Zooplankton associations in a Mediterranean long-term time-series

Coastal zooplankton have been investigated since 1984 at a Long Term Ecological Research station MC (LTER-MC) in the inner Gulf of Naples (Tyrrhenian Sea, Western Mediterranean). The sampling site, located between the littoral and the open sea systems, has very active hydrography that affects plankton communities. The present work was aimed at establishing whether, in such a dynamic and variable environment, species associations and homogeneous periods could be identified as characteristic and stable features of the mesozooplankton over the period 1984–2006. Hierarchical clustering was applied to assess species associations based on a matrix of similarities between species (R-mode), and homogeneous periods based on a matrix of similarities between observations (Q-mode). The Indicator Value index [IndVal, Dufrene and Legendre (1997) Species assemblages and indicator species: the need for a flexible asymmetrical approach. Ecol. Monogr., 67, 345–366] was calculated to identify species characterizing each period. Five taxonomic groups with well-defined composition and abundance were identified as robust associations that likely reflect different modes of community functioning. The temporal course of these associations was largely shaped by strong seasonal forcing comprising both physical and biological (e.g. trophic) signals. These associations persisted over the long term, thus indicating some stable characters in the Naples zooplankton time-series, providing evidence of resilience in communities in highly variable coastal conditions.

Abundance of phytoplankton, heterotrophic nanoflagellates and bacteria through the water column at time series station L4 in the Western English Channel, 2007-2013

short doi:10/rf8 full doi:10.5285/f014becf-d6d6-3bb9-e044-000b5de50f38

Oithona similislikes it cool: evidence from two long-term time series

We compare the long-term and seasonal patterns of abundance and phenology of the cyclopoid copepod Oithona similis at the L4 site (1988–2013) in the North Atlantic and at the LTER-MC site (1984–2013) in the Mediterranean Sea to investigate whether high temperature limits the occurrence of this species with latitudinal cline. The two sites are well suited to testing this hypothesis as they are characterized by similar chlorophyll a concentration (Chl a) but different temperature [sea surface temperature (SST)]. The abundance of O. similis at L4 was ∼10 times higher than at LTER-MC. Moreover, this species had several peaks of abundance during the year at L4 but a single peak in spring at LTER-MC. The main mode of temporal variability in abundance was seasonal at both sites. The abundance of O. similis was negatively correlated with SST only at LTER-MC, whereas it was positively correlated with Chl a at both sites. Oithona similis had a temperature optimum between 15 and 20°C reaching maximum abundance at ∼16.5°C at LTER-MC, but showed no Chl a optimum at either site. We conclude that the abundance of O. similis increases with prey availability up to 16.5°C and that temperature >20°C represents the main limiting factor for population persistence.

How does Calanus helgolandicus maintain its population in a variable environment? Analysis of a 25-year time series from the English Channel

Calanus helgolandicus is a key copepod of the NE Atlantic and fringing shelves, with a distribution that is expanding northwards with oceanic warming. The Plymouth L4 site has warmed over the past 25-years, and experiences large variations in the timing and availability of food for C. helgolandicus. Here we examine the degree to which these changes translate into variation in reproductive output and subsequently C. helgolandicus population size. Egg production rates (eggs female−1 day−1) were maximal in the spring to early-summer period of diatom blooms and high ciliate abundance, rather than during the equally large autumn blooms of autotrophic dinoflagellates. Egg hatch success was lower in spring however, with a greater proportion of naupliar deformities then also. Both the timing and the mean summer abundance of C. helgolandicus (CI–CVI) reflected those of spring total reproductive output. However this relationship was driven by inter-annual variability in female abundance and not that of egg production per female, which ranged only two-fold. Winter abundance of C. helgolandicus at L4 was much more variable than abundance in other seasons, and reflected conditions from the previous growing season. However, these low winter abundances had no clear carry-over signal to the following season’s population size. Overall, the C. helgolandicus population appears to be surprisingly resilient at this dynamic, inshore site, showing no long-term phenology shift and only a four-fold variation in mean abundance between years. This dampening effect may reflect a series of mortality sources, associated with the timing of stratification in the early part of the season, likely affecting egg sinking and loss, plus intense, density-dependent mortality of early stages in mid-summer likely through predation.

Error sensitive historical GIS: Identifying areal interpolation errors in time series data

Historical GIS has the potential to re-invigorate our use of statistics from historical censuses and related sources. In particular, areal interpolation can be used to create long-run time-series of spatially detailed data that will enable us to enhance significantly our understanding of geographical change over periods of a century or more. The difficulty with areal interpolation, however, is that the data that it generates are estimates which will inevitably contain some error. This paper describes a technique that allows the automated identification of possible errors at the level of the individual data values.

Modelling the impact of antibiotic use and infection control practices on the incidence of hospital-acquired methicillin-resistant Staphylococcus aureus: a time-series analysis

Objectives: Methicillin-resistant Staphylococcus aureus (MRSA) is a major nosocomial pathogen worldwide. A wide range of factors have been suggested to influence the spread of MRSA. The objective of this study was to evaluate the effect of antimicrobial drug use and infection control practices on nosocomial MRSA incidence in a 426-bed general teaching hospital in Northern Ireland.

Methods: The present research involved the retrospective collection of monthly data on the usage of antibiotics and on infection control practices within the hospital over a 5 year period (January 2000–December 2004). A multivariate ARIMA (time-series analysis) model was built to relate MRSA incidence with antibiotic use and infection control practices.

Results: Analysis of the 5 year data set showed that temporal variations in MRSA incidence followed temporal variations in the use of fluoroquinolones, third-generation cephalosporins, macrolides and amoxicillin/clavulanic acid (coefficients = 0.005, 0.03, 0.002 and 0.003, respectively, with various time lags). Temporal relationships were also observed between MRSA incidence and infection control practices, i.e. the number of patients actively screened for MRSA (coefficient = -0.007), the use of alcohol-impregnated wipes (coefficient = -0.0003) and the bulk orders of alcohol-based handrub (coefficients = -0.04 and -0.08), with increased infection control activity being associated with decreased MRSA incidence, and between MRSA incidence and the number of new patients admitted with MRSA (coefficient = 0.22). The model explained 78.4% of the variance in the monthly incidence of MRSA.

Conclusions: The results of this study confirm the value of infection control policies as well as suggest the usefulness of restricting the use of certain antimicrobial classes to control MRSA.

Genetic Algorithm Search for Predictive Patterns in Multidimensional Time Series

Based on an algorithm for pattern matching in character strings, we implement a pattern matching machine that searches for occurrences of patterns in multidimensional time series. Before the search process takes place, time series are encoded in user-designed alphabets. The patterns, on the other hand, are formulated as regular expressions that are composed of letters from these alphabets and operators. Furthermore, we develop a genetic algorithm to breed patterns that maximize a user-defined fitness function. In an application to financial data, we show that patterns bred to predict high exchange rates volatility in training samples retain statistically significant predictive power in validation samples.

NON-GAUSSIAN STATISTICS OF OIL PRICING TIME-SERIES: A CASE STUDY

The stochastic nature of oil price fluctuations is investigated over a twelve-year period, borrowing feedback from an existing database (USA Energy Information Administration database, available online). We evaluate the scaling exponents of the fluctuations by employing different statistical analysis methods, namely rescaled range analysis (R/S), scale windowed variance analysis (SWV) and the generalized Hurst exponent (GH) method. Relying on the scaling exponents obtained, we apply a rescaling procedure to investigate the complex characteristics of the probability density functions (PDFs) dominating oil price fluctuations. It is found that PDFs exhibit scale invariance, and in fact collapse onto a single curve when increments are measured over microscales (typically less than 30 days). The time evolution of the distributions is well fitted by a Levy-type stable distribution. The relevance of a Levy distribution is made plausible by a simple model of nonlinear transfer. Our results also exhibit a degree of multifractality as the PDFs change and converge toward to a Gaussian distribution at the macroscales.