Quantifying the role of deterministic assembly and stochastic drift in a natural community of Arctic mosses
Bibliographic record
Abstract
The interpretation of natural plant communities frequently invokes species‐sorting controlled by niche differences along spatial environmental gradients. This process of niche structuring can be explained by reference to functional traits, which provide a mechanistic explanation for community structure. In contrast, models explaining species coexistence obviate the limiting effect of niche difference, by invoking processes which cause species‐level drift, e.g. demographic stochasticity. This paper investigates a simple habitat with strong gradients (moss communities in a patterned arctic wetland) to identify signature‐patterns under‐pinning the relative importance of deterministic assembly and stochastic drift in a natural community. First, ordination analysis was used to confirm community composition structured by a range of nine carefully selected functional traits. Second, to determine whether traits explaining community composition might also explain species richness, local species richness (sR) was compared to (1) observed trait diversity and (2) expected trait diversity based on permutation tests, which are used to simulate null community assembly for different values of sR. Traits explaining species composition, consistent with deterministic niche structuring, do not appear to maintain sR. This surprising result was explained by decomposing the community into individual pair‐wise comparisons, i.e. species niche‐differences and association (χ 2 ). Results support deterministic processes via the sorting of species with similar and contrasting niches, at opposite ends of a composite environmental gradient. Nevertheless, stochastic drift is apparent in the random structure of a majority of pair‐wise associations; in addition, a species’ abundance was in general not related to environmental distance from response‐optima. We suggest therefore that spatial pattern in the moss community is a balance between deterministic forces with respect to species traits and controlling environmental gradients, and stochastic drift, which weakens this deterministic structure.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".