Drylands Emergent Plant Pattern

Thesis presentation, 15 Dec 2008:

(apologies for the low-resolution video source.)

slides: 6mb .pdf or online:

Products

Paper: A peer-reviewed submission of this research is high on my to-do list. Really. For now, please enjoy the thesis version:

Thesis: Self-patterning of piñon-juniper woodlands in the American southwest (5mb pdf)

Poster (single analysis step): Hyperspectral Mapping of Water-Limited Vegetation (2.5mb pdf)

Abstract

Plant-scale water processes are increasingly well understood in U.S. drylands, but the links between plant-level dynamics and landscape-level outcomes are not as well established. Local facilitation of the establishment of new individuals by existing vegetation and the patch-scale diversion of surface water are identified as driving the landscape-level phenomenon of emergent self-organization in conspicuously patterned landscapes in semi-arid systems worldwide, and these plant-level mechanisms are well documented in the American southwest. This form of self-patterning, theorized to be associated with climate sensitivity, has not previously been been proposed as an explanation for the observed grouping of individuals into aggregate vegetative patches in U.S. drylands. Using piñon-juniper woodlands in Arizona and New Mexico as a study system, I tested for self-patterning at 5 sites by measuring the spatial correlation of vegetated patch shape complexity with terrain-based estimates of surface water conditions. Maps of vegetated patches were extracted from aerial imagery, and the degree of spatial structure present in vegetation configuration was measured. Hydrological models of surface flow and soil water content were derived from a digital elevation model (DEM), and spatial regression analyses were conducted to test the correlation of vegetation pattern and modeled hydrological character across each site. The measured relationships suggested close linkages between surface water conditions, vegetation pattern, and vegetation density. Key spatial correlations support the presence of self-patterning for sites in Arizona, where low values of the Wetness Index (WI) of surface water flow were associated with high values of Mean Shape Index (MSI) of spatial structure of patches (pseudo-R2 0.67, p0.01). The Relative Stream Power (RSP) index of surface water flow was also spatially correlated with MSI, although in a positive relationship (pseudo-R2 0.67, p0.01). A second measure of spatial pattern, Area Weighted Mean Patch Fractal Dimension (AWMPFD) was also tested with and yielded similar results. These analyses are consistent with the presence of a self-patterning dynamic not previously identified in American semi-arid ecosystems and linked with threshold sensitivity to climate change.

Status

Fall ’07 (complete): Experimental processing of AVIRIS hyperspectral imagery recorded over a candidate study site near Los Alamos, New Mexico.

Summer ’08 (complete): Final study site selection and field work. Initial analysis.

Fall ’08 (complete): Major analysis. Writing and submission of thesis.

Winter ’09+ (theoretically in progress): preparation of manuscript for journal submission.

Photos

Photos from my field research are here.

Research Goals

The ordered growth patterns of woody vegetation in water-limited landscapes is theorized to be the result of plant-to-plant mechanisms, and is demonstrated to be more rain-efficient than random distribution. I plan to create fine-scale maps of real plant distributions using hyper-spectral and photographic airborne imaging of semi-arid study sites in New Mexico and Arizona. I will measure if the level of spatial organization of those plants and their associated vegetation patches is correlated with topographically-derived estimates of surface water flow in ways which are consistent with the hypothesized mechanisms of water-limited self-organization.

Close coupling of remote sensing and modeling to explore individual-scale mechanism using real-world data is rare, and to my knowledge has not been attempted in these climate-sensitive and highly dynamic systems. Integrating modern data-gathering tools with emerging theories and methodologies in ecology and systems dynamics offers new ways of understanding critical ecosystem functions.