Climate change has limited effect on the growth of <i>Afzelia africana</i> Sm. and <i>Anogeissus leiocarpus (DC.) Guill.</i> and <i>Perr.</i> in riparian forests in the savannas of Ghana
Bibliographic record
Abstract
Abstract The growth of trees in riparian forests in semi-arid savannas is resilient to the natural variations in temperature and precipitation due to the availability of riverine water. Climate change can nevertheless, intensify the evapotranspiration of tree species, altering biodiversity, plant productivity and ecosystem services. Understanding the growth response of riparian tree species to climate change is, therefore, critical for their management and conservation. Here, we used 23 cross-dated stem discs of Anogeissus leiocarpus (DC.) Guill. and Perr. and Afzelia africana Sm. randomly sampled from riparian forests in the humid and dry savanna regions of Ghana to assess their growth response to climate change. A generalized additive mixed model (GAMM) was used to integrate species-specific basal area increments to an array of explanatory variables that may affect growth, including tree size and seasonal temperature and precipitation between 1982 and 2013. We observed significant association between tree size, rainy and dry season temperatures and precipitation variables, and changes in tree growth. Despite the strong fluctuations in tree growth over time, the estimated growth rates of the species from the residuals of the GAMMs showed no significant change in growth. Our findings suggest that these riparian forests are highly resistant to weather extremes and therefore, might persist (up to a certain point) even if climate change continues to intensify.
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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 | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".