Topological structure and interaction strengths in model food webs

Christopher Quince; Paul Higgs; Alan McKane

Home/Archive/arXiv

arXiv · q-bio/0402014

Topological structure and interaction strengths in model food webs

Christopher Quince·Paul Higgs·Alan McKane

View original source ↗Read source document ↗Export metadata ↓

Abstract

We report the results of carrying out a large number of simulations on a coevolutionary model of multispecies communities. A wide range of parameter values were investigated which allowed a rather complete picture of the change in behaviour of the model as these parameters were varied to be built up. Our main interest was in the nature of the community food webs constructed via the simulations. We identify the range of parameter values which give rise to realistic food webs and give arguments which allow some of the structure which is found to be understood in an intuitive way. Since the webs are evolved according to the rules of the model, the strengths of the predator-prey links are not determined a priori, and emerge from the process of constructing the web. We measure the distribution of these link strengths, and find that there are a large number of weak links, in agreement with recent suggestions. We also review some of the data on food webs available in the literature, and make some tentative comparisons with our results. The difficulties of making such comparisons and the possible future developments of the model are also briefly discussed.

Explore related subjects

q-bio.PE cond-mat.stat-mech

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

Christopher Quince, Paul Higgs, Alan McKane. 2004-02-07. Topological structure and interaction strengths in model food webs. https://arxiv.org/abs/q-bio/0402014

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Detection of evolutionary shifts in variance under an Ornsten-Uhlenbeck model

Sudden changes in environmental conditions can lead to evolutionary shifts not only in the optimal trait value (theta), but also in the diffusion variance (sigma^2) under the Ornstein-Uhlenbeck (OU) model. While several methods have been developed to detect shifts in optimal values, few explicitly account for concurrent shifts in both evolutionary variance and diffusion variance. We use a multi-optima and multi-variance OU model to describe trait evolution with shifts in both theta and sigma^2, and analyze how covariance between species is affected when shifts in variance occur along the phylogeny. We propose a new method that simultaneously detects shifts in both variance and optimal values by formulating the problem as a variable selection task using an L1-penalized loss function. Our method is implemented in the R package ShiVa (Detection of evolutionary shifts in variance). Through simulations, we compare ShiVa with existing methods that can automatically detect evolutionary shifts under the OU model (l1ou, PhylogeneticEM, and PCMFit). Our method demonstrates improved predictive ability and significantly reduces false positives in detecting optimal value shifts when variance shifts are present. When only shifts in optimal value occur, our method performs comparably to existing approaches. We apply ShiVa to empirical data on floral diameter in Rafflesiaceae (with Euphorbiaceae outgroups) and buccal morphology in Centrarchidae sunfishes.

q-bio.PE↗

arXiv2026

Using sequence data to study spatial scales of interactions driving spread of Highly Pathogenic Avian Influenza in Great Britain

H5N1 highly pathogenic avian influenza (HPAI) has been recently circulating in previously unseen patterns. As the underlying causes are uncertain, we need a better understanding of the drivers of virus circulation, as they underpin the spread, and inform about the impact of HPAI. We combine infection and bird population data, using Random Forest models to quantify the relationship between spatially aggregated values of possible infection risk factors and viral phylogenetic data from HPAI outbreaks to define scales of interactions that influence virus circulation. As genetic proximity is correlated to transmission proximity, the genetic relationships of sequences and relevant variables indicate the scale and resolution of interactions driving transmission. We used H5N1 Sequences from Great Britain divided into two periods: Dec 2020-May 2022, and Jun 2022-Oct 2024. Final model with selected spatial scales shows that for the first period various wild bird abundance variables were the strong predictors of genetic distance, and for the second period, these were geographical distance, and collection date. Variables related to infections have different spatial scales for analysed periods, while variables describing the environment have more consistent scales. The differences in predictors for the periods bears further investigation, however may be related to differences in the virus, areas of spread, or seasonal factors. This is the first phylogenetic study indicating possible role for game birds in the circulation of HPAI and supports experimental studies but must be viewed with caution as our study does not indicate direct causation. We show that the scale of spatial aggregation is variable dependent and reflects the scale of underlying processes. In the future our findings may be used to study dynamics of HPAI and design targeted surveillance and intervention strategies.

q-bio.PE↗

arXiv2026

Adaptation by Cumulative Selection

Biological systems like long-lived clonal organisms, holobionts and clades challenge traditional evolutionary thinking since they adapt without populations or reproduction. This paper aims to provide an overarching theoretical framework which encompasses standard Darwinian evolution as well as other processes of adaptation. This framework is cumulative selection and I provide a general `recipe' for it to occur. Lewontin's recipe for evolution by natural selection is shown to be a particular example of cumulative selection, but not the only one, with reproduction and populations just one way to perform cumulative selection. I discuss several other examples including clonal organisms, dioecious populations, holobionts, ecosystems, Gaia and neural networks.

q-bio.PE↗