Tracking fish, seabirds, and wildlife population dynamics with diatoms and other limnological indicators
Bibliographic record
Abstract
Introduction The application of diatoms in paleoenvironmental studies has largely focused on tracking past changes in water chemistry (e.g. nutrients, salinity, pH) and habitat features (e.g. lake ice or macrophytes). When used in conjunction with other paleolimnological proxies, however, diatoms can be used to infer past changes in vertebrate populations or harvests such as fish, birds, and whales. This research is particularly insightful because the fossil record of these vertebrates is fragmented and sparsely distributed. Time series of inferred animal population dynamics also provide the much-needed long-term data required to develop sustainable management plans for these often ecologically sensitive and sometimes commercially harvested taxa (e.g. Selbie et al ., 2007). Many studies that are included in this review are focused on population dynamics of migratory animals. A common thread across these studies is that large densities of migratory animals can introduce substantial nutrient loads to lakes. If the animal population is, at any time, contributing the largest source of nutrients to a study lake, then fluctuations in nutrients can be correlated to the animal's population size. Given that diatom community composition is strongly influenced by nutrient status (see Hall and Smol, this volume), the diatoms are then indirect indicators of animal population dynamics. A second field of study included in this review is focused on changes in non-anadromous fish populations. There have been numerous studies showing that fish kills, fish introductions, or human manipulations of fish community structure can influence primary producers and/or water quality.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".