Monitoring and predictive modelling of estuarine benthic macrofauna and their relevance to resource management problems

Practical considerations in estuarine management, as well as prediction of the consequences of global change on coastal protection, urgently require a better understanding and better modeling of estuarine ecosystems as influenced by ecological, physical, chemical and morphological processes. Recent Dutch examples of such questions are: the impact of enhanced dredging in the Schelde estuary, the impact of sea level rise on the Wadden Sea and Delta area, concerns about the loss of salt marsh habitats, etc. Benthic communities are good indicators of biotic integrity and reflect the present state of the estuarine ecosystem. The analysis of benthic infauna is a key element of many marine and estuarine monitoring programs. In the Dutch Delta area (SW-Netherlands) there is a relatively long tradition on estuarine macrozoobenthos monitoring, such as implemented e.g. in the BIOMON program. This program was designed to detect long-term trends in the average density, biomass and species composition of large parts of different systems (e.g. Schelde estuary, Oosterschelde, Grevelingen), in order to obtain insight in the natural development of estuarine and coastal areas and the anthropogenic influences on these systems. Running now for over a decade, these programs, together with other field campaigns, provide a unique data set on benthic macrofauna (e.g. for the Schelde estuary over 5000 samples are available at the moment). Until recently these data were hardly processed and used for further analysis. However, such data sets offer the opportunity to analyze and predict patterns in occurrence of benthic macrofauna in a much more profound way. Recently, within a cooperation between decision makers (Rijkswaterstaat, Directie Zeeland), advisers (RIKZ) and scientists (NIOO-CEMO), the possibilities and limitations of using these data sets for the predictions of benthic macrofauna at scales relevant to resource management problems are evaluated. In our approach we use different statistical methodologies to quantify, model and predict patterns at different spatial and temporal scales, going from patterns on a single tidal flat to inter-estuary comparisons and from monthly patterns to decennial trends. Several examples are shown that illustrate the use of these data, going from simple classification techniques to more sophisticated predictive modeling: Changes and shifts in benthos communities are shown for a land reclamation area of Rotterdam harbour in the Haringvliet-delta using classification techniques. Ordination analysis on the saline lake Grevelingen, a former estuary, showed long-term changes in macrobenthic community structure as a consequence of changes in salinity, light penetration, etc. This case study will be dealt with in more detail in a separate contribution. In the Schelde estuary, a detailed study was performed to unravel the use of environmental data in predicting benthic macrofaunal species distributions at different spatial scales (from a single tidal flat to the whole estuary). Statistical techniques such as geostatistics, hierarchical analysis and logistic regression were applied. At these different scales a distinct relation between the environment (e.g. salinity, sediment characteristics) on the one hand and macrofaunal species distributions on the other hand was observed. As a consequence, predictions of macrofaunal distributions can be made quite successful from environmental data within the Schelde estuary. An inter-estuary comparison between the Schelde estuary and Oosterschelde revealed that predictive models should also incorporate system-wide properties of estuarine systems, such as primary production and suspended matter concentrations, in order to perform in a more generic way. The results clearly show their use in making more sensible long-term decisions about matters having direct environmental effects. The results also provide information on how the design of monitoring programs could be improved or optimized, depending on the questions asked. As such, a more synergetic and flexible approach is urgently necessary, in which decision makers, advisers and scientists communicate in a more efficient way

What is marine biodiversity? Towards common concepts and their implications for assessing biodiversity status

Biodiversity' is one of the most common keywords used in environmental sciences, spanning from research to management, nature conservation, and consultancy. Despite this, our understanding of the underlying concepts varies greatly, between and within disciplines as well as among the scientists themselves. Biodiversity can refer to descriptions or assessments of the status and condition of all or selected groups of organisms, from the genetic variability, to the species, populations, communities, and ecosystems. However, a concept of biodiversity also must encompass understanding the interactions and functions on all levels from individuals up to the whole ecosystem, including changes related to natural and anthropogenic environmental pressures. While biodiversity as such is an abstract and relative concept rooted in the spatial domain, it is central to most international, European, and national governance initiatives aimed at protecting the marine environment. These rely on status assessments of biodiversity which typically require numerical targets and specific reference values, to allow comparison in space and/or time, often in association with some external structuring factors such as physical and biogeochemical conditions. Given that our ability to apply and interpret such assessments requires a solid conceptual understanding of marine biodiversity, here we define this and show how the abstract concept can and needs to be interpreted and subsequently applied in biodiversity assessments

2-Thiabicyclo[3.2.0]hepta-3,6-Dienes. 3. Desulfuration and Sulfuration of 2-Thiabicyclo[3.2.0]hepta-3,6-Dienes and X-Ray Crystal Structure of 3a,6,7,8,9,9a-Hexahydro-3a,5-Dimethylthieno[3,2-B][2]benzothiophene-2,3-Dicarbonitrile

The 2-3.2.0] hepta-3,6-Dienes 1–7 Extrude Sulfur in Solution at 285 °C to Give the 1,2-Benzenedicarbonitriles 8–12 in Yields of 42–56%. 5-(1,1-Dimethylethyl)-3,6-Dimethyl-2-3.2.0] hepta-3,6-Diene-1,7-Dicarbonitrile (6) Reacts at 140 °C to Give a Mixture of the Cope-Rearranged Isomer 13, the 1,2-Benzenedicarbonitrile 11, and possibly a 3a,6a-Dihydrothieno[3,2-B] thiophene (14). Reaction of 2a,5,6,7,8,8a-Hexahydro-2a,4-Dimethylbenzo[C]cyclobuta[B]thiophene-L,2-Dicarbonitrile (15) at 140 °C Gives a Mixture of De8ulfurated (16) and Sulfureted (17) Products in Yields of 88% and 70%, Respectively. Single-Crystal X-Ray Analysis Proved the 3a,6,7,8,9,9a-Hexahydrothieno[3,2-B][2]benzothiophene Structure (17). the Possible Mechanism of the Insertion of Sulfur in the Carbon-Carbon Single Bond of 15 is Discussed. © 1982, American Chemical Society. All Rights Reserved

Alternatieve stortstrategie voor de Westerschelde : Voortzetting monitoringsprogramma proefstorting walsoorden LOT 2: Ecologische monitoring

Contains fulltext : 35149.pdf (publisher's version ) (Open Access)148 p

Geographic and seasonal patterns and limits on the adaptive response to temperature of European Mytilus spp. and Macoma balthica populations

Seasonal variations in seawater temperature require extensive metabolic acclimatization in cold-blooded organisms inhabiting the coastal waters of Europe. Given the energetic costs of acclimatization, differences in adaptive capacity to climatic conditions are to be expected among distinct populations of species that are distributed over a wide geographic range. We studied seasonal variations in the metabolic adjustments of two very common bivalve taxa at European scale. To this end we sampled 16 populations of Mytilus spp. and 10 Macoma balthica populations distributed from 39° to 69°N. The results from this large-scale comprehensive comparison demonstrated seasonal cycles in metabolic rates which were maximized during winter and springtime, and often reduced in the summer and autumn. Studying the sensitivity of metabolic rates to thermal variations, we found that a broad range of Q10 values occurred under relatively cold conditions. As habitat temperatures increased the range of Q10 narrowed, reaching a bottleneck in southern marginal populations during summer. For Mytilus spp., genetic-group-specific clines and limits on Q10 values were observed at temperatures corresponding to the maximum climatic conditions these geographic populations presently experience. Such specific limitations indicate differential thermal adaptation among these divergent groups. They may explain currently observed migrations in mussel distributions and invasions. Our results provide a practical framework for the thermal ecophysiology of bivalves, the assessment of environmental changes due to climate change and its impact on (and consequences for) aquaculture

Learning biophysically-motivated parameters for alpha helix prediction

<p>Abstract</p> <p>Background</p> <p>Our goal is to develop a state-of-the-art protein secondary structure predictor, with an intuitive and biophysically-motivated energy model. We treat structure prediction as an optimization problem, using parameterizable cost functions representing biological "pseudo-energies". Machine learning methods are applied to estimate the values of the parameters to correctly predict known protein structures.</p> <p>Results</p> <p>Focusing on the prediction of alpha helices in proteins, we show that a model with 302 parameters can achieve a Q_{<it>α </it>}value of 77.6% and an SOV_{<it>α </it>}value of 73.4%. Such performance numbers are among the best for techniques that do not rely on external databases (such as multiple sequence alignments). Further, it is easier to extract biological significance from a model with so few parameters.</p> <p>Conclusion</p> <p>The method presented shows promise for the prediction of protein secondary structure. Biophysically-motivated elementary free-energies can be learned using SVM techniques to construct an energy cost function whose predictive performance rivals state-of-the-art. This method is general and can be extended beyond the all-alpha case described here.</p