Studying the spatial structuring of chemical elements through the prism of community and landscape ecology
Bibliographic record
Abstract
Approximatively 25 chemical elements are essential for the maintenance, growth and reproduction of all living organisms. Hence, the movement, distribution, and relative proportions of those elements on the landscape should influence the structure and functioning of biological communities. Yet our basic understanding for the spatial distribution of elements across landscapes is limited. Here, we propose to apply tools from community and landscape ecology to study spatial patterns in elements. We illustrate this framework using tree leaves elemental composition and demonstrate how spatial grain and spatial dissimilarity of elements interact leading to predictable patterns in elemental distributions at various spatial scales. Meanwhile, further analysis revealed that potassium and calcium are the most important elemental contributors to spatial dissimilarity in leaf elements, raising new questions about their role in, or response to, distributions of biodiversity and ecosystem functions. Our framework provides a way to integrate abiotic and biotic processes, demonstrating how we can use community metrics to investigate variability of individual elements across landscapes. We conclude by hypothesizing that changes in the evenness or beta-diversity of elements should reflect the structure of biotic communities, providing a long-sought mechanistic link between community and ecosystem processes that can be measured directly in the field.
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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 | 0.002 |
| 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 | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".