Biology subjects

Daniel Stouffer

Publications and source records attributed to Daniel Stouffer.

4 recordsLinked to original sources

bioRxiv2016

How ecological networks evolve

Ecological networks represent the backbone of biodiversity. As species diversify over macro-evolutionary time-scales, the structure of these networks changes; this happens because species are gained and lost, and therefore add or remove interactions in their communities. The mechanisms underlying such dynamic changes in ecological network structure, however, remain poorly understood. Here we show that several types of ecological interactions share common evolutionary mechanisms that can be parametrised based on extant interaction data. In particular, we found that a model mimicking birth-death processes for species interactions describes the structure of extant networks remarkably well. Moreover, the various types of ecological interactions we considered--seed dispersal, herbivory, parasitism, bacteriophagy, and pollination--only differed in the position they occupy in the parameters multi-dimensional space. Notably, we found no clustering of parameters values between antagonistic and mutualistic interactions. Our results provide a common modelling framework for the evolution of ecological networks that we anticipate will contribute to the greater consideration of the explicit role played by species interactions in models of macro-evolution and adaptive radiations.

Ecology↗

bioRxiv2016

Simulations of biomass dynamics in community food webs

O_LIFood webs are the backbone upon which biomass flows through ecosystems. Dynamical models of biomass can reveal how the structure of food webs is involved in many key ecosystem properties, such as persistence, stability, etc.\nC_LIO_LIIn this contribution, we present BioEnergeticFoodWebs, an implementation of Yodzis & Innes (1992) bio-energetic model, in the high-performance computing language Julia.\nC_LIO_LIWe illustrate how this package can be used to conduct numerical experiments in a reproducible and standard way.\nC_LIO_LIA reference implementation of this widely used model will ease reproducibility and comparison of results across studies.\nC_LI

Ecology↗

bioRxiv2015

Interactions retain the co-phylogenetic matching that communities lost

Both species and their interactions are affected by changes that occur at evolutionary time-scales, and these changes shape both ecological communities and their phylogenetic structure. That said, extant ecological community structure is contingent upon random chance, environmental filters, and local effects. It is therefore unclear how much ecological signal local communities should retain. Here we show that, in a host-parasite system where species interactions vary substantially over a continental gradient, the ecological significance of individual interactions is maintained across different scales. Notably, this occurs despite the fact that observed community variation at the local scale frequently tends to weaken or remove community-wide phylogenetic signal. When considered in terms of the interplay between community ecology and coevolutionary theory, our results demonstrate that individual interactions are capable and indeed likely to show a consistent signature of past evolutionary history even when woven into communities that do not.

Evolutionary Biology↗

bioRxiv2015

The structure of probabilistic networks

O_LIThere is a growing realization among community ecologists that interactions between species vary across space and time, and that this variation needs be quantified. Our current numerical framework to analyze the structure of species interactions, based on graph-theoretical approaches, usually do not consider the variability of interactions. Since this variability has been show to hold valuable ecological information, there is a need to adapt the current measures of network structure so that they can exploit it.\nC_LIO_LIWe present analytical expressions of key measures of network structured, adapted so that they account for the variability of ecological interactions. We do so by modeling each interaction as a Bernoulli event; using basic calculus allows expressing the expected value, and when mathematically tractable, its variance. When applied to non-probabilistic data, the measures we present give the same results as their non-probabilistic formulations, meaning that they can be generally applied.\nC_LIO_LIWe present three case studies that highlight how these measures can be used, in re-analyzing data that experimentally measured the variability of interactions, to alleviate the computational demands of permutationbased approaches, and to use the frequency at which interactions are observed over several locations to infer the structure of local networks. We provide a free and open-source implementation of these measures.\nC_LIO_LIWe discuss how both sampling and data representation of ecological networks can be adapted to allow the application of a fully probabilistic numerical network approach.\nC_LI

Ecology↗