Species composition determines resistance to drought in dry forests of the Great Lakes – St. Lawrence forest region of central Ontario
Bibliographic record
Abstract
Abstract Question Predicted changes in the frequency of short‐term drought events raise concerns about potential effects of climate change on forest ecosystems. Few experiments have investigated the effects of tree species richness and composition in forest assemblages exposed to drought, and general conclusions for these systems are currently lacking. We tested the hypothesis that different species richness and composition affects community‐level stability during a severe short‐term drought event across a gradient of tree species assemblages typical of temperate forest ecosystems on dry substrates. Location Great Lakes – St. Lawrence forest region, central Ontario, Canada. Methods The study assemblages spanned a gradient of 15 different combinations of species richness and composition of four tree species. The drought occurred in 2005 and was characterized by high growing season temperature and low precipitation. Plots ( n = 63) representing replicate assemblages were selected to collect increment cores ( n = 1193) and examine growth responses to past drought at the community and population levels. Cross‐dated tree ring measurements were used to reconstruct basal area increment ( BAI ) and calculate indices of resistance, resilience and productivity over the drought period. We used hierarchical analysis of variance models to estimate the effects of species richness and composition on drought responses. Results We identified a significant effect of species composition on community‐level resistance and productivity. White pine ( Pinus strobus ) abundance was associated with lower community resistance and white birch ( Betula papyrifera ) abundance with higher resistance. Assemblages with greater productivity were often characterized by the high abundance of trembling aspen ( Populus tremuloides ). There was no overall effect of the assemblage gradient on the population‐level stability; however, red pine ( Pinus resinosa ) productivity was higher in combination with trembling aspen and white birch. Conclusion Our study demonstrated that species richness had no effect on the community‐level stability of growth during a drought in temperate forests on dry substrates. Instead, there were important compositional effects that determine some aspects of stability during drought events. If pine forests were managed to maintain a component of deciduous species, the capacity to dampen the community‐level effects of more frequent drought would increase.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".