Species distribution models of a predator-prey system under climate change
Bibliographic record
Abstract
Mechanistic and correlative models are two types of species distribution models (SDMs). They each have distinct foci, conceptual foundations, and levels of dependency on data availability, leading to potentially different estimates of species’ ecological niches and distributions. Mechanistic SDMs integrate detailed biological processes, making it possible to account for species’ biotic interactions. Despite their assumed importance, interactions in species distribution modeling remain uncommon. In this study, we applied an ensemble model of multiple correlative SDMs, a mechanistic SDM of the focal species (prey) alone, and a mechanistic SDM of the predator-prey interactions, to compare the predictions of correlative and mechanistic approaches and assess their relative strengths and limitations. We predict there are considerable and subtle differences in various predictions generated by the correlative and mechanistic approaches for each aphid species, which call for prior knowledge concerning species’ presence data or life histories. Our mechanistic SDMs allowed for the assessment of the relative significance of abiotic and biotic factors, along with their interactions, in determining species’ habitat suitability. Additionally, we predict aphid habitat suitability decreases across continents due to the effect of predation. However, this decrease may be offset or enhanced by the interaction effect between predation and climate change in different regions. This suggests the necessity of accounting for biotic interactions and the interplay between abiotic and biotic factors in mechanistic approaches. Our research highlights the impact of model philosophies in SDM studies and addresses the importance of selecting an appropriate modeling approach in line with the study’s objectives. Furthermore, our study suggests that mechanistic SDMs could serve as a valuable addition for assessing the robustness of correlative SDM predictions.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".