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Why Plants Grow Where They Do

Chris Van de Ven
Geological and Environmental Sciences
Stanford University
June 2001

The goal of my research is to understand why plants grow where they do in the White Mountains in eastern California.  Since each species only grows in a limited range of environmental conditions, I am trying to understand the importance of environmental factors that affect the distribution of major plant species.

The White Mountains are located along the California-Nevada border and lie opposite the Sierra Nevada Mountain Range on the east side of the Owens Valley.  Because the White Mountains lie within the rain shadow of the Sierra Nevada, they are arid and their vegetation is sparse.  The faults bounding the Owens Valley produce dramatic changes in elevation over very short distances:  There are only about twelve miles (about 19 km) between Owens Valley, at 4,000 feet (1,220 m) above sea level, and White Mountain Peak, which is over 14,000 feet (4,270 m). In addition, the White Mountains consist of a variety of rock types, including carbonates, quartzites, and granites.  Due to such diversity, this mountain range makes an excellent location to study topographic and geologic effects on vegetation.

All plants are products of their environments and only grow in those settings to which they are most suited.  The local environments that vegetation inhabits are determined by the local nutrient and microclimatic conditions present. In arid regions, like the White Mountains, topography is the dominant control on microclimate, while the underlying bedrock determines the available nutrients. In order to make a predictive map for each species, I am using field sites where the environmental conditions and the vegetation species are well known to quantify the importance of each environmental variable and determine the breadth of the natural conditions under which each species will grow.

We can begin to comprehend how vegetation might respond to changes in its environment if we understand the conditions under which different plants grow. This research helps to recognize and predict how plants will respond to environmental changes, such as increased temperature or precipitation associated with climate change.  For example, if temperature increases, plants must migrate to higher elevations in order to grow in the same temperatures they previously grew in. As you increase in elevation up a mountain range, the amount of surface area for plants to grow on decreases, and likewise, its ecosystem functions decrease on the landscape scale as well.

The upward migration of plants, however, is more complex than simply shifting their distributions higher up the mountain range.  The dynamics of such climatic shifts, and vegetation responses to those shifts, can be complicated because the environmental conditions governing plants' distributions are determined by numerous interacting environmental parameters. The dominant factor determining the chemical, thermal, and physical properties in the soil is the underlying rock type (lithology), while topography controls the local climatic conditions.  As you go up in elevation, precipitation increases and temperature decreases. Aspect (the direction a slope faces) and slope angle determine how much sunlight strikes the ground (insolation), and therefore modifies the local temperature, making south-facing slopes warmer than north-facing slopes. Topographic position, meaning whether a given location is on a ridge, meadow, slope, or canyon, affects the amount of soil that accumulates and determines how exposed or protected a given location is.  Topographic position and slope angle together influence how much water accumulates in the soil and how quickly the water flows away.  These geologic and topographic factors are the chief determinants of plant distributions.

As examples, this research has confirmed that some species, such as sagebrush (Artemisia tridentata), cover wide ranges of topography. Others, like mountain mahogany (Cercocarpus ledifolius), grow in more limited topographic settings, most often on warm (south-facing) slopes.  Similarly, some species, like rabbitbrush (Chrysothamnus viscidiflorus), are found on nearly any rock type, while others avoid certain rock and soil types.  One example of a substrate-limited species is pinyon pine (Pinus monophylla), which grows on all rock types except nutrient-poor dolomites. If the temperature were to increase, pinyon pines would be forced to migrate to higher elevations over the span of several generations. Dolomite soils lying upslope of their current positions would prohibit the pines' migration and further reduce the available habitat. Once we understand which parameters determine plants' distributions, we can alter those variables to model how the plants would respond.

My research on this project has been underway for a few years. With significant help from a small team of field workers, we have collected field data at over three hundred sites in all major plant communities and environments. At each field site, we determined the percentage of ground covered by each species, by soil and by bedrock, and recorded topographic data.  To determine which (if any) of these environmental parameters are important for each species, I use a type of multivariate analysis called canonical correspondence analysis. Based on the field sites, this method measures each species' distributions relative to the topographic and geologic parameters.  In this way, the relative importance of each environmental variable (elevation, slope angle, slope aspect, topographic position, and geology) is calculated for each species, allowing us to see which of those variables influence each species' distributions.

Once we know the ranges of environments over which each species grows within our field sites, the distributions of the plants can be predicted over the entire mountain range. To map the different species throughout the White Mountains, we use digital elevation models (DEMs) to capture the topographic and geologic variables everywhere within the range.  DEMs are digital topographic maps distributed by the U.S. Geological Survey, where each point, or pixel, has a value that represents the average elevation of a 30 m by 30 m area on the ground. The DEMs were used to calculate slope angle, aspect, and topographic position.

From slope angle and aspect, insolation (the amount of sunlight striking the ground) can also be calculated.  We were able to map the geology with the aid of previously published maps and analyses of aircraft imagery and then, by using the same remotely sensed imagery to estimate the accuracy of vegetation distributions, we were able to map vegetation communities. However, because of the scale of the imagery, only large, dominant vegetation like trees or sagebrush, or generalized vegetation communities, like alpine fell-fields or desert scrub, can be mapped directly from the imagery. Still, to a limited extent, we can use the remotely sensed imagery to evaluate the accuracy of the vegetation distributions predicted using canonical correspondence analysis.  Any discrepancies between the vegetation community maps created from the imagery and the vegetation species maps created using canonical correspondence analysis can be investigated in the field.  In addition, data derived from the imagery can be incorporated into the statistical analyses of the field data to improve the predictions of vegetation distributions.

The remotely sensed imagery is very important for this project because it provides information about what is on the ground at the time the data was taken. We have, for example, imagery acquired during October 1996, which shows that at that time of year most of the vegetation has dried out, except for trees and riparian vegetation along streams or around springs. In addition, we have imagery collected in June 2000, when nearly all vegetation is green and healthy, with the exception of the very highest crest of the range where the snow had just melted.  Because of the seasonal differences, each dataset (one in October and the other in June) provides information on different vegetation communities at different locations.  The differences between the two datasets tell volumes about where types of vegetation are found and how the different communities change from early summer to fall.