Biodiversity affects microclimate - plant diversity and functional traits driving temperature and humidity using proximal sensing.
Bibliographic record
Abstract
Microclimate ecology conveys fundamental information about how organisms react to and feedback to influence climate change. Evidence shows that vegetation and its spatial variation modify microclimate temperature and relative humidity, mediating thermal regulation and energy exchange with the atmosphere by affecting vapour pressure deficit (VPD) [2]. This process influences crucial eco-physiological processes such as carbon capture, nutrient cycling, and flower visitation, promoting ecosystem productivity. Diverse communities typically display complex canopies due to functionally dissimilar species that spatially complement each other. The differences in diverse communities' canopy have the potential to modulate energy exchange and affect canopy surface temperature [1]. However, microclimate measurements are typically made at the coarse spatial scale using climate means based on meteorological stations or satellites, which ignore the bounded exchange between upper and lower canopy layers. Our approach integrates sub-canopy sensors with remotely sensed products and reveals that microclimate is driven by plant functional traits, groups, and the diversity of plant communities [1, 2, 3]. In biodiversity experiments, we assessed microclimate using under- and upper-canopy microclimate sensors and estimated plant canopy structure with a high spatial resolution (proximal sensing such as terrestrial laser scanning). We demonstrate that examining trait-microclimate relationships reveals the potential of diverse communities and communities dominated by species with particular traits to buffer ecosystems from the negative effects of warming and air dryness. Fundamentally, we propose that future work focuses on the facilitative effects of vegetation microclimate, indicating how plant community composition and diversity feedback on vegetative cooling and air humidification under more frequent and intense climate change events.[1] Guimarães-Steinicke et al. (2021) J Ecol. 109: 1969–1985, http://doi.org/10.1111/1365-2745.13631[2] Wright et al. (2024) Journal of Ecology, 112, 2462–2470. https://doi.org/10.1111/1365-2745.14313[3] English et al. (2022) Frontiers, 10, https://doi.org/10.3389/fevo.2022.921472
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".