The ecological and evolutionary assembly of trophic metacommunities
Bibliographic record
Abstract
Despite the important role spatial processes play in natural communities, far too little theoretical work has been devoted to exploring how complex food web communities may be assembled in space, and how the spatial structure of trophic interactions may provide a stabilizing mechanism for complex food web networks. In this thesis I develop a food web metacommunity model based on a classic Levins-type patch-dynamic model which views trophic interactions between species as occurring in a spatially subdivided habitat. I then use this model to explore both simple and complex trophic networks in an ecological and evolutionary context. I first review and evaluate previous attempts at defining a patch-dynamic metacommunity model of trophic interactions. After correcting the flaws in previously published models I develop a corrected model and apply it to simple trophic configurations. I show how the stability of simple trophic interactions, like omnivory loops, depends upon the interacting effects of space and network configuration. I then use the model to study the evolution of dispersal in a simple predator-prey system. Specifically, I investigate how both predator and prey dispersal rates will evolve in response to increasing patch extinction rates caused by locally strong top-down predator effects. I show how the predator's evolutionarily stable (ESS) dispersal rate will increase, as expected, in response to increasing local extinction, while the prey's ESS dispersal rate exhibits a counterintuitive nonmonotonic response – actually decreasing for some ranges of extinction. I explain how the prey's counterintuitive response arises because of the way trophic interactions between species play out at different spatial scales. After applying the model to simple networks I then explore the assembly of complex food webs. I show that, under very simple assumptions, diverse and complex food web networks can be assembled through the creation of network branches which provide opportunities for the build-up species and multiple food chain paths in the food web. I also show how these network branches can emerge simply as the result of the spatial distribution of trophic interactions, and the structural support provided by omnivory and generalist feeding links. I then attempt to determine if natural food webs show a relationship between biodiversity and network branching. To this end I examine a set of empirical food webs and observe a striking linear scaling relationship between food web size and the degree of branching in the minimum spanning tree of a food web. This empirical corroboration of the theory suggests that the theory reported here may be of value as a guide to how space and dispersal interact to structure natural food webs at large scales.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".