A scenario for impacts of water availability loss due to climate change on riverine fish extinction rates
Bibliographic record
Abstract
Summary Current models estimating impact of habitat loss on biodiversity in the face of global climate change usually project only percentages of species ‘committed to extinction’ on an uncertain time‐scale. Here, we show that this limitation can be overcome using an empirically derived ‘background extinction rate–area’ curve to estimate natural rates and project future rates of freshwater fish extinction following variations in river drainage area resulting from global climate change. Based on future climatic projections, we quantify future active drainage basin area losses and combine them with the extinction rate–area curve to estimate the future change in extinction rate for each river basin. We then project the number of extinct species in each river basin using a global data base of freshwater fish species richness. The median projected extinction rate owing to climate change conditions is c . 7% higher than the median background extinction rate. A closer look at the pattern reveals great geographical variations highlighting an amplification of aridity by 2090 and subsequent increase in extinction rates in presently semi‐arid and Mediterranean regions. Among the 10% most‐impacted drainage basins, water availability loss will increase background extinction rates by 18·2 times (median value). Projected numbers of extinct species by 2090 show that only 20 river basins among the 1010 analysed would experience fish species extinctions attributable to water availability loss from climate change. Predicted numbers of extinct species for these rivers range from 1 to 5. Synthesis and applications . Our results strongly contrast with previous alarming predictions of huge surface‐dependent climate change–driven extinctions for riverine fishes and other taxonomic groups. Furthermore, based on well‐documented fish extinctions from Central and North American drainages over the last century, we also show that recent extinction rates are, on average, 130 times greater than our projected extinction rates from climate change. This last result implies that current anthropogenic threats generate extinction rates in rivers far greater than the ones expected from future water availability loss. We thus argue that conservation actions should be preferentially focused on reducing the impacts of present‐day anthropogenic drivers of riverine fish extinctions.
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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".