The value of paleolimnology in reconstructing and managing ecosystem vulnerability: a systematic map
Bibliographic record
Abstract
Vulnerability can measure an ecosystem’s susceptibility to change as a result of pressure or disturbance, but can be difficult to quantify. Reconstructions of past climate using paleolimnological methods can create a baseline to calibrate future projections of vulnerability, which can improve ecosystem management and conservation plans. Here, we conduct a systematic map to analyze the range and extent that paleolimnological published studies incorporated the concept of vulnerability. Additional themes of monitoring, management, conservation, restoration, or ecological integrity were also included. A total of 52 relevant unique articles were found, a majority of which were conducted in Europe or North America since 2011. Common themes identified included management and adaptation, with the latter heavily focussed on climate change or disturbance. From this, we can infer that the use of paleolimnology to discuss the concept of vulnerability is an emerging field. We argue that paleolimnology plays a valid role in the reconstruction of ecosystem vulnerability due to its capacity to broaden the scope of long-term monitoring, as well as its potential to help establish management and restoration plans. The use of paleolimnology in vulnerability analysis will provide a clearer lens of changes over time; therefore, it should be frequently implemented as a tool for vulnerability assessment.
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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 | 0.026 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.045 | 0.018 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".