Typical earthworm assemblages of European ecosystem types
Bibliographic record
Abstract
Abstract For nature conservation and planning, terrestrial ecosystems are commonly classified based on their plant communities. Although soils are fundamental to ecosystem functioning, ecosystem classifications based on soil organisms are rare, and it is poorly understood whether their assemblage compositions follow existing classification schemes. We examined whether commonly used ecosystem types capture variation in earthworm (Lumbricidae) assemblages—a crucial biotic component of soil ecosystems. To this end, we created four ecosystem classifications by combining large‐scale climatic classifications (Biogeographic Regions [BGR] and Holdridge Life Zones [HLZ]) with small‐scale land cover classifications (CORINE Land Cover [CLC] and European Nature Information System [EUNIS]). European earthworm assemblage data from the sWORM and Edaphobase databases were analysed for variation in composition within and among ecosystem types, using Permutational Analysis of Variance and Analysis of Similarities. Additionally, we used Typical Species Analysis to establish typical earthworm assemblages (TAs) for each ecosystem type. Ecosystem classifications using the BGR explained more variance than HLZ, but HLZ showed a higher separation of assemblages between ecosystem types. The differentiation between Atlantic and Continental climates in the BGR could explain the superiority over the HLZ, which had only one category for the cool temperate zone of our study region. The typical assemblages contained on average six species, with some habitat generalists present in most. This study shows that combinations of ecosystem properties from different spatial scales can be used to distinguish between earthworm assemblages at the European level. However, earthworm assemblages across Europe were highly similar due to low species richness and the dominance of a few widespread species. This limits the possibility of applying TAs on large spatial scales, for example, for environmental monitoring. We suggest that future studies should explore the use of more species‐rich groups of soil organisms to characterize ecosystem types.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| 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".