Major drivers of biodiversity loss and their impacts on helminth parasite populations and communities
Bibliographic record
Abstract
The world's biodiversity is in peril. The major threats to biodiversity globally are habitat change, overexploitation, climate change, invasive species and pollution. Not only do these stressors impact free-living organisms, but they affect parasitic ones as well. Herein, this perspective examines the potential consequences of these anthropogenic perturbations on helminth populations and communities, with emphasis on significant developments over the past decade. Furthermore, several case studies are examined in more detail for each of these threats to biodiversity. While effects are widespread and diverse, for the most part all these environmental stressors have negative effects on parasite populations and communities. Those parasites with complex life cycles that are trophically transmitted are often more at risk, although larval parasites with a wide host spectrum, and directly transmitted ectoparasites, appear less threatened and may even benefit. However, differential effects on hosts and parasites, on parasite life cycle stages and on host-parasite interactions made specific predictions difficult and context-dependent. Experimental laboratory and mesocosm studies on specific parasites that test effects on the different life cycle stages, hosts and host-parasite interactions, permitting the determination of net effects of an environmental stressor, yield insightful and sometimes counterintuitive results, although they remain a simplification of real-world complexity. Recent advances in the use of parasites as bioindicators of effects also are 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 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) | 0.003 | 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".