Bibliographic record
Abstract
Individuals of different species may interact in many different ways, such as competition, mutualism, or predation, to name but a few. Recent theory and experiments reveal that whether an interaction is beneficial or detrimental to the dynamics of a population often depends on species densities and other environmental factors. Here, we explore how, for suitable densities, facilitation may arise between two competing species with an Allee effect. We consider two different mechanisms for the Allee effect: 1) plant species with obligate insect pollination, and 2) generalist predation. In the first case, a second plant species, competing for nutrients, may have a facilitative effect by attracting more pollinators. In the second case, another potentially competing species may serve to satiate the same generalist predator and thereby have a facilitative effect. We explore three aspects of facilitation in each of the two systems. The focal species may benefit from the presence of a ‘competitor’ if it experiences 1) the removal of the Allee threshold, 2) a lowering of the Allee threshold, or 3) an increase in carrying capacity. We find that the latter two effects occur in both study systems whereas the first only occurs for the generalist predation system but not for the plant‐pollination system. We give precise conditions on when such a facilitative effect can be expected. We also demonstrate several unexpected outcomes of these two‐species interactions with multiple steady states, such as obligate co‐occurence; we draw parallels to the dynamics of species known as ‘ecosystem engineers’, and we discuss implications for conservation and management.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".