FORUM: Sustaining ecosystem functions in a changing world: a call for an integrated approach
Bibliographic record
Abstract
Summary With ever‐increasing human pressure on ecosystems, it is critically important to predict how ecosystem functions will respond to such human‐induced perturbations. We define perturbations as either changes to abiotic environment (e.g. eutrophication, climate change) that indirectly affects biota, or direct changes to biota (e.g. species introductions). While two lines of research in ecology, biodiversity–ecosystem function ( BDEF ) and ecological resilience ( ER ) research, have addressed this issue, both fields of research have nontrivial shortcomings in their abilities to address a wide range of realistic scenarios. We outline how an integrated research framework may foster a deeper understanding of the functional consequences of perturbations via simultaneous application of (i) process‐based mechanistic predictions using trait‐based approaches and (ii) detection of empirical patterns of functional changes along real perturbation gradients. In this context, the complexities of ecological interactions and evolutionary perspectives should be integrated into future research. Synthesis and applications . Management of human‐impacted ecosystems can be guided most directly by understanding the response of ecosystem functions to controllable perturbations. In particular, we need to characterize the form of a wide range of perturbation–function relationships and to draw connections between those patterns and the underlying ecological processes. We anticipate that the integrated perspectives will also be helpful for managers to derive practical implications for management from academic literature.
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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 | 0.036 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.025 |
| Scholarly communication | 0.016 | 0.032 |
| Open science | 0.006 | 0.018 |
| Research integrity | 0.022 | 0.022 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".