A comparison of asymmetric before-after control impact (aBACI) and staircase experimental designs for testing the effectiveness of stream restoration
Bibliographic record
Abstract
Abstract Before-after-control-impact (BACI) experimental designs are commonly used in large-scale experiments to test for environmental impacts. However, high natural variability of environmental conditions and populations, and low replication in both treatment and control areas in time and space hampers detection of responses. We compare the power of two asymmetric BACI (aBACI) designs to two staircase designs for detecting changes in juvenile steelhead ( Oncorhynchus mykiss ) abundance associated with a watershed-scale stream restoration experiment. We performed a simulation study to estimate the effect of a 25% increase in steelhead abundance using spatial and temporal estimates of variance from an ongoing study, and determined the power of each design. Experimental designs were then applied to three streams and each stream was composed of three 4 km long sections . We compared the power of a single treatment section in one stream (BACI-1), three simultaneous treatments of all sections in one stream (BACI-3), three sequential treatments in one stream (STAIRCASE-1), and three sequential treatments in one section in each stream (STAIRCASE-3). All designs had ≥ 94% power to detect a 25% increase in abundance assuming average variance. Under worst-case variance (i.e., upper 95% confidence limits of historical variance estimates), the STAIRCASE-3 design outperformed the BACI-1, BACI-3, and STAIRCASE-1 designs (i.e., 77%, 41%, 8%, and 33% power respectively). All the designs estimated the effect of the simulated 25% abundance increase, but the length of the confidence interval was much shorter for the STAIRCASE-3 design compared to the other designs, which had confidence intervals 58-596% longer. The STAIRCASE-3 design continued to have high power (88%) to detect a 10% change in abundance, but the power of the other designs was much lower (range 34-56%). Our study demonstrates that staircase designs can have significant advantages over BACI designs and therefore should be more widely used for testing environmental impacts.
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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 | 0.042 | 0.062 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".