Niche concepts and the interaction between environmental filtering and competitive exclusion
Bibliographic record
Abstract
This is Chapter 5 of my PhD thesis, completed at the University of Toronto. I intend to use some of the concepts used here, simplified and clarified, in an upcoming paper, but I do not have the time or inclination to prepare the more complex model I present here for submission to a journal. However, perhaps someone out there might find it useful, and sufficiently so to wade through the extra verbiage of the long-form dissertation style of the chapter. I do think there are some interesting results and discussion in there, even if it could use some editing. Comments welcome. <strong>Abstract</strong> In the community phylogenetics literature, patterns of clustering and overdispersion are taken to be signs of environmental filtering and competitive exclusion, respectively, when phylogeny is a good proxy for ecological similarity. Competitive exclusion of ecologically similar species is often assumed to be an expectation of classic theory on limiting similarity, where ecological similarity refers to niche separation. Critics of community phylogenetics have recently pointed out that coexistence theory shows that ecological similarity can also lead to similarity in species’ relative competitive ability (or fitnesses), which will actually promote coexistence. This means that the expected effect of competition on ecological similarity is ambiguous. To fully understand the implications of this issue for community phylogenetics and related fields, however, requires incorporating a concept of environmental filtering into a model of competition, allowing a full exploration of how these factors interact to determine coexistence. Here, I present a continuous version of a three trophic level competition model – allowing species to compete both through shared resources and shared predators – which incorporates indirect biotic environmental filtering. An analysis of a simplified two species version of the model, suggests that coexistence is maximized at intermediate levels of ecological similarity, and that the strength of environmental filtering affects the position and dispersion of the ideal niche separation. I discuss how the model presented here can serve as an abstract phenomenological model that expresses many of the general characteristics of modern niche concepts.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.023 | 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 teacher head, 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".