Paleoenvironmental Reconstructions Improve Ecosystem Services Risk Assessment: Case Studies from Two Coastal Lagoons in South America
Bibliographic record
Abstract
Paleoenvironmental reconstructions are increasingly being used in conservation biology, ecosystem management, and evaluations of ecosystem services (ES), but their potential to contribute to the ES risk assessment process has not been explored. We propose that the long-term history of the ecosystem provides valuable information that augments and strengthens an ES risk assessment and that it should be considered routinely when undertaking risk assessments. We adjusted a standard ecosystem-based risk management (EBRM) protocol to include paleoenvironmental data, and tested the modified approach on two coastal lagoons in South America. Paleolimnological reconstructions in both lagoons indicate that salinity and nutrients (in Laguna de Rocha), and salinity (in Ciénaga Grande de Santa Marta), as controlled by hydrologic connectivity with the ocean and freshwater tributaries, have been the key variables behind ecosystem’s function. This understanding, applied to inform various components and steps in the EBRM protocol, suggests that the maintenance of hydrological connections should be a management priority to minimize risk to ES. This work illustrates the utility of including paleoenvironmental data in an EBRM context and highlights the need for a more holistic approach to risk management by incorporating the long-term history of ecosystem function.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".