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Thebault E.,Imperial College London | Thebault E.,Wageningen University | Fontaine C.,Imperial College London | Fontaine C.,CNRS Science Conservation Center
Science | Year: 2010

Research on the relationship between the architecture of ecological networks and community stability has mainly focused on one type of interaction at a time, making difficult any comparison between different network types. We used a theoretical approach to show that the network architecture favoring stability fundamentally differs between trophic and mutualistic networks. A highly connected and nested architecture promotes community stability in mutualistic networks, whereas the stability of trophic networks is enhanced in compartmented and weakly connected architectures. These theoretical predictions are supported by a meta-analysis on the architecture of a large series of real pollination (mutualistic) and herbivory (trophic) networks. We conclude that strong variations in the stability of architectural patterns constrain ecological networks toward different architectures, depending on the type of interaction. Source

Pavoine S.,CNRS Science Conservation Center | Pavoine S.,University of Oxford
Methods in Ecology and Evolution | Year: 2012

1. Quadratic entropy (QE) was developed as a fundamental function for measuring the diversity within a collection, such as a community, or population, from indices of abundance and distance among categories, such as species or alleles. Based on a literature review in the fields of genetics, ecology and statistics and new developments, I analyse the potential of this function for biodiversity studies. 2.Quadratic entropy was established as a generalisation of well-known diversity indices, and has been widely used in molecular ecology and genetics research. It is now integrated within more general frameworks for analysing functional and phylogenetic diversity in ecology. 3.Quadratic entropy can be maximised by removing categories, and several collections can share the maximum diversity, even with highly distinct compositions. Clarifying these statements, I identify all potential indices of the abundance of the categories that maximise QE. 4.By quantifying changes in diversity when mixing collections together, QE can measure differences among collections. Here, I provide a geometric interpretation of these differences that demonstrates their relevance as classical geometric distances. 5.A critical aspect of these distances is obtained if QE is strictly concave; that is, diversity always strictly increases by mixing distinct collections together. More generally, QE can be used to evaluate the effects of various factors on diversity in a framework designated ANOQE (analysis of QE). Generalising ANOVA (analysis of variance), ANOQE uses QE to measure distances between centroids. 6.Importantly, QE is estimated from sampled data and thus requires estimators. Based on these estimators, tests have been developed to compare levels of diversity. Tests of factor effects are evaluated by parametric, jackknife, bootstrap and permutational approaches. However, the procedures associated with these tests that have been suggested thus far only treat a few factors. 7.There is an urgent need for the development of such approaches in biology to deal with experimental factors, observed population and community structure, and different spatial and temporal scales. Together, QE and the ANOQE procedure are likely to have a critical impact on all scientific disciplines interested in any form of diversity. © 2012 The Author. Methods in Ecology and Evolution © 2012 British Ecological Society. Source

Fontaine B.,CNRS Science Conservation Center | Perrard A.,French Natural History Museum | Bouchet P.,French Natural History Museum
Current Biology | Year: 2012

A large part of biodiversity is still unknown, and it is estimated that, at the current pace, it will take several centuries to describe all species living on Earth. In the context of the ongoing 'sixth extinction', accelerating the completion of the inventory of living biota is an issue that reaches far beyond the taxonomic community. However, the factors that influence the accretion of known species remain poorly understood. Here, we study how long it takes from the first collection of a specimen of a new species to its formal description and naming in the scientific literature [1,2] - a period we refer to as a species' 'shelf life'. Based on a random set of species described in 2007 across all kingdoms of life, we determine that the average shelf life between discovery and description is 21 years. The length of the shelf life is impacted by biological, social and geopolitical biases. © 2012 Elsevier Ltd. Source

Robert A.,CNRS Science Conservation Center
BMC Evolutionary Biology | Year: 2011

Background: While the ultimate causes of most species extinctions are environmental, environmental constraints have various secondary consequences on evolutionary and ecological processes. The roles of demographic, genetic mechanisms and their interactions in limiting the viabilities of species or populations have stirred much debate and remain difficult to evaluate in the absence of demography-genetics conceptual and technical framework. Here, I computed projected times to metapopulation extinction using (1) a model focusing on the effects of species properties, habitat quality, quantity and temporal variability on the time to demographic extinction; (2) a genetic model focusing on the dynamics of the drift and inbreeding loads under the same species and habitat constraints; (3) a demo-genetic model accounting for demographic-genetic processes and feedbacks. Results: Results indicate that a given population may have a high demographic, but low genetic viability or vice versa; and whether genetic or demographic aspects will be the most limiting to overall viability depends on the constraints faced by the species (e.g., reduction of habitat quantity or quality). As a consequence, depending on metapopulation or species characteristics, incorporating genetic considerations to demographically-based viability assessments may either moderately or severely reduce the persistence time. On the other hand, purely genetically-based estimates of species viability may either underestimate (by neglecting demo-genetic interactions) or overestimate (by neglecting the demographic resilience) true viability. Conclusion: Unbiased assessments of the viabilities of species may only be obtained by identifying and considering the most limiting processes (i.e., demography or genetics), or, preferentially, by integrating them. © 2011 Robert; licensee BioMed Central Ltd. Source

Elias M.,CNRS Systematics, Biodiversity and Evolution Institute | Fontaine C.,CNRS Science Conservation Center | Frank Van Veen F.J.,University of Exeter
Current Biology | Year: 2013

Uncovering the processes that shape the architecture of interaction networks is a major challenge in ecology. Studies have consistently revealed that more closely related taxa tend to show greater overlap in interaction partners, fuelling the idea that interactions are phylogenetically conserved [1-8]. However, local ecological processes such as exploitative or apparent competition (indirect interactions) might instead cause a decrease in overlap in interacting partners. Because of the taxonomic and geographic coarseness of existing studies [2-5, 7], the structuring effect of such processes has been overlooked. Here, we assess the relative importance of phylogeny and ecological processes in a local, highly resolved, four-level antagonistic network. Across all network levels we consistently find that phylogenetic relatedness among resource species is correlated with consumer overlap but that phylogenetic relatedness among consumer species is not or negatively correlated with resource overlap. This pervasive pattern indicates that the antagonistic network has been shaped by both phylogeny on resource range and by exploitative competition limiting resource overlap among closely related consumer species. Intriguingly, the strength of phylogenetic signal varies in a consistent way across the network levels. We discuss the generality of our findings and their implications in a changing world. © 2013 Elsevier Ltd. Source

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