Concomitant impacts of climate change, fragmentation and non‐native species have led to reorganization of fish communities since the 1980s
Bibliographic record
Abstract
Abstract Aim In response to climate change, species distribution shifts resulting from local extinctions, colonizations and variations in population abundances potentially lead to community‐level reorganizations. Here, we assess changes over time in stream fish communities, quantify the extent to which these changes are attributable to population declines or increases and identify their main drivers. Location France. Time period 1980–2012. Major taxa studied Stream fish species. Methods We used abundance‐monitoring data to quantify changes in composition and uniqueness for 332 stream fish communities between a cold historical period (1980–1993) and a warm contemporary period (2004–2012). Then, we used a model‐averaging procedure to test the impacts of factors related to climate, land use and non‐native species density and their interacting effects in shaping community reorganization. Results We observed biotic homogenization over time in stream fish communities, although some communities experienced differentiation. Changes in composition mainly resulted from population declines and were favoured by an increase in temperature seasonality and in non‐native species density. Population declines decreased with fragmentation and changes in non‐native species density, whereas population increases were negatively driven by changes in precipitation and positively by fragmentation. Our results provide evidence that environmental changes can interact with other factors (e.g., upstream–downstream, fragmentation intensity) to determine community reorganization. Main conclusions In the context of global change, fish assemblage reorganizations mainly result from population declines of species. These reorganizations are spatially structured and driven by both climatic and human‐related stressors. Here, we emphasize the need to take into account several components of global change, because the interplay between stressors might play a key role in the ongoing biodiversity changes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".