Bibliographic record
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, p<0.01). 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, p<0.01). 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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".