Impacts of anthropogenic sedimentation on shell-bed habitats in Lake Tanganyika, Africa
Bibliographic record
Abstract
Lake Tanganyika, in central Africa, contains a diverse and endemic fauna under threat from global climate change, overfishing, and nearshore sediment pollution. Previous studies of sediment pollution focused justifiably on impacts along rocky shorelines where diversity is high, but Lake Tanganyika also contains widespread shelly accumulations (shell beds) unprecedented in the modern East African lakes, but where impacts are less constrained. Here we integrate multiple datasets from three sites along the Tanzanian shoreline to explore how variation in sedimentation rates and sediment quality impacts shell-bed substrate and diversity and abundance of ostracodes and sponges across sites that exhibit varying watershed characteristics. Taphonomic overprinting of the shells are similar over the three sites, suggesting lake-wide processes control their accumulation. However, shell bed distribution and sediment volume and compositions vary. There are also differences in the abundance of studied taxa. Where organic matter is diluted by clastic mud, ostracodes are less abundant and less diverse. Where sediment is pervasive and shell density is low, fewer sponges occur. Using the fallout radionuclide 210Pb, the two sites with discontinuous shell beds show sedimentation rates at least twice as high as the site where shell beds are more continuous. These differences are likely related to modest differences in watershed morphology, urbanization, and land cover. Our study suggests that modern sediment pollution creates sediment blankets that cover extant shell beds and likely reduce live populations of the snails that contribute to the accumulations. This has important conservation implications as planning must focus on large watersheds where agriculture and urbanization tend to be higher.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".