Catastrophic wind damage and subsequent forest recovery are scale-dependent phenomena and both spatial and temporal scales are important in understanding effects of catastrophic winds. As Levin (1992) pointed out, "no single mechanism explains pattern on all scales." Consequently, it is essential to clarify both the spatial and temporal scale over which wind damage and recovery patterns occur and are examined.

Windstorms are often distributed over a broad range of spatial scales, and certain damage effects and recovery patterns can only be observed at a specific spatial scale in the context of specific processes (Foster & Boose, 1992, 1994, 1995, 2000). For example, the geographic and meteorological factors that control the formation and movement of hurricanes can be only be understood on a continental scale (~5000 km), whereas wind velocity, local topography (variation in site exposure), and individual stand attributes are the controlling factors of hurricane damage at the landscape scale (~10 km). At small scales biotic factors become more significant. For example, Peterson (2004) found that within-stand variation in damage can be largely explained in the context of tree size and species. Our study in the Piedmont forests of the southeastern United States also showed that at the stand scale, tree size (i.e., its vertical stratum) and resistance to wind are the most important indicators of mortality probability and damage type during a major hurricane (Xi, 2005; Xi et al., 2008a). It is important to clarify the temporal scale across which the research is conducted and ecological patterns are compared. Recovery time from catastrophic windstorms varies tremendously between forests from a few years to a predicted period of several hundred years, depending on wind intensity and the regeneration capability of the damaged forest. Ecologists often divide windstorm impacts and post-disturbance forest responses into three temporal categories: immediate (a few months to one year, e.g., Walker et al., 1992), short-term (few months to several years, e.g., Vandermeer et al., 2000; Pascarella et al., 2004) and long-term (few decades to centuries, e.g., Hibbs, 1983; Foster, 1988; Burslem et al., 2000). Moreover, forest recovery processes also vary with time. For example, during and immediately after a hurricane, mortality processes dominate, whereas the recruitment process becomes important in the years immediately after the wind damage. Consequently, the timing of surveys of wind-disturbed forests is critical for understanding the damage, mortality and recovery. The predictability of forest damage from catastrophic winds and the subsequent recovery pattern generally is scale-dependant. Although wind conditions are highly variable in all aspects during a windstorm, wind gusts are more random at smaller scales. The predictability of forest damage at the stand scale (~1 km) is, therefore, relatively low due to the random effects of wind gusts and the complex interactions among their neighbour individuals. The larger-scale forest damage patterns and recovery processes (e.g., at landscape and regional scale) can be predicted reasonably well (Fig. 1). For example, forest damage patterns across post-hurricane landscapes are predictable based on wind speeds, topography (site exposure), stand structure, disturbance, and land-use history (e.g., Foster, 1998; Foster & Boose, 1992; DeCoster, 1996; Xi, 2005; Xi et al., 2008a).

Renewable Energy 101

Renewable Energy 101

Renewable energy is energy that is generated from sunlight, rain, tides, geothermal heat and wind. These sources are naturally and constantly replenished, which is why they are deemed as renewable. The usage of renewable energy sources is very important when considering the sustainability of the existing energy usage of the world. While there is currently an abundance of non-renewable energy sources, such as nuclear fuels, these energy sources are depleting. In addition to being a non-renewable supply, the non-renewable energy sources release emissions into the air, which has an adverse effect on the environment.

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