Species, Functional Groups, and Thresholds in Ecological Resilience
Bibliographic record
Abstract
The cross-scale resilience model states that ecological resilience is generated in part from the distribution of functions within and across scales in a system. Resilience is a measure of a system's ability to remain organized around a particular set of mutually reinforcing processes and structures, known as a regime. We define scale as the geographic extent over which a process operates and the frequency with which a process occurs. Species can be categorized into functional groups that are a link between ecosystem processes and structures and ecological resilience. We applied the cross-scale resilience model to avian species in a grassland ecosystem. A species' morphology is shaped in part by its interaction with ecological structure and pattern, so animal body mass reflects the spatial and temporal distribution of resources. We used the log-transformed rank-ordered body masses of breeding birds associated with grasslands to identify aggregations and discontinuities in the distribution of those body masses. We assessed cross-scale resilience on the basis of 3 metrics: overall number of functional groups, number of functional groups within an aggregation, and the redundancy of functional groups across aggregations. We assessed how the loss of threatened species would affect cross-scale resilience by removing threatened species from the data set and recalculating values of the 3 metrics. We also determined whether more function was retained than expected after the loss of threatened species by comparing observed loss with simulated random loss in a Monte Carlo process. The observed distribution of function compared with the random simulated loss of function indicated that more functionality in the observed data set was retained than expected. On the basis of our results, we believe an ecosystem with a full complement of species can sustain considerable species losses without affecting the distribution of functions within and across aggregations, although ecological resilience is reduced. We propose that the mechanisms responsible for shaping discontinuous distributions of body mass and the nonrandom distribution of functions may also shape species losses such that local extinctions will be nonrandom with respect to the retention and distribution of functions and that the distribution of function within and across aggregations will be conserved despite extinctions.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".