Implications of Sampling Frequency for Detecting Temporal Patterns during Environmental Effects Monitoring
Bibliographic record
Abstract
Abstract To estimate the effects of sampling frequency on detecting temporal patterns during environmental effects monitoring, we used multivariate analyses and data subsampling to investigate long-term (spanning 20 years) patterns in benthic invertebrate community structure downriver of a large pulp mill in southern British Columbia. Patterns in invertebrate abundance sampled yearly were related to long-term patterns in several physicochemical variables measured in the river using multidimensional scaling ordination. The only available physicochemical variables that were significantly correlated with invertebrate community structure over the 20-year period were the mill outputs of total phosphorus and suspended solids, and these were associated with increased abundances of five families of mayflies, stoneflies and caddisflies. To evaluate the implications of sampling on a more coarse (than yearly) time scale, the full data set spanning 20 years was subsampled to produce a series of smaller data sets, each simulating a sampling frequency of once every three years. Ordination of the subsample data sets showed that an average of 71% of the important taxa and 50% of the important physicochemical variables highlighted in the full analysis were missed in the subset analyses. These results underscore the importance of ensuring adequate temporal replication of sampling effort when a major goal is to directly measure or test for temporal patterns of stressor impacts.
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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 | 0.003 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".