pine and oak forests of the southern and central Appalachians. Occasionally,
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more intense, stand-replacing fires also occurred (Sutherland et al. 1995). The fire
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history analyses also reveal a sharp decline in fire frequency during the mid-1900s. This
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change was a consequence of efforts to exclude fire from the forests.
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Recent work demonstrates that the abundance of more shade-tolerant, and less
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fire-tolerant, species has increased in xerophytic pine- and oak-dominated stands of the
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Appalachians during the era of fire exclusion, and suggests that successional replacement
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of pine and oak may be occurring (Harmon 1984; Williams and Johnson 1990; Abrams
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1992; Harrod et al. 1998; Williams 1998; Harrod et al. 2000; Shumway et al. 2001; Lafon
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and Kutac 2003). Acer rubrum L.(red maple), Nyssa sylvatica Marsh.(black gum), Pinus
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strobus L., (eastern white pine, a subgenus Haploxylon Koehne pine), and Tsuga
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canadensis (L.) Carr.(eastern hemlock) are among the species becoming more abundant
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on xeric sites in the southern Appalachians. At the same time, regeneration of yellow
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pine and oak appears to be declining. These trends suggest that in the continued absence
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of fire, pine and oak stands will be replaced by more mesophytic vegetation, although the
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rates and specific directions of change will vary spatially and temporally. Oaks
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themselves are among the potential replacing species in the more xerophytic yellow pine
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forests (Williams and Johnson 1990; Williams 1998; Welch et al. 2000). Storms,
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droughts, and native and exotic insects and diseases likely will accelerate these
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successional trends (Schowalter et al. 1981; McGee 1984; Fajvan and Wood 1996; Lafon
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and Kutac 2003; Waldron et al, in press).
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Assessing the potential consequences of different disturbance regimes, such as
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burning versus fire exclusion, for long-term forest dynamics is difficult because of the
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long lifespan of the trees. Simulation modeling provides a useful tool for exploring long-
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term forest dynamics. In this paper, we apply LANDIS, a computer model that simulates
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disturbance and succession on forested landscapes (He et al., 1996; Mladenoff et al.,
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1996; He and Mladenoff, 1999a, 1999b; He et al., 1999a, 1999b; Mladenoff and He,
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1999), to the simulation of forest dynamics in the southern Appalachian Mountains,
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USA. LANDIS originally was developed for the Great Lakes region of North America
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(Mladenoff 2004), but has been adapted for use in other locations, including the Ozark
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Plateau (Shifley et al., 1998; Shifley et al., 2000), the southern California foothills
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(Franklin et al,. 2001; Franklin, 2002; Syphard and Franklin, 2004), northeastern China
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(He et al., 2002; Xu et al,. 2004), Fennoscandia (Pennanen and Kuuluvainen, 2002),
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Quebec (Pennanen et al., 2004), and the Georgia Piedmont (Wimberly, 2004). Our work
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extends the application of LANDIS to the floristically diverse and environmentally
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heterogeneous landscape of the Appalachian Mountains.
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Southern Appalachian forests are affected by various agents of natural and
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anthropogenic disturbance, in addition to fire. LANDIS is designed to be able to simulate
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multiple disturbances. However, in this study we focus solely on fire because it is
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thought to be the key disturbance process in pine- and oak- dominated forests (SAMAB,
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1996; Williams, 1998; Dey, 2002; Lafon and Kutac, 2003), and because of the
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widespread interest in using fire for ecosystem restoration. Simulation modeling is
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employed frequently to evaluate the role of a specific disturbance process independent of
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the influences of other disturbances (e.g., Le Guerrier et al., 2003; Hickler et al., 2004;
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Lafon, 2004; Sturtevant et al., 2004). Simulating the role of fire will establish the
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template onto which other disturbances can be imposed. The work reported in this paper
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is a step within a larger effort that will use LANDIS to assess the influences of fire,
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Dendroctonus frontalis Zimmermann (southern pine beetle), and other disturbances (e.g.,
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Adelges tsugae Annand (hemlock wooly adelgid), Adelges piceae Ratzeburg (balsam
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wooly adelgid), Phytophthora ramorum Werres, de Cock & Man in’t Veld. (sudden oak
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death disease)) on the spatial and temporal dynamics of forests on southern Appalachian
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landscapes, and to investigate the implications of restoration efforts.
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The landscape simulated in this study is an idealized landscape that captures the
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predominant physical gradients (elevation and moisture) that influence vegetation
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distribution in the southern Appalachian Mountains (Whittaker, 1956). Such idealized
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landscapes commonly are used in simulation modeling studies to facilitate the
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straightforward interpretation of model projections (e.g., Mladenoff and He, 1999;
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Pennanen et al, 2004; Syphard and Franklin, 2004; Waldron et al., in press). An idealized
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landscape is useful for this initial application of LANDIS to our study area, because we
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seek to elucidate successional dynamics on the individual site types (“landtypes” in
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LANDIS parlance), without the influences of spatial complexities. Understanding
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projected successional patterns on this simple landscape will inform our interpretation of
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subsequent modeling investigations using the same landtypes in more complex
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arrangements. The subsequent analyses will explore specifically the implications of
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landscape structure for vegetation patterns and for disturbance dynamics such as southern
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pine beetle infestations and the spread of fires.
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Methods
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Study area
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The southern Appalachians region is a mountainous area with a humid,
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continental climate (Bailey 1978). Temperature and precipitation exhibit pronounced
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fine-scale spatial patterns because of the mountainous terrain. Oak forests are the
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predominant land cover type, occupying xeric, subxeric, and submesic sites (Stephenson
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et al. 1993; SAMAB 1996). Because of their topographic complexity, however,
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Appalachian landscapes contain a variety of community types. These range from
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mesophytic hemlock-hardwood forests on the moist valley floors, to yellow pine
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woodlands on ridgetops; and from temperate deciduous forests in the low elevations to
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Picea Dietr.-Abies Mill.( spruce-fir) stands on the high summits (Whittaker 1956;
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Stephenson et al. 1993). The landscape we simulate is based on Great Smoky Mountains
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National Park (35°35' N, 83°25' W), in which most major ecosystems of the southern
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Appalachians are represented, and for which the general topographic distribution of
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communities and tree species has been described (Whittaker 1956). For this paper, we
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focus our discussion on the dry, pine- and oak-covered sites only.
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Model description
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LANDIS 4.0 operates on a raster-based landscape in which the presence or
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absence of 10-year age classes of each tree species is simulated for each cell. Succession
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on each cell is influenced by dispersal, shade-tolerance, and the suitability of the habitat
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for each tree species. With respect to habitat suitability, the landscape can be divided
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into a series of “landtypes,” each of which represents different conditions of topography,
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elevation, soil, and/or climate. For each landtype, an establishment coefficient between 0
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and 1 is assigned to each species to govern the relative growth capability of the species
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on that site (He and Mladenoff, 1999b).
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LANDIS 4.0 permits the simulation of disturbance by fire, wind, harvesting, and
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biological agents such as insects and disease (Sturtevant et al., 2004). Fire ignition,
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initiation, and spread are stochastic processes (Yang et al., 2004). The probability that a
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fire will initiate and spread becomes higher as time since last fire increases. Fire spreads
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until it reaches a pre-defined maximum possible size or encounters a fire break (e.g., a
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recently burned patch) (Yang et al., 2004). Different fire regimes can be defined within a
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single landscape by assigning different fire parameters (e.g., ignition density, frequency,
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intensity) to different landtypes. Low-intensity fires kill only the most fire-sensitive trees
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(young trees and/or fire-intolerant species), while fires of higher intensity kill larger trees
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and more fire-tolerant species (He and Mladenoff, 1999b). Because burning is simulated
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as a stochastic process, fire interval varies temporally, fluctuating around the mean for
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each landtype. These variations in fire interval also lead to temporal variability in fire
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intensity, which is greater after a long fire-free interval than after a shorter interval with
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minimal time for fuel to accumulate. In the absence of disturbance, mortality occurs only
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when a tree cohort approaches the maximum age for the species.
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Detailed sensitivity analyses of the LANDIS model have been conducted
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(Mladenoff and He, 1999; Syphard and Franklin, 2004; Wimberly, 2004; Xu et al., 2004),
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and indicate that model projections are relatively insensitive to differences in fire size,
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species establishment coefficient, habitat (landtype) heterogeneity, and initial forest
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conditions. Model results are moderately sensitive to variations in the fire return interval
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and the level of spatial aggregation (i.e., model performance declines with increasing cell
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size), and are especially sensitive to differences in seed dispersal.
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Model application
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We used LANDIS 4.0 to simulate forest dynamics over a 1000-year period on a
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120-ha idealized landscape. The landscape was a 100- × 120-cell grid with a cell size of
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10 m × 10 m, the smallest cell size permitted. Using this small cell size allowed us to
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operate at approximately the scale of the individual canopy tree, following the logic of
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gap models (cf. Botkin, 1993). The landscape was divided into 18 rectangles, each
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representing an individual landtype. The arrangement of the 18 landtypes follows the
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mosaic chart used by Whittaker (1956) to depict the elevation and moisture gradients on
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the Great Smoky Mountains landscape. The landtypes are arranged in three rows of six
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rectangles. The three rows represent different elevation zones, with elevation increasing
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from the bottom row to the top. The elevation zones are low (400–915 m), middle (916–
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1370 m), and high (1371–2025 m). The six rectangles in each row represent different
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topographic moisture classes. Moisture availability increases from right to left, as
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follows: (1) ridges and peaks (hereafter “ridgetops”); (2) slopes facing southeast, south,
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southwest, or west (hereafter “south- and west-facing slopes”); (3) slopes facing
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northwest, north, northeast, or east (hereafter “north- and east-facing slopes”); (4)
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sheltered slopes; (5) flats, draws, and ravines; and (6) coves and canyons. Elevation also
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influences moisture availability, hence, for example, a low-elevation ridgetop would have
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drier conditions than a mid-elevation ridgetop. Although the simulated landscape
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incorporates the full range of environments in the Great Smoky Mountains, our interest in
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this paper is only on the successional patterns for ridgetops, south- and west-facing
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slopes, and north- and east-facing slopes at low and middle elevations.
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Thirty tree species (the maximum allowable in LANDIS 4.0) were used in the
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simulations (Table 1). We selected these species based on their importance in
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Whittaker’s (1956) study of vegetation in the Great Smoky Mountains. The 30-species
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limit necessitated the exclusion of some minor tree species from the simulations, but did
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not constrain our ability to characterize the general successional dynamics of the major
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tree species. Also, because of the focus on montane vegetation, some of the species that
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are common on the nearby lowlands (e.g., Pinus echinata Mill. (shortleaf pine)) were
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absent from Whittaker’s dataset and were not represented in our simulations.
We based the species parameters listed in Table 1 on Burns and Honkala (1990),
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which contains an extensive array of life-history data for North American trees, and
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which has served as the basis for a number of previous forest modeling studies (e.g.,
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Lafon, 2004; Sturtevant et al., 2004; Wimberly, 2004). Identical dispersal capabilities
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were assigned to all species (a likelihood of 0.95 that seeds will disperse within 30 m, and
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a likelihood of 0.05 that seeds will disperse between 30–50 m) (Waldron et al., in press).
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The assignment of identical dispersal attributes minimized the effect of this parameter,
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which was not of primary interest for our study, in order to simplify the interpretation of
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successional patterns.
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For the establishment coefficient parameter for each species, we consulted data
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about the spatial distributions of tree species along the elevation and moisture gradients
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in the Great Smoky Mountains (Whittaker, 1956). We sought to incorporate into the
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establishment coefficient some of the constraints on tree growth that are hypothesized to
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control the spatial and temporal dynamics of vegetation along moisture gradients (Smith
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and Huston, 1989). Specifically, lower establishment coefficients were assigned to
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drought- or shade-tolerant species than to the less tolerant species to account for tradeoffs
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between the ability to grow rapidly and the ability to tolerate low resource levels.
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Consequently, although our establishment coefficients permit drought-tolerant species to
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grow on moist landtypes, they are not competitive with the mesophytic species
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encountered there. Shade-tolerant species are not permitted to inhabit the driest
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landtypes, consistent with tradeoffs between drought- and shade-tolerance (Smith and
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Huston 1989), and with the observed pattern of tree distribution (Whittaker, 1956).
Initially, a single species was assigned to each cell on the landscape. The number
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of cells inhabited by each species was based on its relative abundance in the landtype
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(Figure 1), as inferred from Whittaker (1956). We distributed each species randomly to
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the appropriate number of cells within each landtype.
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We conducted simulations for two disturbance scenarios: (1) fire exclusion (no
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burning) and (2) restoring fire at a frequency approximating the pre-suppression fire
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regime. For the burning scenario, a target fire return interval for each landtype was
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identified from published work on the fire regimes that characterized Appalachian
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landscapes prior to fire exclusion. Dendroecological data about past fire return intervals
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are available for xeric sites (south-, southwest-, and west-facing slopes) in the southern
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and central Appalachians (Harmon, 1982; Sutherland et al., 1995; Shumway et al., 2001;
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Armbrister, 2002; Shuler and McClain, 2003), and are useful for guiding the selection of
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input parameters for LANDIS. We derived fire return intervals for mesic sites from
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Wade et al. (2000). We calibrated the return interval for each landtype by adjusting fire
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parameters until the mean return interval for ten 1000-year simulations was within 10%
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of the target interval (cf. Wimberly, 2004). The target return interval was 10 years for
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ridgetops and south- and west-facing slopes, and 20 years for north- and east-facing
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slopes. The moister landtypes had return intervals of 200–1000 years. Rates of fuel
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accumulation, and hence fire severity, also varied across the simulated landscape, with
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the highest levels on xeric sites (He and Mladenoff, 1999b). The fire disturbances
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imposed in this study are not intended to replicate actual fire size or the patterns of fire
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spread with respect to landscape structure. Rather, our focus is on applying fire to each
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landtype at an appropriate frequency in order to evaluate the influence of fire on forest
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succession at individual landtypes.
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Results
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Under contemporary conditions pines dominate the ridgetops at both low and
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middle elevations (Figure 1). LANDIS simulations in the absence of fire suggest that this
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is not sustainable. In middle elevation pine forests, oak species become more important
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over time (Figure 2A). At low elevations pines remain abundant on the landscape;
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however, Pinus virginiana Mill. (Virginia pine) and P. rigida Mill. (pitch pine) are
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replaced by white pine (Figure 2C). Quercus prinus L. (chestnut oak) also becomes a
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dominant species in the absence of fire.
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When fire occurs within the LANDIS simulations, yellow pine-dominated stands
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persist on the ridgetops in both elevation zones (Figure 2B, D). Many of the cells do not
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have trees (Figure 3A, D). These open woodland conditions contrast with the continuous
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forest cover that develops under fire exclusion.
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The mid-elevation south- and west-facing slopes show dominance by chestnut
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oak, Quercus alba L. (white oak), and Q. rubra L. (northern red oak) under conditions of
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fire exclusion (Figure 4A). Black gum displays a steady rise in abundance over the
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course of succession, ultimately becoming a dominant species. The yellow pines decline,
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while the abundance of many of the minor species remains relatively stable throughout
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the simulation. On low-elevation south- and west-facing slopes, yellow pines also
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decline, while chestnut oak and white pine increase to become the dominant species
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(Figure 4C). As in the middle elevations, black gum expands, albeit more slowly.
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When fire is allowed to occur on the landscape, the south- and west-facing slopes
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at middle elevations retain considerably different species, with chestnut oak, white oak,
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and Table Mountain pine dominating the forest (Figure 4B). For the lower elevation
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sites, pitch pine and chestnut oak are the dominant species when fire occurs on the
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landscape, and Virginia pine declines (Figure 4D). Burning maintains open stands on the
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low-elevation site (Figure 3E).
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Forests on north- and east-facing slopes are dominated initially by chestnut oak,
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red maple, Oxydendron arboretum (L.) DC. (sourwood), and northern red oak (Figure
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1A, D). When fire is excluded from the landscape, these land types retain high species
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diversity (Figure 5A, C). At middle elevations, white oak, chestnut oak, and red maple
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continue to be abundant across the landscape. However, some fire-intolerant mesophytic
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species steadily increase in abundance throughout the simulation. Eastern hemlock and
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Tilia heterophylla Vent.(white basswood) show this pattern. Hemlock is particularly
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important in this regard because it shifts from a rare species in this land type to the most
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abundant species by the end of the simulation. Dramatic changes in species abundance
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occur at low elevations as well. Chestnut oak begins as a dominant species and increases
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in abundance, while white pine and black gum exhibit strong, steady rises over the course
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of succession. Burning results in dominance of the north- and east-facing slopes by
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chestnut oak and white oak (Figure 5B, D), and maintains conditions that are slightly
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more open than under fire exclusion (Figure 3C, F).
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Discussion
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Fire promotes yellow pine and oak dominance on ridgetops and dry slopes of the
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simulated landscape. The pines are especially dependent on fire. Even on dry ridgetops,
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pines do not maintain dominance without fire, and they virtually disappear from south-
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and west-facing slopes. Under the burning scenario, however, they persist at high levels
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on both the ridgetops and the south- and west-facing slopes.
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With respect to the individual species of yellow pine, Table Mountain pine and
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pitch pine fare well under the burning regime we impose, but Virginia pine declines. The
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species is less fire-tolerant than the other two yellow pines, and consequently frequent
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burning reduces its abundance. In fact, burning can be used to eliminate Virginia pine
16
1
from mixed pine stands (Wade et al., 2000). Virginia pine is a relatively short-lived
2
pioneer species that apparently thrives in a regime of less frequent, but more intense, fire
3
(Iverson et al., 1999; Wade et al., 2000). Its abundance in Whittaker’s (1956) dataset,
4
and hence in our input file, may reflect (1) a history of intense, stand-replacing fires on
5
some of the low-elevation ridgetops in the Smokies or (2) establishment of the species in
6
abandoned pastures (Pyle, 1988). Our study does not consider either of these disturbance
7
regimes. In any case, the endemic Table Mountain pine of the middle elevations is of
8
greater concern for resource managers, and the fire regime we impose seems appropriate
9
for maintaining that species.
10
The consequences of burning versus fire exclusion are mixed for oaks. Fire
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exclusion favors oak on the driest sites, which otherwise would be dominated by yellow
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pine. This result matches previous suggestions that fire exclusion in the Appalachians
13
promotes the successional replacement of yellow pines by oaks (Williams and Johnson,
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1990; Williams, 1998; Welch et al., 2000). On moister sites, however, the oaks seem to
15
benefit from burning, because it reduces the abundance of competing species that are less
16
fire-tolerant. Chestnut oak and white oak are the most fire-tolerant oak species, and
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thrive under a regime of frequent burning. These species often dominate forests on
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moderately dry sites in the Appalachians, and did so historically as well (Whittaker,
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1956; Stephenson et al., 1993; Abrams, 2003); our results suggest that their importance is
20
largely a consequence of periodic burning, without which a diverse mix of mesophytic
21
and xerophytic species would develop.
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The negative influence of disturbance on the species diversity of dry sites is
2
consistent with empirical observations in the southern Appalachian Mountains (Harrod et
3
al., 1998), and it also agrees with ecological theory. In particular, the dynamic
4
equilibrium model of Huston (1979, 1994) predicts that in the absence of disturbance,
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species diversity will be high on dry sites because of the slow growth rates of vegetation
6
(and hence relatively low rates of competitive displacement). Frequent disturbances
7
reduce diversity in dry environments because the low growth rates prevent the
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populations of some species from recovering between successive disturbance events.
9
Such patterns of diversity have been simulated using individual-based gap models of
10
forest succession (Smith and Huston, 1989; Huston, 1994). Our results demonstrate that
11
LANDIS can generate a similar pattern, and imply that LANDIS is capable of
12
incorporating vegetation processes (e.g., interspecific competition, life-history tradeoffs)
13
in a manner sufficient to simulate diversity dynamics that agree with ecological theory
14
pertinent to biodiversity conservation and ecosystem restoration.
15
In our simulations, fire exclusion favors northern red oak, which becomes one of
16
the dominant species at middle elevations in the absence of burning. This trend reflects
17
that (1) northern red oak is more fire-sensitive than chestnut oak and white oak and (2) it
18
has a relatively high establishment coefficient. These results are consistent with an
19
expansion of northern red oak observed in oak forests throughout the eastern U.S. as a
20
consequence of reduced fire activity and also more frequent canopy disturbances (e.g.,
21
cutting, chestnut blight) (Stephenson et al., 1993; Abrams, 2003). However, the
22
simulations may overestimate the increase in northern red oak on the driest, pine-
18
1
dominated sites, where conditions may become too stressful for the species during
2
periodic extreme drought events.
3
Our results suggest that frequent burning creates open woodland conditions,
4
rather than continuous closed-canopy forest, on xeric sites. Such open-canopy
5
woodlands may have been typical on xeric sites in the southern Appalachian Mountains
6
prior to fire exclusion (Delcourt and Delcourt, 1998; Harrod et al., 2000). Currently these
7
open conditions are a restoration target for forest managers in the region (e.g., USDA
8
Forest Service, 2004a, 2004b). Under fire exclusion, our simulations imply that denser
9
forests with a more continuous canopy develop. Indeed, such conditions have arisen on
10
southern Appalachian landscapes. The work of Harrod et al. (1998) suggests that canopy
11
tree density nearly tripled during four decades without fire or other anthropogenic
12
disturbances on xeric sites in the Great Smoky Mountains. The recent southern pine
13
beetle outbreak that devastated yellow pine stands throughout the region likely was a
14
consequence of this change in vegetative structure and underscores the need for restoring
15
these ecosystems to a more sustainable condition.
16
Other model projections also correspond with the successional changes occurring
17
as a result of fire exclusion in the Appalachian Mountains. In particular, white pine,
18
black gum, red maple, and eastern hemlock are favored under the fire exclusion scenario,
19
precisely the pattern observed in field studies in the region (Williams and Johnson, 1990;
20
Harrod et al., 1998; Shumway et al., 2001). Our results suggest that the successional
21
trends inferred from these field studies will continue in the future and result in
22
pronounced shifts in tree species composition. One of the most dramatic changes
19
1
projected is the gradual expansion of hemlock throughout the wettest landtype considered
2
here (north- and east-facing slope at middle elevations). The likelihood of this slow-
3
growing, shade-tolerant species actually attaining dominance on a subxeric site may be
4
low, even without fire, because of other disturbances (timber harvest, windstorms,
5
droughts, ice storms, and hemlock wooly adelgid) not considered in this paper.
6
7
Conclusions
8
9
As a spatially explicit model capable of simulating vegetation dynamics across
10
entire landscapes, LANDIS lacks detail with respect to mechanisms (e.g., individual tree
11
growth, tree life-history tradeoffs) that help drive forest succession (Mladenoff, 2004).
12
Nonetheless, the model appears to account for such processes in a fashion that is
13
adequate for representing successional dynamics on a southern Appalachian landscape,
14
and that is also able to generate results consistent with biodiversity theory.
15
The model projections in this paper underscore the critical role of fire in
16
xerophytic forests of the Appalachian Mountains, where burning appears to be necessary
17
for maintaining yellow pine and oak dominance. Simulation modeling augments field
18
studies of Appalachian fire ecology by providing a means to explore long-term vegetation
19
dynamics, and by permitting the examination of a single disturbance agent under
20
controlled conditions. Elucidating LANDIS projections for these simple scenarios of
21
burning versus fire exclusion is an important step in simulating Appalachian forest
22
dynamics under multiple-disturbance scenarios, in which fire is a key process. It is also
20
1
important because burning is one of the primary management tools for restoring
2
xerophytic forests.
3
We anticipate that simulating multiple disturbance agents on more complex
4
landscapes will yield greater realism in some respects, for example, the issue of hemlock
5
dominance noted above. Biotic disturbances (herbivory, disease) may amplify the
6
successional changes identified under the no burning scenario in this paper. This is
7
because an increase in tree density under fire exclusion likely would exacerbate the
8
extent and severity of biotic disturbances (Schowalter et al., 1981; Savage, 1997), leading
9
to more precipitous declines in the dominant pines (Paine et al., 1984; Paine et al., 1985;
10
Coulson et al., 1998) and oaks, and to more rapid rates of successional replacement.
11
Recently, LANDIS has been extended to simulate biotic disturbances (Sturtevant et al.,
12
2004); incorporating southern pine beetle outbreaks and other biotic disturbances will
13
enable us to investigate potential consequences of disturbance interactions and restoration
14
efforts.
15
16
17
Implications for Practice

Despite simplifying assumptions, LANDIS can represent ecological processes
18
that lead to results consistent with ecological theory and field studies of
19
vegetation change in the southern Appalachian Mountains. The simulations
20
imply that ongoing vegetation changes linked to fire exclusion will contribute to
21
long-term declines in pine and oak abundance.
21
1

2
3
4
Reintroducing fire appears to be necessary if pine and oak stands are to be
restored and maintained in the southern Appalachian Mountains.

Reintroducing fire likely will restore more open stand conditions similar to those
thought to exist prior to fire exclusion.
5
6
Acknowledgements
7
8
This research was supported through USDA Forest Service Southern Research
9
Station cooperative agreement SRS03-CA-11330129-168. Portions of the work were
10
supported by a grant from the National Science Foundation to DMC (BCS-9808989).
11
Special thanks go to David Loftis, Henry McNab, Tom Waldrop, and Jim Vose for their
12
input at various stages of this project. We also are grateful to Brian Sturtevant and Rob
13
Scheller for providing us with the LANDIS program and aiding in its use. Thanks to Jae
14
Yu for his programming assistance.
15
22
1
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1
Table 1
2
Species abbreviations and life history parameters
Species
Longevity
Maturity
Shade
Fire
Vegetative
abbreviation
(years)
(years)
tolerance
tolerance
reproduction
Abies fraseri (Pursh) Poir.
abfr
150
70
5
1
0
Acer rubrum L.
acru
150
55
3
1
0.4
Acer saccharum Marsh.
acsa
200
60
4
1
0.2
Aesculus octandra Marsh.
aeoc
200
60
4
2
0.1
Betula allegheniensis Britt.
beal
300
70
2
2
0.1
Betula lenta L.
bele
200
45
2
2
0.1
Carya glabra (Mill.) Sweet
cagl
300
75
2
2
0.3
Carya tomentosa (Poir.) Nutt.
cato
200
40
2
2
0.4
Fagus grandifolia Ehrh.
fagr
300
60
5
1
0.3
Fraxinus americana L.
fram
200
55
3
1
0.3
Halesia carolina L.
haca
100
60
4
2
0.2
Liriodendron tulipifera L.
litu
300
45
2
1
0.3
Magnolia acuminata L.
maac
150
55
3
2
0.4
Magnolia fraseri Walt.
mafr
70
55
3
1
0.2
Nyssa sylvatica Marsh.
nysy
200
55
3
3
0.3
Oxydendrum arboreum (L.) DC.
oxar
100
55
3
3
0.4
Picea rubens Sarg.
piru
400
70
5
1
0
Pinus pungens Lamb.
pipu
250
35
1
5
0
Pinus rigida Mill.
piri
200
35
1
5
0.2
Pinus strobus L.
pist
400
30
2
3
0
Pinus virginiana Mill.
pivi
100
35
1
4
0
Prunus serotina Ehrh.
prse
200
30
1
1
0.4
Species
34
Quercus alba L.
qual
450
50
3
4
0.3
Quercus coccinea Muenchh.
quco
130
50
1
3
0.4
Quercus prinus L.
qupr
350
55
3
4
0.4
Quercus rubra L.
quru
300
50
2
3
0.4
Quercus velutina Lam.
quve
150
40
2
3
0.3
Robinia pseudoacacia L.
rops
100
15
1
1
0.4
Tilia heterophylla Vent.
tihe
250
60
4
2
0.4
Tsuga canadensis (L.) Carr.
tsca
450
70
5
1
0
1
Maturity: age of sexual maturity; Shade tolerance: between 1-5 (intolerant to tolerant);
2
Fire tolerance: between 1-5 (intolerant to tolerant); Vegetative reproduction: probability
3
of vegetative reproduction following mortality of a parent cohort on a cell
4
35
1
Figure Captions
2
3
Figure 1. Number of cells initially occupied by each species as a percentage of the six
4
land types. A) Middle elevation, north- and east-facing slopes, B) Middle elevation,
5
south- and west-facing slopes, C) Middle elevation ridgetops, D) Low elevation, north-
6
and east-facing slopes, E) Low elevation, south- and west-facing slopes, F) Low
7
elevation ridgetops. Species abbreviations are provided in Table 1.
8
9
Figure 2. LANDIS simulation results for ridgetop sites. Results are presented for the
10
middle elevation range (A & B) and the low elevation sites (C & D). Fire exclusion
11
conditions are shown on the left (A & C) and results from simulations with fire are shown
12
on the right (B & D). Only species that occur on more than 10% of the landscape at any
13
time during the simulation are shown. Species abbreviations are provided in Table 1.
14
15
Figure 3. Proportion of empty cells over time for each simulated landscape type for
16
simulations with (solid lines) and without (dotted lines) fire. A) Middle elevation
17
ridgetops, B) Middle elevation south- and west-facing slopes , C) Middle elevation north-
18
and east-facing slopes, D) Low elevation ridgetops, E) Low elevation south- and west-
19
facing slopes, F) Low elevation north- and east-facing slopes.
20
21
Figure 4. LANDIS simulation results for south- and west-facing slopes. Results are
22
presented for the middle elevation range (A & B) and the low elevation sites (C & D).
36
1
Fire exclusion conditions are shown on the left (A & C) and results from simulations with
2
fire are shown on the right (B & D). Only species that occur on more than 10% of the
3
landscape at any time during the simulation are shown. Species abbreviations are
4
provided in Table 1.
5
6
Figure 5. LANDIS simulation results for north- and east-facing slopes. Results are
7
presented for the middle elevation range (A & B) and the low elevation sites (C & D).
8
Fire exclusion conditions are shown on the left (A & C) and results from simulations with
9
fire are shown on the right (B & D). Only species that occur on more than 10% of the
10
landscape at any time during the simulation are shown. Species abbreviations are
11
provided in Table 1.
12
37
1
Figure 1
2
3
38
1
Figure 2
2
3
39
1
Figure 3
2
3
40
1
Figure 4
2
3
41
1
Figure 5
2
42