Determinism and stochasticity in the spatial–temporal continuum of ecological communities: the case of tropical mountains
Bibliographic record
Abstract
Ecological communities are assembled in a spatial–temporal continuum. However, we still have a poor understanding of the relative importance of different mechanisms structuring community composition (i.e. beta‐diversity) in space and time. In this study, we start by introducing a conceptual model that capitalizes upon the core‐occasional species concept to predict that the assembly process in tropical mountains is driven by the deterministic turnover of core species in space via habitat sorting, but the turnover of occasional species through time via stochastic events of colonization and local extinctions. We then propose a general analytical framework that allows assessing these predictions by partitioning the total variance of a species‐by‐site‐by‐time matrix (i.e. total beta‐diversity) among its purely spatial (variation in space independent of time), purely temporal (variation in time independent of space) and spatiotemporal (i.e. variation across different sites across different moments in time) components. Through simulation models, we provided theoretical support that the proposed analytical framework is suitable to test the predictions derived from our conceptual model. We then used this framework to identify general patterns and quantify the relative importance of processes underlying the spatial and temporal organization of ten distinct insect metacommunities along a tropical elevational gradient. As predicted, we found that, across taxa, spatial beta‐diversity was mainly explained by environmental variation alone: a pattern that indicates the spatial turnover of core species. In contrast, temporal beta‐diversity could not be distinguished from the expectation of null models where communities are simply represented by random draws from species pools: a pattern that indicates a temporal turnover of occasional species within communities. Taken together, our findings illustrate how our conceptual model and quantitative framework can articulate a better understanding of community assembly in space and time.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".