Landscape diversity promotes landscape functioning in North America
Bibliographic record
Abstract
Biodiversity–ecosystem functioning experiments have established generally positive species richness-productivity relationships in plots of single ecosystem types, typically grassland or forest. However, it remains unclear whether these findings apply in real-world landscapes that resemble a heterogeneous mosaic of different ecosystem and plant types that interact through biotic and abiotic processes. Here, we show that landscape-level diversity, measured as number of land-cover types (different ecosystems) per 250×250 m, is positively related to landscape-wide remotely-sensed primary production across all of North America, covering 16 of 18 ecoregions of Earth. At higher landscape diversity, productivity was temporally more stable, and 20-year greening trends were accelerated. These effects occurred independent of local species diversity, suggesting emergent mechanisms at hitherto neglected levels of biological organization. Specifically, mechanisms related to interactions among land-cover types unfold at the scale of entire landscapes, similar to, but not necessarily resulting from, interactions between species within single ecosystems. Landscape-level diversity – the number of different ecosystems in a 250 m by 250 m area – is positively related to landscape-wide primary production across North America, according to remote sensing observations and statistical analyses.
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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 | 0.000 | 0.001 |
| 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) | 0.002 | 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".