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Daufresne, T.

Publications and source records attributed to Daufresne, T..

2 recordsLinked to original sources

Doallochthonous flows explain deviations from the Redfield ratio in lakes?

Lakes or streams show strong deviations of their stoichiometry compared to the Redfield ratio measured in oceans. Allochthonous inflows of resources might contribute to those deviations directly, by changing composition of detritus in ecosystems, but also indirectly, by shaping local community dynamics and stoichiometric constrains within aquatic ecosystems. Here, we developed a stoichiometric model to understand those direct and indirect mechanisms through which allochthonous inflows affect seston stoichiometry. Our results emphasize that increasing allochthonous inflows promotes heterotrophic functioning, and relax decomposer carbon limitation. This release of stoichiometric constrain of decomposers (i) destabilizes the aquatic ecosystem by promoting competition between phytoplankton and decomposers, (ii) decreases the ability of the lake to regulate allochthonous flows, and (iii) push seston stoichiometry away from the Redfield ratio. Our study emphasize how the quantity and the stoichiometry of inflows shape community dynamics and the elemental constraints within the ecosystem, and open perspectives for stoichiometry at the landscape extent.

ecology↗

Galton-Watson Process And Bayesian Inference: A Turnkey Method For The Viability Study Of Small Populations

O_LISharp prediction of extinction times is needed in biodiversity monitoring and conservation management. C_LIO_LIThe Galton-Watson process is a classical stochastic model for describing population dynamics. Its evolution is like the matrix population model where offspring numbers are random. Extinction probability, extinction time, abundance are well known and given by explicit formulas. In contrast with the deterministic model, it can be applied to small populations. C_LIO_LIParameters of this model can be estimated through the Bayesian inference framework. This enables to consider nonarbitrary scenarios. C_LIO_LIWe show how coupling Bayesian inference with the Galton-Watson model provides several features: i) a flexible modelling approach with easily understandable parameters ii) compatibility with the classical matrix population model (Leslie type model) iii) A non-computational approach which then leads to more information with less computing iv) a non-arbitrary choice for scenarios, parameters... It can be seen to go one step further than the classical matrix population model for the viability problem. C_LIO_LITo illustrate these features, we provide analysis details for two examples whose one of which is a real life example. C_LI

ecology↗