Characterizing biotic interactions within the Order Lagomorpha using Joint Species Distribution Models at 3 different spatial scales
Bibliographic record
Abstract
Species Distribution Models (SDMs) rarely incorporate biotic interactions, even though the latter may have great impacts on biogeographical patterns, because interactions can be difficult to model in time and space. In addition, the resolution of input data can have dramatic effects on results, with coarser resolutions unlikely to capture climatic variation at small scales, particularly in mountainous regions. Joint SDMs can be used to explore distributions of multiple, coexisting species and characterize modeled biotic interactions; however, the influence of scale on predictions is yet to be tested. We produced Joint SDMs for European lagomorph species at 3 hierarchical resolutions and calculated residual and environmental correlations that could explain why species may or may not co-occur, thereby suggesting biotic interactions. European lagomorph species exhibited similar environmental and biotic responses at all 3 resolutions (50 km, 25 km, and 10 km), with models at finer resolutions producing more precise estimates but requiring considerable computing time. The majority of pairwise residual responses were negative, indicating that European lagomorph species co-occur less than expected given their similarity in environmental responses, and suggesting modeled biotic interactions consistent with those reported in the literature. Fine-scale data and models offer greater precision but are not always necessary for multispecies models. However, caution is advised when inferring biotic interactions using data and models based on a coarser scale.
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 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".