Reappraisal of the Dilution and Amplification Effect Framework: A Case Study in Lyme Disease
Bibliographic record
Abstract
Abstract The role of biodiversity in regulating zoonotic disease in ecological communities has been broadly referred to as the biodiversity-disease relationship in disease ecology. Whether biodiversity decreases or increases disease risk, known as a dilution or amplification effect respectively, remains unclear. The literature has focused on the strength, generality, nature, and context dependencies that could explain contradictory evidence. We suggest that a continued focus on this approach to resolving the biodiversity-disease debate detracts from a more foundational problem with testing these dilution and amplification hypotheses, in that these hypotheses are not falsifiable as proposed. When tested and interpreted as net effects in a system, these hypotheses do not possess a true null outcome and they are vulnerable to ad hoc explanations. To remedy this problem, we propose that biodiversity and disease risk can be modelled as latent variables in multivariate causal models to test specific mechanistic pathways. We present a case study on Lyme disease through a systematic review and meta-analysis, concluding that there is little evidence for a net dilution or amplification effect. We demonstrate how testing these net effect hypotheses falls short of providing robust evidence for its underlying mechanisms. While these hypotheses have previously been helpful in conceptualizing this idea of biodiversity as a potentially protective factor for human health, they need further specificity and clarity to move forwards with greater unity.
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 | 0.176 | 0.228 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.007 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".