An innovative bivariate approach to detect joint temporal trends in environmental conditions: Application to large French rivers and diadromous fish
Bibliographic record
Abstract
Most key life-events of organisms are synchronized by complex interactions of several environmental cues to ensure optimal survival and growth of individuals and their offspring. However, global change is known to affect multiple components of ecosystems and cues at the same time. Therefore, detecting joint trends in covariate time series is a crucial challenge in global change ecology that has rarely been addressed so far. In this context, we designed an innovative combination of kernel density estimations and Mann-Kendall trend tests to detect joint temporal trends in a pair of environmental variables. This methodological framework was tested on >30 years (1976-2019) of water temperature and discharge data for 6 large French rivers (the Garonne, Dordogne, Rhône, Rhine, Loire and Vienne rivers). The implications of such trends in both temperature and discharge for diadromous species key life-cycle processes were then explored by checking if significant bivariate environmental changes occurred during seasons of upstream and downstream migration, and reproductive activities. Results were contrasted between rivers and seasons: many rivers displayed an increase in the number of days with high water temperature and low river discharge, but local discharge regulation measures could have mitigated the trend in discharge. Our findings showed that species migrating or spawning in spring were likely to be strongly impacted by the new environmental conditions in the Garonne, Loire and Rhône rivers, given the marked changes in water temperature and discharge associations detected by our new method. Conditions experienced by fall-running and spawning species have been strongly affected in all the rivers studied. This innovative methodology was implemented in a new R package, ChocR, for application to other environments and ecosystems.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".