Partitioning fish communities into guilds for ecological analyses: an overview of current approaches and future directions
Bibliographic record
Abstract
A major strength of the guild approach is its ability to simplify community analysis by aggregating species with similar roles or functions into groups. These groups can be used to study a number of important ecological concepts, including functional diversity, community response to disturbance, and food-web dynamics. Despite increased use, guild membership can be based on subjective criteria that are arbitrarily chosen, leading to inconsistencies across studies. Additionally, studies using the guild approach generally ignore ontogenetic changes in diet and habitat use and therefore do not fully capture the complexity of aquatic communities. Although these issues have been discussed in the literature, much has changed since the last review was published a decade ago. In our examination, we discuss data requirements and consequences of data availability and reliability on guild formation. We identify bootstrapping and permutation techniques developed to address limitations through cluster validation and the identification of ontogenetic shifts prior to guild delineation. Lastly, we provide a step-by-step guide to guild analysis, accompanied by a decision tree, to facilitate objective and informed guild creation.
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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 | 0.022 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".