Host taxonomy constrains the properties of trophic transmission routes for parasites in lake food webs
Bibliographic record
Abstract
Some parasites move from one host to another via trophic transmission, the consumption of the parasite (inside its current host) by its future host. Feeding links among free-living species can thus be understood as potential transmission routes for parasites. As these links have different dynamic and structural properties, they may also vary in their effectiveness as trophic transmission routes. That is, some links may be better than others in allowing parasites to complete their complex life cycles. However, not all links are accessible to parasites as most are restricted to a small number of host taxa. This restriction means that differences between links involving host and non-host taxa must be considered when assessing whether transmission routes for parasites have different food web properties than other links. Here we use four New Zealand lake food webs to test whether link properties (contribution of a link to the predator's diet, prey abundance, prey biomass, amount of biomass transferred, centrality, and asymmetry) affect trophic transmission of parasites. Critically, we do this using both models that neglect the taxonomy of free-living species and models that explicitly include information about which free-living species are members of suitable host taxa. Although the best-fit model excluding taxonomic information suggested that transmission routes have different properties than other feeding links, when including taxonomy, the best-fit model included only an intercept. This means that the taxonomy of free-living species is a key determinant of parasite transmission routes and that food-web properties of transmission routes are constrained by the properties of host taxa. In particular, many intermediate hosts (prey) attain high biomasses and are involved in highly central links while links connecting intermediate to definitive (predator) hosts tend to be dynamically weak.
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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".