The Distribution and Abundance of Parasites in Aquatic Ecosystems in a Changing Climate: More than Just Temperature
Bibliographic record
Abstract
SynopsisEvaluation of the potential response of parasites of aquatic organisms to climate change illustrates the complexity of host-parasite relationships and the difficulty of making accurate predictions for these biological systems. In recent years, trematodes have proven to be a useful model to evaluate potential effects of climate change on host-parasite systems. In the first part of this article, I review and summarize results from the recent use of trematodes and specifically their early life cycle stages in testing effects of temperature and other climate-driven variables on life history traits and host-parasite interactions. However, metazoan parasites in aquatic systems respond directly to changes in temperature and also to changes in other climate-driven abiotic parameters that are mediated directly on the parasite or indirectly through changes in the distribution and abundance of their hosts. In addition, though most research to date has focused on the effects of temperature, it is imperative to explore effects of precipitation, eutrophication, acidification, water levels and flow rates, habitat loss and fragmentation, extreme weather, and other forms of anthropogenic interference on the distribution of both hosts and parasites, as these biotic and abiotic factors and stressors do not operate independently of climate. In the second part of this article, the effects of some of these factors derived from our own field studies, as well as other investigations both in the laboratory and the field, on the distribution, abundance, and community structure of parasites in aquatic ecosystems will be reviewed and discussed.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".