The Hologenome Across Environments and the Implications of a Host-Associated Microbial Repertoire
Bibliographic record
Abstract
Our understanding of the diverse interactions between hosts and microbes has grown profoundly over the past two decades and, as a product, has revolutionized our knowledge of the life sciences. Through primarily laboratory experiments, the current framework for holobionts and their respective hologenomes aims to decipher the underpinnings and implications of symbioses between host and microbiome. However, the laboratory setting restricts the full spectrum of host-associated symbionts as compared to those found in nature; thus, limiting the potential for a holistic interpretation of the functional roles the microbiome plays in host biology. When holobionts are studied in nature, associated microbial communities vary considerably between conditions, resulting in more microbial associates as part of the "hologenome" across environments than in either environment alone. We review and synthesize empirical evidence suggesting that hosts may associate with a larger microbial network that, in part, corresponds to experiencing diverse environmental conditions. To conceptualize the interactions between host and microbiome in an ecological context, we suggest the "host-associated microbial repertoire," which is the sum of microbial species a host may associate with over the course of its life-history under all encountered environmental circumstances. Furthermore, using examples from both terrestrial and marine ecosystems, we discuss how this concept may be used as a framework to compare the ability of the holobiont to acclimate and adapt to environmental variation, and propose three "signatures" of the concept.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".