De vitaliteit van de nationale staat in een internationaliserende wereld

Political Scienc

Evaluating the ecological realism of plant species distribution models with ecological indicator values

Species distribution models (SDMs) are routinely applied to assess current as well as future species distributions, for example to assess impacts of future environmental change on biodiversity or to underpin conservation planning. It has been repeatedly emphasized that SDMs should be evaluated based not only on their goodness of fit to the data, but also on the realism of the modelled ecological responses. However, possibilities for the latter are hampered by limited knowledge on the true responses as well as a lack of quantitative evaluation methods. Here we compared modelled niche optima obtained from European-scale SDMs of 1,476 terrestrial vascular plant species with empirical ecological indicator values indicating the preferences of plant species for key environmental conditions. For each plant species we first fitted an ensemble SDM including three modeling techniques (GLM, GAM and BRT) and extracted niche optima for climate, soil, land use and nitrogen deposition variables with a large explanatory power for the occurrence of that species. We then compared these SDM-derived niche optima with the ecological indicator values by means of bivariate correlation analysis. We found weak to moderate correlations in the expected direction between the SDM-derived niche optima and ecological indicator values. The strongest correlation occurred between the modelled optima for growing degree days and the ecological indicator values for temperature. Correlations were weaker for SDM-derived niche optima with a more distal relationship to ecological indicator values (notably precipitation and soil moisture). Further, correlations were consistently highest for BRT, followed by GLM and GAM. Our method gives insight into the ecological realism of modelled niche optima and projected core habitats and can be used to improve SDMs by making a more informed selection of environmental variables and modeling techniques

The EAGLE concept - A vision of a future European Land Monitoring Framework

Abstract. This paper describes the EAGLE concept, an object-oriented data model for land moni-toring. It highlights the background situation in the field of land monitoring, identifies the team in-volved, explains the technical and strategic considerations behind the concept, describes the cur-rent status of the harmonization and the developments made and outlines the future activities and requirements. After the structure and the content of the data model and matrix are explained, ex-amples are given on how to use the matrix. Besides its possible function as a semantic translation tool between different classification systems, it also can help to analyze class definitions to find semantic gaps, overlaps and inconsistencies and can serve as data model for new mapping initia-tives. On the long-term, the EAGLE concept aims at sketching a vision of a future integrated and harmonized European land monitoring system, which is designed to store all kinds of environmen-tally relevant information on the Earth´s surface, coming from both national and European data sources. Being still in the state of development, some first applications and test cases are under way. This paper also dedicates a chapter referring to the context between the concept and remote sensing in general as well as the relation between land monitoring and the principles of the Euro

Aspects of data on diverse relationships between agriculture and the environment

The main objectives presented in the report are: 1) To examine data gaps in the field of ecologically valuable grasslands and land at risk of abandonment by gathering existing data and making recommendations on how the gaps might best be filled to underpin the present and future policy process in these fields 2) To gather existing data and providing best/less good practice examples in relation to the environmental impacts of afforestation in agricultural lands in order to underpin the present and future policy process and environmental policy objectives 3) To find and present best/less good practice examples in relation to optimal design and management of riparian buffer strips in the context of environmental policy objectives

Severity of left ventricular remodeling defines outcomes and response to therapy in heart failure Valsartan heart failure trial (Val-HeFT) echocardiographic data

AbstractObjectivesThe objective of this study was to test the hypothesis that the severity of left ventricular remodeling predicts the response to treatment and outcomes in chronic heart failure.BackgroundReversal of remodeling should produce the most favorable outcome in patients with the most severe remodeling.MethodsIn 5,010 heart failure patients on background therapy and randomized to valsartan and placebo, serial recordings of left ventricular internal diastolic diameter (LVIDd) and ejection fraction (EF) were read at sites that had to meet qualifying standards before participating. Baseline LVIDd and EF were pooled across treatments and retrospectively grouped by quartiles Q1 to Q4, representing best to worst. Kaplan-Meier survival curves were obtained by the log-rank test. Q1 was compared with Q4 for mortality and combined mortality and morbidity (M + M) from Cox regression risk ratios (RRs). Valsartan versus placebo changes from baseline in LVIDd and EF were analyzed by quartiles from analysis of covariance. Valsartan and placebo were compared by RRs for M + M.ResultsSurvival rates were greater in the better quartiles for LVIDd and EF (p < 0.00001). The RR for Q1 versus Q4 in events approached 0.5 for both LVIDd and EF (p < 0.0001). An LVIDd decrease and EF increase were quartile-dependent and greater with valsartan than placebo at virtually all time points. The RR for M + M outcomes favored valsartan in the worse quartiles.ConclusionsStratification by baseline severity of remodeling showed that patients with worse LVIDd and EF are at highest risk for an event, yet appear to gain the most anti-remodeling effect and clinical benefit with valsartan treatment

Assessing the role of EO in biodiversity monitoring: options for integrating in-situ observations with EO within the context of the EBONE concept

The European Biodiversity Observation Network (EBONE) is a European contribution on terrestrial monitoring to GEO BON, the Group on Earth Observations Biodiversity Observation Network. EBONE’s aims are to develop a system of biodiversity observation at regional, national and European levels by assessing existing approaches in terms of their validity and applicability starting in Europe, then expanding to regions in Africa. The objective of EBONE is to deliver: 1. A sound scientific basis for the production of statistical estimates of stock and change of key indicators; 2. The development of a system for estimating past changes and forecasting and testing policy options and management strategies for threatened ecosystems and species; 3. A proposal for a cost-effective biodiversity monitoring system. There is a consensus that Earth Observation (EO) has a role to play in monitoring biodiversity. With its capacity to observe detailed spatial patterns and variability across large areas at regular intervals, our instinct suggests that EO could deliver the type of spatial and temporal coverage that is beyond reach with in-situ efforts. Furthermore, when considering the emerging networks of in-situ observations, the prospect of enhancing the quality of the information whilst reducing cost through integration is compelling. This report gives a realistic assessment of the role of EO in biodiversity monitoring and the options for integrating in-situ observations with EO within the context of the EBONE concept (cfr. EBONE-ID1.4). The assessment is mainly based on a set of targeted pilot studies. Building on this assessment, the report then presents a series of recommendations on the best options for using EO in an effective, consistent and sustainable biodiversity monitoring scheme. The issues that we faced were many: 1. Integration can be interpreted in different ways. One possible interpretation is: the combined use of independent data sets to deliver a different but improved data set; another is: the use of one data set to complement another dataset. 2. The targeted improvement will vary with stakeholder group: some will seek for more efficiency, others for more reliable estimates (accuracy and/or precision); others for more detail in space and/or time or more of everything. 3. Integration requires a link between the datasets (EO and in-situ). The strength of the link between reflected electromagnetic radiation and the habitats and their biodiversity observed in-situ is function of many variables, for example: the spatial scale of the observations; timing of the observations; the adopted nomenclature for classification; the complexity of the landscape in terms of composition, spatial structure and the physical environment; the habitat and land cover types under consideration. 4. The type of the EO data available varies (function of e.g. budget, size and location of region, cloudiness, national and/or international investment in airborne campaigns or space technology) which determines its capability to deliver the required output. EO and in-situ could be combined in different ways, depending on the type of integration we wanted to achieve and the targeted improvement. We aimed for an improvement in accuracy (i.e. the reduction in error of our indicator estimate calculated for an environmental zone). Furthermore, EO would also provide the spatial patterns for correlated in-situ data. EBONE in its initial development, focused on three main indicators covering: (i) the extent and change of habitats of European interest in the context of a general habitat assessment; (ii) abundance and distribution of selected species (birds, butterflies and plants); and (iii) fragmentation of natural and semi-natural areas. For habitat extent, we decided that it did not matter how in-situ was integrated with EO as long as we could demonstrate that acceptable accuracies could be achieved and the precision could consistently be improved. The nomenclature used to map habitats in-situ was the General Habitat Classification. We considered the following options where the EO and in-situ play different roles: using in-situ samples to re-calibrate a habitat map independently derived from EO; improving the accuracy of in-situ sampled habitat statistics, by post-stratification with correlated EO data; and using in-situ samples to train the classification of EO data into habitat types where the EO data delivers full coverage or a larger number of samples. For some of the above cases we also considered the impact that the sampling strategy employed to deliver the samples would have on the accuracy and precision achieved. Restricted access to European wide species data prevented work on the indicator ‘abundance and distribution of species’. With respect to the indicator ‘fragmentation’, we investigated ways of delivering EO derived measures of habitat patterns that are meaningful to sampled in-situ observations

A Generic Bio-Economic Farm Model for Environmental and Economic Assessment of Agricultural Systems

Bio-economic farm models are tools to evaluate ex-post or to assess ex-ante the impact of policy and technology change on agriculture, economics and environment. Recently, various BEFMs have been developed, often for one purpose or location, but hardly any of these models are re-used later for other purposes or locations. The Farm System Simulator (FSSIM) provides a generic framework enabling the application of BEFMs under various situations and for different purposes (generating supply response functions and detailed regional or farm type assessments). FSSIM is set up as a component-based framework with components representing farmer objectives, risk, calibration, policies, current activities, alternative activities and different types of activities (e.g., annual and perennial cropping and livestock). The generic nature of FSSIM is evaluated using five criteria by examining its applications. FSSIM has been applied for different climate zones and soil types (criterion 1) and to a range of different farm types (criterion 2) with different specializations, intensities and sizes. In most applications FSSIM has been used to assess the effects of policy changes and in two applications to assess the impact of technological innovations (criterion 3). In the various applications, different data sources, level of detail (e.g., criterion 4) and model configurations have been used. FSSIM has been linked to an economic and several biophysical models (criterion 5). The model is available for applications to other conditions and research issues, and it is open to be further tested and to be extended with new components, indicators or linkages to other models