Monitoring the vegetation start of season (SOS) across the island of Ireland using the MERIS global vegetation index

The aim of this study was to develop a methodology, based on satellite remote sensing, to estimate the vegetation Start of Season (SOS) across the whole island of Ireland on an annual basis. This growing body of research is known as Land Surface Phenology (LSP) monitoring. The SOS was estimated for each year from a 7-year time series of 10-day composited, 1.2 km reduced resolution MERIS Global Vegetation Index (MGVI) data from 2003 to 2009, using the time series analysis software, TIMESAT. The selection of a 10-day composite period was guided by in-situ observations of leaf unfolding and cloud cover at representative point locations on the island. The MGVI time series was smoothed and the SOS metric extracted at a point corresponding to 20% of the seasonal MGVI amplitude. The SOS metric was extracted on a per pixel basis and gridded for national scale coverage. There were consistent spatial patterns in the SOS grids which were replicated on an annual basis and were qualitatively linked to variation in landcover. Analysis revealed that three statistically separable groups of CORINE Land Cover (CLC) classes could be derived from differences in the SOS, namely agricultural and forest land cover types, peat bogs, and natural and semi-natural vegetation types. These groups demonstrated that managed vegetation, e.g. pastures has a significantly earlier SOS than in unmanaged vegetation e.g. natural grasslands. There was also interannual spatio-temporal variability in the SOS. Such variability was highlighted in a series of anomaly grids showing variation from the 7-year mean SOS. An initial climate analysis indicated that an anomalously cold winter and spring in 2005/2006, linked to a negative North Atlantic Oscillation index value, delayed the 2006 SOS countrywide, while in other years the SOS anomalies showed more complex variation. A correlation study using air temperature as a climate variable revealed the spatial complexity of the air temperature-SOS relationship across the Republic of Ireland as the timing of maximum correlation varied from November to April depending on location. The SOS was found to occur earlier due to warmer winters in the Southeast while it was later with warmer winters in the Northwest. The inverse pattern emerged in the spatial patterns of the spring correlates. This contrasting pattern would appear to be linked to vegetation management as arable cropping is typically practiced in the southeast while there is mixed agriculture and mostly pastures to the west. Therefore, land use as well as air temperature appears to be an important determinant of national scale patterns in the SOS. The TIMESAT tool formed a crucial component of the estimation of SOS across the country in all seven years as it minimised the negative impact of noise and data dropouts in the MGVI time series by applying a smoothing algorithm. The extracted SOS metric was sensitive to temporal and spatial variation in land surface vegetation seasonality while the spatial patterns in the gridded SOS estimates aligned with those in landcover type. The methodology can be extended for a longer time series of FAPAR as MERIS will be replaced by the ESA Sentinel mission in 2013, while the availability of full resolution (300m) MERIS FAPAR and equivalent sensor products holds the possibility of monitoring finer scale seasonality variation. This study has shown the utility of the SOS metric as an indicator of spatiotemporal variability in vegetation phenology, as well as a correlate of other environmental variables such as air temperature. However, the satellite-based method is not seen as a replacement of ground-based observations, but rather as a complementary approach to studying vegetation phenology at the national scale. In future, the method can be extended to extract other metrics of the seasonal cycle in order to gain a more comprehensive view of seasonal vegetation development.

Identifying sources of global contention in constraint satisfaction search

Much work has been done on learning from failure in search to boost solving of combinatorial problems, such as clause-learning and clause-weighting in boolean satisfiability (SAT), nogood and explanation-based learning, and constraint weighting in constraint satisfaction problems (CSPs). Many of the top solvers in SAT use clause learning to good effect. A similar approach (nogood learning) has not had as large an impact in CSPs. Constraint weighting is a less fine-grained approach where the information learnt gives an approximation as to which variables may be the sources of greatest contention. In this work we present two methods for learning from search using restarts, in order to identify these critical variables prior to solving. Both methods are based on the conflict-directed heuristic (weighted-degree heuristic) introduced by Boussemart et al. and are aimed at producing a better-informed version of the heuristic by gathering information through restarting and probing of the search space prior to solving, while minimizing the overhead of these restarts. We further examine the impact of different sampling strategies and different measurements of contention, and assess different restarting strategies for the heuristic. Finally, two applications for constraint weighting are considered in detail: dynamic constraint satisfaction problems and unary resource scheduling problems.

Investigation of global regulators influencing styrene metabolism and bioplastic synthesis in Pseudomonas putida CA-3

The genetics and biochemistry involved in the biodegradation of styrene and the production of polyhydroxyalkanoates in Pseudomonas putida CA-3 have been well characterised to date. Knowledge of the role played by global regulators in controlling these pathways currently represents a critical knowledge gap in this area. Here we report on our efforts to identify such regulators using mini-Tn5 transposon mutagenesis of the P. putida CA-3 genome. The library generated was subjected to phenotypic screening to identify mutants exhibiting a reduced sensitivity to the effects of carbon catabolite repression of aromatic pathway activity. Our efforts identified a clpX disrupted mutant which exhibited wild-type levels of growth on styrene but significantly reduced growth on phenylacetic acid. RT-PCR analysis of key PACoA catabolon genes necessary for phenylacetic acid metabolism, and SDS-PAGE protein profile analyses suggest that no direct alteration of PACoA pathway transcriptional or translational activity was involved. The influence of global regulators affecting the accumulation of PHAs in P. putida CA-3 was also studied. Phenotypic screening of the mini-Tn5 library revealed a gacS sensor kinase gene disruption resulting in the loss of PHA accumulation capacity in P. putida CA-3. Subsequent SDS-PAGE protein analyses of the wild type and gacS mutant strains identified post-transcriptional control of phaC1 synthase as a key point of control of PHA synthesis in P. putida CA-3. Disruption of the gacS gene in another PHA accumulating organism, P. putida S12, also demonstrated a reduction of PHA accumulation capacity. PHA accumulation was observed to be disrupted in the CA-3 gacS mutant under phosphorus limited growth conditions. Over-expression studies in both wild type CA-3 and gacS mutant demonstrated that rsmY over-expression in gacS disrupted P. putida CA-3 is insufficient to restore PHA accumulation in the cell however in wild type cells, over-expression of rsmY results in an altered PHA monomer compositions.