High order explicit symplectic integrators for the Discrete Non Linear Schr\"odinger equation

We propose a family of reliable symplectic integrators adapted to the Discrete Non-Linear Schr\"odinger equation; based on an idea of Yoshida (H. Yoshida, Construction of higher order symplectic integrators, Physics Letters A, 150, 5,6,7, (1990), pp. 262.) we can construct high order numerical schemes, that result to be explicit methods and thus very fast. The performances of the integrators are discussed, studied as functions of the integration time step and compared with some non symplectic methods

Efficient control of accelerator maps

Recently, the Hamiltonian Control Theory was used in [Boreux et al.] to increase the dynamic aperture of a ring particle accelerator having a localized thin sextupole magnet. In this letter, these results are extended by proving that a simplified version of the obtained general control term leads to significant improvements of the dynamic aperture of the uncontrolled model. In addition, the dynamics of flat beams based on the same accelerator model can be significantly improved by a reduced controlled term applied in only 1 degree of freedom

Coupling growth and mortality models to detect climate drivers of tropical forest dynamics

Climate models for the coming century predict rainfall reduction in the Amazonian region, including deep changes in water availability for tropical rainforests. Here, we develop an integrated modeling framework in order test the extent to which climate variables related to water regime, temperature and irradiance shape the long-term dynamics of neotropical forests. In a first step, a Bayesian hierarchical model was built to couple tree growth and tree mortality processes into a single modeling framework. Coupling a longitudinal growth model with a punctual mortality model was not an easy task. Past growth, related to the expected growth, was used as an indicator of the individual tree vigor, which is supposed to play a key role in the mortality process. A MCMC approach is used to estimate all the parameters simultaneously. The individual-centered model was explicitly designed to deal with diverse sources of uncertainty, including the complexity of the mortality process itself and the field data, especially historical data for which taxonomic determinations were uncertain. Functional traits are integrated as proxies of the ecological strategies of the trees and permit generalization among all species in the forest community. Data used to parameterize the model were collected at Paracou study site, a tropical rain forest in French Guiana, where 20,408 trees have been yearly censured over 18 years. Climate covariates were finally added as external drivers of the forest dynamics. These drivers are selected in a list of climate variables for which future predictions are available thanks to the IPCC scenario. Amongst climate variables, we highlight the predominant role of water availability in determining interannual variation in the dynamic of neotropical forests. And we stressed the need to include these relationships into forest simulators to test, in silico, the impact of different climate scenarios on the future dynamics of the rainforest. (Texte intégral

Machine learning algorithms to infer trait-matching and predict species interactions in ecological networks

Ecologists have long suspected that species are more likely to interact if their traits match in a particular way. For example, a pollination interaction may be more likely if the proportions of a bee's tongue fit a plant's flower shape. Empirical estimates of the importance of trait‐matching for determining species interactions, however, vary significantly among different types of ecological networks. Here, we show that ambiguity among empirical trait‐matching studies may have arisen at least in parts from using overly simple statistical models. Using simulated and real data, we contrast conventional generalized linear models (GLM) with more flexible Machine Learning (ML) models (Random Forest, Boosted Regression Trees, Deep Neural Networks, Convolutional Neural Networks, Support Vector Machines, naïve Bayes, and k‐Nearest‐Neighbor), testing their ability to predict species interactions based on traits, and infer trait combinations causally responsible for species interactions. We found that the best ML models can successfully predict species interactions in plant–pollinator networks, outperforming GLMs by a substantial margin. Our results also demonstrate that ML models can better identify the causally responsible trait‐matching combinations than GLMs. In two case studies, the best ML models successfully predicted species interactions in a global plant–pollinator database and inferred ecologically plausible trait‐matching rules for a plant–hummingbird network from Costa Rica, without any prior assumptions about the system. We conclude that flexible ML models offer many advantages over traditional regression models for understanding interaction networks. We anticipate that these results extrapolate to other ecological network types. More generally, our results highlight the potential of machine learning and artificial intelligence for inference in ecology, beyond standard tasks such as image or pattern recognition

Uncertainties in the value and opportunity costs of pollination services

Pollination is an ecosystem service that directly contributes to agricultural production, and can therefore provide a strong incentive to conserve natural habitats that support pollinator populations. However, we have yet to provide consistent and convincing pollination service valuations to effectively slow the conversion of natural habitats. We use coffee in Kodagu, India, to illustrate the uncertainties involved in estimating costs and benefits of pollination services. First, we fully account for the benefits obtained by coffee agroforests that are attributable to pollination from wild bees nesting in forest habitats. Second, we compare these benefits to the opportunity cost of conserving forest habitats and forgoing conversion to coffee production. Throughout, we systematically quantify the uncertainties in our accounting exercise and identify the parameters that contribute most to uncertainty in pollination service valuation. We find the value of pollination services provided by one hectare of forest to be 25% lower than the profits obtained from converting that same surface to coffee production using average values for all parameters. However, our results show this value is not robust to moderate uncertainty in parameter values, particularly that driven by variability in pollinator density. Synthesis and applications. Our findings emphasize the need to develop robust estimates of both value and opportunity costs of pollination services that take into account landscape and management variables. Our analysis contributes to strengthening pollination service arguments used to help stakeholders make informed decisions on land use and conservation practices. © 2019 The Authors. Journal of Applied Ecology © 2019 British Ecological Societ