Earthworm invasion reduces above-belowground biodiversity and ecosystem multifunctionality
Bibliographic record
Abstract
Abstract Global change alters abiotic and biotic conditions across the globe with unprecedented consequences for the functional integrity of affected ecosystems. However, most studies addressing global-change impacts focus on a very limited number of environmental variables, taxa, and ecosystem functions. Evidence is mounting that many belowground ecosystems are subject to an underappreciated aspect of global change: the invasion of earthworms. While we know that earthworm invasion can impact the physical, chemical, and biological properties of invaded ecosystems, it remains poorly understood how these changes are interconnected and how they concurrently affect the overall functioning of an ecosystem. To fill this gap, we collected data on six environmental variables, ten functional groups of microbes, plants, and animals, and 16 ecosystem functions from four forests in the USA and Canada, well-known hotspots of earthworm invasion. We used a multi-step Structural-Equation Modeling approach to disentangle the direct and indirect effects of earthworm invasion on environmental conditions and the ecosystem multidiversity and multifunctionality of invaded forests at different levels of resolution. Our analysis revealed that earthworm invasion reduced total multidiversity (combined microbial, plant, and animal multidiversity). Ecosystem multifunctionality was reduced via a combination of direct and indirect effects, with the latter involving both effects mediated by altered environmental conditions and by total multidiversity. In contrast, when resolving total multidiversity into taxon-specific multidiversity indices for microbes, plants, and animals, the above-mentioned effects of earthworm invasion on multifunctionality via multidiversity disappeared. Invasion effects on single ecosystem functions differed in their mediators but were net negative across the board. Given these differences across the differently-resolved analyses, the results suggest that a whole- ecosystem perspective is paramount to comprehensively understanding the impacts of biological invasions. Combining a multi-step analytical design with multiple biodiversity indices and multiple ecosystem functions assessed at the same time and place, our study represents the most complete assessment of the mechanisms and ecosystem-level consequences of earthworm invasion to date.
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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 | 0.001 |
| 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) | 0.001 | 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".