An ecosystem resilience index that integrates measures of vegetation function, structure, and composition
Why this work is in the frame
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Bibliographic record
Abstract
As ecosystem disturbances increase due to human induced global change, accurately quantifying ecosystem resilience has never been more critical. This study introduces a spatially explicit Ecosystem Resilience Index (ERI), that integrates vegetation function, structure, and composition recovery metrics. We provide proof-of-concept for this index by applying it to a wildfire in northwestern Montana by leveraging novel and existing remote sensing datasets to evaluate ecosystem resilience and environmental drivers. First, we independently assessed each metric of ecosystem recovery, and examined how each recovery metric was influenced by abiotic and biotic environmental drivers. We found that ecosystem structure, as estimated by canopy height, showed the highest level of recovery (62 %), followed by composition as measured by relative vegetation abundance (60 %) and function as measured by primary productivity (35 %) over 17 years. Our study revealed that each ecosystem recovery metric is influenced by distinct environmental drivers. Specifically, structural recovery was strongly predicted by distance to seed source, and solar radiation. Compositional recovery was predominantly driven by solar radiation and available soil water capacity. Lastly, burn severity and the terrain ruggedness index were the primary drivers of functional recovery. Finally, we synthesized each ecosystem recovery metric into our ERI, revealing that the overall resilience in our study domain was 54 %. Our estimated ERI rate of 3 %/yr indicates that this forested ecosystem located within the Western Canadian Rockies Ecoregion remains resilient compared to its historical fire return interval of 120 years would yield a 100 % ERI. ERI was driven by solar radiation, distance to seed source, and burn severity. Our findings illustrate that different ecosystem recovery metrics may not provide similar estimates of ecosystem resilience and that recovery metrics may be sensitive to different environmental drivers. Thus an index that incorporates multiple recovery metrics provides a more comprehensive understanding of ecosystem resilience.
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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 | 0.000 |
| 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) | 0.000 | 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 it