The Adaptive Metropolis algorithm as a tool for model selection given irregular and imperfect time-series data

Oceanographic and other ecological observations often yield irregular time-series of data, including substantial uncertainties. Models, which express quantitatively our hypotheses about the underlying processes, are useful and in many cases essential tools for interpreting such observations. However, given the uncertainties in both the data and models, rigorous model-data comparison is a daunting task. Here I will present details from a previously published example (Smith et al. J. Plankton Res. 38, doi: 10.1093/plankt/fbv038, 2016) using the Adaptive Metropolis (AM) algorithm (Haario et al. Bernoulli 7, p. 223-242, 2001) to combine oceanic time-series observations with plankton ecosystem modelling. Time-series observations consisted of nutrients, chlorophyll, and primary production, from the K2S1 project (https://ebcrpa.jamstec.go.jp/k2s1/en/). The Bayesian statistical foundation of the AM algorithm allows for systematically combining: 1) prior knowledge of parameter values, 2) irregular time-series data of different types, each with its own uncertainties, and 3) different model formulations. The algorithm generates an ensemble of model simulations tuned to match the range of observations. It thus provides: 1) quantitative metrics for determining which model formulation is best supported by the available data (i.e., model selection), 2) posterior distributions of model parameter values and corresponding model outputs, and 3) uncertainty estimates (from Gibbs sampling) of the model-data mismatch for each observation type, respectively. Such Bayesian approaches provide a systematic means of quantifying uncertainties and evaluating competing hypotheses given irregular time-series data. This is important for interpreting large, modern data sets consisting of multiple data types, each having different units and associated uncertainties. It also works well with complex models having non-linear dynamics. However, even these computationally efficient statistical methods require thousands to many millions of model simulations, depending on data quality and the number of parameters being optimized. Until recently this has limited their use to 0-D models, but fast modern computers have recently made it possible to apply them with 1-D ocean models as well (Chen and Smith. Geosci. Model Dev., doi: 10.5194/gmd-11-467-2018?, 2018), expanding their potential for studying the combined effects of physical, ecological, and biogeochemical processes.XMAS-IV Xiamen Symposium on Marine Environmental Sciences (Xiamen, China, January 6-9, 2019

北太平洋に棲むプランクトンの多様性と生産力

2018年度JAMSTEC横浜研究所一般公開立ち寄りセミナー（2018年10月27日, 海洋研究開発機構横浜研究所

Diel vertical migration promotes zooplankton horizontal patchiness

Spatial patchiness of plankton enhances fishery production and carbon export in the ocean. While diel vertical migration (DVM) has been identified as an important factor contributing to vertical patchiness, its effect on horizontal patchiness has never been investigated. We use a simple individual based zooplankton model to examine the effect of DVM on the horizontal patchiness of four zooplankton groups with differing DVM patterns in a two-dimensional ocean circulation model. We find that zooplankton horizontal patchiness can be induced by two mechanisms: 1) in stratified waters, DVM can synchronize zooplankton vertical positions with the horizontal current velocities that drive them, resulting in horizontal patchiness; and 2) migrating zooplankton tend to aggregate in deep waters when they encounter sea bottom. Due to these mechanisms, zooplankton horizontal patchiness may be ubiquitous in the ocean, enhancing secondary production and fisheries

Optimality-based approach for computationally efficient modeling of phytoplankton growth, chlorophyll-to-carbon, and nitrogen-to-carbon ratios

To increase the efficiency of computing phytoplankton growth rate (μ), chlorophyll-to-carbon (θ) and nitrogen- to-carbon ratios (QN) in three-dimensional ocean circulation models, it is preferable to directly calculate θ and QN from ambient environmental factors instead of treating them as independent tracers. Optimality-based modeling has emerged as a novel and efficient approach to fulfill this task. However, it is still unclear precisely how the response of optimality-based models differs from conventional models. We compare a recent optimality- based phytoplankton model (PAHLOW model), based on which the familiar Droop function can be derived, to a commonly used Monod-type (MONOD) model. The two models generate similar patterns of μ with some im- portant differences. Compared to the MONOD model, the PAHLOW model predicts higher μ under light lim- itation. The PAHLOW model also predicts that θ decreases with decreasing light under dim light and predicts decreasing QN with increasing light even at constant nutrient levels. Compared to the MONOD model, these features of the PAHLOW model qualitatively agree better with laboratory data. The PAHLOW model also suffers from a few shortcomings including the underestimation of θ under very low light and two times of computation time compared to the MONOD model. The two models generate striking differences of QN and θ in a one- dimensional implementation. Validation of such patterns will require more direct in situ measurements of μ, θ and QN

Optimality-based modeling of planktonic organisms

On the basis of the assumption that natural selection should tend to produce organisms optimally adapted to their environments, we consider optimality as a guiding concept for abstracting the behavior of aquatic microorganisms (plankton) to develop models to study and predict the behavior of planktonic organisms and communities. This is closely related to trait-based ecology, which considers that traits and functionality can be understood as the result of the optimization inherent in natural selection, subject to constraints imposed by fundamental processes necessary for life. This approach is particularly well suited to plankton because of their long evolutionary history and the ease with which they can be manipulated in experiments. We review recent quantitative modeling studies of planktonic organisms that have been based on the assumption that adaptation of species and acclimation of organisms maximize growth rate. Compared with mechanistic models not formulated in terms of optimality, this approach has in some cases yielded simpler models, and in others models of greater generality. The evolutionary success of any given species must depend on its interactions with both the physical environment and other organisms, which depend on the evolving traits of all organisms concerned. The concept of an evolutionarily stable strategy (ESS) can, at least in principle, constrain the choice of goal functions to be optimized in models. However, the major challenge remains of how to construct models at the level of organisms that can resolve short-term dynamics, e.g., of phytoplankton blooms, in a way consistent with ESS theory, which is formulated in terms of a steady state

Effect of phytoplankton size diversity on primary productivity in the North Pacific : trait distributions under environmental variability

While most biodiversity and ecosystem functioning (BEF) studies have found positive effects of species richness on productivity, it remain unclear whether similar patterns hold for marine phytoplankton with high local richness. We use the continuous trait-based modelling approach, which assumes infinite richness and represents diversity in terms of the variance of the size distribution, to investigate the effects of phytoplankton size diversity on productivity in a three-dimensional ocean circulation model driven by realistic physics forcing. We find a slightly negative effect of size diversity on primary production, which we attribute to several factors including functional trait-environment interactions, flexible stoichiometry and the saturation of productivity at low diversity levels. The benefits of trait optimisation, whereby narrow size distributions enhance productivity under relatively stable conditions, tend to dominate over those of adaptive capacity, whereby greater diversity enhances the ability of the community to respond to environmental variability

Search for vectorlike B quarks in events with one isolated lepton, missing transverse momentum, and jets at √s = 8 TeV with the ATLAS detector

A search has been performed for pair production of heavy vectorlike down-type (B) quarks. The analysis explores the lepton-plus-jets final state, characterized by events with one isolated charged lepton (electron or muon), significant missing transverse momentum, and multiple jets. One or more jets are required to be tagged as arising from b quarks, and at least one pair of jets must be tagged as arising from the hadronic decay of an electroweak boson. The analysis uses the full data sample of pp collisions recorded in 2012 by the ATLAS detector at the LHC, operating at a center-of-mass energy of 8 TeV, corresponding to an integrated luminosity of 20.3 fb −1 . No significant excess of events is observed above the expected background. Limits are set on vectorlike B production, as a function of the B branching ratios, assuming the allowable decay modes are B → Wt/Zb/Hb. In the chiral limit with a branching ratio of 100% for the decay B → Wt, the observed (expected) 95% C.L. lower limit on the vectorlike B mass is 810 GeV (760 GeV). In the case where the vectorlike B quark has branching ratio values corresponding to those of an SU(2) singlet state, the observed (expected) 95% C.L. lower limit on the vectorlike B mass is 640 GeV (505 GeV). The same analysis, when used to investigate pair production of a colored, charge 5/3 exotic fermion T 5/3 , with subsequent decay T 5/3 → Wt, sets an observed (expected) 95% C.L. lower limit on the T 5/3 mass of 840 GeV (780 GeV)

Search for strong gravity in multijet final states produced in pp collisions at √s=13 TeV using the ATLAS detector at the LHC

A search is conducted for new physics in multijet final states using 3.6 inverse femtobarns of data from proton-proton collisions at √s = 13TeV taken at the CERN Large Hadron Collider with the ATLAS detector. Events are selected containing at least three jets with scalar sum of jet transverse momenta (HT) greater than 1TeV. No excess is seen at large HT and limits are presented on new physics: models which produce final states containing at least three jets and having cross sections larger than 1.6 fb with HT > 5.8 TeV are excluded. Limits are also given in terms of new physics models of strong gravity that hypothesize additional space-time dimensions

Operation and performance of the ATLAS semiconductor tracker

The semiconductor tracker is a silicon microstrip detector forming part of the inner tracking system of the ATLAS experiment at the LHC. The operation and performance of the semiconductor tracker during the first years of LHC running are described. More than 99% of the detector modules were operational during this period, with an average intrinsic hit efficiency of (99.74±0.04)%. The evolution of the noise occupancy is discussed, and measurements of the Lorentz angle, δ-ray production and energy loss presented. The alignment of the detector is found to be stable at the few-micron level over long periods of time. Radiation damage measurements, which include the evolution of detector leakage currents, are found to be consistent with predictions and are used in the verification of radiation background simulations

Measurement of the correlation between flow harmonics of different order in lead-lead collisions at √sNN = 2.76 TeV with the ATLAS detector

Correlations between the elliptic or triangular flow coefficients vm (m=2 or 3) and other flow harmonics vn (n=2 to 5) are measured using √sNN=2.76 TeV Pb+Pb collision data collected in 2010 by the ATLAS experiment at the LHC, corresponding to an integrated luminosity of 7 μb−1. The vm−vn correlations are measured in midrapidity as a function of centrality, and, for events within the same centrality interval, as a function of event ellipticity or triangularity defined in a forward rapidity region. For events within the same centrality interval, v3 is found to be anticorrelated with v2 and this anticorrelation is consistent with similar anticorrelations between the corresponding eccentricities, ε2 and ε3. However, it is observed that v4 increases strongly with v2, and v5 increases strongly with both v2 and v3. The trend and strength of the vm−vn correlations for n=4 and 5 are found to disagree with εm−εn correlations predicted by initial-geometry models. Instead, these correlations are found to be consistent with the combined effects of a linear contribution to vn and a nonlinear term that is a function of v22 or of v2v3, as predicted by hydrodynamic models. A simple two-component fit is used to separate these two contributions. The extracted linear and nonlinear contributions to v4 and v5 are found to be consistent with previously measured event-plane correlations