Natural History Collections are Needed to Resolve Host Sampling Gaps in Parasitology: Insights from Avian Haemosporidians
Bibliographic record
Abstract
The field of parasitology, and thus biodiversity research more broadly, is faced with the unfortunate reality that our understanding of parasite biodiversity can be only as good as our ability to sample parasite host species. Although the sampling of many host species is trivial, there typically remains a subset of species across any host group of interest that is difficult to sample due to rarity, habitat, body size, or some other trait. The result is a blind spot in our understanding of parasite biodiversity that is centered around parasite species that infect hosts that are rarely sampled by humans. However, for many groups of hosts, the daunting task of obtaining host samples has already been done, and these samples exist in the form of natural history collections at institutions across the world. With avian malaria parasites and other haemosporidians as an example, I demonstrate that significant host sampling gaps exist in the United States and Canada. Bird species that have not been sampled for molecular haemosporidian research typically are associated with aquatic habitats, significantly greater masses, and more restricted geographic distributions than are bird species that have already been sampled. These unsampled host species are likely to be infected with a high richness of previously undiscovered avian haemosporidian genetic lineages. However, natural history collections in the United States can be used to nearly completely address these sampling gaps with tissue samples currently housed in these institutions. The result of this analysis indicates that the future of parasite biodiversity research is dependent on the use and support of natural history collections and other biorepositories.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".