Detecting signatures of competition from observational data: a combined approach using <scp>DNA</scp> barcoding, diversity partitioning and checkerboards at small spatial scales
Bibliographic record
Abstract
Summary Competitive interactions are assumed to play a role in governing species distributions but are difficult to infer using observational data. In addition, morphological identification may overlook species, obscuring patterns of competitive exclusion. To address these limitations, we showcase a molecular (DNA barcoding) approach to species‐unit delineation and sample stream insect communities at small spatial scales where environmental filtering and dispersal limitations are minimised. Rocks from riffles were sampled for stream insects, in particular caddisfly (Trichoptera), beetle (Coleoptera) and blackfly (Simuliidae) larvae from filterer and grazer guilds at White Clay Creek, Pennsylvania, U.S.A. (20 June, 2013). Additive partitioning of species diversity was used to establish at which spatial scale(s) variation in the distribution of species occurs; this informed us of the spatial scale at which to conduct checkerboard analyses, which tested for signatures of species segregations (e.g. competitive exclusion). Only a small portion of the total species diversity occurred at the smallest spatial level (mean = 30% on individual rocks). Distributional variation in species was greatest at the kilometre scale (where beta‐diversity was 37% and 41% for filterers and grazers respectively). At the scale of White Clay Creek sampled (c. 3 km extent), species of filterers showed a strong tendency to segregate on individual rocks, while grazers showed random structure approaching aggregation. This study demonstrates a potential approach to assessing the role of competition in structuring communities using observational data and highlights the importance of accurate species units for analysis (e.g. DNA barcoding). Using a combination of analyses, we were able to link patterns of segregation to competitive interactions among stream insect species in a filterer guild, while determining these interactions were not important in a grazer guild.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".