Uncertainty in ensemble forecasting of species distribution
Bibliographic record
Abstract
Abstract Species distribution modelling has been widely applied in order to assess the potential impacts of climate change on biodiversity. Many methodological decisions, taken during the modelling process and forecasts, may, however, lead to a large variability in the assessment of future impacts. Using measures of species range change and turnover, the potential impacts of climate change on French stream fish species and assemblages were evaluated. Our main focus was to quantify the uncertainty in the projections of these impacts arising from four sources of uncertainty: initial datasets (Data), statistical methods [species distribution models (SDM)], general circulation models (GCM), and gas emission scenarios (GES). Several modalities of the aforementioned uncertainty sources were combined in an ensemble forecasting framework resulting in 8400 different projections. The variance explained by each source was then extracted from this whole ensemble of projections. Overall, SDM contributed to the largest variation in projections, followed by GCM, whose contribution increased over time equalling almost the proportion of variance explained by SDM in 2080. Data and GES had little influence on the variability in projections. Future projections of range change were more consistent for species with a large geographical extent (i.e., distribution along latitudinal or stream gradients) or with restricted environmental requirements (i.e., small thermal or elevation ranges). Variability in projections of turnover was spatially structured at the scale of France, indicating that certain particular geographical areas should be considered with care when projecting the potential impacts of climate change. The results of this study, therefore, emphasized that particular attention should be paid to the use of predictions ensembles resulting from the application of several statistical methods and climate models. Moreover, forecasted impacts of climate change should always be provided with an assessment of their uncertainty, so that management and conservation decisions can be taken in the full knowledge of their reliability.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".