Sedimentary DNA and pigments show increasing abundance and toxicity of cyanoHABs during the Anthropocene
Bibliographic record
Abstract
Abstract Cyanobacterial harmful algal blooms (cyanoHABs) are assumed to be increasing in abundance and toxicity, but comprehensive analysis of change through time is limited, in part, because some key taxa (e.g., Microcystis) leave ambiguous evidence of historical abundance and toxicity. Sedimentary DNA (sedDNA) can allow the reconstruction of the cyanobacteria community as well as the frequency of genes specific to cyanotoxin production, enabling us to determine which taxa are present and their potential for toxin‐production. Using a combination of droplet digital polymerase chain reaction (ddPCR) and high‐throughput sequencing (HTS), we quantified the abundance of cyanobacterial genes of known function and changes in cyanobacteria taxa from sedDNA over the last century in nine lakes along a gradient of lake size, depth and trophic state in Minnesota, U.S.A. Using ddPCR, we quantified genes associated with microcystin toxin‐producing potential (mcyE), total cyanobacteria (CYA, 16S rRNA) and the genus Microcystis (MICR, 16S rRNA). Using HTS on a subset of lakes, we investigated how the abundance of this toxin‐producing gene covaried with the cyanobacteria community composition. We also compared ddPCR and HTS data to fossil pigments, a well‐established palaeolimnological method used to track changes in primary producers over time. Our results showed a significant correlation between MICR and the quantity of mcyE gene and cyanobacterial taxa with known toxin‐production potential. The abundance of both genes likewise increased concomitantly through time. Community analyses of HTS data showed significant change in cyanobacterial communities commencing c. 1950 when major land‐use change in this region led to increased lake productivity, and c. 1990 when Dolichospermum and Microcystis genera increased in abundance, and the subtropical exotic cyanobacteria Raphidiopsis raciborskii and Sphaerospermopsis aphanizomenoides became abundant. Cyanobacteria pigment data reflected these changes only in deeper lakes, suggesting issues related to benthic production or biomarker preservation in shallower systems. This study provides evidence for historical development of increasingly toxic cyanoHABs across a diverse set of lakes and illustrates how sedDNA may help link changes in the cyanobacteria community to the expression of potentially toxic genes.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 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".