Microbiome transfer from native to invasive species may increase invasion risk and shorten invasion lag
Bibliographic record
Abstract
Abstract In a fast-changing world, understanding how organisms adapt to their environment is a pressing necessity. Research has focused on genetic adaptation, while our understanding of non-genetic modes is still in its infancy. Particularly, the host-associated microbiome may strongly influence an organism’s ability to cope with its environment. The presence of certain microbes in the gut, for example, can facilitate the utilization of dietary resources, provide protection from pathogens, and increase resilience to diverse abiotic conditions. However, the role that the microbiome may play in species’ adaptation to novel challenges is largely unexplored, experimentally as well as theoretically. Here, we study the possibility of such adaptation in invasive species. We present and explore a new hypothesis: Invasive species may rapidly adapt to local conditions by adopting beneficial microbes of similar co-occurring native species. Ironically, due to competition, these native species are also those most likely to suffer from the invaders’ spread. We formulate a mathematical framework to investigate how the transfer of beneficial microbes between a native and an introduced species can alter their competitive dynamics. We suggest that, non-intuitively, the presence of a related native species may facilitate the success of an invasive species’ establishment. This occurs when the invader’s fitness is strongly influenced by adaptation to local conditions that is provided by microbes acquired from the natives’ microbiomes. Further, we show that in such cases a delayed acquisition of native microbes may explain the occurrence of an invasion lag, and we discuss biological systems that could lend themselves for the testing of our hypotheses. Overall, our results contribute to broadening the conceptualization of rapid adaptation via microbiome transfer and offer possible insights for designing early intervention strategies for invasive species management during their lag phase.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".