Increasing synchrony opposes stabilizing effects of species richness on terrestrial communities
Bibliographic record
Abstract
Abstract Aim Ecological theory has predicted that species richness should stabilize communities, with mechanisms including species synchrony and population variability determining the net impacts. While these theories have been supported empirically, results can be sensitive to taxonomic bias as studies are often focussed on plants. Trophic differences between consumers and primary producers can lead to varying stabilizing effects of species richness. Here, we compared the impact of species richness on community variability in four taxonomic groups: terrestrial birds, mammals, invertebrates and plants. Location Global. Method Using data from 6763 time series globally (BioTIME) for four terrestrial taxa, we quantified community and population variability and species synchrony based on abundance fluctuations over time. Results Species richness destabilized communities through increasing synchrony and stabilized communities through reducing population variability in all taxa. Such opposing effects weakened the net impacts of species richness on communities. Population variability had higher importance relative to synchrony in plant communities. By contrast, synchrony had more comparable (or even higher) importance compared with population variability in animal communities. When synchrony and population variability were not controlled, stabilizing impacts of species richness were detected in plant communities only. Main Conclusions Our results highlight how species richness drives stabilizing and destabilizing mechanisms simultaneously across all taxa, with strong taxonomic variation in the relative importance of these mechanisms in regulating community variability. This questions the generality of previous findings on stabilizing impacts of species richness based on limited taxonomic coverage. Additionally, our results indicate the need to understand how the importance of stabilizing and destabilizing mechanisms differs in determining community variability across organisms and environments.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".