
Maury, O., & Poggiale, J.  C. (2013). From individuals to populations to communities: A dynamic energy budget model of marine ecosystem sizespectrum including life history diversity. Journal of Theoretical Biology, 324, 52–71.



Carteron, A., Jeanmougin, M., Leprieur, F., & Spatharis, S. (2012). Assessing the efficiency of clustering algorithms and goodnessoffit measures using phytoplankton field data. Ecol. Inform., 9, 64–68.
Résumé: Investigation of patterns in beta diversity has received increased attention over the last years particularly in light of new ecological theories such as the metapopulation paradigm and metacommunity theory. Traditionally, beta diversity patterns can be described by cluster analysis (i.e. dendrograms) that enables the classification of samples. Clustering algorithms define the structure of dendrograms, consequently assessing their performance is crucial. A common, although not always appropriate approach for assessing algorithm suitability is the cophenetic correlation coefficient c. Alternatively the 2norm has been recently proposed as an increasingly informative method for evaluating the distortion engendered by clustering algorithms. In the present work, the 2norm is applied for the first time on field data and is compared with the cophenetic correlation coefficient using a set of 105 pairwise combinations of 7 clustering methods (e.g. UPGMA) and 15 (dis)similarity/distance indices (e.g. Jaccard index). In contrast to the 2norm, cophenetic correlation coefficient does not provide a clear indication on the efficiency of the clustering algorithms for all combinations. The two approaches were not always in agreement in the choice of the most faithful algorithm. Additionally, the 2norm revealed that UPGMA is the most efficient clustering algorithm and Ward's the least. The present results suggest that goodnessoffit measures such as the 2norm should be applied prior to clustering analyses for reliable beta diversity measures. (C) 2012 Elsevier B.V. All rights reserved.



Granger, V., Fromentin, J.  M., Bez, N., Relini, G., Meynard, C., GAERTNER, J.  C., et al. (2015). Largescale spatiotemporal monitoring highlights hotspots of demersal fish diversity in the Mediterranean Sea. Progress in Oceanography, 130, 65–74.
Résumé: Increasing human pressures and global environmental change may severely affect the diversity of species assemblages and associated ecosystem services. Despite the recent interest in phylogenetic and functional diversity, our knowledge on large spatiotemporal patterns of demersal fish diversity sampled by trawling remains still incomplete, notably in the Mediterranean Sea, one of the most threatened marine regions of the world. We investigated large spatiotemporal diversity patterns by analysing a dataset of 19,886 hauls from 10 to 800 m depth performed annually during the last two decades by standardized scientific bottom trawl field surveys across the Mediterranean Sea, within the MEDITS program. A multicomponent (eight diversity indices) and multiscale (local assemblages, biogeographic regions to basins) approach indicates that only the two most traditional components (species richness and evenness) were sufficient to reflect patterns in taxonomic, phylogenetic or functional richness and divergence. We also put into question the use of widely computed indices that allow comparing directly taxonomic, phylogenetic and functional diversity within a unique mathematical framework. In addition, demersal fish assemblages sampled by trawl do not follow a continuous decreasing longitudinal/latitudinal diversity gradients (spatial effects explained up to 70.6% of deviance in regression tree and generalized linear models), for any of the indices and spatial scales analysed. Indeed, at both local and regional scales species richness was relatively high in the Iberian region, Malta, the Eastern Ionian and Aegean seas, meanwhile the Adriatic Sea and Cyprus showed a relatively low level. In contrast, evenness as well as taxonomic, phylogenetic and functional divergences did not show regional hotspots. All studied diversity components remained stable over the last two decades. Overall, our results highlight the need to use complementary diversity indices through different spatial scales when developing conservation strategies and defining delimitations for protected areas.



Catherine, A., Selma, M., Mouillot, D., Troussellier, M., & Bernard, C. (2016). Patterns and multiscale drivers of phytoplankton species richness in temperate periurban lakes. Science of The Total Environment, 559, 74–83.
Résumé: Local species richness (SR) is a key characteristic affecting ecosystem functioning. Yet, the mechanisms regulating phytoplankton diversity in freshwater ecosystems are not fully understood, especially in periurban environments where anthropogenic pressures strongly impact the quality of aquatic ecosystems. To address this issue, we sampled the phytoplankton communities of 50 lakes in the Paris area (France) characterized by a large gradient of physicochemical and catchmentscale characteristics. We used large phytoplankton datasets to describe phytoplankton diversity patterns and applied a machinelearning algorithm to test the degree to which species richness patterns are potentially controlled by environmental factors. Selected environmental factors were studied at two scales: the lakescale (e.g. nutrients concentrations, water temperature, lake depth) and the catchmentscale (e.g. catchment, landscape and climate variables). Then, we used a variance partitioning approach to evaluate the interaction between lakescale and catchmentscale variables in explaining local species richness. Finally, we analysed the residuals of predictive models to identify potential vectors of improvement of phytoplankton species richness predictive models. Lakescale and catchmentscale drivers provided similar predictive accuracy of local species richness (R2 = 0.458 and 0.424, respectively). Both models suggested that seasonal temperature variations and nutrient supply strongly modulate local species richness. Integrating lake and catchmentscale predictors in a single predictive model did not provide increased predictive accuracy; therefore suggesting that the catchmentscale model probably explains observed species richness variations through the impact of catchmentscale variables on inlake water quality characteristics. Models based on catchment characteristics, which include simple and easy to obtain variables, provide a meaningful way of predicting phytoplankton species richness in temperate lakes. This approach may prove useful and costeffective for the management and conservation of aquatic ecosystems.



Reichel, K., Masson, J.  P., Malrieu, F., ArnaudHaond, S., & Stoeckel, S. (2016). Rare sex or out of reach equilibrium? The dynamics of FIS in partially clonal organisms. BMC Genet., 17, 76.
Résumé: Background: Partially clonal organisms are very common in nature, yet the influence of partial asexuality on the temporal dynamics of genetic diversity remains poorly understood. Mathematical models accounting for clonality predict deviations only for extremely rare sex and only towards mean inbreeding coefficient (FIS) over bar < 0. Yet in partially clonal species, both FIS < 0 and FIS > 0 are frequently observed also in populations where there is evidence for a significant amount of sexual reproduction. Here, we studied the joint effects of partial clonality, mutation and genetic drift with a stateandtime discrete Markov chain model to describe the dynamics of FIS over time under increasing rates of clonality. Results: Results of the mathematical model and simulations show that partial clonality slows down the asymptotic convergence to FIS = 0. Thus, although clonality alone does not lead to departures from HardyWeinberg expectations once reached the final equilibrium state, both negative and positive FIS values can arise transiently even at intermediate rates of clonality. More importantly, such “transient” departures from Hardy Weinberg proportions may last long as clonality tunes up the temporal variation of FIS and reduces its rate of change over time, leading to a hyperbolic increase of the maximal time needed to reach the final mean (FIS,Finfinity) over bar value expected at equilibrium. Conclusion: Our results argue for a dynamical interpretation of FIS in clonal populations. Negative values cannot be interpreted as unequivocal evidence for extremely scarce sex but also as intermediate rates of clonality in finite populations. Complementary observations (e.g. frequency distribution of multiloci genotypes, population history) or time series data may help to discriminate between different possible conclusions on the extent of clonality when mean (FIS) over bar values deviating from zero and/or a large variation of FIS over loci are observed.

