Assessing the enhanced vegetation index as a proxy for macrophyte species abundance in a brackish lake in Northern Chilean Patagonia
Bibliographic record
Abstract
Climate change may amplify the effects of human perturbations on lakes. The main goal of this study was to examine the relationship between the Enhanced Vegetation Index (EVI) and the presence of macrophyte species in Budi Lake, a shallow brackish lake in southern Chile. We used EVI as a discriminative index for macrophyte species to study their spatial and temporal dynamics. The EVI’s ability to operate at multiple temporal scales, decadal, annual, seasonal, monthly, and daily, allowed us to identify patterns and correlations between environmental variables and macrophyte abundance. This multiscale approach is essential for understanding ecological or anthropogenic processes influencing lake ecosystems over time. We used a combination of frequency, correlation, and principal component analyses (PCA) and found that macrophyte abundance inferred by the EVI declined from 2000 to 2017, and its relationship with environmental variables varied with the time scale used (i.e., decadal, annual, seasonal, monthly, daily). We suggest that salinity changes from managing lake-ocean connectivity in synergy with environmental variability could drive the dynamics of macrophyte abundance in Budi Lake. Our 2D PCA further revealed that this reconnection event coincides with the years of lowest EVI values (2007 and 2008), highlighting the relationship between brackish intrusion and reduced macrophyte abundance. Our findings provide valuable information about using remote sensing monitoring as a potential methodological approach for assessing macrophyte dynamics in lakes, which may contribute to managing lake ecosystems under global environmental change.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".