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Record W4315776986 · doi:10.1101/2023.01.10.523527

Spatial exclusion leads to tug-of-war ecological dynamics between competing species within microchannels

2023· preprint· en· W4315776986 on OpenAlexafffund
Jeremy Rothschild, Tianyi Ma, Joshua N. Milstein, Anton Zilman

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2023
Typepreprint
Languageen
FieldSocial Sciences
TopicEvolutionary Game Theory and Cooperation
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsFixation (population genetics)EcologySpatial ecologyExtinction (optical mineralogy)BiologyPopulationEcosystemSpatial distributionCompetition (biology)Evolutionary biologyStatistical physicsBiological systemPhysicsMathematicsStatistics

Abstract

fetched live from OpenAlex

Abstract Competition is ubiquitous in microbial communities, shaping both their spatial and temporal structure and composition. Many classic minimal models, such as the Moran model, have been employed in ecology and evolutionary biology to understand the role of fixation and invasion in the maintenance of a population. Informed by recent experimental studies of cellular competition in confined spaces, we extend the Moran model to explicitly incorporate spatial exclusion through mechanical interactions among cells within a one-dimensional, open microchannel. The results of our spatial exclusion model differ significantly from those of its classical counterpart. The fixation/extinction probability of a species sharply depends on the species’ initial relative abundance, and the mean time to fixation is greatly accelerated, scaling logarithmically, rather than algebraically, with the system size. In non-neutral cases, spatial exclusion tends to attenuate the effects of fitness differences on the probability of fixation, and the fixation times increase as the relative fitness differences between species increase. Successful fixation by invasive species, whether through mutation or immigration, are also less probable on average than in the Moran model. Surprisingly, in the spatial exclusion model, successful fixations occur on average more rapidly in longer channels. The mean time to fixation heuristically arises from the boundary between populations performing either quasi-neutral diffusion, near a semi-stable fixed point, or quasi-deterministic avalanche dynamics away from the fixed point. These results, which can be tested in microfluidic monolayer devices, have implications for the maintenance of species diversity in dense bacterial ecosystems where spatial exclusion is central to the competition, such as in organized biofilms or intestinal crypts. The results may be broadly applied to any system displaying tug-of-war type dynamics with a region of quasi-neutral diffusion centered around regions of deterministic population collapse. Author summary Competition for territory between different species has far reaching consequences for the diversity and fate of bacterial communities. In this study, we theoretically and computationally study the competitive dynamics of two bacterial populations competing for space in confined environments. The model we develop extends classical models that have served as paradigms for understanding competitive dynamics but did not explicitly include spatial exclusion. We find that spatial effects drastically change the probability of one species successfully outcompeting the other and accelerates the mean time it takes for a species to exclude the other from the environment. In comparison to the predictions of population models that neglect spatial exclusion, species with higher selective advantages are less heavily favoured to outcompete their rival species. Moreover, spatial exclusion influences the success of an invasive species taking over a densely populated community. Compared to classical well-mixed models, there is a reduction in the effectiveness of an invaders fitness advantage at improving the chances of taking over the population. Our results show that spatial exclusion has rich and unexpected repercussions on species dominance and the long-time composition of populations. These must be considered when trying to understand complex bacterial ecosystems such as biofilms and intestinal flora.

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 imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.630
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.031
GPT teacher head0.264
Teacher spread0.233 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2023
Admission routes2
Has abstractyes

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