Predicting ecosystem functions from biodiversity and mutualistic networks: an extension of trait‐based concepts to plant–animal interactions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Research linking biodiversity and ecosystem functioning (BEF) has been mostly centred on the influence of species richness on ecosystem functions in small‐scale experiments with single trophic levels. In natural ecosystems, many ecosystem functions are mediated by interactions between plants and animals, such as pollination and seed dispersal by animals, for which BEF relationships are little understood. Largely disconnected from BEF research, network ecology has examined the structural diversity of complex ecological networks of interacting species. Here, we provide an overview of the most important concepts in BEF and ecological network research and exemplify their applicability to natural ecosystems with examples from pollination and seed‐dispersal studies. In a synthesis, we connect the structural approaches of network analysis with the trait‐based approaches of BEF research and propose a conceptual trait‐based model for understanding BEF relationships of plant–animal interactions in natural ecosystems. The model describes the sequential processes that determine the BEF relationship, i.e. the responses of species to environmental filters, the matching of species in ecological networks and the functionality of species in terms of their quantitative and qualitative contributions to plant demography and ecosystem functioning. We illustrate this conceptual integration with examples from mutualistic interactions and highlight its value for predicting the consequences of biodiversity loss for multispecies interactions and ecosystem functions. We foresee that a better integration between BEF and network research will improve our mechanistic understanding of how biodiversity relates to the functioning of natural ecosystems. Our conceptual model is a step towards this integration between structural and functional biodiversity research.
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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 | 0.000 | 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 it