Nutritional interactions in mixed species forests: a synthesis
Bibliographic record
Abstract
For more than a century, scientists have considered whether mixtures of tree species may differ in nutrition and yield relative to monocultures. We review the empirical evidence on the nutritional interactions of tree species in mixtures, including information on foliar nutrition, soil nutrient supply, rates of nutrient input, and patterns of root distribution. Linear effects were most common, with mixtures intermediate in value between monocultures. In some cases, values for mixtures were lower than expected, indicating an antagonistic interaction. A few cases that included nitrogen- fixing species showed a synergistic interaction, with mixtures showing higher values than monocultures. Nutrient concentrations in foliage of Sitka spruce (Picea sitchensis (Bong.) Carrière) were improved in mixtures with other conifers in three studies, in contrast to four studies with mixtures of various conifers and hardwoods that showed no effect of mixtures on foliage nutrient concentrations. Mixtures that combine species with and without the ability to fix atmospheric nitrogen have shown a full range of foliar responses from decreases to increases in phosphorus, to increases in nitrogen, to no effect of mixtures. Rates of litter decomposition usually showed no effect of species mixtures, but a few cases demonstrated both increases and decreases in decomposition relative to monocultures. Pools of soil nutrients generally did not differ between mixtures and monocultures. Root distributions in mixtures of Norway spruce (Picea abies (L.) Karst.) and beech (Fagus sylvatica L.) were altered in mixtures; compared with monocultures, spruce rooted more shallowly in mixtures with beech, and beech rooted more deeply in mixtures with spruce. General conclusions are limited by the small number of studies that directly addressed mixed-species effects in forests, and the wide variety of observed interactions. Further research would be particularly helpful in identifying situations where nonlinear interactions may develop, including the species and site conditions that promote nonlinear interactions. Neighborhood methods, which analyze the relationship between stand composition and nutritional properties on a small spatial scale, offer great potential for exploring nutritional effects in mixed-species stands.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".