Utilizing gradient simulations for quantifying community‐level resistance and resilience
Bibliographic record
Abstract
Abstract Resilience is a complex, multidimensional property of ecosystems that describes how ecosystems respond to disturbance and likely results from the interactions of species and their environments across temporal and spatial scales. Due to the complexity in how ecosystems function and respond to disturbance, measuring resilience is a challenge. Gradient analysis provides a familiar, yet somewhat neglected framework for understanding and characterizing resilience. With simulations parameterized on existing biomonitoring data, we used distance‐based measures in ordination space to characterize community‐level resilience, here defined as a function of resistance and recovery. Our simulations and analyses involved five steps: (1) We generated regional species pools by simulating species distributions across environmental gradients; (2) we sampled from these regional species pools to emulate temporal changes in reference (i.e., minimally disturbed) and impacted communities responding to disturbance; (3) we performed ordinations on observations from both impacted and reference communities to summarize multivariate data; (4) we calculated distance‐based measures for individual community trajectories in the ordinations to quantify their relative resistance and resilience; and (5) we compared these distance‐based metrics between reference and impacted communities. We conclude with an empirical example demonstrating the lack of resistance of the Harp Lake (Ontario, Canada) zooplankton community to invasion relative to the changes observed among minimally disturbed reference communities. Overall, distance measures on ordinations provide a simple and effective visual framework to quantify the relative resistance and resilience of communities to disturbance, and our simulation approach provides a novel technique to develop and evaluate quantitative metrics related to ecosystem or community‐level processes.
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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 | 0.000 | 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 | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 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".