Spatiotemporal dynamics of abiotic and biotic properties explain biodiversity–ecosystem‐functioning relationships
Bibliographic record
Abstract
Abstract There is increasing evidence that spatial and temporal dynamics of biodiversity and ecosystem functions play an essential role in biodiversity–ecosystem‐functioning (BEF) relationships. Despite the known importance of soil processes for forest ecosystems, belowground functions in response to tree diversity and spatiotemporal dynamics of ecological processes and conditions remain poorly described. We propose a novel conceptual framework integrating spatiotemporal dynamics in BEF relationships and hypothesized a positive tree species richness effect on soil ecosystem functions through the spatial and temporal stability of biotic and abiotic soil properties based on species complementarity and asynchrony. We tested this framework within a long‐term tree diversity experiment in Central Germany by assessing soil ecosystem functions (soil microbial properties and litter decomposition) and abiotic variables (soil moisture and surface temperature) for two consecutive years in high spatial and temporal resolution. Tree species richness and identity had significant effects on soil properties (e.g., soil microbial biomass). Structural equation modeling revealed that overall soil microbial biomass was partly explained by (1) enhanced temporal stability of soil surface temperature and (2) decreased spatial stability of soil microbial biomass. Overall, spatial stability of soil microbial properties was positively correlated with their temporal stability. These results suggest that spatiotemporal dynamics are indeed crucial determinants in BEF relationships and highlight the importance of vegetation‐induced microclimatic conditions for stable provisioning of soil ecosystem functions and services.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".