Biological and environmental time series obtained from Point B, Bay of Villefranche

Ecological succession provides a widely accepted description of seasonal changes in phytoplankton and mesozooplankton assemblages in the natural environment, but concurrent changes in smaller (i.e. microbes) and larger (i.e. macroplankton) organisms are not included in the model because plankton ranging from bacteria to jellies are seldom sampled and analyzed simultaneously. Here we studied, for the first time in the aquatic literature, the succession of marine plankton in the whole-plankton assemblage that spanned 5 orders of magnitude in size from microbes to macroplankton predators (not including fish or fish larvae, for which no consistent data were available). Samples were collected in the northwestern Mediterranean Sea (Bay of Villefranche) weekly during 10 months. Simultaneously collected samples were analyzed by flow cytometry, inverse microscopy, FlowCam, and ZooScan. The whole-plankton assemblage underwent sharp reorganizations that corresponded to bottom-up events of vertical mixing in the water-column, and its development was top-down controlled by large gelatinous filter feeders and predators. Based on the results provided by our novel whole-plankton assemblage approach, we propose a new comprehensive conceptual model of the annual plankton succession (i.e. whole plankton model) characterized by both stepwise stacking of four broad trophic communities from early spring through summer, which is a new concept, and progressive replacement of ecological plankton categories within the different trophic communities, as recognised traditionally.

The effect of introducing radiographer reporting on the availability of reports for Accident and Emergency and General Practitioner examinations: a time-series analysis

This study explores whether the introduction of selectively trained radiographers reporting Accident and Emergency (A&E) X-ray examinations or the appendicular skeleton affected the availability of reports for A&E and General Practitioner (GP) examinations at it typical district general hospital. This was achieved by analysing monthly data on A&E and GP examinations for 1993 1997 using structural time-series models. Parameters to capture stochastic seasonal effects and stochastic time trends were included ill the models. The main outcome measures were changes in the number, proportion and timeliness of A&E and GP examinations reported. Radiographer reporting X-ray examinations requested by A&E was associated with it 12% (p = 0.050) increase in the number of A&E examinations reported and it 37% (p

Concentrations and d13C compositions of n-alkanes, n-alcohols, n-alkanoic acids in a 34-month time series of suspended sediments from the outflow of the Congo River

The concentrations, distributions, and stable carbon isotopes (d13C) of plant waxes carried by fluvial suspended sediments contain valuable information about terrestrial ecosystem characteristics. To properly interpret past changes recorded in sedimentary archives it is crucial to understand the sources and variability of exported plant waxes in modern systems on seasonal to inter-annual timescales. To determine such variability, we present concentrations and d13C compositions of three compound classes (n-alkanes, n-alcohols, n-alkanoic acids) in a 34-month time series of suspended sediments from the outflow of the Congo River. We show that exported plant-dominated n-alkanes (C25-C35) represent a mixture of C3 and C4 end members, each with distinct molecular distributions, as evidenced by an 8.1 ± 0.7 per mil (±1Sigma standard deviation) spread in d13C values across chain-lengths, and weak correlations between individual homologue concentrations (r = 0.52-0.94). In contrast, plant-dominated n-alcohols (C26-C36) and n-alkanoic acids (C26-C36) exhibit stronger positive correlations (r = 0.70-0.99) between homologue concentrations and depleted d13C values (individual homologues average <= -31.3 per mil and -30.8 per mil, respectively), with lower d13C variability across chain-lengths (2.6 ± 0.6 per mil and 2.0 ± 1.1 per mil, respectively). All individual plant-wax lipids show little temporal d13C variability throughout the time-series (1 Sigma <= 0.9 per mil), indicating that their stable carbon isotopes are not a sensitive tracer for temporal changes in plant-wax source in the Congo basin on seasonal to inter-annual timescales. Carbon-normalized concentrations and relative abundances of n-alcohols (19-58% of total plant-wax lipids) and n-alkanoic acids (26-76%) respond rapidly to seasonal changes in runoff, indicating that they are mostly derived from a recently entrained local source. In contrast, a lack of correlation with discharge and low, stable relative abundances (5-16%) indicate that n-alkanes better represent a catchment-integrated signal with minimal response to discharge seasonality. Comparison to published data on other large watersheds indicates that this phenomenon is not limited to the Congo River, and that analysis of multiple plant-wax lipid classes and chain lengths can be used to better resolve local vs. distal ecosystem structure in river catchments.

Biogenic particle and biomarker flux from a mooring time-series at AWI HAUSGARTEN

Since 2000 long-term measurements of vertical particle flux have been performed with moored sediment traps at the long-term observatory HAUSGARTEN in the eastern Fram Strait (79°N/4°E). The study area, which is seasonally covered with ice, is located in the confluence zone of the northward flowing warm saline Atlantic water with cold, low salinity water masses of Arctic origin. Current projections suggest that this area is particularly vulnerable to global warming. Total matter fluxes and components thereof (carbonate, particulate organic carbon and nitrogen, biogenic silica, biomarkers) revealed a bimodal seasonal pattern showing elevated sedimentation rates during May/June and August/September. Annual total matter flux (dry weight, DW) at ~ 300 m depth varied between 13 and 32 g/m**2/a during 2000 and 2005. Of this total flux 6-13 % was due to CaCO3, 4-21 % to refractory particulate organic carbon (POC), and 3-8 % to biogenic particulate silica (bPSi). The annual flux of all biogenic components together was almost constant during the period studied (8.5-8.8 g/m**2/a), although this varied from 27 to 67 % of the total annual flux. The fraction was lowest in a year characterized by the longest duration of ice coverage (91 and 70 days for the calendar year and summer season, May-September, respectively). Biomarker analyses revealed that organic matter originating from marine sources was present in excess of terrigenious material in the sedimented matter throughout most of the study period. Fluxes of recognizable phyto- and protozooplankton cells amounted up to 60x106 m**2/d. Diatoms and coccolithophorids were the most abundant organisms. Diatoms, mainly pennate species, dominated during the first years of the investigation. A shift in the composition occurred during the last year when numbers of diatoms declined considerably, leading to a dominance of coccolithoporids. This was also reflected in a decrease in the sedimentation of bPSi. The sedimentation of biogenic matter, however, did not differ from the amount observed during the previous years. Among the larger organisms, pteropods at times contributed significantly to both the total matter and CaCO3, fluxes.

Planktonic foraminiferal flux and shell geochemistry data in the sediment trap time series JAM 1-3 off south Java

Results from sediment trap experiments conducted in the seasonal upwelling area off south Java from November 2000 until July 2003 revealed significant monsoon-, El Niño-Southern Oscillation-, and Indian Ocean Dipole-induced seasonal and interannual variations in flux and shell geochemistry of planktonic foraminifera. Surface net primary production rates together with total and species-specific planktonic foraminiferal flux rates were highest during the SE monsoon-induced coastal upwelling period from July to October, with three species Globigerina bulloides, Neogloboquadrina pachyderma dex., and Globigerinita glutinata contributing to 40% of the total foraminiferal flux. Shell stable oxygen isotopes (d18O) and Mg/Ca data of Globigerinoides ruber sensu stricto (s.s.), G. ruber sensu lato (s.l.), Neogloboquadrina dutertrei, Pulleniatina obliquiloculata, and Globorotalia menardii in the sediment trap time series recorded surface and subsurface conditions. We infer habitats of 0-30 m for G. ruber at the mixed layer depth, 60-80 m (60-90 m) for P. obliquiloculata (N. dutertrei) at the upper thermocline depth, and 90-110 m (100-150 m) for G. menardii in the 355-500 mm (>500 µm) size fraction corresponding to the (lower) thermocline depth in the study area. Shell Mg/Ca ratio of G. ruber (s.l. and s.s.) reveals an exponential relationship with temperature that agrees with published relationships particularly with the Anand et al. (2003) equations. Flux-weighted foraminiferal data in sediment trap are consistent with average values in surface sediment samples off SW Indonesia. This consistency confirms the excellent potential of these proxies for reconstructing past environmental conditions in this part of the ocean realm.

Time series analysis of a Web search engine transaction log

In this paper, we use time series analysis to evaluate predictive scenarios using search engine transactional logs. Our goal is to develop models for the analysis of searchers’ behaviors over time and investigate if time series analysis is a valid method for predicting relationships between searcher actions. Time series analysis is a method often used to understand the underlying characteristics of temporal data in order to make forecasts. In this study, we used a Web search engine transactional log and time series analysis to investigate users’ actions. We conducted our analysis in two phases. In the initial phase, we employed a basic analysis and found that 10% of searchers clicked on sponsored links. However, from 22:00 to 24:00, searchers almost exclusively clicked on the organic links, with almost no clicks on sponsored links. In the second and more extensive phase, we used a one-step prediction time series analysis method along with a transfer function method. The period rarely affects navigational and transactional queries, while rates for transactional queries vary during different periods. Our results show that the average length of a searcher session is approximately 2.9 interactions and that this average is consistent across time periods. Most importantly, our findings shows that searchers who submit the shortest queries (i.e., in number of terms) click on highest ranked results. We discuss implications, including predictive value, and future research.

Stochastic modelling of financial time series with memory and multifractal scaling

Financial processes may possess long memory and their probability densities may display heavy tails. Many models have been developed to deal with this tail behaviour, which reflects the jumps in the sample paths. On the other hand, the presence of long memory, which contradicts the efficient market hypothesis, is still an issue for further debates. These difficulties present challenges with the problems of memory detection and modelling the co-presence of long memory and heavy tails. This PhD project aims to respond to these challenges. The first part aims to detect memory in a large number of financial time series on stock prices and exchange rates using their scaling properties. Since financial time series often exhibit stochastic trends, a common form of nonstationarity, strong trends in the data can lead to false detection of memory. We will take advantage of a technique known as multifractal detrended fluctuation analysis (MF-DFA) that can systematically eliminate trends of different orders. This method is based on the identification of scaling of the q-th-order moments and is a generalisation of the standard detrended fluctuation analysis (DFA) which uses only the second moment; that is, q = 2. We also consider the rescaled range R/S analysis and the periodogram method to detect memory in financial time series and compare their results with the MF-DFA. An interesting finding is that short memory is detected for stock prices of the American Stock Exchange (AMEX) and long memory is found present in the time series of two exchange rates, namely the French franc and the Deutsche mark. Electricity price series of the five states of Australia are also found to possess long memory. For these electricity price series, heavy tails are also pronounced in their probability densities. The second part of the thesis develops models to represent short-memory and longmemory financial processes as detected in Part I. These models take the form of continuous-time AR(∞) -type equations whose kernel is the Laplace transform of a finite Borel measure. By imposing appropriate conditions on this measure, short memory or long memory in the dynamics of the solution will result. A specific form of the models, which has a good MA(∞) -type representation, is presented for the short memory case. Parameter estimation of this type of models is performed via least squares, and the models are applied to the stock prices in the AMEX, which have been established in Part I to possess short memory. By selecting the kernel in the continuous-time AR(∞) -type equations to have the form of Riemann-Liouville fractional derivative, we obtain a fractional stochastic differential equation driven by Brownian motion. This type of equations is used to represent financial processes with long memory, whose dynamics is described by the fractional derivative in the equation. These models are estimated via quasi-likelihood, namely via a continuoustime version of the Gauss-Whittle method. The models are applied to the exchange rates and the electricity prices of Part I with the aim of confirming their possible long-range dependence established by MF-DFA. The third part of the thesis provides an application of the results established in Parts I and II to characterise and classify financial markets. We will pay attention to the New York Stock Exchange (NYSE), the American Stock Exchange (AMEX), the NASDAQ Stock Exchange (NASDAQ) and the Toronto Stock Exchange (TSX). The parameters from MF-DFA and those of the short-memory AR(∞) -type models will be employed in this classification. We propose the Fisher discriminant algorithm to find a classifier in the two and three-dimensional spaces of data sets and then provide cross-validation to verify discriminant accuracies. This classification is useful for understanding and predicting the behaviour of different processes within the same market. The fourth part of the thesis investigates the heavy-tailed behaviour of financial processes which may also possess long memory. We consider fractional stochastic differential equations driven by stable noise to model financial processes such as electricity prices. The long memory of electricity prices is represented by a fractional derivative, while the stable noise input models their non-Gaussianity via the tails of their probability density. A method using the empirical densities and MF-DFA will be provided to estimate all the parameters of the model and simulate sample paths of the equation. The method is then applied to analyse daily spot prices for five states of Australia. Comparison with the results obtained from the R/S analysis, periodogram method and MF-DFA are provided. The results from fractional SDEs agree with those from MF-DFA, which are based on multifractal scaling, while those from the periodograms, which are based on the second order, seem to underestimate the long memory dynamics of the process. This highlights the need and usefulness of fractal methods in modelling non-Gaussian financial processes with long memory.

Evaluating Specification Tests for Markov-Switching Time-Series Models

We evaluate the performance of several specification tests for Markov regime-switching time-series models. We consider the Lagrange multiplier (LM) and dynamic specification tests of Hamilton (1996) and Ljung–Box tests based on both the generalized residual and a standard-normal residual constructed using the Rosenblatt transformation. The size and power of the tests are studied using Monte Carlo experiments. We find that the LM tests have the best size and power properties. The Ljung–Box tests exhibit slight size distortions, though tests based on the Rosenblatt transformation perform better than the generalized residual-based tests. The tests exhibit impressive power to detect both autocorrelation and autoregressive conditional heteroscedasticity (ARCH). The tests are illustrated with a Markov-switching generalized ARCH (GARCH) model fitted to the US dollar–British pound exchange rate, with the finding that both autocorrelation and GARCH effects are needed to adequately fit the data.