Robust species distribution predictions of predator and prey responses to climate change
Bibliographic record
Abstract
Abstract Aim Species distribution models (SDMs) can be correlative or mechanistic, which have very different assumptions, leading to potentially different estimates of the ecological niches and distributions of the species. The model predictions from correlative and mechanistic approaches are incomparable due to their distinct assumptions. Yet, seeking their agreements can identify robust predictions that are relatively independent of the assumptions used to generate them. However, the search for robust model predictions among SDM models remains understudied and rarely considers the effect of biotic interactions. It is essential to identify robust predictions from SDMs for policy making. Location Global. Time Period 1970–2000/1980–2000 and 2081–2100. Major Taxa Studies Aphids. Methods In this study, we selected five aphid species as examples, and applied an ensemble model of multiple correlative SDMs (eC‐SDM), a mechanistic SDM of the prey species alone (M‐SDM) and a mechanistic SDM of the predator–prey interactions (M‐BI‐SDM), to predict the habitat suitability of these aphids under climate change and seek robust predictions from both approaches, as well as evaluate the importance of biotic interactions in SDM studies. Results Our results show that the five aphid species have different habitat suitability patterns predicted by both correlative and mechanistic approaches. However, there is a notable consensus between the model predictions for parts of North America and eastern Asia, indicating that the predictions in these regions are robust. Additionally, our mechanistic models allow us to assess the importance of predation on SDM predictions, revealing that predation can quantitatively affect species' habitat suitability both directly and indirectly. Main Conclusions Our study suggests that mechanistic SDM could serve as a valuable addition to assess the robustness of the correlative SDM predictions, by providing additional biological realism. It highlights the importance of using diverse modelling approaches to achieve robust model predictions.
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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 | 0.001 |
| 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".