Competitive exclusion over broad spatial extents is a slow process: evidence and implications for species distribution modeling
Bibliographic record
Abstract
There is considerable debate about the role of competition in shaping species distributions over broad spatial extents. This debate has practical implications because predicting changes in species' geographic ranges in response to ongoing environmental change would be simpler if competition could be ignored. While this debate has been the subject of many reviews, recent literature has not addressed the rates of relevant processes. This omission is surprising in that ecologists hypothesized decades ago that regional competitive exclusion is a slow process. The goal of this review is to reassess the debate under the hypothesis that competitive exclusion over broad spatial extents is a slow process. Available evidence, including simulations presented for the first time here, suggests that competitive exclusion over broad spatial extents occurs slowly over temporal extents of many decades to millennia. Ecologists arguing against an important role for competition frequently study modern patterns and/or range dynamics over periods of decades, while much of the evidence for competition shaping geographic ranges at broad spatial extents comes from paleoecological studies over time scales of centuries or longer. If competition is slow, as evidence suggests, the geographic distributions of some, perhaps many species, would continue to change over time scales of decades to millennia, even if environmental conditions did not continue to change. If the distributions of competing species are at equilibrium it is possible to predict species distributions based on observed species–environment relationships. However, disequilibrium is widespread as a result of competition and many other processes. Studies whose goal is accurate predictions over intermediate time scales (decades to centuries) should focus on factors associated with range expansion (colonization) and loss (local extinction), as opposed to current patterns. In general, understanding of modern range dynamics would be enhanced by considering the rates of relevant processes.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".