A plant‐based index of biological integrity in permanent marsh wetlands yields consistent scores in dry and wet years
Bibliographic record
Abstract
ABSTRACT Plant‐based assessments can contribute to wetland conservation and management by providing a standardized method to monitor biological communities in relation to human activity. One major challenge, however, is that their measurements must be fairly insensitive to temporal variation in community composition, which can be difficult since marsh plant communities are known to be influenced by natural climatic cycles. Variation in the scores for an index of biological integrity (IBI) was evaluated in relation to plant community changes that occurred between years with differing precipitation inputs throughout the growing season (dry: 240 mm in 2008, 198 mm in 2009; wet: 324 mm in 2010, 329 mm in 2011). Species composition and IBI scores were measured by sampling macrophytes in the centre of the wet meadow zone at 47 semi‐permanent to permanent natural and constructed marshes. Non‐metric multidimensional scaling (NMS) ordinations revealed that although plant community composition shifted between dry and wet years, IBI scores were sensitive to only 21% of the total inter‐annual variation in species composition. The first NMS axis was positively correlated with IBI scores (r = 0.85) as well as several environmental parameters (r 2 > 0.2), including dissolved organic carbon, total nitrogen, potassium, shoreline slope, and salinity. The wet meadow IBI also yielded consistent scores between dry and wet years (Spearman's rho = 0.82; Wilcoxon z‐score = 0.53, P ‐value = 0.60) and was able to distinguish a change in biological condition among sites against a backdrop of natural variation (F‐test = 3.0, P < 0.001). These findings provide support for the continued use of plants as indicators of wetland condition in permanent northern prairie marshes, providing that the range in water levels is moderate. Copyright © 2013 John Wiley & Sons, Ltd.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".