Effect of habitat complexity attributes on species richness
Bibliographic record
Abstract
Habitat destruction is a leading cause of biodiversity loss worldwide. Destruction involving structural simplification tends to be a large contributing factor to this loss as many studies have reported a positive relationship between habitat complexity and taxonomic richness. However, the aspects of complexity that are most important for this relationship are still unclear. We tested whether several attributes of complexity contribute significantly to the effects of habitat complexity on macroinvertebrate richness. We sampled macroinvertebrates associated with several species of macrophytes covering a wide complexity gradient in freshwater coastal wetlands. Macrophyte complexity was quantified by measuring vertical and horizontal interstitial distances. Multiple regression was used to assess the relative importance of complexity attributes including the overall complexity as a space size/frequency index, space‐size heterogeneity as the variation in space sizes, as well as the more commonly used macrophyte biomass, number of stems and the number of macrophyte species. Our results indicate that space‐size heterogeneity is a more important contributor to taxonomic richness than overall complexity and the other complexity attributes examined. The results of this study have implications for the use of this concept in habitat restoration by the enhancement of habitat structures.
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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".