Removing invasive stream macrofauna shifts nontarget invertebrate mesofauna through facilitation
Bibliographic record
Abstract
Abstract Positive interactions among non‐native species can drive invasional meltdowns to the detriment of native biota. Here, we assessed whether targeted control of aquatic invasive species (AIS) can benefit native species by eliminating synergies among invaders. We did so by monitoring changes in the abundance of native and non‐native benthic invertebrates following the targeted removal of non‐native fish and crustaceans in 10 streams on the island of Oʻahu (Hawaiʻi, USA). Benthic invertebrate sampling was conducted in paired control reaches and removal reaches in each stream immediately following removals, one month following removals, and then at 2‐ to 3‐month intervals for 16 months. Temporal variations in the abundance, composition, and diversity of the native and non‐native invertebrates were compared among streams and between treatment reaches using multivariate data visualization and mixed‐effects models. We observed both seasonal shifts in overall community composition and treatment‐specific effects on the abundance of common taxa that were mediated by the number of AIS fish removed. Most notably, as removal of non‐native poeciliid fish increased, we observed concordant decreases (−32 ± 13% mean ± SE) in non‐native caddisfly ( Cheumatopsyche analis ) and increases (122 ± 69%) in partially native chironomid midges in the treatment reaches relative to the control reaches. Our results provide experimental evidence supporting the hypothesis that predation by introduced poeciliids on midges indirectly facilitates non‐native caddisfly populations via competitive release. Our findings indicate that removal of poeciliids allows midges to outcompete non‐native caddisflies and increase their abundance. Our study illustrates that targeted removal of non‐native species can have cascading negative effects on other introduced taxa. Understanding trophic relationships among invasive and native species can thus enhance management efforts by maximizing benefits to native species relative to effort and cost.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".