Explaining ecosystem multifunction with evolutionary models
Bibliographic record
Abstract
Ecosystem function is the outcome of species interactions, traits, and niche overlap - all of which are influenced by evolution. However, it is not well understood how the tempo and mode of niche evolution can influence ecosystem function. In evolutionary models where either species differences accumulate through random drift in a single trait or species differences accumulate through divergent selection among close relatives, we should expect that ecosystem function is strongly related to diversity. However, when strong selection causes species to converge on specific niches or when novel traits that directly affect function evolve in some clades but not others, the relationship between diversity and ecosystem function might not be very strong. We test these ideas using a field experiment that established plant mixtures with differing phylogenetic diversities and we measured ten different community functions. We show that some functions were strongly predicted by species richness and mean pairwise phylogenetic distance (MPD, a measure of phylogenetic diversity), including biomass production and the reduction of herbivore and pathogen damage in polyculture, while other functions had weaker (litter production and structural complexity) or nonsignificant relationships (e.g., flower production and arthropod abundance) with MPD and richness. However, these divergent results can be explained by different models of niche evolution. These results show that diversity-ecosystem function relationships are the product of evolution, but that the nature of how evolution influences ecosystem function is complex.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".