Palaeolimnological insights for biodiversity science: an emerging field
Bibliographic record
Abstract
1. Decreases in biodiversity are so widespread that they are now considered a form of global change in their own right. Given the grave nature of this issue, rapid advances in understanding are needed to mitigate further impacts. In this Opinion paper, we argue that palaeolimnological studies have important contributions to make to biodiversity science. 2. Given that long-term community data are sparse in their geographic coverage and tend to span no more than 5 years, greater insight into biodiversity dynamics can be obtained from palaeoecological analyses. One such approach is palaeolimnology, which is a field that can provide long-term data on changes in both physico-chemical and biological components of lake ecosystems. 3. To date, a handful of quantitative palaeolimnological studies have addressed biodiversity questions, focussing primarily on defining the drivers of change in species richness or identifying functional traits that best capture ecosystem processes. Several studies have also quantified the role of spatial variables in determining assemblage structure, a necessary first step in addressing how metacommunity interactions influence biodiversity–ecosystem processes. Overall, these early studies show that palaeolimnological approaches can address both similar and novel questions compared with contemporary ecological studies. However, palaeolimnology allows for a great expansion of the temporal scale of investigation, the quantification of rates of change to stressors and the possibility of conducting experiments by applying resurrection techniques. 4. As an emerging field, there are numerous exciting applications of palaeolimnology to biodiversity science. It is an opportune time to create synergy between contemporary aquatic ecologists and palaeolimnologists.
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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 | 0.000 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".