Changes in Abundance of Least Bitterns in Ontario,1995-2019
Bibliographic record
Abstract
Populations of many breeding marsh bird species continue to decline in the southern Great Lakes basin, although this is not the case for the threatened Least Bittern. Recent analysis based on data from the Great Lakes Marsh Monitoring Program of Birds Canada shows that its abundance has increased consistently since the mid-2000s throughout the lower Great Lakes in the U.S. and Canada, with the highest abundance occurring in recent years. In this study, we expanded on these findings by assessing patterns in abundance of Least Bitterns among different geographical locations in Ontario and across years from 1995 to 2019. We found that abundance was relatively consistent in Ontario from 1995 to 2016, but notably higher from 2017 to 2019, largely due to increases in abundance of Least Bitterns at Great Lakes coastal locations (i.e., those directly influenced by fluctuating Great Lakes water levels) compared to inland, particularly in the Lake Erie basin. We also found strong evidence that the increase in abundance was closely tied to increasing water levels during the breeding season on Lake Erie and Lake Ontario. Although this appears to be a good-news story for this species of priority conservation concern, it should be emphasized that Great Lakes water levels naturally fluctuate over time, so it is reasonable to expect a decline in abundance of Least Bitterns when water levels eventually begin to recede. It is also important to realize that the increase in abundance reported here may be due to a change in distribution of Least Bitterns moving into our study area during high water rather than an increase in the total size of the population. Nonetheless, the recent increase that we observed, if it represents a genuine increase in total population size, is encouraging for this species at risk in Ontario and Canada.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 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".