Quantifying the influence of tree species richness on community drought resistance using drone-derived NDVI and ground-based measures of Plant Area Index and leaf chlorophyll in a young tree diversity experiment
Bibliographic record
Abstract
Abstract Tree diversity may buffer the negative impact of drought events according to the diversity insurance hypothesis. During the extreme pan-European drought of 2018, we tested whether tree species richness modulated drought impacts on communities of a young tree diversity experiment in Freiburg, Germany. We utilized drone-based hyperspectral images to capture early symptoms of drought stress indicated by variation in Normalized Difference Vegetation Index (NDVI), and ground-based measures that are indicative of tree canopy function, including Plant Area Index (PAI) and leaf chlorophyll content (Chl). Measures were taken the fifth growing season after planting on six broadleaved and six conifer species originating from Europe and North America in monocultures, two-, four- and six-species mixtures. NDVI decreased successively in response to the summer drought. In contrast to our expectation, tree species richness did not reduce declines in NDVI at the plot level. NDVI values were generally lower in six-species mixtures, compared to monocultures or two-species mixtures, which may be indicative of antagonistic tree species interactions in mixtures of high diversity. Changes in PAI in response to the drought were similar to changes in NDVI; however, modeled differences in PAI between mixtures were not statistically significant. Chl initially decreased, but recovered within a period of two months following the drought. Remotely sensed NDVI facilitated monitoring of the drought response of our large field experiment and could aid in monitoring canopy health in response to extreme drought events. Our observations suggest that NDVI responses are likely more strongly related to leaf shedding within tree canopies than decreases in Chl. Tree stands with a high abundance of deciduous trees may therefore be especially sensitive in triggering NDVI changes in response to drought during the stages leading up to tree mortality. Future studies of drought-related canopy responses should include monitoring of leaf shedding as well as the Chl of shed leaves.
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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) | 0.000 | 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 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".