The effects of depleted, current and elevated growth [CO2] in wheat are modulated by water availability

Drought is the main constraint on wheat yield in Mediterranean conditions. The photosynthesis, chlorophyll fluorescence and plant growth parameters of durum wheat (Triticum turgidum, L. var. durum) were compared at three [CO2] (i.e., depleted 260 ppm, current 400ppm and elevated 700 ppm) in plants subjected to twowater regimes (i.e.,well-wateredWW, and mildwater stress by drought orwater deficit WS), during pre-anthesis, post-anthesis and the end of grain filling. We showed that [CO2] effects on plants are modulated by water availability. Plants at depleted [CO2] showed photosynthetic acclimation (i.e., up-regulation) and reduced plant biomass and Harvest Index, but depleted [CO2] combined with WS has a more negative impact on plants with decreases in C assimilation and biomass. Plants at elevated [CO2] had decreased plant growth and photosynthesis in response to a down-regulation mechanism resulting from a decrease in Rubisco and N content, but plants exposed to a combination of elevated [CO2] and WS were the most negatively affected (e.g., on plant biomass).

Foliar photochemical processes and carbon metabolism under favourable and adverse winter conditions in a Mediterranean mixed forest, Catalonia (Spain)

Evergreen trees in the Mediterranean region must cope with a wide range of environmental stresses from summer drought to winter cold. The mildness of Mediterranean winters can periodically lead to favourable environmental conditions above the threshold for a positive carbon balance, benefitting evergreen woody species more than deciduous ones. The comparatively lower solar energy input in winter decreases the foliar light saturation point. This leads to a higher susceptibility to photoinhibitory stress especially when chilly (< 12 C) or freezing temperatures (< 0 C) coincide with clear skies and relatively high solar irradiances. Nonetheless, the advantage of evergreen species that are able to photosynthesize all year round where a significant fraction can be attributed to winter months, compensates for the lower carbon uptake during spring and summer in comparison to deciduous species. We investigated the ecophysiological behaviour of three co-occurring mature evergreen tree species (Quercus ilex L., Pinus halepensis Mill., and Arbutus unedo L.). Therefore, we collected twigs from the field during a period of mild winter conditions and after a sudden cold period. After both periods, the state of the photosynthetic machinery was tested in the laboratory by estimating the foliar photosynthetic potential with CO2 response curves in parallel with chlorophyll fluorescence measurements. The studied evergreen tree species benefited strongly from mild winter conditions by exhibiting extraordinarily high photosynthetic potentials. A sudden period of frost, however, negatively affected the photosynthetic apparatus, leading to significant decreases in key physiological parameters such as the maximum carboxylation velocity (Vc,max), the maximum photosynthetic electron transport rate (Jmax), and the optimal fluorometric quantum yield of photosystem II (Fv/Fm). The responses of Vc,max and Jmax were highly species specific, with Q. ilex exhibiting the highest and P. halepensis the lowest reductions. In contrast, the optimal fluorometric quantum yield of photosystem II (Fv/Fm) was significantly lower in A. unedo after the cold period. The leaf position played an important role in Q. ilex showing a stronger winter effect on sunlit leaves in comparison to shaded leaves. Our results generally agreed with the previous classifications of photoinhibition-tolerant (P. halepensis) and photoinhibitionavoiding (Q. ilex) species on the basis of their susceptibility to dynamic photoinhibition, whereas A. unedo was the least tolerant to photoinhibition, which was chronic in this species. Q. ilex and P. halepensis seem to follow contrasting photoprotective strategies. However, they seemed equally successful under the prevailing conditions exhibiting an adaptive advantage over A. unedo. These results show that our understanding of the dynamics of interspecific competition in Mediterranean ecosystems requires consideration of the physiological behaviour during winter which may have important implications for long-term carbon budgets and growth trends.

Energy dispersive X-Ray fluorescence : measuring elements in solid and liquid matrices

The subject of this project is about ���Energy Dispersive X-Ray Fluorescence ��� (EDXRF).This technique can be used for a tremendous variety of elemental analysis applications.It provides one of the simplest, most accurate and most economic analytical methods for thedetermination of the chemical composition of many types of materials.The purposes of this project are:- To give some basic information about Energy Dispersive X-ray Fluorescence.- To perform qualitative and quantitative analysis of different samples (water-dissolutions,powders, oils,..) in order to define the sensitivity and detection limits of the equipment.- To make a comprehensive and easy-to-use manual of the ���ARL QUANT���X EnergyDispersive X-Ray Fluorescence��� apparatus

Effect of nitrogen and phosphorus supply on growth, chlorophyll content and tissue composition of the macroalga Chaetomorpha linum (O.F. Mull), Kutz, in a Mediterranean Coastal Lagoon

The effect of dissolved nutrients on growth, nutrient content and uptake rates of Chaetomorpha linum in a Mediterranean coastal lagoon (Tancada, Ebro delta, NE Spain) was studied in laboratory experiments. Water was enriched with distinct forms of nitrogen, such as nitrate or ammonium and phosphorus. Enrichment with N, P or with both nutrients resulted in a significant increase in the tissue content of these nutrients. N-enrichment was followed by an increase in chlorophyll content after 4 days of treatment, although the difference was only significant when nitrate was added without P. P-enrichment had no significant effect on chlorophyll content. In all the treatments an increase in biomass was obseved after 10 days. This increase was higher in the N+P treatments. In all the treatments the uptake rate was significantly higher when nutrients were added than in control jars. The uptake rate of N, as ammonium, and P were significantly higher when they were added alone while that of N as nitrate was higher in the N+P treatment. In the P-enriched cultures, the final P-content of macroalgal tissues was ten-fold that of the initial tissue concentrations, thereby indicating luxury P-uptake. Moreover, at the end of the incubation the N:P ratio increased to 80, showing that P rather than N was the limiting factor for C. linum in the Tancada lagoon. The relatively high availability of N is related to the N inputs from rice fields that surround the lagoon and to P binding in sediments.

Self-Organising Maps and Correlation Analysis as a Tool to Explore Patterns in Excitation-Emission Matrix Data Sets and to Discriminate Dissolved Organic Matter Fluorescence Components

Dissolved organic matter (DOM) is a complex mixture of organic compounds, ubiquitous in marine and freshwater systems. Fluorescence spectroscopy, by means of Excitation-Emission Matrices (EEM), has become an indispensable tool to study DOM sources, transport and fate in aquatic ecosystems. However the statistical treatment of large and heterogeneous EEM data sets still represents an important challenge for biogeochemists. Recently, Self-Organising Maps (SOM) has been proposed as a tool to explore patterns in large EEM data sets. SOM is a pattern recognition method which clusterizes and reduces the dimensionality of input EEMs without relying on any assumption about the data structure. In this paper, we show how SOM, coupled with a correlation analysis of the component planes, can be used both to explore patterns among samples, as well as to identify individual fluorescence components. We analysed a large and heterogeneous EEM data set, including samples from a river catchment collected under a range of hydrological conditions, along a 60-km downstream gradient, and under the influence of different degrees of anthropogenic impact. According to our results, chemical industry effluents appeared to have unique and distinctive spectral characteristics. On the other hand, river samples collected under flash flood conditions showed homogeneous EEM shapes. The correlation analysis of the component planes suggested the presence of four fluorescence components, consistent with DOM components previously described in the literature. A remarkable strength of this methodology was that outlier samples appeared naturally integrated in the analysis. We conclude that SOM coupled with a correlation analysis procedure is a promising tool for studying large and heterogeneous EEM data sets.

Technical Note: Dissolved organic matter fluorescence a finite mixture approach to deconvolve excitation-emission matrices

The analysis of the shape of excitation-emission matrices (EEMs) is a relevant tool for exploring the origin, transport and fate of dissolved organic matter (DOM) in aquatic ecosystems. Within this context, the decomposition of EEMs is acquiring a notable relevance. A simple mathematical algorithm that automatically deconvolves individual EEMs is described, creating new possibilities for the comparison of DOM fluorescence properties and EEMs that are very different from each other. A mixture model approach is adopted to decompose complex surfaces into sub-peaks. The laplacian operator and the Nelder-Mead optimisation algorithm are implemented to individuate and automatically locate potential peaks in the EEM landscape. The EEMs of a simple artificial mixture of fluorophores and DOM samples collected in a Mediterranean river are used to describe the model application and to illustrate a strategy that optimises the search for the optimal output.

Self-Organising Maps and Correlation Analysis as a Tool to Explore Patterns in Excitation-Emission Matrix Data Sets and to Discriminate Dissolved Organic Matter Fluorescence Components

Dissolved organic matter (DOM) is a complex mixture of organic compounds, ubiquitous in marine and freshwater systems. Fluorescence spectroscopy, by means of Excitation-Emission Matrices (EEM), has become an indispensable tool to study DOM sources, transport and fate in aquatic ecosystems. However the statistical treatment of large and heterogeneous EEM data sets still represents an important challenge for biogeochemists. Recently, Self-Organising Maps (SOM) has been proposed as a tool to explore patterns in large EEM data sets. SOM is a pattern recognition method which clusterizes and reduces the dimensionality of input EEMs without relying on any assumption about the data structure. In this paper, we show how SOM, coupled with a correlation analysis of the component planes, can be used both to explore patterns among samples, as well as to identify individual fluorescence components. We analysed a large and heterogeneous EEM data set, including samples from a river catchment collected under a range of hydrological conditions, along a 60-km downstream gradient, and under the influence of different degrees of anthropogenic impact. According to our results, chemical industry effluents appeared to have unique and distinctive spectral characteristics. On the other hand, river samples collected under flash flood conditions showed homogeneous EEM shapes. The correlation analysis of the component planes suggested the presence of four fluorescence components, consistent with DOM components previously described in the literature. A remarkable strength of this methodology was that outlier samples appeared naturally integrated in the analysis. We conclude that SOM coupled with a correlation analysis procedure is a promising tool for studying large and heterogeneous EEM data sets.