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“To Read” list

  • Weber and Deutsch (2012): “Oceanic nitrogen reservoir regulated by plankton diversity and ocean circulation”
  • Fox-Kemper and Lumpkin (2012): On the possible use of Lagrangian-mean analyses to diagnose eddy fluxes.
  • Berdalet and Estrada (2005): “Effects of small-scale turbulence on the physiological functioning of marine microalgae”, for the proposal on diapycnal mixing.
  • Wang et al. (2013): “Incorporating genomic information and predicting gene expression patterns”. The title says it all!
  • Wunsch (2013): “Baroclinic Motions and Energetics as Measured by Altimeters” or How to deduce baroclinic motions from surface signals. May be useful to re-estimate eddy-driven transport, for instance.
  • Williams (2013): “Phage-induced diversification improves host evolvability”, evolutionary-ecological model of phage-host dynamics. It is the same Williams as Williams and Lenton (2007).
  • Doebeli and Ispolatov (2010): “Complexity and diversity” about the conditions for which frequency-dependent selection (the fitness of a phenotype is either proportional or inversely proportional to its frequency with respect to other phenotypes; see the corresponding Wiki page on this) is likely to occur and lead to sympatric speciation (speciation from non-isolated population; see also the Wiki page on this). This process is called adaptive speciation and the theory is named adaptive dynamics, a component of which is that phenotypes can influence and modify the environment and so they can modify also their fitness, positively or negatively (see the introduction of Herron and Doebeli 2013 for a summary). See Dieckmann and Law (1996), Doebeli and Dieckmann (2000) and the monograph by Doebeli Adaptive Diversification (2011) for the basis of the theory, as well as the recent Herron and Doebeli (2013) for experimental evidence on E. coli. In the latter study, the authors show that the speciation process does not occur due to many small steps but instead due to sporadic large steps; given that the same type of mutations are observed, they also speculate that these mutations might actually be predictable to some extent.
  • Watkins (2013): On the use of stochastic models to simulate extreme events in geosciences.
  • Gaultier et al. (2012): On the use of SST and FSLE to improve the surface velocity field using a Bayesian approach.
  • On Maximum Entropy Production principle: Dewar (2009; read), Dewar and Maritan (2011?), Martyushev (2013).
  • On Maximum Entropy Production principle and life: Kleidon (2010; 2010b)
  • A review of Evolutionary Game Theory by Lewis and Dumbrell (2013)
  • On prediction of tipping points: read Boettiger and Hastings (2012) on bias of particular statistical models and on an unbiased alternative way to predict the tipping point. See also Scheffer et al. (2009), Lade and Gross (2012), Boettiger and Hastings (2012b) and Pierini (2012).
  • Read the book Hierarchical Modelling for the Environmental Sciences : Statistical Methods and Applications by Clark et al. (2006; on my ebrary account) on hierarchical Bayesian modelling and an application on a marine ecosystem model by Dowd and Meyer (2003).
  • Rose and Allen (2013) on models of marine ecosystems used to predict effects of climate change.
  • Schymanski et al. (2010) on an example of using MEP principle to parameterize subgrid-scale processes.
  • Sornette and Ouillon (2012): On the predictability of chaotic phenomena.
  • Judgment under uncertainty: Heuristics and biases by Tversky and Kahneman, a Science paper that seems to be a summary of their book of the same name. This paper was referenced by Wunsch (2007) concerning the thought process of human beings in face of uncertainty.
  • Statistically accurate low-order models for uncertainty quantification in turbulent dynamical systems by Sapsis and Majda (2013)
  • On the parameterization of eddy-induced transport: Read Klocker et al. (2012a, b) and Aberthaney et al. (2013), Bachman and Fox-Kemper (2013). Summarize this paper in my note on the literature of eddy-driven flows.
  • An ecological model of sardine that involves artificial neural network and genetic algorith (cool!): Okunishi et al. (2009) “A simulation model for Japanese sardine (Sardinops melanostictus) migrations in the western North Pacific”.
  • More generally on IBM with or without behavior/adaptation, read Dagorn et al. (1997), Huse et al. (1999), Strand et al. (2002), Okunishi et al. (2009), Neuheimer et al. (2010), Willis (2011).
  • The book on Artificial Life edited by Langton.
  • Various application of Bayesian analysis: Raftery et al. (1995) about inference from a deterministic population dynamics model, Arhonditsis et al. (2008) and Lignell et al. (2013) on the calibration of a marine ecosystem model, Gende et al. (2011) on the role of ship speed on whale-ship encounters, Mutshinda et al. (2013) on the use of a hierarchical Bayesian model with “variable selection” to get the important factors that control the abundance of various phytoplankton species.
  • On Bayesian view and the Ockham’s razor by Jefferys and Berger (1991).
  • On confidence interval versus Bayesian intervals by Jaynes (1976). See also Efron (1980, 1987).
  • Application to artificial neural network/genetic algorithm: Ecosystem model (Lek and Guegan 1999), vertical migration of fishes (Rosland and Giske 1994)
  • Monte Carlo Strategies in Scientific Computing by Jun. S. Liu (look for MCbook.pdf in RESEARCH/LITERATURE/Monte_Carlo
  • Read The Bayesian Choice by Robert (look for Robert_The_Bayesian_Choice_Book.pdf in RESEARCH/LITERATURE/Bayesian_Inference)
  • On Bayesian inference: Edwards et al. (1963), Berger and sellke (1987) and Berger and Delampady (1987)