Ecological knowledge and North Sea environmental policies

In an analysis of North Sea eutrophication science and policies, focusing on the period 1980–2005, it was investigated how scientific information was used in policy-making. The analysis focused on the central assumptions of the rational policy-making model, i.e. that scientific information can be used to formulate decisions, based upon objective scientific information (rational decision-making), and secondly, can support implementing these decisions (rational management). In general terms, the following was concluded:

• •More knowledge has increased rather than reduced uncertainty;
• •In order to handle the problem of dealing with complexity and uncertainty at the political level, a simplification of facts has occurred, in this case focusing on nutrients as the main cause of the problem, at the same time excluding other possible causes;
• •Both the limited scientific view (i.e. the nutrient view) and the exaggeration of the seriousness of the problem (impacts, scope) have been used as an authoritative basis for the justification of political decisions. Both were not supported by the majority of the scientific community;
• •New scientific knowledge, not in support of existing policies, has been excluded from the policy process;
• •The science–policy interface, mainly consisting of “civil-servant scientists”, that emerged and increased its influence over the period of investigation, has been the central element in the simplification and exclusion process.

The main lesson learned is that work at the interface of science and policy must be subject to democratic principles, i.e. be transparent and involving all parties with a stake in the issue under consideration.     

Application of chemometric analysis based on physicochemical and chromatographic data for the differentiation origin of plant protection products containing chlorpyrifos

The aim of this study was to investigate the similarities and dissimilarities between the pesticide samples in form of emulsifiable concentrates (EC) formulation containing chlorpyrifos as active ingredient coming from different sources (i.e., shops and wholesales) and also belonging to various series. The results obtained by the Headspace Gas Chromatography–Mass Spectrometry method and also some selected physicochemical properties of examined pesticides including pH, density, stability, active ingredient and water content in pesticides tested were compared using two chemometric methods. Applicability of simple cluster analysis and also principal component analysis of obtained data in differentiation of examined plant protection products coming from different sources was confirmed. It would be advantageous in the routine control of originality and also in the detection of counterfeit pesticides, respectively, among commercially available pesticides containing chlorpyrifos as an active ingredient.     

How the DPSIR framework can be used for structuring problems and facilitating empirical research in coastal systems

As pressures on coastal zones mount, there is a growing need for frameworks that can be used to conceptualize complex sustainability challenges and help organize research that increases understand about interacting ecological and societal processes, predicts change, and supports the management, persistence, and resilience of coastal systems. The Driver–Pressure–State–Impact–Response (DPSIR) framework is one such approach that has been adopted in some coastal zones around the world. Although the application of the DPSIR framework has considerable potential to bridge the gap between scientific disciplines and link science to coastal policy and management, current applications of DPSIR in coastal environments have been limited and new innovations in the application of the DPSIR model are needed. We conducted a structured review of literature on the DPSIR framework as applied to the function, process and components of complex coastal systems. Our specific focus was on how the DPSIR framework has been used as a tool to organize sophisticated empirical scientific research, support transdisciplinary knowledge at a level appropriate for building understanding about coastal systems, and how adopting a DPSIR approach can help stakeholders to articulate and structure challenges in coastal systems and use the framework to support policy and management outcomes. The review revealed that DPSIR models of coastal systems have been largely used to support and develop conceptual understanding of coastal social–ecological systems and to identify drivers and pressures in the coastal realm. A limited number of studies have used DPSIR as a starting point for semi-quantitative or quantitative analyses, although our review highlights the continued need for, and potential of, transformative quantitative analyses and transdisciplinary applications of the DPSIR framework. The DPSIR models we reviewed were predominantly single sector, encompassing ecological or biophysical factors or focusing primarily on socio-cultural dimensions rather than full integration of both types of information. Only in eight of 24 shortlisted articles did researchers actively engage decision-makers or citizens in their research: given the potential opportunity for using DPSIR as a tool to successfully engage policy-makers and stakeholders, it appears that the DPSIR framework has been under-utilized in this regard.     

The Importance of Being Spatial (and Reserved): Assessing Northern Spotted Owl Habitat Relationships with Hierarchical Bayesian Models

Abstract:  Regional conservation planning increasingly draws on habitat suitability models to support decisions regarding land allocation and management. Nevertheless, statistical techniques commonly used for developing such models may give misleading results because they fail to account for 3 factors common in data sets of species distribution: spatial autocorrelation, the large number of sites where the species is absent (zero inflation), and uneven survey effort. We used spatial autoregressive models fit with Bayesian Markov Chain Monte Carlo techniques to assess the relationship between older coniferous forest and the abundance of Northern Spotted Owl nest and activity sites throughout the species' range. The spatial random-effect term incorporated in the autoregressive models successfully accounted for zero inflation and reduced the effect of survey bias on estimates of species–habitat associations. Our results support the hypothesis that the relationship between owl distribution and older forest varies with latitude. A quadratic relationship between owl abundance and older forest was evident in the southern portion of the range, and a pseudothreshold relationship was evident in the northern portion of the range. Our results suggest that proposed changes to the network of owl habitat reserves would reduce the proportion of the population protected by up to one-third, and that proposed guidelines for forest management within reserves underestimate the proportion of older forest associated with maximum owl abundance and inappropriately generalize threshold relationships among subregions. Bayesian spatial models can greatly enhance the utility of habitat analysis for conservation planning because they add the statistical flexibility necessary for analyzing regional survey data while retaining the interpretability of simpler models.     

Silver-Russell syndrome as a cause for early intrauterine growth restriction

The diagnosis of Silver–Russell syndrome is based on the characteristic growth restriction and the presence of typical dysmorphic features. We present the prenatal and postnatal findings of a case that was treated at our perinatal center. The suspected diagnosis Silver–Russell syndrome was confirmed after delivery by our medical genetic and neonatology services. The authors want to point out that SRS should be considered in the differential diagnosis of early asymmetric intrauterine growth restriction. Copyright © 2005 John Wiley & Sons, Ltd.     

High efficiency chlorine removal from polyvinyl chloride (PVC) pyrolysis with a gas–liquid fluidized bed reactor

In this research a gas–liquid fluidized bed reactor was developed for removing chlorine (Cl) from polyvinyl chloride (PVC) to favor its pyrolysis treatment. In order to efficiently remove Cl within a limited time before extensive generation of hydrocarbon products, the gas–liquid fluidized bed reactor was running at 280–320 °C, where hot N₂ was used as fluidizing gas to fluidize the molten polymer, letting the molten polymer contact well with N₂ to release Cl in form of HCl. Experimental results showed that dechlorination efficiency is mainly temperature dependent and 300 °C is a proper reaction temperature for efficient dechlorination within a limited time duration and for prevention of extensive pyrolysis; under this temperature 99.5% of Cl removal efficiency can be obtained within reaction time around 1 min after melting is completed as the flow rate of N₂ gas was set around 0.47–0.85 Nm³ kg^?1 for the molten PVC. Larger N₂ flow rate and additives in PVC would enhance HCl release but did not change the final dechlorination efficiency; and excessive N₂ flow rate should be avoided for prevention of polymer entrainment. HCl is emitted from PVC granules or scraps at the mean time they started to melt and the melting stage should be taken into consideration when design the gas–liquid fluidized bed reactor for dechlorination.     

Influence of aeration modes on leachate characteristic of landfills that adopt the aerobic–anaerobic landfill method

As far as the optimal design, operation, and field application of the Aerobic–Anaerobic Landfill Method (AALM) are concerned, it is very important to understand how aeration modes (different combinations of aeration depth and air injection rate) affect the biodegradation of organic carbon and the transformation of nitrogen in landfill solid waste. Pilot-scale lysimeter experiments were carried out under different aeration modes to obtain detailed information regarding the influence of aeration modes on leachate characteristics. Results from these lysimeter experiments revealed that aeration at the bottom layer was the most effective for decomposition of organic carbon when compared with aeration at the surface or middle layers. Moreover, the air injection rate led to different nitrogen transformation patterns, unlike the lesser influence it has on organic carbon decomposition. Effective simultaneous nitrification and denitrification were observed for the aeration mode with a higher air injection rate (=1.0 L/min). On the other hand, the phenomenon of sequenced nitrification and denitrification could be observed when a low air injection rate (=0.5 L/min.) was employed. Finally, it is concluded that, for AALM, air injection with a higher air injection rate at the deepest layer near the leachate collection pipe tends to accelerate the stabilization of landfill waste as defined in terms of the enhancement of denitrification as well as organic carbon decomposition.     

Rapid estimation of compost enzymatic activity by spectral analysis method combined with machine learning

The aim of this study was to investigate the feasibility of using visible near-infrared (VisNIR) diffuse reflectance spectroscopy (DRS) as an easy, inexpensive, and rapid method to predict compost enzymatic activity, which traditionally measured by fluorescein diacetate hydrolysis (FDA-HR) assay. Compost samples representative of five different compost facilities were scanned by DRS, and the raw reflectance spectra were preprocessed using seven spectral transformations for predicting compost FDA-HR with six multivariate algorithms. Although principal component analysis for all spectral pretreatments satisfactorily identified the clusters by compost types, it could not separate different FDA contents. Furthermore, the artificial neural network multilayer perceptron (residual prediction deviation = 3.2, validation r² = 0.91 and RMSE = 13.38 μg g^?1 h^?1) outperformed other multivariate models to capture the highly non-linear relationships between compost enzymatic activity and VisNIR reflectance spectra after Savitzky–Golay first derivative pretreatment. This work demonstrates the efficiency of VisNIR DRS for predicting compost enzymatic as well as microbial activity.     

Uncertainties in the assessment of “significant effect” on the Dutch Natura 2000 Wadden Sea site – The mussel seed fishery and powerboat race controversies

Natura 2000, the nature network based on the European Bird and Habitat Directives, is explicitly grounded on ecological science. To acquire a permit under the Dutch Nature Conservation Act, an appropriate assessment of significant effects must be conducted based on the best available scientific knowledge. In this way the scientific and policy world are directly linked. This article focuses on ‘significant effect’ as a boundary object to analyse how science–policy interactions shape the meaning and assessment of significant effect and how these interpretations influence the decision-making process. To this end, two conflicts over significant effect are investigated: the conflict over the 2006-spring permit for the mussel seed fishery, and the 2011 permit for the planned World Championship powerboat races. In both cases nature organisations started a court process against the government-granted permits in protest to the “no significant effect” claim, stating that there was insufficient certainty for this conclusion. These conflicts are approached as controversies between discourse coalitions with different interpretations of the ecological knowledge. We show how significant effect became a focal point in the controversies, limiting the debate to ecological arguments and science-based expertise, but also creating options for parties to advance their protest by articulating uncertainties. Only uncertainty of incomplete knowledge was explicitly addressed, excluding ambiguity of values and unpredictability of the actual ecosystem. We suggest that acknowledging the value aspect in disputes on significant effect would leave more space for effective solutions of the problems under debate.     

A Bayesian network highlighting the linkages between landscape structure and the local economy: the case of agritourism in lowland areas of Northern Italy

Linking landscapes to socio-economic benefits necessarily requires considering the usability of landscape structure. To do so, however, depends on the interaction between users and producers of landscape-related services. We illustrate this interaction with a Bayesian Belief Network (BBN) in a case study analysing the connection between residents' perceptions of landscape structure and agritourism restaurants in the eastern lowlands of Ferrara (Italy). We use estimates of prior and conditional probabilities from a mix of different data: land use, survey data, regional statistics, and expert judgements to show the likely effects of the landscape structure on the local economy by using intermediate forms of services (i.e. second-order services). The second-order service is highly influenced by the agritourism density and by the frequency with which customers dine at agritourism restaurants and less by landscape attractiveness, confirming the importance of the supply and demand of second-order services in the provision of landscape-related services.