Mixed-species groups and aggregations: shaping ecological and behavioural patterns and processes
Bibliographic record
Abstract
Mixed-species groups of birds, fishes and mammals have traditionally been described in taxa-specific journals. However, mixed-species systems are actually more widely found when one includes aggregative (non-moving) systems, such as those common in amphibians and invertebrates. The objective of this special issue is to dispel the idea that mixed-species phenomena are a 'niche topic' to ecology and instead explore how taking a mixed-species perspective can change our conception of important ecological patterns and processes. A mixed-species perspective starts by understanding the relative abundance and positioning of individuals of different species and their behavioural synchrony; it is enriched by understanding differences between species in their vulnerability/attractiveness to predators, their potential for competing with other group participants and their use as a source of public information. Contributions to the special issue show how the mixed-species perspective can change our ideas about invasion ecology, island biogeography, keystone species, mimicry, predator eavesdropping and more. Rather than seeking synthesis, the special issue celebrates the taxonomic and conceptual breadth of the field of mixed-species groups, with detailed descriptions of many distinctive systems. This article is part of the theme issue 'Mixed-species groups and aggregations: shaping ecological and behavioural patterns and processes'.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".