Integrated assessment models for ecologists: the present and the future
Bibliographic record
Abstract
Abstract Aim Human impacts on the biosphere are a matter of urgent and growing concern, with ecologists increasingly being asked to project biodiversity futures. The Intergovernmental Platform on Biodiversity and Ecosystem Services ( IPBES ) is likely to comprehensively assess such projections, yet despite being widely used and potentially critical tools for analysing socio‐environmental futures, integrated assessment models ( IAM s) have received little attention from ecological modellers. We aim to raise awareness and understanding of IAM s among ecologists by describing the structure and composition of IAM s, assessing their utility for biodiversity projections and identifying limitations that hamper greater interaction between scientists using IAMs and those using ecological models. We also hope to inspire more accessible and applicable models by suggesting development needs for IAM s. Methods We conduct a systematic review of four state‐of‐the‐art IAM s, which describes and contrasts key model features and analyses six aspects of IAM s that are of fundamental interest to ecologists. Conclusions IAM s could be valuable for modelling biodiversity futures; however, current IAM s were not developed for this application and challenges remain for ecologists looking to use their outputs. Separating and understanding the differences resulting from IAM formulation and those resulting from specific scenario assumptions is currently problematic, and current IAM s may be unable to accurately represent environmental conditions for both Earth‐system projections and for building robust models of biodiversity because key ecological processes are absent. We suggest that model intercomparisons would identify differences in model dynamics, and detailed studies of how dynamical interactions between components influence behaviour would address why such differences arise. Bio‐economic fisheries models and agriculture pollination models provide starting points for integrating key ecological feedbacks within IAM s. Ultimately, making IAM s more accessible within the multidisciplinary study of global change, drawing on user‐centred research, would enable more resolved, reliable and accurate assessment of how Earth's socio‐ecological system is approaching planetary boundaries.
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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 | 0.000 | 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) | 0.000 | 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".