Biology subjects

Timothée Poisot

Publications and source records attributed to Timothée Poisot.

6 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↗

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↗

bioRxiv2014

Connecting people and ideas from around the world: global innovation platforms for next-generation ecology and beyond

We present a case for using global community innovation platforms (GCIPs), an approach to improve innovation and knowledge exchange in international scientific communities through a common and open online infrastructure. We highlight the value of GCIPs by focusing on recent efforts targeting the ecological sciences, where GCIPs are of high relevance given the urgent need for interdisciplinary, geographical, and cross-sector collaboration to cope with growing challenges to the environment as well as the scientific community itself. Amidst the emergence of new international institutions, organizations, and dedicated meetings, GCIPs provide a stable international infrastructure for rapid and long-term coordination that can be accessed by any individual. The accessibility can be particularly important for researchers early in their careers. Recent examples of early career GCIPs complement an array of existing options for early career scientists to improve skill sets, increase academic and social impact, and broaden career opportunities. In particular, we provide a number of examples of existing early career initiatives that incorporate elements from the GCIPs approach, and highlight an in-depth case study from the ecological sciences: the International Network of Next-Generation Ecologists (INNGE), initiated in 2010 with support from the International Association for Ecology and 18 member institutions from six continents.

Preprint↗

bioRxiv2014

Ongoing worldwide homogenization of human pathogens

BackgroundInfectious diseases are a major burden on human population, especially in low- and middle-income countries. The increase in the rate of emergence of infectious outbreaks necessitates a better understanding of the worldwide distribution of diseases through space and time.\n\nMethodsWe analyze 100 years of records of diseases occurrence worldwide. We use a graph-theoretical approach to characterize the worldwide structure of human infectious diseases, and its dynamics over the Twentieth Century.\n\nFindingsSince the 1960s, there is a clear homogenizing of human pathogens worldwide, with most diseases expanding their geographical area. The occurrence network of human pathogens becomes markedly more connected, and less modular.\n\nInterpretationHuman infectious diseases are steadily expanding their ranges since the 1960s, and disease occurrence has become more homogenized at a global scale. Our findings emphasize the need for international collaboration in designing policies for the prevention of outbreaks.\n\nFundingT.P. is funded by a FRQNT-PBEE post-doctoral fellowship, and through a Marsden grant from the Royal Academy of Sciences of New-Zealand. Funders had no input in any part of the study.

Ecology↗

bioRxiv2014

A continuum of specialists and generalists in empirical communities

Understanding the persistence of specialists and generalists within ecological communities is a topical research question, with far-reaching consequences for the maintenance of functional diversity. Although theoretical studies indicate that restricted conditions may be necessary to achieve co-occurrence of specialists and generalists, analyses of larger empirical (and species-rich) communities reveal the pervasiveness of coexistence. In this paper, we analyze 175 ecological bipartite networks of three interaction types (animal hosts-parasite, plant-herbivore and plant-pollinator), and measure the extent to which these communities are composed of species with different levels of specificity in their biotic interactions. We find a continuum from specialism to generalism. Furthermore, we demonstrate that diversity tends to be greatest in networks with intermediate connectance, and argue this is because of physical constraints in the filling of networks.

Ecology↗

bioRxiv2014

Beyond species: why ecological interaction networks vary through space and time

Community ecology is tasked with the considerable challenge of predicting the structure, and properties, of emerging ecosystems. It requires the ability to understand how and why species interact, as this will allow the development of mechanism-based predictive models, and as such to better characterize how ecological mechanisms act locally on the existence of interspecific interactions. Here we argue that the current conceptualization of species interaction networks is ill-suited for this task. Instead, we propose that future research must start to account for the intrinsic variability of species interactions, then scale up from here onto complex networks. This can be accomplished simply by recognizing that there exists intra-specific variability, in traits or properties related to the establishment of species interactions. By shifting the scale towards population-based processes, we show that this new approach will improve our predictive ability and mechanistic understanding of how species interact over large spatial or temporal scales.

Ecology↗