Establishing perennial strips in a cultivated floodplain for habitat restoration: monitoring over 4 years and success drivers
Bibliographic record
Abstract
Floodplains, one of the most biologically diverse and productive ecosystems, are under threat from intensive crop production. Implementing perennial strips alongside agricultural ditches and streams could reduce negative impacts of intensive agriculture and restore wildlife habitats in cultivated floodplains. To successfully set up perennial strips, it is important to understand the parameters that drive their establishment. Here we assessed the establishment success of reed canarygrass (RCG; Phalaris arundinacea ) strips in the lake Saint Pierre (LSP) floodplain, Québec, Canada by monitoring RCG biomass and vegetation height over 4 years and identify the factors driving its establishment. A total of 26 RCG strips across six municipalities of LSP were monitored. Biomass and vegetation height of RCG increased over time to reach an average of 5048 kg/ha in year 4 and 104 cm in year 3 in established strips. The RCG established successfully in 62% of surveyed plots and three environmental parameters explained 61% of this success. Establishment of RCG was most successful when a first rain came right after seeding (<3 days). High clay content and low elevation were associated with establishment failures. Overall, our results highlight the ability of RCG strips to restore dense perennial vegetation cover in cultivated floodplain, thereby providing suitable habitat for fish spawning during spring floods. This study provides significant insight into the drivers of establishment of perennial grass strips in highly constrained cultivated areas such as floodplains.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 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".