Spatio‐temporal variation of macroinvertebrate metacommunity organization in a monsoon‐climate region
Bibliographic record
Abstract
Abstract Aim Disentangling the drivers of community assembly and species diversity in space and time is critical to elucidating metacommunity theory. However, our understanding of how metacommunity structuring will change over time remains insufficient, especially in rivers in the monsoon climate zone. We examined whether (1) the idealized metacommunity structure is different among seasons and (2) the relative importance of underlying mechanisms on community assembly varies within and across tributaries as well as among seasons. Location Five tributaries in the Hanjiang River Basin. Taxon Macroinvertebrates. Methods Benthic macroinvertebrates and environmental data were collected seasonally (i.e. spring, summer, autumn and winter) from the same 70 sampling sites. The elements of metacommunity structure (EMS) framework was employed to identify which idealized topology best characterizes the actual metacommunity. Redundancy analysis and variance partitioning procedures were applied to determine key environmental and spatial factors and to examine their relative contributions to variation in community structure in different seasons. Results The best‐fit metacommunity typologies were consistent over time, with metacommunity structure in each season displaying Clementsian gradients, characterized by high degrees of coherence and turnover, and positive boundary clumping. Environmental control prevailed over spatial processes in structuring macroinvertebrate communities, but their relative influence on community variation was context‐dependent. Particularly, environmental filtering was the predominant mechanism at the intermediate spatial scale. However, spatial processes had gradually stronger effects at smaller and larger extents. Main conclusions Both EMS analysis and the variance partitioning approach suggested environmental filtering was the principal structuring processes for macroinvertebrate assemblages throughout the year, although mass effects and dispersal limitation also played significant roles at smaller and larger spatial extents respectively. Considering that the most influential environmental factors shaping macroinvertebrate communities were variable across seasons, we argue that spatio‐temporal investigations would provide more information on community assembly than single snapshot studies.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".