Tree Species Richness Promotes Invertebrate Herbivory on Congeneric Native and Exotic Tree Saplings in a Young Diversity Experiment
Bibliographic record
Abstract
Tree diversity in forests is an important driver of ecological processes including herbivory. Empirical evidence suggests both negative and positive effects of tree diversity on herbivory, which can be, respectively, attributed to associational resistance or associational susceptibility. Tree diversity experiments allow testing for associational effects, but evidence regarding which pattern predominates is mixed. Furthermore, it is unknown if herbivory on tree species of native vs. exotic origin is influenced by changing tree diversity in a similar way, or if exotic tree species escape natural enemies, resulting in lower damage that is unrelated to tree diversity. To address these questions, we established a young tree diversity experiment in temperate southwestern Germany that uses high planting density (49 trees per plot; plot size 13 m2). The species pool consists of six congeneric species pairs of European and North American origin (12 species in total) planted in monocultures and mixtures (1, 2, 4, 6 species). We assessed leaf damage by leaf-chewing insects on more than 5,000 saplings of six broadleaved tree species. Plot-level tree species richness increased leaf damage, which more than doubled from monocultures to six-species mixtures, strongly supporting associational susceptibility. However, leaf damage among congeneric native and exotic species pairs was similar. There were marked differences in patterns of leaf damage across tree genera, and only the genera likely having a predominately generalist herbivore community showed associational susceptibility, irrespective of the geographical origin of a tree species. In conclusion, an increase in tree species richness in young temperate forests may result in associational susceptibility to feeding by generalist herbivores.
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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 | 0.000 |
| 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) | 0.001 | 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".