An Integrated Multi-Disciplinary Approach for Studying Multiple Stressors in Freshwater Ecosystems: Daphnia as a Model Organism
Bibliographic record
Abstract
The increased overexploitation of freshwater ecosystems and their extended watersheds often generates a cascade of anthropogenic stressors (e.g., acidification, eutrophication, metal contamination, Ca decline, changes in the physical environment, introduction of invasive species, over-harvesting of resources). The combined effect of these stressors is particularly difficult to study, requiring a coordinated multi-disciplinary effort and insights from various sub-disciplines of biology, including ecology, evolution, toxicology, and genetics. It also would benefit from a well-developed and broadly accepted model systems. The freshwater crustacean Daphnia is an excellent model organism for studying multiple stressors because it has been a chosen focus of study in all four of these fields. Daphnia is a widespread keystone species in most freshwater ecosystems, where it is routinely exposed to a multitude of anthropogenic and natural stressors. It has a fully sequenced genome, a well-understood life history and ecology, and a huge library of responses to toxicity. To make the case for its value as a model species, we consider the joint and separate effects of natural and three anthropogenic stressors-climatic change, calcium decline, and metal contaminants on daphniids. We propose that integrative approaches marrying various subfields of biology can advance our understanding of the combined effects of stressors. Such approaches can involve the measuring of multiple responses at several levels of biological organization from molecules to natural populations. For example, novel interdisciplinary approaches such as transcriptome profiling and mutation accumulation experiments can offer insights into how multiple stressors influence gene transcription and mutation rates across genomes, and, thus, help determine the causal mechanism between environmental stressors and population/community effects as well as long-term evolutionary patterns.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".