The interplay of nested biotic interactions and the abiotic environment regulates populations of a hypersymbiont
Bibliographic record
Abstract
The role of biotic interactions in shaping the distribution and abundance of species should be particularly pronounced in symbionts. Indeed, symbionts have a dual niche composed of traits of their individual hosts and the abiotic environment external to the host, and often combine active dispersal at finer scales with host-mediated dispersal at broader scales. The biotic complexity in the determinants of species distribution and abundance should be even more pronounced for hyper symbionts (symbionts of other symbionts). We use a chain of symbiosis to explore the relative influence of nested biotic interactions and the abiotic environment on occupancy and abundance of a hypersymbiont. Our empirical system is the epibiont ciliate Lagenophrys discoidea, which attaches to an ostracod that is itself ectosymbiotic on crayfish (the basal host). We applied multimodel selection and variance partitioning for GLMM to assess the relative importance of (a) traits of symbiotic hosts (ostracod sex and abundance), (b) traits of basal hosts (crayfish body weight, abundance and intermoult stage), (c) the abiotic environment (water chemistry and climate) and (d) geospatial autocorrelation patterns (capturing potential effects of crayfish dispersal among localities). Our models explained about half of the variation in prevalence and abundance of the hypersymbiont. Variation in prevalence was partly explained, in decreasing order of importance (18%-4%) by shared effects of symbiotic host traits and the abiotic environment, pure fixed effects of symbiotic hosts, abiotic environment and geospatial patterns (traits of basal hosts were not relevant). Hypersymbiont abundance was most strongly explained by random effects of host traits (mainly the symbiotic host), in addition to weaker fixed effects (mostly abiotic environment). Our results highlight the major role of the interplay between abundance of symbiotic hosts and water physico-chemistry in regulating populations of a hypersymbiotic ciliate, which is likely critical for dispersal dynamics, availability of attachment resources and suitability of on-host living conditions for the ciliate. We also found moderate signal of regulation by the basal host, for which we propose three mechanisms: (a) modulation of microhabitat suitability (crayfish-created water currents); (b) concentration of symbiotic hosts within crayfish; and (c) dispersal mediated by crayfish.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".