Routine production of population trends from citizen science data: insights into the dynamics of common bird and plant species in France
Bibliographic record
Abstract
The ongoing environmental crisis, driven by human activities, has resulted in significant biodiversity losses across various taxa, affecting ecosystem functioning. To deal with this crisis, policymakers have notably established the Kunming-Montreal Global Biodiversity Framework, which includes targets to mitigate biodiversity loss by 2050. To achieve this goal, reliable and ecologically relevant indicators are essential to quantify and qualify biodiversity changes. Temporal trends in species abundance or occurrence have been proposed as useful indicators. In France, the Vigie-Nature program engages volunteers in biodiversity monitoring through various schemes, thereby producing relevant data to estimate country-wide temporal trends for various taxonomic groups. Some indicators of population trends are already produced for some taxa, but the analysis pipelines remain unpublished and need extensions to accommodate monitoring schemes collecting presence/absence instead of abundance data, such as the Vigie-flore plant monitoring scheme. Here, we present a newly developed analysis pipeline to estimate population trends, which handles different data types and protocol specificities, and goes beyond linear population trends by considering multiple time periods and visualizing non-linear dynamics. In addition to introducing the methodology and making it available, we ran this pipeline to produce population trends for 148 bird and 181 plant species in France, based on abundance data from STOC (French Breeding Bird Survey) and occurrence data from Vigie-flore schemes. Results show as many increasing as decreasing bird population trends over the past 23 years, and a tendency for more decreasing than increasing plant population trends over the past 15 years, thereby revealing significant changes in community composition. Specifically, for birds, most habitat generalist species showed stable or increasing population trends, while most habitat specialist species showed stable or decreasing population trends, suggesting biotic homogenization. This pipeline and first analyses provide an unprecedented overview of bird and plant population trends, and contribute to the production of biodiversity indicators based on open science and reproducible research.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".