Bibliographic record
Abstract
Predicting the effects of climate change requires understanding complex interactions among multiple abiotic and biotic factors. By influencing key interactions among host species, parasites can affect community and ecosystem structuring. Yet, our understanding of how multiple parasites and abiotic factors interact to alter ecosystem structure remains limited. To empirically test the role of temperature variation and parasites in shaping communities, we used a multigenerational mesocosm experiment composed of four sympatric freshwater crustacean species (isopods and amphipods) that share up to four parasite species. Mesocosms were assigned to one of four different treatments with contrasting seasonal temperatures (normal and elevated) and parasite exposure levels (continuous and arrested (presence or absence of parasite larvae in mesocosm)). We found that parasite exposure and water temperature had interactive effects on the host community. Continuous exposure to parasites altered the community structure and differences in water temperature altered species abundance. The abundance of the amphipod Paracalliope fluviatilis decreased substantially when experiencing continuous parasite exposure and elevated water temperatures. Elevated temperatures also led to parasite-induced mortality in another amphipod host, Paracorophium excavatum. Contrastingly, isopod hosts were affected much less, suggesting increasing temperatures in conjunction with higher parasite exposure might increase their relative abundance in the community. Changes in invertebrate host populations have implications for other species such as fish and birds that consume crustaceans as well as having impacts on ecosystem processes, such as aquatic primary production and nutrient cycling. In light of climate change predictions, parasite exposure and rise in average temperatures may have substantial impacts on communities and ecosystems, altering ecosystem structure and dynamics.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.095 | 0.056 |
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".