Tracing plant–environment interactions from organismal to planetary scales using stable isotopes: a mini review
Bibliographic record
Abstract
Natural isotope variation forms a mosaic of isotopically distinct pools across the biosphere and flows between pools integrate plant ecology with global biogeochemical cycling. Carbon, nitrogen, and water isotopic ratios (among others) can be measured in plant tissues, at root and foliar interfaces, and in adjacent atmospheric, water, and soil environments. Natural abundance isotopes provide ecological insight to complement and enhance biogeochemical research, such as understanding the physiological conditions during photosynthetic assimilation (e.g. water stress) or the contribution of unusual plant water or nutrient sources (e.g. fog, foliar deposition). While foundational concepts and methods have endured through four decades of research, technological improvements that enable measurement at fine spatiotemporal scales, of multiple isotopes, and of isotopomers, are advancing the field of stable isotope ecology. For example, isotope studies now benefit from the maturation of field-portable infrared spectroscopy, which allows the exploration of plant-environment sensitivity at physiological timescales. Isotope ecology is also benefiting from, and contributing to, new understanding of the plant-soil-atmosphere system, such as improving the representation of soil carbon pools and turnover in land surface models. At larger Earth-system scales, a maturing global coverage of isotope data and new data from site networks offer exciting synthesis opportunities to merge the insights of single-or multi-isotope analysis with ecosystem and remote sensing data in a data-driven modeling framework, to create geospatial isotope products essential for studies of global environmental 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".