Seeing through the static: The temporal dimension of plant–animal mutualistic interactions
Bibliographic record
Abstract
Most studies of plant--animal mutualistic networks have been temporally static. This approach has revealed many general patterns in the structure of complex webs of mutualistic interactions, but limits our ability to understand the ecological and evolutionary processes that shape these networks, and to predict the consequences of natural and human-driven disturbance on species interactions. The growing availability of temporally explicit data is allowing ecologists to move beyond this static perspective. We review the growing literature dealing with temporal dynamics in plant--animal mutualistic networks including pollination, seed dispersal and ant defence mutualisms. We identify general patterns of temporal variation in these networks across temporal scales. We discuss potential mechanisms underlying variation in interactions, ranging from behavioural and physiological processes at the narrowest temporal scales to ecological and evolutionary processes operating over much broader temporal scales. We conclude by discussing priorities for future research, including an improved understanding of the abiotic and biotic factors driving temporal network change, and further development and refinement of analytical tools. Our review highlights the key role of the importance of considering the temporal dimension for our understanding of the ecology and evolution of complex webs of mutualistic interactions.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.007 |
| 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".