Effects of species traits, motif profiles, and environment on spatial variation in multi‐trophic antagonistic networks
Bibliographic record
Abstract
Abstract Understanding drivers of antagonistic interactions across temporal and spatial scales is important for predicting community structure. In particular, studies examining spatial variation in ecological networks are critical for anticipating community responses to anthropogenic change. Most studies examining spatial interaction turnover focus on bipartite networks begging the question of whether the results are also reflected in unipartite, multi‐trophic networks. To examine the spatial turnover in food web interactions, the environmental and ecological drivers of this, and the influence of interaction turnover on the preservation of individual species’ roles, we used a spatially expansive multi‐trophic antagonistic ecological network data set of 129 lakes spanning over 1000 kms. We used β‐diversity metrics to quantify spatial turnover in interactions and calculated the relative contributions of interaction rewiring and turnover in top, intermediate, and basal species to network turnover. We then investigated the relative and combined role of multiple ecological drivers (e.g., abundance, thermal tolerance) and environmental drivers (e.g., latitude, total phosphorus) on internal network structure. Finally, we used a motif analysis to measure the effect of spatial interaction turnover on the variation in individual species’ roles. We observed high interaction turnover across lakes, driven primarily by turnover in basal species but also the rewiring of interactions among shared species, driven, in part by underlying environmental gradients (e.g., species richness). Contrary to previous food web models applied to single sites, none of the ecological drivers we considered were effective predictors of lake‐specific interactions perhaps indicating an important distinction between network model accuracy at regional and local extents. Finally, despite high spatial turnover in interactions, species’ roles were highly conserved across the study lakes demonstrating the potential of species’ roles for predicting community structure. These findings demonstrate how integrating species’ fundamental roles into trait‐based approaches may improve our predictions of ecological networks at local scales.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".