Can datasets from long-term biomonitoring programs detect climate change effects on stream benthos?
Bibliographic record
Abstract
We analyzed datasets from a long-term monitoring program of stream ecosystems in British Columbia, Canada, to determine whether or not it could detect climate change effects. In the Fraser River Basin (monitoring timespan 1994-2019), there was a marked (∼50%) increase in alpha diversity in reference streams, while BC North Coast (2004-2021) streams showed a modest trend of decreasing diversity and Columbia River Basin (2003-2018) and Vancouver Island (2001-2019) streams showed modestly increasing diversity. In all four regions, diversity across all sites in a specific period was primarily a function of sampling effort during this period rather than a temporal trend. Across all the regions, only three of 21 groups of faunally similar sites defined by Reference Condition Approach predictive modeling showed a suggestion of a directional change in community structure over time. Only 1 of 15 reference sites that were repeatedly sampled over several years showed a pattern that may indicate a response to changing climate. Three, not mutually exclusive, reasons why we did not see a clear effect of climate change on BC stream ecosystems were: 1) Little or no effect of climate change relative to other, potentially interacting biotic and abiotic factors, 2) The timespan of monitoring was too short to detect cumulative effects of climate change, and, most importantly, 3) The sampling design and protocol were unable to detect climate change effects. To better detect and characterize the effects of climate change on streams in monitoring programs, we recommend annual re-sampling of a few reference sites and detailed analysis of the natural and human environment of the sites along with better characterization of the benthic community (e.g. with eDNA) at all monitored sites.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".