Congruence of biodiversity measures among larval dragonflies and caddisflies from three Canadian rivers
Bibliographic record
Abstract
Summary 1. Scientists tasked with collecting taxon richness and assemblage variation data for conservation purposes have identified biomonitoring studies as potential sources of information. This approach assumes that biodiversity patterns revealed by biomonitoring reflect those of the wider community, an assumption not thoroughly tested in riverine ecosystems. 2. We compared patterns of taxon richness and assemblage variation in an important biomonitoring group (Trichoptera) with a group with high conservation significance (Odonata) at 34 sites across three fifth‐order catchments. We also explored the effect of abundance on observed patterns by rarefying the larval Trichoptera data set. 3. Our results indicate that Trichoptera do not fully reflect site‐scale taxon richness or assemblage variation in Odonata. The magnitude of odonate assemblage variation was much greater than that of Trichoptera for one of the catchments. Odonata and Trichoptera richness was moderately correlated in two catchments, while assemblage variation was strongly correlated in another pair of catchments. However, comparisons based on rarefied data eliminated differences in the magnitude of assemblage variation and strengthened correlations in richness and assemblage variation, suggesting the lack of congruence in these measures might be due to differences in abundance among groups. Further, incomplete taxonomy may mask additional assemblage variation, particularly in Trichoptera. 4. Conservation planning in riverine ecosystems based on proxies derived from biomonitoring data should proceed cautiously until we understand how well the resulting information reflects biodiversity patterns in under‐sampled taxa and habitats. Future studies of biodiversity congruence should consider both richness and assemblage variation as each provides valuable information for conservation‐related decisions. The taxonomic resolution and relative abundance of comparison groups can potentially impact the strength, direction and statistical significance of patterns. Researchers should employ species‐level taxonomy and account for differences in abundance among groups through rarefaction where at all possible and DNA‐based taxonomy methods can support this.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".