Exotic Mammals Disperse Exotic Fungi That Promote Invasion by Exotic Trees
Bibliographic record
Abstract
Biological invasions are often complex phenomena because many factors influence their outcome. One key aspect is how non-natives interact with the local biota. Interaction with local species may be especially important for exotic species that require an obligatory mutualist, such as Pinaceae species that need ectomycorrhizal (EM) fungi. EM fungi and seeds of Pinaceae disperse independently, so they may use different vectors. We studied the role of exotic mammals as dispersal agents of EM fungi on Isla Victoria, Argentina, where many Pinaceae species have been introduced. Only a few of these tree species have become invasive, and they are found in high densities only near plantations, partly because these Pinaceae trees lack proper EM fungi when their seeds land far from plantations. Native mammals (a dwarf deer and rodents) are rare around plantations and do not appear to play a role in these invasions. With greenhouse experiments using animal feces as inoculum, plus observational and molecular studies, we found that wild boar and deer, both non-native, are dispersing EM fungi. Approximately 30% of the Pinaceae seedlings growing with feces of wild boar and 15% of the seedlings growing with deer feces were colonized by non-native EM fungi. Seedlings growing in control pots were not colonized by EM fungi. We found a low diversity of fungi colonizing the seedlings, with the hypogeous Rhizopogon as the most abundant genus. Wild boar, a recent introduction to the island, appear to be the main animal dispersing the fungi and may be playing a key role in facilitating the invasion of pine trees and even triggering their spread. These results show that interactions among non-natives help explain pine invasions in our study area.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".