Research outlook /

Description based on: [5th] (1980); title from cover

Environmental implications of trends in agriculture and silviculture /

Mode of access: Internet.

Seasonal dynamic pattern analysis in service of climate change research. A methodical case-study - monitoring and simulation based on an aquatic insect community

Our aim was to approach an important and well-investigable phenomenon – connected to a relatively simple but real field situation – in such a way, that the results of field observations could be directly comparable with the predictions of a simulation model-system which uses a simple mathematical apparatus and to simultaneously gain such a hypothesis-system, which creates the theoretical opportunity for a later experimental series of studies. As a phenomenon of the study, we chose the seasonal coenological changes of aquatic and semiaquatic Heteroptera community. Based on the observed data, we developed such an ecological model-system, which is suitable for generating realistic patterns highly resembling to the observed temporal patterns, and by the help of which predictions can be given to alternative situations of climatic circumstances not experienced before (e.g. climate changes), and furthermore; which can simulate experimental circumstances. The stable coenological state-plane, which was constructed based on the principle of indirect ordination is suitable for unified handling of data series of monitoring and simulation, and also fits for their comparison. On the state-plane, such deviations of empirical and model-generated data can be observed and analysed, which could otherwise remain hidden.

Trends in research on the possible effects of climate change concerning aquatic ecosystems with special emphasis on the modelling approach

Knowledge on the expected effects of climate change on aquatic ecosystems is defined by three ways. On the one hand, long-term observation in the field serves as a basis for the possible changes; on the other hand, the experimental approach may bring valuable pieces of information to the research field. The expected effects of climate change cannot be studied by empirical approach; rather mathematical models are useful tools for this purpose. Within this study, the main findings of field observations and their implications for future were summarized; moreover, the modelling approaches were discussed in a more detailed way. Some models try to describe the variation of physical parameters in a given aquatic habitat, thus our knowledge on their biota is confined to the findings based on our present observations. Others are destined for answering special issues related to the given water body. Complex ecosystem models are the keys of our better understanding of the possible effects of climate change. Basically, these models were not created for testing the influence of global warming, rather focused on the description of a complex system (e. g. a lake) involving environmental variables, nutrients. However, such models are capable of studying climatic changes as well by taking into consideration a large set of environmental variables. Mostly, the outputs are consistent with the assumptions based on the findings in the field. Since synthetized models are rather difficult to handle and require quite large series of data, the authors proposed a more simple modelling approach, which is capable of examining the effects of global warming. This approach includes weather dependent simulation modelling of the seasonal dynamics of aquatic organisms within a simplified framework.

The impact of perceived corporate environmental performance on the behavior of capital market decision makers : analysis of food industry companies

Environmental protectionism and sustainable development has been gaining increased attention among governments, investors and consumers alike. As a result, firms are facing growing pressure from the various stakeholders to improve their environmental performance. This study is focusing on the food industry, which in recent years has been a subject of increased scrutiny due to their role in resource consumption, waste generation and unsustainable production practices. Our research is aiming to examine how the financial community evaluates the environmental stewardship of food industry companies as proxied by market reactions in response to environmental news. Are all company related environmental news items evaluated equally, and which financial and non-financial firm-specific attributes can influence market responses? Have there been changes in reactions on the stock exchange in the past two decades?

Data compilation on the biological response to ocean acidification: environmental and experimental context of data sets and related literature

The exponential growth of studies on the biological response to ocean acidification over the last few decades has generated a large amount of data. To facilitate data comparison, a data compilation hosted at the data publisher PANGAEA was initiated in 2008 and is updated on a regular basis (doi:10.1594/PANGAEA.149999). By January 2015, a total of 581 data sets (over 4 000 000 data points) from 539 papers had been archived. Here we present the developments of this data compilation five years since its first description by Nisumaa et al. (2010). Most of study sites from which data archived are still in the Northern Hemisphere and the number of archived data from studies from the Southern Hemisphere and polar oceans are still relatively low. Data from 60 studies that investigated the response of a mix of organisms or natural communities were all added after 2010, indicating a welcomed shift from the study of individual organisms to communities and ecosystems. The initial imbalance of considerably more data archived on calcification and primary production than on other processes has improved. There is also a clear tendency towards more data archived from multifactorial studies after 2010. For easier and more effective access to ocean acidification data, the ocean acidification community is strongly encouraged to contribute to the data archiving effort, and help develop standard vocabularies describing the variables and define best practices for archiving ocean acidification data.

TERENO (Terrestrial Environmental Observatories) wild bee monitoring in six agriculturally dominated landscapes of Saxony-Anhalt (Germany)

In 2001 we started as part of the EU FP5 project Greenveins monitoring of insect communities in the normal landscape of Saxony-Anhalt (Germany), which is dominated by agricultural use. We selected four landscape sites of 4x4 km and recorded insects using combined flight traps, combining the ideas of window and yellow pan traps (see Duelli et al., 1999). Traps consist of a yellow funnel (25 cm diameter) filled with water (preserving agent added) and two perspex windows mounted in a way that they are crossed in the center. Within each square km of a site one trap was placed at ecotones between semi-natural habitats and agricultural fields (16 traps per site). Traps were operated in late spring-early summer (three sampling rounds) and late summer (three sampling rounds). Follow-up sampling started in 2010 as long-term monitoring within the TERENO project (www.tereno.net), contributing to the LTER network (Long-Term Ecosystem Research) in Germany (www.lter-d.de) and internationally as well (www.lter-europe.net). Metadata about the sites and related activities and data sets can be found in the DEIMS Repository for Research Sites and Datasets (https://data.lter-europe.net/deims/). In 2010 another two landscapes were added and yearly sampled in the same way. Due to long processing time of trapped insects data of follow-up years will be available about 18 months after trapping.

A review of LCADesign

Objective The review addresses two distinct sets of issues: 1. specific functionality, interface, and calculation problems that presumably can be fixed or improved; and 2. the more fundamental question of whether the system is close to being ready for ‘commercial prime time’ in the North American market. Findings Many of our comments relate to the first set of issues, especially sections B and C. Sections D and E deal with the second set. Overall, we feel that LCADesign represents a very impressive step forward in the ongoing quest to link CAD with LCA tools and, more importantly, to link the world of architectural practice and that of environmental research. From that perspective, it deserves continued financial support as a research project. However, if the decision is whether or not to continue the development program from a purely commercial perspective, we are less bullish. In terms of the North American market, there are no regulatory or other drivers to press design teams to use a tool of this nature. There is certainly interest in this area, but the tools must be very easy to use with little or no training. Understanding the results is as important in this regard as knowing how to apply the tool. Our comments are fairly negative when it comes to that aspect. Our opinion might change to some degree when the ‘fixes’ are made and the functionality improved. However, as discussed in more detail in the following sections, we feel that the multi-step process — CAD to IFC to LCADesign — could pose a serious problem in terms of market acceptance. The CAD to IFC part is impossible for us to judge with the information provided, and we can’t even begin to answer the question about the ease of using the software to import designs, but it appears cumbersome from what we do know. There does appear to be a developing North American market for 3D CAD, with a recent survey indicating that about 50% of the firms use some form of 3D modeling for about 75% of their projects. However, this does not mean that full 3D CAD is always being used. Our information suggests that AutoDesk accounts for about 75 to 80% of the 3D CAD market, and they are very cautious about any links that do not serve a latent demand. Finally, other system that link CAD to energy simulation are using XML data transfer protocols rather than IFC files, and it is our understanding that the market served by AutoDesk tends in that direction right now. This is a subject that is outside our area of expertise, so please take these comments as suggestions for more intensive market research rather than as definitive findings.

Ecological sustainability : what role for public health education?

This article explores the notion of ecological sustainability in the context of public health education and the contribution Universities can make in creating environments that include ecologically sustainable practices. It considers the important role of environmental health in building a sustainable future for the population as a central plank of public health. It presents the evidence for the need for comprehensive approaches to ecological sustainability within the University and offers suggestions about how this can take place. It concludes by arguing that to date there is a substantial gap between the rhetoric and the reality in the University context.

What measure of temperature is the best predictor of mortality?

Hot and cold temperatures significantly increase mortality rates around the world, but which measure of temperature is the best predictor of mortality is not known. We used mortality data from 107 US cities for the years 1987–2000 and examined the association between temperature and mortality using Poisson regression and modelled a non-linear temperature effect and a non-linear lag structure. We examined mean, minimum and maximum temperature with and without humidity, and apparent temperature and the Humidex. The best measure was defined as that with the minimum cross-validated residual. We found large differences in the best temperature measure between age groups, seasons and cities, and there was no one temperature measure that was superior to the others. The strong correlation between different measures of temperature means that, on average, they have the same predictive ability. The best temperature measure for new studies can be chosen based on practical concerns, such as choosing the measure with the least amount of missing data.