Changes in the direction of the diversity–productivity relationship over 15 years of stand development in a planted temperate forest
Bibliographic record
Abstract
Abstract Experiments manipulating diversity in both forests and grasslands have often observed a positive diversity–productivity relationship (DPR) which tends to strengthen during plant community development. This pattern is generally attributed to an increase in niche complementarity or facilitation. Most analyses do not examine species dominance and density, which also change over time. Moreover, how neighbourhood‐scale interactions among tree species affect the DPR is not well‐understood. We analysed growth and mortality data from the Simplex experiment, a part of the BIOTREE tree diversity experiment. Simplex consists of 36 plots each planted with four common and commercially important tree species to create a gradient of tree species evenness at two tree densities (6667 and 3556 trees ha −1 ). We test whether: (a) the effect of evenness on total above‐ground biomass productivity increase with stand development (year), (b) the effect of evenness on productivity is stronger in dense plots; and (c) intraspecific competition from neighbours negatively affect the growth of dominant species more strongly compared to co‐dominant species, and whether this negative effect is stronger in denser plots. The direction of DPR was initially negative because the fast‐growing long‐lived pioneer Douglas fir ( Pseudotsuga menziesii [Mirb.] Franco) dominated. However, with time, shade‐tolerant Norway spruce ( Picea abies [L.] Karst.) and European beech ( Fagus sylvatica L.) increased in abundance (by biomass), and the relationship between evenness and biomass increment changed from negative to positive in high‐density plots. Neighbourhood analyses revealed that for Douglas fir and Norway spruce, conspecifics reduced individual growth rates across density levels and years. Synthesis . We observed a shift in the diversity–productivity relationship over 15 years in our experiment. Over time, increasing intraspecific competition limited the increment of the abundant Douglas fir in uneven plots, and a persistent increase in the abundance (by biomass) of shade‐tolerant Norway spruce and European beech in even plots led to a higher community biomass increment, which led to a positive DPR. Emergence of a positive DPR in temperate forest plantations requires significant time and is importantly promoted by diversity at the neighbourhood scale (intimate mixtures) as well as higher density planting.
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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 | 0.001 | 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 | 0.000 |
| 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".