Productivity of riparian <i>Populus</i> forests: Satellite assessment along a prairie river with an environmental flow regime
Bibliographic record
Abstract
Abstract In semiarid regions, the growth and survival of cottonwoods (riparian Populus species) depend on river water supplementing the limited precipitation. Indicators of growth and productivity are needed to assess how altered streamflow regimes on regulated rivers impact cottonwood trees and the riparian forest ecosystems they support. We used satellite imagery from the Landsat program to make historical (1984–2020) assessments of ecosystem productivity in a riparian cottonwood forest along a regulated prairie river in southern Alberta, Canada, with an environmental flow regime that increased the minimum flows implemented in 1993. A version of the near‐infrared reflectance of vegetation scaled with incoming sunlight (NIRvP) was calculated from Landsat images to provide a proxy for primary production. Near‐infrared reflectance of vegetation scaled with incoming sunlight was correlated strongly with gross primary production measurements from eddy covariance and basal area increment measurements from tree ring analyses, supporting its use as a practical proxy. The lowest NIRvP values occurred in drought years with low flows and dry weather during the growing season, while the highest values occurred in wet years with high flows, including floods. Across all years, NIRvP was positively correlated with streamflow and a weather‐driven soil moisture index. This indicated that ecosystem productivity was limited by water supply, which is sourced from river water and local precipitation. Subsequently, cottonwood forests in this region would be vulnerable to drought from declines in streamflow, due to climatic variations or human water withdrawals, and reductions in shallow soil moisture from limited local precipitation. This satellite proxy should be broadly applicable and can provide a diagnostic indicator for assessing riparian forest health and responses to varying weather, changing climate, and streamflow regulation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.002 | 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 teacher head, 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".