Utility of Dendrochronology Crossdating Methods in the Development of Arctic Coralline Red Algae Clathromorphum compactum Growth Increment Chronology for Sea Ice Cover Reconstruction
Bibliographic record
Abstract
Paleoclimate and paleoenvironmental reconstructions from increment-yielding archives strongly depend on precise age models. Like bivalves, corals, trees, and speleothems, the coralline alga Clathromorphum compactum produces annual growth increments and shows considerable promise as an environmental archive for arctic and subarctic regions. Though their growth increment widths correlate with temperature and sea ice cover in high Arctic regions, existing timeseries have not been crossdated. In fact, previous studies have shown a lack of inter-sample correlation in non-crossdated timeseries suggesting possible age model dating errors. Here, we use dendrochronology crossdating techniques and COFECHA software to ensure and validate synchrony between C. compactum timeseries (<141 years) from three specimens collected near Beechey Island, Nunavut, Canada. Results showed that non-crossdated timeseries constructed by four coralline red algae researchers using annual increments of the same C. compactum samples were highly variable and showcase the likelihood of dating errors in non-crossdated timeseries. Crossdating improved inter-series correlations, and correlations to sea ice-related records, suggesting that at least three crossdated timeseries are required to isolate paleoclimate signals. Our findings suggest that future reconstructions with C. compactum should employ crossdating techniques to reduce dating errors and allow for more precise climate reconstructions. Lay Abstract Long-term environmental records provide a critical baseline to examine how humans have impacted Earth’s natural climate. An important piece to consider is sea ice’s role in natural climate variability because its brightness limits warming by reflecting solar irradiation back to space. However, instrumental records of sea ice rarely extend beyond the early satellite era (late 1970s), limiting our understanding of how sea ice affects natural climate variability in the preindustrial era. A lack of historical baseline prompted the development of sea ice proxies, including the long-lived marine alga, Clathromorphum compactum. Similar to tree-rings, C. compactum produces a new mineralized layer each year, and layer thicknesses have been shown to respond to sea ice cover, making them useful to record long-term sea ice variability. However, a recent study showed that records had replicability problems, maybe due to dating mistakes. Our study applies tree-ring dating methods (dendrochronology) to match annual algal growth layers across algal specimens. Results showed that these new methods reduced dating errors, allowing for more precise past sea ice cover reconstructions.
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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 | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".