Uncertainties in coupled species distribution–metapopulation dynamics models for risk assessments under climate change
Bibliographic record
Abstract
Abstract Aim Species distribution models ( SDM s) coupled with metapopulation dynamics models can integrate multiple threats and population‐level processes that influence species distributions. However, multiple sources of uncertainties could lead to substantial differences in model outputs and jeopardize risk assessments. We evaluate uncertainties in coupled species distribution –metapopulation dynamics models and focus on two often underappreciated sources of uncertainty: the choice of general circulation model ( GCM ) and demographic parameter uncertainty of the metapopulation model. We rank the risks associated with potential climate changes and habitat loss on projected range margin dynamics of the Hooded Warbler ( Setophaga citrina ). Location Breeding range of the Hooded Warbler, North America. Methods Using SDM s, we quantified variability in projected future distributions using four GCM s and a consensus model at the biogeographic scale and assessed the propagation of uncertainty through to metapopulation viability projections. We applied a global sensitivity analysis to the coupled species distribution –metapopulation models to rank the influence of choice of GCM , parameter uncertainty and simulated effects of habitat loss on metapopulation viability, thereby addressing error propagation through the whole modelling process. Results The Hooded Warbler range was consistently projected to shift north: choice of GCM s influenced the magnitude of change, and variability was spatially structured. Variability in the choice of GCM s propagated through to metapopulation viability at the northern range boundary. Although viability measures were sensitive to the GCM used, measures of direct habitat loss were more influential. Despite the high ranking of vital rates in the global sensitivity analysis, direct habitat loss had a larger negative influence on extinction risk than potential future climate changes. Main conclusions This work underscores the importance of a global sensitivity analysis framework applied to coupled models to disentangle the relative influence of uncertainties on projections. The use of multiple GCM s enabled the exploration of a range of possible outcomes relative to the consensus GCM , helping to inform risk estimates. Ranking uncertainties informs the prioritization of management actions for species affected by dynamic anthropogenic threats over multiple spatial scales.
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 | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".