Do metacommunity mass effects predict changes in species incidence and abundance?
Bibliographic record
Abstract
Metacommunity matrices contain data on species incidence or abundance across sites, compactly portraying community composition and how it varies over sites. We constructed models based on an initial metacommunity matrix of either species incidence or abundance to test whether such data suffice to predict subsequent changes in incidence or abundance at each site. The models reflect both species and site mass effects in using products of the row and column totals to predict the incidence or abundance of each species expected at each site. We tested these models against empirical data on vascular plant incidence and abundance collected from two sets of forested sites in both the 1950s and 2000s. Predictions from these models parallel observed changes in species incidence and abundance in these distinctly different metacommunities and differ greatly from null model predictions. The abundance model shows greater power than the incidence model reflecting its higher information content. Predictions were more accurate for the more diverse forests of southern Wisconsin that are changing quickly in response to succession and fragmentation. Simulations show that these results are robust but sensitive to sampling intensity. Because these models require no data on site conditions or species’ characteristics, they provide a useful baseline to assess more complex models based on species’ functional traits, local site conditions, or landscape context. They may also prove useful to conservation biologists seeking to predict local population declines and extinction risks.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".