Impacts of an invasive plant are fundamentally altered by a co‐occurring forest disturbance
Bibliographic record
Abstract
Invasive species frequently co-occur with other disturbances, which can impact the same ecosystem functions as the invader. Yet invasion studies rarely control for the presence of these other disturbances, although their overlapping effects may influence the direction and magnitude of impacts attributed to the invader alone. Here, we ask whether controlling for the presence of a co-occurring disturbance, as well as the time since disturbance, yields different values of an invader's ecosystem effects than when these factors remain unaddressed. We used a chronosequence of six forest stands at a single site: five logged stands that each contained paired invaded-uninvaded plots of the forest understory invasive grass Microstegium vimineum, as well as one unlogged and uninvaded control stand. By controlling for the presence of both logging and invasion, we untangled the effects of each through time. We found that the co-occurring disturbance of logging can dramatically alter the measured effects of M. vimineum by amplifying, dampening, negating, or entirely reversing the direction of the invader's impacts. During its period of peak impact, logging amplified the invader's positive effect on the size of the soil microbial biomass pool by 24%, reduced the invader's positive effect on soil water holding capacity by 5%, negated the invader's positive effect on the particulate organic matter carbon pool (from a 9% increase to no significant effect), and reversed the direction of the invader's impact on net nitrogen mineralization rate from a 51% increase to a 52% decrease. Furthermore, the influence of logging on the invader's impacts was not static, but dynamic through time. The results from our site therefore demonstrate that failure to account for the impacts of a co-occurring disturbance, as well as the time since disturbance, can result in flawed inference about the nature of an invader's effects. Future research should determine how widespread such flawed inference might be among other invasive species and across different environmental contexts. To help guide such research, we describe a general framework for disentangling the overlapping effects of invasions and co-occurring disturbances through time.
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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 | 0.000 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".