Regime shifts in marine ecosystems: detection prediction and management

Regime shifts are abrupt changes between contrasting, persistent states of any complex system. The potential for their prediction in the ocean and possible management depends upon the characteristics of the regime shifts: their drivers (from anthropogenic to natural), scale (from the local to the basin) and potential for management action (from adaptation to mitigation). We present a conceptual framework that will enhance our ability to detect, predict and manage regime shifts in the ocean, illustrating our approach with three well-documented examples: the North Pacific, the North Sea and Caribbean coral reefs. We conclude that the ability to adapt to, or manage, regime shifts depends upon their uniqueness, our understanding of their causes and linkages among ecosystem components and our observational capabilities.

Causes and prediction of abrupt climate-driven ecosystem shifts in the North Atlantic

Warming of the global climate is now unequivocal and its impact on Earth’ functional units has become more apparent. Here, we show that marine ecosystems are not equally sensitive to climate change and reveal a critical thermal boundary where a small increase in temperature triggers abrupt ecosystem shifts seen across multiple trophic levels. This large-scale boundary is located in regions where abrupt ecosystem shifts have been reported in the North Atlantic sector and thereby allows us to link these shifts by a global common phenomenon. We show that these changes alter the biodiversity and carrying capacity of ecosystems and may, combined with fishing, precipitate the reduction of some stocks of Atlantic cod already severely impacted by exploitation. These findings offer a way to anticipate major ecosystem changes and to propose adaptive strategies for marine exploited resources such as cod in order to minimize social and economic consequences.

TRANSIENT LIPOPOLYSACCHARIDE-INDUCED CYTOKINE RESPONSES IN THE MATERNAL AND FETAL GUINEA PIG

The aim of this study was to further investigate the role of pro-inflammatory cytokines in the pathogenesis of fetal cererbral white matter injury associated with chorioamnionitis by charaterizing the time course of the cytokine response in the pregnant guinea pig following a maternal inflammatory insult. Chorioamnionitis increases the risk for fetal brain injury. In the guinea pig, a threshold maternal inflammatory response must be reached for significant fetal brain injury to occur. However, a previous study demonstrated that, by seven days after an acute maternal inflammatory insult, cytokine levels in both maternal and fetal compartments are not different from controls. The purpose of this study, therefore, was to test the hypothesis that a significant cytokine response occurs within the first seven days following an acute maternal inflammatory response. Pregnant guinea pigs (n=34) were injected intraperitoneally with 100µg/kg lipopolysaccharide (LPS) at 70% gestation and euthanized at 24 hours, 48 hours or 5 days following endotoxin exposure. Control animals were euthanized at 70% gestation without exposure. Concentrations of interleukin-6, interleukin 1-β and tumour necrosis factor-α (IL-6, IL-1β, TNF-α) were quantified in the maternal serum and amniotic fluid by enzyme-linked immunosorbent assay. IL-6 and IL-1β concentrations were elevated in the maternal serum at 24 hours and returned to control levels by five days. In the amniotic fluid, IL-6 peaked at 48 hours and IL-1β at 24 hours. TNF-α levels were not significantly increased. A single maternal LPS injection produces transient increases in cytokine concentrations in the maternal serum and amniotic fluid. This further implicates the cytokines as potential mediators of fetal white matter damage. Although this response might not be sufficient to produce the brain injury itself, it may initiate harmful pro-inflammatory cytokine cascades, which could even continue to harm the fetus following delivery. A human diagnostic protocol was developed to assess the use of serial serum biomarkers, including IL-6 and TNF-α, in the prediction of histological chorioamnionitis. Preliminary analysis of the pilot study suggests that certain biomarkers might be worthy of further investigation in a larger-scale study.

Investigation of the effect of intrauterine inflammation and infection on fetal brain injury using human and animal models

In recent years, increased focus has been placed on the role of intrauterine infection and inflammation in the pathogenesis of fetal brain injury leading to neurodevelopmental disorders such as cerebral palsy. At present, the mechanisms by which inflammatory processes during pregnancy cause this effect on the fetus are poorly understood. Our previous work has indicated an association between experimentally-induced intrauterine infection, increased proinflammatory cytokines, and increased white matter injury in the guinea pig fetus. In order to further elucidate the pathways by which inflammation in the maternal system or the fetal membranes leads to fetal impairment, a number of studies investigating aspects of the disease process have been performed. These studies represent a body of work encompassing novel research and results in a number of human and animal studies. Using a guinea pig model of inflammation, increased amniotic fluid proinflammatory cytokines and fetal brain injury were found after a maternal inflammatory response was initiated using endotoxin. In order to more closely monitor the fetal response to chorioamnionitis, a model using the chronically catheterized fetal ovine was carried out. This study demonstrated the adverse effects on fetal white matter after intrauterine exposure to bacterial inoculation, though the physiological parameters of the fetus were relatively stable throughout the experimental protocol, even when challenged with intermittent hypoxic episodes. The placenta is an important mediator between mother and fetus during gestation, though its role in the inflammatory process is largely undefined. Studies on the placental role in the inflammatory process were undertaken, and the limited ability of proinflammatory cytokines and endotoxin to cross the placenta are detailed herein. Neurodevelopmental disorders can be monitored in animal models in order to determine effective disease models for characterization of injury and use in therapeutic strategies. Our characterizations of postnatal behaviour in the guinea pig model using motility monitoring and spatial memory testing have shown small but significant differences in pups exposed to inflammatory processes in utero. The data presented herein contributes a breadth of knowledge to the ongoing elucidation of the pathways by which fetal brain injury occurs. Determining the pathway of damage will lead to discovery of diagnostic criteria, while determining the vulnerabilities of the developing fetus is essential in formulating therapeutic options.

A note on the prediction of liquid hold-up with the stratified roll wave regime for gas/liquidco-current flow in horizontal pipes.

This note presents a simple model for prediction of liquid hold-up in two-phase horizontal pipe flow for the stratified roll wave (St+RW) flow regime. Liquid hold-up data for horizontal two-phase pipe flow [1, 2, 3, 4, 5 and 6] exhibit a steady increase with liquid velocity and a more dramatic fall with increasing gas rate as shown by Hand et al. [7 and 8] for example. In addition the liquid hold-up is reported to show an additional variation with pipe diameter. Generally, if the initial liquid rate for the no-gas flow condition gives a liquid height below the pipe centre line, the flow patterns pass successively through the stratified (St), stratified ripple (St+R), stratified roll wave, film plus droplet (F+D) and finally the annular (A+D, A+RW, A+BTS) regimes as the gas rate is increased. Hand et al. [7 and 8] have given a detailed description of this progression in flow regime development and definitions of the patterns involved. Despite the fact that there are over one hundred models which have been developed to predict liquid hold-up, none have been shown to be universally useful, while only a handful have proven to be applicable to specific flow regimes [9, 10, 11 and 12]. One of the most intractable regimes to predict has been the stratified roll wave pattern where the liquid hold-up shows the most dramatic change with gas flow rate. It has been suggested that the momentum balance-type models, which give both hold-up and pressure drop prediction, can predict universally for all flow regimes but particularly in the case of the difficult stratified roll wave pattern. Donnelly [1] recently demonstrated that the momentum balance models experienced some difficulties in the prediction of this regime. Without going into lengthy details, these models differ in the assumed friction factor or shear stress on the surfaces within the pipe particularly at the liquid–gas interface. The Baker–Jardine model [13] when tested against the 0.0454 m i.d. data of Nguyen [2] exhibited a wide scatter for both liquid hold-up and pressure drop as shown in Fig. 1. The Andritsos–Hanratty model [14] gave better prediction of pressure drop but a wide scatter for liquid hold-up estimation (cf. Fig. 2) when tested against the 0.0935 m i.d. data of Hand [5]. The Spedding–Hand model [15], shown in Fig. 3 against the data of Hand [5], gave improved performance but was still unsatisfactory with the prediction of hold-up for stratified-type flows. The MARS model of Grolman [6] gave better prediction of hold-up (cf. Fig. 4) but deterioration in the estimation of pressure drop when tested against the data of Nguyen [2]. Thus no method is available that will accurately predict liquid hold-up across the whole range of flow patterns but particularly for the stratified plus roll wavy regime. The position is particularly unfortunate since the stratified-type regimes are perhaps the most predominant pattern found in multiphase lines.

Modelling and Prediction of NOx Emission in a Coal-Fired Power Generation Plant

In this paper NOx emissions modelling for real-time operation and control of a 200 MWe coal-fired power generation plant is studied. Three model types are compared. For the first model the fundamentals governing the NOx formation mechanisms and a system identification technique are used to develop a grey-box model. Then a linear AutoRegressive model with eXogenous inputs (ARX) model and a non-linear ARX model (NARX) are built. Operation plant data is used for modelling and validation. Model cross-validation tests show that the developed grey-box model is able to consistently produce better overall long-term prediction performance than the other two models.