Empirically tuned theory reveals why symbiont abundance ‘mite’ vary across hosts
Bibliographic record
Abstract
Research Highlight: del Mar Labrador, M., Serrano, D., Doña, J., Aguilera, E., Arroyo, J. L., Atiénzar, F., Barba, E., Bermejo, A., Blanco, G., Borràs, A., Calleja, J. A., Cantó, J. L., Cortés, V., de la Puente, J., de Palacio, D., Fernández-González, S., Figuerola, J., Frías, Ó., Fuertes-Marcos, B. Garamszegi, L. Z., Gordo, Ó., Gurpegui, M., Kovács, I., Martínez, J. L., Meléndez, L., Mestre, A., Møller, A. P., Monrós, J. S., Moreno-Opo, R., Navarro, C., Pap, P. L., Pérez-Tris, J., Piculo, R., Ponce, C., Proctor, H., Rodríguez, R., Sallent, Á., Senar, J., Tella, J. L., Vágási, C. I., Vögeli, M., & Jovani, R. (2023). Host space, not energy or symbiont size, constrains feather mite abundance across passerine bird species. Journal of Animal Ecology, https://doi.org/10.1111/1365-2656.14032. Symbionts represent crucial links between species in ecosystems. Consequently, understanding their patterns of abundance is a major goal in the study of symbioses. However, multiple biotic and abiotic factors may regulate symbionts, and disentangling the mechanisms that drive variation in their abundance across host species is challenging. One promising strategy to approach this challenge is to incorporate biologically relevant data into theoretical models. In a recent study, Labrador et al. (2023) used this strategy to investigate the poorly understood symbiosis between feather mites and their avian hosts. They integrate a remarkable amount of empirical data with models based on the metabolic theory of ecology to determine what factors limit feather mite abundance across European passerines. Their quantitative analyses indicate that the number of feather barbs limits mite abundance across host species, suggesting that mite populations are spatially, but not energetically, constrained. These findings not only reveal mechanisms that may drive the variation in feather mite abundances across hosts, but also advance our understanding of the ecology of interspecific interactions more generally.
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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 | 0.009 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".