Increasing land use drives changes in plant phylogenetic diversity and prevalence of specialists
Bibliographic record
Abstract
Increased human land use has resulted in the increased homogenization of biodiversity between sites, yet we lack sufficient indicators to predict which species decline and the consequence of their potential loss on ecosystem services. We used comparative phylogenetic analysis to (1) characterize how increasing conversion of forest and grasslands to grazing pasturelands changes plant diversity and composition; (2) examine how changes in land use relate to declines in functional trait diversity; and (3) specifically investigate how these changes in plant composition affect the prevalence of zygomorphy and the possible consequences that these changes may have on pollinator functional groups. As predicted, we found that the conversion to grazing pasturelands negatively impacted species richness and phylogenetic composition. Clades with significantly more represented taxa in grasslands (GL) were genera with a high representation of agricultural weeds, while the composition was biased towards clades of subalpine herbaceous wildflowers in Mixed Forest (MF). Changes in community composition and structure had strong effects on the prevalence of zygomorphic species likely driven by nitrogen-fixing abilities of certain clades with zygomorphic flowers (e.g., Fabaceae). Land conversion can thus have unexpected impacts on trait distributions relevant for the functioning of the community in other capacities (e.g., cascading effects to other trophic levels (i.e., pollinators). Finally, the combination of traits represented by the current composition of species in GL and MF might enhance the diagnostic value of productivity and ecosystem processes in the most eroded ecosystems.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".