Bibliographic record
Abstract
Climate change has become a huge threat to terrestrial and aquatic ecosystems globally. While most attention has been focused on potential effects on free-living organisms, there also has been widespread interest on effects on pathogens and disease. Parasites will no doubt be impacted by climate change, but whether they increase or decrease in abundance, or increase or decrease their distribution, is not obvious. Because transmission of parasites is linked to abundance and diversity of potential hosts, climate change effects on free-living organisms will also affect their parasites. Herein, advances in effects of increasing temperature on parasites and host–parasite interactions are explored, with emphasis on recent advances in acclimation effects and the application of metabolic models to particular host–parasite systems. Climate change will also affect the immune function and physiology of aquatic organisms. Furthermore, climate change does not operate in a vacuum, and there are numerous associated abiotic effects that will impact parasites and their hosts. Effects of precipitation and drought, hydrological changes, eutrophication, acidification, salinity and contaminants in relation to a warming climate are examined for parasites of freshwater and marine ecosystems. Nor do these effects operate independently, and combined effects of multiple stressors on host–parasite systems are discussed. This chapter concludes with an examination of higher order ecological effects and ecosystem consequences of parasitism in a warming climate, and finally, a series of case studies on effects of climate change on particularly well-studied aquatic parasites.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".