Bibliographic record
Abstract
The competitive exclusion principle postulates that due to abiotic constraints, resource usage, inter-species interactions, and other factors, ecosystems can be divided into ecological niches, with each niche supporting only one species in steady state. Seemingly in conflict with this principle, remarkable biodiversity exists in biomes such as the human microbiome, the ocean surface, and every speck of soil. Despite their importance in human health and conservation biology, the long term dynamics, diversity, and stability of communities of multiple interacting species that occupy similar niches are still not fully understood. Biodiversity decreases as species go extinct and increases as new species establish themselves, and both extinction and invasion are moderated by interactions with other species. Classically, the theory of niches describes biodiversity, as relatively static. More recently popular, neutral theory models biodiversity as a balance of successive extinctions and invasions. Stochastic fluctuations allow mathematical models, like the neutral Moran model, to exhibit extinction. Stochasticity also connects neutral and niche theories, with Moran dynamics being one limit of a Lotka-Volterra model. The extinction times in the neutral and niche limits are qualitatively different, indicative respectively of exclusion and coexistence of two species, yet the transition between these limits has not been fully investigated. I identify the nature of the transition by calculating the mean extinction time with an arbitrarily accurate technique, discovering that competing species can coexist unless their ecological niches entirely overlap, which implies that extinction and loss of biodiversity is less common than predicted by neutral models. Biodiversity is also maintained by new species entering the system, a process I represent with a single invader in the Lotka-Volterra model and repeated immigrants into the Moran model. I demonstrate that greater niche overlap leads to longer invasion times, and less likelihood of success of an invasion attempt. With the Moran model I find the critical immigration rate at which immigrants are likely to maintain their presence in the system.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| 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".