Functional markers to predict forest ecosystem properties along a rural‐to‐urban gradient
Bibliographic record
Abstract
Abstract Questions With increasing focus on urban sustainability, new tools are needed to manage urban woodlands for resilience and ecosystem service provision. Functional traits can provide quantitative and testable links between urban plant communities and specific ecosystem properties (functional markers). We ask whether commonly described multivariate patterns of trait association with urbanization (trait syndromes) capture changes in ecosystem properties associated with urbanization. Given that environmental heterogeneity can generate weak or non‐linear trait–ecosystem property relationships, we ask whether linear methods can yield functional markers with significant power for different ecosystem properties. Location Montreal metropolitan area (Canada). Methods We documented the functional composition of 43 woodlands along an urbanization gradient and measured proxies of three ecosystem properties: plant colonization, soil water infiltration and organic matter decomposition. We use redundancy analysis to identify traits associated with urbanization, and multiple linear regression and model selection to identify response and effect traits that best predict actual differences in ecosystem properties. We compare the resulting linear model with a non‐linear equivalent. Results Traits associated with urbanization (urban syndrome) were inconsistently selected as best predictors of ecosystems properties (functional markers). Although predictive power varied between ecosystem properties, all three could be significantly predicted from community‐weighted traits (functional markers), with both response and effect traits contributing to the final model. When we fitted equivalent non‐linear models, we found that traits had largely non‐linear relationships with ecosystem properties. Conclusions Our results demonstrate that community‐weighted traits of urban woodlands can yield functional markers that capture ecosystem properties, but these are inconsistently identified by “trait syndrome” approaches. In linear combinations, such functional markers provide a testable and generalizable way to quantify ecosystem properties in urban woodlands. Capturing such properties is one important step toward management of woodlands for their continued ability to provide ecosystem services into the future.
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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 | 0.001 | 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 | 0.000 |
| 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 | 0.001 |
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".