A cross‐scale assessment of productivity–diversity relationships
Bibliographic record
Abstract
Abstract Aim Biodiversity and ecosystem productivity vary across the globe, and considerable effort has been made to describe their relationships. Biodiversity and ecosystem functioning research has traditionally focused on how experimentally controlled species richness affects net primary productivity ( S → NPP) at small spatial grains. In contrast, the influence of productivity on richness (NPP → S ) has been explored at many grains in naturally assembled communities. Mismatches in spatial scale between approaches have fuelled debate about the strength and direction of biodiversity–productivity relationships. Here, we examine the direction and strength of the influence of productivity on diversity (NPP → S ) and the influence of diversity on productivity ( S → NPP) and how these vary across spatial grains. Location Contiguous USA. Time period 1999–2015. Major taxa studied Woody species (angiosperms and gymnosperms). Methods Using data from North American forests at grains from local (672 m 2 ) to coarse spatial units (median area = 35,677 km 2 ), we assess relationships between diversity and productivity using structural equation and random forest models, while accounting for variation in climate, environmental heterogeneity, management and forest age. Results We show that relationships between S and NPP strengthen with spatial grain. Within each grain, S → NPP and NPP → S have similar magnitudes, meaning that processes underlying S → NPP and NPP → S either operate simultaneously or that one of them is real and the other is an artefact. At all spatial grains, S was one of the weakest predictors of forest productivity, which was largely driven by biomass, temperature and forest management and age. Main conclusions We conclude that spatial grain mediates relationships between biodiversity and productivity in real‐world ecosystems and that results supporting predictions from each approach (NPP → S and S → NPP) serve as an impetus for future studies testing underlying mechanisms. Productivity–diversity relationships emerge at multiple spatial grains, which should widen the focus of national and global policy and research to larger spatial grains.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".